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    1. 125F668823802361389153289>300018,40050231Eosinophils 17%, fungal scrapes—positive

      Case#: 12, M, 5 y.o., Ethnicity: Indian.

      CasePresentingHPOs: HP:0001945 (Fever), HP:0001824 (Weight loss), HP:0002716 (Lymphadenopathy/FHL), HP:0003212 (Increased circulating IgE level), HP:0002716 (Lymphadenopathy), HP:0009098 (Chronic oral candidiasis), HP:0002841 (Recurrent fungal infections), HP:0032326 (Methicillin-resistant Staphylococcus aureus infection), HP:0020271 (Increased lymph-node eosinophils), HP:0100827 (Lymphocytosis), HP:0003237 (Increased circulating IgG level), HP:0002090 (Pneumonia)

      CaseHPOFreeText: Eosinophils 17%, fungal scrapes—positive. Methicillin-resistant Staphylococcus aureus pneumonia, oral candidiasis/Hyper IgE.

      Suspected recurring pneumonia.

      CaseNotHPOs: N/A.

      CaseNotHPOFreeText: N/A.

      CasePreviousTesting: N/A.

      CaseMethod1: N/A.

      CaseMethod2: N/A.

      CaseGenotypingMethod: Sanger sequencing and NGS targeting a customized panel of genes.

      Variant: NM_005026.5:c.2296G>A.

      ClinVar: 846790.

      CAID: CA577485.

      gnomAD: 0.00001611. https://gnomad.broadinstitute.org/variant/1-9722305-G-A?dataset=gnomad_r4.

      VariantEvidence: N/A.

      CaseAddInfo: N/A.

      CasePMIDs: N/A.

    1. P03Mukukuk*G52DReduced––17.2PathogenicukNANAAlive

      Case#: P03, Male, Age: N/A, ethnicity: N/A, Alive at the time of article's publication

      DiseaseAssertion: N/A

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs:N/A

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: The percentage of transendocytosis using either CD80-GFP or CD80-mScarlet CHO cells was determined in eight LRBA-deficient patients. No difference in the percentage of transendocytosis was observed between CTLA4-variant carriers (GFP median=5.4%; mScarlet median= 49.8%) and LRBA-deficient patients (GFP median=9.9%; mScarlet median, 48.6%). However, significantly lower percentages of transendocytosis were observed in LRBA-deficient patients compared to healthy donors (HD) when using CD80-mScarlet CHO cells (median, 48.6% vs. 65.5% in HD) (Fig. ​(Fig.4e),4e). This difference was not observed with CD80-GFP CHO cells (patients median of 9.9% in patients vs. 13.9% in HD). In conclusion, the CTLA4 transendocytosis method using CD80-mScarlet CHO cells enables the functional verification of LRBA deficiency, but it cannot differentiate between LRBA deficiency and CTLA4 insufficiency.

      GenotypingMethod: NGS and Sanger sequencing

      PreviouslyPublished: N/A

      Variant: NM_005214.5(CTLA4):c.155G>A (p.Gly52Asp)

      ClinVar ID: 871301

      gnomAD: not found in any gnomAD version.

      SupplementalData: Yes, all data regarding the patient was found in Table1.

    2. P22FGermanukuk*L163Sfs*24Reduced––37.2Pathogenicuk42.86Severely affectedAlive

      Case#: P22, Female, German, age: n/a , alive at the time of publication

      DiseaseAssertion: Severely affected based on a CHAI score of 42.86%

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs:N/A

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: The percentage of transendocytosis using either CD80-GFP or CD80-mScarlet CHO cells was determined in eight LRBA-deficient patients. No difference in the percentage of transendocytosis was observed between CTLA4-variant carriers (GFP median=5.4%; mScarlet median= 49.8%) and LRBA-deficient patients (GFP median=9.9%; mScarlet median, 48.6%). However, significantly lower percentages of transendocytosis were observed in LRBA-deficient patients compared to healthy donors (HD) when using CD80-mScarlet CHO cells (median, 48.6% vs. 65.5% in HD) (Fig. ​(Fig.4e),4e). This difference was not observed with CD80-GFP CHO cells (patients median of 9.9% in patients vs. 13.9% in HD). In conclusion, the CTLA4 transendocytosis method using CD80-mScarlet CHO cells enables the functional verification of LRBA deficiency, but it cannot differentiate between LRBA deficiency and CTLA4 insufficiency.

      GenotypingMethod: NGS and Sanger sequencing

      PreviouslyPublished: N/A

      Variant: ENSP00000497102.1:p.Leu163Ser

      ClinVar ID:

      CAID: PA2850594025

      gnomAD: Not found

      SupplementalData: Yes, all data regarding the patient was found in Table1.

    3. P21FAmericanukuk*P156LReduced––36.7PathogenicukNANAAlive

      Case#: P21, female, American, age: n/a, alive at the time of publication

      DiseaseAssertion: n/a

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs:N/A

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: The percentage of transendocytosis using either CD80-GFP or CD80-mScarlet CHO cells was determined in eight LRBA-deficient patients. No difference in the percentage of transendocytosis was observed between CTLA4-variant carriers (GFP median=5.4%; mScarlet median= 49.8%) and LRBA-deficient patients (GFP median=9.9%; mScarlet median, 48.6%). However, significantly lower percentages of transendocytosis were observed in LRBA-deficient patients compared to healthy donors (HD) when using CD80-mScarlet CHO cells (median, 48.6% vs. 65.5% in HD) (Fig. ​(Fig.4e),4e). This difference was not observed with CD80-GFP CHO cells (patients median of 9.9% in patients vs. 13.9% in HD). In conclusion, the CTLA4 transendocytosis method using CD80-mScarlet CHO cells enables the functional verification of LRBA deficiency, but it cannot differentiate between LRBA deficiency and CTLA4 insufficiency.

      GenotypingMethod: NGS and Sanger sequencing

      PreviouslyPublished: N/A

      Variant: NM_005214.5(CTLA4):c.467C>T (p.Pro156Leu)

      ClinVar ID: 1035066

      gnomAD: 0.00002292 https://gnomad.broadinstitute.org/variant/2-203871387-C-T?dataset=gnomad_r4

      SupplementalData: Yes, all data regarding the patient was found in Table1.

    4. P20MGerman2222.7T147Rfs*8Reduced––30.7Pathogenic[12]42.11Severely affectedDead

      Case#: P20, male, German, 22 years old at the time of clinical diagnosis, 22.7 years old at the time of genetic diagnosis, dead at the time of publication

      DiseaseAssertion: Severely affected based on a CHAI score of 42.11%

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs:N/A

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: The percentage of transendocytosis using either CD80-GFP or CD80-mScarlet CHO cells was determined in eight LRBA-deficient patients. No difference in the percentage of transendocytosis was observed between CTLA4-variant carriers (GFP median=5.4%; mScarlet median= 49.8%) and LRBA-deficient patients (GFP median=9.9%; mScarlet median, 48.6%). However, significantly lower percentages of transendocytosis were observed in LRBA-deficient patients compared to healthy donors (HD) when using CD80-mScarlet CHO cells (median, 48.6% vs. 65.5% in HD) (Fig. ​(Fig.4e),4e). This difference was not observed with CD80-GFP CHO cells (patients median of 9.9% in patients vs. 13.9% in HD). In conclusion, the CTLA4 transendocytosis method using CD80-mScarlet CHO cells enables the functional verification of LRBA deficiency, but it cannot differentiate between LRBA deficiency and CTLA4 insufficiency.

      GenotypingMethod: NGS and Sanger sequencing

      PreviouslyPublished: N/A

      Variant: NP_001032720.1:p.Thr147Arg

      ClinVar ID:

      CAID: PA2850594024

      gnomAD: Not found

      SupplementalData: Yes, all data regarding the patient was found in Table1.

    5. P19FItalianukukN145SNormal16.9Non-pathogenic––[9]NANAAlive

      Case#: P19, Female, Italian, age: n/a, alive at the time of diagnosis

      DiseaseAssertion: n/a

      FamilyInfo: N/A

      CasePresentingHPOs: N/A

      CaseHPOFreeText: N/A

      CaseNotHPOs:N/A

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: The percentage of transendocytosis using either CD80-GFP or CD80-mScarlet CHO cells was determined in eight LRBA-deficient patients. No difference in the percentage of transendocytosis was observed between CTLA4-variant carriers (GFP median=5.4%; mScarlet median= 49.8%) and LRBA-deficient patients (GFP median=9.9%; mScarlet median, 48.6%). However, significantly lower percentages of transendocytosis were observed in LRBA-deficient patients compared to healthy donors (HD) when using CD80-mScarlet CHO cells (median, 48.6% vs. 65.5% in HD) (Fig. ​(Fig.4e),4e). This difference was not observed with CD80-GFP CHO cells (patients median of 9.9% in patients vs. 13.9% in HD). In conclusion, the CTLA4 transendocytosis method using CD80-mScarlet CHO cells enables the functional verification of LRBA deficiency, but it cannot differentiate between LRBA deficiency and CTLA4 insufficiency.

      GenotypingMethod: NGS and Sanger sequencing

      PreviouslyPublished: N/A

      Variant: ENST00000295854.10:c.434A>G

      ClinVar ID:

      CAID: CA350138791

      gnomAD: 6.197e-7 https://gnomad.broadinstitute.org/variant/2-203870910-A-G?dataset=gnomad_r4

      SupplementalData: Yes, all data regarding the patient was found in Table1.

    1. Comprehensive comparison between 222 CTLA-4 haploinsufficiency and 212 LRBA deficiency patients: a systematic review

      PMID: 33788257

      Gene: CTLA4

      HGNC: 2505

      Disease: autoimmune lymphoproliferative syndrome due to CTLA4 haploinsufficiency

      MONDO: 0014493

      InheritancePattern: autosomal dominant

      Prevalence: <1 in 1,000,000

      Penetrance: The penetrance of the phenotype among carriers of pathogenic variants is incomplete.

    1. 5.6.2: Iterating through arrays in code.

      Lab Notes: Good practice to show you how to loop. Up and down max down to up changing numbers in an array from negative to possitive and more at the beginning...

    1. eLife Assessment

      This study presents an important finding regarding the effect of Yoda molecules on PIEZO2 function, challenging the assumption that they selectively activate PIEZO1. The evidence supporting this claim is solid, but several methodological and conceptual issues need to be addressed. Overall, this work will be of broad interest to researchers working with PIEZO channels across various biological scales.

    2. Reviewer #1 (Public review):

      Summary:

      In this work, T. Wijerathne et al. investigated and reported the agonistic effect of Yoda1 and Yoda2 over PIEZO2 function using patch clamp electrophysiology, Ca2+ imaging, and molecular dynamics. They find that Yoda1 sensitizes PIEZO2 to membrane tension, can induce Ca2+ influx, and decreases its inactivation to a lesser degree than it does to PIEZO1 channels. Additionally, their data shows that Yoda2 sensitizes PIEZO2 channels to membrane indentation to a greater extent, but it has a weaker effect on channel inactivation than Yoda1. Interestingly, they report that a mutation in a conserved arginine between PIEZO channels can be used to abolish PIEZO1-mediated Ca2+ flux in response to Yoda molecules. As a whole, the results presented here should be put into perspective against previous and future works involving systems where both PIEZO1 and PIEZO2 might be expressed. This is especially true for works where Yoda1 has been used as a basis for determining the absence of PIEZO2.

      Strengths:

      The authors use multiple techniques to investigate how Yoda molecules affect the three most important biophysical aspects of PIEZO channels that, when changed, result in pathophysiological responses: a) sensitivity to mechanical stimuli, b) Ca2+ entry, and c) channel inactivation. Lastly, they find a specific amino acid/region that could be exploited for drug design and/or development.

      Weaknesses:

      The methods and discussion sections are lacking enough detail to fully evaluate the findings and put them into perspective, respectively.

    3. Reviewer #2 (Public review):

      Summary:

      This manuscript challenges the long-standing assumption that Yoda1 and Yoda2 are PIEZO1-selective activators. Using patch-clamp electrophysiology and calcium imaging in HEK293TΔPZ1 cells overexpressing PIEZO2, the authors demonstrate that Yoda1 potentiates PIEZO2 stretch-activated currents to a similar extent as PIEZO1 and slows PIEZO2 poking-current inactivation (albeit with lower efficacy). They further show that the more potent analog Yoda2 affects PIEZO2 at nanomolar concentrations and use mutagenesis and molecular dynamics simulations to propose that Yoda2's benzoic acid group forms a transient salt bridge with R1724 in the putative Yoda binding pocket, explaining its enhanced potency.

      Strengths:

      The authors are established Piezo/biophysics experts; the study is highly important, technically competent, and carries significant implications for the reinterpretation of prior work that used Yoda compounds as PIEZO1-selective probes.

      The core finding that Yoda1 modulates PIEZO2 stretch currents is convincing and important. However, several conceptual, methodological, and presentational issues need to be addressed before acceptance, as detailed below.

      Weaknesses:

      (1) The abstract states that Yoda1 potentiates PIEZO2 "as efficaciously as PIEZO1." This claim is accurate only for stretch currents and single-channel open probability, but the paper itself demonstrates important asymmetries: i) Yoda molecules slow PIEZO2 poking-current inactivation ~2-fold, versus ~5-10 fold for PIEZO1 (Figure 3b and ref #60). ii) Spontaneous Ca²⁺ entry via PIEZO2 requires non-physiological conditions (high extracellular Ca²⁺, hypertonic solutions) that are unlikely to occur in native cells.

      The abstract should be revised to clearly qualify where equivalence holds and where efficacy differences exist. IMO, the current wording risks overcorrecting the historical bias (PIEZO1-only) by going too far in the other direction.

      (2) Related concern: the PIEZO2 Ca²⁺ signal in Figure 2 is only detectable using a Ca²⁺-boosted solution (CBS ie 30 mM Ca²⁺). Physiological extracellular Ca²⁺ and cells normally do not experience sustained hypertonicity at these magnitudes. The authors should explicitly clarify that the practical implication of their findings is primarily for electrophysiological (patch-clamp) experiments and that the Ca²⁺ imaging caveat applies only under amplified conditions. Specifically, the authors should state that in standard Ca²⁺ imaging assays with physiological buffers, PIEZO2 is unlikely to confound Yoda1 results.

      Related point: Can cytochalasin D (CytoD) restore a Yoda1-dependent Ca²⁺ signal in physiological saline? This would help determine whether the weak PIEZO2 response is primarily a membrane tension issue (cytoskeletal tethering) versus intrinsically lower channel expression or permeability. The authors already have tagged PIEZO1/2 constructs and could, in principle, normalize by surface expression.

      (3) The mean inactivation tau values for wild-type PIEZO2 poking currents in both DMSO and Yoda1 conditions (Figure 3b, approximately 15-40 ms range) appear substantially higher than values reported in published literature (typically 5-10 ms; eg, PMID: 20813920). This discrepancy needs to be addressed.

      (4) The authors perform all MD simulations on a truncated PIEZO1 model and justify this choice by noting that the Yoda binding region is highly conserved between homologs. This is a reasonable and defensible starting point given the availability of well-validated PIEZO1 simulation set ups in their lab. A few points are nonetheless worth addressing: While PIEZO2 simulations are not strictly required, the authors are encouraged to briefly discuss whether any long-range structural differences between PIEZO1 and PIEZO2 (outside the binding site itself) could influence Yoda2 binding dynamics, particularly in light of the chimera data showing that PIEZO2 sequence in repeat A abolishes Yoda1 sensitivity. This reviewer still doesn't understand the reason behind this discrepancy despite it being acknowledged in the text.

      Another MD-related comment is that three simulation replicas (which is impressive for such a big system) show markedly different salt bridge occupancy (82.6%, 49.7%, 99.8%; stated in the text). This wide variation suggests incomplete sampling in at least one replica. The authors should provide RMSD plots for ligand and protein backbone to assess convergence and possibly discuss whether the 49.7% replica represents a genuinely distinct binding mode or incomplete equilibration.

      (5) The Discussion proposes that PIEZO2's weaker Ca²⁺ response to Yoda1 could partly reflect lower membrane expression. Since the authors already have fluorescently tagged PIEZO1 and 2 constructs, a simple fluorescence intensity comparison between the two (acknowledging it would reflect total rather than surface expression) could provide at least indirect support for this claim. Alternatively, if such a comparison is not feasible, the authors may consider removing membrane expression from the list of proposed explanations or explicitly acknowledging that this remains unsubstantiated speculation. The max poking currents may somewhat and roughly indicate the level expression difference too, if done exactly side by side.

      (6) The abstract or concluding remarks should highlight that Dooku1 is not PIEZO1-selective in its agonist-like action on PIEZO2, and that Cmpd15/Cmpd64 appear to be better PIEZO1-selective tools. This nuance is buried in the Results section.

      (7) The authors should not cite PMID 31015490. Clearly, any work on MCC13 is confounded by the overwhelming expression of PIEZO1 (PMID: 42084270). Instead, the authors should also cite the literature from others who have clearly recorded stretch currents from PIEZO2 before the cited studies (eg, PMID: 37590348).

    4. Reviewer #3 (Public review):

      Summary:

      The manuscript reports that Yoda1 and Yoda2 agonize PIEZO2 in a manner similar to PIEZO1, increasing open probability and stretch sensitivity, but the mechanism underlying this sensitivity is incomplete. Mutagenesis was shown exclusively in PIEZO1, with no corresponding mutagenesis in PIEZO2, so the proposed mechanism in PIEZO2 is inferred by homology rather than directly tested. All experiments use mouse PIEZO2, and the human ortholog should be used before generalizing the proposed reinterpretation of the field.

      Strengths:

      The pressure-clamp electrophysiology demonstrating a shift in half-activation pressure for PIEZO2 is compelling evidence in support of the central claim.

      Weaknesses:

      (1) In the single-channel recordings (Figure 1a), it's unclear how many channels were present in those patches. After applying -60 mmHg pressure, multiple channels would be activated (as seen in Figure 1e). The number of channels in the patch and their inactivation rate could significantly influence the open probability in such experiments. To overcome this, in the original Yoda1 article (Syeda, Ruhma, et al. eLife 2015), no additional pressure was used. Additionally, the reported open probability comparison (n=7 Yoda1 vs n=17 DMSO patches) has an SEM nearly as large as the effect itself (0.30 {plus minus} 0.11), consistent with a small number of outliers driving this. The underlying mean open and shut times are reported without any statistical test; only the derived open probability receives a p-value. Additionally, in Figure 1a, the Yoda1 condition noise is different from the control. This should be stated if noise filtering was applied and how, given that this could affect open probability analysis.

      (2) The calcium imaging data in Figure 2 raise significant concerns regarding the chemical activation claim. The calcium-boosted solution (30 mM Ca2+) is not physiological and appears to be generally stressing cells rather than specifically activating PIEZO2: the control condition under CBS already shows an elevated signal, consistent with cells being unwell at this calcium concentration, and adding Yoda1 on top of this shifted baseline raises further questions about specificity rather than confirming it. Separately, it is unclear why DMSO alone produces measurable PIEZO2-associated calcium influx in HBSS, a result that is not addressed in the text. Figure 2 should clearly indicate when DMSO/Yoda1 perfusion was initiated, and y-axis labels are missing from panels A and B.

      (3) In the poke experiments, an activation threshold should be calculated and reported, and amplitude data (e.g., peak current versus indentation depth) should be shown rather than only inactivation tau values. It is also unclear why mClover3- and N-GFP-tagged constructs were used in these experiments, since electrophysiological recording already confirms channel expression without requiring a fluorescent tag.

      (4) For inactivation kinetics (Figure 3b), the authors use unpaired comparisons across separate cells, whereas the deactivation experiments (Figure 3c) use paired; it should be applied to the inactivation experiments as well. Deactivation kinetics for PIEZO2 itself should be shown. If the claim is that Yoda1 acts on PIEZO2 through the same mechanism proposed for PIEZO1, then a PIEZO1/2 chimera should be expected to show a corresponding effect on deactivation tau; instead, this chimera is reported as completely Yoda1-insensitive despite both parental channels being Yoda1-sensitive, as shown in this study.

      (5) Given that this reflects a different experimental paradigm for Yoda EC50, PIEZO1 should be included within Figure 4b. Additionally, EC50 bar plots should be present on this figure. The inactivation time constant for PIEZO2 without Yoda1 is inconsistent across figures, below 20 ms in Figure 3b but above 20 ms in Figure 4c.

      (6) Finally, the modeling is performed exclusively on PIEZO1, whereas the manuscript's central focus is PIEZO2. It is therefore unclear whether the proposed structural mechanism, including the basis for Yoda2's reduced efficacy on PIEZO2, can be directly extrapolated to PIEZO2.

    1. eLife Assessment

      In this manuscript, the authors describe a new member of the KCNE auxiliary subunits of potassium channels from a lamprey. This new subunit represents an early evolutionary member which confers new properties when expressed along with KCNQ channels. The authors present convincing evidence from several experimental approaches. The contents of this manuscript are important and should be relevant to understanding both the mechanism of modulation of KCNQ channels by KCNE subunits and the evolutionary history of these subunits, which this manuscript now extends to the divergence of early vertebrates.

    2. Reviewer #1 (Public review):

      Summary:

      In this study, the authors describe an early diverging vertebrate KCNE gene present in jawless lampreys that they denote KCNE0.

      Three forms of the protein are isolated from different lampreys, which have 95% homology to each other, but only moderate homology to KCNE1-6.

      Co-expression with lamprey KCNQ1 produced a non-inactivating current, whereas co-expression with mammalian KCNQ1 resulted in less modulation. Introduction of a tetra-leucine motif from KCNE4 into KCNE0 reduced current on co-expression with KCNQ1, conferring an inhibitory effect.

      Strengths:

      This is an interesting and uncontroversial report of a new KCNE isoform from lower vertebrates that gives insight into the evolutionary progression of the sequence and functional properties of the accessory protein.

      Weaknesses:

      (1) No error bars visible for lamprey Q1 isoforms (open symbols) in Figure 2G. No statistical comparison was provided to indicate whether lamprey Q1 isoform V1/2s are significantly different (nor in Supplementary Table 1).

      (2) There is the same issue in Figures 3 and 4. No appropriate statistical comparison is made between V1/2s for different truncations of PmKCNE0 (Figure 3), or between KCNQ1 species isoforms with and without PmE0.

    3. Reviewer #2 (Public review):

      Summary:

      This study functionally characterizes a single KCNE-like gene, kcne0, from a jawless vertebrate. The authors conducted multiple experiments, including TEVC, VCF, RT-PCR, and RNA-seq to show that KCNQ1 and kcne0 exhibited a broadly overlapping organ distribution in lamprey species, and KCNE0 produced a constitutively active current when co-expressed with lamprey KCNQ1, similar to the effects of human KCNE3 on KCNQ1. This modulation was species-specific, as co-expression of KCNE0 with other species' KCNQ1 was less effective. Moreover, the authors found that truncating the N-terminal had a more significant reduction of the modulatory effects than truncating the C-terminal of KCNE0. Interestingly, the introduction of the tetra-leucine motif from human KCNE4 into KCNE0 conferred KCNE0 with comparable effects of human KCNE4 on KCNQ1.

      Strengths:

      The authors clearly introduced an early-diverging member of the KCNE family, and convincingly demonstrated the function of this gene, KCNE0. The results are supported by experiments of multiple approaches and are clearly written. The work is significant and will interest readers from the extended research area.

      Weaknesses:

      No major concerns were identified with the manuscript in general.

    1. Well, I argue that we discussAAVE

      Johnsons doesn't say students should not use SAE but says they should learn when SAE is expected while still respecting AAVE.

    2. However, there hasbeen a lot of pushback from Blackfamilies in labeling their studentsas ESL students, which has inpart made readily available anddownloadable content hard to find.

      If a student use's an at home dialect it shows how unfair admissions expectations can be to students.

    3. it is rare for my students tohave to codeswitch into SAEoutside of school. However, theysee examples of their elderscodeswitching when talking on thephone, visiting the doctor’s office,

      Students may not practice SAE that often because of their environment/community. Students are expected to adjust langauge depending on their setting wich connects to Standard English.

    4. AAVE has its owndistinct grammar and syntax lawsthat have been developed overcenturies of use.

      AAVE is not broken English. It has rules and is structured.

    5. They were writing ina completely different language:African American VernacularEnglish (AAVE).

      If AAVE has its own grammar why do schools treat it as incorrect instead of different?

    Annotators

    1. culture of fear

      This section of the article appears to drop the theory all together and use real data from YRDSB and PDSB. It exposes the 'culture of fear' where white administrators used nepotism, favouritism and unposted jobs to hire their friends while deliberately passing over skilled and qualified BIPOC candidates. This again establishes a power dynamic used to control the narrative within their systems.

    2. 1993 Policy Program Memorandum

      From this, the takeaway is that Ontario has talked about inclusion and diversifying their teacher populations since 1993, but their main strategy to use neutral hiring doesn't appear to work. The province identified that they provided zero proof that this method closes the diversity gap. Instead, they have continued to ignore the real systemic racism that keeps BIPOC educators out while continuing to feed the privileges that favour white candidates.

    3. onscious and unconscious biases contribute to hiring decisions

      This whole section is interesting because the official leadership framework in Ontario is providing guidance on how to bring changes through administration by determining their own conscious and unconscious biases, but nothing actually forces them to look at their own privilege or question why they keep promoting the way they are.

    4. colour-blindness

      I understand the use of CRT and ACL to argue that we need to stop pretending the school system is totally 'colour-blind', but leaders in these higher power positions cannot simply follow passive rules; rather, they need to actively look within their biases to fix the unfair setup of the system. As situated further down, there needs to be a collaboration to shift the narrative.

    5. Over 90

      This seems to be a key stat within this article, 90% of principals and VPs are white and middle class even though almost 30% of the population identifies as BIPOC. This speaks to the power that is held, and means the power to hire and promote is stuck with one specific group .

    6. BIPOC educators are underrepresente

      While Ontario is lauded as highly diverse, this section of the article specifically calls to question the education infrastructure and how it remains a system that privileges whiteness. The lack of representation has real-world consequences such as BIPOC students being suspended at way higher rates because the individuals in charge don't understand the communities in which they work.

    7. pplied Critical Leadership (ACL)

      Equity cannot only be achieved with an “equal” employment opportunity standard, as the social location of the administrators greatly impacts what they will pay attention to, value, and reward. Abawi (2021) used Applied Critical Leadership (ACL) to demonstrate that the dominance of whiteness occurs in leadership roles, placing BIPOC educators in more precarious positions of exclusion. Structural power is discussed as relevant to hiring practices. If Ontario schools want truly representative positions and voices, administrators need to commit to reflecting, building allyship, and being accountable.

    8. true for school administrators

      The central argument in the article is that fair recruitment will be ineffective unless administrators do some serious soul-searching concerning their positionality, bias, and complicity in perpetuating racial inequity.

    9. representation

      I recognize that the Leadership FOS group in this cohort is quite small (and we will talk about our fields and areas of research interest soon: had it on the menu for today but we had that much deeper circle conversation, which I remain grateful for) …BUT I think a leadership lens on representation is always of value as educational researchers often end up in leadership conversations, or lead decisions about who gets included in X research project. Hence these two articles, one on racial representation and one on queer representation...no matter your fields of study, thinking about the ways axes of identity shape access to educational experiences is part of building the transformative and reflexive practice this paper calls for :)

    1. Students should learn dialect patterns before reading literature with a lot of dialect. Contrastive analysis is helpful because it shows students that every dialect has its own system, and this makes tough texts easier to understand, which is good for respecting different languages and dialects.

    2. Dialect spellings typically reflect pronunciation. They're used by writers to capture authentic speech, and that's why authors such as Zora Neale Hurston often write dialogue that differs from Standard English. This approach helps to explain the variations in spelling.

    3. Dialects are legitimate language varieties with their own grammar rules. It's time to move away from the idea that one English dialect is better than another. Common stereotypes about "proper" English are overturned, and authors are recognizing the value of diverse dialects.

    4. Authors don't buy into the notion that one English dialect is superior to another. Dialects have their own grammar rules, and it's about time they're seen as legitimate language varieties. What this means is that common stereotypes about "proper" English get turned on their head.

    5. Learning dialects takes practice. It's like picking up any new language variety. Students will need support, and teachers should be ready to provide it. Speaking a dialect doesn't mean someone can read or write it. Literacy skills are different from speaking skills. Schools need to consider this when deciding how to teach students. And they should think about teaching students to read literature in different dialects, but is this the best use of their time?

    6. Students learn to compare dialects, rather than replace one with another, through these instructional strategies. This approach respects their linguistic identities and teaches academic English at the same time. Language use is tied to social inequality, as the article suggests. How people speak can influence others' opinions of them, and this makes language a matter of power, not just communication.

    7. Teachers should not just define unfamiliar dialect words, according to the authors. Students need to learn how to understand different language varieties on their own.

    8. Students can use their knowledge of African American Vernacular English (AAVE) to better understand literature. It's a matter of language experience becoming an academic strength. Dialect helps readers understand characters and cultural context; it doesn't have to confuse them.

    9. Students come from different language backgrounds. Dialect differences aren't deficits. Teachers need to support students without making them feel bad about their home language. What's the best way to do this?

    1. Naming the Standard Units at Work

      I suggest placing the learning objectives at the beginning of each lesson so that learners are guided from the start and have a clear understanding of what is expected of them.

    1. use the word “queer”

      I am curious what your perspective is on the use of an umbrella term to represent a constellation of identities. Personally, I find it empowering to reclaim a term that has been weaponized in the past; however, I acknowledge that that term was never wielded on me. I am curious if all members of the 2SLGBTQIA+ community of all ages reclaim this term or feel as though it is minimizing a wide range of social experiences into a singular identity.

    2. Someuniversities even went beyond silencing andenacted aggressive measures of public shamingby “flushing out” suspected gays and lesbians

      "Flushing out" is a very telling phrase in this context. It is a very brutal phrase which reveals the very brutal logic of an institution's need to purify, to get rid of the queer contaminant. That the university is using the language of sanitation to talk about how they are going to find this queer student, and then flush him out, is a very telling commentary on the university's active role in violence in order to purify the campus of the “other” in order to maintain the illusion of a pure, heteronormative space. The shame is always on the suspected not the suspector. This institution is filled with such deep-seated shame and desperation to cleanse itself of difference.

    3. We describe how queerpeople are being represented in the literature

      Describing representation is not neutral; it is participation. By providing a list of ways in which queer people are represented in literature, the researcher acts as a kind of curator of established narratives in studies of representation. In this way, the researcher decides which stories of representation will be studied in any given study of representation. The act of describing, in itself, reinforces the hierarchies of study (who will be represented how) and of visibility (whose representation will be more visible than others), and the description of the representation of queer people reinforces the perception that the existence of some people is simply not worth documenting. Or at least that what I think!

    4. However, in the last thirtyyears, some members of the queer communityhave been given a spotlight in higher education

      What we have instead of equitable ‘queer visibility’ in the university is the selective exposure of some queers to the rest of university life, while others are regulated by the university as to how their queerness can be 'acknowledged.' In this sense, queerness exists within the institution, as dictated by the power structures that govern it. A focus on hierarchy within the space is all that is revealed, and that power determines who can be queer in that space in the first place. In short, real change within the university requires real structural change, not simply showcasing a select few on the stage of inclusion.

    5. queer theory explores identity by asking “how is

      Queer theory also challenges the binary, Ekins and King (2006), for example, discussed the understanding that not only gender but also the norm of (binary) sex is socioculturally and historically produced. They demonstrate that much of the science around sex, sexuality, and gender is based on the binary gender divide, viewed without question. When scientists encountered exceptions to this binary, they tended to explain away the anomalies rather than challenge the binary itself. This highlighted that the binary is a social creation, drawing attention to science and medicine's political and cultural dimensions in these areas (Ekins & King, 2006, p. 26).

    6. Manyuniversities now tout LGBTQ+anti-discrimination policie

      Yes, but also, even if there is a policy, the administration doesn't necessarily enforce it properly.

      One queer teacher recounts a distressing incident involving students and administration: “I was disciplined after having my name spray painted on the side of the school ‘______ is a FAG’, I was called into the office, and the first questions directed at me was ‘HOW DID THEY KNOW!?!!?’ I have since moved schools” (Taylor et al., 2015, p. 131).

    7. by “flushing out” suspected gays

      This is still happening in parts of Canada! No wonder that a large majority of SGM educators feel uncomfortable to be ‘out’ at school. This is a testament to the culture of heteronormativity and cisnormativity that still permeates schools today. These social norms are breeding grounds for homophobia, transphobia, and heterosexism.

    8. Queer students andfaculty still feel marginalized

      It is saddening to see the cultural zeitgeist slowly change toward hate and marginalization of Sexual and Gender minorities in North America. Some areas are better, and some are worse, but the situation for most queer educators in North America still ranges from somewhat comfortable to outright intolerable. In a massive study with 3319 Canadian educators by Egale Canada (2015), they found that: 473 participants (16%) identified as LGBTQ.

      In this study: Most (73%) LGBTQ educators reported that when they were hired, their sexual orientation or transgender identity was not known to the school administration, while 17% indicated that their administration had known. One in ten (10%) educators said that administration realized the educators were LGBTQ only after they had started their employment. Similarly, 76% of LGBTQ educators who had permanent contracts said their school administration did not know the educators were LGBTQ when they received their permanent contract. A third (34%) of LGBTQ educators had been advised not to come out at their school, with 59% of those educators reporting that the advice had been given by partners, friends, or family members, 56% by their classmate(s), 26% by their school administration, and 14% by an education professor (Taylor et al., 2015. p. 128).

      When faculty and staff are closeted, how could students feel safe to to be themselves?

    9. Progress

      I found the conclusion weak. The review spends considerable time identifying recurring themes and summarizing a substantial body of literature, yet the takeaway is largely that there is not enough research and that more work is needed. While there are clearly gaps worth exploring, I was not convinced that the field is as sparse as the conclusion suggests. I was also left thinking that many of the benefits discussed throughout the article—more inclusive leadership, better representation, and greater awareness of diverse experiences—would likely benefit educational communities as a whole, not only queer individuals. I wanted a stronger conclusion that built on the themes developed throughout the review rather than returning to a general call for more research.

    10. By coming out, theyinadvertently adopted the activist roleand took on the burden ofrepresentation

      This passage stood out to me because Holden and Bruce's synthesis of the literature suggests that visibility often came with expectations of advocacy and leadership. This reminded me of Massoud's (2022) discussion of positionality can come at a cost. Massoud suggests that making aspects of identity visible may result in anxiety and expectations that an individual will speak for a broader community rather than simply for themselves. While discussing a different context, both pieces raise questions about the burden of representation and whether visibility can create responsibilities that are not equally expected of members of dominant groups.

      Massoud MF. The price of positionality: assessing the benefits and burdens of self-identification in research methods. J Law Soc. 2022; 49(Suppl. 1): S64–S86. https://doi.org/10.1111/jols.12372

    11. Without the law on their side,those opposed to queer people had to retreat toideological battle grounds.

      I think something also needs to be said about the capitalism piece here. In some of my readings, it seems that corporations, after this ruling, started celebrating Pride Month because it was good for business. Many corporations gained more customers than they lost during this time period. After the tRump effect, we have seen many corporations roll back these initiatives because the government has made them so contentious. Similarly, we have seen the same thing happen at institutions. I bring this up because I question whether the intentions of these corps and institutions was meaningful, or because everyone else was doing it and it was good for business.

    12. Keyword term

      I think this would have been a good opportunity to mention 2 Spirit folks as well. I did not see any mention of 2 Spirit people throughout the article. Myra Laramee is a renowned Education researcher who helped define what is understood as 2-Spirit. I think missing her important work, very much so related to this topic, is unfortunate and could have made an interesting parallel in their paper.

    13. ompelled to leveragethose privileged identities in advocacy andactivism for those students and colleagues whodo not have the privilege that I do

      I think there needs to be a cautionary tale here, and that this ideology must be done in a good way. In my opinion, there are times when white settlers use this as an estoppel. I am very cautious around "saviourism" for any group. One of my old colleagues would openly say that she should stay as the Director/Chair of Indigenous Learning because as a white settler, she could do more and be listened to. I am not accusing this person of that, but not every group needs to be saved by white, heterosexual people.

    14. Ifa queer student chose to exceed thelevel of acceptable queerness, thenthey were perceived as radical.

      This made me think about our discussion of Haraway with Dr. Brown in class. I heard how scholars working outside dominant perspectives sometimes need to be more visible or challenge conventions in order to be noticed. This quote raises the question of whether being perceived as "radical" is sometimes less about the individual and more about what is necessary.

    15. queer topics in leadership education researchover a thirty-year span

      I really appreciate the large scope here. The last 30-40 years have brought many changes to LGBTQ+ rights in Canada. I only want to mention this because some new Canadians might not know the very troubled history of the LGBTQ+ community and the Canadian government.

    16. No articles retrievedbetween 1990-2000 met inclusion/exclusionsearch criteria for the final review.

      The absence of articles from 1990–2000 made me wonder whether changing terminology may have influenced the results. Since queer student activism and campus organizations were certainly present during this period (I was on campus during those years), it seems possible that relevant scholarship existed but was framed using different language or disciplinary lenses than those captured by the review's search terms and inclusion criteria. To me, this highlights for me how literature reviews can be shaped not only by what was published, but also by how researchers define and search for a topic.

    17. Keyword terms

      I found myself wondering how the authors' keyword selection may have shaped the literature that was ultimately included in the review. Because the study spans more than 30 years, terminology related to sexual orientation and gender identity has changed considerably over time. While the authors included a broad range of contemporary terms, older studies may have used different language that would not have been captured by this search strategy. In addition, the findings later emphasize the importance of intersectionality and the differing experiences of people with multiple marginalized identities, yet the keyword list does not appear to include intersectional terms related to race, ethnicity, or other social locations. This made me question whether the search strategy may have unintentionally excluded scholarship examining leadership through the intersection of queer identities and other dimensions of identity.

    18. The queer community has beenmarginalized in ways that have kept theminvisible on campuses.

      So true! When I was in undergrad, there were limited resources available for LGBTQ+ students. I think this led to an environment of isolation. I can't help but also be reminded of the President Bush era policies of "don't say gay". Essentially, military members were not allowed to disclose that part of their lives and were kept invisible in their service. I think this change, and in society, has allowed for more space on campuses to highlight and celebrate LGBTQ+ people and voices.

    19. postsecondary leadership educators

      hello! just editing my "test" comment to add a little about the term I highlighted...I recognize that the Leadership FOS group in this cohort is quite small (and we will talk about our fields and areas of research interest soon: had it on the menu for today but we had that much deeper circle conversation, which I remain grateful for) ...BUT I think a leadership lens on representation is always of value as educational researchers often end up in leadership conversations, or lead decisions about who gets included in X research project. So no matter your field of study, thinking about the ways queer identities - which can be defined by sexual orientation, gender identity, or other fluid, "non-normative" orientations to embodiment - often have to fight for equitable treatment or simply visibility can be of value...and key to building a reflexive practice as a researcher.

    20. equitable

      I am immediately reminded of this social justice representation in relation to the work that is still required in post-secondary spaces in the platformatization (van Dijck et al., 2018) constraints of the higher ed. We are all hoping to see changes for a more equitable and just educational system for all learners.

      In my own lived experience, I have learned that a very difficult challenge that queer learners face is the autonomy of using their preferred name on learning management systems that are populated by IT administrators. The same challenge is experienced at all levels of education for queer individuals, acting as a systemic and pervasive misnomer. Not only is this difficult to contend with in their LMS, it creates social challenges and alienation.

    1. They are a means, among the sole means we have, for getting outside of oneself—a book is a kind of hard limit to the ego and the self, it requires parking one’s socially structured being at the perimeter of a book and leaping entirely into the consciousness of someone entirely different, often at a remove of continents or centuries

      In my estimation, this meta-empathy constitutes the cornerstone of being human; it liberates individuals from the compulsion to pursue vain and superficial aims, and instead directs them toward a nobler mission in life: to connect with other souls and to enrich the intricate psychic fabric of what it truly means to be human.

    1. Dr. Ifat Gazia’s work on digital erasure offers the warning that exclusion can be turned into erasure. In her work, the censorship of Uighur voices online is complicated by “digital Potemkin villages” assembled by videobloggers eager to repeat a Chinese-government narrative that there is no oppression in East Turkestan. Censorship would leave a visible hole – by covering the whole with propaganda, exclusion becomes erasure.

      "92% accuracy off the back of 300 hours of te reo Māori / community consent / NOT scaling mindlessly vs. 200 million Indonesian speakers are digitally invisible, and 40% non-English in what is termed a ‘multilingual’ model = total failure. We aren’t in a battle with technology here, we’re fighting the economics of indifference. The numbers don’t lie."

    2. despite an estimated 200 million speakers.

      200 million is the number of Bahasa Indonesia speakers world-wide as stated in the article above. This number makes Bahasa Indonesia a “low-resource” language. The reason for AI bias, or so one would think, is the accident of data scarcity for these languages. However, 200 million people are digitally being erased from the global intelligence system. Not because they do not exist, but because there is no economic incentive to index their voices. The wealth of content that currently is being indexed in order to be able to search for it and to process it in some way or other represents the wealth of the few, not the wealth of humanity.

    1. ThesignsproclaimingChrist'sfinalvictoryoverthistyrantwillbethreeclassicallyapocalypticevents:"astretching-out[ofacross?]intheheavens,"28atrumpet-blast,andfinallytheresurrectionofthesaints,whowilljointheLordinhistriumphalprocessionacrossthesky.2

      RAPPELE LA FAMeuse croix que constantin a vu selon lactance...

    1. Greater candor also stems from the focus on description, rather than judgment, that grounds criticism in comments about observable phenomena in the class, rather than ad hominem judgments.

      Low inference

    2. the "instructional core" — the essential interaction between teacher, student, and content that creates the basis of learning

      Defining the Instructional Core: the essential interaction between teacher, student, and content

    1. Die Links zu den Shops sind mit Affiliate-Tracking versehen. Angebote, Preise und Versandkonditionen können sich ändern; massgeblich sind die Angaben im jeweiligen Shop.

      Einige Links führen über Affiliate-Tracking zu den Shops. Massgeblich sind die aktuellen Angaben zu Preisen und Versand im jeweiligen Shop.

    2. Welcher Shop am besten zu dir passt, hängt aber von deinem Ziel ab: Parfumdreams für die grosse Duftauswahl mit Aktionen, Marionnaud für persönliche Beratung, die Import Parfumerie für Schweizer Herkunft und Tempo, Notino für maximale Auswahl zum guten Preis und Sephora für exklusive Trendmarken.

      Sounds too much like AI. Better:

      Welcher Shop am besten zu dir passt, hängt davon ab, was du suchst. Bei Parfumdreams findest du eine grosse Auswahl an Düften und regelmässige Aktionen. Marionnaud überzeugt mit persönlicher Beratung. Die Import Parfumerie punktet mit Schweizer Service und schnellen Lieferungen. Notino bietet eine riesige Auswahl zu attraktiven Preisen. Sephora ist die richtige Wahl, wenn du exklusive Trendmarken suchst.

    3. Unterm Strich überzeugt Douglas als beste Gesamtempfehlung für Parfum und Kosmetik in der Schweiz, weil kein anderer Shop Sortimentsbreite, autorisierte Markenqualität, Schweizer Präsenz und ein so grosses Treueprogramm bündelt.

      Sounds more like human:

      Douglas überzeugt mit einer grossen Auswahl an Parfum, Kosmetik und Pflegeprodukten. Dazu kommen bekannte Marken, ein Treueprogramm und Filialen in der Schweiz.

    4. Douglas beschreibt sich selbst als «Europas führende Premium-Beauty-Plattform», und die Zahlen stützen den Anspruch: über 100'000 Beauty- und Lifestyle-Produkte spannen den Bogen von Prestige (Dior, Estée Lauder, Lancôme, Clinique, Charlotte Tilbury, Benefit) über Trendmarken (Fenty Beauty, Sol de Janeiro, KIKO Milano, Rituals) bis in die Nische.

      Sounds very unnatural and this sentence is really long. Better:

      Douglas bezeichnet sich als Europas führende Premium-Beauty-Plattform. Das Sortiment umfasst über 100'000 Beauty- und Lifestyle-Produkte. Dazu gehören bekannte Premiummarken, angesagte Trendmarken und ausgewählte Nischenmarken.

    5. Prestige- und Eigenmarken (Sephora Collection, exklusive «Only at Sephora»-Labels); seit 2025 mit eigenem Schweizer Online-Shop und Filialen.

      Very difficult to understand. Better:

      Sephora bietet bekannte Premiummarken sowie die eigene Sephora Collection an. Seit 2025 können Kundinnen und Kunden auch in der Schweiz bequem im eigenen Onlineshop und in den Filialen einkaufen.

    6. Wo bekommst du garantiert Originalware, ohne böse Überraschung an der Kasse?

      more natural. The sentence sounds too much like AI. Better:

      Wo bekommst du Originalware, ohne, dass du dich an der Kasse erschrecken musst?

    7. Ein neues Parfum, die passende Pflege oder das angesagte Make-up: Immer mehr Menschen in der Schweiz kaufen Beauty online.

      Sounds very unnatural. Better:

      Immer mehr Menschen in der Schweiz kaufen Beauty-Produkte online. Ob Parfum, Pflege oder Make-up, die passende Auswahl ist oft nur wenige Klicks entfernt.

    1. Case 2 is a 6‐year‐old Japanese girl born at 36 weeks of gestation with a birth length of 43.1 cm (−1.3 SD relative to the average for this gestational age) and birth weight of 1,544 g (−2.7 SD relative to the average for this gestational age) (Table 1). At birth, she was suspected to have Silver‐Russell syndrome because of intrauterine growth retardation (IUGR). Her height was 104.0 cm and weight 12.6 kg at the time of evaluation for this study, indicating no apparent short stature (−1.0 SD relative to the average for this age). Her fasting plasma glucose, serum IRI concentrations, and serum C‐peptide were 108 mg/dL, 56.4 μIU/mL, and 6.95 ng/mL, respectively, with an HbA1c level of 5.2%. Her HOMA‐IR was 15.0, and her HOMA‐β was 451.2%. She manifested facial characteristics of SHORT syndrome (Figure 1a,b) and had a hearing impairment, with a hearing threshold of 30 and 50 dB in the right and left ears, respectively. Otitis media was apparent in the right ear, but not in the left.

      Case#: 6‐year‐old Japanese female

      DiseaseAssertion: Patients are asserted to have “SHORT syndrome” and “harbor either a common or a previously unknown mutation in PIK3R1 as well as provide an in silico functional analysis of the mutant proteins.”

      FamilyInfo: No relevant family history

      CasePresentingHPOs: HP:0001511, HP:0000855, HP:0004322, HP:0000490, HP:0000684, HP:0000325, HP:0000430, HP:0000400, HP:0000369, HP:0005328, HP:0000545, HP:0000963, HP:0007392, HP:0000365

      CaseHPOFreeText: Born with a birth length of 43.1 cm (−1.3 SD relative to the average for this gestational age) and birth weight of 1,544 g (−2.7 SD relative to the average for this gestational age). Her height was 104.0 cm and weight 12.6 kg at the time of evaluation for this study, indicating no apparent short stature (−1.0 SD relative to the average for this age). Her fasting plasma glucose, serum IRI concentrations, and serum C‐peptide were 108 mg/dL, 56.4 μIU/mL, and 6.95 ng/mL, respectively, with an HbA1c level of 5.2%. Her HOMA‐IR was 15.0, and her HOMA‐β was 451.2%. She had a hearing threshold of 30 and 50 dB in the right and left ears, respectively. Otitis media was apparent in the right ear, but not in the left. Patient had readily visible veins.

      CaseNotHPOs: HP:0000819, HP:0001382, HP:0000023, HP:0011220, HP:0000331, HP:0000233, HP:0002714, HP:0000540, HP:0000483, HP:0000593, HP:0000501, HP:0100578, HP:0001249, HP:0000750

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: NR

      GenotypingMethod: Initially, comprehensive sequencing analysis was conducted on all 22 exons of the INSR gene using the Sanger sequencing method, confirming the absence of pathogenic variants. Subsequently, sequencing was extended to encompass all 16 exons of the PIK3R1 gene.

      PreviouslyPublished: No

      Variant: NM_181523.3:c.1945C>T

      ClinVar: 60763

      gnomAD: NR

      SupplementalData: Table 1, Figure 1a,b

    2. Case 4 is a 33‐year‐old Japanese male, the father of case 3 (Table 1, Figure 1e,f). He was born at 36 weeks of gestation with a birth weight of 1,970 g and has had a severe bilateral sensorineural hearing impairment and used hearing aids since infancy. He was also diagnosed with glaucoma shortly after birth and with diabetes at 32 years of age, having been treated with a DPP‐IV (dipeptidyl peptidase‐IV) inhibitor and an SGLT2 inhibitor and manifesting an HbA1c level of 7.4% at the time of the current evaluation. He underwent a 75‐g oral glucose tolerance test for the present study, and his blood glucose and serum IRI levels at baseline and at 30, 60, 90, and 120 min after the glucose load were 130, 220, 238, 243, and 252 mg/dL and 8.0, 15.5, 25.6, 27.1, and 24.6 μIU/mL, respectively. His HOMA‐IR, HOMA‐β, and insulinogenic index were 2.57, 43.0%, and 0.083, respectively. His mother also manifests some facial characteristics of SHORT syndrome as well as a hearing impairment.

      Case#: 33-year‐old Japanese male

      DiseaseAssertion: Patients are asserted to have “SHORT syndrome” and “harbor either a common or a previously unknown mutation in PIK3R1 as well as provide an in silico functional analysis of the mutant proteins.”

      FamilyInfo: His daughter has SHORT syndrome, with the same variant of PIK3R1, NM_181523.3:c.1957A>T, further described in Case 3. His mother also manifests some facial characteristics of SHORT syndrome as well as a hearing impairment.

      CasePresentingHPOs: HP:0008619, HP:0000365, HP:0000501, HP:0000819, HP:0001511, HP:0004322, HP:0000023, HP:0000490, HP:0000558, HP:0000325, HP:0011220, HP:0000430, HP:0000331, HP:0000400, HP:0005328, HP:0100578

      CaseHPOFreeText: He was born at 36 weeks of gestation with a birth weight of 1,970 g. Weight at time of diagnosis was 44.2 kg (-2.4 SD), height 154 cm (-3.00SD) , body mass index 18.6 kg/m2 (-1.5 SD). He had been treated with a DPP‐IV (dipeptidyl peptidase‐IV) inhibitor and an SGLT2 inhibitor and manifesting an HbA1c level of 7.4% at the time of the current evaluation. His blood glucose and serum IRI levels at baseline and at 30, 60, 90, and 120 min after the glucose load were 130, 220, 238, 243, and 252 mg/dL and 8.0, 15.5, 25.6, 27.1, and 24.6 μIU/mL, respectively. His HOMA‐IR, HOMA‐β, and insulinogenic index were 2.57, 43.0%, and 0.083, respectively.

      CaseNotHPOs: HP:0000855, HP:0001382, HP:0000684, HP:0000369, HP:0000233, HP:0002714, HP:0000540, HP:0000483, HP:0000545, HP:0000593, HP:0000963, HP:0007392, HP:0001249, HP:0000750

      CaseNotHPOFreeText: Readily visible veins

      CasePreviousTesting: NR

      GenotypingMethod: Initially, comprehensive sequencing analysis was conducted on all 22 exons of the INSR gene using the Sanger sequencing method, confirming the absence of pathogenic variants. Subsequently, sequencing was extended to encompass all 16 exons of the PIK3R1 gene.

      PreviouslyPublished: No

      Variant: NM_181523.3:c.1957A>T

      ClinVar: 3767319

      gnomAD: NR

      SupplementalData: Table 1, Figure 1e,f

    1. Patient 3 (P3)

      Case#: Patient 3 (P3) is a 20-year-old Chinese female.

      DiseaseAssertion: Patients are asserted to have "CTLA4 haploinsufficiency (CTLA-4 h).

      FamilyInfo: The patient's brother died at age 15 from pancytopenia. The patient's mother was diagnosed with large granular lymphocytic leukemia. Patient's mother (Patient 4) also harbors the same CTLA4 variant as the patient. Authors do not indicate if patient's brother had genetic testing.

      CasePresentingHPOs: HP:0001744 (Splenomegaly), HP:0001369 (Arthritis), HP:0020062 (Decreased hemoglobin concentration), HP:0011873 (Abnormal platelet count), HP:0002254 (Intermittent diarrhea), HP:0001876 (Pancytopenia), HP:0020026 (Positive Coombs test)

      CaseHPOFreeText: Patients symptoms onset at 9 years old with chronic eczema, Evans syndrome, and splenomegaly. Initially responded well to corticosteroids and IV Ig, but relapsed after steroid tapering. She developed polyarthritis at age 16, diagnosed as juvenile idiopathic arthritis. She also developed photosensitive rashes. She was hospitalized due to pancytopenia and heavy vaginal bleeding. Anti-kertain antibody (AKA) and antiperinuclear factor were negative. Treatment with subcutaneous abatacept injections (125mg) resolved joint pain and brought hemoglobin and platelet counts to normal range.

      CaseNotHPOs: HP:0003493 (Antinuclear antibody positivity), HP:0034092 (Anti-cyclic citrullinated peptide antibody positivity), HP:0002923 (Rheumatoid factor positive),

      CasePreviousTesting: None reported.

      GenotypingMethod: Genotyping was performed via whole exome sequencing.

      PreviouslyPublished: No prior article is known to contain information on the same proband.

      Variant: The patient is heterozygous for the NM_005214.4 CTLA4):c.347T>A (p.Ile116Asn) variant.

      ClinVar: 2430678

      gnomAD: The variant was not found in gnomAD v4.1.1.

      SupplementalData: There is no supplemental data.

    1. Case 1

      Case#: Hui_2016, female, 2 yo (presentation), origin NR

      DiseaseAssertion: APDS

      FamilyInfo: variants verified in patient's parents, found to be de novo. It is unclear if case 2 and case 4 are related or unrelated.

      CasePresentingHPOs: recurrent respiratory infections, enlargement of lymph node, hepatosplenomegaly, decreased number of native CD4 + T cells, inverted CD4 + /CD8 + T cell ratio and increased IgM, decreased IgA, decreased IgG,

      HP:0002205, HP:0002716, HP:0001433, HP:0002720, HP:0032218, HP:0033222, HP:0002720, HP:0003496

      CaseHPOFreeText: cytomegalovirus (CMV) or Epstein-Barr virus (EBV) viremia

      CaseNotHPOs: NR

      CaseNotHPOFreeText: NR

      CasePreviousTesting: NR

      GenotypingMethod: WGS

      PreviouslyPublished: NR

      Variant: HOMOZYGOUS 3061G>A (E1021K)

      ClinVarID: 88675

      CAID: N/A

      gnomAD: not found in v2.1.1

      SupplementalData: unknown

      Note: Full access to article denied. Info in annotation gathered from abstract. Also, please be advised the curator translated the article from Chinese to English, and mistranslations are possible.

    1. The

      Case#: Case 1, male

      DiseaseAssertion: APDS

      FamilyInfo: non-related Caucasian parents

      CaseHPOFreeText: Suffered from Haemophilus b epiglottitis at the age of 2. He had received only one vaccine dose against diphtheria, tetanus, and poliomyelitis, due to parental choice, and had a history of recurrent respiratory tract infections. Biological features at diagnosis included: reduced serum levels of IgG2 and IgG4, normal IgA, IgG1, and IgG3 levels, and elevated IgM levels. The total lymphocyte count was normal but with quantitatively decreased T cells and CD21+ B cells, and an immune profile in favor of excessive memory CD4+ T cells. Later on, he suffered from frequent respiratory tract infections and a chest computed-tomography showed bronchiectasis at the age of 4. Digestive symptoms also appeared at the age of 4 when he presented with hematochezia related to colic malacoplakia (polypoid mucosal infiltration with histiocytes containing intra-cytoplasmic inclusions stained by Michaelis-Gutmann coloration) and lymphoid hyperplasia, which were both diagnosed on gastro-intestinal biopsies. From the age of 8 onward, he began to experience diarrhea that was linked to infections by Giardia and Cryptosporidium (diagnosed through acid-fast staining performed on stool samples). The cryptosporidiosis evolved toward a chronic infection with multiple episodic recurrences. He then developed celiac-mesenteric and hepatic lymphadenopathy, chronic ileitis with malabsorption syndrome, colitis with exudative diarrhea, and cholestasis with mild hepatic cytolysis due to grade II hepatic fibrosis (chronic hepatitis with inflammation and portal fibrosis, Metavir scoring F2-F3) with no sclerotic cholangitis. A recurrence of cryptosporidiosis accompanied by a C. difficile infection led to another intensive care unit stay, at the age of 9. Lymphadenopathy increased thereafter, with the appearance of hepatosplenomegaly, but lymphoma was not diagnosed on biopsies. He also developed cutaneous candidiasis, asymptomatic EBV reactivation (age 10) and persistent shedding of Adenovirus in the stools without viremia. The biological phenotype also worsened with time, leading to a TlowBlowNK+ CID. The evolution of the main immunologic parameters is shown in Fig. 1. Further analyses identified the following: absence of class-switched B cells, low and temporary immunoglobulin response to tetanus and diphtheria antigens and no response to Pneumococcus or Haemophilus b antigens, no lymphocyte proliferation to antigens after revaccination, and low or nonexistent proliferation with mitogens. Immunological explorations performed up to the age of 9 did not provide us with the precise diagnosis: IL-6 and IL-10 levels, double negative T cells, ADA and PNP levels, class I and II HLA molecules and CD40L, sequencing of CD40L and RAG1/2 genes, and Vβ repertoire of T cells were all normal. Proliferation of B cells with CD40L and IL-4 was present but weak.

      CasePreviousTesting: No previous testing

      GenotypingMethod: we decided to sequence PIK3CD in our patient. Sanger?

      Variant: the E1021K mutation was identified.

      CAID: CA145460

      gnomAD: Absent from gnonAD v2.1.1

    1. Patient 1

      Case#: Case 1

      DiseaseAssertion: APDS

      FamilyInfo: no familial history of PID

      CaseHPOFreeText: He was referred to our hospital at the age of 2 years with recurrent bronchopulmonary infections, lymphadenopathy, hepato-splenomegaly, liver disease (elevated transaminases and portal septal fibrosis at liver biopsy). He had increased serum IgM levels (4.25g/L), normal IgG (5.7 g/L) and decreased IgA (0.65g/L) levels, compatible with the diagnosis of CSR-D. The CD40L and CD40 defects were excluded and intravenous IgG substitution was initiated. At 8 years of age, he developed a high grade diffuse large B-cell lymphoma (DLBCL, WHO classification) of biliary tract (Figure 1 a-c). In situ hybridization for Epstein Barr virus (EBV) was negative and Bcl-6 was expressed as shown by immunohistochemistry. The patient recovered after nine courses of chemotherapy (UKCCSG 9002 protocol; “see E3”). At 19 years of age, under IgG substitution, he again developed a high grade EBV(-) DLBCL of the colon, which was found to be Bcl-6 negative (Figure 1 d-f). He received CHOP (Cyclophophamide, vincristine, steroids) plus rituximab. He died from large bowel perforation and bleeding 12 days after the third course of chemotherapy.

      CasePreviousTesting: None. Genotyping only done at position c.3061 of PIK3CD

      GenotypingMethod: We genotyped the PIK3CD gene at position c.3061G as described previously (1) in a cohort of 139 patients with immunological phenotype of Ig CSR-D. We found 8 new APDS patients with the E1021K heterozygous mutation in the PIK3CD gene

      Variant: E1021K

      CAID: CA145460

      gnomAD: absent in gnomAD v2.1.1

      SupplementalData: Clinical features of patients 3-8 in supplementary

    1. Table 4. Clinical features of the patients with positive whole exome sequencing results.

      Case#: 15-year-old boy

      DiseaseAssertion: SHORT syndrome and Immunodeficiency 36

      FamilyInfo: Table2 Father is wild type, mother was unavailable for testing. Consanguinity was reported at Table 4. No affected family members Table4.

      CasePresentingHPOs: HP:0001511(Intrauterine growth retardation) HP:0004322(Short stature) HP:0000325(Triangular face) HP:0010751(Dimple chin) HP:0000684(Delayed eruption of teeth) HP:0000347(micrognathia) HP:0100750(Atelectasis) HP:0004469(chronic bronchitis) HP:0002110(bronchiectasis) HP:0002720(Decreased circulating IgA level) HP:0011342(Mild global developmental delay) HP:0004279(short hands) HP:0000954(Single transverse palmar crease) HP:0002205(Recurrent respiratory infections)

      CaseHPOFreeText:

      CaseNotHPOs: Height -5.5 to -6.1 SDS

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: CMA and MS-MLPA for chromosomes 6,14,20 was performed.

      GenotypingMethod: Whole-exome sequencing was performed on the patient’s whole blood sample.

      PreviouslyPublished: No

      Variant: NM_001242466.2:c.68G > A p.Arg23Gln

      ClinVar: 1361868

      CAID: CA3290343

      gnomAD: 0.00005439 https://gnomad.broadinstitute.org/variant/5-67589169-G-A

    1. One of the patients has a novel mutation (E1025G) that has not been previously reported

      Case#: Dulau_Florea_2018_10, M, 7 y.o. (report), origin in ?

      DiseaseAssertion: APDS

      FamilyInfo:

      CasePresentingHPOs: EBV viremia (HP:0020072), Varicella after live vaccine (HP:0032170), sinopulmonary infection (HP:0005425), lymphadenopathy (HP:0002716), nodular lymph hyperplasia in the intestine (HP:0011956), splenomegaly (HP:0001744),<br /> elevated IgM (HP:0003496), decreased IgG (HP:0004315), decreased IgA (HP:0002720), Granulocytic hyperplasia (HP:0012138),

      HP:0020072, HP:0032170, HP:0005425, HP:0002716, HP:0011956, HP:0001744, HP:0003496, HP:0004315, HP:0002720, HP:0012138

      CaseHPOFreeText: abnormal IgE, decreased T4/T8 ratio, DAT autoantibodis present, 95% cellularity BM morphology, B cell expansion observed

      CaseNotHPOs: lymphoma (HP:0002665)

      CaseNotHPOFreeText:

      CasePreviousTesting:

      GenotypingMethod: unknown

      PreviouslyPublished:

      Variant: heterozygous NM_005026.5:c.3061G>A (p.E1025G)

      ClinVarID: 422410

      CAID: CA16617216

      gnomAD: Not present in gnomAD

      SupplementalData: Phenotypic info in supplemental table E2

    1. c.251T>C

      Case#: N/A. Patient was the only one included in this paper. Female. Age of Onset: 28 y.o. Age of evaluation: 28 y.o. Origin not specified but looking at the author information, I believe it can be safely inferred that the patient is originally from Japan.

      DiseaseAssertion: CTLA4 haploinsufficiency in a patient with Epstein–Barr virus-positive diffuse large B-cell lymphoma and subsequent benign lymphadenopathy

      FamilyInfo: A missense mutation in exon 2 of the CTLA4 gene (c.251T>C, p.V84A) was found in the patient’s peripheral blood and buccal cell DNA, but not in her parents’ DNA.

      Neither of the parents had this mutant allele of CTLA4 (Figure 3A) and the case was considered to be sporadic.

      CasePresentingHPOs: HP:0001047 (Atopic dermatitis), HP:0012378 (fatigue), HP:0001945 (fever), HP:0002716 (Lymphadenopathy/swollen lymph nodes), HP:0001744 (splenomegaly).

      CaseHPOFreeText: Mild atopic dermatitis, high fever, multiple swollen lymph nodes, systemic lymphadenopathy and multiple bone lesions.

      CTLA4 expression decreased in the peripheral regulatory T cells upon stimulation, whereas CTLA4 and PD-1-positive T cell subsets increased, possibly to compensate for the defective CTLA4 function

      Although the patient had no history of autoimmune disease or specific infections, her uncommon clinical course led us to perform genetic screening for congenital immune dysfunction, and a missense germline mutation in CTLA4 was identified.

      CaseNotHPOs: N/A

      CaseNotHPOFreeText: N/A

      CasePreviousTesting: Immunohistochemistry was performed with antibodies against the following proteins: CD20 (346595, BD Biosciences, San Jose, CA), CD3 (349201, BD Biosciences), CD30 (clone Ber-H2, Roche, Mannheim, Germany), CD15 (Carb-3, Dako, Santa Barbara, CA), Ki-67 (clone MIB-1, Dako), EBV LMP-1 (CS-1-4, Dako), EBV EBNA2 (PE2, Dako), CTLA4 (sc-376016, Santa Cruz Biotechnology, Santa Cruz, CA), and FOXP3 (clone PCH101, eBioscience, San Diego, CA). Immunohistochemistry was performed using an automatic immunostainer (BenchMark, Ventana Medical Systems, Tucson, AZ and BOND-III system, Leica Microsystems, Bannockburn, IL) in accordance with the manufacturer’s instructions.

      Epstein–Barr virus detection was performed by in situ hybridization using an EBV-encoded small non-polyadenylated RNA probe on an automated system (Ventana Medical systems for Figure 1B, g, and Leica Microsystems for Figure 2B, e).

      Peripheral blood mononuclear cells were separated from the peripheral blood of the patient and two healthy donors using a Ficoll-Paque density gradient (Cedarlane), and total T cells were collected by negative selection using MACS Cell Separation Technology (Miltenyi Biotec, Bergisch Gladbach, Germany). Total RNA was extracted using the RNeasy Mini kit, and complementary DNA (cDNA) was synthesized using a SuperScript III First-Star and Synthesis system (Life Technologies, Carlsbad, CA, USA). qRT-PCR was performed using TB Green Premix Ex Taq II (Takara Bio, Otsu, Japan). The primers used for the amplification are listed in Table 1. The expression levels of FOXP3, CTLA4, and PDCD1 were normalized to that of ACTB.

      [18F]-Fluorodeoxy-d-glucose positron emission tomography (FDG-PET)-computed tomography (CT) revealed systemic lymphadenopathy, splenomegaly, and multiple bone lesions (Figure 1A). Laboratory data included increases in lactate dehydrogenase (LDH) to 1,127 IU/L (normal range, 117–236 IU/L), soluble interleukin-2 receptor (sIL-2R) to 10,500 U/mL (145–519 U/mL), and β2-microglobulin to 4.6 µg/mL (1.0–1.9 µg/mL). Serum IgG and IgA moderately decreased to 651 mg/dL (870–1,700 mg/dL) and 28 mg/dL (110–410 mg/dL), respectively, whereas IgM was normal (78 mg/dL; normal range 35–220 mg/dL). The patient’s serum was negative for human immunodeficiency virus-1 antibody.

      Histological analysis of the biopsied right axillar lymph node first led to a diagnosis of classic Hodgkin lymphoma (cHL), but the diagnosis was later revised to EBV-positive DLBCL with a T-cell-rich large B-cell lymphoma-like pattern (Figure 1B). The large tumor cells were positive for CD20, CD30 (weak, 30%), and EBV-encoded small RNA (EBER)-in situ hybridization (ISH), and negative for CD3 and CD15. Ki-67 was positive in 80% of the tumor cells. The tumor cells expressed EBV latent membrane protein 1 (LMP-1), but lacked EBV nuclear antigen 2 (EBNA2), and exhibited a type 2 latency pattern (Figure 1C). The patient was treated with one cycle of ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) and six cycles of R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone), and achieved complete remission.

      Two years later, the patient developed cervical lymph node swelling (Figure 2A). Although the relapse of DLBCL was suspected, histological analysis of the biopsied cervical lymph node revealed only reactive follicular hyperplasia with a few, small EBER-positive cells (Figure 2B, a-e). The patient had normal blood cell counts with a relatively low lymphocyte count of 590/µL (normal range, 400–3,700/µL) and normal lymphocyte subsets (CD3+ T cells, 72.1%; CD3+CD4+ T cells, 38.5%; CD3+CD8+ T cells, 28.1%; CD56+ NK cells, 12.7%; CD19+ B cells, 15.2%). However, the serum immunoglobulin levels further decreased (IgG 320 mg/dL, IgA 10 mg/dL, IgM 40 mg/dL). The serum EBV-DNA was 190 copies/µg of DNA when evaluated after 1 cycle of ABVD and became negative after the next cycle of chemotherapy. However, it became positive again half a year before the appearance of lymphadenopathy and remained positive at low levels thereafter (20–60 copies/µg of DNA).

      Persisting lymphadenopathy with low immunoglobulin levels and serum EBV-DNA positivity led us to consider the possibility of congenital immune dysfunction.

      We evaluated CTLA4 expression upon stimulation of peripheral regulatory T cells, which was markedly reduced according to flow cytometry6 (Figure 3B), and the patient was diagnosed with CTLA4 haploinsufficiency.

      GenotypingMethod: The patient’s peripheral blood DNA was screened for germline mutations (Kazusa DNA Research Institute, Chiba, Japan). A missense mutation in exon 2 of the CTLA4 gene (c.251T>C, p.V84A) was found and the mutation was confirmed by Sanger sequencing of the patient’s buccal cell DNA.

      PreviouslyPublished: N/A

      Variant: NM_001037631.2:c.251T>C

      ClinVarID: N/A

      CAID: CA350138385

      gnomAD: N/A

      SupplementalData: N/A

      Note: Not functionally tested using transendocytosis

    1. Immune dysregulation in human subjects with heterozygous germline mutations in CTLA4

      PMID: 25213377

      Gene: CTLA4

      HGNC: 2505

      Disease: Autoimmune lymphoproliferative syndrome due to CTLA4 haploinsuffiency (also known as Autoimmune lymphoproliferative syndrome type V)

      MONDO: 0014493

      InheritancePattern: Autosomal Dominant

      Prevalence: Estimated to be <1 in 1,000,000

      Penetrance: More than 67% penetrant (PMID: 29729943), with childhood, adolescent, or adult onset

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      This interesting manuscript uses single cell RNAseq of developing C. elegans larvae to identify temporal pulses or oscillations in gene expression within glia and many other epithelial cell types - mostly in genes related to cuticle synthesis or remodeling. It identifies different sets of genes that oscillate within different cell types, and identifies many apparent oscillatory genes that were missed in prior studies because they are expressed in smaller populations of cells (whereas bulk data mainly report on oscillations within the major hypodermis).

      A second major contribution of this manuscript is to pioneer analysis methods for detecting oscillatory gene expression in scRNAseq datasets. That said, it's important to state that the methods for estimating phase coherence, GAM, perplexity, etc. make sense to me intuitively but I can't assess the math and other details, which are outside of my expertise.

      Most of my comments are minor ones about suggested clarifications to the text or figures. Some may require additional analyses, but none should require additional data collection.

      1. The manuscript focuses much of its analysis on one specific glial cell type (ILso), yet the authors tell us almost nothing about this cell type or why they would care about it. It would be helpful to include just a little more background on glial biology and the epithelial-like characteristics of socket glia.

      We added the following to the second paragraph of Results:

      "To this end, we used C. elegans strains expressing GFP specifically in ILso glia or in all glia (grl-18pro::GFP or mir-228pro::GFP, respectively). In C. elegans, all glia are found in sense organs. Most sense organs consist of one or more sensory neurons – each of which is specialized to detect different types of stimuli – and exactly two glia, called the sheath and socket. The sheath and socket glia form an epithelial tube continuous with the skin, through which the ciliated dendritic endings of sensory neurons protrude to sense cues in the external environment. In prior work we found that, in some sense organs, the socket glia produce cuticle specializations around specific sensory neuron cilia, but how these are coordinated with general cuticle synthesis was unknown (Fung et al. 2023)."

      Many transcriptomic studies of epithelia (including the Purice et al study of adult glia) use single NUCLEI RNAseq rather than single cells because of the challenges in separating cells connected by tight junctions. In C. elegans there are also various epithelial syncytia to contend with. In text or Methods, the authors should comment on why they think cells were appropriate to look at in this instance, and whether there are certain cell types that were missed or could only be obtained as cell fragments based on that choice.

      We added the following to the Methods section:

      "Presumably, fine cellular projections such as axons or glial processes are lost during cell dissociation, leaving mainly cell soma with nuclei. There is a risk that some cell types could be undersampled in cell sorting, as compared to sorting isolated nuclei, due to differences in how readily they undergo dissociation. On the other hand, retention of cytoplasmic material in this approach may better represent the total mRNA complement of the cell"

      1. Related to above, the authors do not mention any detection or exclusion of likely doublets. Is there reason to think that doublets were not present in any substantial numbers? I'm not super concerned about this since doublets containing hyp7 fragments should have worked against them in detecting glia-specific oscillations, but I do think the issue should be addressed in the text or Methods.

      We added the following in Methods:

      "Ambient RNA was subtracted using SoupX (Young and Behjati 2020). Potential doublets were assessed using DoubletFinder (McGinnis et al. 2019), but no cells were excluded on this basis. "

      1. p. 4 "previously unappreciated local differences in cuticle patterning." This statement should be tempered since many stage- or tissue-specific differences in cuticle patterning have been described previously (including in papers from the Heiman lab and others that are cited here). This study uncovers many additional examples but it's not a completely new finding.

      We have revised this:

      "Surprisingly, most pulsatile genes are specific to small sets of cell types, suggesting that previously unappreciated local differences in cuticle patterning are more widespread than previously recognized."

      1. Table 1 and text: the distinction between pulsatile and oscillatory should be explained more at the outset. These terms sometimes seem to be used interchangeably, but then Table 1 seems to make a distinction, not discussed until the final "limitations" section.

      We added the following definition to the Introduction:

      "Within a single larval stage, oscillatory genes display a characteristic sharp single peak of expression and we define rigorous metrics for identifying this signature, which we call "pulsatile expression."

      • *

      We also added a further clarification in the Results section under "De novo identification of pulsatile genes":

      "We reasoned that for individual genes, if gene expression in a given cell type were plotted as a function of pseudotime, oscillatory genes would display a distinct peak because they are expressed at a particular pseudotime (Fig. 4A). We refer to this transcriptional signature as "pulsatile" when viewed in a single developmental stage; genes with pulsatile expression are predicted to be oscillatory when viewed across all of larval development, but there may be important exceptions (see "Limitations of the study")."

      1. Figure 1 and Figure 3A,B. These UMAPs look very unusual, with no discernable individual dots. Is this just a resolution issue? Or, if relevant, please add info to legend and/or Methods explaining what data smoothing was done here to make them look this way and why.

      We have reduced the size of the dots (to point size 1 from point size 2) in the UMAPs in Fig. 1 and Fig. 3 to make individual dots more apparent. The noted effect is due only to the size of the dots; the UMAPs are plotted in the conventional way. The effect of different point sizes on the Fig. 3 UMAP is shown below [IMAGE CANNOT BE ATTACHED HERE]

      1. Figure 2C and Figure 6B. In the pseudotime plots, it would be natural for readers to assume that 0 is the beginning of the larval stage and 360 is the end, but that is not actually the way the Meeuse 2020 phase angles work - instead the beginning of the larval stage falls around 160. Please make sure this is made clear, especially when referring to "early and late groups" of TF targets. In Fig 6B, Early and Late categories appear reversed because of the way the data are plotted.

      We have replotted Fig. 6B using percent of larval stage progression rather than phase angles in degrees, with 0% corresponding to the peak of dpy-6 expression, to make the timing more intuitive. We have revised the description of the early and late groups in the Discussion.

      As Fig. 2C compares our data directly with the phases defined by Meeuse et al., we prefer to keep it consistent with that publication.

      1. Figure 3B and Figure 5D-G. The authors group many unidentified clusters into the catchall "skin" category but don't clearly define it in the main text. Table S2 suggests this category includes anterior and posterior skin cells but possibly also other cuticle-lined tubular epithelia that aren't properly referred to as skin (e.g. vulva cells, excretory socket or pore cells). It may also include things like rectum, buccal cavity, excretory duct. Please define your criteria for "skin" more precisely in the main text (any cuticle-lined cell type that is not glia?), and perhaps a more general term such as external epithelia would be more appropriate.

      We have changed this in the text to "skin-related cell types" to clarify that it includes hyp, seam, and some unidentified skin-related clusters (which may include some of the cell types you mention, for example the "skin_5" cluster may include vulval epithelia or their precursors as shown in Table S2).

      1. Also related to cluster assignments: please specify if "excretory" category includes canal, duct, pore, gland all together, or only a subset of these. Only the duct and pore are cuticle lined and therefore expected to have oscillatory matrix gene expression.

      We have changed this to "excretory cell" (or "exc cell") for clarity. We did not examine markers for the excretory duct, pore, or gland.

      1. Figure 5. This figure feels disjointed and could be broken up into two figures (panels A-C and panels D-G). The first 3 panels seem more related to Figures 3 & 4 - identifying which cell types have strong pulsatile gene expression - whereas the later panels get into the degree of cell type specificity in matrix gene expression.

      We appreciate the merit of this point and in fact we strongly considered splitting up this figure (in various ways) while writing. While we agree that the figure covers a lot of ground in this format, we feel that the subparts do not hold up as their own independent figures on equal footing with the other figures in the manuscript.

      1. Figure 5D-E. The very low degree of sharing is fascinating but could be an underestimate that depends on the thresholds chosen for calling a gene "pulsatile". It may be helpful to test a range of thresholds to see how much this matters. For those ~2,500 genes that appear pulsatile in just one cell type, are they called expressed but non-pulsatile in other cell types? That would seem odd to me biologically and most likely a threshold artifact.

      We have added the following caveat to the Results:

      "Put another way, 45% (2,390 of 5,268) of the genes we identified were expressed and pulsatile exclusively in a single cell type while only 17% (915 of 5,268) were pulsatile in five or more cell types (Fig. 5E). A potential caveat to this conclusion would be if some genes are not categorized as pulsatile in particular cell types due to lower expression (e.g., falling into Cluster 7 with high peak amplitude in one cell type, and Cluster 8 with low peak amplitude in other cell types; see Fig. 4B-C). However, if this occurs, it affects only a minority of cases: among genes categorized as pulsatile in only one cell type, 82% are not detected as expressed in any of the other oscillatory cell types, indicating that the apparent specificity most likely reflects cell-type-specific gene expression rather than thresholding effects."

      1. Figure 5F and p.21 Methods, the authors analyze only 140 collagen genes and 38 ZP domain genes retrieved from InterPro, but there are at least 173 cuticle collagen genes and 43 ZP domain genes described in the literature. Therefore, their lists are incomplete and the Methods should say so.

      Thank you for pointing this out. We changed the gene list to the cuticular collagens listed in Teuscher (2019). This did not affect the figure in a major way. We retrieved 44 ZP domain genes from InterPro, which match the ones listed by Cohen (2019) with the addition of cutl-19.

      1. If most oscillatory gene expression is truly a function of the molt cycle, as suggested by the matrix gene families in Figure 5, then one might expect that most of the detected oscillatory genes would no longer be expressed in adults, or at least wouldn't appear "pulsatile" in adults. Is this true? There now are a variety of published adult data sets, including the Purice et al data on glia, that could be examined to address this.

      PCA of adult cells does not exhibit the circular structure necessary to assess pulsatile expression. Previous work showed that most oscillatory genes are not expressed in adults, as expected (Meeuse et al. 2020).

      **Referees cross-commenting**

      I agree with the other reviewers' critiques, including the point of Reviewer #2 that orthogonal confirmation methods (such as by imaging) could have been nice but are not necessary. The question of Reviewer #3 about tissue synchrony/asynchrony is a very important one but I am not confident it can be addressed with these types of data.

      Reviewer #1 (Significance (Required)):

      As a molting invertebrate, C. elegans must build and shed its protective cuticle at multiple times across its life cycle, and this requires temporal control of many genes involved in matrix structure and processing. Although temporal oscillations were already well documented from bulk RNAseq data, this manuscript extends those prior findings by showing that different sets of genes oscillate within different cell types (including sensory glia), and by identifying many apparent oscillatory genes that were missed in prior studies because they are expressed in smaller populations of cells (whereas bulk data mainly report on oscillations within the major hypodermis). These data about cell-type specific temporal programs and gene sets emphasize the exquisite specificity of apical matrix and will be broadly useful to researchers in the C. elegans community.

      A second major contribution of this manuscript is to pioneer analysis methods for detecting oscillatory gene expression in scRNAseq datasets, even where bulk temporal data may not exist. This will be valuable for others doing sRNAseq studies in nematodes but also in other systems where cells may have molt cycle- or circadian-regulated oscillations.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SECTION A - Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      The authors use single cell sequencing (scRNA-Seq ) of cells obtained from larval stages of C elegans -- primarily the L4 stage, but also the L2. Worms are disrupted and individual cells are sorted by expression of fluorescent markers specific to glial cells, a cell type that is relatively rare in the population, and of particular interest to the focus of the study. In this fashion, the samples were enriched for glial cells but, bedcause the soting is not perfect, also contain representations of other cell populations, including hypodermal (skin) and other epithelial cells, muscles, and neurons. 2D representation of the scRNA-Seq data in principle component (PC) space reveals sets of cells of the same cell type (for example glia or skin cells) arranged in roughly circular patterns, indicative of rhythmic gene expression in those cell types. Much of this circular PC behavior is shown to be driven by genes that had previously been shown, by bulk RNAseq of staged larvae, to undergo rhythmic gene expression in conjunction with the larval stages and the molts that punctuate the larval stages. Based on the previously published relative timing of expression of these cycling genes, and the pattern of peak expression of each gene in the scRNA-Seq 2D PC space, the authors could calculate a phase angle of expression of each gene relative to its peak in each cell, and thereby calculate an average phase angle of all cycling gene for each cell, and place that metric in register with the roughly circular pattern of cell types in the 2D PC plot.

      The authors show that many of these rhythmic genes encode extracellular matrix (ECM) proteins or other proteins related to cuticle synthesis and assembly, or molting. Cell types exhibiting cyclic gene expression included skin, pharyngeal epithelial, as well as several types of glia, notably socket glia, which synthesize a specialized ECM that surrounds and protects sensory neurons.

      Finally, the authors analyze the patterns of cyclic gene expression in several cell types with respect to the expression of transcription factors (TFs) that are expressed in the cell type, including TFs that appear to likewise cycle, and whose predicted targets are enriched for cycling genes. From this computational analysis, the authors derive sets of hypothetical transcriptional regulatory circuits underlying phased expression of cycling genes.

      Major comments:

      • Are the key conclusions convincing?

      1) Yes, the data support the conclusion that the authors' approach and methodology can take a list of genes known to cycle in expression level at larval stages and identify the cycling gene expression profiles of those genes in single cell sequencing datasets. It is also convincing that the authors' data analysis methods can identify cycling genes from the scRNA-Seq data that had not been previously identified as cycling from bulk RNAseq. Furthermore, the enrichment of genes encoding collagens and other ECM components is clear from the data.

      2) The above being said, it is noteworthy that the conclusions of the manuscript - including the sets of predicted novel cycling genes, and the predicted transcription factor-target circuits -- were not confirmed experimentally using independent samples or orthogonal methodology. I think it is OK for the authors to leave these predictions for later experimental confirmation, but it would be appropriate for the authors to discuss this caveat about the need for strategic experimental tests to confirm the more novel findings presented here, while at the same time pointing out predictions from their analysis that fit with previous experimental findings (for example cases such as NHR-85 and NHR-23 where previous studies support that the relevant TF is involved in regulating molting-associated transcriptional activity.)

      We have added the following sentence to "Limitations of the study":

      "Further, while our results are consistent with other studies (Meeuse et al. 2020; Gaidatzis et al. 2025) and successfully identify known regulators such as NHR-23 and NHR-85, it will be important in future work to test expression of the novel oscillatory genes and the roles of novel regulators we have predicted."

      3) There is an issue of concern that is perhaps about terminology, and not necessarily conceptual: Throughout the manuscript the authors variously use the terms, "oscillatory", "transient", and "pulsatile" to refer to cyclic gene expression. It seems that each of these terms could have distinct meanings, based on their English usage: The term "oscillatory" gene expression would seem to be a general term for gene expression that varies in a regular, rhythmic fashion. "Transient" gene expression seems like a general term for ON/OFF dynamics, albeit not necessarily oscillatory. "Pulsatile" gene expression implies oscillatory dynamics where the rise and fall of gene expression is relatively abrupt and might also imply ON/Off dynamics (between zero to some positive value). These terms are used seemingly interchangeably in the early parts of the manuscript, and then later, "pulsatile" is used increasingly, so the reader starts to wonder why. The authors should define these terms precisely and use the terminology deliberately and consistently.

      We have clarified the important point about oscillatory vs. pulsatile in the text. Please see our response to Reviewer 1, Point 5. Additionally, we have removed the use of "transient" except in the context of the phrase "transient aECM" that has been established in the literature.

      4) Related to the above, the authors should address how lowly-expressed genes behave in scRNA-Seq data, where the transcriptome is not fully sampled in each cell, and how that phenomenon could affect the apparent variation of gene expression within a population. My understanding is that if the expression level of a gene goes below some threshold percentage of the total transcriptome, it may not show up at all in the reads from that cell, even though the gene may still be expressed. Therefore, a gene can display apparent on/off behavior within a population of cells whilst the underlying variation in mRNA levels for that gene may be far less abrupt. How might this phenomenon affect the interpretation of a gene's dynamics as "pulsatile"?

      We added the following to clarify that sampling variation among cells was mitigated by applying a smoothing function based on each cell's five nearest neighbors in PCA space:

      "We then fitted the expression pattern of each gene with a generalized additive model (GAM) to obtain smoothed expression profiles. Because the GAM is fitted across many cells ordered along pseudotime, it captures the underlying expression trend even when individual cells show zero counts due to incomplete transcriptome sampling (e.g., Fig. 4A, black dots at y = 0)."

      As further described in Methods, our pipeline also incorporates several features that mitigate this valid concern:

      • First, before fitting gene-level dynamics, we retain only genes detected in at least 20 cells and in at least 5% of cells of a given cell type (Methods). While this filter may exclude some genuinely low-expressed oscillating genes, it ensures that pulsatile calls are made on genes where expression is reliably measurable.
      • Second, we apply two levels of smoothing. Prior to PCA, k-nearest-neighbor smoothing ensures that each cell's expression profile reflects a local average of transcriptionally similar cells rather than a single noisy measurement. When modeling gene expression along pseudotime, we fit a generalized additive model (GAM) with cyclic cubic splines, pooling information across many cells. The curves we score as pulsatile therefore reflect averaged expression across neighborhoods of cells, rather than raw per-cell counts subject to dropout.
      • Critically, dropouts arising from incomplete transcriptome sampling are independent of pseudotime (e.g., see dnj-1 in Fig. S3A). Our pulsatility criterion explicitly requires a low baseline combined with a well-shaped, high-amplitude peak in a specific pseudotime window, which dropout noise alone cannot generate. Indeed, as shown in Fig. 4A, the method readily identifies peaks even when many individual cells have zero detected reads (black dots at y = 0), demonstrating that the smoothed fit recovers the underlying dynamics from sparse data.
      • Finally, during development we also tested a logistic GAM that models the probability of detecting at least one read per cell, rather than read counts directly, which produced comparable results, though it saturated for highly expressed genes.
        • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      5) Page 12: "Taken together, our results suggest that cuticle formation is the main commonality among pulsatile genes, and that distinct cell types use very different gene expression programs during this process. Thus, while cuticle aECM is typically perceived as a single homogeneous meshwork, our results suggest that the cuticle is actually a patchwork matrix with different patterning and composition contributed by distinct cell types."

      It is not necessarily surprising that the cuticle made by skin cells could have composition non-identical to the cuticle made by glial cells or pharyngeal cells. But by describing the cuticle as a 'patchwork' elicits in the reader's mind an image of the skin of the animal (seam + Hyp) being mosaic for distinct cuticle compositions. Is that what the authors intend to say? It would be interesting if there were differences in composition of cuticle between skin cell types, and so it would be helpful if the authors could comment on how the transcript profiles compare for hypodermal seam cells vs multinucleate Hyp cells.

      We have expanded on this idea:

      "Taken together, our results suggest that cuticle formation is the main commonality among pulsatile genes, and that distinct cell types use very different gene expression programs during this process. Classical work showed that the cuticle exhibits regionalized specializations – for example, alae are present only over seam cells; annuli and struts are present over hyp7 but not near the nose; the vulval cuticle is thought to present structural or chemical signatures for recognition during mating; and the pharyngeal cuticle exhibits three short projections in the buccal cavity, sieve-like fingers between the metacorpus and isthmus, and grinder elements in the posterior bulb. However, the extent to which these structural differences correspond to distinct molecular composition was not known. Thus, while cuticle aECM is typically perceived as a single homogeneous meshwork, our Our results suggest that the cuticle is actually a patchwork matrix with different patterning and molecular composition contributed by distinct cell types."

      • Would additional experiments be essential to support the claims of the paper?

      6) The data here are mostly from L4 stage larvae, with a possible (but unknown) contribution from L2 larvae. It would be helpful, in terms of broader understanding of their roles in larval progression, if some of the oscillatory genes identified here (especially the novel ones) were tested by orthogonal methodology (such as fluorescent protein tagging) for oscillatory expression at other stages. However, these experiments are arguably beyond the scope of this paper, and as long as the authors note the importance of such confirmatory experiments in their Discussion, I don't think that further experimentation is critical for this paper.

      We agree about the importance of these confirmatory experiments, and have added a comment in the Discussion (see response to Point 2 above).

      • Are the data and the methods presented in such a way that they can be reproduced?

      7) In general, yes.

      • Are the experiments adequately replicated and statistical analysis adequate?

      8) Yes.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      9) Page 4: Regarding the single cell sequencing approach, the authors should comment on the extent to which mRNAs are efficiently recovered from hypodermal syncytial cells (Hyp), which are multinucleate. Could the data from Hyp be chiefly from nuclear transcripts? If so, how might that affect the interpretation of the data?

      We have added a comment in Methods related to caveats of cell sorting vs. nuclei sorting (see response to Reviewer 1, Point 2). As the proportion of immature (unspliced) mRNA and reads corresponding to the mitochondrial genome are not noticeably different in the hypodermal cells than in other cell types, we do not think the data are chiefly from nuclear transcripts.

      10) It is confusing that Table S1 lists male-enriched samples that were apparently sequenced, but only hermaphrodite data were analyzed for the paper. To prevent confusion, the male samples should not be listed.

      We have clarified in the Methods that these samples are included in Table S1 because we wanted to share the datasets with the community:

      "(Supp. Table S1; note this table includes related samples that were not used in the present analysis but that are deposited in the Gene Expression Omnibus (GEO) repository as a public resource)"

      11) Page 5, bottom: The following analysis requires clarification (at least for this reader): "To test if such oscillatory gene expression is present in ILso glia, we computed the average phase of each cell (Fig. 2B). Specifically, for each cell, we computed a weighted circular average of the peak phases of oscillating genes (derived from the previous bulk RNA-Seq data), using the gene expression levels in that cell as weights."

      In reading this part of the main text, this reader struggled to understand how one can compute the phase angle for a given gene in a cell by comparing its level of expression in that cell to measurement of the level of that gene in previous bulk RNA-Seq data. Of course, there is far more to the analysis than that, which the Methods and Materials section on page 18 describes in more detail, where one learns that the level if each gene in each cell is scaled to its maximum expression across all the cells analyzed, and that the previous bulk sequence analysis is used to simply provide a phase angle for the gene's peak expression relative to an arbitrary framework (which corresponds to a larval stage, one assumes). The presentation of this analysis on Page 5 in the main text should be revised to include a full description of what was done so the reader can follow along and understand it without having to read the Methods section. But moreover, the Methods section treatment of this analysis is still not entirely clear; for example, certain variables (W, s, and c) are not defined. The presentation of the mathematics should be clarified so that the reader can understand the analysis without having to look up scTransform-normalization.

      We have expanded and clarified this section:

      "To test if such oscillatory gene expression is present in ILso glia, we computed the average phase of each cell (Fig. 2B). Specifically, for each cell in our dataset, we considered its expression level of each of the 3,739 previously described oscillatory genes (Meeuse et al. 2020). To avoid biasing towards inherently highly-expressed genes (e.g., those encoding structural proteins), the expression of each gene in a given cell was scaled to its maximum expression across all cells. We then computed the average phase of each cell by taking the known phase for each gene (Meeuse et al. 2020) and calculating a weighted circular average, using the scaled expression of each gene in that cell as weights (Fig. 2B; each colored line represents one oscillatory gene with its angle representing its known phase and its length representing its scaled expression in that cell). we computed a weighted circular average of the peak phases of oscillating genes (derived from the previous bulk RNA-Seq data), using the gene expression levels in that cell as weights. This average results in a vector whose direction reflects the average phase of genes expressed in that cell, and whose length reflects how consistently the genes’ peak times align in that cell (Fig. 2B, black arrow)."

      In the Methods, we have moved the definitions of W, s, and c so that they precede the formula for the average angle .

      • Are prior studies referenced appropriately?

      12) yes

      • Are the text and figures clear and accurate? - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      13) Figure S3 Panel A: What does the green line mean? Figure S3 Panel C: The use of the "predictors" tem is confusing because on page 8, the part of the narrative referring to Figure S3, the term used is "descriptors". Is that an meningful switch in terminology?

      We have expanded and clarified the Supp. Fig. S3 legend. For simplicity, we now use the term "metrics" to refer to the parameters used for hierarchical clustering of pulsatile expression (peak amplitude, baseline, fit, shape). This replaces our previous uses of "predictors" and "descriptors".

      14) The legend to Figure S3 requires more details to enable the reader understand the Figure. The same critique applies to most of the Supplemental Figure legends, where more details are required to allow the reader to understand each Figure without having to refer back to the main text.

      Thank you for pointing this out. We have revised and expanded all of the Supplemental Figure legends.

      15) Page 19: "The cells were grouped by cell type independent of the stage of collection (L2 or L4), and each cell type was processed individually."

      Why were L2s and L4s pooled? How does this affect the analysis and/or the outcomes? Could there be confounding effects from pooling the samples that could affect the analysis or the conclusions?

      We added the following clarification in the main text:

      "Because we found that cells clustered together based on their cell type rather than developmental stage, L2 and L4 cells of the same cell type were pooled for all downstream analyses (see Methods)."

      as well as the following explanation in the Methods:

      "The cells corresponding to the same cell type at different stages were then merged for subsequent analysis. After annotation, cells of the same cell type from L2 and L4 datasets were pooled for downstream analysis, such that each cell type is represented as a single combined cluster across stages. This provides two advantages: it increases statistical power by increasing the number of cells, and it favors genes that are oscillating in both larval stages. Because L2 representation is more limited (Table S1), the pooled pseudotime is dominated by L4 dynamics, ensuring that L2 cells are anchored on the L4-defined trajectory."

      **Referees cross-commenting**

      There is substantial agreement amongst all three reviewers, regarding the signifcance of the findings and that the conclusions are well enough supported by the data such that no additional experiments are required. We all recommend revisions to clarify or expand the description of the experiments and/or analysis. Many comments are reiterated by more than one Reviewer. I agree with all the other reviewers' critiques.

      Reviewer #2 (Significance (Required)):

      SECTION B - Significance

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      The finding of oscillatory and molting-related gene expression patterns in glial cells emphasizes the importance of molting-related ECM/cuticle production by these sensory-accessory cells and will serve as a platform for further studies and further understanding the structural and molecular basis of glial cell support functions, especially in the context of changing roles for sensory neurons during developmental progression.

      The methodology and data analysis of C. elegans scRNA-Seq data presented here offers several significant advances, especially since it had been known that thousands of genes cycle in rhythm with the C. elegans molting cycle, yet that was based on previous bulk sequencing, so it was not possible to resolve cell-type specific expression. This paper presents methods for analysis of cycling gene expression in specific cell types.

      The manuscript derives hypothetical TF-target regulatory interactions that are proposed do underly cyclic gene expression in specific cell types. This is a significant resource for future work to explore and delineate upstream oscillator mechanisms, and answer questions such as, Is there a central oscillator for all of larval stage rhythmic gene expression? and, How are different genes expressed with different phases of the larval stages? etc.

      • Place the work in the context of the existing literature (provide references, where appropriate).

      The authors cite important previous studeis that used scRNA-Seq to profile gene expression in specific C. elegans cell type in various developmental and physiological settings, and previous studies that used bulk RNAseq to identify genes whose transcripts cycle along with the larval stages. This manuscript reports the first study to examine cyclic gene express in C. elegans on the single-cell level.

      • State what audience might be interested in and influenced by the reported findings.

      Moderately broad audience of biologists interested in biological oscillators; developmental biologists interested in gene regulatory control of developmental cell fate timing and reiterative developmental processes; neurobiologists interested in glial cell function in developmental contexts.

      • Define your field of expertise with a few keywords to help the authors contextualize your point of view.

      • C. elegans larval development; temporal control of cell fate progression. Are there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Honestly, some of the mathematical analysis is beyond my ability to judge whether the chosen approach is the best choice for the particular setting.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      This study describes both new scRNA-seq data from C. elegans, targeting glia/epidermal cell types and especially the ILso glial cell, and analytical approaches to identify periodically expressed genes in the dataset. Overall the data appear of high quality so have value as a resource, and the analysis provides a substantial improvement in our understanding of how different cell types vary in their cyclic expression across the molt cycles. While I have make many suggestions, overall this is a very nice study as is and definitely seems likely to be an impactful publication.

      Major

      Figure 2:

      • The dataset includes cells from multiple stages (L2 and L4 mentioned in the text, adult as well listed in Supplemental Table 1). There is a superficial display in Figure S1 which seems to imply that whether the same cell type clusters together across stages vs making stage specific clusters might be complex. But this isn't really discussed at all in the paper. For Figure 2 specifically it seems critical to know whether stages were pooled or separated for this analysis, and the question of whether the cyclic program varies across stages (at least for well sampled cells like ILso) is important.

      Please see our response to Reviewer 2, Point 15.

      Figure 3

      • This approach overall is good for cells that cycle in a way their signature comes up in Meeuse but would it detect rare cell type cycles? Maybe the PCA space velocity approach could be a way to screen for cell types that cycle in a way that isn't detected in the whole organism time course data (or rule out the presence of large cycling gene sets)? For example "pharyngeal gland" seems to have a weak cycling signature using the Meeuse gene set (Fig. 3D) but fairly clear "circular UMAP" structure (Fig 3B).

      We added the following to emphasize that our rationale for developing the perplexity metric is to identify oscillatory cell types de novo, i.e. without relying on the Meeuse et al. dataset:

      "We hypothesized that this [perplexity] metric would distinguish between pulsatile cell types (corresponding to relatively high perplexity) and non-pulsatile cell types (corresponding to low perplexity), without relying on prior bulk annotations that may be insensitive to rare cell types."

      Consistent with the reviewer's intuition, this approach does identify cell types missed by the Meeuse-based local phase coherence analysis, specifically coelomocytes and PHsh glia, which have perplexity >30 but did not reach significance by phase coherence (Fig. 5C).

      Regarding the pharyngeal gland, this cell type has intermediate scores by both metrics and falls below our conservative thresholds (Fig. 5C). It is possible that it has a genuine but weak oscillatory program that our methods are underpowered to detect given the number of cells recovered for this cell type.

      We considered using RNA velocity, but we have not succeeded in developing a satisfying quantitative score; therefore, the perplexity metric serves this role in our current analysis.

      • Do these data say anything about the question of whether all cell types in an organism are synchronized in the same phase as each other, or whether some might be systematically earlier or later in the cycle at a given time? It seems like if individual samples have enough stage bias (as an illustrative but made up example, if sample "230421_AM" has mostly early L4 while "230421_PM" has mostly late L4), then these data could be used to see if for example ILso cells tend to have earlier or later phases in the same sample compared to hyp cells. In my view, this is an important enough general question in the field to be worth addressing if the data are sufficient. And it could also provide an independent way to support/refute the presence of additional cycling cells (see Fig. 5 comments)

      This is an interesting idea, but unfortunately our synchronization at the population level is not sufficiently precise, and each sample contains cells spanning nearly the full phase range (shown below [IMAGE CANNOT BE ATTACHED HERE]). Under these conditions, between-sample phase offsets are dominated by within-sample dispersion, and we cannot reliably estimate systematic phase differences between cell types.

      Figure 5

      • This seems a nice approach to address the earlier question about detecting cell type specific oscillations. But then only results for the cell types previously identified as oscillatory are reported. It seems important to report the potential cycling genes for the other cell types (PHsh, Coelomocytes, maybe Pharyngeal Gland) so their cycling status could be tested by others in the future.

      We revised the text to highlight that these gene lists are in Supp. Tables S3 and S4.:

      "We limit our subsequent analyses to the 17 high-confidence cell types that appear oscillatory using both approaches (local phase coherence and perplexity), which together contain 5,268 pulsatile genes. Pulsatile gene lists for these and other cell types are provided in Supp. Tables S3 and S4 to facilitate independent assessment of their cycling status. A summary of pulsatile genes in these 17 cell types is shown in Table 1."

      • Regarding the last section ("only 17% were pulsatile in five or more cell types", "only 10 genes were pulsatile in all 17 oscillatory cell types" etc) - thresholding of a dataset like this can lead to false negatives resulting from incomplete (and cell type specific differences in power) which is a common source of technical non-overlap in this type of comparison. Indeed it is notable that the highest overlap was with ILso (specific sort target, likely to be especially well powered) and CEPso. There are various approaches to estimate not just confident overlap but also confident non-overlap, for example the "irreproducible discovery rate" (IDR) approach commonly used for ChIP-seq data. While clearly based on gene set enrichment there is cell type specificity, I'd suggest toning down the interpretation of the fractional overlap in the text if this can't be resolved.

      We toned down the interpretation of the fractional overlap:

      "To what extent are the same sets of pulsatile genes shared between cell types? To address this question, we examined the overlap between the pulsatile genes we identified in each cell type (Fig. 5D), noting that because power to detect pulsatile expression varies across cell types, the overlap values we report are likely to underestimate the true sharing between cell types.

      […]

      Put another way, 45% (2,390 of 5,268) of the genes we identified were detected as expressed and pulsatile exclusively in a single cell type while only 17% (915 of 5,268) were pulsatile in five or more cell types (Fig. 5E)."

      Minor

      Figure 1:

      • I was a little unclear about the coloring in Fig 1C (are the colors by annotated tissue or something else like clusters?) - suggest specifying in the legend.

      We updated the legend: "UMAP of the same cells as in B, with each cell colored by its annotated tissue identity."

      • More details on clustering and annotations approaches in the methods would be useful.

      We have substantially expanded the corresponding section.

      • I have mixed feelings about the word "skin" in the figure panels - while more accessible to a broad audience, hypodermis or hyp subset labels (hyp 7 etc) might be more precise.

      We have changed many of these to "skin-related." We cannot use the anatomical terms because we cannot confidently distinguish, for example, hyp1 vs hyp2, due to the lack of known markers for each cell type. We therefore refer to skin-related cluster 3 as "skin 3," because calling it "hyp 3" would lead to confusion with the anatomical term.

      • Table S2 would benefit from including the number of cells annotated with each cell type name

      We have added the number of cells per cell type to Supp. Table S2, with separate columns for L2, L4, and the total.

      Figure 2

      • Fig 2B is nice - clearly shows the difference in expression of phase specific genes in the two example cells and conceptual framework for averaging. I was struck by the relatively broad range of phase values though (For example the bottom cell has highly expressed genes with phases ranging from ~100 degrees to ~280). It seems this could reflect technical noise in the single cell data or imprecision in the phase calls in Meeuse. But there is also the interesting possibility that there is biological flexibility in the order/expression of phased genes at this single cell level. Not sure if there is an obvious way to address this or whether it should be in the scope of this work but maybe at least worthy of a mention

      A parsimonious explanation for the broad range of phase values in a single cell is the shape of the peak: examining the data from Meeuse et al, oscillatory genes do not generally display a sharp peak, but rather elevated expression over a span of ~3h (out of a larval stage of ~8h), which would correspond to expression over 100°. Indeed, the decentered genes in Fig. 2B correspond to the genes F53F4.2 and cutl-10 which have their peak expression at ~135° (26 h of larval development in the Meeuse dataset) but are still expressed at ~180° (28 h of larval development in the Meeuse dataset). Importantly, expression peaks tend to be roughly symmetric around the cell's true phase and therefore reduce the length of the phase vector but do not affect the average phase itself.

      Figure 3

      • The class Alter et al SVD paper https://www.pnas.org/doi/full/10.1073/pnas.97.18.10101 was the first use case of SVD/PCA in genome wide expression data and used (cell cycle) periodic expression as the main use case. The plots in Figures 2 and 3 are very similar to that approach, which basically used the relevant (~sin and ~cos correlated) principle components to define phases of both samples and genes. I mention this mostly in case it is useful to see how they approached the question and maybe as a relevant citation.

      We added the citation.

      Figure 4

      • Minor method clarification - how was DTW adapted to deal with circular data, specifically to identify cases where the peak is centered at pseudotime == 0/1? It seems from the figures that maybe some approach was used to center the raw data on the peak but I didn't see a description of how this was done (apologies if I missed it)

      We edited the Methods to make the connection with the previous section more explicit:

      "We used the trained model to predict expression of each gene along a grid of 128 regularly spaced pseudotime values, resulting in a smoothed expression profile. Further, for each gene, we shifted the pseudotime values to center the maximal expression value, and fitted a GAM as described above. To facilitate comparison of profile shapes across genes with different peak times, we additionally produced a centered version of each profile. For each gene, we identified the pseudotime at which the uncentered profile reached its maximum, then circularly shifted the pseudotime values so that this maximum fell at the center of the range (pseudotime 0.5). A new GAM was fit on the shifted data as described above, and used to predict expression along the same regular grid. This yielded a centered, smoothed expression profile for each gene in which all genes have their peak at the center of the pseudotime axis. These centered profiles were used in the subsequent section to compute both the baseline and shape metrics of each gene.

      […]

      We then scaled the curve by its maximum value and centered it around its maximal value. We then scaled the centered smoothed expression profile (defined in the previous section) by its maximum value. The Dynamic Time Warp distance between the scaled and centered expression and an ideal sharp peak defined as the density of a normal distribution of mean 0.5 and standard deviation 0.01 was computed with the dtw package."

      • The 2-PC view of ILso seems to align well with phase, but some of the other cell types (such as Seam in Figure 3C) are more complex - and also it seems possible that there could be cell types where the phase information is in e.g. PC2 and 3 instead of 1 and 2; how customizable is the approach and how dependent is it on a clean circular pattern in the PC plot?
      • *

      We have expanded the Discussion to include this point:

      "This could indicate either a genuine absence of oscillatory programs; the presence of oscillations driven by only a few genes that are insufficient to shape PCA structure; or oscillations that are present but reside in higher principal components dominated in PCs 1-2 by other sources of cell-to-cell variation."

      By using an Elastic Principal Cycle (ElPiGraph) to fit pseudotime rather than relying on angle from the origin (as is common for this type of data), we accommodate trajectories within PCs 1-2 that deviate from perfect circularity, including elongated or asymmetric shapes such as in seam cells (Fig. 3C). However, when phase information resides in higher-order PCs, in the absence of an independent timing reference there is no principled way to identify which PCs carry oscillatory signal versus other gradients of cell-to-cell variation. Recovering oscillations in such cell types would therefore require complementary approaches, such as synchronized time-course sampling, rather than a modification of the current pipeline.

      • It would be useful to annotate the examples (Fig 4A, lower panels) with whether they were newly identified or known from the bulk time course. And consider a larger supplemental figure with a sampling of newly identified genes in a similar format across a range of amplitudes etc

      We added Supplementary Figure S4C with examples across a range of amplitudes.

      • The examples are all relatively tight peaks (width We developed an approach to quantify the width of peaks in the Meeuse data (Methods); we display the distribution of peak width for genes expressed in ILso and seam cells in the new Supplementary Figure S4A. Our approach did not display a systematic bias to detect narrow or wide peaks. We added the following in the Results:

      “More generally, our classification captures a range of expression profile morphologies without apparent bias (Supp. Fig. S4).”

      Figure 5

      • There are important caveats in the interpretation of perplexity. For example a cell type that oscillates but with the vast majority of genes expressed uniformly or at one specific phase, would get a low perplexity, while a cell with multiple distinct states that don't cycle (this may be why body muscle has a modestly elevated sore) might achieve high perplexity. Worth addressing at some point.

      We added these caveats:

      "Potential caveats are that some non-oscillatory cell types might have high perplexity, for example if there are other sources of complex transcriptional heterogeneity among cells, while some oscillatory cell types might have low perplexity, for example if oscillating genes do not dominate the PCA structure."

      • Fig 5C raises the question of how power (for each of these metrics) relates to number of cycling genes in a cell type and the density of its sampling across time (for example is glia 4 just a poorly sampled cell type, or is it qualitatively different in what fraction of its transcriptome is cycling?). Just recoloring this plot by number of single cells per annotation might touch on this, or could try a subsampling approach.

      We added this with the new Supp. Fig. S5C:

      "To test whether differences in perplexity could be explained by differences in the number of sampled cells, we recomputed perplexity after subsampling to progressively smaller numbers of cells for several representative cell types. Perplexity values were largely stable across subsample sizes, indicating that the classification of cell types as oscillatory or non-oscillatory is not driven by differences in statistical power (Supp. Fig. S5C)."

      • The identification of pharyngeal muscle and epithelial oscillatory genes is a nice resource aspect of this paper given past work by EM showing these cells changing across the life cycle; it appears these cells have distinct enrichments (Fig 5G) and I think talking about these differences more explicitly could add to the closing paragraph in this section about aECM heterogeneity

      We have added the following:

      "In addition, the nematode astacin (NAS) metalloproteases appeared enriched in pulsatile genes in glia and pharynx, but not hypodermis (Fig. 5G, Supp. Table S6), consistent with ultrastructural observations that the pharyngeal muscle becomes secretory during molts and that the protease NAS-6 is required to digest the old pharyngeal cuticle (Sparacio et al., 2020)."

      Figure 6

      • This section is great and a very useful resource for future work. A detailed analysis may be beyond the scope of this work, but for Fig 6B I wondered whether the TF oscillation phase matched/preceded the timing of its predicted targets (for the subset of TFs that were themselves oscillatory in that cell type)? Even a qualitative analysis of this would be informative.

      We added a new supplementary Figure S7 and commented on it in the text:

      "__For the subset of TFs that are themselves pulsatile, we asked whether their peak expression coincides with or precedes that of their predicted targets. We found that pulsatile targets are modestly enriched in a temporal window around the TF's own peak (Supp. Fig S7), consistent with near-simultaneous expression of TFs and their targets, as previously observed for nhr-23 (Johnson et al., 2023). This temporal enrichment was most consistent for nhr-23 and nhr-25, which showed significant enrichment across most cell types, while other TFs showed more variable patterns (Supp. Fig. S7)."__

      Open-ended/discretionary

      A general challenge in single cell data analysis is that standard methods like clustering can give misleading or hard to interpret results when multiple processes occur simultaneously. For example, cells can have signatures of cell fate and cell cycle and depending on the genes used for clustering and strengths of those signals, naïve clustering may cause them to group by either fate of cell cycle phase. This is a long-winded way to say an application of the approach reported here would be to identify cycling genes shared between cell types that could be removed from the "variably expressed genes" lists prior to clustering to improve cell type separation, or used exclusively to allow clustering by phase rather than cell type. (definitely discretionary to consider this but could be mentioned in Discussion as a possible application)

      **Referees cross-commenting**

      I agree with all of this, including Reviewer #1 that asynchrony may be hard to address with current data, and with both reviewers that the dataset stands on its own.

      Reviewer #3 (Significance (Required)):

      This paper addresses the problem of how to identify cycling genes in single cell data, using the C. elegans larval/molt cycle as a model system. The system has emerged as a powerful model for understanding regulation of periodic gene expression, with past bulk RNA-seq time course have identified 1000s of cycling genes. However, how cyclic gene expression varies across cell types was not known. This study uses single cell RNA-seq and develops new analysis approaches to identify cycling genes across dozens of C. elegans cell types. Strengths are the generation of a new single cell data enriched for larval glia, identification of cyclic gene expression across many C. elegans cell types, an improved analytical framework for identifying cycling genes that could be applied in other datasets, and substantial analysis of pathways and regulators involved. Weaknesses are limited, and include minor overinterpretations of the data and missed opportunities for additional analyses. The work should be of interest to a broad audience including not just C. elegans researchers but also the single cell and chronobiology communities.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      This study describes both new scRNA-seq data from C. elegans, targeting glia/epidermal cell types and especially the ILso glial cell, and analytical approaches to identify periodically expressed genes in the dataset. Overall the data appear of high quality so have value as a resource, and the analysis provides a substantial improvement in our understanding of how different cell types vary in their cyclic expression across the molt cycles. While I have make many suggestions, overall this is a very nice study as is and definitely seems likely to be an impactful publication.

      Major

      Figure 2:

      • The dataset includes cells from multiple stages (L2 and L4 mentioned in the text, adult as well listed in Supplemental Table 1). There is a superficial display in Figure S1 which seems to imply that whether the same cell type clusters together across stages vs making stage specific clusters might be complex. But this isn't really discussed at all in the paper. For Figure 2 specifically it seems critical to know whether stages were pooled or separated for this analysis, and the question of whether the cyclic program varies across stages (at least for well sampled cells like ILso) is important.

      Figure 3

      • This approach overall is good for cells that cycle in a way their signature comes up in Meeuse but would it detect rare cell type cycles? Maybe the PCA space velocity approach could be a way to screen for cell types that cycle in a way that isn't detected in the whole organism time course data (or rule out the presence of large cycling gene sets)? For example "pharyngeal gland" seems to have a weak cycling signature using the Meeuse gene set (Fig. 3D) but fairly clear "circular UMAP" structure (Fig 3B).
      • Do these data say anything about the question of whether all cell types in an organism are synchronized in the same phase as each other, or whether some might be systematically earlier or later in the cycle at a given time? It seems like if individual samples have enough stage bias (as an illustrative but made up example, if sample "230421_AM" has mostly early L4 while "230421_PM" has mostly late L4), then these data could be used to see if for example ILso cells tend to have earlier or later phases in the same sample compared to hyp cells. In my view, this is an important enough general question in the field to be worth addressing if the data are sufficient. And it could also provide an independent way to support/refute the presence of additional cycling cells (see Fig. 5 comments)

      Figure 5

      • This seems a nice approach to address the earlier question about detecting cell type specific oscillations. But then only results for the cell types previously identified as oscillatory are reported. It seems important to report the potential cycling genes for the other cell types (PHsh, Coelomocytes, maybe Pharyngeal Gland) so their cycling status could be tested by others in the future.
      • Regarding the last section ("only 17% were pulsatile in five or more cell types", "only 10 genes were pulsatile in all 17 oscillatory cell types" etc) - thresholding of a dataset like this can lead to false negatives resulting from incomplete (and cell type specific differences in power) which is a common source of technical non-overlap in this type of comparison. Indeed it is notable that the highest overlap was with ILso (specific sort target, likely to be especially well powered) and CEPso. There are various approaches to estimate not just confident overlap but also confident non-overlap, for example the "irreproducible discovery rate" (IDR) approach commonly used for ChIP-seq data. While clearly based on gene set enrichment there is cell type specificity, I'd suggest toning down the interpretation of the fractional overlap in the text if this can't be resolved.

      Minor

      Figure 1:

      • I was a little unclear about the coloring in Fig 1C (are the colors by annotated tissue or something else like clusters?) - suggest specifying in the legend.
      • More details on clustering and annotations approaches in the methods would be useful.
      • I have mixed feelings about the word "skin" in the figure panels - while more accessible to a broad audience, hypodermis or hyp subset labels (hyp 7 etc) might be more precise.
      • Table S2 would benefit from including the number of cells annotated with each cell type name

      Figure 2

      • Fig 2B is nice - clearly shows the difference in expression of phase specific genes in the two example cells and conceptual framework for averaging. I was struck by the relatively broad range of phase values though (For example the bottom cell has highly expressed genes with phases ranging from ~100 degrees to ~280). It seems this could reflect technical noise in the single cell data or imprecision in the phase calls in Meeuse. But there is also the interesting possibility that there is biological flexibility in the order/expression of phased genes at this single cell level. Not sure if there is an obvious way to address this or whether it should be in the scope of this work but maybe at least worthy of a mention

      Figure 3

      • The class Alter et al SVD paper https://www.pnas.org/doi/full/10.1073/pnas.97.18.10101 was the first use case of SVD/PCA in genome wide expression data and used (cell cycle) periodic expression as the main use case. The plots in Figures 2 and 3 are very similar to that approach, which basically used the relevant (~sin and ~cos correlated) principle components to define phases of both samples and genes. I mention this mostly in case it is useful to see how they approached the question and maybe as a relevant citation.

      Figure 4

      • Minor method clarification - how was DTW adapted to deal with circular data, specifically to identify cases where the peak is centered at pseudotime == 0/1? It seems from the figures that maybe some approach was used to center the raw data on the peak but I didn't see a description of how this was done (apologies if I missed it)
      • The 2-PC view of ILso seems to align well with phase, but some of the other cell types (such as Seam in Figure 3C) are more complex - and also it seems possible that there could be cell types where the phase information is in e.g. PC2 and 3 instead of 1 and 2; how customizable is the approach and how dependent is it on a clean circular pattern in the PC plot?
      • It would be useful to annotate the examples (Fig 4A, lower panels) with whether they were newly identified or known from the bulk time course. And consider a larger supplemental figure with a sampling of newly identified genes in a similar format across a range of amplitudes etc
      • The examples are all relatively tight peaks (width <0.5 pseudotime units) - is this approach able to identify genes with wider "plateau" patterns (and do such patterns exist?) Figure 5
      • There are important caveats in the interpretation of perplexity. For example a cell type that oscillates but with the vast majority of genes expressed uniformly or at one specific phase, would get a low perplexity, while a cell with multiple distinct states that don't cycle (this may be why body muscle has a modestly elevated sore) might achieve high perplexity. Worth addressing at some point.
      • Fig 5C raises the question of how power (for each of these metrics) relates to number of cycling genes in a cell type and the density of its sampling across time (for example is glia 4 just a poorly sampled cell type, or is it qualitatively different in what fraction of its transcriptome is cycling?). Just recoloring this plot by number of single cells per annotation might touch on this, or could try a subsampling approach.
      • The identification of pharyngeal muscle and epithelial oscillatory genes is a nice resource aspect of this paper given past work by EM showing these cells changing across the life cycle; it appears these cells have distinct enrichments (Fig 5G) and I think talking about these differences more explicitly could add to the closing paragraph in this section about aECM heterogeneity

      Figure 6

      • This section is great and a very useful resource for future work. A detailed analysis may be beyond the scope of this work, but for Fig 6B I wondered whether the TF oscillation phase matched/preceded the timing of its predicted targets (for the subset of TFs that were themselves oscillatory in that cell type)? Even a qualitative analysis of this would be informative.

      Open-ended/discretionary

      A general challenge in single cell data analysis is that standard methods like clustering can give misleading or hard to interpret results when multiple processes occur simultaneously. For example, cells can have signatures of cell fate and cell cycle and depending on the genes used for clustering and strengths of those signals, naïve clustering may cause them to group by either fate of cell cycle phase. This is a long-winded way to say an application of the approach reported here would be to identify cycling genes shared between cell types that could be removed from the "variably expressed genes" lists prior to clustering to improve cell type separation, or used exclusively to allow clustering by phase rather than cell type. (definitely discretionary to consider this but could be mentioned in Discussion as a possible application)

      Referees cross-commenting

      I agree with all of this, including Reviewer #1 that asynchrony may be hard to address with current data, and with both reviewers that the dataset stands on its own.

      Significance

      This paper addresses the problem of how to identify cycling genes in single cell data, using the C. elegans larval/molt cycle as a model system. The system has emerged as a powerful model for understanding regulation of periodic gene expression, with past bulk RNA-seq time course have identified 1000s of cycling genes. However, how cyclic gene expression varies across cell types was not known. This study uses single cell RNA-seq and develops new analysis approaches to identify cycling genes across dozens of C. elegans cell types. Strengths are the generation of a new single cell data enriched for larval glia, identification of cyclic gene expression across many C. elegans cell types, an improved analytical framework for identifying cycling genes that could be applied in other datasets, and substantial analysis of pathways and regulators involved. Weaknesses are limited, and include minor overinterpretations of the data and missed opportunities for additional analyses. The work should be of interest to a broad audience including not just C. elegans researchers but also the single cell and chronobiology communities.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      The authors use single cell sequencing (scRNA-Seq ) of cells obtained from larval stages of C elegans -- primarily the L4 stage, but also the L2. Worms are disrupted and individual cells are sorted by expression of fluorescent markers specific to glial cells, a cell type that is relatively rare in the population, and of particular interest to the focus of the study. In this fashion, the samples were enriched for glial cells but, bedcause the soting is not perfect, also contain representations of other cell populations, including hypodermal (skin) and other epithelial cells, muscles, and neurons. 2D representation of the scRNA-Seq data in principle component (PC) space reveals sets of cells of the same cell type (for example glia or skin cells) arranged in roughly circular patterns, indicative of rhythmic gene expression in those cell types. Much of this circular PC behavior is shown to be driven by genes that had previously been shown, by bulk RNAseq of staged larvae, to undergo rhythmic gene expression in conjunction with the larval stages and the molts that punctuate the larval stages. Based on the previously published relative timing of expression of these cycling genes, and the pattern of peak expression of each gene in the scRNA-Seq 2D PC space, the authors could calculate a phase angle of expression of each gene relative to its peak in each cell, and thereby calculate an average phase angle of all cycling gene for each cell, and place that metric in register with the roughly circular pattern of cell types in the 2D PC plot.

      The authors show that many of these rhythmic genes encode extracellular matrix (ECM) proteins or other proteins related to cuticle synthesis and assembly, or molting. Cell types exhibiting cyclic gene expression included skin, pharyngeal epithelial, as well as several types of glia, notably socket glia, which synthesize a specialized ECM that surrounds and protects sensory neurons.

      Finally, the authors analyze the patterns of cyclic gene expression in several cell types with respect to the expression of transcription factors (TFs) that are expressed in the cell type, including TFs that appear to likewise cycle, and whose predicted targets are enriched for cycling genes. From this computational analysis, the authors derive sets of hypothetical transcriptional regulatory circuits underlying phased expression of cycling genes.

      Major comments:

      • Are the key conclusions convincing?

      1) Yes, the data support the conclusion that the authors' approach and methodology can take a list of genes known to cycle in expression level at larval stages and identify the cycling gene expression profiles of those genes in single cell sequencing datasets. It is also convincing that the authors' data analysis methods can identify cycling genes from the scRNA-Seq data that had not been previously identified as cycling from bulk RNAseq. Furthermore, the enrichment of genes encoding collagens and other ECM components is clear from the data.

      2) The above being said, it is noteworthy that the conclusions of the manuscript - including the sets of predicted novel cycling genes, and the predicted transcription factor-target circuits -- were not confirmed experimentally using independent samples or orthogonal methodology. I think it is OK for the authors to leave these predictions for later experimental confirmation, but it would be appropriate for the authors to discuss this caveat about the need for strategic experimental tests to confirm the more novel findings presented here, while at the same time pointing out predictions from their analysis that fit with previous experimental findings (for example cases such as NHR-85 and NHR-23 where previous studies support that the relevant TF is involved in regulating molting-associated transcriptional activity.)

      3) There is an issue of concern that is perhaps about terminology, and not necessarily conceptual: Throughout the manuscript the authors variously use the terms, "oscillatory", "transient", and "pulsatile" to refer to cyclic gene expression. It seems that each of these terms could have distinct meanings, based on their English usage: The term "oscillatory" gene expression would seem to be a general term for gene expression that varies in a regular, rhythmic fashion. "Transient" gene expression seems like a general term for ON/OFF dynamics, albeit not necessarily oscillatory. "Pulsatile" gene expression implies oscillatory dynamics where the rise and fall of gene expression is relatively abrupt and might also imply ON/Off dynamics (between zero to some positive value). These terms are used seemingly interchangeably in the early parts of the manuscript, and then later, "pulsatile" is used increasingly, so the reader starts to wonder why. The authors should define these terms precisely and use the terminology deliberately and consistently.

      4) Related to the above, the authors should address how lowly-expressed genes behave in scRNA-Seq data, where the transcriptome is not fully sampled in each cell, and how that phenomenon could affect the apparent variation of gene expression within a population. My understanding is that if the expression level of a gene goes below some threshold percentage of the total transcriptome, it may not show up at all in the reads from that cell, even though the gene may still be expressed. Therefore, a gene can display apparent on/off behavior within a population of cells whilst the underlying variation in mRNA levels for that gene may be far less abrupt. How might this phenomenon affect the interpretation of a gene's dynamics as "pulsatile"? - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      5) Page 12: "Taken together, our results suggest that cuticle formation is the main commonality among pulsatile genes, and that distinct cell types use very different gene expression programs during this process. Thus, while cuticle aECM is typically perceived as a single homogeneous meshwork, our results suggest that the cuticle is actually a patchwork matrix with different patterning and composition contributed by distinct cell types."

      It is not necessarily surprising that the cuticle made by skin cells could have composition non-identical to the cuticle made by glial cells or pharyngeal cells. But by describing the cuticle as a 'patchwork' elicits in the reader's mind an image of the skin of the animal (seam + Hyp) being mosaic for distinct cuticle compositions. Is that what the authors intend to say? It would be interesting if there were differences in composition of cuticle between skin cell types, and so it would be helpful if the authors could comment on how the transcript profiles compare for hypodermal seam cells vs multinucleate Hyp cells. - Would additional experiments be essential to support the claims of the paper?

      6) The data here are mostly from L4 stage larvae, with a possible (but unknown) contribution from L2 larvae. It would be helpful, in terms of broader understanding of their roles in larval progression, if some of the oscillatory genes identified here (especially the novel ones) were tested by orthogonal methodology (such as fluorescent protein tagging) for oscillatory expression at other stages. However, these experiments are arguably beyond the scope of this paper, and as long as the authors note the importance of such confirmatory experiments in their Discussion, I don't think that further experimentation is critical for this paper. - Are the data and the methods presented in such a way that they can be reproduced?

      7) In general, yes. - Are the experiments adequately replicated and statistical analysis adequate?

      8) Yes.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      9) Page 4: Regarding the single cell sequencing approach, the authors should comment on the extent to which mRNAs are efficiently recovered from hypodermal syncytial cells (Hyp), which are multinucleate. Could the data from Hyp be chiefly from nuclear transcripts? If so, how might that affect the interpretation of the data?

      10) It is confusing that Table S1 lists male-enriched samples that were apparently sequenced, but only hermaphrodite data were analyzed for the paper. To prevent confusion, the male samples should not be listed.

      11) Page 5, bottom: The following analysis requires clarification (at least for this reader): "To test if such oscillatory gene expression is present in ILso glia, we computed the average phase of each cell (Fig. 2B). Specifically, for each cell, we computed a weighted circular average of the peak phases of oscillating genes (derived from the previous bulk RNA-Seq data), using the gene expression levels in that cell as weights."

      In reading this part of the main text, this reader struggled to understand how one can compute the phase angle for a given gene in a cell by comparing its level of expression in that cell to measurement of the level of that gene in previous bulk RNA-Seq data. Of course, there is far more to the analysis than that, which the Methods and Materials section on page 18 describes in more detail, where one learns that the level if each gene in each cell is scaled to its maximum expression across all the cells analyzed, and that the previous bulk sequence analysis is used to simply provide a phase angle for the gene's peak expression relative to an arbitrary framework (which corresponds to a larval stage, one assumes). The presentation of this analysis on Page 5 in the main text should be revised to include a full description of what was done so the reader can follow along and understand it without having to read the Methods section. But moreover, the Methods section treatment of this analysis is still not entirely clear; for example, certain variables (W, s, and c) are not defined. The presentation of the mathematics should be clarified so that the reader can understand the analysis without having to look up scTransform-normalization. - Are prior studies referenced appropriately?

      12) yes - Are the text and figures clear and accurate? - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      13) Figure S3 Panel A: What does the green line mean? Figure S3 Panel C: The use of the "predictors" tem is confusing because on page 8, the part of the narrative referring to Figure S3, the term used is "descriptors". Is that an meningful switch in terminology?

      14) The legend to Figure S3 requires more details to enable the reader understand the Figure. The same critique applies to most of the Supplemental Figure legends, where more details are required to allow the reader to understand each Figure without having to refer back to the main text.

      15) Page 19: "The cells were grouped by cell type independent of the stage of collection (L2 or L4), and each cell type was processed individually."

      Why were L2s and L4s pooled? How does this affect the analysis and/or the outcomes? Could there be confounding effects from pooling the samples that could affect the analysis or the conclusions?

      Referees cross-commenting

      There is substantial agreement amongst all three reviewers, regarding the signifcance of the findings and that the conclusions are well enough supported by the data such that no additional experiments are required. We all recommend revisions to clarify or expand the description of the experiments and/or analysis. Many comments are reiterated by more than one Reviewer. I agree with all the other reviewers' critiques.

      Significance

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      The finding of oscillatory and molting-related gene expression patterns in glial cells emphasizes the importance of molting-related ECM/cuticle production by these sensory-accessory cells and will serve as a platform for further studies and further understanding the structural and molecular basis of glial cell support functions, especially in the context of changing roles for sensory neurons during developmental progression.

      The methodology and data analysis of C. elegans scRNA-Seq data presented here offers several significant advances, especially since it had been known that thousands of genes cycle in rhythm with the C. elegans molting cycle, yet that was based on previous bulk sequencing, so it was not possible to resolve cell-type specific expression. This paper presents methods for analysis of cycling gene expression in specific cell types.

      The manuscript derives hypothetical TF-target regulatory interactions that are proposed do underly cyclic gene expression in specific cell types. This is a significant resource for future work to explore and delineate upstream oscillator mechanisms, and answer questions such as, Is there a central oscillator for all of larval stage rhythmic gene expression? and, How are different genes expressed with different phases of the larval stages? etc. - Place the work in the context of the existing literature (provide references, where appropriate).

      The authors cite important previous studeis that used scRNA-Seq to profile gene expression in specific C. elegans cell type in various developmental and physiological settings, and previous studies that used bulk RNAseq to identify genes whose transcripts cycle along with the larval stages. This manuscript reports the first study to examine cyclic gene express in C. elegans on the single-cell level. - State what audience might be interested in and influenced by the reported findings.

      Moderately broad audience of biologists interested in biological oscillators; developmental biologists interested in gene regulatory control of developmental cell fate timing and reiterative developmental processes; neurobiologists interested in glial cell function in developmental contexts. - Define your field of expertise with a few keywords to help the authors contextualize your point of view.

      C. elegans larval development; temporal control of cell fate progression.

      Are there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Honestly, some of the mathematical analysis is beyond my ability to judge whether the chosen approach is the best choice for the particular setting.

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      Referee #1

      Evidence, reproducibility and clarity

      This interesting manuscript uses single cell RNAseq of developing C. elegans larvae to identify temporal pulses or oscillations in gene expression within glia and many other epithelial cell types - mostly in genes related to cuticle synthesis or remodeling. It identifies different sets of genes that oscillate within different cell types, and identifies many apparent oscillatory genes that were missed in prior studies because they are expressed in smaller populations of cells (whereas bulk data mainly report on oscillations within the major hypodermis).

      A second major contribution of this manuscript is to pioneer analysis methods for detecting oscillatory gene expression in scRNAseq datasets. That said, it's important to state that the methods for estimating phase coherence, GAM, perplexity, etc. make sense to me intuitively but I can't assess the math and other details, which are outside of my expertise.

      Most of my comments are minor ones about suggested clarifications to the text or figures. Some may require additional analyses, but none should require additional data collection.

      1. The manuscript focuses much of its analysis on one specific glial cell type (ILso), yet the authors tell us almost nothing about this cell type or why they would care about it. It would be helpful to include just a little more background on glial biology and the epithelial-like characteristics of socket glia.
      2. Many transcriptomic studies of epithelia (including the Purice et al study of adult glia) use single NUCLEI RNAseq rather than single cells because of the challenges in separating cells connected by tight junctions. In C. elegans there are also various epithelial syncytia to contend with. In text or Methods, the authors should comment on why they think cells were appropriate to look at in this instance, and whether there are certain cell types that were missed or could only be obtained as cell fragments based on that choice.
      3. Related to above, the authors do not mention any detection or exclusion of likely doublets. Is there reason to think that doublets were not present in any substantial numbers? I'm not super concerned about this since doublets containing hyp7 fragments should have worked against them in detecting glia-specific oscillations, but I do think the issue should be addressed in the text or Methods.
      4. p. 4 "previously unappreciated local differences in cuticle patterning." This statement should be tempered since many stage- or tissue-specific differences in cuticle patterning have been described previously (including in papers from the Heiman lab and others that are cited here). This study uncovers many additional examples but it's not a completely new finding.
      5. Table 1 and text: the distinction between pulsatile and oscillatory should be explained more at the outset. These terms sometimes seem to be used interchangeably, but then Table 1 seems to make a distinction, not discussed until the final "limitations" section.
      6. Figure 1 and Figure 3A,B. These UMAPs look very unusual, with no discernable individual dots. Is this just a resolution issue? Or, if relevant, please add info to legend and/or Methods explaining what data smoothing was done here to make them look this way and why.
      7. Figure 2C and Figure 6B. In the pseudotime plots, it would be natural for readers to assume that 0 is the beginning of the larval stage and 360 is the end, but that is not actually the way the Meeuse 2020 phase angles work - instead the beginning of the larval stage falls around 160. Please make sure this is made clear, especially when referring to "early and late groups" of TF targets. In Fig 6B, Early and Late categories appear reversed because of the way the data are plotted.
      8. Figure 3B and Figure 5D-G. The authors group many unidentified clusters into the catchall "skin" category but don't clearly define it in the main text. Table S2 suggests this category includes anterior and posterior skin cells but possibly also other cuticle-lined tubular epithelia that aren't properly referred to as skin (e.g. vulva cells, excretory socket or pore cells). It may also include things like rectum, buccal cavity, excretory duct. Please define your criteria for "skin" more precisely in the main text (any cuticle-lined cell type that is not glia?), and perhaps a more general term such as external epithelia would be more appropriate.
      9. Also related to cluster assignments: please specify if "excretory" category includes canal, duct, pore, gland all together, or only a subset of these. Only the duct and pore are cuticle lined and therefore expected to have oscillatory matrix gene expression.
      10. Figure 5. This figure feels disjointed and could be broken up into two figures (panels A-C and panels D-G). The first 3 panels seem more related to Figures 3 & 4 - identifying which cell types have strong pulsatile gene expression - whereas the later panels get into the degree of cell type specificity in matrix gene expression.
      11. Figure 5D-E. The very low degree of sharing is fascinating but could be an underestimate that depends on the thresholds chosen for calling a gene "pulsatile". It may be helpful to test a range of thresholds to see how much this matters. For those ~2,500 genes that appear pulsatile in just one cell type, are they called expressed but non-pulsatile in other cell types? That would seem odd to me biologically and most likely a threshold artifact.
      12. Figure 5F and p.21 Methods, the authors analyze only 140 collagen genes and 38 ZP domain genes retrieved from InterPro, but there are at least 173 cuticle collagen genes and 43 ZP domain genes described in the literature. Therefore, their lists are incomplete and the Methods should say so.

      https://pubmed.ncbi.nlm.nih.gov/33543001/ https://pubmed.ncbi.nlm.nih.gov/30409789/ 13. If most oscillatory gene expression is truly a function of the molt cycle, as suggested by the matrix gene families in Figure 5, then one might expect that most of the detected oscillatory genes would no longer be expressed in adults, or at least wouldn't appear "pulsatile" in adults. Is this true? There now are a variety of published adult data sets, including the Purice et al data on glia, that could be examined to address this.

      Referees cross-commenting

      I agree with the other reviewers' critiques, including the point of Reviewer #2 that orthogonal confirmation methods (such as by imaging) could have been nice but are not necessary. The question of Reviewer #3 about tissue synchrony/asynchrony is a very important one but I am not confident it can be addressed with these types of data.

      Significance

      As a molting invertebrate, C. elegans must build and shed its protective cuticle at multiple times across its life cycle, and this requires temporal control of many genes involved in matrix structure and processing. Although temporal oscillations were already well documented from bulk RNAseq data, this manuscript extends those prior findings by showing that different sets of genes oscillate within different cell types (including sensory glia), and by identifying many apparent oscillatory genes that were missed in prior studies because they are expressed in smaller populations of cells (whereas bulk data mainly report on oscillations within the major hypodermis). These data about cell-type specific temporal programs and gene sets emphasize the exquisite specificity of apical matrix and will be broadly useful to researchers in the C. elegans community.

      A second major contribution of this manuscript is to pioneer analysis methods for detecting oscillatory gene expression in scRNAseq datasets, even where bulk temporal data may not exist. This will be valuable for others doing sRNAseq studies in nematodes but also in other systems where cells may have molt cycle- or circadian-regulated oscillations.

    1. This is a real technical achievement in bottom-up synthetic biology, but the framing outruns the data, and the clearest example is the word "replication." What the paper demonstrates is Phi29 rolling-circle amplification of plasmids — templates copied in bulk — not cell-cycle genome replication: there is no origin-controlled, once-per-cycle initiation and no segregation machinery to partition copies to daughters. The gap matters, because the "self-reproducing cell" narrative depends on the second while the experiments show the first. The same over-reach runs throughout: feeding relies on externally supplied feeder liposomes, division is triggered by externally added streptavidin, and no experiment shows growth, replication, division, and inheritance together in a single tracked lineage — each is demonstrated separately, in some regime or population. So an accurate description is a chemically-assisted liposome workflow that cycles, not an autonomous cell with a complete life cycle. None of this makes the platform less impressive — it's a genuine step — but "selection," "Darwinian," and "complete cell cycle" are doing more work than the experiments support, and the paper would be stronger with language narrowed to match what was shown.

    1. In Kolumbien wird der neugewählte rechte Präsident einen Rollback in der Umweltpolitik und bei der Energiewende einleiten. Er wird dabei auf gut organisierten Widerstand lokaler Communities und der Linken stoßen. Trotz der Politik der Regierung Gustavo Petro ist Kolumbien noch immer von Rohstoffexporten abhängig. Interessierte Machtgruppen setzen vor allem auf die Expansion des Kupfer-Bergbaus.

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      Reply to the reviewers

      __Reviewer #1 (Evidence, reproducibility and clarity (Required)):____

      Summary In this study, Phalora et al identified the selective autophagy receptor SQSTM1/p62 as a MR1 interacting protein by proteomics approach using a cell line overexpressing MR1. While SQSTM1/p62 is implicated in autophagy regulation and autophagosome formation, genetic ablation of SQSTM1/p62 resulted in enhanced MAIT cell activation upon challenge with E. coli, but not with a synthetic agonist 5-OP-RU. In contrast, knockout of Atg5 and Atg7, both of which are involved in phagophore expansion engendered increased activation of MAIT cells upon both stimuli. From these data, the authors concluded that some factors in autophagy controlled the MR1 activity, thus the autophagy is a pivotal regulator of cellular antigen presentation.

      Major comments: 1. The notion that "This regulation appears to occur at an early step in the trafficking pathway." in the summary appears not to be compatible with the present data. What the authors have shown in the study is possible implication of autophagy components such as SQSTM1/p62, Atg5, and Atg7 that are implicated in autophagosome and phagophore formation. Should the authors highlight an "early step of trafficking", Atg14L, Atg13, and/or Atg101 must be analyzed by genetic knockout in addition to PI3 kinase inhibitors that are supposed to affect an early step in autophagy. Such an approach could confirm whether the regulation of MR1 occurs at an early step of trafficking, or at least, at an early step of autophagy.__


      The reviewer may have misinterpreted our conclusion. When we state ‘an early step in the trafficking pathway’ we are referring to MR1 trafficking (from the ER to the PM) and not to early steps in the autophagy pathway. We have modified the text to make this clearer.

      __ In Figure 2, while the degree of β2M depletion from B1 appears to be superior to that in B6 (Figure 2A), why the former was more potent in producing IFN-γ relative to the latter upon E. coli and 5-OP-RU (Figure 2D)?__


      We cannot conclusively say why MAIT cell activation is reduced to a greater extent in clone B6 compared to clone B1, whereas the protein depletion is not as pronounced. Most likely as these are clonal cells there may be genetic/phenotypic differences apart from depletion of B2M that may impact upon antigen presentation. Importantly both B1 and B6 are significantly decreased in terms of MR1 surface expression and MAIT cell activation compared to the control as would be expected.

      __ In Figure 3B, right column, what is Ac-6-FP? The left histograms show MR1 expression level upon DMSO, E. coli, and 5-OP-RU challenge. There is no explanation.__


      We thank the reviewer for pointing this out. The bar chart was mislabelled and should read 5-OP-RU (as in the histogram). This has now been corrected in the figure.

      __ Also in the same figure, was MR1 geomeans in Control, 5-1, 5-2, 5-3, 7-1, 7-2, and 7-3 upon Ac-6-FP superior to DMSO? If so or not, please explain the rational.__


      The difference in MR1 geomeans between DMSO and 5-OP-RU treated cells was significantly different. However as stated in the text the difference between control and Atg depleted cells for each condition was not statistically significant although there is a trend for increasing MR1 expression in KD cells.


      __ Figure 3C is highly intentional. If the authors put two left panels together (Control, 5-1, 5-2, and 5-3), is there still statistical difference among them?__


      The data for Atg5.3 was displayed separately as the experiments for this cell line were performed at a later timepoint using different donor cells. Therefore, it would be inappropriate to combine and/or compare them with the data for Atg 5.1 and 5.2. For clarity the figure has now been modified and this explanation added to the figure legend.

      __ There was no explanation for Figure 4B why the authors used Hela-MR1-HA. Other cell lines were used in the rest of the experiments. It is highly desirable to perform the experiment with THP1-MR1-HA in terms of logical development.__


      As the reviewer correctly states, it would be ideal to use Thp.MR1.HA cells for these microscopy experiments as they have been used throughout the rest of the paper. However, Thp1 cells can be difficult to image and HeLa cells which are more amenable to this technique are commonly used instead, generally and for MR1 studies. We have validated the HeLa.MR1.HA cell lines and can show that they upregulate MR1 at the cell surface in response to antigen and can activate MAIT cells. This data is now included as a supplementary figure (Supplementary Figure 11) and the rationale for the use of these cells explained in the main text.

      __ In addition, Figure 4B represent only the non-activated status. Given that association of SQSTM1/p62 with MR1 is dependent on E.coli and/or 5-OP-RU (Figure 1A), the same immuno-fluorescent imaging in the presence of the inhibitors upon stimulation with these reagents would also be desirable. It will uncover whether MR1 and SQSTM1/p62 colocalize upon stimulation, and such colocalization is perturbed in the presence of the inhibitors.__


      The aim of this microscopy experiment was to demonstrate that perturbations to the autophagy pathway induced by different drug treatments also affected MR1 localisation and/or expression to complement the other experiments in that figure (Figure 4A and 4C). SQSTM1 expression was included as a control as it is known to be regulated by autophagy. Although assessing the interaction between MR1 and SQSTM1 under different autophagy conditions may be of interest we did not find it to be particularly relevant in this case as our focus shifted to the autophagy pathway in general rather than the specific interaction between MR1 and SQSTM1.

      __ Whereas the authors addressed the question as to at which stage MR1 is regulated in trafficking in Figure 5, there was no experiments with 5-OP-RU (an agonist for MAIT cells). This casts the doubt whether observed phenotype really represented the true MR1 trafficking, because there is no guarantee that the trafficking pathway for antagonist (Ac-6-FP) is same as that for agonist.__


      5-OP-RU and Ac-6-FP are small chemically synthesised molecules and an agonist and antagonist of MR1 antigen presentation respectively There is no evidence to suggest that apart from activation of MAIT cells (5-OP-RU is stimulatory, Ac-6-FP is not) that they would behave any differently in terms of trafficking and interaction with MR1. Indeed, both are used interchangeably in the MR1 field.

      __ Given the importance of MR1 overexpression in showing the association between MR1 and SQSTM1/p62, it is worthwhile to consider performing the knockout experiments with Thp1-MR1-HA rather than Thp1. It will further clarify the role(s) of SQSTM1/p62, Atg5, and Atg7 in MR1 trafficking and resultant MAIT cell activation.__

      The interaction studies had to be performed with overexpressed MR1 as the endogenous protein is very difficult to detect for these types of experiments. The majority of the functional studies were performed with the endogenous protein which avoids any issues concerned with the use of overexpressed and tagged proteins and addresses concerns that interactions observed with the overexpressed protein are simply artifactual. As the functional assays validate the interaction data, we believe it is not necessary to repeat the depletion experiments in the MR1 overexpressed cell lines.



      __ Minor comments: 1.Please explain why the authors failed to detect IL23A in the coimmunoprecipitation. Should MR1-IL23A interaction be specific, what is a biological significance?__


      This point is addressed in the discussion. It is sometimes the case that interactions identified by mass spec cannot be recapitulated by co-immunoprecipitation and alternative methods may need to be employed to verify the interaction. Since this work concentrates on the autophagy pathway further experiments involving IL23A were deemed beyond the scope of this manuscript. Of note, IL23A will be strongly induced over very low background levels by E coli, which would amplify the impact of any weak interactions.

      __ When Hela-MR1-HA was used, did the authors obtain the same results as Thp1-MR1-HA as shown in Figure 1C-D? This is relevant to the specificity in the interaction between MR1 and SQSTM1/p62 as shown in Figure 4B.__


      The interaction between MR1 and SQSTM1 in the presence of E.coli was not confirmed in the HeLa.MR1.HA cells. SQSTM1 is included as a positive control as it is known to be regulated by autophagy. As these experiments were performed in the absence of any antigen, we would not expect to observe an interaction in this instance.


      __ While S1, S2, S3, and S4 showed a similar degree of SQSTM1 depletion in Figure 2A, there was difference in the potential of IFN-γ production from MAIT cells among the clones. Only S4 showed decreased potential for IFN-γ upon 5-OP-RU, though E. coli failed to so. Contrary to 5-OP-RU, S1-S3 showed an enhanced potential while S4 failed to do so. Why is that so?__


      As the SQSTM1 knockout cells are clonal cells there may be other genetic/phenotypic differences, besides depletion of SQSTM1, that can account for the observed differences in MAIT cell activation. To mitigate for these differences, we tested 4 different clonal cell lines, with 3 out of 4 clones displaying the same phenotype with respect to activation of MAIT cells.

      __ Given that there was little correlation between MR1 expression level and the potential of S1-S4 to promote or inhibit the ligand-dependent production of IFN-γ (Figure 2C right panel and Figure 2D), it is difficult to conclude that the factors implicated in autophagy play a pivotal role in MR1-dependent MAIT cell activation.__


      Surface MR1 levels on the whole are difficult to detect even in the presence of antigen as MR1 surface expression appears to be very tightly controlled. Although MR1 surface expression levels between the different SQSTM1 clones appeared to be somewhat variable, in the Atg depleted cells they showed a more consistent upregulation compared to the control (although these differences were not statistically significant). In both cases, stimulation with E.coli resulted in increased MAIT activation demonstrating that these autophagy proteins did affect MR1 presentation and that small (perhaps undetectable in some cases) changes in surface expression did impact MR1 function. Therefore, we have concluded that autophagy factors are able to regulate MR1 antigen presentation but to what extent and how remains unclear. We have removed the word ‘pivotal’ from the abstract as we agree with the reviewer that the impact of these interactions has not been conclusively established.

      __ There was no consistency in the experimental design for Figure 5. Please explain the rational why the authors have used 7.1 in A and C, but not in B, D and E?__


      For some of the experiments it was not possible to display and thus quantify all the cell lines in one figure eg the western blot data for the EndoH experiments (Figure 5D). Therefore, one representative cell line from Atg5 and Atg7 depleted cells was chosen, as on the whole all the cell lines behaved similarly. This rationale is now included in the main text.

      __ The control appeared to behave as 7.1 did. Was there statistical difference between 7.1 and 7.2 in Figure 5C? If so, what is the interpretation.__


      As the reviewer correctly notes, in Figure 5C the Atg7.1 cell line had similar kinetics to the control cell line in terms of MR1 surface expression. In other experiments Atg7.1 shows increased MR1 surface expression compared to the control (Figure 3B, although not statistically significant). One major difference between these experiments is the timing, Figure 3B is measured after an overnight incubation while Figure 5C is measured over 6 hours. It may be the case that in this cell line MR1 takes slightly longer to accumulate at the cell surface compared to Atg7.2. As these are heterogenous cell populations, there may be other factors that account for these differences apart from depletion of Atg7. Statistical analysis has now also been included for this data.

      __ Time course over 6 h will be required to assess the MR1 expression in Figure 5C.__

      It has been demonstrated by others that MR1 is able to reach the cell surface within 4 hours of antigen exposure (McWilliams et al, 2016), therefore a time course over 6 hours to measure MR1 surface expression was deemed sufficient.__

      Reviewer #1 (Significance (Required)):

      The present study uncovered the possible implication of autophagy factors in MR1 trafficking, in other words, MAIT cell activation. Although the previous study has demonstrated the importance of the protein loading factors (McWilliam et al., PNAS,117 24974-24985 2020), this study adds another pathway for MAIT cell activation. However, the conceptual significance is limited in that depletion of the factors pertinent to autophagy such as Atg5 and Atg7 in Thp1 resulted in rather weak interference in terms of MR1 trafficking and MAIT cell activation. Thus, this study will interest those who work in basic immunology, in particular, in regulation of antigen-presentation molecules and T cells as well as those who are in the field of MAIT cell biology. Although the field of this reviewer covers biochemistry, molecular biology, developmental biology, immunology and regenerative medicine, proteomics approach (in detailed technique) as seen here to identify the associated molecules is somewhat beyond the reviewer's expert.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary

      The authors used a mass spectrometry proteomics approach to screen for proteins which interact with the MHC-I-related molecule MR1. In addition to expected interacting partners, they identified SQSTM1/p62, a selective autophagy mediator, and demonstrated that MAIT cell responses to fixed E. coli were increased with knockout of SQSTM1. The authors further investigated the role of autophagy in regulating MR1 ligand presentation through knockout of two key autophagy proteins, Atg5 and Atg7, or treatment with various autophagy inhibitors. MR1 surface expression and MAIT cell activation were variably increased following interruption of autophagy in the context of fixed E. coli or synthetic ligand treatment of human monocytes and B cell lines. The authors concluded that preformed pools of MR1 are regulated by autophagy.

      Major comments

      Overall, this is an interesting study that is the first to identify autophagy as a potential regulatory mechanism for MR1. There are a number of conceptual questions relevant to the model system. The main concerns regard a number of the conclusions made, given the analysis of the data as presented. These concerns are described in more detail below.

      Conceptual concerns:

      1. The investigators rightly note the challenge in studying MR1 protein due to low endogenous expression. However, the use of over-expressed MR1 protein begs some questions with regard to the identification of ER degradation and autophagy proteins (which as they note are also involved in the degradation of damaged and defective cellular components). Although they have previously shown that MR1-HA tagged protein goes to the cell surface and presents antigen, it is impossible to know what proportion of the over-expressed molecules are functional, and it is plausible that a proportion of these molecules that end up in ER degradation or autophagy pathways identified, but would still IP with the HA tag. In the data shown, it is not entirely clear that the impacts of the molecules are actually impacting MR1 protein absent overexpression. Example: In Figure 2, there is very little impact of the complete KO of SQSTM1 on MR1 protein expression in WT THP1 cells, despite this protein only interacting with MR1 in E.coli infected cells. In contrast, in the 5-OP-RU incubated cells, there is a difference in MR1 expression in the SQSTM1 mutant clones, but no impact to MAIT cell activation. The authors note these issues and discuss the possibility that the other functions of SQSTM1 are coming in to play and further look at Atg5 and Atg7, however the absence of these proteins also have no significant impact on the expression of MR1 protein. Can the authors comment on this? The authors state that the increase in MAIT cell responses to fixed E. coli-treated polyclonal populations of SQSTM1 KO cells (same cells as SF2D) was blocked by the use of an anti-MR1 antibody, but do not show this data. Why not done with clonal populations? It is unclear why this data was not shown as it would help to support that the impact of inhibited autophagy is really on the functional MR1 protein pool, rather than a pool of non-functional but still HA tagged MR1 that has been shunted to degradation or autophagy pathways.__

      The reviewer rightly acknowledges the challenges associated with detecting endogenous MR1 protein levels which can be difficult to measure even after antigen exposure. For this reason, many researchers use a tagged protein in overexpressing cell lines to study MR1 as we (and others) have done for the proteomics analysis and validation studies. The use of tagged overexpressed proteins can be problematic because they may not recapitulate endogenous protein structure, localisation and/or function. Although we have previously demonstrated that HA tagged MR1 behaves similarly to its endogenous counterpart in terms of trafficking to the cell surface and presentation to MAIT cells (Ussher et al, 2016), there is still a possibility that there is a population of non-functional protein that is targeted for degradation. As we understand, it is the reviewer’s concern that it is this protein pool that is immunoprecipitating with autophagy components.

      Firstly, although the interaction studies were necessarily performed using tagged overexpressed protein, the majority of the functional studies (ie measuring surface MR1 levels and MAIT cell activation in SQSTM1 and Atg depleted cells, Figures 2 and 3) were performed in wildtype Thp1 cells, expressing endogenous levels of MR1. As explained in response to reviewer 1, MR1 surface levels as displayed in Figures 2C and 3B, are very tightly controlled and can be difficult to detect even after antigen exposure as demonstrated in the accompanying histograms. Therefore, subtle differences in MR1 surface levels are to be expected especially when measuring an increase rather than a decrease in expression. Although for SQSTM1 depletion there was some variability in MR1 surface levels, for Atg depletion there was a clear trend towards increased expression, although these differences were not statistically significant. In both cases (depletion of SQSTM1 and Atg) there was a definite effect on MR1 presentation as MAIT cell activation was increased in nearly all cases. It is well established in the literature that MAIT activation, in the 5 hour timecourse of our experiments, is wholly MR1 dependent. Therefore, these subtle, and perhaps sometimes undetectable, differences on endogenous MR1 surface expression do have an effect on MR1 function and we believe this validates the data from the interaction studies using overexpressed protein.

      In addition, experiments performed with the MR1 blocking antibody would not necessarily address the reviewers concerns as again these were done on Thp1 cells expressing endogenous levels of MR1 and not the overexpressing cell lines. However, for completeness this data has now been included as a supplementary figure.

      Secondly one of the top hits from the proteomics analysis was B2M, a protein known to associate with MR1 and to be functionally important. Other proteins identified by our screen include components of the peptide loading complex which have also been reported to be important for MR1 trafficking and antigen presentation. It should also be noted that SQSTM1 was identified in a similar proteomics screen performed by a different lab (McWilliams et al, 2020). Therefore, we believe that these findings also validate use of the HA tagged MR1 construct to generate true protein interactions.

      __ The conclusion that "regulation of MR1 by autophagy is not dependent on new protein synthesis and is most likely occurring on pre-existing pools of MR1" is not strongly supported by the data. If MR1 is processed normally through the golgi in Atg5 and 7 deficient cells (Figure 5D), how can the conclusion be made that the pre-existing pools of MR1 are in the ER? There is a non-significant decrease in MR1 surface expression from CHX treatment in the context of Ac-6-FP stimulation in Atg KO cells. This data is not clear enough to support a firm conclusion in either direction. Have the authors performed this experiment using 5-OP-RU or fixed E. coli as ligand sources? Is there a similar trend seen using the Atg KO C1R cells? Further supporting experiments may be necessary to conclude whether or not this trend is biologically relevant.__


      We thank the reviewer for their comment. The statement that pre-existing pools of MR1 are in the ER is based on reports from the literature where it has been shown that unbound ligand receptive MR1 remains in the ER until it comes into contact with antigen. Since we were able to show that MR1 trafficked normally through the Golgi in Atg depleted cells, the effects of autophagy on MR1 expression and function must occur prior to Golgi processing. This would indicate the ER population of MR1 as the likely targets of regulation by autophagy especially considering the function of SQSTM1 which binds to proteins in the ER.

      The experiments with CHX treatment were used to establish whether it was new or pre existing protein that was targeted by autophagy. Since CHX had no effect on MR1 surface expression this would indicate that new protein synthesis is not required for MR1 trafficking in Atg depleted cells.

      __

      Analysis of Western Blot data:

      1. There are many places throughout the manuscript where statements are made with regard to increases and decreases in the protein expression level with treatment, or comparisons between control and knockout samples. Although the legends generally indicate these experiments were based on at least 3 replicates (except some cases, where noted), there is no quantification of any western blotting data. There is no information in the legends or methods as to how much sample was loaded. Specific examples:

      a. Figure 1/Supp Figure 1: Figure 1C and 1D: There are several differences in the inputs between the 2 blots, including differences in the no antigen samples (which should be the same) or presence of multiple bands in one blot for a given marker but not the other. Fig 1C: the band for Calreticulin in the immunoprecipitated E. coli-treated Thp1.MR1.HA samples (right lane) is very weak. Fig. 1D: the bands are weak and there is no clear difference for Calnexin in the immunoprecipitated 5-OP-RU treated Thp1.MR1.HA samples (right lane) compared to no ligand despite the conclusion that Calnexin weakly associates with MR1 in the context of 5-OP-RU ligand. Are some of these weak associations visible due to different inputs? Why are the input blots for anti-HA so different between the no antigen controls in the E coli vs 5-OP-RU blots? Supp Figure 1B: the +5-OP-RU pulldown of MR1.HA appears as to be more (like with E.coli), but no quantification. Why does so little B2M IP with 5-OP-RU MR1? Supp Figure 1D (and others): statements are made about increases and decreases without quantification. All: Presumably HSP90 is used as a loading control for the input, but this is not discussed nor is there quantification.__


      We thank the reviewer for this comment. As western blotting is a multi-step process often over more than one day, there are numerous points at which variation can occur between blots no matter how carefully the conditions are controlled to minimise this. It is for this reason that it is generally not good practice to compare samples that have been run on different gels. Therefore, we do not believe that comparisons between blots in Figures 1C and 1D, relating to differences in input proteins for example, are appropriate nor informative. If we take the top anti-HA blot for Figure 1C there is a big increase in protein expression in the Ip of E.coli treated cells (final lane) which is not as pronounced with 5-OP-RU treatment (Figure 1D, top blot, final lane). This sample will dictate the exposure time of the blot (so as to prevent saturation of this sample) which then affects detectable expression of less well expressing samples on the same blot (such as the input samples in Figure 1C). Therefore there may appear to be less input protein in Figure 1C than Figure 1D but there is also more protein in the E.coli treated pull down than in the 5-OP-RU treated one, which also needs to be taken into account. This is one example of why it is difficult to compare samples across blots. To accurately and correctly compare these input samples they would need to be run on the same gel.

      The only useful comparison that can be drawn is between samples from the same blot, so comparing input protein in the presence and absence of E.coli for instance. To take the reviewer’s example, for the anti-calreticulin blot in Figure 1C, there is a weak interaction of calreticulin with MR1 in the presence of E.coli. If we compare the input lanes on this blot (effectively the loading control), there actually appears to be slightly more protein in the E.coli negative sample that the positive one. This would argue against the reviewers claim that this weak interaction is actually due to differences in the input and it is instead more likely to simply be a weak interaction. It is important to point out that this interaction, and others involving components of the peptide loading complex, have also been validated by other groups.


      With regards to Calnexin association with MR1 in the presence of 5-OP-RU, we did not mean to imply that this association was only in the presence of 5-OP-RU as it is evident from the data that Calnexin weakly associates even in the absence of antigen. The text has now been changed to make this clearer.


      The anti-HSP90 blot has been included to show that a random protein, not identified by our proteomics screen, does not spuriously associate with MR1, and not as a loading control for the input samples per se. This explanation has now been included in the text.

      Finally with regard to quantification of the co-immunoprecipitation blots, while quantification of western blots in some cases can be informative (eg relative expression of a protein compared to a control), it is at best only a semi-quantitative technique and not generally applied to co-immunoprecipitation data. As we are looking for a binary result (presence/absence of a particular protein) rather than a relative value, we do not see how quantification of this data will make it any more informative. We have included more detail of the sample loading in the methods section as requested by the reviewer and have added quantification of other blots where appropriate. __

      b. Supp Figure 5: The authors conclude there are no difference in protein interactions with MR1 in Atg5 or 7 deficient cells. By eye, there appear to in fact be differences, but there is no quantification to support the conclusions either iway. These data are subsequently used to make interpretive statements about the data in Figure 5. There is no indication of the number of times this experiment was performed.__

      In supplementary figure 5, we aimed to determine whether depletion of Atg 5 or 7 negatively affected the MR1 proteome ie whether interactions that were previously observed were disrupted and whether this contributed to the effects on MR1 antigen presentation observed in these cell lines. The interactions between MR1 and the tested proteins remained intact in Atg depleted cells. However, as Atg depletion increased MR1 protein expression some of these interactions are more pronounced in the depleted cell lines compared to the control cell line. Thus, the reviewer is correct in stating that there are differences in the protein interactions between the cell lines but in all cases the protein interactions remain intact which was the focus of our analysis. We have modified the text to make this clearer. The figure legend now also includes the number of replicates for this experiment.

      __ Figure 4A: No quantification to support conclusions. Unclear why both blocking and inducing autophagy would both increase the amount of MR1 in cells.__


      Quantification of this western blot data has now been included in the figure. Blocking autophagy (3MA and Wort) has a much greater effect on total MR1 protein levels, while inducing autophagy (EBSS) has minimal effects compared to the control. As autophagy is a highly dynamic process with western blotting providing just a snapshot of this process, inhibiting and inducing autophagy can both lead to the same observed phenotype of increased autophagosomes, due to blocking fusion with lysosomes and increased autophagosome formation respectively.

      __

      Analysis of Fluorescence microscopy data (Figure 4B):

      1. There are several concerns with the conclusions drawn from the fluorescence microscopy images (Figure 4B). How many images/fields were taken and cells analyzed per condition? How were individual fields chosen for imaging to be unbiased? Overall, the conclusions are observational and require quantification. For example, the authors indicate "an increase in MR1 cytoplasmic signal intensity following treatment...", but there is not data analysis to support this statement. This could be quantified by analyzing average MR1-HA fluorescence intensity across the cell volume compared to the bright fluorescence intensity of the non-cytoplasmic MR1-HA regions. Similarly, the number and intensity of the SQSTM1 foci should be quantified. Quantification is required to make the stated conclusions.__

      We thank the reviewer for their helpful suggestions regarding the microscopy experiments. Quantification of the data has now been added, including MR1 fluorescent intensity and the number of SQSTM1 foci, which supports the data from Figure 4A. The methods and figure legend have been updated to include more details of the analysis pipeline.


      __ Other statistical concerns:

      1. Some of the figure legends do not clearly state the number of independent experiments performed (2D, 3C-D, 5A, SF2, SF3, SF5). If these experiments were only performed once, additional repeats and appropriate statistical analysis are necessary to validate any conclusions drawn from these results.__

      The number of replicates for each experiment are now included in the figure legends. __

      1. Was statistical analysis performed on the MR1 mRNA expression in Figure 5A, and how many independent experiments are shown? There appears to be a decrease in MR1 expression in the Stg7.1 KO cells, which might impact the overall MR1 expression. Also, statistical analysis seems to be missing from 5B and 5C.__

      This figure has now been altered to reflect the number of replicates (2 biological replicates each consisting of 3 technical replicates) and statistical analysis has also been included. Statistical analysis for Figures 5B and 5C has also now been included. __

      1. In figure 5E, were there statistical comparisons between the Atg KO and control cells in the Ac-6-FP-treated non-CHX condition? It is unclear whether the statement "As previously observed, there was an increase in surface MR1 levels in Atg-depleted cells compared to the control in the presence of Ac-6-FP" is referring to the non-significant results in 3B or to this data presented in 5E. This statement should be revised to reflect the statistical significance of these data.__

      We thank the reviewer for this point. This figure has been amended to include a timecourse of CHX treatment in control and Atg depleted cell lines and statistical analysis has also been included. The statement has been clarified to highlight the point that CHX treatment does not affect the level of MR1 upregulation in control and knockdown cell lines.

      __

      Throughout the figures, several bar plots are missing the individual data points of experimental or technical replicates.__


      All bar plots display either the individual data points where donor cells were used or the average of 3 or more independent experiments with error bars denoting the standard deviation for experiments using cell lines.__

      1. The data in Figures 3C-D could be presented and analyzed as paired data (comparing the response from MAIT cells of each PBMC donor to the Ctrl cells vs the Atg KO clones) to better represent the impact of the KO.__

      We thank the reviewer for this suggestion. We believe that analysis via ANOVA is more appropriate in this instance due to the number of comparisons made with the control cells.__

      Other minor concerns:

      1. The conclusion "Overall, in the absence of SQSTM1, cellular changes induced by E. coli result in increased antigen presentation, which is not replicated with 5-OP-RU where MAIT activation may be adversely affected, implying that regulation of MR1 function by SQSTM1 may be dependent on the nature of the antigen" (page 6) is confusing and may need re-wording.__

      We are sorry for the confusion and have reworded this sentence to make it clearer. __

      1. The x-axis in the bar plots of Fig 3B labels the right group as "Ac-6-FP" in contrast to the histogram label and figure legend, which indicate the cells were treated with 5-OP-RU.__

      We thank the reviewer for pointing this out, the bar plot was indeed mislabelled and has now been corrected. __

      1. The presentation of data in Figure 5B is confusing. Perhaps the DMSO and Ac-6-FP conditions are mis-labeled? For the DMSO-treated samples, it appears that the data presented are percent surface MR1 GeoMean compared to the 0hr timepoint per cell lines. However, treating cells with Ac-6-FP should result in an increased surface MR1 expression (as seen in the non-CHX samples of Fig 5E, for example). If the data presented are percent of the 0hr DMSO control, wouldn't the % MR1 expression be higher for the Ac-6-FP samples than the DMSO samples? Alternately, it might be clearer to separate these two conditions onto separate plots, with % MR1 calculated relative to the 0 hr control of DMSO or Ac-6-FP treatment, respectively.__

      We thank the reviewer for pointing this out, the graph was indeed mislabelled and has now been corrected. The DMSO and Ac-6-FP treated samples are normalised to their own 0-hour timepoint (set at 100%) in order to directly compare the rate of decline of MR1 surface expression between the two conditions. This is now more clearly explained in the figure legend.

      __ Unclear in Figures 3 and 5 (and supplements) why all or only some of the Atg5 and 7 clones are used from experiment to experiment.__


      Please see our response to reviewer 1 on this point.__

      1. The discussion mentions "we found no evidence of an interaction between MR1 and AAKI" on page 9. What data supports this statement?__

      We found no evidence of an interaction between MR1 and AAK1 from our proteomics screen, this is now explained in the text.

      __ The discussion indicates that "This increase in SQSTM1 protein levels still resulted in increased MR1 surface levels and activation of MAIT cells, the same phenotype observed in SQSTM1-depleted cells" as it relates to the presence of E.coli. This statement is not fully supported by the data as SQSTM1 depletion did not lead to an increase in surface MR1 in E.coli treated cells.__


      We thank the reviewer for pointing this out, this sentence has now been corrected. __

      1. In the Proteomics/Mass Spec methods section on page 13, the citations to MaxQuant and Andromeda may need to be fixed.__

      We thank the reviewer for pointing this out, this has now been corrected. __

      1. There is no materials/methods section in the supplement. While most of this is covered by the main manuscript M/M section, there is no information on the IL12 and IL18 cytokine treatment, or treating with il12/il18 or isotype blocking antibody in SF1.__

      A methods section for the supplementary data has now been included. __

      1. Throughout the manuscript, several full stops are missing following in-text citations (ex: page 1, line 6 "...and Granzyme B 2-4 The microbial...").__

      We thank the reviewer for pointing this out, this has now been corrected.__

      1. The figure 1 legend should read "LC-MS/MS" rather than "LC-LC/MS"__

      We thank the reviewer for pointing this out, this has now been corrected.__

      1. Several of the citations need updating. They are listed as "Preprint available at ..." but for several of these references, the DOI links to the fully peer-reviewed publications, not a preprint.__

      We thank the reviewer for pointing this out, this has now been corrected.

      __

      Reviewer #2 (Significance (Required)):

      Significance

      Overall, this work expands the field knowledge of MR1 regulation and antigen presentation. The authors are the first to describe the putative role of key autophagy mediators like SQSTM1 and Atg5/7 in regulating MR1/MAIT cell activation. This report builds upon previous works exploring MR1 trafficking (Huang et al. JEM 2008, McWilliam et al. Nat Imm 2016, Harriff et al. PLoS Path 2016, Karamooz et al. Sci Rep 2019, McWilliam PNAS 2020, Huber et al. Sci Rep 2020) and MR1 protein stability (Abós et al. Biochem Biophys Res Commun 2011, Ussher et al. Eur J Immunol 2016, McWilliam et al. PNAS 2020, Kulicke et al. JBC 2022).

      This report would be of interest to researchers in the field of MR1 trafficking and antigen presentation, particularly in the context of increasing interest in targeting MR1 therapeutically (e.g. in cancer immunobiology or autoimmunity). From these results, future work could include characterization of the specific autophagy mechanisms which target MR1 for degradation, the role of SQSTM1 in modulating MR1 function via direct binding through autophagy or additional mechanisms, the variable mechanisms of MR1 trafficking and antigen presentation in the context of internal vs external ligand sources, and exploring if bacterial modulation of autophagy might impact MR1 antigen presentation.

      Expertise: MR1 trafficking and antigen presentation, MAIT cell activation, cell and molecular techiques, statistical analyses. Difficult to assess: the relevance of these marker in the autophagy field and evaluating the technical methods for LC-MS/MS.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In the current report, Phalora et al., have identified a number of proteins that bind to human MR1. Some of them, including those associated with the peptide-loading complex, such as tapasin, have been identified by others as well. However, these authors found that molecules associated with autophagy-specifically, SQST1/p62-were negative regulators of MR1 surface expression. In other words, knocking out the gene encoding this protein enhanced MR1 expression in THP-1 cells pulsed with E. coli and consequent MAIT cell activation. Moreover, CRISPR/Cas9-mediated deletion of the autophagy proteins, Atg5 and Atg7, resulted in an even greater enhancement of MR1 surface expression. Chemicals that block autophagy had similar effects in both THP-1 and primary PBMC monocytes. Thus, for the first time, it has been demonstrated that, like in classical HLA class I molecules, autophagy plays a role in the surface expression of the MR1 antigen presenting molecule. Overall, the study is very interesting and technically well-done. I do have a few questions, concerns and criticisms that are indicated in the sections below.

      Major Comments: 1. It was stated in the text that they used an anti-MR1 mAb to demonstrate the effects on MAIT cell activation were indeed MR1-dependent, yet these data were not shown. Those experiments should be included in the supplemental data section.__


      We thank the reviewer for this suggestion, this data has now been included as a supplementary figure.

      __ The Discussion lacks a "big picture" assessment/speculation about how these observations fit within a particular disease or set of diseases__


      The discussion has now been revised to include assessment of how these findings fit into the wider scope of MR1 restricted T cells in health and disease.

      __ THP-1 and C1R are essentially cancer cells and it has been shown that MR1T cells likely recognize a tumor antigen presented by MR1. Rather than using purified MAIT cells for this study, the authors used purified CD8+ T cells. MAIT cells represent a portion of them. How many of the non-MAIT cells were activated by THP-1 and/or C1R cells? One could compare MAIT vs. MR1T cell activation depending on the APC type.__


      We thank the reviewer for this suggestion. We re-analysed some of the data to focus on the non-MAIT population but we were unable to identify a population of non-MAIT cells stimulated by co-incubation with Thp1 or CR1 cells. In general, MR1T cells are quite rare and difficult to isolate solely from the non-MAIT cell population.


      __ As autophagy proteins have been shown to be important for MHC class I and, thanks to this work, MR1, it would have been helpful to discuss other antigen presenting molecules (e.g., CD1d) and what this could mean in immune responses overall. How does this help the host?__


      We have now included a section in the discussion to address the wider significance of these findings for immune responses via antigen presentation and the implications for other antigen presenting molecules.

      __ Minor Comment: 1. Some parts of some figures (e.g., Fig. 1B) have text so small that it is extremely difficult to read. This would be problematic in a journal article.__


      We thank the reviewer for pointing this out, the text in the figure has now been adjusted to make it easier to read.

      __

      Reviewer #3 (Significance (Required)):

      This study shows, for the first time, that autophagy processes impact cell surface expression of MR1 and this depends upon the antigen. Because this phenomenon has been demonstrated previously for classical MHC class I molecules (ref. 28) and the lipid-presenting antigen presenting molecule CD1d (Autophagy 13:1025-1036, 2017), the novelty of their findings is somewhat diminished.

      An audience who would be interested in this work would include investigators who study antigen presentation to both classical and innate T cells.

      Keywords: antigen presentation; MAIT cells; MR1; autophagy; innate immunity__

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      In the current report, Phalora et al., have identified a number of proteins that bind to human MR1. Some of them, including those associated with the peptide-loading complex, such as tapasin, have been identified by others as well. However, these authors found that molecules associated with autophagy-specifically, SQST1/p62-were negative regulators of MR1 surface expression. In other words, knocking out the gene encoding this protein enhanced MR1 expression in THP-1 cells pulsed with E. coli and consequent MAIT cell activation. Moreover, CRISPR/Cas9-mediated deletion of the autophagy proteins, Atg5 and Atg7, resulted in an even greater enhancement of MR1 surface expression. Chemicals that block autophagy had similar effects in both THP-1 and primary PBMC monocytes. Thus, for the first time, it has been demonstrated that, like in classical HLA class I molecules, autophagy plays a role in the surface expression of the MR1 antigen presenting molecule. Overall, the study is very interesting and technically well-done. I do have a few questions, concerns and criticisms that are indicated in the sections below.

      Major Comments:

      1. It was stated in the text that they used an anti-MR1 mAb to demonstrate the effects on MAIT cell activation were indeed MR1-dependent, yet these data were not shown. Those experiments should be included in the supplemental data section.
      2. The Discussion lacks a "big picture" assessment/speculation about how these observations fit within a particular disease or set of diseases
      3. THP-1 and C1R are essentially cancer cells and it has been shown that MR1T cells likely recognize a tumor antigen presented by MR1. Rather than using purified MAIT cells for this study, the authors used purified CD8+ T cells. MAIT cells represent a portion of them. How many of the non-MAIT cells were activated by THP-1 and/or C1R cells? One could compare MAIT vs. MR1T cell activation depending on the APC type.
      4. As autophagy proteins have been shown to be important for MHC class I and, thanks to this work, MR1, it would have been helpful to discuss other antigen presenting molecules (e.g., CD1d) and what this could mean in immune responses overall. How does this help the host?

      Minor Comment:

      1. Some parts of some figures (e.g., Fig. 1B) have text so small that it is extremely difficult to read. This would be problematic in a journal article.

      Significance

      This study shows, for the first time, that autophagy processes impact cell surface expression of MR1 and this depends upon the antigen. Because this phenomenon has been demonstrated previously for classical MHC class I molecules (ref. 28) and the lipid-presenting antigen presenting molecule CD1d (Autophagy 13:1025-1036, 2017), the novelty of their findings is somewhat diminished.

      An audience who would be interested in this work would include investigators who study antigen presentation to both classical and innate T cells.

      Keywords: antigen presentation; MAIT cells; MR1; autophagy; innate immunity

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The authors used a mass spectrometry proteomics approach to screen for proteins which interact with the MHC-I-related molecule MR1. In addition to expected interacting partners, they identified SQSTM1/p62, a selective autophagy mediator, and demonstrated that MAIT cell responses to fixed E. coli were increased with knockout of SQSTM1. The authors further investigated the role of autophagy in regulating MR1 ligand presentation through knockout of two key autophagy proteins, Atg5 and Atg7, or treatment with various autophagy inhibitors. MR1 surface expression and MAIT cell activation were variably increased following interruption of autophagy in the context of fixed E. coli or synthetic ligand treatment of human monocytes and B cell lines. The authors concluded that preformed pools of MR1 are regulated by autophagy.

      Major comments

      Overall, this is an interesting study that is the first to identify autophagy as a potential regulatory mechanism for MR1. There are a number of conceptual questions relevant to the model system. The main concerns regard a number of the conclusions made, given the analysis of the data as presented. These concerns are described in more detail below.

      Conceptual concerns:

      1. The investigators rightly note the challenge in studying MR1 protein due to low endogenous expression. However, the use of over-expressed MR1 protein begs some questions with regard to the identification of ER degradation and autophagy proteins (which as they note are also involved in the degradation of damaged and defective cellular components). Although they have previously shown that MR1-HA tagged protein goes to the cell surface and presents antigen, it is impossible to know what proportion of the over-expressed molecules are functional, and it is plausible that a proportion of these molecules that end up in ER degradation or autophagy pathways identified, but would still IP with the HA tag. In the data shown, it is not entirely clear that the impacts of the molecules are actually impacting MR1 protein absent overexpression. Example: In Figure 2, there is very little impact of the complete KO of SQSTM1 on MR1 protein expression in WT THP1 cells, despite this protein only interacting with MR1 in E.coli infected cells. In contrast, in the 5-OP-RU incubated cells, there is a difference in MR1 expression in the SQSTM1 mutant clones, but no impact to MAIT cell activation. The authors note these issues and discuss the possibility that the other functions of SQSTM1 are coming in to play and further look at Atg5 and Atg7, however the absence of these proteins also have no significant impact on the expression of MR1 protein. Can the authors comment on this? The authors state that the increase in MAIT cell responses to fixed E. coli-treated polyclonal populations of SQSTM1 KO cells (same cells as SF2D) was blocked by the use of an anti-MR1 antibody, but do not show this data. Why not done with clonal populations? It is unclear why this data was not shown as it would help to support that the impact of inhibited autophagy is really on the functional MR1 protein pool, rather than a pool of non-functional but still HA tagged MR1 that has been shunted to degradation or autophagy pathways.
      2. The conclusion that "regulation of MR1 by autophagy is not dependent on new protein synthesis and is most likely occurring on pre-existing pools of MR1" is not strongly supported by the data. If MR1 is processed normally through the golgi in Atg5 and 7 deficient cells (Figure 5D), how can the conclusion be made that the pre-existing pools of MR1 are in the ER? There is a non-significant decrease in MR1 surface expression from CHX treatment in the context of Ac-6-FP stimulation in Atg KO cells. This data is not clear enough to support a firm conclusion in either direction. Have the authors performed this experiment using 5-OP-RU or fixed E. coli as ligand sources? Is there a similar trend seen using the Atg KO C1R cells? Further supporting experiments may be necessary to conclude whether or not this trend is biologically relevant.

      Analysis of Western Blot data:

      1. There are many places throughout the manuscript where statements are made with regard to increases and decreases in the protein expression level with treatment, or comparisons between control and knockout samples. Although the legends generally indicate these experiments were based on at least 3 replicates (except some cases, where noted), there is no quantification of any western blotting data. There is no information in the legends or methods as to how much sample was loaded. Specific examples:
        • a. Figure 1/Supp Figure 1: Figure 1C and 1D: There are several differences in the inputs between the 2 blots, including differences in the no antigen samples (which should be the same) or presence of multiple bands in one blot for a given marker but not the other. Fig 1C: the band for Calreticulin in the immunoprecipitated E. coli-treated Thp1.MR1.HA samples (right lane) is very weak. Fig. 1D: the bands are weak and there is no clear difference for Calnexin in the immunoprecipitated 5-OP-RU treated Thp1.MR1.HA samples (right lane) compared to no ligand despite the conclusion that Calnexin weakly associates with MR1 in the context of 5-OP-RU ligand. Are some of these weak associations visible due to different inputs? Why are the input blots for anti-HA so different between the no antigen controls in the E coli vs 5-OP-RU blots? Supp Figure 1B: the +5-OP-RU pulldown of MR1.HA appears as to be more (like with E.coli), but no quantification. Why does so little B2M IP with 5-OP-RU MR1? Supp Figure 1D (and others): statements are made about increases and decreases without quantification. All: Presumably HSP90 is used as a loading control for the input, but this is not discussed nor is there quantification.
        • b. Supp Figure 5: The authors conclude there are no difference in protein interactions with MR1 in Atg5 or 7 deficient cells. By eye, there appear to in fact be differences, but there is no quantification to support the conclusions either iway. These data are subsequently used to make interpretive statements about the data in Figure 5. There is no indication of the number of times this experiment was performed.
        • c. Figure 4A: No quantification to support conclusions. Unclear why both blocking and inducing autophagy would both increase the amount of MR1 in cells.

      Analysis of Fluorescence microscopy data (Figure 4B):

      1. There are several concerns with the conclusions drawn from the fluorescence microscopy images (Figure 4B). How many images/fields were taken and cells analyzed per condition? How were individual fields chosen for imaging to be unbiased? Overall, the conclusions are observational and require quantification. For example, the authors indicate "an increase in MR1 cytoplasmic signal intensity following treatment...", but there is not data analysis to support this statement. This could be quantified by analyzing average MR1-HA fluorescence intensity across the cell volume compared to the bright fluorescence intensity of the non-cytoplasmic MR1-HA regions. Similarly, the number and intensity of the SQSTM1 foci should be quantified. Quantification is required to make the stated conclusions.

      Other statistical concerns:

      1. Some of the figure legends do not clearly state the number of independent experiments performed (2D, 3C-D, 5A, SF2, SF3, SF5). If these experiments were only performed once, additional repeats and appropriate statistical analysis are necessary to validate any conclusions drawn from these results.
      2. Was statistical analysis performed on the MR1 mRNA expression in Figure 5A, and how many independent experiments are shown? There appears to be a decrease in MR1 expression in the Stg7.1 KO cells, which might impact the overall MR1 expression. Also, statistical analysis seems to be missing from 5B and 5C.
      3. In figure 5E, were there statistical comparisons between the Atg KO and control cells in the Ac-6-FP-treated non-CHX condition? It is unclear whether the statement "As previously observed, there was an increase in surface MR1 levels in Atg-depleted cells compared to the control in the presence of Ac-6-FP" is referring to the non-significant results in 3B or to this data presented in 5E. This statement should be revised to reflect the statistical significance of these data.
      4. Throughout the figures, several bar plots are missing the individual data points of experimental or technical replicates.
      5. The data in Figures 3C-D could be presented and analyzed as paired data (comparing the response from MAIT cells of each PBMC donor to the Ctrl cells vs the Atg KO clones) to better represent the impact of the KO.

      Other minor concerns:

      1. The conclusion "Overall, in the absence of SQSTM1, cellular changes induced by E. coli result in increased antigen presentation, which is not replicated with 5-OP-RU where MAIT activation may be adversely affected, implying that regulation of MR1 function by SQSTM1 may be dependent on the nature of the antigen" (page 6) is confusing and may need re-wording.
      2. The x-axis in the bar plots of Fig 3B labels the right group as "Ac-6-FP" in contrast to the histogram label and figure legend, which indicate the cells were treated with 5-OP-RU.
      3. The presentation of data in Figure 5B is confusing. Perhaps the DMSO and Ac-6-FP conditions are mis-labeled? For the DMSO-treated samples, it appears that the data presented are percent surface MR1 GeoMean compared to the 0hr timepoint per cell lines. However, treating cells with Ac-6-FP should result in an increased surface MR1 expression (as seen in the non-CHX samples of Fig 5E, for example). If the data presented are percent of the 0hr DMSO control, wouldn't the % MR1 expression be higher for the Ac-6-FP samples than the DMSO samples? Alternately, it might be clearer to separate these two conditions onto separate plots, with % MR1 calculated relative to the 0 hr control of DMSO or Ac-6-FP treatment, respectively.
      4. Unclear in Figures 3 and 5 (and supplements) why all or only some of the Atg5 and 7 clones are used from experiment to experiment.
      5. The discussion mentions "we found no evidence of an interaction between MR1 and AAKI" on page 9. What data supports this statement?
      6. The discussion indicates that "This increase in SQSTM1 protein levels still resulted in increased MR1 surface levels and activation of MAIT cells, the same phenotype observed in SQSTM1-depleted cells" as it relates to the presence of E.coli. This statement is not fully supported by the data as SQSTM1 depletion did not lead to an increase in surface MR1 in E.coli treated cells.
      7. In the Proteomics/Mass Spec methods section on page 13, the citations to MaxQuant and Andromeda may need to be fixed.
      8. There is no materials/methods section in the supplement. While most of this is covered by the main manuscript M/M section, there is no information on the IL12 and IL18 cytokine treatment, or treating with il12/il18 or isotype blocking antibody in SF1.
      9. Throughout the manuscript, several full stops are missing following in-text citations (ex: page 1, line 6 "...and Granzyme B 2-4 The microbial...").
      10. The figure 1 legend should read "LC-MS/MS" rather than "LC-LC/MS"
      11. Several of the citations need updating. They are listed as "Preprint available at ..." but for several of these references, the DOI links to the fully peer-reviewed publications, not a preprint.

      Significance

      Overall, this work expands the field knowledge of MR1 regulation and antigen presentation. The authors are the first to describe the putative role of key autophagy mediators like SQSTM1 and Atg5/7 in regulating MR1/MAIT cell activation. This report builds upon previous works exploring MR1 trafficking (Huang et al. JEM 2008, McWilliam et al. Nat Imm 2016, Harriff et al. PLoS Path 2016, Karamooz et al. Sci Rep 2019, McWilliam PNAS 2020, Huber et al. Sci Rep 2020) and MR1 protein stability (Abós et al. Biochem Biophys Res Commun 2011, Ussher et al. Eur J Immunol 2016, McWilliam et al. PNAS 2020, Kulicke et al. JBC 2022).

      This report would be of interest to researchers in the field of MR1 trafficking and antigen presentation, particularly in the context of increasing interest in targeting MR1 therapeutically (e.g. in cancer immunobiology or autoimmunity). From these results, future work could include characterization of the specific autophagy mechanisms which target MR1 for degradation, the role of SQSTM1 in modulating MR1 function via direct binding through autophagy or additional mechanisms, the variable mechanisms of MR1 trafficking and antigen presentation in the context of internal vs external ligand sources, and exploring if bacterial modulation of autophagy might impact MR1 antigen presentation.

      Expertise: MR1 trafficking and antigen presentation, MAIT cell activation, cell and molecular techiques, statistical analyses. Difficult to assess: the relevance of these marker in the autophagy field and evaluating the technical methods for LC-MS/MS.

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary

      In this study, Phalora et al identified the selective autophagy receptor SQSTM1/p62 as a MR1 interacting protein by proteomics approach using a cell line overexpressing MR1. While SQSTM1/p62 is implicated in autophagy regulation and autophagosome formation, genetic ablation of SQSTM1/p62 resulted in enhanced MAIT cell activation upon challenge with E. coli, but not with a synthetic agonist 5-OP-RU. In contrast, knockout of Atg5 and Atg7, both of which are involved in phagophore expansion engendered increased activation of MAIT cells upon both stimuli. From these data, the authors concluded that some factors in autophagy controlled the MR1 activity, thus the autophagy is a pivotal regulator of cellular antigen presentation.

      Major comments:

      1. The notion that "This regulation appears to occur at an early step in the trafficking pathway." in the summary appears not to be compatible with the present data. What the authors have shown in the study is possible implication of autophagy components such as SQSTM1/p62, Atg5, and Atg7 that are implicated in autophagosome and phagophore formation. Should the authors highlight an "early step of trafficking", Atg14L, Atg13, and/or Atg101 must be analyzed by genetic knockout in addition to PI3 kinase inhibitors that are supposed to affect an early step in autophagy. Such an approach could confirm whether the regulation of MR1 occurs at an early step of trafficking, or at least, at an early step of autophagy.
      2. In Figure 2, while the degree of β2M depletion from B1 appears to be superior to that in B6 (Figure 2A), why the former was more potent in producing IFN-γ relative to the latter upon E. coli and 5-OP-RU (Figure 2D)?
      3. In Figure 3B, right column, what is Ac-6-FP? The left histograms show MR1 expression level upon DMSO, E. coli, and 5-OP-RU challenge. There is no explanation.
      4. Also in the same figure, was MR1 geomeans in Control, 5-1, 5-2, 5-3, 7-1, 7-2, and 7-3 upon Ac-6-FP superior to DMSO? If so or not, please explain the rational.
      5. Figure 3C is highly intentional. If the authors put two left panels together (Control, 5-1, 5-2, and 5-3), is there still statistical difference among them?
      6. There was no explanation for Figure 4B why the authors used Hela-MR1-HA. Other cell lines were used in the rest of the experiments. It is highly desirable to perform the experiment with THP1-MR1-HA in terms of logical development.
      7. In addition, Figure 4B represent only the non-activated status. Given that association of SQSTM1/p62 with MR1 is dependent on E.coli and/or 5-OP-RU (Figure 1A), the same immuno-fluorescent imaging in the presence of the inhibitors upon stimulation with these reagents would also be desirable. It will uncover whether MR1 and SQSTM1/p62 colocalize upon stimulation, and such colocalization is perturbed in the presence of the inhibitors.
      8. Whereas the authors addressed the question as to at which stage MR1 is regulated in trafficking in Figure 5, there was no experiments with 5-OP-RU (an agonist for MAIT cells). This casts the doubt whether observed phenotype really represented the true MR1 trafficking, because there is no guarantee that the trafficking pathway for antagonist (Ac-6-FP) is same as that for agonist.
      9. Given the importance of MR1 overexpression in showing the association between MR1 and SQSTM1/p62, it is worthwhile to consider performing the knockout experiments with Thp1-MR1-HA rather than Thp1. It will further clarify the role(s) of SQSTM1/p62, Atg5, and Atg7 in MR1 trafficking and resultant MAIT cell activation.

      Minor comments:

      1.Please explain why the authors failed to detect IL23A in the coimmunoprecipitation. Should MR1-IL23A interaction be specific, what is a biological significance? 2. When Hela-MR1-HA was used, did the authors obtain the same results as Thp1-MR1-HA as shown in Figure 1C-D? This is relevant to the specificity in the interaction between MR1 and SQSTM1/p62 as shown in Figure 4B. 3. While S1, S2, S3, and S4 showed a similar degree of SQSTM1 depletion in Figure 2A, there was difference in the potential of IFN-γ production from MAIT cells among the clones. Only S4 showed decreased potential for IFN-γ upon 5-OP-RU, though E. coli failed to so. Contrary to 5-OP-RU, S1-S3 showed an enhanced potential while S4 failed to do so. Why is that so? 4. Given that there was little correlation between MR1 expression level and the potential of S1-S4 to promote or inhibit the ligand-dependent production of IFN-γ (Figure 2C right panel and Figure 2D), it is difficult to conclude that the factors implicated in autophagy play a pivotal role in MR1-dependent MAIT cell activation. 5. There was no consistency in the experimental design for Figure 5. Please explain the rational why the authors have used 7.1 in A and C, but not in B, D and E? 6. The control appeared to behave as 7.1 did. Was there statistical difference between 7.1 and 7.2 in Figure 5C? If so, what is the interpretation. 7. Time course over 6 h will be required to assess the MR1 expression in Figure 5C.

      Significance

      The present study uncovered the possible implication of autophagy factors in MR1 trafficking, in other words, MAIT cell activation. Although the previous study has demonstrated the importance of the protein loading factors (McWilliam et al., PNAS,117 24974-24985 2020), this study adds another pathway for MAIT cell activation. However, the conceptual significance is limited in that depletion of the factors pertinent to autophagy such as Atg5 and Atg7 in Thp1 resulted in rather weak interference in terms of MR1 trafficking and MAIT cell activation. Thus, this study will interest those who work in basic immunology, in particular, in regulation of antigen-presentation molecules and T cells as well as those who are in the field of MAIT cell biology.

      Although the field of this reviewer covers biochemistry, molecular biology, developmental biology, immunology and regenerative medicine, proteomics approach (in detailed technique) as seen here to identify the associated molecules is somewhat beyond the reviewer's expert.

    1. Yes, send me the two monthly emails. The Deliverability Dispatch (deliverability know-how) and Notes from Yanna-Torry (her take on the industry, plus the occasional Review My Emails product update). Manage or leave any time from any email. Track my opens and clicks to tailor what I get. Optional, off any time.

      this is the correct form that should be on all the pages at the bottom of the page that they have it but we still have the little bug from yesterday where the checkbox is centered in like two columns and a text in another column we need the checkbox to the complete left at th level as where the email field starts and then the tax for the fields need to be closer to the checkbox so it doesn't end up being 27 lines

    2. ghts

      I don't know why it seems to be in a column on the left there's nothing on the right so either center it and make the insights waitlist just a little bit longer so that it's not so tight and small

    1. ionKeeps you signed in to the dashboard. Required for the service to work af

      for now there's no app so it should be as well the free tools I don't know if that works there but if they give us a email we will remember them as long as they don't clear the cookies for extra amount of time

    1. It’s not a mystery.

      I need something stronger like maybe not it's not a scam but something along the lines at good business cards makes good friends or if one sex if one of us succeed we all do kind of thing and maybe something along the lines that we're not too small for any kind of help everybody needs to grow some way in I don't know I don't have $1 million to put in Google art I would much rather work with partners then spend my time marketing myself and why not share the knowledge I have with one person who was lots of customers then try to manage individually I want to change the way people think about email their knowledge s let's give access to everyone to grow together

    2. ou do not need to manage the relationship after the referral. We handle the cleaning, the report, the walkthrough. You just need to make the introduction. 1 introduction. That is your whole side of the work. Drop the link, make the connection. 48h average turnaround from upload to a report your client can act on. Our side. 15% of every credit purchase they ever make comes back to you. Monthly, net 30.

      let's make the text on the left a smaller column so that the table with the one $40.15 percent has more space and I think the font still too small

    1. Because I have been stuck before too. Staring at bounce logs at midnight, Googling error codes, not sure who to ask. Most email questions fall into a handful of common patterns. Answering them takes me five minutes and saves you five hours. If you need more than guidance, I also do done-for-you list reviews, but no pressure.

      replace with Trust beats tactics.

      We offer real help without charging because it is the right thing to do. Most questions fall into a few common patterns I see every week. Some people need a quick answer. Some need deeper support. Either way, the conversation starts the same.

      If you need more than guidance, we offer done-for-you list reviews with expert interpretation included. But there is no pressure. The SOS Hotline exists to help, not to sell.

    2. Litmus Coach 2020·Ask a Deliverability Expert·8,000+ lists reviewed·Lenovo Twinning Finalis

      Litmus Coach Award 2020 MARsum Top 100 Marketing & Advertising Leaders 2021 Marketing 2.0 Best Business Award 2022 Lenovo Twinning Finalist 2026 this issue seems to have persisted across the website other than the 8000+ less reviewed unless it's specifically done this way we need to add the rest of the awards

    1. Start free

      We need to save a copy of this page as is but we need to update the text a little bit or at least the call to action because we're telling him go clean your list but the app isn't coming out for a couple more weeks so what we can do is offer them to sign up with their email no password and sign it with the newsletter to get 15,000 free extra credit on top of the 1000 and unless they click the checkbox for the newsletter they will only get an email to let them know that the app is available online and that they got their 15 while they're 16,000 credits the wood free gets you free credits and everything like that is fine is just that the apps already yet so many people to get on the waiting list

    1. Runs the things that make every customer feel handled. Makes sure no question gets dropped and every customer leaves understanding what we did.

      here is her job description, maybe something better you can peopose?DESCRIPTION The Head of Operations & Growth is the operational backbone of Review My Emails and Shipshape. She runs point on everything that keeps the two businesses moving day to day: localization and translation across English and Vietnamese, marketing and social planning, Vietnamese market development, competitive and customer research, freelancer management, and serving as the founder's primary sparring partner on new ideas. The role sits at the intersection of operations, marketing, and growth, and its scope is intentionally broad. If it is not the founder's job to do directly, it is very likely hers RESPONSIBILITIES Translate and localize content across English and Vietnamese for both Review My Emails and Shipshape, including product copy, marketing content, customer communication, partner outreach, and training material. Tone and cultural fit matter as much as literal accuracy. Own the marketing and social media calendar across both brands. Plan content, brief designers and writers, coordinate publishing across channels, and monitor engagement and performance. Develop the Vietnamese market for both companies. Identify partners, prospects, and channels. Coordinate introductions, meetings, and follow-through with customers, resellers, agencies, and communities in Vietnam. Run research on competitors, pricing, new tools, and new market segments. Return usable, decision-ready findings, not raw information dumps. Manage the freelancer bench across both companies. Recruit, brief, review deliverables, coordinate payment, and hold quality and turnaround standards. Serve as the founder's sparring partner. Hear new ideas first, pressure-test them, flag risks, push back when something is half-baked, and translate loose ideas into concrete plans, briefs, and next steps. Coordinate the day-to-day operational tasks that keep the businesses running: recordkeeping, small procurement decisions, tool subscriptions, freelancer contracts, and cross-team logistics. Represent the founder's voice and standards internally and externally with freelancers, partners, and vendors. Decisions made in this role carry the same weight as the founder's own. Contribute to strategic planning across both businesses (goals, priorities, quarterly focus) and hold the team accountable to what has been agreed. Other responsibilities as the businesses evolve. Both companies are early stage and scope will shift as they grow.

    2. Builds the backend, then tries to break it. Tests every path and checks every edge, so what we ship is actually right.

      here is her description frok linked in, maybe something better you can peopose?Software Engineer with experience designing, developing, and deploying intelligent solutions in production environments. Strong background in AI-driven systems, automation, and data-centric software engineering.

      Focused on building and operationalizing AI pipelines and inference systems, integrating predictive models into scalable applications using DevOps and MLOps best practices, with emphasis on code quality, reliability, and production readiness.

      Experienced in LLM-based agents, RAG systems, computer vision, data pipelines, and inference APIs, working with Python, Flask, Django, Docker, SQL databases, vector stores, and cloud-native environments.

      Hands-on and collaborative professional, comfortable in technically complex projects and multidisciplinary teams, delivering AI solutions that generate measurable business impact.

    3. Built the reasoning layer behind the verdicts. Not the kind of AI that writes your emails for you. The kind that reads dozens of signals per address and explains, in plain English, why one is risky and what to do about it.

      here is his description frok linked in, maybe something better you can peopose?Bruno S. Brasil is a Cloud Engineer with a deep-rooted expertise in Linux, which he has used since childhood. His career began in on-premises environments, transitioning smoothly to cloud computing as he embraced the DevOps culture. Specializing in Google Cloud Platform, Bruno has successfully led and contributed to various projects across industries such as digital banking, marketplaces, and startups, both as a consultant and engineer. He is dedicated to promoting best practices in infrastructure as code, advocating for DevOps principles, and implementing SRE strategies. A passionate Open Source community supporter, Bruno firmly believes it plays a crucial role in fostering innovation and the development of new talent and technologies.

    4. Runs security and Google Cloud infrastructure. Keeps the app protected and the platform humming, so the engine that checks millions of addresses stays fast, safe, and reliable.

      here is his description frok linked in, maybe something better you can peopose?:About 🚀 Cloud Architect | DevOps & Platform Engineer | Product-Focused | Problem Solver

      Since 2017, I’ve been designing, securing, and scaling cloud-native infrastructure for high-impact environments—serving millions of users across global markets.

      With hands-on experience across GCP, AWS, Kubernetes, and Terraform, I specialize in building robust, scalable platforms using GitOps and modern DevSecOps practices. My work consistently balances developer experience, security, and compliance, especially for organizations with high regulatory demands like FinTech.

      🔧 Recent Wins:

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      ⚡ Boosted CI/CD performance by 25% while meeting strict security audit requirements

      🌍 Managed infra supporting 60M+ users in 60+ countries

      🚀 Led cloud migration and built scalable, self-healing systems from the ground up

      I’m passionate about Open Source, love solving complex challenges, and thrive in collaborative, diverse environments. Open to remote/hybrid opportunities—currently based in Amsterdam.

      Let’s build what’s next.

    1. Decompose the word, then choose features. This first page teaches the mechanics.

      This is a little confusing with the title being a call to action. Could we make the title be "Mechanics", Then replace tha paragraph to be more of an intro, similar to other pages.

    1. Client lists now reviewed before the first send 0 of 12 → 12 of 12

      this clashes with the sent3nce thag this agency was cleaning each list. need better sent3nce. maybe something along the lines of "the result gave them even more strategoes to fix or things outsode the list itself that needed to be abalyzed and fixed

    2. If your authentication is broken, no list cleaning will save you. Start here.

      replace this with something thst wxplains what is below. based on rwlesult of free check they do themselves they can maybe see really ovious checks. if they still dont know if they need it, lsos page link or talk to yt

    1. eLife Assessment

      In this important study, Boudjema et al. use cell culture models and high quality advanced microscopic imaging to provide detailed analyses of the cellular processes underlying centriole amplification, apical migration, and assembly of hundreds of motile cilia in multi-ciliated cells. The authors present convincing evidence showing that in these cells all the molecular and cellular steps controlling centriole biogenesis that in cycling cells extend over almost two cell cycles, occur within a single cell cycle variant. This work provides a better understanding of the regulation and order of these processes and is of interest to all cell biologists and in particular researchers studying centrioles and cilia.

    2. Reviewer #1 (Public Review):

      The manuscript by Boudjema et al. describes the cellular events underlying centriole amplification and apical migration to allow the assembly of hundreds of motile cilia in multi-ciliated cells. For this, they use cell culture models in combination with fixed and live cell imaging using antibody staining and fluorescence from endogenously tagged centriole and deuterostome markers, respectively. The work is largely descriptive and functional analyses are restricted to treatment with the microtubule depolymerizing drug nocodazole. The imaging is state-of-the-art including confocal microscopy, live imaging with optical sectioning and high optical and temporal resolution, as well as super-resolution imaging by ultra-expansion microscopy.

      The study does a good job of providing a very detailed description of the dynamics of centrioles and deuterostomes that lead to centriole amplification and apical migration in multiciliated cells. This detailed view was missing in previous work. It also reveals the involvement of microtubules at multiple steps: the formation of a cloud of deuterostome precursors, the nuclear envelope tethering of newly formed centrioles, their separation, and their migration to the apical surface.

      It would have been useful to expand the analysis of the role of microtubules by including analyses of the requirement for specific microtubule motors, for a better understanding and additional evidence that microtubule-based transport is involved. A weak point is that there is no visualization of microtubules together with deuterosomes and centrioles at the different steps of centriole amplification and migration, to directly address how these structures may interact with and move along microtubules.

      Overall, apart from experimental aspects and since this is largely a descriptive study, the manuscript would benefit from more precise language and a better description of the complex events underlying centriole amplification and movements.

      Comments on revised version.

      The authors have significantly improved the manuscript, by refocusing it, introducing text and figure changes, and by adding new data including functional analyses. The revised version now has convincing data that support the claims. All my remaining concerns have been addressed.

    3. Reviewer #3 (Public review):

      Summary:

      In this manuscript, Boudjerna and Balagé et al. aim to elucidate the spatial origin of centriole amplification and the mechanisms behind the formation of an apical basal body patch in multiciliated cells (MCCs). To this end, they focused on the role of microtubules and developed new tools for spatiotemporal and high-resolution analysis of different stages of centriole amplification, including the centrosome stages, A-stage, G-stage, MCC-stage. Among these tools, the MEF-MCC cells grown on micropatterns stands out for its versatility as it is not tissue-specific and does not require epithelial cell-to-cell contact for differentiation. Additionally, the Cen2-GFP; mRuby-Deup1 knock-in mouse model was used to study different stages of centriole amplification in physiological brain MCCs. This model offers an advantage over the previously described Cen2-GFP model by enabling the resolution of early events in centriole amplification through the visualization of Deup1-positive structures and their dynamics. Finally, the authors leveraged powerful imaging techniques, including super-resolution microscopy, the U-ExM and high-resolution live cell imaging in order to detect and track centriole amplification, elongation, disengagement, and migration.

      By combining the MEF-MCC and knock-in mouse model with spatiotemporal imaging in control and nocodazole-treated cells(treated acutely or chronically), the authors define the sequence of events during centriole amplification, revealing the critical roles of microtubules for the first time. Initially, the centrosome-mediated microtubule network forms, organizing a pericentrosomal nest from which procentrioles and deuterosomes emerge. Their findings indicate the importance of microtubules in recruiting and maintaining pericentriolar material clouds that contain DEUP1, PCNT, SAS6, PLK1, PLK4, and tubulins. Following the amplification stage, the procentrioles mature, leading to cells displaying numerous MTOCs, as demonstrated by regrowth experiments. Mature centrioles then disengage from deuterosomes, attach to the nuclear envelope, and migrate to the apical surface facilitated by microtubules.

      Strengths:

      The manuscript provides new insights into the regulatory function of microtubules and microtubule-based transport in different stages of differentiation in brain MCCs. Addressing the role of microtubules during different stages of centriole amplification required development of new tools to study brain MCCs, which will be useful in future studies of MCCs. A notable strength of this manuscript is the authors' thorough and quantitative spatiotemporal analysis of highly dynamic processes in MCCs. The precision and detail in describing these dynamic events are impressive and are further strengthened in the revised version through additional analysis and adoption of new methods. This comprehensive analysis advances our understanding of MCC biology regarding the involvement of microtubules.

      Comments on revised version.

      The revised manuscript is substantially improved, and given the scope, it is appropriate that it primarily establishes a detailed spatiotemporal framework. That said, a few points would further strengthen clarity and impact. First, several observations naturally raise follow-up mechanistic questions, for example whether additional cytoskeletal systems such as actin contribute to steps like centriole apical migration. A slightly more detailed framing of these open questions would help guide future work. Second, some terminology introduced to label observed microtubule-based structures (for example "nest") may not be essential. Finally, while the authors have increased quantification, some analyses would benefit from super plot-style displays with replicate-level comparisons, particularly for intensity-based readouts.

    4. Author response:

      The following is the authors’ response to the original reviews.

      We have carefully addressed the insightful comments provided by the reviewers which thoroughly increased our comprehension of the dynamics of centriole amplification. The manuscript has been revised accordingly and put in the context of the two papers we published since our last submission, showing that MCC differentiation is a genuine cell cycle variant. A point by point answer to all reviewer comments is provided below.

      Briefly:

      We have streamlined terminology and nomenclature in text and figures / better define experimental conditions with nocodazole

      We have tested the role of dyneins in the dynamics of centriole amplification

      We have done correlative light and electron microscopy on the early stages of centriole amplification

      We have analyzed a new single cell RNA seq dataset comparing canonical and MCC cell cycle variants in mouse brain progenitors

      Collectively, this allowed us to make a clearer parallel with what occurs during centriole duplication and to demonstrate that centriole biogenesis in the MCC cell cycle is marked by the superimposition of 2 canonical centriole cycles.

      We believe the manuscript will interest a broader readership since it now provides more fundamental insights on the mechanism of centriole biogenesis.

      Public Reviews:

      Reviewer #1 (Public Review):

      The manuscript by Boudjema et al. describes the cellular events underlying centriole amplification and apical migration to allow the assembly of hundreds of motile cilia in multi-ciliated cells. For this, they use cell culture models in combination with fixed and live cell imaging using antibody staining and fluorescence from endogenously tagged centriole and deuterostome markers, respectively. The work is largely descriptive and functional analyses are restricted to treatment with the microtubule depolymerizing drug nocodazole. The imaging is state-of-the-art including confocal microscopy, live imaging with optical sectioning and high optical and temporal resolution, as well as super-resolution imaging by ultra-expansion microscopy.

      The study does a good job of providing a very detailed description of the dynamics of centrioles and deuterostomes that lead to centriole amplification and apical migration in multiciliated cells. This detailed view was missing in previous work. It also reveals the involvement of microtubules at multiple steps: the formation of a cloud of deuterostome precursors, the nuclear envelope tethering of newly formed centrioles, their separation, and their migration to the apical surface.

      It would have been useful to expand the analysis of the role of microtubules by including analyses of the requirement for specific microtubule motors, for a better understanding and additional evidence that microtubule-based transport is involved. A weak point is that there is no visualization of microtubules together with deuterosomes and centrioles at the different steps of centriole amplification and migration, to directly address how these structures may interact with and move along microtubules.

      Overall, apart from experimental aspects and since this is largely a descriptive study, the manuscript would benefit from more precise language and a better description of the complex events underlying centriole amplification and movements.

      We have streamlined terminology and nomenclature, clarified the description of the complex events, and test the role of dyneins in centriole amplification. Microtubules density in MCC does not allow to extract information from imaging. In addition, we have done correlative light and electron microscopy on the early stages of centriole amplification and analyzed a new single cell RNA seq dataset comparing canonical and MCC cell cycle variants in mouse brain progenitors. We also replied points by points to the reviewer specific comments.

      Altogether, our new data allowed to demonstrate that centriole biogenesis in the MCC cell cycle is marked by the superimposition of 2 canonical centriole cycles. We believe the manuscript will interest a broader readership since it now provides more fundamental insights on the mechanism of centriole biogenesis.

      Reviewer #2 (Public Review):

      This important work will be of interest to centriole and cilia cell biologists. It describes in detail how microtubules control multiple aspects of centriole amplification in brain multiciliated cells. This study provides a greater time-resolved and molecular proteomic mapping of the different steps involved, with or without microtubule disruption. Boudjema et al. show that microtubules are important throughout the centriole amplification process, from the early stages, where the procentrioles emerge from a pericentriolar "nest", through the growth stage where microtubules maintain the perinuclear localisation, to the detachment stage, where microtubules assist in perinuclear disengagement and apical migration. The results are generally well supported by the evidence, but the manuscript would benefit significantly from some heavy editing to introduce more niche terms, standardize abbreviations in text, and labels on figures to help bring the readers, especially non-specialists, along with them - increasing the accessibility of their work.

      We thank the reviewer for his/her enthusiasm. We have streamlined terminology and nomenclature and clarified the description of the complex events to increase the accessibility of our work. We also replied points by points to his/her specific comments.

      Reviewer #3 (Public Review):

      Summary:

      In this manuscript, Boudjerna and Balagé et al. aim to elucidate the spatial origin of centriole amplification and the mechanisms behind the formation of an apical-basal body patch in multiciliated cells (MCCs). To this end, they focused on the role of microtubules and developed new tools for spatiotemporal and high-resolution analysis of different stages of centriole amplification, including the centrosome stages, A-stage, G-stage, and MCC-stage. Among these tools, the MEF-MCC cells grown on micropatterns stands out for its versatility as it is not tissue-specific and does not require epithelial cell-to-cell contact for differentiation. Additionally, the CEN2-GFP; mRuby-DEUP1 knock-in mouse model was used to study different stages of centriole amplification in physiological brain MCCs. This model offers an advantage over the previously described CEN2-GFP model by enabling the resolution of early events in centriole amplification through the visualization of DEUP1-positive structures and their dynamics. Finally, the authors leveraged powerful imaging techniques, including super-resolution microscopy, the U-ExM, and high-resolution live cell imaging in order to detect and track centriole amplification, elongation, disengagement, and migration.

      By combining the MEF-MCC and knock-in mouse model with spatiotemporal imaging in control and nocodazole-treated cells (treated acutely or chronically), the authors define the sequence of events during centriole amplification, revealing the critical roles of microtubules for the first time. Initially, the centrosome-mediated microtubule network forms, organizing a pericentrosomal nest from which procentrioles and deuterosomes emerge. Their findings indicate the importance of microtubules in recruiting and maintaining pericentriolar material clouds that contain DEUP1, PCNT, SAS6, PLK1, PLK4, and tubulins. Following the amplification stage, the procentrioles mature, leading to cells displaying numerous MTOCs, as demonstrated by regrowth experiments. Mature centrioles then disengage from deuterosomes, attach to the nuclear envelope, and migrate to the apical surface facilitated by microtubules.

      Strengths:

      The manuscript provides new insights into the regulatory function of microtubules in centriole amplification. Addressing the role of microtubules during different stages of centriole amplification required the development of new tools to study brain MCCs, which will be useful in future studies of MCCs. A notable strength of this manuscript is the authors' thorough and quantitative analysis of highly dynamic processes in MCCs. The precision and detail in describing these dynamic events are impressive. This comprehensive analysis advances our understanding of MCC biology.

      Weaknesses:

      The role of microtubules and other molecular players during different stages of centriole amplification in brain MCCs can be further studied and strengthened using the tools developed in the manuscript. A more quantitative description of some of the analysis performed in the manuscript is required to strengthen the conclusions.

      We thank the reviewer for his/her enthusiasm. We have tested the role of dyneins in the dynamics of centriole amplification, done correlative light and electron microscopy on the early stages of centriole amplification and analyzed a new single cell RNA seq dataset comparing canonical and MCC cell cycle variants in mouse brain progenitors. We also replied points by points to the reviewer specific comments.

      Recommendations for the authors:

      As you will see, all reviewers felt that the analyses of the involvement of microtubules should be strengthened by including controls and additional experiments. Also, they agree that significant text editing would help to improve the manuscript's accessibility and readability.

      Specifically, they would suggest (1) streamline terminology and nomenclature in text and figures; (2) better define experimental conditions with nocodazole (concentrations used, effect on microtubules, effect on canonical centriole duplication); and (3), in the absence of other complementary genetic perturbation experiments, add a limitations paragraph in the discussion about conclusions drawn from nocodazole treatment alone.

      Reviewer #1 (Recommendations For The Authors):

      Main issues:

      (1) The authors use variable terminology to describe the same or similar events/structures. For example, in Figure 1 they refer to "centrosome stage" where they observe a pericentrin "cloud", which they later refer to as a "nest". In all other figures the first stage is not referred to as the "centrosome stage" but as the "cloud stage". Again, they also describe the "cloud" as a "nest" occasionally, but not always. In the cartoon, the nest is termed "centrosome cradle". The variable and inconsistent use of terms is confusing and the authors do not provide any explanation for the use of one vs. another.

      The text is now corrected. The centrosome stage corresponds to the stage preceding the beginning of centriole amplification in MCC progenitor. The pericentrosomal cloud of centriole and deuterosome elements forms later on, during the amplification A-stage. The formation of this cloud marks the beginning of A-stage, and persists up to G-stage where it dissolves. When we show that the cloud hosts the first stages of centriole biogenesis, we defined it as a “nest”. We do not use anymore the term craddle.

      (2) What prompted the authors to use the term "nest"? It gives the impression that they describe aspecific physical entity/structure (also depicted in this way in Figure 3P, with microtubules outside of this structure), but what is the evidence for this?

      The cloud is the spatial entity and the term “nest” is used to define a function of this transient compartment. We decided to keep the term “nest” as we now identified it with correlative light and electron microscopy, in addition to U-ExM, and show that the accumulation of centriole and deuterosome elements is accompanied by the formation of immature procentrioles, deprived of MT walls, as well as immature and empty deuterosomes. The scheme with MT outside the cloud/nest is misleading as we see MT organized by the mother centriole. We have now changed this.

      (3) The "nest" may simply be a dynamic accumulation of precursor particles around the centrosome, similar to what has been described for centriolar satellites. Rather than proposing a new entity, I suggest testing whether the "nest" particles may colocalize with PCM1 and thus may be related to centriolar satellites. Based on the data, the nest would simply be the centrosomal MTOC that organizes a radial microtubule array on which particles move around its center. In the absence of other evidence, I am not convinced that a new term is needed.

      We totally agree with the reviewer: the centrosome, as MTOC, concentrates centriolar and deuterosome components. This cloud is consistently dissolved when MT are depolymerized or dyneins inhibited. So, the physical entity is a “cloud”. We used the term “nest” to propose one function for this cloud which is to form deuterosomes and centrioles, before they move away for maturation. In fact, deuterosome and centriole formation are hindered when the cloud is dissolved. We have tried to edit the text all over the manuscript to make it clearer.

      (4) Role of MTs: are microtubules required or do they just facilitate some of the investigated events?

      The reason why the role of MT has not been tested yet during centriole amplification is probably because MT not only constitute the cell cytoskeleton on which molecular motors ride to transport cargos or distribute forces, they are also the core component of the structures we are studying. This is why we have tested a range of nocodazole concentrations and used concentrations where MT are perturbed but not entirely depolymerized, allowing centrioles to be produced (Fig. 4 Supplementary 1A-B). This may lead to an underestimation of the role of MT but we cannot study the role of MT on centriole amplification if centrioles cannot be formed.

      Does multi-ciliation in these models eventually occur normally under the concentrations and treatment conditions used here? This should be tested and discussed in the context of whether microtubules are indeed required and at what step of the entire process (amplification, migration, ciliogenesis) they may be critical.

      We did both chronic and acute treatments.

      Chronic treatments were done to test the overall efficiency of centriole amplification when MT (or dyneins) are perturbed. Chronic treatments were used to assess the role of MT (or dyneins) on the global efficiency of centriole and deuterosome formation (number of cells able to amplify, number/size/loading of deuterosomes, final number of centrioles (Fig. 4H-I, Fig. 4 Supplementary 2 B-D). In these chronic treatment, we focused on centriole amplification and not ciliation since it was the scope of this study. Also, we did not take ciliation as a readout of amplification because ciliation is relying on MT polymerization.

      Then, we also did acute treatments to test the role of MT (or dyneins) at each stage of amplification (A-amplification, G-growth, D-disengagement, M-migration; Fig. 4, 5, 7, 8 and associated supplementary figures). Since one stage is dependent on the precedent one, this enabled us to decipher the direct role of MT (or dyneins) on each single stage. We have now edited text, methods, legends and pictograms to be clear on whether acute or chronic treatment was done.

      (5) Can the authors include control (non-amplifying) progenitors in their analyses? It would be useful to know what the signal and distribution of each specific marker are before differentiation begins (before the cloud stage).

      Non amplifying progenitors are analyzed and constitute the so-called “centrosome stage”. We have now precised it and called it the “progenitor stage”.

      (6) Figure 2: Again, the terminology is confusing, since the authors describe that DEUP1 forms a "cloud" with centrin during the A stage.

      Corrections have been done as explained in point 1.

      (7) Description Figure 3: the authors introduce yet another term: "halo" A-stage. Is this the early A stage? Again, this is not explained and confusing. More systematic and consistent description is needed.

      Corrections have been done as explained in point 1. The term halos is used un the lab as it was the first term we used in our Nature paper in 2014 in reference to the halo described by Erich Nigg when they overexpressed Plk4. It was an error to use it in the manuscript.

      (8) Nocodazole treatments: the used concentrations are quite high.

      MCC develop a very dense and stable MT network that is not comparable to cycling cells. MT are very difficult to depolymerize entirely (Fig. 4 Supplementary 1A-B).

      (a) To avoid non-specific effects the authors should test what the minimal concentration is that completely depolymerizes microtubules in their cell model and perform analyses at this concentration.

      We have of course tested a range of nocodazole concentrations at the beginning of the study (Fig. 4 supplementary 1A-B), and used concentrations where MT are perturbed but not entirely depolymerized, allowing centrioles to be produced (see answer to point 4). In case it was not clear, we refer to this now several time and more clearly in the text and methods.

      (b) They should demonstrate depolymerization of microtubules by microtubule staining in the acute and chronic noc treatments and at the different noc concentrations used.

      This is, and was, in supplementary material (same, Fig. 4 supplementary 1A).

      (c) The authors should demonstrate that the used nocodazole concentrations do not impair normal centriole biogenesis during the cell cycle in these cells; if so, impaired assembly of centriole wall MTs may contribute to the observed effects in Figure 4.

      As mentioned in point 8b, we have of course tested a range of nocodazole concentrations at the beginning of the study (Fig. 4 supplementary 1A), and used concentrations where MT are perturbed but not entirely depolymerized, allowing centrioles to be produced (see answer to point 4). The ability of the cells to form centrioles during chronic treatments were always assessed using immunostainings of SAS6 and/or CEN2-GFP signals (now exemplified in Fig. 4 Supplementary 1B). We also did EM analysis on cells treated with the highest doses of nocodazole (Nocodazole 10 uM for 24h) and this showed that centrioles can form with, what seems to be MT walls, in cells totally deprived of cytoplasmic MT fibers (Fig. 4 Supplementary 3-4). However, this does not show that all the cells can, because the number of cells that can be analyzed by EM are not sufficient to conclude. Also, one cannot assess whether MT walls are properly polymerized. However, the absence of MT walls should not change the results of the Figure 4, which are based on DEUP1, SAS6 or CEN2-GFP signals for deuterosomes and centrioles. Also MT depolymerization affects the formation of deuterosomes, which should not be altered by MT wall defects as it is not affected, even when centriole formation is blocked (LoMastro et al., 2024). Last but not least, we now show that blocking dyneins, as a comparable and even greater effect, on the formation of the cloud, deuterosomes and centrioles (Fig. 4C-I and Supplementary Fig. 4), which confirms that MTOC function, rather that MT wall formation, explain the centriole biogenesis alteration shown in Figure 4.

      (9) The authors repeatedly refer to the centriole-to-centrosome conversion of amplified centrioles and how this resembles centriole-to-centrosome conversion during the cell cycle. However, they incorrectly claim that this occurs at the G2/M transition. PLK1-dependent modification occurs at this stage, but conversion and PCM recruitment only occur after mitosis (see original work by the Tsou lab, which needs to be cited here).

      We agree with the reviewer. We have now added additional data to show clearly that centriole biogenesis, which requires two cell cycles to proceed in cycling cells, is accelerated during the MCC cell cycle variant where the elongation and maturation cycles are superimposed. This is now clearly shown in Fig. 3, 5, 9 and discussed.

      (10) Figure 6H-J: the authors claim that at low noc concentration, more D-stage cells showed incomplete disengagement than in controls, but the effect is shown only for the highest 10 µM concentration. Do any eof the phenotypes in Figure 6 also occur at the lowest noc concentration (assuming it depolymerizes MTs)? Again, it is crucial to demonstrate this, to exclude unspecific effects not linked to MT depolymerization.

      An error was made on the figure (but not in the legend). In Figure 6, chronic treatments are at 1 or 5 µM. Only acute treatments were done using 10 µM. In both cases, MT are not entirely depolymerized in these experiments (Fig. 4 supplementary 1A).

      (11) Disengagement, Figure 7: The authors describe that DEUP1 signal spreads all over the cytoplasm and becomes diffuse during this process, but one cannot see a diffusive signal throughout cells in the figures.

      We pushed the contrast to make it clearer but the deuterosomes are still bright at this stage and it is difficult to have both signal clear (now in Fig. 6B). We have also changed the example in video (now video 19) to show it more clearly with DEUP1 channel alone.

      (12) Figure 7: localization of disengaged centrioles at microtubule "nodes" is not clear from the images. There are many centrioles and random colocalization may be expected simply based on the high number. Higher resolution and/or magnification and quantification would be needed.

      We have edited and now say that centrioles “colocalize” with MT which, since centrioles nucleate MT, seems normal. We agree that it could be random, but given the density of MT, and the number of centrioles, it does not seem opportune to us to quantify. We can just say that we never see centrioles is regions that are deprived of MT.

      (13) The term "diffusive" to describe slow centriole movements in Figure 8 suggests that it is not motor or force-dependent, but there is no evidence for that. Movement based on opposing forces could produce a similar result, but would not be considered diffusive.

      We agree. We have changed “diffusive” by “diffusive-like”.

      (14) The manuscript would greatly benefit from the analysis of some candidate motor activities that may drive the movement and migrations of centrioles in this system. This would support the importance of the microtubule network for the specific steps in these processes, and better define its role beyond "being required". Dynein may be a candidate or minus end-directed kinesins. Since chemical inhibitors are available, these types of experiments would be straightforward.

      We formerly tested ciliobrevin but had hard time because of the small stability of the drug. Since our submission to eLife, we tested dynapyrazol and dynarestin and found dynapyrazol very efficient in dissolving the Golgi, a good readout of dynein inhibition. We sought to test the role of dyneins, using dynapyrazol, on (i) the formation of the pericentrosomal cloud in A-stage, (ii) the oscillation of DEUP1+ structures during A-stage, (iii) the number, size, loading of deuterosome, (iv) the final number of centrioles, (v) the migration to the nuclear membrane and (vi) the final apical migration of centrioles. The results are now inserted in main and associated Fig. 4, 5, 7, 8, 9.

      (15) Discussion:

      "the role microtubules" lacks "of"

      This is now edited.

      "This lack is..." Lack of what?

      This is now edited.

      "reflexive link" - meaning of "reflexive" is not clear in this context

      We have removed it.

      In my opinion, the study does not identify a nest composed of DEUP1, PCNT, and Centrin2; it only shows that these components accumulate as particles around the centrosome, which functions as MTOC. Consequently, it seems that the "nest" does not exist when MT is depolymerized. One could consider the center of the centrosomal MT array as a nest in this context, but there is no evidence of a specific new structure as suggested by the way the term is used in the manuscript.

      This is what we want to say: the center of the MT array become a nest in this context. We do not state that there is a specific new structure. We just say that MT and dynein dependent concentration of centriole and deuterosome components exists and that this region nests the birth of centrioles and deuterosomes. Also, this compartment is restricted in time and space, which justifies to use a specific term. The MTOC exists in the progenitor cell, while this compartment, marked by DEUP1, Centrin, PCNT accumulation, appears at the beginning of amplification and grows during A-stage to be dissolved at G-stage when all the deuterosomes and centrioles have moved away.

      What is the evidence that "DEUP1 is a centrosomal protein before building deuterosome structures"? It would be good to refer to the specific experiment. Does DEUP1 localize at centrioles also in the absence of microtubules? If not, I would not consider it a centrosomal protein.

      We have removed this statement to avoid misinterpretation.

      "This reminds the centriole-to-centrosome conversion..." the sentence is missing an "of"; also, again the authors confuse the order of events during the cell cycle, where centrosome conversion occurs after completion of mitosis, not at G2/M transition.

      We have removed this statement to avoid misinterpretation. Also, see Point 9.

      "microtubule dependent nuclear migration" should be rephrased; it sounds as if the nucleus migrates.

      This has been changed

      The following discussion of disengagement being linked to association with the nuclear envelope and resembling the process in cycling cells is misleading. In cycling cells movement of centrioles along the nuclear envelope occurs at G2/M and drives centrosome separation (separation of centriole pairs) in preparation for mitosis, not centriole disengagement.

      We are now clearer. We compare centriole-loaded deuterosome organization around the nuclear membrane to the migration of new centrosomes during early prophase (Fig. 5F-H, Fig. 5 Supplementary 2G-K).

      Regarding the possibility that forces by microtubules generated by the daughter centriole drive disengagement also in cycling cells, I would argue that this is unlikely since the daughter centriole can only nucleate microtubules after disengagement has occurred (and conversion to centrosome/PCM recruitment). Once this happens, it may physically separate the disengaged centrioles, which is a different type of activity. Indeed, originally the term "disengagement" was coined to specifically describe the loss of the perpendicular engagement of daughter centrioles with their mothers (Tsou and Stearns, Nature, 2006).

      We have removed this statement to avoid misinterpretation. The perpendicular engagement is difficult to assess on deuterosomes but we do see by live imaging, that attachment changes during D-stage, before centrioles detach clearly from deuterosomes.

      "high resolutive" should be "high resolution"

      Edit done.

      "splitted" should be "split"

      Edit done.

      "Consistently, when the mitotic oscillator is dis-inhibited and cells enter pseudo-mitotic events, centrioles show clear and rapid cell-cycle like clustering" This sentence is not understandable without further explanation; what does mitotic oscillator refer to? What are pseudo-mitotic events? What is cell cycle-like clustering?

      We have removed this statement.

      Minor:

      (1) Abstract: "Centriole number must be restricted to two..." Since cells are born with two centrioles and have 4 centrioles (2 pairs) when they enter mitosis, this sentence is inaccurate.

      The sentence has changed.

      (2) Abstract: "reflexive link"; I am not sure what the term "reflexive" refers to?

      We have removed this statement to avoid misinterpretation.

      (3) Figure 1C, D: it should be described better that the larger magnification panels represent overlays of many cells and what marker they show. This is not obvious since the smaller single-cell panels always show two different markers. Also, it would be more useful to show also single cells in the magnified view. The overlay does not allow us to see if a marker forms a cloud or a single dot, which is as important as the cell-to-cell variation in distribution.

      We have clarified this in the text and the legend. The cell-to-cell variation cannot be estimated with the overlay, but the projection from several cells (number precised) allows to see that the signal is confined in a restricted region. Or not. Which is what we wanted to analyze.

      Related to the above, the authors say that pericentrin forms a cloud at the top left in panel D, but there is only one confined centrosomal dot in the single-cell panel.

      The sentence has changed.

      (4) Results, Figure 2F; video 4: The authors claim connection and disconnection of DEUP1 aggregates with centrosomal centrioles; can the authors comment on the spatial resolution including in z in this movie to support this claim? Can they exclude that the structures are in proximity of each other rather than "connected"?

      This is a single z-section of 500nm. The resolution in xy is 128nm/pixel. Given the sizes of deuterosomes and a mature centriole, and given the fact that we observed this dynamics in several cells in live, we can state that the structures are connected. This is consistent with deuterosomes frequently observed “kissing” the daughter centriole by EM in the present manuscript (Fig. 2D, Fig. 2 supplementary 3 and 4 and Fig. 4 Supplementary 3-4). One has to look carefully at the daughter centriole (marked “dc”) and span in on the serial sections to see the connected deuterosome (marked by a star): this is at very early stage and therefore it is small. We have not zoomed in since previous manuscript have already described this at later stages with bigger deuterosomes. You can refer to main or supplementary figures in previous manuscripts (Al Jord 2014, Khoury Damaa 2024) where serial sections span the entire deuterosomes and daughter centrioles and show, with nanometric resolution, that both structures are frequently sticked to each others on tens of nanometers.

      (5) The term "dynamics" as used in the manuscript should be plural.

      It has been used plural, except when for “dynamic microtubules” and “dynamic attachment to the nucleus”, which we think is ok? We have not found any other singular uses in our manuscript.

      (6) Figure 5: what does "YL1/2 procentriole intensity" refer to in panel F? This should be the intensity of microtubule asters.

      This has been modified.

      (7) Figure 6 - supplement 1B: contrary to the claim in the text, one cannot see tight colocalization with the nuclear pore marker. This seems to be a very small subset of particles and even in those cases colocalization is not tight. Also, what is the relevance of nuclear pore colocalization?

      We edit and change the phrasing as ‘colocalization with NPC’ is not the good term. What we want to say is that there is a tight connection with the nuclear envelope as shown by the localization of NPC on the same z-section as centrioles. This is why we present a single z, to show that centrioles and NPC are on the same z-plane of 500nm. NPC are stained to outline the nuclear membrane. This is also clearly visible for G-stage centrioles in the XY plane. We have now added an entire z-stack on video 18.

      Reviewer #2 (Recommendations For The Authors):

      To improve accessibility of their manuscript, we would suggest making the following edits:

      (1) Define 'specialist' or 'niche' terms each time you introduce them, such as 'pericentrosomal nest', or 'flower-like structures'.

      This has been clarified.

      (2) Have a think about abbreviations, again ones that work for people outside the project- this paper uses 'PC' for 'procentriole' but for many 'PC' is 'Parental centriole' or Figure 6J talks about 'D total' or 'D partial', leaves readers confused.

      This has been clarified.

      (3) Standardize your abbreviations throughout particularly for your treatments- sometimes Noco sometimes, NOCO, or your imaging experiments sometimes Cen-GFP, sometime CEN2-GFP (Figure 7A, D vs. Figure 6) or DEUP1- mRuby, DEUP1-mRuby3 or mRuby3-DEUP1?

      We now use Nocodazole or Noco in the text and the figure respectively, CEN2-GFP and mRubyDEUP1.

      (4) About 10% of the population, including several key figures in this field, are red-green color blind. Although 4 colour fluorescence is difficult to get right for everyone, choosing palettes (especially for two colour panels) is inclusive. More so, greyscale or inverted monochrome images make it easier for everyone to visualize changes in localization, size, and intensity. Red on black small foci is particularly difficult to discern. For example, Figure 3 - more individual channels in grayscale with arrows to mc, dc, and cilia would be helpful - difficult to distinguish stainings.

      We thank the reviewer for this comment and for this recommendation of being more inclusive. We have done the changes.

      To improve the conclusions drawn, we suggest some revisions below:

      (1) Since the paper really hangs on it, a clearer description of the rationale for when, how long and how much nocodazole treatment was done is needed. The logic currently is difficult to follow seemingly random jumps 10x concentration are used. Microtubules control many aspects of cell biology and could be impacted. For example, I particularly found Figures 6D and H difficult to follow i.e. the timing for 6H seems off.

      MCC develop a very dense and stable MT network that is not comparable to cycling cells. MT are very difficult to depolymerize entirely. We have of course tested a range of nocodazole concentrations at the beginning of the study and shown the extent of MT depolymerization under each treatment. We used concentrations where MT are perturbed but not entirely depolymerized, allowing centrioles to be produced (see answer to point 4 reviewer 1). The level of perturbation of MT and consequences on centriole formation at the different timings and doses were done for each experiment and are exemplified in Fig. 4 supplementary 1A-B. This figure was already present in the first version of the manuscript but we have now edited text, methods and pictograms to clarify this.

      (2) Perhaps an extension of this point- in general how interdependent are the processes? If there is a defect at the nest stage, how much are the later defects secondary to this, or do MTs genuinely play direct roles at all stages or are these knock-on effects? How do the authors rule this out? Defects in the nest, lead to smaller and more DEUP1+ foci, with defects in concentrating procentriole factors and centrin, which lead to... For example, Figure 4B looks like centrin is reduced upon noco treatment? Does noco treatment affect Cetn2GFP levels globally? Individual channels grayscale would help visualise this better.

      See also our answer to reviewer 1 point 8c.

      The stages are indeed interdependent. This is why we did both chronic and acute treatments. Chronic treatments were done to test the overall efficiency of centriole amplification when MT are perturbed. We typically used low dose of 1µM because nocodazole remains 48h in the culture medium. Acute treatments were done to test the role of MT at each stage of amplification (A-amplification, G-growth, D-disengagement, M-migration). Most of the acute treatments were done live and nocodazole was applied after the first time point of live monitoring. We used 10µM to have a rapid effect, and because nocodazole remains only several hours in the culture medium. This allowed to monitor the stage “n”, in cells where the stage “n-1” was completed without any drug which allowed to analyze a stage without having perturbed the precedent one.

      We now also test the consequences of dynein inhibition using both acute and chronic dynapyrazole treatments. We show that except for centriole migration, dynein inhibition phenocopies MT depolymerization (centriole number, perinuclear organization and disengagement as well as deuterosome number/loading/size).

      Nocodazole chronic treatments do affect intensity of CEN2-GFP at G-stage centrioles suggesting an altered A-to-G transition. In D-stage, CEN2-GFP signal seems normal. We now mention this in the text and in the Fig. 4 Supplementary 1B.

      (3) The authors nicely show the importance of MTs in the structure of the nest from which procentrioles and DEUP1 positive structures emerge. They suggest this nest may be what supports procentriole generation in the absence of DEUP1 and parental centrioles. Firstly how does this nest look in the absence of DEUP1 and/or parental centrioles (centrinone treatment)? This may be what they are trying to show in Figure 5 Supplement 1 but it currently is very difficult to digest what it is showing relative to controls and whether this is significant in the way it is plotted.

      The nest is conserved in the DEUP1KO with or without centrosomal centrioles, as shown by accumulation of Centrin and PCNT at the center of the self-organised MT network (Mercey et al., 2019). This is in fact what motivated our study on the role of MT in centriole amplification. We have edited the legend to precise the quantification done, which is not related to this question. In this quantification, we show that the increased propensity to accumulate PCNT by centriole-loaded deuterosomes between A and G-stage is maintained in the absence of deuterosomes, indicating that centrioles themselves accumulate/recruit PCNT.

      (4) Can you do CLEM on DEUP1-Ruby and these early foci at the cloud stage to see if they are visible at the ultrastructural level, relative to procentrioles, microtubules, and other electron-dense structures?

      We thank the reviewer for this question. We have done CLEM on the pericentrosomal cloud during very early steps of centriole amplification. This showed that DEUP1 early accumulation at the centrosome corresponds to a region rich in fibro granular aggregates, suggesting that DEUP1 may be translated here, through locally concentrated centriolar sattelites, known to be involved in local translation. Then, small deuterosomes and immature centrioles are formed, within this cloud of sattelites, confirming that the pericentrosomal cloud is a nest for centriole biogenesis (Fig. 2C-D + Fig. 2 Supplementary 2-6 for control and Fig. 4 Supplementary 3-4 for nocodazole treated cells). This also shows that immature deuterosomes are not necessarily round shaped, and can be deprived of centriole loading.

      (5) Check the scale bars- see Fig 4E. Check throughout.

      Done.

      (6) Figure 3 Supplement 1 and 2 don't match the legend and are likely reversed - which one is right?

      Done.

      (7) Technical issue - I couldn't play videos 6 or 16? Check these work.

      Done.

      (8) Nomenclature mammalian proteins- mouse or human- should be all caps DEUP1, PLK4, SAS6,etc. Watch your units- space between number and unit.

      This has been done.

      (9) Many of the graphs involve three biological replicates but why not plot the mean of each of the three experiments and do stats? The number of events measured may conflate the significance. Try using Superplots.

      Here is how we proceed: we count the number of occurrence of the phenotype we monitor, and the total number of cells. We apply a X<sup>2</sup> to test whether there is a significative difference between our replicates in each condition. If not, we pool the number of occurrence of the phenotype we monitor and the total number of cells for the 3 replicates, and for each condition. Finally we apply a X<sup>2</sup> between the different conditions. This is how we usually proceed to avoid comparing a mean of percentages. This is now explained in the methods.

      Minor points:

      (1) "DEUP1 is a centrosomal protein and assembles deuterosomes in the pericentrosomal region in brain MCC". I am not sure you have evidence that DEUP1 is a centrosomal protein. You don't seem to study the relationship between centrosomes and DEUP1? Rewrite this title and tone down this claim.

      This has been modified.

      (2) Why the crossbow micropattern (versus some other shape) - seems very specific but not discussed?

      We wanted a shape where centrosome is not localized at the center of mass of the nucleus. Among the corresponding patterns, the crossbow was the one where differentiating cells had less propensity to detach.

      (3) Figure 2 - are the foci of DEUP1 at the cloud stage smaller than at A stage? How do they grow? Measure the diameter at cloud stage, just after they leave the cloud and then once they move away from centrosomal cloud and each other. If so, and they do indeed grow in size from the cloud stage to the growth stage which I think your images suggest - do you envision this happening with the gradual addition of DEUP1 rather than fusion?

      Early deuterosomes are not easy to detect by light microscopy, because of accumulation of DEUP1 in the cloud. We did CLEM on the cloud of early A-stage cells to resolve the earliest deuterosomes which are often very small (see Fig. 2D, Fig. 2 Supplementary 2-6) suggesting that they grow, either by fusion, which we never observe in our movies at later A-stage, or by accretion of DEUP1. However, by light microscopy, we can detect very early but big deuterosomes, which we see splitting later on into smaller ones. So, we cannot conclude on the mechanism that regulate deuterosome size. This is now discussed in the discussion of the manuscript.

      You say in the discussion:

      "Consistently, we never observed fusion events of DEUP1 condensates in our time-lapse experiments. More importantly, we did FRAP experiments on endogenously tagged mRuby-DEUP1 in cells at the different stages of centriole amplification, and did not find significant recovery, supporting that centrosomal DEUP1+ foci and deuterosomes are not liquid-like structures (Figure 8 Supplementary 2)." How do you prove there is no fusion of deuterosomes?

      It is always difficult to prove the absence of something, we agree! But we did tens of movies with high temporal resolution and never observed fusion events. But, as we say in the previous question, the very early deuterosomes can be very small and we do not distinguish them from the DEUP1+ cloud by live imaging. So at this stage, we cannot say. But later on, during A- or G-stage and when deuterosomes are outside the cloud to be easily observed, we very often observe deuterosomes bumping into each others and stay in close contact for minutes, but then moving away. This, for us, supports the lack of fusion properties. But the question remains open. We now explain this in the manuscript and have added an example in video 28.

      If they are getting bigger as I think your imaging suggests from cloud to growth stage, then how is this happening?

      MT depolymerisation and dynein inhibition leads to the formation of very small deuterosomes. Dynein inhibition can even lead to a block in the formation of new deuterosomes suggesting that DEUP1 concentration is a crucial parameter for condensation into deuterosomes. Deuterosome growth may happen through oligomerization of DEUP1 molecules allowed by their dyne-independent concentration. Sorokin in 1968 proposed that a supersaturation of deuterosome components may lead to their solid crystallization into deuterosomes. Deuterosome size can also be regulated by a more complex molecular cascade, involving post-translational modifications of DEUP1 or PCM, such as phosphorylations driven by the cell cycle machinery. This would be consistent with the fact that deuterosomes are very big in the absence of CCNO, a cyclin required for entering the MCC cell cycle variant. This will need further investigations.

      I'm not sure FRAP actually proves fusion doesn't happen.

      Agreed, this is not what we wanted to say, we clarified. The FRAP experiment just suggests that it is not liquid-like.

      It is technically difficult to laser ablate individual or only subsets of deuterosomes...

      This is what was done but anyway, FRAP does not firmly show that deuterosome compartments are not liquid-like as we now precise.

      (4) How do you fix your cells for expansion as you have no preservation of cytoplasmic microtubules? You are saying that there is a "nest" of MTs but beta tubulin ONLY stains the cilia and centriole - why is this? Tyrosinated tubulin on regular confocal shows strong cytoplasmic staining. See Figure 3.

      Cytoplasmic microtubules do not preserve well through the expansion process. We did try a few different fixations and pre-extraction methods but they come at a trade-off to preserving centrioles. i.e. we could either preserve cytoplasmic tubes or centrioles but not both with the same processing method.

      (5) "PCNT puncta partially overlap with centrin (Figure 3 Supplementary 2C). At this stage, PLK4, the master regulatory kinase, and SAS6, one of the first centriolar components are either absent or present as small foci within the cloud, often on the wall of the parent centrioles (Figure 3B-C)." some arrows to highlight this would be useful - difficult to see?

      We have tried to make arrows on what is now Fig. 3 Supplementary 1 G, but there is to many CENTRIN colocalizing with PCNT. We have enhanced the contrast of the merge to make it more visible.

      (6) Figure 3I legend - what are the arrows pointing at? Yellow and white on inserts? ". Around the same time as tubulin, centrin is also recruited to procentrioles (Figure 3I). This stage is probably the stage that we previously documented as A"

      However you see centrin at DEUP1 foci in D, and you don't show any eg. SAS6 or PLK4 positive DEUP1+ structures lacking centrin specifically, centrin seems to be present on all the procentrioles in Figure 3I. Did I miss it where you show centrin negative procentrioles in the cloud?

      Fig. 3I (now Fig. Supplementary 1J), yellow arrows are pointing at centrioles with non-acetylated MT while white arrows point at acetylated MT. This is now indicated in the legend.

      Regarding CENTRIN, it is present as a diffuse staining around the centrosome since the very beginning of amplification (now in Fig. 3 Supplementary 1A with different contrasts), in addition to compose the parental centrioles. This staining can therefore overlap with DEUP1 staining when DEUP1 appears (Fig. 3 Supplementary 1B, E) but not necessarily. In live we observe that CENTRIN and DEUP1 foci can move independently at early stages (Fig. 2 Supplementary 1B, video 2). This is later on, as shown now in Fig. 3 Supplementary 1J (previously Fig. 3I), that procentrioles are all strongly positive for CENTRIN.

      A new paper (Laporte et al., Cell 2024) recently showed that the recruitment of CENTRIN on duplicating procentrioles first occurs at the distal end, visible by a small dot, and then appears gradually at the level of the inner scaffold when procentriole reach 160nm, the stage where POC5 appears, which corresponds to the A-to-G transition in our MCC progenitors (Al Jord et al., 2014). One can therefore consider that the same is happening in our cells, and that, with the CENTRIN cloud, we have difficulties to detect the distal CENTRIN dot. We have changed the text to add this reference and discuss CENTRIN apparition in MCC procentrioles.

      (7) " The DEUP1 asymmetry previously described at the centrosomal daughter centriole (Al Jord etal., 2014) becomes visible in some cells during the cloud stage (Figure 3B, N; Figure 3 Supplementary 2B) and in a majority of cells" difficult to see - maybe enlarge and single channel from Figure 3F-H in the supplemental Figure 3 to emphasise this?

      We have either changed the pictures or the contrast to be more representative with the quantifications. This is visible in Fig. 3A, D, E, G; Fig3. Supplementary 1E and now using correlative light and EM in Fig. 2 Supplementary 2, 3, 4 and Fig. 4 Supplementary 3-4. One has to look carefully at the daughter centriole (marked “dc”). We have not zoomed in since previous manuscript have already described this at later stages with bigger deuterosomes. You can refer to main or supplementary figures in previous manuscripts (Al Jord 2014, Khoury Damaa 2024) where serial sections span the entire deuterosomes and daughter centrioles and show, with nanometric resolution, that both strutures are frequently sticked to each others on tens of nanometers.

      (8) Do you have videos of DEUP1 oscillations with nocodazole to show a lack of oscillations?

      We have now added videos of DEUP1 oscillations under nocodazole and dynapyrazole treatments.

      (9) "In addition, co-staining of centrioles and nuclear pore proteins show a tight colocalization(Figure 6 Supplementary 1B)." I see the colocalisation in panel 1 but less obvious with panel 2 maybe have some more zoomed in panels and some quantification of the colocalization? Is it more striking at the G stage than the D stage?

      We edit and change the phrasing as ‘colocalization with NPC’ is not the good term. There is too many centrioles and NPC, they cannot do otherwise than colocalize… What we want to say is that there is a tight connexion with the nuclear envelope. This is why we present a single z, to show that centrioles and NPC are on the same z-plane. This is also clearly visible for centrioles that are loaded on deuterosomes that are around the nuclear membrane in the XY plane. We also added a video to show an entire z-stack of this kind of staining.

      (10) "Indeed, SAS6 normally disappears from procentrioles when centrioles are docked, just beforeciliation (Al Jord et al., 2014). This suggests that centrioles were able to degrade SAS6, a process also dependent on APC/C (Strnad et al., 2007), but failed to disengage from deuterosomes." Figure 6 Supplement 1E-F - are you sure it wasn't that Sas6 wasn't loaded correctly at the earlier stage and so is reduced recruitment rather than premature disengagement of Sas6? If it is indeed premature disengagement of Sas-6 - what about CP110 - does the CP110 get loaded and is it still present in noco treated cells arrested in the D phase?

      We do not observe SAS6-negative procentrioles on deuterosomes at G-stage but only on deuterosomes in D-stage cells (cells with partly disengaged procentrioles). This is why we hypothesize that, because of the long duration of D-stage and knowing that SAS6 is finally degraded at the end of amplification (Al Jord et al., 2014), we are in the presence of cells where SAS6 has been degraded but where centrioles did not manage to disengage. This is now clarified in the text.

      (11) Can you track deuterostome splitting live? Maybe not enough spatial or time resolution?

      One has to monitor in 3D (multiple z because deuterosomes move a lot), 2 colors, high temporal resolution (dt=2-5’; to be able to track a single deuterosome), and long duration (deuterosomes are sometimes touching each other and then moving away, giving the impression that they split). This eventually leads to the bleaching of the mRuby fusion protein… We have put an example of what we think is a deuterosome splitting in Fig. 6E (former Fig. 7D). But we decided to finally monitor with low temporal resolution (dt=40’) to avoid photobleaching, and analyze numerous deuterosomes and cells to quantify the number and size of deuterosomes over time in single cells.

      (12) The MT nodes - can you segment the tyrosinated MTs and define nodes and then quantify theDEUP1 presence on them?

      Please see answer to reviewer 1 regarding this point.

      (13) Figure 8 supp 1 (E): Representative XY distribution of CEN2-GFP+ centrioles at the end of migration (Sas6 negative) in brain MCCs treated with DMSO, Nocodazole 1µM and 5µM (48h). Scale bar, 5µm Bit more detail on how you define fully migrated vs still migrating centrioles in z. You say you are using Sas-6 negativity to define fully migrated cells in the legend, yet you say noco treatment leads to premature sas-6 negativity, and yet the apical migration takes longer upon noco treatment?

      Nocodazole does not lead to premature SAS6 negativity but to a partial disengagement which lead to SAS6 negative “mature” centrioles being still connected to deuterosomes. We define complete migration when all the centrioles are on the apical side of the nucleus. We now clearly define what “apical” migration stands for in the main text and changed the pictograms in Fig. 8G to clarify this.

      (14) Figure 8H and video 18 - it isn't obviously clear to me that the noco-treated cells are "more erratic" or how you decide what counts as apically migrated successfully. How do you control for drift in z? Can you track individual centrioles as you did in untreated and define what is "erratic about their movement?

      Erratic means that the centrioles are moving away from each others, and back, in a non-predictable way, instead of migrating up and gathering. The drift in z of the whole cell is visible because there is always some centrioles, that are apically located at the beginning, that remains on the apical membrane, probably because they are already docked.

      We have indeed followed the centrioles individually in the nocodazole condition. However, in the control, the XYZ coordinates of one of the centrioles of the centrosome, which normally don’t move, are substracted to the coordinates of all the other centrioles as explained in the method section. This allows to have a subcellular reference, and to circumvent the movements of the cell, which are non-negligible at all at this timescale. In the nocodazole treated cells, the centrosomal centrioles share the erratic movements of the other centrioles and can migrate up and down, which exclude them as a reference. Since the nucleus is also moving a lot, we were left with no reference point.

      (15) Figure 8 supplement 1E can you quantify the final area of centriole patch in XY upon noco treatment?

      It was in main Fig. 8J and is now in Fig. 8 Supplementary 1F.

      (16) Figure 8J legend- MBB is never defined as an acronym.

      Thank you for pointing this.

      (17) Define what is the frequency and how is it calculated - Figure 8J.

      This is the MBB patch area in µm<sup>2</sup>

      Text edits:

      (1) "Altogether, these results suggest that, in this non-tissue-specific proxy of MCC progenitors, microtubules organize the onset of centriole amplification in the pericentrosomal region."

      Sentences have changed.

      (2) "Increasing the temporal resolution to 5-15s reveals that DEUP1+ foci observe an exhibit oscillatory dynamics to at the centrosome (Figure 2E, colored arrows, Video 3, 5/10 cells observed for 1-4min)."

      Sentences have changed.

      (3) "stage procentrioles were involved in this perinuclear migration and distribution. In fact, this dynamic is reminiscent of the centrosome migration that occurs during the G2-to-M progression in cycling cells in preparation for mitotic spindle organization. In cycling cells, this" Grammar - maybe change to "stage procentrioles were involved in this perinuclear migration and distribution. This is reminiscent of the centrosome migration that occurs during the G2-to-M".

      Sentences have changed.

      (4) "We then wondered whether these microtubule-dependent dynamics was were required for an efficient subsequent centriole disengagement during the following D-stage."

      Sentences have changed.

      (5) "Then, monitoring tens of disengagement movies, we identified a transient stage during which disengaging procentrioles redistribute isotropically in the 3 dimensions, along the nuclear membrane (Figure 6A, 4:30, Video 7) before losing its contact to migrate to the apical surface (Figure 6A, 6:30 to 14:00)."

      Sentences have changed.

      (6) Discussion: "Since pioneer electron microscopy studies on basal body production in quail oviduct MCC 35 years ago (Boisvieux-Ulrich et al., 1987, 1990; Boisvieux-Ulrich et al., 1989), this work is the first to assess the role of microtubules in the now finely described centriole amplification process. This"

      Sentences have changed.

      (7) "Using live imaging on brain MCC, we highlight the existence of a nest composed of DEUP1, PCNT and Centrin2, pre-assembled before the onset of centriole amplification onset."

      Sentences have changed.

      (8) "Recently, formation of DEUP1 pure condensates in solution as well as FRAP experiments after overexpression of DEUP1 in MCC progenitors suggested that deuterosomes where are not liquidlike structures (Yamamoto & Kitagawa, 2019). Consistently, we never observed fusion events of DEUP1."

      Sentences have changed.

      (9) "This reminds is reminiscent of the centriole-to-centrosome conversion occurring at the G2-M transition followed by the associated microtubule dependent nuclear migration of new centrosomes at mitosis onset (Agircan et al., 2014)."

      Sentences have changed.

      (10) "Following individual trajectories requires high resolutive resolution spatio-temporal live imaging while avoiding excessive light exposure which disturbs centriole migration (Boudjema et al., 2024)."

      Sentences have changed.

      (11) "Using high temporal resolution microscopy, we further identify that individual dynamics is are complex and can be splitted between divided into the baso-apical migration, where centrioles move in a processive and more..."

      Sentences have changed.

      Reviewer #3 (Recommendations For The Authors):

      (1) Growing MEF-MCCs on micropatterns has successfully mimicked the dynamics of centriole amplification in brain MCCs, allowing the authors to study the spatial origin of procentrioles. Since this is a powerful system, a more quantitative description of the system will be informative and beneficial for future studies. For example: What is the efficiency of this system? Do the cilia that form in MEF-MCCs motile?

      The system of MEF-MCCs has been described in a previous paper from the Kintner lab. It seems that growing the MEF-MCCs on micropatterns did not ameliorate the ciliation which is partial, probably due to the absence of an apico-basal polarity.

      (2) Figure 2: The analogy drawn by the authors between DEUP1 oscillatory dynamics and centriolar satellites is intriguing. In early amplifying cells within the cloud, do these DEUP1 structures co-localize with the satellite marker PCM1?

      We have added immuno stainings of PCM1 in mRuby-DEUP1 / CEN2-GFP cells in Fig. Supplementary 2E. Within the centrosomal cloud, DEUP1 colocalizes with PCM1. Interestingly, this PCM1 concentration at the centrosome is dependent, at least in part, on dyneins. Then, PCM1 can localize around the deuterosomes, but it is never colocalized with deuterosomes (not shown). This is also showed by immuno-EM in Zhao et al., 2019. Although it was shown that PCM1 is a proximity interactor of DEUP1 (called ccdc67 at that time) by Firat-Karalar et al., 2014., absence of PCM1 staining on deuterosomes does not favor the hypothesis of PCM1 and DEUP1 being part of the same entities. One could hypothesizes that DEUP1 is transcribed locally within the satellites, explaining the colocalization of the 2 proteins and the + BioID results, and then form PCM1negative deuterosomes.

      (3) The authors propose a physical link between deuterosomes and centrosomes based on their oscillatory behavior. How are the oscillatory dynamics of DEUP1 affected by nocodazole treatment or inhibition of microtubule motors (i.e ciliobrevin treatment)?

      These oscillations are inhibited by nocodazole (Fig. 4D). They are also inhibited by dynapyrazole (Fig. 4D). We never succeeded in having a nice disruption of the Golgi apparatus with ciliobrevin and therefore we did not used it.

      (4) In addition to nocodazole treatment, it would be important to determine the consequences of microtubule stabilization by taxol and inhibition of microtubule motors during critical stages of centriole amplification where microtubules are reported to play a role for the first time in this manuscript. Another interesting area of investigation will be to study the extent to which microtubule PTMs contribute to these processes.

      We now blocks dyneins during the different stages of amplification. The results are in main and associated Fig. 4, 5, 7, 8. The role of microtubule PTM, is not in the scope of this manuscript.

      (5) Describing microtubule dynamics along with Centrin/DEUP1 dynamics will be informative in assessing whether these structures associate and/or move along microtubules? Have the authors performed their imaging experiments with SIR tubulin?

      Yes, we have tried hard! But we have encountered different obstacles:

      3-color video microscopy is phototoxic,

      siRTubulin is bleaching very rapidly

      The density of microtubules in MCC makes the observation hardly informative

      (6) Figure 5: The role of PLK1 in centriole-centrosome conversion and generation of multiple MTOCs can be tested with a PLK1 inhibitor for further confirmation.

      We have also tried but inhibiting Plk1 blocks the A-to-G and G-to-D transitions so it was not possible to uncouple the role of Plk1 in stage transitions versus centriole maturation.

      (7) Figure 6: The tight co-localization of nuclear pore proteins with centrioles poses questions about the role of nuclear pore proteins or other nuclear proteins that are associated with centrioles during centriole disengagement and migration. Considering the existing literature on centrosome-nucleus attachments, can there be a way to test this question within the scope of this manuscript?

      We have tried to deplete Nup133 but it’s killing the cells. Our additional experiments now show that the nuclear migration of centrioles during G-stage is dynein dependent, reinforcing the parallel with centrosome migration in prophase. We also added results from our scRNA sequencing (Fig. 5 Supplementary 1) showing that some key players of centriole migration to the nuclear membrane are conserved in the MCC cell cycle variant, and expressed with a comparable dynamics as to the canonical cell cycle.

      (8) Figure 8: Manually tracking a subset of migrating centrioles to define their dynamics during centriole migration and docking provides valuable analysis for determining the molecular mechanism of these processes. In addition to microtubules, does actin contribute to this process? Since centrioles eventually migrate to the apical side in nocodazole-treated cells, there should be other molecular players involved in this process.

      We did block actin polymerization but we found that the different stages were affected and that it would be better to dedicate a whole manuscript on the role of actin during each stage of amplification. We discuss the migration mechanism, and the putative role of actin, in the discussion.

      (9) The legends for Supplementary Figures 1 and 2 in Figure 3 are mixed and need correction.

      Figures have been remodelled.

      (10) In Figure 3P, the term "PLK4+" is labeled in bright green, which is not clearly visible. It maybe beneficial to change the color of this label for better visibility.

      We have tried to correct this.

      (11) Figure 6F quantifies "% tethered flowers" on the nuclear membrane. When quantifying, is the3D localization of DEUP1 flowers in both DMSO- and Noc-treated cells considered? A flower may appear to be on the nucleus in 2D, but it could be detached from the membrane in a 3D view.

      The quantifications are done in 3D. However, flowers that are below or above the nucleus are not quantified since the space is confined and the resolution in z to small to see whether they are connected or not. This is now precised in the legend.

      Before the editors proceed with an updated assessment, they've requested that we pass on some of the comments that have arisen as part of the evaluation of your revised manuscript. They feel that these concerns should be addressed before we proceed with issuing a formal assessment and publishing the revised Reviewed Preprint:

      We thank the reviewers and the editors for the corrections and insighfull comments. We apologize for our delayed answer and hope our corrections in the main text and some of the figures will give them satisfaction.

      The revised manuscript is greatly improved with nice new data regarding the role of microtubules. It also has changed quite a bit including the title. The new focus is on the cell and centriole cycle variants in MCC. While this helped to focus the study, there remains an important issue related to the interpretation of the data and the proposed 2-in-1 cycle model. Before providing the final updated assessment, we ask you to address the following points (which were raised already in the first round of review): The manuscript still contains statements that are not aligned with published work and the current view in the field regarding the timing of events during canonical centriole biogenesis. These timings are in conflict with your model that 2 centriole cycles are "superposed" in the MCC cell cycle variant, as currently presented. An alternative straightforward interpretation would be that multiciliogenesis uses an accelerated centriole duplication cycle where key steps occur concomitantly or in short succession instead of being separated by mitotic divisions as in the canonical cycle.

      We do agree with the acceleration of all steps into only one cycle, this is actually what we think we have proposed. When correcting our confusions as regard to centriole-to-centrosome conversion (as explained below) and putting the events in a scheme, this reveals that the events of the two canonical cycles nicely superpose, both in term of molecular composition and dynamics (corrected Fig. 9). We therefore maintain that the null hypothesis is that the acceleration is done through a superposition of events that; although driven by the same molecular machinery, are normally occuring in two consecutive cell cycle. We explain ourself briefly in two paragraphs, before answering point by point to the questions of the reviewers.

      As regard to centriole-to-centrosome conversion:

      We thank the reviewer for pointing out that we used “MTOC conversion” for what is normally called “centrosome maturation”. We have removed the term “centriole-to-centrosome conversion” during the first round of revision but we now realize that “MTOC conversion” leads to the same misinterpretation as regard to the literature on centriole duplication.

      The reviewer asks us to refer to the work of the Tsou lab (Wang 2011, reference now added in the manuscript) showing that daughter centrioles are “modified” (e.g. recruit PCM, become competent for MT nucleation and duplication) during late M/early G1. This “centriole-to-centrosome conversion” can’t occur for our procentrioles at this stage since they are not even born during the mitosis that precedes MCC differentiation. Also, in our cells, such modification does not include the capacity to become competent for duplication since we know that procentrioles become basal bodies without making any round of duplication (Al Jord et al., 2014).

      Also, we have not done the experiments to tackle the question on when our centriole become “modified-like”. What we can say is that during A-stage, they become progressively positive for PCM (Fig. 5 Supplementary 2) and a weak signal shows that some MT are seen emerging from them (Fig. 5 and Fig. 5 Supplementary 2, and see point by point answer).

      What we do see is that, at the A-to-G transition, they increase their PCM recruitment, show clear and strong MTOC ability (sometimes as strong as the centrosomal centrioles), and that this is associated with migration and separation of centrosome/deuterosomes around the nuclear membrane (Fig. 5). We therefore connect this to what occurs at the G2/M transition which is an increased recruitment of PCM protein, an increased ability to nucleate MT, associated with centrosome migration and separation at the nuclear membrane. Since this process in the canonical cell cycle is called “centrosome maturation”, we therefore should refer to this term in our study. However, centrioles in the MCC variants are not organized in centrosomes, so we now compare what we see to the “centrosome maturation” of the canonical cell cycle with an associated reference (Joukov et al., 2018), but name it “centriole maturation”.

      We have modified the text (track changes visibles) and the schemes (Fig. 5, Fig. 5 Supplementary 1 and 2, Fig. 9, Fig. 9 Supplementary S1; new versions uploaded) accordingly.

      As regard to 1.5 or 2 cell cycles

      Except for the “MTOC conversion” that we have now changed, as explained above, we think our work does suggest (depicted on Fig. 9) what the reviewer states for centriole duplication: “In the current view, centriole biogenesis starts in early S, elongation proceeds through G2/M and by early G1 it is complete. During M/early G1 centrioles disengage and newly formed daughters recruit PCM (centrosome conversion). Then these centrioles go through another complete cell cycle and when they reach early G1 again they have acquired DAs and SDAs. Key here is that biogenesis and disengagement/centrosome conversion are separated by the first mitosis (ensuring duplication occurs only once), and acquisition of DAs and SDAs is separated by another mitosis (ensuring that cells only form a single cilium)”.

      We feel that going from early S to a G1 phase, after 2 mitosis, is what one can call “2 cell cycles”. One of the paper that inspired us a lot when studying how the cell cycle machinery can drive centriole amplification in MCC is a paper from Jadranka Loncarek team (Kong et al., 2014) where they also state that “nascent centrioles gradually mature through 2 cell cycles”. Very interestingly, in this study they show that when they enhance Plk1 activation, they could erase centriole age and new procentrioles are able to recruit PCM and appendages within only 1 cell cycle, without mitotic progression, like what we see in MCC. We have added the reference in our discussion.

      Point by point answer

      (1) Original work on canonical centriole disengagement and centriole-to-centrosome conversion should be cited (e.g. PMID: 16862117, PMID: 21576395)

      As explained earlier, we used the wrong term since the begining. We do not speak about the centriole-to-centrosome (nor MTOC) conversion since we do not test when centriole modification (Wang et al., 2011) occurs in the MCC cell cycle variant. We know that PCNT is present on the procentrioles during A-stage (as shown in Fig. 5 Supplementary 2B), but we do not know when it is recruited (UExM did not work properly with this antibody). We quantify a weak MT staining in regrowth experiment during A-stage and see that procentrioles can be connected to MT in both brain MCC and MEFs (as shown in Fig. 5D, E for brain MCC and Fig. 5 Supplementary 2F for MEFs) , but we do not know when during A-stage they become competent for nucleation. We therefore did not speak about this process that we do not document. What we clearly document/quantify is the enhanced MT nucleation capacities at the A-to-G transition, concomitent with the nuclear migration (easily defined with Cen2-GFP or GT335 stainings) and that we compare to centrosome maturation occuring at the canonical G2/M transition.

      (2) The authors state in several places that canonical centriole formation and maturation takes two iterations of the canonical cell cycle. This is imprecise. Based on the above work and work by others, the broadly accepted view is that it takes 1.5 cell cycles. This difference matters for the final proposed model (see below). Reviewed e.g. here: PMID: 20869612; PMID: 30601682

      Our answer is in the preamble.

      (3) "Centriole maturation cycle superposes with centriole elongation cycle in the MCC cell cycle variant": Your description of the canonical cycle differs from the current view in the field. In the current view, centriole biogenesis starts in early S, elongation proceeds through G2/M and by early G1 it is complete. During M/early G1 centrioles disengage and newly formed daughters recruit PCM (centrosome conversion). All this occurs in 0.5 cycles. Then these centrioles go through another complete cell cycle and when they reach early G1 again they have acquired DAs and SDAs (total of 1.5 cell cycles). Key here is that biogenesis and disengagement/centrosome conversion are separated by the first mitosis (ensuring duplication occurs only once), and acquisition of DAs and SDAs is separated by another mitosis (ensuring that cells only form a single cilium).

      (4) Fig 5A, B and Fig. 9

      (a) Are 2 separate figures needed for the model? They seem redundant.

      We find it easier not to wait Fig. 9 to have the first part depicted.

      (b) The model shows loss of SAS6 throughout G1, but this already occurs during M/early G1

      Thanks. It was already ok in Fig. 9, we have modified for Fig. 5.

      The model shows "MTOC capacity/conversion" during S phase, but this occurs during early G1

      Thanks a lot, as explained earlier, we used the term MTOC conversion occurring in G1 for what is normally called centrosome maturation occurring in G2/M, as explained earlier. We do not speak anymore of MTOC conversion since we have not tackled this question (explained above). We have therefore removed MTOC conversion in the texts and the schemes and replaced it by “centrosome maturation” for the duplication cycle, and by “enhanced MT nucleation capacity” for the MCC cycle. To be clearer and schematize that procentrioles are competent for MT nucleation before G2/M or A/G transitions, we have added some MT nucleated from G1 procentrioles during the canonical cycle, and from late A-stage procentrioles during the MCC cycle.

      The model shows disengagement only in the second M phase, but this occurs already at the first M phase, directly following centriole biogenesis, right before centosome conversion.

      This is a big edition error in both Fig. 5 and 9. Of course the daughter centriole disengage during the first M-phase. This has been changed. Thanks a lot for spotting it. This, however does not contradict the hypothesis of superposition.

      We also added the acquisition of distal appendage which was written in Fig. 5 but not in Fig.

      9 for duplication during the second M-phase.

      When the correct timings are incorporated in the figure, the proposed superposition of two cycles is not an accurate description of the events. Instead, your data seem consistent with a model where MCC incorporates all steps in one cell cycle variant that lacks mitoses, so that disengagement and MTOC conversion occur together with centriole elongation, followed immediately by acquisition of DAs and SDAs.

      We do agree with the acceleration of all steps into only one cycle, this is actually what we tried to propose. When putting the events in a scheme, this reveals that the events of the two canonical cycles nicely superpose, both in term of molecular composition and dynamics (Fig. 9). We therefore maintain that the null hypothesis is that the acceleration is done through a super opposition of events that; although driven by the same molecular machinery, are normally occurring in two consecutive cell cycle. This is notably consistent with the findings of Kong et al., 2014 cited previously.

      (5) While all reviewers felt that there was no need to introduce the new term "nest", they leave it to the authors to keep it. However, the authors may want to consider that the term is still not introduced and explained properly, which may confuse readers. For example, while this section reads like an introduction to the term: "Correlative DEUP1 live-imaging and EM highlights the existence of a pericentrosomal "nest" in brain MCC", the term is already used two times before without explanation. The first mentioning is at the beginning of the results section and is followed by citations, which gives the impression that these studies describe the nest, which is not the case.

      The first mention of “nest” is in the end of introduction resuming the findings of the paper where the term is in the following context: “we found that centriole amplification emerges in a pericentrosomal “nest” concentrating core centriole/deuterosome elements”. We looked at nest definition in the Collins Dictionnary : “a structure or other place where creatures, esp. birds, give birth or leave their eggs to develop”, we felt this was clear. We added quotation marks around the term nest.

      Then, the result section opens with this sentence: “The origin of amplified centrioles in MCC remains controversial. Some live imaging experiments and electron microscopy suggest that the centrosome could constitute a nest for centriole and deuterosome biogenesis (Al Jord et al., 2014; Kalnins et al., 1972; Mori et al., 2017), but others have proposed that procentriole-loaded deuterosomes emerge independently from the centrosome location, all over the cytoplasm (Nanjundappa et al., 2019; Sorokin, 1968; Zhao et al., 2013, 2019).”. Here, the term nest is again used as a place of birth for centrioles and deuterosomes which is what is actually proposed in these papers. First, Kalnins el al., in 1969 (we made an error on the reference date, this has been changed), resume in their abstract “This observation suggests that all of the clusters may form initially in close association with the diplosomal centrioles”. Then, not to mention Al Jord 2014 which comes from our lab, the title of Mori et al. is “Cytoplasmic E2f4 forms organizing centres for initiation of centriole amplification during multiciliogenesis”, and in the paper, they show that E2F4 accumulates at the centrosome. This is now also proposed by collaborators for MCIDAS (Lu et al., 2025). We feel that these references, which are often omitted, are appropriated at this location.

      Then we continue with: “To test whether microtubules drive the organization of a centrosomal nest from which procentrioles emerge”, which keeps the notion of the place of birth.

      Then the title "Correlative DEUP1 live-imaging and EM highlights the existence of a pericentrosomal "nest" in brain MCC" arrives. In this section we first speak about a pericentriosomal cloud on which we zoom in using CLEM, to then conclude at the end of the section “Altogether live imaging mRuby-DEUP1/CEN2-GFP during early A-stage suggests that core deuterosome and centriole components are concentrated in a primordial cloud around the centrosome, which constitutes a nest where centrioles and deuterosomes concomitantly form before they move away from the centrosomal region (Fig. 2F)”.

      Finally, we begin the discussion section regarding the nest by: “We named this transitory compartment a “nest” since deuterosomes and procentrioles emerge specifically in this region and grow while moving away from it.”

      During the first revision, we tried to make it clearer. If this is still not the case after and the reviewer has another proposition of definitions/phrasing, we will be glad to consider it.

      As replied to the other reviewer, the term “nest” does not need to be retained as a new terminology. It is just a way for us to identify the transitory region and to best define one of its function/characteristic which is to host the birth of new deuterosomes and centrioles.

      The following comments from Reviewer #3 may also provide further context regarding the editors' remaining concerns:

      The authors have done an excellent job addressing the points I raised overall, and the revision is substantially improved in focus and clarity. That said, some concerns raised by other reviewers, particularly regarding terminology and statistical analysis, could have been addressed more fully. One issue remains insufficiently resolved. Several quantitative analyses (for example Fig. 5C and 5E) still appear to rely on pooled single-event measurements collected across three independent experiments. This approach can overstate statistical significance. The authors indicate in their rebuttal that they use chi-square tests to compare proportions and to justify pooling across replicates. However, I am not convinced this addresses the issue for the intensity-based and single event distributions shown in the panels specified above. I recommend that these key analyses be represented with biological replicates shown explicitly (superplot-style, with replicates distinguished).

      Our reply was for the comparison of proportions and not the intensity-based and single event distributions shown in the panels Fig. 5C and Fig. 5E. We have now changed our plots to represent biological replicates explicitly (superplot-style, with replicates distinguished). As for the statistical analysis: we evaluated differences in marker intensity between A-stage and G-stage samples using a linear regression model, with stages as the main effect and replicate as a fixed covariate, to account for batch variation. Statistical significance was assessed using Type II ANOVA.

      Separately, I continue to feel that some newly introduced terminology (for example, the "nest") may not be necessary at this stage. It may be sufficient to describe these structures and focus on their spatiotemporal behavior, composition, and measurable features, rather than assigning new names. Having read the authors' response, I understand that they would like to retain this terminology, which is acceptable; however, it may not be readily adopted by the field.

      The term “nest” does not need to be retained as a new terminology. It is just a way for us to identify the region and to best define one of its function/characteristic which is to host the birth of new deuterosomes and centrioles.

      Minor correction (remove "in MCCs" part from the following sentence):

      In MCC, PCM1 depletion alters deuterosome formation and centriole production in brain and airway MCC (Hall et al., 2023; Zhao et al., 2021).

      Done

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      i sent myself a photo got the email from the response but couldnt open file. if we csnt fix this todsy due to credits, we should just remove this photo for now.

    1. 15 questions

      we write the number of quedtion to read here is 15 ( same for other blovks here. they dont go to the actually currated reads. we had done the magoc math of which articles to bunch together. we dont have a lot of token left. we have 8% left until sundsy so maybe we hide this sectioj if it takes too much to rebuild the pages under this with links to content without duplicating it but still makimg it eady to see all 15 questions to read with next next or sokething clever so we dont have duplicated content.