make this: die Karte

make this: – und damit

make this: die Mastercard GOLD von Advanzia

make this: .

make this: . D

delete

make this: sind jeweils

make this: deckt weder Gepäckversicherung noch Such- und Rettungskosten ab

make this: –

make this: abgedeckten Personenkreis

make this: schneidet sie gut ab

make this: . Vorausgesetzt,

make this: –

make this: oder

In Summe ist der Versicherungsschutz allerdings nicht vergleichbar mit der Platinum Card.

make this: . D

make this: ist aber

delete

delete

make this: an die Grenze

make this: Gepäckschutz

make this: Tagen Reisedauer

what is this supposed to mean. please rephrase to clarify

make this: großzügiger

make this: die bei den medizinischen Kernpositionen durchgehend oben steht.

This is a lot to take in. I'm guessing none of all that info could be left out?

make this: zeigt die folgende Tabelle im Abschnitt "Medizinische Absicherung". Zudem gibt sie einen Überblick über die wichtigsten Fakten und Bedingungen pro Kreditkarte.

make this: Genügt andererseits laut Police

make this: adäquaten

make this: relevante

make this: Daraus folgt

delete

make this: Platinum Card von American Express

make this: Rangfolge in diesem Vergleich

make this: oft angenommen

make this: manchen

delete

make this: gegebenenfalls nicht ausreichend

make this: Eine

capitalize the first words after the colon for the next 5 bullet points too

make this: Für diesen Vergleich sind daher sechs Kriterien entscheidend

make this: nichts

make this: mehr als

delete

make this: Dieser

make this: im Falle einer Erkrankung einen

Does this mean Continental Europe or the European Union? Please clarify

make this: Wie

make this: ab,

make this: weiter unten beschriebenen

make this: Dieser Artikel vergleicht sechs Karten auf Basis der offiziellen Versicherungsbedingungen der Anbieter.

make this: die ähnliches zu versprechen scheinen

make this: Versicherungen, mit denen für eine Kreditkarte geworben wird

Testicular

eLife Assessment

In this valuable study, the authors identified a rare population of Nestin-expressing cells within the external granule layer of the early postnatal mouse cerebellum. They demonstrated that these cells are distinct from Sox2+ progenitors and can give rise to medulloblastoma. Collectively, the findings provide convincing evidence that this unique Nestin+ population is susceptible to oncogenic transformation and may underlie the preferential emergence of Sonic hedgehog-driven medulloblastomas.

Reviewer #1 (Public review):

Summary:

GCPs, which drive postnatal cerebellar growth and can give rise to SHH-MB, are not uniform. The authors show that GCPs include a rare Nestin-expressing subpopulation with distinct molecular features. This subpopulation is spatially restricted, enriched for stem cell-like properties, and shows a high competency for tumor formation comparable to larger GCP pools, with tumors preferentially arising in the posterior-lateral cerebellum. Overall, the findings indicate that SHH-MB might originate preferentially from this small, tumor-competent Nestin-expressing GCP subset.

Strengths:

(1) The authors use a breadth of approaches from histology, mouse genetics, and single-cell RNA sequencing.

(2) Throughout, this paper uses very elegant genetic approaches, such as the double Nes-FlpoER; Atoh1-FSF-Cre; LSL-Smo-M2, to generate tumors only from Atoh1+; Nes+ double-positive cells. This intersectional genetic experiment makes for a very clear answer.

(3) The findings reported in this manuscript are valuable since they reveal a novel GCP subpopulation defined by spatial and molecular identity. Some of their experiments suggest that these cells could represent the main cell-of-origin of SHH MB. The experiments are carefully performed, and the evidence is convincing.

Weaknesses or elements that could be improved:

(1) A transgenic Nestin-CFP mouse is used in this study. However, it is not clear whether CFP accurately reflects the Nestin protein. Figure 1: After the promoter is turned off, these cells might remain positive for CFP for longer than they are positive for Nestin, due to CFP protein stability. Is the Nestin protein present in these cells? Nestin double immunofluorescence with CFP and Sox2 and Barhl1 could be performed to address this. Related to this comment, it is also important to note that this is a rat promoter transgene. So the transgene might not reflect exactly the endogenous Nestin expression.

(2) Could the posterior restriction of Nestin-CFP be due to the timing (P1) at which the authors looked? In other words, if they look earlier, would the authors see Nestin-CFP cells more anterior?

(3) Since only one medulloblastoma mouse model (Smo-M2) is used to conclude that "the Nes-expressing GCP population in the normal cerebellum is transcriptionally closer to SHH MB tumor cells than the remainder of the GCPs", the findings might not apply to other SHH-MB models. This should be mentioned.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors studied transgenic reporter mice to profile Nestin expression in the postnatal mouse cerebellum. They discovered a small population of Nestin+; Atoh1+ granule neuron precursors (GNPs) in the external granule cell layer (EGL). Using immunostaining, qPCR, and RNA-sequencing, the authors showed that these Nestin+ cells are not identical to Sox2+ cells (e.g., the majority of Nestin+ cells are Sox2-). Using various mouse genetic strategies, including an elegant intersectional strategy that specifically targets Nestin+; Sox2+ cells, the authors showed that Nestin+ cells are capable of initiating Sonic hedgehog (SHH) medulloblastoma when they express the SmoM2 allele that drives constitutively active SHH signaling. Lastly, the authors profiled the transcriptomes of these cells and showed that they display enriched stem cell genes and are closer to the transcriptomes of GNP-like cells in medulloblastoma compared to Nestin- GNPs in the developing cerebellum.

Strengths:

(1) The comprehensive mouse genetics experiments, in combination with immunostaining, lineage tracing, and RNA-seq studies, provided compelling evidence that rare Nestin+ cells are present in the EGL, predominantly at the posterior lateral cerebellum in early postnatal mice.

(2) The intersectional genetics experiment unequivocally show that Nestin+; Atoh1+ cells can be oncogenically transformed by SmoM2, leading to SHH medulloblastoma.

(3) The more stem cell-like transcriptomic features of the Nestin+ GNPs compared to Nestin- GNPs provide support for the heterogeneity of this transient progenitor cell population, with implications for development, congenital diseases, and tumors from the cerebellum.

Weaknesses:

Main comments:

My main concern relates to whether these Nestin+; Atoh1+ cells are restrictively localized in the EGL. Both the title "A Rare Nestin-Expressing Granule Cell Precursor Subpopulation Underlies SHH Medulloblastoma Formation" and what the authors described throughout the manuscript propose that Nestin+; Atoh1+ cells in the EGL are the cell-of-origin of SHH medulloblastoma. To definitively conclude this, the authors need to comprehensively analyze all regions of the developing cerebellum.

Most importantly, are Nestin+; Atoh1+ cells present in the rhombic lip? Are there any rhombic lip cells genetically labeled in their intersectional mouse mutants (e.g., the Atoh1Frt-Cre/+; Nes-FlpoER; R26LSL-SsmoM2/+ mice)?

If Nestin+; Atoh1+ cells are present at non-EGL regions in the developing cerebellum, the authors would have to reconsider many of their conclusions and also the title of this paper.

Additional comments:

(1) To investigate Nestin expression, the authors used Nes-CFP transgenic mice expressing CFP from promoter/enhancer sequences from the rat Nes gene (Encinas et al., 2006). Given that Nestin expression is of central importance for this study, it is important to validate that these reporter mice faithfully report Nestin protein expression (e.g., by co-labeling CFP with Nestin antibody and systemically comparing signals throughout the cerebellum, ideally in several developmental stages).

(2) The authors mostly presented immunostaining data of the cerebellum from P1 mice. It is important to systematically profile the appearance and disappearance of these Nestin+, Atoh1+ cells in mouse cerebellum across developmental stages (e.g., embryonic, early, and late postnatal stages).

(3) How different is the proliferative ability of the Nestin+ versus Nestin- GNPs at various developmental stages? Also, the difference between EdU+; Barhl1+; Nestin+ and EdU+; Barhl1+; Nestin- cells is quite small despite statistical difference (Figure 1N). Do the authors think this very small EdU incorporation difference can translate into a biological difference (in developmental and/or disease context)?

(4) Lines 145-147: "Compared to double-negative cells, Atoh1 and Nes were significantly higher in the double-positive fraction, supporting the identity of the cells as a previously unrecognized rare population of GCPs at P1 that expresses both the GCP marker Atoh1 and ventricular zone marker Nes." Nestin is not a ventricular zone marker. This should be rephrased.

(5) In Figure 3, the authors showed mouse survival data and concluded that Nes-driven and Atho1-driven SHH medulloblastoma models show similar tumor penetrance. This is not an entirely accurate description of their data. The Nes-SmoM2 mice displayed significantly longer survival compared to the Atoh1-SmoM2 mice (Figure 3B). This conclusion needs to be revised.

(6) In Figure 5, the authors showed that genes enriched in cluster 10 included Sox2, Nes, Wls, and Wnt1, while Neurod1 and Rbfox3 were preferentially expressed in the other GCP clusters. They conclude that cluster 10 represents a less differentiated, more stem-like GCP state, potentially positioned upstream in the lineage hierarchy. While these few markers are useful, it is more informative to formally support this conclusion by comparing the stem cell transcriptomic signature (using a larger gene list) between cluster 10 and other GCPs.

(7) In Figure 5, the authors performed gene ontology analysis and showed that cluster 10 is enriched for biological processes linked to WNT signaling and proposed that this molecular profile supports their identity as a transient, developmentally plastic population within the GCP lineage related to the rhombic lip. The authors are recommended to use an orthogonal approach (i.e., immunostaining to compare nuclear localization of beta-Catenin) to validate their transcriptome-based finding.

akia wurde mindestsent zweimal genantn. was bedeutet dieses wort, ich kenne es nciht

ohne dass es definirt wird

shortens teh sentence

reisegepaecksversicherung,

i hope they still allow for reisegepaeck hahah

cut this part of the sentence and replace with "hilfreich"

zu problemen fuehren kann (statt praktisch relevan tist)

30 Tagen, no need to say miettagen

what do you mean mit kollisionsanteil?

viel zu lange, zu detailiert, aber ist trotzdem nicht klar, wenn jemadn aus deutschland in die schweiz kommt, zaehlen dann schweizer versicherungen?

jedenfalls bitte deutlich detulich kuerzen, auch umbenenne, weil hier werden die bewertungskriterien genannt, entsprechend sollte der titel auch so etwas sein, welche kriterien zaehlen und warum....

eu acho que recomendaria isso para meus alunos. o texto em si achei muito esforco no sentido de ser completo, cobrir tudo, mas nem sempre isso é bom. investiria mais em como conectar as partes, como ser mais objetivo. talvez começar com a checklist, na ordem que faz mais sentido checar, e depois adicionar detalhes para cada idem da lista, podesse levar a uma estrutura que fosse mais util para quem quiser usar o guia.

bom mesmo seria ter skills e scripts derivados disso! que orientassem as pessoas sobre o que falta fazer na preparacao dos artefatos dos projetos delas

we should add orgs to run cloud at the same time

don't we have this already via the docs? docs.run.cloud

I don't think we need to solve this yet

Is this true? I think we support this no?

Claude, can we verify this now? If yes, lets put another appendix for this bit

i didn't quite get this question / problem

the local venue driver is still booting desktop app local sessions from what i understand, so this is an internal newly behaviour

i didn't quite get this question / problem

always other runcloud customers win, its okay to have downtime on newly, its not okay to have downtime for customers

i would say run.cloud should offer otel, other runcloud customers might want to use it too

good question, idk

Tim to decide

separate instance imo

run.cloud imo

tim is already fixing it

this describes the current implementation (not the proposed one); and it proposes a few places with easy wins

instead of hardcoding a custom external field, we could just suport a "metadata" field which would be a json blob, and our cutsomers could send whatever they want in the metadata? wdyt?

Metro part i understand, but what about otel spans/traces from sim fleet? how will they propagate back to newly?

what a reviewer might ask is "why is this a problem?"

my answer: - imagine a scenartio where Newly needs a new feature; this new feature lands on run.cloud automatically, even though run.cloud does not need that feature; - imagine a scenario where run.cloud needs a new feature; thsi new feature lands on Newly automatically, even though Newly does not need that feature

this becomes unmaintainable very fast

what are rungs A and B?

newly desktop app or web app

what is Baggage?

Papaya is codename for this rfc / rafactor

test comment

repete também o que foi dito em outra secao. seria melhor concentrar e apenas referenciar

duplica com a secao de documentacao

apareceu antes, em outra seção

com tantas seções e subseções, sem numeração, a estrutura às vezes não ficou clara

meio que aparece do nada. sinto falta de uma narrativa que conecte as partes, para não parecer uma lista de práticas desconectadas

se eu vou fazer isso, as recomendações anteriores dessa seção já vão ser válidas. então não bastaria isso? ou isso não deveria ser primeiro.

meu sentimento é de que houve uma priorização de completude, de cobrir muita coisa, mas a conexão entre as partes deixa a desejar em pontos como esse e no de containers, pelo menos... é como se precisasse amadurecer essas conexões

muita gente documenta tudo isso num docker/container. pelo menos mencionar que pode ser interessante o uso de containers

vi que depois menciona, mas a conexão é fraca, quase como se container não fosse uma forma de documentar

apareceu meio do nada, sem explicações

achei bem completo, mas longo e meio enfadonho. mas o que importa aqui é descobrir o que os alunos acham.

gostei da terminologia.

senti falta de dizer que a terminologia não é padrão, citar a da ACM, e tentar fazer uma relação grosseira entre as duas

cut this sentence

greift die leistung praktisch nie

incorrect german rephrase

please cut this compeltetly

应该指的是黑格尔哲学下的某种社会关系获得合法地位，被社会承认。

eLife Assessment

This manuscript presents important new findings showing that the transcription factor and regulator of lipid and glucose metabolism PPARγ is methylated by the enzyme SETD6. The data convincingly demonstrate that methylation of PPARγ by SETD6 regulates its function in controlling transcription of lipid storage and metabolism genes and thereby modulates lipid accumulation in liver cells. This work uncovers a new role for post-translational modulation by lysine methylation in controlling transcription factor activity and lipid metabolism in a relevant physiological context.

Reviewer #1 (Public review):

Summary:

In this manuscript from the Levy lab, the authors investigate whether SETD6 regulates hepatic lipid accumulation through direct methylation of PPARγ. They show that SETD6 binds and mono-methylates PPARγ at K170 and provide evidence that this modification enhances PPARγ occupancy at target promoters, promotes expression of lipid metabolism genes, as well as facilitates lipid droplet accumulation in HepG2 cells. The authors also find a positive feedback loop or circuit in which PPARγ activates SETD6 transcription in a methylation-dependent manner, thereby reinforcing this lipogenic program. Overall, the work presents a novel SETD6-PPARγ regulatory axis linking lysine methylation to transcriptional control of lipid storage genes, with possible relevance to NAFLD-associated biology.

In all, I find this to be an important paper that describes and advances a new regulatory pathway that has significance to human health and disease. It would also be of interest to a broad audience. That said, there are also some concerns that the authors should address, as outlined below.

Major concerns (pertains to rigor - highest priority)

(1) Overall, the work presented is of high quality and the data nicely support the conclusions; however, a few panels should be strengthened that have missing controls or information:<br /> a. The co-IP panel in Fig. 1B lacks a lane where HA SETD6 is expressed without PPARγ. This control is needed to verify that the SEDT6-HA signal depends on PPARγ.<br /> b. In Fig. 1C, the authors should show that the co-IP works in both directions (include IP for PPARγ/blot for SETD6). I am a bit confused also over the labeling with IP on the left and on top of the panel next to the beads label. More importantly, the data would be stronger if the authors take advantage of a deletion line to validate the co-IP is specific to the presence of both.<br /> c. The same IP labeling issue exists for Fig 3B (label is on the same and on top).<br /> d. Antibody information (e.g., where the pan-methyl Ab comes from and at what dilutions they are used at) is missing.

Nice to have experiments (medium priority - strongly consider)

(2) A missing gap is how K170me1 contributes to DNA binding and gene transcription. One possibility is that methylation enhances the DNA binding activity of PPARγ. Given the authors have all of the reagents, it would be possible to perform a gel shift assay (or other approach) with and without SETD6-mediaetd methylation. Is DNA binding affected/enhanced?

(3) Along these lines, I wonder if there is another possibility: could SETD6-mediated methylation of PPARγ drive SETD6-PPARγ interaction? In other words, in the K170R, is SETD6 still even associated with PPARγ, and this interaction is required for promoter recruitment? Alternatively, would a catalytic dead version of SETD6 fail to associate with PPARγ? Currently, no experiments test the impact of an unmethylatable version of PPARγ or catalytic dead version of SETD6 on SETD6-PPARγ interaction or SETD6 recruitment to promoters.

Minor concerns (text and figure display)

(4) The text has multiple typos and grammatical errors.

Comments on revised version.

Great job on addressing the comments. It is a nice study.

Reviewer #2 (Public review):

Summary:

In this work, the authors investigated the regulation of the transcription factor PPARγ by the post-translational modification lysine methylation The data demonstrate that the lysine methyltransferase SETD6 targets PPARγ for methylation using biochemical and cell-based assays. Methylation of PPARγ occurs in its DNA binding domain, and the authors demonstrate that loss of methylation limits PPARγ chromatin binding, particularly to lipid storage and metabolism genes promoters. As a physiological output, the authors demonstrate that deletion of SETD6 and loss of PPARγ methylation also disrupt lipid droplet accumulation in hepatocytes. In addition, the authors uncover a positive feedback loop in which SETD6 methylation of PPARγ also regulates its binding to the SETD6 promoter and expression of the gene.

Strengths:

One of the key strengths of this manuscript is the novelty of the findings in terms of identifying a new mode of regulation of PPARγ that modulates its chromatin association in cells and thereby regulating lipid metabolism genes. The authors nicely combine biochemical studies of SETD6 activity with cell-based assays investigating PPARγ and SETD6 function in regulating lipid storage. Data supporting this conclusion is largely convincing and frequently, multiple assays are used to provide sufficient support to the conclusions. This work therefore expands regulatory modes of PPARγ and identifies a new target for SETD6, an enzyme that targets a number of other transcription factors. Furthermore, the regulatory loop that controls SETD6 expression via PPARγ methylation is likely important for understanding SETD6 function in different cell types that have high levels of lipid accumulation or regulation. The gene expression and lipid accumulation assays are useful for testing the physiological outcome of loss of SETD6 activity or PPARγ methylation directly. In the revised manuscript, the authors have added useful structural modeling to better define potential roles of methylation of PPARγ in regulating its function, particularly relative to DNA binding, and to better define the physical interaction between PPARγ and SETD6.

Weaknesses:

The revised manuscript substantially improved on the presentation of the data and broadened the discussion to provide more context to both the role of SETD6 and to elaborate on potential mechanisms by which methylation impacts PPARγ function and under what physiological conditions this interaction and regulation is important. This improves and strengthens the manuscript and its impact overall.

Comments on revised version.

The authors addressed all of my major concerns following this round of review and I do not have additional recommendations. The presentation of the manuscript including text and figures is improved compared to the previous version. I have updated my public review to reflect these changes.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript from the Levy lab, the authors investigate whether SETD6 regulates hepatic lipid accumulation through direct methylation of PPARγ. They show that SETD6 binds and monomethylates PPARγ at K170, and provide evidence that this modification enhances PPARγ occupancy at target promoters, promotes expression of lipid metabolism genes, as well as facilitates lipid droplet accumulation in HepG2 cells. The authors also find a positive feedback loop or circuit in which PPARγ activates SETD6 transcription in a methylation-dependent manner, thereby reinforcing this lipogenic program. Overall, the work presents a novel SETD6PPARγ regulatory axis linking lysine methylation to transcriptional control of lipid storage genes, with possible relevance to NAFLD-associated biology.

In all, I find this to be an important paper that describes and advances a new regulatory pathway that has significance to human health and disease. It would also be of interest to a broad audience. That said, there are also some concerns that the authors should address, as outlined below.

We are grateful to the reviewer for the positive feedback and appreciation of our work.

Major concerns (pertains to rigor - highest priority)

(1) Overall, the work presented is of high quality, and the data nicely support the conclusions; however, a few panels should be strengthened that have missing controls or information:

(a) The co-IP panel in Figure 1B lacks a lane where HA SETD6 is expressed without PPARγ. This control is needed to verify that the SEDT6-HA signal depends on PPARγ.

We thank the reviewer for this valuable suggestion. The overexpression co-immunoprecipitation experiment referred to by the reviewer has been moved to Supplementary Figure S1 in the revised manuscript. In this experiment, immunoprecipitation was performed using an anti-FLAG antibody to pull down FLAG-tagged PPARγ. In the absence of FLAG-PPARγ, the anti-FLAG immunoprecipitation does not recover a bait protein, and therefore HA-SETD6 is not expected to be specifically immunoprecipitated. Thus, an HA-SETD6-only condition would primarily serve as a negative control for the anti-FLAG pull-down rather than provide additional information regarding the specificity of the interaction.

Importantly, in the revised manuscript we have substantially strengthened the evidence supporting the SETD6–PPARγ interaction by adding two independent complementary experiments. First, we included a reciprocal endogenous co-immunoprecipitation (new Figure 2B), demonstrating that endogenous PPARγ co-immunoprecipitates with endogenous SETD6. Second, we added an independent proximity ligation assay (PLA) (new Figure 2D), which further confirms the interaction between SETD6 and PPARγ in cells. Together with the in vitro binding assay presented in Figure 2A, these orthogonal approaches provide compelling evidence for the specificity of the SETD6–PPARγ interaction. Therefore, we believe that the requested HA-SETD6-only control would not provide additional mechanistic insight beyond the comprehensive validation now included in the revised manuscript.

(b) In Figure 1C, the authors should show that the co-IP works in both directions (include IP for PPARγ/blot for SETD6). I am a bit confused also over the labeling with IP on the left and on top of the panel next to the beads label. More importantly, the data would be stronger if the authors took advantage of a deletion line to validate that the co-IP is specific to the presence of both.

We thank the reviewer for this helpful suggestion. We have revised the manuscript to strengthen the evidence supporting the endogenous interaction between SETD6 and PPARγ. Specifically, we now include a reciprocal endogenous co-immunoprecipitation (new Figure 2B), demonstrating that endogenous SETD6 co-immunoprecipitates with endogenous PPARγ and, conversely, that endogenous PPARγ co-immunoprecipitates with endogenous SETD6. These reciprocal experiments independently validate the specificity of the interaction.

In addition, we have revised the figure layout and labeling to more clearly distinguish the immunoprecipitating antibody from the bead control, thereby addressing the reviewer's concern regarding the presentation of the co-immunoprecipitation data.

Although we did not perform the co-immunoprecipitation in a depletion/knockout background, we believe that the combination of reciprocal endogenous co-immunoprecipitation (New Figure 2B), the independent proximity ligation assay (New Figure 2D), and the direct in vitro binding assay (Figure 2A) provides multiple orthogonal lines of evidence supporting a specific interaction between SETD6 and PPARγ.

(c) The same IP labeling issue exists for Figure 3B (label is on the same and on top).

We have revised the labeling in Figure 3B to clearly distinguish the immunoprecipitating antibody from the bead control, thereby improving the clarity of the figure.

(d) Antibody information (e.g., where the pan-methyl Ab comes from and at what dilutions they are used at) is missing.

We thank the reviewer for pointing this out. We have now added the missing information regarding the pan-methyl antibody to the Materials and Methods section, including the supplier, catalogue number, and experimental conditions used. Specifically, the pan-methyl antibody used in this study was purchased from Abcam (ab23366) and was used at a 1:500 dilution for western blot analysis and 2 μg per reaction for immunoprecipitation experiments.

Nice to have experiments (medium priority - strongly consider)

(2) A missing gap is how K170me1 contributes to DNA binding and gene transcription. One possibility is that methylation enhances the DNA-binding activity of PPARγ. Given that the authors have all of the reagents, it would be possible to perform a gel shift assay (or other approach) with and without SETD6-mediated methylation. Is DNA binding affected/enhanced?

We thank the reviewer for raising this important point. To investigate whether K170 methylation could directly affect PPARγ binding to DNA, we performed structural modeling based on the available co-crystal structure of PPARγ bound to DNA (PDB: 3DZU). As shown in the new Figure 6F, K170 is positioned near the DNA-binding region; however, the modeled K170me1 side chain is predicted to face away from the DNA interface and does not appear to sterically interfere with the PPARγ–DNA interaction. In addition, modeling of multiple K170me1 rotamers did not suggest any major disruption of the DNA-bound conformation.

These observations suggest that K170 methylation is unlikely to directly alter the intrinsic DNA-binding affinity of PPARγ. In contrast, our ChIP-qPCR experiments demonstrate that K170 methylation positively regulates PPARγ occupancy at target promoters in cells. Together, these findings support a model in which K170 methylation promotes PPARγ chromatin association and transcriptional activity through mechanisms other than direct modulation of DNA binding, such as altered cofactor recruitment or protein–protein interactions.

We agree with the reviewer that future biochemical approaches, including EMSA/gel shift assays or quantitative DNA-binding measurements, will be valuable to directly determine whether K170 methylation affects the intrinsic DNA-binding affinity of PPARγ. We have incorporated this new structural analysis and the corresponding discussion into the revised manuscript.

(3) Along these lines, I wonder if there is another possibility: could SETD6-mediated methylation of PPARγ drive SETD6-PPARγ interaction? In other words, in the K170R, is SETD6 still even associated with PPARγ, and this interaction is required for promoter recruitment? Alternatively, would a catalytic dead version of SETD6 fail to associate with PPARγ? Currently, no experiments test the impact of an unmethylatable version of PPARγ or a catalytic dead version of SETD6 on SETD6-PPARγ interaction or SETD6 recruitment to promoters.

We thank the reviewer for this insightful suggestion. To address whether SETD6 catalytic activity is required for its association with PPARγ, we performed an additional PLA experiment comparing SETD6 WT and the catalytic mutant SETD6 Y285A. As shown in the revised New Figure 2D, both SETD6 WT and SETD6 Y285A showed comparable proximity to PPARγ in cells, indicating that SETD6 catalytic activity is not required for the physical association between SETD6 and PPARγ.

These findings support a model in which SETD6 first recognizes and binds PPARγ independently of its catalytic activity. Subsequent methylation of PPARγ at K170 is therefore likely to regulate the downstream functional consequences of this interaction, including enhanced chromatin occupancy and transcriptional activation, rather than the initial SETD6–PPARγ association itself.

In addition, we generated using AlphaFold a structural model of the SETD6–PPARγ complex as a supportive visualization (New figure S2). Given the limited confidence of the prediction, we interpret this model cautiously and include it in the Supplementary Information rather than the main figures. We agree with the reviewer that future studies examining SETD6 recruitment to PPARγ target promoters and the effect of the PPARγ K170R mutant on SETD6–PPARγ association will further refine the molecular mechanism.

Minor concerns (text and figure display)

(4) The text has multiple typos and grammatical errors, and there are some issues with the figure display.

We thank the reviewer for this comment. We carefully revised the manuscript to correct typographical and grammatical errors throughout the text and also addressed the figure display issues noted by the reviewer.

Reviewer #2 (Public review):

Summary:

In this work, the authors investigated the regulation of the transcription factor PPARγ by the post-translational modification lysine methylation. The data demonstrate that the lysine methyltransferase SETD6 targets PPARγ for methylation using biochemical and cell-based assays. Methylation of PPARγ occurs in its DNA binding domain, and the authors demonstrate that loss of methylation limits PPARγ chromatin binding, particularly to lipid storage and metabolism gene promoters. As a physiological output, the authors demonstrate that deletion of SETD6 and loss of PPARγ methylation also disrupt lipid droplet accumulation in hepatocytes. In addition, the authors uncover a positive feedback loop in which SETD6 methylation of PPARγ also regulates its binding to the SETD6 promoter and expression of the gene.

Strengths:

One of the key strengths of this manuscript is the novelty of the findings in terms of identifying a new mode of regulation of PPARγ that modulates its chromatin association in cells and thereby regulates lipid metabolism genes. The authors nicely combine biochemical studies of SETD6 activity with cell-based assays investigating PPARγ and SETD6 function in regulating lipid storage. Data supporting this conclusion is largely convincing, and frequently, multiple assays are used to provide sufficient support to the conclusions. This work therefore expands regulatory modes of PPARγ and identifies a new target for SETD6, an enzyme that targets a number of other transcription factors. Furthermore, the regulatory loop that controls SETD6 expression via PPARγ methylation is likely important for understanding SETD6 function in different cell types that have high levels of lipid accumulation or regulation. The gene expression and lipid accumulation assays are useful for testing the physiological outcome of loss of SETD6 activity or PPARγ methylation directly.

We thank the reviewer for his/her positive feedback on the manuscript.

Weaknesses:

The data presented in the manuscript are largely convincing in support of the authors' conclusions; however, there are some errors in the presentation of the figures and some issues in the text that would benefit from editing. Furthermore, there are some important questions not fully addressed in the results or discussion. 

It would be great if the authors could speculate more on the diverse roles of SETD6 in methylated transcription factors and/or provide more context regarding the conditions that are likely to support methylation of PPARγ by SETD6. 

We thank the reviewer for this important suggestion. In the revised Discussion, we expanded the manuscript to better place our findings within the broader context of SETD6-mediated regulation of transcription factors. Previous studies from our group and others demonstrated that SETD6 methylates multiple chromatin-associated transcriptional regulators, including RelA, E2F1, TWIST1, and BRD4, thereby modulating transcriptional selectivity, chromatin occupancy, and cofactor recruitment. We now discuss the possibility that SETD6 functions as a context-dependent signalling integrator that selectively regulates transcription factor activity through lysine methylation under distinct physiological conditions.

In addition, we expanded the Discussion regarding potential cellular contexts that may favour PPARγ methylation by SETD6. Because PPARγ activity is strongly induced during lipid overload and fatty acid exposure, conditions associated with steatosis and metabolic stress may enhance the functional importance of SETD6-dependent methylation. We also discuss the possibility that chromatin accessibility, ligand-dependent activation of PPARγ, and metabolic signaling pathways may collectively influence the formation and stability of the SETD6–PPARγ complex.

Also, while a potential cross-talk between methylation and phosphorylation is described in the discussion, it would be great to provide more structural insight into how this might regulate DNA binding of PPARγ and/or discuss whether there are other possibilities given the location of the target lysine in the DNA binding domain.

We thank the reviewer for this valuable suggestion. To provide additional structural insight into the potential interplay between methylation and phosphorylation within the PPARγ DNA-binding domain, we performed structural modeling based on the published PPARγ–DNA co-crystal structure (PDB: 3DZU). As shown in the new Figure S6, the modeled K170me1 side chain is predicted to face away from the DNA interface and does not introduce steric clashes with DNA, suggesting that K170 methylation is unlikely to directly alter the DNA-binding interface through steric effects. In contrast, phosphorylation of the neighboring residue T166 is predicted to introduce multiple intramolecular steric clashes within the DNA-binding domain. These structural changes could influence the local conformation or dynamics of the DNA-binding domain and thereby indirectly modulate PPARγ DNA binding or transcriptional activity.

In addition, as suggested by the reviewer, we expanded the Discussion to consider alternative mechanisms by which K170 methylation may regulate PPARγ function. While our ChIP-qPCR experiments demonstrate that K170 methylation positively regulates PPARγ chromatin occupancy at target promoters, the structural modeling suggests that this effect is unlikely to arise from direct steric modulation of the DNA interface. Instead, K170 methylation may influence chromatin occupancy by regulating protein–protein interactions, cofactor recruitment, local conformational dynamics, or other chromatin-associated mechanisms. We have incorporated these new structural analyses and the expanded discussion into the revised manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The KEGG panels are too low resolution to read.

We thank the reviewer for this comment. We improved the resolution of the KEGG pathway enrichment panels and, as suggested by the reviewer (see below), we separated the upregulated and downregulated gene sets to improve clarity and readability. These changes are now reflected in the revised new Figures 5C, 5D, 6B and 6C.

(2) Figure 3 panel D is hard to visualize; a better version or repeat experiment is needed.

We thank the reviewer for this comment. To address this concern, we replaced the original Figure 3D with a new independent experiment that more clearly demonstrates the methylation of endogenous PPARγ by SETD6. We believe that the new data provide substantially stronger evidence and improve the clarity of the revised manuscript.

(3) There is a typo in Figure 2A (line present in "S" of Signal).

We thank the reviewer for pointing out this typo. The error in Figure 2A has now been corrected in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

Overall, the experiments and analyses presented are sufficient to support the conclusions and interpretations of the work. However, there are some issues of presentation and writing that are worth addressing. These comments are listed below:

(1) My only substantial recommendation is to improve the discussion to provide more context to the findings in terms of both the larger role of SETD6 in methylating transcription factors (some of whom also regulate its expression) and the potential modes through which PPARγ DNA binding activity could be regulated by methylation.

We thank the reviewer for this insightful suggestion. In the revised manuscript, we substantially expanded the Discussion to better place our findings within the broader context of SETD6mediated regulation of transcription factors. We now discuss previous studies demonstrating that SETD6 methylates multiple chromatin-associated transcriptional regulators, including RelA, E2F1, TWIST1, and BRD4, thereby modulating chromatin occupancy, cofactor recruitment, and transcriptional selectivity. We further propose that SETD6 functions as a context-dependent signaling regulator that integrates distinct cellular pathways through the selective lysine methylation of transcription factors.

In addition, we expanded the Discussion regarding the potential mechanisms by which PPARγ K170 methylation regulates transcriptional activity. We incorporated new structural modeling based on the published PPARγ–DNA co-crystal structure (PDB: 3DZU), which suggests that K170 methylation is unlikely to directly alter the DNA-binding interface through steric effects, whereas phosphorylation of the neighboring residue T166 may induce intramolecular steric clashes within the DNA-binding domain. We also expanded the Discussion to consider alternative mechanisms by which K170 methylation may regulate PPARγ function, including modulation of chromatin occupancy, local conformational dynamics, protein–protein interactions, and recruitment of transcriptional cofactors or chromatin-associated proteins. Finally, we discuss that future biochemical studies will be important to determine whether K170 methylation also influences the intrinsic DNA-binding affinity of PPARγ.

Can the authors incorporate any other published structural data to speculate on the role of methylation or describe more about how it is expected that the methylation-phosphorylation crosstalk modulates DNA binding? This type of discussion would better highlight the potential importance of this new modification on PPARγ.

We thank the reviewer for this important suggestion. In the revised manuscript, we expanded the Discussion and incorporated additional structural analyses (new Figure 6F and new figure S6) based on the published PPARγ–DNA co-crystal structure (PDB: 3DZU). K170 is positioned within the DNA-binding domain, between the two zinc-finger motifs that mediate DNA recognition and stabilization on PPRE-containing DNA. Our structural modeling predicts that the K170me1 side chain is oriented away from the DNA interface and does not introduce steric clashes with DNA, suggesting that methylation is unlikely to directly alter the DNA-binding interface through steric effects. Nevertheless, lysine methylation can influence protein surface properties, protein– protein interactions, and recognition by regulatory binding partners, raising the possibility that K170 methylation modulates PPARγ chromatin occupancy or promoter selectivity through indirect mechanisms.

In contrast, structural modeling predicts that phosphorylation of the neighboring residue T166 introduces multiple intramolecular steric clashes within the DNA-binding domain. These clashes could alter the local conformation or dynamics of the DNA-binding domain and thereby indirectly influence PPARγ–DNA interactions and transcriptional activity. Together, these observations suggest that K170 methylation and T166 phosphorylation may represent a regulatory crosstalk that fine-tunes PPARγ function through distinct structural mechanisms.

We further expanded the Discussion to consider additional, non-mutually exclusive mechanisms by which K170 methylation may regulate PPARγ function, including modulation of chromatin occupancy, protein–protein interactions, cofactor recruitment, and stabilization of transcriptional complexes at target genes. While these possibilities require further mechanistic investigation, we agree with the reviewer that these structural considerations highlight the potential regulatory importance of this newly identified PPARγ modification.

(2) In the gene expression experiments presented in Figure 5, it would be useful if the GO terms were described as enriched in either the up- or down-regulated gene sets. From the way it is presented, it is not clear if specific categories of genes are found enriched in those upregulated or downregulated upon KO of SETD6. This is also true for the experiments presented in Figure 6 regarding the PPARγ mutant.

We thank the reviewer for this important suggestion. In the revised manuscript, we separated the pathway enrichment analyses into upregulated and downregulated gene sets for both the SETD6 knockout RNA-sequencing experiments (Figure 5) and the PPARγ WT versus K170R mutant analysis (Figure 6). This revision provides improved clarity regarding which biological pathways are positively or negatively associated with SETD6 depletion or disruption of PPARγ K170 methylation. The updated KEGG enrichment analyses are now presented in the revised new Figures 5C, 5D, 6B and 6C.

In addition, if there is a significant overlap in genes misregulated in both mutants, this could be shown in the figure.

We thank the reviewer for this suggestion. We compared the differentially expressed genes identified in the SETD6 KO cells and the PPARγ K170R mutant cells to evaluate the extent of overlap between the two datasets. However, we did not observe a substantial or statistically significant overlap in misregulated genes under the thresholds used in our analysis. Therefore, we decided not to include this comparison in the revised figure. Nevertheless, both datasets consistently showed enrichment for pathways associated with lipid metabolism and PPAR signaling, supporting a functional connection between SETD6 and PPARγ-mediated transcriptional regulation.

(3) There are some typos and grammatical errors throughout the work, and it should be carefully edited. One example is the following heading: PPARγ K170 methylation by SETD6 regulates mediates lipid droplets formation.

We thank the reviewer for this comment. The manuscript was carefully revised to correct typographical and grammatical errors throughout the text. In particular, the heading mentioned by the reviewer was corrected in the revised manuscript.

Minor errors in the figures:

(1) Formatting issue in the labeling for the x-axis of Figure 1E.

We thank the reviewer for pointing out this formatting issue. The labeling of the x-axis in Figure 1E has been corrected in the revised manuscript.

(2) Figure 2B - HA-SETD6 should be labeled as minus for the first lane.

We thank the reviewer for pointing this out. We corrected the labeling in Figure 2B (now Figure S1), and the first lane is now properly indicated as negative for HA-SETD6.

(3) Figure 2D - Labeling needs improvement. Is this FLAG-PPARγ? "NC" was not defined in the legend. If negative control, what type?

We thank the reviewer for this comment. We improved the labeling and figure legend of Figure 2D for clarity. Specifically, we now clearly indicate that the experiment was performed using Flag-PPARγ, and we defined “NC” in the legend as the negative control condition. In addition, during the revision process we noticed that the original PLA experiment was performed in HeLa cells rather than HepG2 cells, as previously indicated. This has now been corrected throughout the revised manuscript.

(4) Figure 3 - The CRSIPR control should be described somewhere in the legend or the methods.

We thank the reviewer for this comment. We clarified the description of the CRISPR control cells in the Materials and Methods section. Specifically, we now explicitly state that the CRISPR control (CT) cells were generated using the empty lentiCRISPR vector without SETD6-targeting sgRNAs.

(5) Figure 5B and 6A - The legend is not labeled nor defined in the text- fold-change, log2 fold change, z score?

We thank the reviewer for pointing this out. We revised the figure legends for Figures 5B and 6A to explicitly define the heatmap scale and normalization method. Specifically, we now indicate that the heatmaps represent normalized gene expression values displayed as Z-scores.

eLife Assessment

This study presents valuable data suggesting that ATP-induced modulation of alveolar macrophage (AM) functions is associated with NLRP3 inflammasome activation and enhanced phagocytic capacity. While the in vivo and in vitro data reveal an interesting phenotype, the evidence provided is incomplete and does not fully support the paper's conclusions. Additional investigations would be of value in complementing the data and strengthening the interpretation of the results. This study should be of interest to immunologists and the mucosal immunity community.

Reviewer #1 (Public review):

Summary:

Alveolar macrophages (AMs) are key sentinel cells in the lungs, representing the first line of defense against infections. There is growing interest within the scientific community in the metabolic and epigenetic reprogramming of innate immune cells following an initial stress, which alters their response upon exposure to a heterologous challenge. In this study, the authors show that exposure to extracellular ATP can shape AM functions by activating the P2X7 receptor. This activation triggers the relocation of the potassium channel TWIK2 to the cell surface, placing macrophages in a heightened state of responsiveness. This leads to the activation of the NLRP3 inflammasome and, upon bacterial internalization, to the translocation of TWIK2 to the phagosomal membrane, enhancing bacterial killing through pH modulation. Through these findings, the authors propose a mechanism by which ATP acts as a danger signal to boost the antimicrobial capacity of AMs.

Strengths:

This is a fundamental study in a field of great interest to the scientific community. A growing body of evidence has highlighted the importance of metabolic and epigenetic reprogramming in innate immune cells, which can have long-term effects on their responses to various inflammatory contexts. Exploring the role of ATP in this process represents an important and timely question in basic research. The study combines both in vitro and in vivo investigations and proposes a mechanistic hypothesis to explain the observed phenotype.

Weaknesses:

Although these findings are convincing and intrinsically interesting, they do not support the conclusion that ATP induces trained immunity. By definition, trained immunity refers to long-lasting metabolic and epigenetic reprogramming initiated by a primary stimulus. Importantly, some of these changes persist after the cells have returned to a basal activation state, thereby generating an altered response upon secondary stimulation (https://doi.org/10.1038/s41590-020-00845-6). In the present study, the data demonstrate a sustained increase in inflammasome activation and enhanced microbicidal activity for up to seven days following ATP exposure. While this sustained activation is noteworthy as well as metabolic shift, it does not demonstrate the existence of trained immunity. The terms priming or sustained activation would therefore be more appropriate than trained immunity.

Similarly, the observation of increased chromatin accessibility at inflammasome-related genes is expected given the robust activation of this pathway. The presence of open chromatin at these loci does not, by itself, constitute evidence for long-term trained immunity. The authors should therefore be cautious with their terminology and avoid overinterpreting their findings.

The authors have revised the manuscript to address the comments raised during the first rounds of review. However, several figures, figure legends, and methodological sections still require additional adjustments and clarification.

The Methods section remains incomplete and requires substantial revision. For instance, the methodology used to quantify immune cell populations presented in Figure 2 is still not described. It is not stated how immune cells were isolated and identified (e.g. flow cytometry from lung tissue). No information is provided regarding tissue digestion, cell isolation procedures, or gating strategy (presumably by flow cytometry). These details are essential and should be included, together with the corresponding gating strategy and absolute cell numbers.

There are inconsistencies throughout the manuscript. For example, the authors report n = 3 in the figure legend 2 and 3 independent experiments, whereas 3 or 4 points are represented in the graphs. This discrepancy is unclear and should be clarified.

Overall, while the study addresses an interesting biological question, the manuscript would benefit from substantial revision prior to publication. In particular, clarifications and improvements regarding the methodology, data presentation, and interpretation are required to strengthen the rigor and reproducibility of the conclusions. Several of the conclusions extend beyond what is directly supported by the data. In particular, the interpretation that these findings demonstrate trained immunity should be revised, and additional methodological clarifications and corrections are required.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Please include an uptake control (early time point) or time‑course to distinguish phagocytosis from intracellular killing.

We agree that distinguishing uptake from intracellular killing is important for interpreting bactericidal assays. Due to a recent transition, we are unable to conduct additional early‑time‑point assays. To address this transparently, we have revised the manuscript to clarify that our measurements represent overall bacterial load reduction, reflecting the combined effects of uptake and killing.

The normalization as ‘fold killing’ is non‑standard; please report absolute CFU (log scale).

We have retrieved the raw data and re‑expressed all bactericidal activity measurements as absolute CFU. All relevant figures, legends, and text have been updated accordingly.

Authors report quantification of cytokine concentrations, yet no information is provided regarding how these measurements were performed.

Cytokine concentrations were quantified by ELISA. We have now added details regarding assay kits, sample preparation, and detection parameters to the Methods section.

While the choice of IL‑1β and IL‑6 is straightforward, the focus on IL‑18 requires explicit justification.

IL‑18 is a macrophage‑associated pro‑inflammatory cytokine with established links to inflammasome activation and trained immunity pathways, providing a clear justification for its inclusion.

The methodology used to quantify immune cell populations presented in Figure 2 is not described.

We have added a detailed description of the flow cytometry methodology, including staining and gating strategies.

Immune cell quantification would be expected in the context of the challenge experiment as well.

While we agree that such data would be valuable, additional mouse experiments cannot be performed because the animals used in the challenge model are not currently available during the transition period. If feasible, we are exploring ex vivo flow cytometry data from TWIK2 mutant versus wild‑type macrophages.

AMs are not considered recruited immune cells; this should be corrected.

We have corrected this in the figure legend and throughout the manuscript.

The authors report n = 5 for the survival curves in the figure legend, whereas n = 7 is stated in the Methods section.

We have corrected the sample size to ensure consistency between the figure legend and Methods.

ATAC‑seq peaks are referred to as ‘genes’ and ‘differentially expressed genes’.

We have corrected the terminology in the manuscript. ATAC‑seq identifies differentially accessible chromatin regions, which are then annotated to the nearest downstream gene.

In Figure 7, trained WT and Nlrp3-/- mice display similar levels of bacterial clearance. How should this result be interpreted?

A portion of this phenotype is via the normalization of phagocytosis to ‘fold killing’. Presentation of the raw CFU data shows a trend towards reduced bacterial clearance in Nlrp3-/- mice.

Reviewer #2 (Public review):

Sample numbers for experiments 1, 2, and 6 are not provided.

We have added explicit n values for all experiments and verified their accuracy against the original records.

The Discussion would benefit from a clear summary of study caveats.

We agree and have added a dedicated paragraph outlining key Caveats as described.

Specific identities of DEGs are not provided; only pathway enrichment is shown.

We have now included a supplementary table listing the differentially expressed genes identified in our analysis.

Controls for subcellular fractionation and dye microscopy should be included.

Controls for subcellular fractionation have added in figure 3B and controls for dye microscopy have now been added in supplementary figure 1.

The text states that protease inhibitors diminish ATP‑induced training effects, but the figure does not show significance.

We have re‑examined the data and updated the figure to include statistical testing where appropriate.

eLife Assessment

This manuscript reports valuable results on the role of MDC1 and Treacle in DSB repair in rDNA repeats. It has been previously established that MDC1 is replaced by Treacle as the main adaptor in the nucleolar DNA damage response. This work provides convincing evidence that MDC1 is required for the recruitment of RAD51 and BRCA1 to DSBs in rDNA. The work involves multiple MDC1 knockout models and establishes that RFN8-RNF168 act downstream of MDC1in parallel with the RAP80-ABRAXAS pathway in the recruitment of the HR machinery to nucleolar DSBs.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The revised version of the manuscript addresses the previous concerns. Importantly, a major role of the RAP80-ABRAXAS pathway is now demonstrated in the recruitment of BRCA1, PALB2, and RAD51 to nucleolar DSBs.]

This study elucidates the molecular linkage between the mobilization of damaged rDNA from the nucleolus to its periphery and the subsequent repair process by HDR. The authors demonstrate that the nucleolar adaptor protein Treacle mediates rDNA mobilization, and the MDC1-RNF8-RNF168 pathway coordinates the recruitment of the BRCA1-PALB2-BRCA2 complex and RAD51 loading. This stepwise regulation appears to prevent aberrant recombination events between rDNA repeats. This work provides compelling evidence for the recruitment of the Treacle-TOPBP1-NBS1 complex to rDNA DSBs and demonstrates the critical role of MDC1 in the rDNA damage response. There are some issues with the over-interpretation of results as described subsequently. Some aspects could be strengthened, for example, a potential role of the RAP80-Abraxas axis, the origin of the repair synthesis (HDR vs. NHEJ), and a direct comparison of the RNF8 and RNF168 recruitment in the absence or presence of MDC1.

Reviewer #2 (Public review):

Summary:

DNA double-strand breaks (DSB) in repeated DNA pose a challenge for repair by homologous recombination (HR) due to the potential of generating chromosomal aberrations, especially involving repeats on different chromosomes. This conceptual caveat led to a long-held notion that HR is not active in repeated DNA, which was disproven in groundbreaking work by Chiolo showing in Drosophila that DSBs in pericentromeric repeats are mobilized to the nuclear periphery for repair by HR. A similar mechanism operates in mouse cells, as shown by the Gautier laboratory, but the mobilization goes to the nucleolar periphery, called nucleolar caps. In this manuscript, the authors reexamine the role of MDC1 in the mobilization of DSBs in rDNA in human cells. Previous work has shown that MDC1 is replaced by Treacle, the gene associated with Treacher Collins syndrome 1, in its role as the main adaptor of the DNA damage response, and these results are confirmed here. The novelty of this contribution lies in the discovery that MDC1 is required downstream in the recruitment of BRCA1 and RAD51 to nucleolar DSBs that were mobilized to the nucleolar cap. Using multiple MCD knockout models and DSBs induced by the nuclease PpoI, which cleaves at nuclear sites as well as in the 28S rDNA, convincingly documents this role of MDC1 and shows that it acts upstream of the RNF8-RNF168 ubiquitylation axis. Using a proxy assay of co-localization of EdU incorporation at DSBs (gammaH2AX), evidence is provided that MDC1 is required for HR in rDNA. MDC1 was not required for RAD51 recruitment to IR-induced foci, but it is unclear whether this is related to the different DSB chemistry (enzymatic versus IR) or to the localization of the DSB (rDNA versus unique sequence genome).

Strengths:

(1) The manuscript is well-written, and the experimental evidence is nicely presented.

(2) Multiple MDC1 knockout models are used to validate the results.

(3) Convincing back-complementation data clarify the relationship between MDC1 and RNF8.

Author response:

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

Reviewer #1 (Public review):

This study elucidates the molecular linkage between the mobilization of damaged rDNA from the nucleolus to its periphery and the subsequent repair process by HDR. The authors demonstrate that the nucleolar adaptor protein Treacle mediates rDNA mobilization, and the MDC1-RNF8-RNF168 pathway coordinates the recruitment of the BRCA1-PALB2-BRCA2 complex and RAD51 loading. This stepwise regulation appears to prevent aberrant recombination events between rDNA repeats. This work provides compelling evidence for the recruitment of the Treacle-TOPBP1-NBS1 complex to rDNA DSBs and demonstrates the critical role of MDC1 in the rDNA damage response. There are some issues with the over-interpretation of results as described subsequently. Some aspects could be strengthened, for example, a potential role of the RAP80-Abraxas axis, the origin of the repair synthesis (HDR vs. NHEJ), and a direct comparison of the RNF8 and RNF168 recruitment in the absence or presence of MDC1.

We thank the reviewer for the positive assessment of our work and for the constructive suggestions. We agree that certain aspects of the manuscript required clarification, in particular the potential contribution of the RAP80–ABRAXAS pathway, the interpretation of the repair synthesis assay, and the role of RNF168 recruitment. We have addressed these points experimentally where feasible and have revised the manuscript accordingly to avoid overinterpretation.

Reviewer #1 (Recommendations for the authors):

Major comments

(1) In Figures 4C, 4D, and S4B-D, BRCA1 and RAD51, recruitment to nucleolar caps is partially reduced upon RNF168 depletion. Despite this, the authors broadly conclude that recruitment mainly depends on the MDC1-RNF8-RNF168 pathway. Since the RAP80-Abraxas pathway may also contribute, as briefly mentioned in the Discussion, siRNA knockdown of Abraxas would help clarify the relative roles of these two pathways.

We thank the reviewer for this important suggestion. To directly address the potential contribution of the RAP80–ABRAXAS pathway, we depleted RAP80 by siRNA and analysed BRCA1 and RAD51 recruitment to nucleolar caps following I-PpoI-induced rDNA damage.

Strikingly, RAP80 depletion strongly impaired the formation of both BRCA1 and RAD51 nucleolar caps in two independent cell lines (U2OS and RPE1) (new Figure 5). These findings demonstrate that the RAP80–ABRAXAS pathway plays a critical role in BRCA1 recruitment at nucleolar caps.

Together with our observation that RNF168 depletion only partially reduces BRCA1 and RAD51 recruitment, these results indicate that both RNF168-dependent and RAP80–ABRAXAS-dependent pathways contribute to HDR factor recruitment downstream of RNF8-mediated chromatin ubiquitylation.

We have revised the model Figure (Figure 9) and the Results and Discussion sections accordingly to reflect this dual-pathway model and to avoid overemphasising the contribution of RNF168.

(2) In Figure 7C, the EdU-γH2AX PLA assay detects DNA synthesis at rDNA breaks, but it remains unclear whether this signal reflects HDR- or NHEJ-mediated repair. Since MDC1 functions upstream of the DSB repair pathway choice, the observed reduction in PLA signal upon MDC1 depletion does not necessarily reflect impaired HDR alone. Synchronizing cells in G2 or using cell cycle markers would help clarify the repair context and strengthen the interpretation.

We thank the reviewer for raising this important point. We agree that the EdU–gH2AX PLA assay does not exclusively report on HDR-mediated DNA synthesis and may also capture other forms of repair-associated DNA synthesis.

In the revised manuscript, we have therefore tempered our interpretation and now describe this assay more cautiously as a readout of DNA synthesis at sites of rDNA damage, rather than as a direct measure of HDR activity.

Importantly, our conclusion that MDC1 promotes HDR factor recruitment at nucleolar caps is based primarily on the reduced accumulation of BRCA1, PALB2, and RAD51, which are well-established markers of HDR. The PLA assay is now presented as supportive evidence for ongoing DNA synthesis at these sites rather than as a definitive indicator of HDR.

We agree that further experiments, such as cell cycle synchronization or the use of phase-specific markers, would help to more precisely define the repair context, and we have included this point in the Discussion.

(3) The authors propose that MDC1 is essential for RNF8-RNF168 recruitment, specifically at nucleolar rDNA breaks. A side-by-side comparison of RNF8 or RNF168 localization in the presence and absence of MDC1, with IR-treated conditions, would provide important validation of this model. Including representative images in Figure S2C would further support the claim.

We agree with the reviewer that a direct analysis of RNF8 and RNF168 recruitment in the presence and absence of MDC1 would provide valuable mechanistic insight. We therefore attempted to address this experimentally.

However, despite testing multiple antibodies, we were unable to obtain specific and reproducible signals for RNF8 and RNF168 at nucleolar caps, precluding a reliable analysis of its recruitment under these conditions.

Given this technical constraint, we have revised the manuscript to avoid overinterpretation regarding direct RNF168 recruitment and instead focus on functional readouts of downstream ubiquitylation-dependent signalling, such as BRCA1 and RAD51 accumulation.

We note that the requirement for MDC1 in BRCA1 and RAD51 recruitment at nucleolar caps is consistent with a role of MDC1 upstream of RNF8-dependent chromatin ubiquitylation, in line with its established function at IR-induced DSBs.

Minor comments:

(1) The legend for Figure 8 should more clearly explain the proposed mechanism and include concise titles or descriptions for each sub-panel.

We agree with the reviewer that the model should be described in the Figure legend. We have thus updated the model to accommodate the new data and wrote a legend that concisely explains the proposed model. We do not think that titles for each sub-panel are required. Instead, we separately referred to the sub-panels in the legend.

(2) Typos:

(a) Page 8: PRE1 MDC1, as "RPE1 MDC1;

(b) S3 Figure legend: Dhermacon";

(c) Page 29: "80.103"-please clarify or correct.

We thank the reviewer for pointing out these errors. These have been corrected in the revised manuscript

Reviewer #2 (Public review):

Summary:

DNA double-strand breaks (DSB) in repeated DNA pose a challenge for repair by homologous recombination (HR) due to the potential of generating chromosomal aberrations, especially involving repeats on different chromosomes. This conceptual caveat led to a long-held notion that HR is not active in repeated DNA, which was disproven in groundbreaking work by Chiolo showing in Drosophila that DSBs in pericentromeric repeats are mobilized to the nuclear periphery for repair by HR. A similar mechanism operates in mouse cells, as shown by the Gautier laboratory, but the mobilization goes to the nucleolar periphery, called nucleolar caps. In this manuscript, the authors reexamine the role of MDC1 in the mobilization of DSBs in rDNA in human cells. Previous work has shown that MDC1 is replaced by Treacle, the gene associated with Treacher Collins syndrome 1, in its role as the main adaptor of the DNA damage response, and these results are confirmed here. The novelty of this contribution lies in the discovery that MDC1 is required downstream in the recruitment of BRCA1 and RAD51 to nucleolar DSBs that were mobilized to the nucleolar cap. Using multiple MCD knockout models and DSBs induced by the nuclease PpoI, which cleaves at nuclear sites as well as in the 28S rDNA, convincingly documents this role of MDC1 and shows that it acts upstream of the RNF8-RNF168 ubiquitylation axis. Using a proxy assay of co-localization of EdU incorporation at DSBs (gammaH2AX), evidence is provided that MDC1 is required for HR in rDNA. MDC1 was not required for RAD51 recruitment to IR-induced foci, but it is unclear whether this is related to the different DSB chemistry (enzymatic versus IR) or to the localization of the DSB (rDNA versus unique sequence genome).

Strengths:

(1) The manuscript is well-written, and the experimental evidence is nicely presented.

(2) Multiple MDC1 knockout models are used to validate the results.

(3) Convincing back-complementation data clarify the relationship between MDC1 and RNF8.

Weaknesses:

(1) The recruitment of BRCA2 was not directly demonstrated. This caveat could be recognized, as IF for BRCA2 is challenging.

(2) PpoI also induces DSBs in the non-rDNA genome. These DSBs would be an ideal control to establish nucleolar specificity of the events described and clarify whether the difference between IR and PpoI is the chemical structure of the DSB or the location of the DSB.

We thank the reviewer for the positive and insightful evaluation of our work. We appreciate the recognition of the conceptual advance and the robustness of our experimental approaches. We have carefully considered the reviewer’s suggestions and have revised the manuscript to clarify interpretation where appropriate, particularly regarding BRCA2 recruitment and the specificity of I-PpoI-induced DNA damage. Where possible, we have also added new analyses to strengthen the conclusions.

Reviewer #2 (Recommendations for the authors):

(1) The claim that the BRCA1-PALB2-BRCA2 is recruited (abstract, end of results section, discussion page 15) should be qualified as BRCA2 recruitment was not directly demonstrated.

We thank the reviewer for this important point. We agree that BRCA2 recruitment was not directly demonstrated in our study, as reliable immunofluorescence detection of BRCA2 remains technically challenging.

We have therefore revised the manuscript throughout (Abstract, Results, and Discussion) to avoid overstatement and now refer more precisely to the recruitment of BRCA1, PALB2, and RAD51, rather than implying direct recruitment of a BRCA1–PALB2–BRCA2 complex.

We note that BRCA2 function is supported indirectly by the observed RAD51 loading, which depends on BRCA2 activity. However, we have clarified this point to ensure that our conclusions remain fully supported by the presented data.

(2) The temporal sequence established in Figure 1, 1hr BRCA1 and 2 hrs PALB2, argues against recruitment of a stable BRCA1-PALB2-(BRCA2) complex. This should be acknowledged.

We thank the reviewer for this insightful observation. We agree that the temporal separation between BRCA1 accumulation (1 h) and PALB2/RAD51 recruitment (2 h) argues against the recruitment of a pre-assembled, stable BRCA1–PALB2–BRCA2 complex.

We have revised the manuscript to reflect this interpretation and now describe the recruitment of HDR factors as a sequential process rather than as the assembly of a pre-formed complex. This is consistent with current models in which BRCA1 promotes subsequent PALB2 and BRCA2 recruitment, ultimately leading to RAD51 loading.

(3) The model predicts that MDC1-KO cells are proficient for transcriptional repression after nucleolar DSB induction. Has this been tested?

We did not specifically test this in the current work, but previous results published by our group revealed that siRNA-mediated depletion of MDC1 in human cells had a minimal effect on rDNA transcriptional inhibition after DNA damage (Larsen et al., 2024).

(4) The nuclear PpoI DSBs could be analyzed as a specificity control, and clarify whether the difference between IRIF and PpoI DSBs relates to the DSB chemistry or location.

We thank the reviewer for this important point. We agree that I-PpoI induces DNA breaks both within rDNA repeats and at additional genomic loci.

To address this, we have now analysed the formation of gH2AX-positive nucleolar caps and non-nucleolar gH2AX foci over time following I-PpoI expression (new Figure 1–figure supplement 2). We find that nucleolar caps form rapidly and are prominent at early time points, whereas gH2AX foci accumulate more gradually.

These results indicate that nucleolar caps and non-nucleolar DNA damage responses can be distinguished both spatially and temporally, and support the use of nucleolar caps as a specific readout for rDNA damage in our study.

In addition, we note that RAD51 recruitment to IR-induced foci is not affected by MDC1 loss, suggesting that the requirement for MDC1 in RAD51 loading is specific to nucleolar rDNA breaks rather than reflecting differences in DSB chemistry alone. We have clarified this point in the Discussion.

Additional points:

(5) Page 4 top: Shieldin.

Corrected.

(6) The general reader will be interested to learn about the connection of the Treacle function with Treacher Collins syndrome. Maybe a paragraph could be added to discuss this?

We thank the reviewer for this suggestion. We agree that the relationship between Treacle and Treacher Collins syndrome may be of interest to a broad readership. Since the developmental pathology of Treacher Collins syndrome is currently thought to arise primarily from impaired ribosome biogenesis and nucleolar dysfunction rather than defective nucleolar DNA damage signalling, we felt that an extensive discussion would be beyond the scope of the present study. We have, however, added a brief statement introducing Treacle as the product of the TCOF1 gene mutated in Treacher Collins syndrome and noting that whether its DNA damage response function contributes to disease pathology remains an open question.

(7) Figure 7: A short explanation could be added as to why hypoxia conditions were chosen for the p53-deficient cell lines.

We thank the reviewer for pointing this out. We have added a brief explanation in the figure legend to clarify that hypoxia conditions were used to stabilise replication stress and enhance detection of DNA repair intermediates in p53-deficient cells.

(8) A short statement on whether the repair of nuclear DBS is affected by Treacle could be added.

We thank the reviewer for this interesting point. While our study focuses on nucleolar DNA damage, we did not observe evidence that Treacle is required for the repair of non-nucleolar DSBs. We have added a brief statement in the Discussion to clarify that Treacle appears to function specifically in the nucleolar DNA damage response.

eLife Assessment

This study offers valuable insights into the role of post-translational modifiers, specifically SUMO2ylation at K81 in p66Shc, and its impact on endothelial function through reactive oxygen species. A series of compelling experiments demonstrated that lysine 81 of p66Shc is the site of SUMO2 conjugation, which is crucial for mitochondrial localization and essential for S36 phosphorylation, leading to specific pathological effects. The combination of cell overexpression and animal studies provides solid data supporting this mechanistic link.

Reviewer #2 (Public review):

Summary:

The manuscript titled "p66Shc Mediates SUMO2-induced Endothelial Dysfunction" by Kumar et al. builds upon established literature demonstrating that both p66Shc and SUMOylation are essential players in nitric oxide (NO)-mediated endothelial vascular homeostasis and development (PMID: 10580504, 28760777, and 35187108).

In this study, the authors uncover a novel mechanism showing how the SUMO2ylation of p66Shc drives reactive oxygen species (ROS) production in endothelial cells. Specifically, they identify Lysine 81 (K81) as the critical residue on p66Shc conjugated to SUMO2, proving it is essential for the protein's mitochondrial localization.

The authors convincingly demonstrate that:

p66Shc is actively SUMO2ylated at the K81 site in cellular models.

Phosphorylation at Serine 36 (S36) is significantly reduced upon the loss of this critical SUMOylation site.

Conclusion:

Overall, this study provides strong evidence for a novel regulatory axis in endothelial cells. It successfully opens the door for further dissection of the complex mechanistic crosstalk between three key post-translational modifications on p66Shc: S36 phosphorylation, K81 SUMO2ylation, and acetylation.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors describe a role of sumoylation at K81 in p66Shc which affects endothelial dysfunction. This explores a new mechanism for understanding the role of PTMs in cellular processes.

Strengths:

The experiments are well planned and the results are well represented.

Vascular tonality experiments were carried out nicely, given the amount of time and effort one needs to put in to get clean results from these experiments.

Weaknesses:

(1) The production of ROS has been measured in a very superficial way.

The term "ROS" confers a plethora of chemical species which exerts different physiological effects on different cells and situations.

Mitochondria through one of the source, but not the only source of ROS production. Only measuring ROS with mitosox do not reflect the cellular condition of ROS in a specific condition. I would suggest authors consider doing IF of oxidative stress specific markers, carbonyl group and also, maybe, Amplex red for determining average oxidative stress and ros production in the cells.

As suggested, we employed an additional ROS-sensitive probe, H₂DCFDA, which revealed an overall increase in intracellular ROS levels upon SUMO2 overexpression; this effect was reversed by knockdown of p66Shc. In addition, we performed the Amplex Red assay on conditioned media to assess extracellular ROS release. SUMO2 overexpression did not alter ROS levels detected in the media, whereas a paradoxical increase was observed following p66Shc knockdown.

Author response image 1.

Amplex Red assay performed in HUVECs with and without knockdown of p66Shc expressing SUMO2 (Ad-SUMO2) or a control virus (Ad-LacZ).

Amplex Red predominantly detects hydrogen peroxide (H₂O₂), which may originate from NADPH oxidases or be generated through the dismutation of superoxide. In contrast to superoxide, H₂O₂ is relatively stable, membrane-permeable, and well recognized as a second messenger in endothelial signaling, where it modulates kinase and phosphatase activity and supports physiological vascular functions. Thus, the elevated H₂O₂ detected following p66Shc knockdown may represent a signaling-competent redox state rather than a pathological increase in oxidative stress. Moreover, the SUMO2-p66Shc-mediated increase in mitochondrial ROS may be efficiently buffered by cellular antioxidant defense mechanisms, thereby limiting detectable extracellular ROS release.

(2) 8-OHG signal seems very confusing in Figure 7E. 8-ohg is supposed to be mainly in the nucleus and to some extent in mitochondria. The signal is very diffused in the images. I would suggest a higher magnification and better resolution images for 8-ohg. Also, the VWF signal is pretty weak whereas it should be strong given the staining is in aorta. Authors should redo the experiments.

We have provided a better image for figure 7E. We repeated the staining with another antibody for vWF which showed stronger signal for vWF.

(3) PCA analysis is quite not clear. Why is there a convergence among the plots? Authors should explain. Also, I would suggest that the authors do the analysis done in Figure 8B again with R based packages. IPA, though being user-friendly, mostly does not yield meaningful results and the statistics carried out is not accurate. Authors should redo the analysis in R or Python whichever is suitable for them.

We thank the reviewer for their valuable feedback and insightful suggestions.

Regarding the PCA analysis, the observed convergence among the data points reflects the underlying biological similarity between the samples within each group. Given the relatively low abundance and limited number of quantified peptides in our dataset, the variance captured by PCA is modest, leading to partial overlap between groups. This convergence is therefore likely due to shared biological characteristics and inherent sample variability, rather than technical issues.

In response to the reviewer’s suggestion on pathway analysis, we have re-performed the analysis using R-based approaches. Specifically, we utilized established pipelines PROGENy-based signaling pathway activity inference. These methods provide statistically robust and reproducible results. The updated analyses and corresponding figures have been included in the revised manuscript, replacing the previous IPA-based results (Fig. 8). We believe these additions strengthen the interpretation of signaling pathway alterations in our dataset.

(4) The MS analysis part seems pretty vague in methods. Please rewrite.

We have revised the methodology for MS.

Reviewer #2 (Public review):

Summary:

The article builds on the earlier work that both p66Shc and SUMOylation are essential nitric oxide (NO) based development of endothelial vasculature (PMID: 10580504; 28760777 and 35187108). The current manuscript brings forward a finding of how SUMO2ylation of p66Shc mediated ROS production which is essential for endothelial cells. They further identify that lysine 81 of p66Shc is the residue which is conjugated to SUMO2 and is crucial for mitochondrial localization. They further show that K81 SUMO2ylation is essential for S36 phosphorylation.

Strengths:

Convincingly shows that p66Shc is SUMO2ylated on lysine 81 in cells and also shows that the phosphorylation (serine 36) reduces upon loss of this critical SUMOylation site.

Weaknesses:

All the experiments performed here are in overexpression background therefore, it would be crucial to show that p66Shc is SUMO2ylated at physiological levels.

As detecting endogenous SUMO2-p66Shc is technically challenging considering the almost 92% homology between SUMO2 and SUMO3 and the absence of a specific antibody to detect p66Shc, we generated a custom-made antibody which can detect SUMO2-p66Shc (YenZym, CA). Using this antibody, we performed immunoprecipitation which showed endogenous SUMO2-p66Shc at a molecular weight higher than p66Shc suggesting the SUMO2 modification of p66Shc at physiological level.

Reviewer #3 (Public review):

Summary:

The authors set out to determine how SUMO2 impairs endothelial function through direct modification of the protein p66Shc. p66Shc is known to promote reactive oxygen species production, and here the authors demonstrate that SUMO2 modifies p66Shc at lysine-81, resulting in increased phosphorylation, mitochondrial translocation. These are prosed to mediate the detrimental effects of SUMO2 in a mouse model of hyperlipidemia.

Strengths:

A major strength of this work is the multi-pronged approach combining biochemical assays, proteomic analyses, and a genetically modified mouse model expressing a SUMOylation resistant mutant of p66Shc. These experiments comprehensively illustrate that lysine-81 SUMOylation of p66Shc is necessary for the observed endothelial dysfunction in hyperlipidemic conditions.

Weaknesses:

One notable weakness is that the link between the observed cellular changes and the ultimate in vivo phenotype remains only partially explored. While the authors successfully show that p66ShcK81R knockin mice are protected from endothelial dysfunction in a hyperlipidemic context, additional experiments characterizing the broader tissue-specific roles, or examining further endothelial assays in vivo, would strengthen the mechanistic conclusions. It would also be beneficial to see more direct evaluations of p66Shc subcellular localization in the protective knockin mice to complement the proteomic findings.

We agree with the reviewer’s suggestion. However, due to limited resources, we could not pursue additional studies.

Despite these gaps, the data broadly support the authors' main conclusions. The authors lay out a plausible mechanistic pathway for how hyperlipidemia and increased global SUMOylation can converge on the oxidative stress pathway to provoke vascular dysfunction.

The likely impact of this work on the field is noteworthy. Beyond clarifying how a single post-translational modification event can influence the pathophysiology of endothelial cells, the study provides a model for investigating broader roles of SUMO2 in other cardiovascular conditions and highlights the importance of identifying additional SUMOylation sites and their downstream impact.

In conclusion, by demonstrating the direct SUMOylation of p66Shc at lysine-81 and linking that modification to endothelial dysfunction in a hyperlipidemic mouse model, this paper offers valuable insights into how broadly acting post-translational modifiers can evoke specific pathological effects.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Please rearrange the figures. It is very hard to follow.

We rearranged some of the figures for a better presentation.

Reviewer #2 (Recommendations for the authors):

**Please note that I am not an expert of mouse work therefore I will not be able to comment on the mouse work (Figure 7).

The following suggested changes major concerns will strengthen the work, and the minor concerns will increase the paper's accessibility to eLife's broad scientific community.

Major concerns.

(1) All the work done here is based on overexpression studies, therefore it will be very useful to show that p66Shc gets SUMO2ylated under physiological conditions using SUMO-Trap beads or using tandem SUMO-Interacting Motifs (as shown in Silva-Ferrada et al., 2013: https://doi.org/10.1038/srep01690).

We have demonstrated endogenous SUMO2 conjugation of p66Shc under physiological conditions by immunoprecipitation using a custom-generated antibody specific for SUMO2-p66Shc. This approach allows detection of SUMO2ylated p66Shc without reliance on overexpression systems, thereby confirming that SUMO2 modification of p66Shc occurs endogenously.

(2) The authors claim that K81 is the only site of SUMO2ylation and based on the HUVEC experiments (Fig 3C, D) it appears that p66Shc K81R mutant still gets SUMO2ylated indicating that there are other residues which can get SUMO2ylated. Do you see the other sites getting SUMO2ylated in your mass spec data?

Our mass spectrometry analysis did not identify SUMO2 modification on lysine residues other than K81. However, we acknowledge that SUMOylation detected in vitro on recombinant protein may differ from SUMOylation occurring in a cellular context, where protein conformation, interacting partners, and local enzyme availability can influence modification patterns. These differences may account for the appearance of multiple SUMOylated p66Shc species in cell lysates despite the absence of additional SUMOylation sites in the mass spectrometry dataset. Our emphasis on K81 is based on its localization within the CH2 domain of p66Shc, a region unique to the p66 isoform. Previous studies have demonstrated that post-translational modifications within the CH2 domain, such as phosphorylation, are critical for activation of the oxidative and pro-apoptotic functions of p66Shc. Accordingly, we focused our mechanistic analyses on SUMO2ylation at K81. Nevertheless, we do not exclude the possibility that additional lysine residues within the PTB or CH1 domains of p66Shc may also undergo SUMOylation in cells. Importantly, our functional data support the conclusion that SUMO2ylation at K81 is a key regulatory modification driving the oxidative activity of p66Shc.

(3) In Figure 4 the authors claim that SUMO2ylation at K81 is a prerequisite for S36 phosphorylation, however the phosphorylation changes are marginal (Figure 4 A, E) and why do they not see the higher molecular weight bands in the western blots.

We appreciate the critique and agree with the reviewer that our data do not provide direct evidence that K81 SUMO2ylation and S36 phosphorylation occur simultaneously on the same p66Shc molecule. Rather, our findings suggest that SUMO2ylation at K81 may facilitate or enhance S36 phosphorylation, but is not an absolute requirement for this modification. Accordingly, we have revised the wording in the main text to avoid implying a strict prerequisite relationship and to more accurately reflect the magnitude of the observed changes in S36 phosphorylation.

(4) The authors further claim that the SUMO2ylation at K81 effects S36 phosphorylation which has consequences for mitochondrial localization. However, K81R still translocate to mitochondria which is indicative of SUMO2ylation is important but not necessary (Fig 5A, C).

We agree with the reviewer’s observation and have revised the main text accordingly, as described in our prior response, to clarify that K81 SUMO2ylation facilitates, but is not essential for, S36 phosphorylation and mitochondrial translocation.

(5) To know if the K81 SUMO2ylation had any effect on phosphorylation it would be interesting to see how the phosphomimic (S36D) mutant along with/without K18R will behave in mitochondrial localization experiments.

We agree that examining the double mutant (S36D/K81R) would provide valuable mechanistic insight into the relationship between K81 SUMO2ylation and S36 phosphorylation in regulating mitochondrial localization. Although we are currently unable to perform these experiments due to constraints in manpower and resources, we have acknowledged this as a limitation in the Discussion and plan to pursue these studies in future work to further validate the mechanism.

Minor Concerns

(a) In Figure 1A the authors claim that SUMO2ylation of p66Shc promotes ROS production however they do not include any well-known ROS induced proteins in the western blots such as KEAP1 or NRF2 or any other marker.

We agree that NRF2 is a well-established transcriptional regulator of antioxidant responses. However, the primary aim of Figure 1A was to directly examine the effect of SUMO2ylation on p66Shc-mediated ROS production. While NRF2 and other ROS-responsive proteins reflect downstream adaptive responses, they do not provide a direct measure of ROS generation. Therefore, we focused on measuring SUMO2-induced changes in cellular ROS levels. To further strengthen this conclusion, we have performed additional complementary ROS assays, which are now included in the revised manuscript.

(b) In Figure 2D, the concentration of Anacardic acid used is low and therefore the SUMO2ylation is not completely inhibited. It would be advisable if the authors go to concentration of complete inhibition.

We acknowledge that some residual SUMO2ylation is visible in the immunoblots following anacardic acid treatment. However, the assay demonstrates a clear, dose-dependent reduction in SUMO2ylation levels, which is sufficient to interpret the effect of SUMO2 inhibition on p66Shc function. Increasing the concentration further could introduce off-target effects, and the current assay provides a reliable and physiologically relevant readout.

(c) The figure panels need uniform fonts and also the size/resolution of the western blots needs to be improved

We thank the reviewer for this suggestion. All figures have been updated to ensure uniform fonts, and the western blot images have been improved for size and resolution in the revised manuscript.

(d) Supplemental figures are very low resolution.

High-resolution images have been provided.

(e) Please introduce abbreviations before using them (like LDLr and ND).

The abbreviations have been explained.

(f) Figure 6A seems like data that belongs in the SI rather than the main text.

We kept it in main figure as the knock-in mouse was generated for this study.

(a) Figure 7B needs a WT HFD control.

It is a valid suggestion. However, it is known in the literature (we have prior experience as well) that wild-type mice do not exhibit a drastic increase in serum lipid level with high-fat diet feeding.

(b) The differences in Figure 7E are not profound and immediately obvious. Is there a better way to show the data, potentially with quantification?

We agree, the difference is not that huge, we have provided the qualification as Fig. 7F.

(h) Figure panels 6E-H could use some labels to differentiate the data from WT vs p66ShcK81R mutant mice within the figure panel.

We mentioned that on the top and have inserted a vertical line to separate the groups.

(i) Lines 165-168, 182-185, 214-218 and 282-286: These sentences can be rewritten for more clarity.

We have modified these sentence for clarity.

(j) Line 512 should read, "Two-way ANOVA, ***P<0.001."

Thank you, we have corrected the mistake.

eLife Assessment

This valuable study introduces an innovative experimental design to address a crucial and timely issue in microbial ecology: the potential bias in soil microbial community analyses caused by extracellular DNA degradation. The work contributes to the field by providing convincing evidence that variable extracellular DNA degradation rates impact microbial ecology studies. This research will appeal to microbial ecologists and researchers interested in using molecular techniques to evaluate microbial community structure.

Reviewer #1 (Public review):

Summary:

This manuscript investigates the degradation dynamics of extracellular DNA in soils and its impact on estimates of microbial abundance and diversity. By combining a broad geographic sampling design with a primer-labeling strategy, qPCR quantification, amplicon sequencing, and PMA treatment, the authors aim to disentangle total versus intracellular DNA signals and explore sequence-specific degradation patterns. The topic is relevant, particularly given the increasing awareness of relic DNA as a confounding factor in microbial ecology. The experimental design is ambitious and potentially impactful. However, several conceptual inconsistencies, methodological ambiguities, and statistical limitations currently weaken the robustness of the conclusions. These issues need to be addressed.

Strengths:

The manuscript addresses a timely and important question in microbial ecology, particularly given the growing recognition that relic DNA can bias interpretations of community composition derived from amplicon sequencing. The study is ambitious in scope, incorporating a broad geographic sampling design across multiple soil types, which enhances the generalizability of the findings. The use of a controlled microcosm experiment combined with a primer-labeling strategy to track extracellular DNA dynamics is conceptually innovative and provides a structured framework to investigate degradation processes.

In addition, the integration of multiple approaches, including qPCR for absolute quantification, high-throughput sequencing for community profiling, and PMA treatment to differentiate extracellular from intracellular DNA, represents a comprehensive attempt to disentangle complex sources of bias in soil microbiome analyses. The effort to link degradation dynamics with environmental variables and to explore sequence-level patterns further demonstrates the authors' intent to move beyond descriptive analyses toward a mechanistic understanding.

Weaknesses:

Several conceptual and methodological issues currently limit confidence in the study's conclusions. Key terms such as "sequence-specific degradation" are not clearly defined or supported by a mechanistic or structural hypothesis, making it difficult to interpret the biological meaning of the results. In addition, the bioinformatic workflow presents inconsistencies, particularly the use of ASVs followed by clustering at 97% similarity, which undermines the resolution required to support sequence-level inferences. Statistical analyses are also insufficiently described, including unclear definitions of "T values," a lack of detail on pairing structure, and no indication of multiple testing correction.

Furthermore, important methodological details are missing or unclear, including primer design (e.g., GAPDH tag vs ACTF), Illumina library preparation (e.g., adapter and indexing strategy), and validation of PMA treatment efficiency. The interpretation of PMA-treated samples as representing "living communities" is likely overstated, given the known limitations of the method in soil systems. Finally, typographical errors, inconsistent terminology, and unclear phrasing throughout the manuscript reduce readability and further complicate interpretation.

Reviewer #2 (Public review):

Summary:

This manuscript describes the results of an interesting study examining the rate of degradation of extracellular DNA in soil ecosystems using a clever experimental approach. 16S ribosomal RNA genes were amplified from soil samples, and then purified PCR amplicons, containing a 5' linker sequence on the forward primer, were introduced to soils and monitored over time using real-time quantitative PCR and NGS amplicon sequencing. The study was able to measure rates of overall extracellular DNA degradation, but also sequence-specific degradation rates. I like the idea and execution of the study, and the results are interesting. The manuscript needs some help to improve the overall readability. Please see general and editorial comments below.

Strengths:

Innovative experimental design that is well deployed across a large number of soil types, revealing interesting variability in extracellular DNA degradation.

Weaknesses:

(1) The manuscript needs another review to improve the readability of the document.

(2) The authors have used 16S genes to look at sequence-specific degradation. But 16S rRNA genes are actually pretty well conserved, and there isn't as much genetic variation across this gene among organisms as there is for other genes. It might be more relevant to look at metagenomic DNA degradation from high AT, high GC organisms, etc. This would be more generalizable than 16S genes.

(3) Consideration of differential cell lysis during soil DNA extraction needs to be considered as well.

(4) It is not clear why the authors didn't put GAPDH linkers on the reverse primer as well. This would have given an easier amplicon to amplify (no degeneracies at all).

Author response:

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

We sincerely appreciate you and the reviewers for investing time and effort in evaluating our manuscript. After carefully reading the comments and suggestions, we found they are insightful, constructive, and critical for improving the quality of our work. Based on these valuable recommendations, we have substantially revised the manuscript as summarized below.

Abstract: Inappropriate or ambiguous statements have been revised to improve clarity.

Introduction: (1) The study purpose and hypotheses have been re-organized in a clearer and more concise way. (2) A mechanistic rationale for sequence-specific degradation has been provided and the use of PMA treatment has been explained. (3) The terminologies related to extracellular DNA and 16S rRNA gene amplicons have been clarified.

Materials and Methods: 1) More detailed description of the microcosm experiment has been added. 2) The design and rationale of GAPDH F-tagged primers and the use of fusion primers for Illumina library preparation have been clarified. 3) More details about PCR amplification, DNA purification, and pooling strategies have been added. 4) We have corrected and standardized primer naming throughout the manuscript; 5) More details about bioinformatic workflow have been added. 6) We have defined statistical parameters and multiple testing corrections. 7) All abbreviations have been defined and standardized.

Results: 1) The terminology for PMA-treated DNA has been revised and it has been clarified interpretation as “PMA-treated prokaryotic community” rather than “living community”. 2) The figures and legends have been updated for clarity, and the explicit explanation of “ASV I” and “ASV II” in pairwise comparisons have been added. 3) the figures (e.g., Figs. 2–5, S2–S8) have been reorganized to better reflect results; 4) Inappropriate statements or misleading interpretations have been removed.

Discussion: A detailed section on technical limitations have been added. The limiatons added mainly include: 1) PCR amplification bias and recommendations for spike-in standards or multi-primer approaches; 2) differential DNA extraction efficiency due to variable cell lysis; and 3) limitations of using 16S rRNA amplicons as proxies for natural extracellular DNA and the limitations of PMA treatment efficiency in soil matrices;

eLife Assessment

This valuable study introduces an innovative experimental design to address a crucial and timely issue in microbial ecology: the potential bias in soil microbial community analyses caused by extracellular DNA degradation. While the evidence showing variable degradation rates of extracellular DNA is convincing, additional conceptual, methodological, and statistical clarifications could reinforce the claims and the study's contribution to the field. This research will appeal to microbial ecologists and researchers interested in using molecular techniques to evaluate microbial community structure.

We sincerely appreciate the editors for the careful assessment of our work and for recognizing the value of addressing extracellular DNA degradation in soil microbial community analyses. We also greatly appreciate the reviewers’ constructive feedbacks concerning the need for additional conceptual, methodological, and statistical clarifications. We agree that further refinement in these areas will strengthen our claims and enhance the study’s contribution to the field. Based on these insightful suggestions, we have carefully revised the manuscript to provide clearer conceptual framework, more detailed methodological descriptions, and more rigorous statistical analyses. We believe these revisions have substantially improved the clarity and robustness of our work. More details about the revisions have been provided in the following responses.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript investigates the degradation dynamics of extracellular DNA in soils and its impact on estimates of microbial abundance and diversity. By combining a broad geographic sampling design with a primer-labeling strategy, qPCR quantification, amplicon sequencing, and PMA treatment, the authors aim to disentangle total versus intracellular DNA signals and explore sequence-specific degradation patterns. The topic is relevant, particularly given the increasing awareness of relic DNA as a confounding factor in microbial ecology. The experimental design is ambitious and potentially impactful. However, several conceptual inconsistencies, methodological ambiguities, and statistical limitations currently weaken the robustness of the conclusions. These issues need to be addressed.

We sincerely appreciate the reviewer for the constructive assessment of our work. We also appreciate the reviewer’s critical insights regarding the conceptual inconsistencies, methodological ambiguities, and statistical limitations that currently weaken the robustness of the conclusions. We agree with the reviewer that addressing these issues is essential to strengthen our work. Based on these valuable comments, we have carefully revised the manuscript to clarify the conceptual framework. Additionally, we have provided more detailed methodological descriptions, and enhance the statistical rigor of our analyses. We believe these revisions have substantially improved the clarity, consistency, and overall robustness of our conclusions.

Strengths:

The manuscript addresses a timely and important question in microbial ecology, particularly given the growing recognition that relic DNA can bias interpretations of community composition derived from amplicon sequencing. The study is ambitious in scope, incorporating a broad geographic sampling design across multiple soil types, which enhances the generalizability of the findings. The use of a controlled microcosm experiment combined with a primer-labeling strategy to track extracellular DNA dynamics is conceptually innovative and provides a structured framework to investigate degradation processes.

In addition, the integration of multiple approaches, including qPCR for absolute quantification, high-throughput sequencing for community profiling, and PMA treatment to differentiate extracellular from intracellular DNA, represents a comprehensive attempt to disentangle complex sources of bias in soil microbiome analyses. The effort to link degradation dynamics with environmental variables and to explore sequence-level patterns further demonstrates the authors' intent to move beyond descriptive analyses toward a mechanistic understanding.

We sincerely thank the reviewer for the positive and encouraging comments of our work.

Weaknesses:

Several conceptual and methodological issues currently limit confidence in the study's conclusions. Key terms such as "sequence-specific degradation" are not clearly defined or supported by a mechanistic or structural hypothesis, making it difficult to interpret the biological meaning of the results. In addition, the bioinformatic workflow presents inconsistencies, particularly the use of ASVs followed by clustering at 97% similarity, which undermines the resolution required to support sequence-level inferences. Statistical analyses are also insufficiently described, including unclear definitions of "T values," a lack of detail on pairing structure, and no indication of multiple testing correction.

Furthermore, important methodological details are missing or unclear, including primer design (e.g., GAPDH tag vs ACTF), Illumina library preparation (e.g., adapter and indexing strategy), and validation of PMA treatment efficiency. The interpretation of PMA-treated samples as representing "living communities" is likely overstated, given the known limitations of the method in soil systems. Finally, typographical errors, inconsistent terminology, and unclear phrasing throughout the manuscript reduce readability and further complicate interpretation.

We sincerely appreciate the reviewer’s thorough and critical evaluation of the manuscript’s weaknesses. The issues raised regarding conceptual clarity, bioinformatic consistency, statistical rigor, methodological transparency, and the interpretation of PMA treatment have been fully acknowledged. We also recognized that typographical errors, inconsistent terminologies, and unclear phrasing largely reduced readability. In response to these valuable comments, the manuscript has been carefully revised as follows. (1) The clearer definition of the term “sequence-specific degradation” has been provided. (2) The bioinformatic workflow was streamlined to ensure consistency. (3) The descriptions of statistical analyses were substantially expanded, including explicit definitions of “t values,” detailed clarification of the pairing structure, and the application of appropriate multiple testing corrections. (4) Missing details regarding primer design, Illumina library preparation, and PMA treatment validation have been added to the Method section. (5) Interpretations of PMA-treated samples have been revised to more accurately reflect methodological limitations in soil systems. (6) The manuscript has been thoroughly proofread to correct typographical errors, standardize terminology, and enhance overall clarity. These revisions are believed to substantially address the concerns raised and significantly strengthen the manuscript. A point-by-point response to the specific comments is provided below.

Reviewer #2 (Public review):

Summary:

This manuscript describes the results of an interesting study examining the rate of degradation of extracellular DNA in soil ecosystems using a clever experimental approach. 16S ribosomal RNA genes were amplified from soil samples, and then purified PCR amplicons, containing a 5' linker sequence on the forward primer, were introduced to soils and monitored over time using real-time quantitative PCR and NGS amplicon sequencing. The study was able to measure rates of overall extracellular DNA degradation, but also sequence-specific degradation rates. I like the idea and execution of the study, and the results are interesting. The manuscript needs some help to improve the overall readability. Please see general and editorial comments below.

We sincerely thank the reviewer for the positive and encouraging assessment of our study. We have carefully revised the manuscript to enhance clarity, streamline the presentation, and refine the language throughout. We believe these improvements have made the manuscript more readable and easier to follow. We are also grateful for the general and editorial comments provided, which have been addressed as outlined below.

Strengths:

Innovative experimental design that is well deployed across a large number of soil types, revealing interesting variability in extracellular DNA degradation.

We sincerely thank the reviewer for the positive and encouraging assessment of our work.

Weaknesses:

(1) The manuscript needs another review to improve the readability of the document.

We thank the reviewer for this helpful suggestion. We fully agree that improving readability is essential for effectively communicating our findings. Based on the comment, we have carefully revised the manuscript to enhance clarity and readability. We have streamlined sentence structures, standardized terminology, corrected typographical errors, and improved the logical organization of the text. We believe these revisions have substantially improved the overall readability of the manuscript.

(2) The authors have used 16S genes to look at sequence-specific degradation. But 16S rRNA genes are actually pretty well conserved, and there isn't as much genetic variation across this gene among organisms as there is for other genes. It might be more relevant to look at metagenomic DNA degradation from high AT, high GC organisms, etc. This would be more generalizable than 16S genes.

We thank the reviewer for this insightful comment. We agree with the reviewer that 16S rRNA genes are relatively conserved compared to functional genes or whole metagenomic DNA, and that studying degradation of more variable sequences (e.g., high‑AT, high‑GC regions, or metagenomic DNA) would provide greater generalizability. However, we would like to clarify the rationale for using 16S rRNA gene amplicons in the present study. First, the 16S rRNA gene remains the most widely used phylogenetic marker in soil microbial ecology (Knight et al., 2018). Demonstrating sequence‑specific degradation with this well‑established marker directly informs a large body of existing research that relies on 16S RNA gene‑based community analyses. Second, despite its conserved nature, the targeted fragment in this study is belong to the highly varied region (V4) of 16S rRNA gene. Accordingly, we indeed observed significant sequence‑specific variation in degradation rates among different 16S rRNA gene amplicon sequence variants (ASVs) (Fig. 2c, 3a). This indicates that even within a conserved marker gene, sequence‑dependent degradation biases exist and can affect diversity estimates. Third, our study was designed as a proof‑of‑concept to establish a methodological framework for quantifying both overall and sequence‑specific degradation rates. Using a single, well‑characterized marker allowed us to develop and validate the primer‑labeling and qPCR/sequencing workflow without the additional complexity of metagenomic DNA (e.g., variable fragment lengths and complex mineral associations). In the revised manuscript, we have added the following sentence to the Discussion section to address the concerns from the reviewer.

L294-305

“Despite the high-resolution insights afforded by our methodology, several limitations should be considered. First, utilizing PCR-amplified 16S rRNA gene fragments as proxies oversimplifies the structural and sequence complexity of natural soil eDNA pools. In natural environments, eDNA varies widely in fragment length and conformation, and exhibits complex interactions with mineral surfaces, all of which fundamentally affect degradation dynamics (Levy-Booth et al., 2007; McKinney and Dungan, 2020). Additionally, the highly conserved nature of the 16S rRNA gene means that the nucleotide variability explored here (e.g., GC content gradients) does not fully capture the genomic heterogeneity of entire metagenomes (Knight et al., 2018). Consequently, our reported degradation rates indicate the decay potential of highly accessible linear eDNA rather than a universal rate for all soil DNA fractions. Future studies incorporating diverse metagenomic DNA, especially those with extreme AT or GC contents, are essential for building a more generalizable predictive framework for eDNA persistence (Morrissey et al., 2015)”

(3) Consideration of differential cell lysis during soil DNA extraction needs to be considered as well.

We thank the reviewer for raising this important technical consideration. We agree that differential cell lysis during soil DNA extraction is a well‑recognized source of bias in microbial community analysis. Different microbial taxa (e.g., Gram‑positive vs. Gram‑negative bacteria, spores, or fungi) vary in their cell wall structure and susceptibility to lysis, which can lead to under‑representation of certain groups and over‑representation of others in the extracted DNA. This bias affects both total DNA extracts and PMA‑treated fractions, potentially influencing our estimates of the relative contributions of intact‑cell derived DNA versus extracellular DNA. However, currently, eliminating these biases are still challenging, and thus we have added the following sentence to the Discussion to address this concern.

L305-311

“Second, methodological biases inherent in quantifying the intracellular community must be acknowledged (Du et al., 2025). Although PMA treatment is widely used to exclude eDNA, its efficiency in complex soil matrices can be compromised by limited light penetration in turbid suspensions and competitive adsorption to soil particles (Nocker et al., 2007; Carini et al., 2016; Heise et al., 2016). Compounding this issue, downstream DNA recovery is subject to differential cell lysis, as taxa with robust cell walls (e.g., Gram-positive bacteria) may resist extraction (Frostegård et al., 1999; Albertsen et al., 2015).”

(4) It is not clear why the authors didn't put GAPDH linkers on the reverse primer as well. This would have given an easier amplicon to amplify (no degeneracies at all).

The decision to place the GAPDH linker only on the forward primer (515F) was intentional to balance the need for tracking exogenous extracellular DNA with amplification efficiency, sequencing quality, and cost-effectiveness. Adding linkers to both primers would increase the total amplicon length, potentially reducing amplification efficiency, especially in complex soil samples with degraded or low-quality DNA. More importantly, the reverse primer used in our study is a degenerate primer designed to target the 16S rRNA gene across diverse bacterial taxa, and extending it with an additional GAPDH linker could introduce further complexity, decrease amplification efficiency, and increase primer-dimer formation. Additionally, single-end labeling allows the usage of standard 16S rRNA reverse primers with existing barcodes, whereas dual-end labeling would require synthesis of new barcode-labeled primers, increasing both cost and time. Our preliminary experiments confirmed that single-end labeling produced reproducible amplification curves (~85% efficiency) and high-quality sequencing reads, which were sufficient for quantifying degradation rates. We have added a clarification in the Methods section to explain this rationale.

L365-371

“The GAPDH was incorporated only into the forward primer for several reasons. Methodologically, adding a long linker to the degenerate reverse primer (806R) could reduce amplification efficiency or introduce bias. Economically, single-end labeling allowed us to use the standard reverse primer already carrying sample-specific barcodes, avoiding the costly synthesis of a full set of dual-labeled barcoded primers. This design minimized the risk of secondary structure and primer-dimer artifacts while maintaining sufficient specificity and compatibility with downstream qPCR and sequencing.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Major Comments

(1) Inconsistency between ASV inference and 97% sequence recruitment

The bioinformatic pipeline presents a major conceptual inconsistency. ASVs are inferred using UNOISE3, but reads are subsequently mapped to ASVs at a 97% similarity threshold, effectively reintroducing OTU-level clustering. Given that the manuscript's central claim is sequence-specific degradation, this step undermines the single-nucleotide resolution that ASVs provide and may obscure biologically meaningful differences. The authors should either reanalyze the data using a consistent ASV framework (exact matching), or explicitly treat the analysis as OTU-like and moderate claims of sequence specificity.

We appreciate the reviewer's critical evaluation of our bioinformatic pipeline. We also apologized for our unclear statements in our original manuscript. We understand the concern that mapping reads to ASVs at 97% similarity might appear to reintroduce OTU-level clustering. Actually, we used the default pipeline provided by the authors of USEARCH with otutab command to generate the ASV table.

Following the logic and recommendations of the USEARCH/UNOISE3 developer (Robert Edgar), this approach is a standard procedure for robust noise management rather than a conceptual inconsistency. First, in our pipeline, ASVs (ZOTUs) are first inferred using the UNOISE3 algorithm, which effectively identifies “true” biological sequences at single-nucleotide resolution. Secondly, according to the USEARCH manual, while the ASVs themselves represent exact biological sequences, the raw reads inevitably contain stochastic sequencing errors. Using “exact matching” for recruitment would discard a significant portion of the data that originates from a specific ASV but carries minor random errors. Mapping at 97% identity is the recommended method to recruit these noisy reads back to their correct biological origin (the ASV centroid). Meanwhile, during the recruitment process, reads are not randomly assigned to any ASV within the 97% identity radius. Instead, the algorithm follows a “highest similarity first” principle. For instance, if a specific read exhibits 98% similarity to ASV1 and 97% similarity to ASV2, it is strictly assigned to ASV1. A read is only discarded if its highest similarity to any ASV falls below the 97% threshold. Unlike traditional OTU clustering (where sequences are clustered together based on similarity from the start), our approach maintains the ASV as a fixed biological reference. The quantification of degradation rates is performed on these high-resolution centroids. Thus, our claims regarding sequence-specific degradation remain valid, as the underlying biological variation is defined by the ASVs. To avoid any possible confusion, we have rewritten the relevant paragraph in the Methods section (subsection 4.6) as follows.

L446-457

“ASVs were generated using the UNOISE3 non‑clustering denoising algorithm (Edgar, 2016), which infers 100% exact sequence variants by distinguishing biological sequences from PCR/sequencing errors. ASVs with total sequence counts fewer than 9 across all samples were removed to reduce noise. To quantify the abundance of each ASV, an ASV table was generated by mapping the quality‑filtered raw reads back to the ASV set using the otutab command. A 97% similarity threshold was applied for this recruitment to accommodate stochastic sequencing noise while maintaining biological resolution. Crucially, the mapping followed a best-hit priority rule, where each read was assigned to the ASV with the highest per cent identity within the 97% radius. This approach ensures that reads derived from the same biological template are accurately counted toward their respective ASV, preventing the underestimation of abundances that would occur with exact matching while strictly preserving the single-nucleotide resolution of the ASV framework.”

(2) Undefined "ASV I" and "ASV II" groups

The manuscript refers to "ASV I" and "ASV II" groups in pairwise comparisons of degradation rates (e.g., Fig. 3), but these groups are not defined anywhere in the text.

It is unclear whether these represent: predefined biological categories, arbitrary pairwise ASV comparisons, or groupings based on taxonomy, abundance, or degradation rate.

In addition, the pairing structure underlying these comparisons is not described. While a paired t-test is mentioned, it is unclear how ASVs were paired (e.g., within sites, across samples, or across time points).

The current terminology ("groups") is potentially misleading and suggests biological structure where none may exist. The authors should explicitly define these terms, clarify the pairing scheme, and revise terminology if these are simply pairwise comparisons.

We thank the reviewer for this keen observation. We completely agree that the terms "ASV I" and "ASV II" were poorly defined and potentially misleading.

We would like to clarify that "ASV I" and "ASV II" were not intended to represent predefined biological categories (such as groupings based on taxonomy, abundance, or degradation rates). Instead, they were merely used as a labeling convention to indicate the directionality of pairwise comparisons within the heatmap matrix. Specifically, "ASV I" referred to the ASVs represented in the rows, while "ASV II" referred to those in the columns. In the original Fig. 3, blue indicated that the degradation rate of the row ASV was significantly lower than that of the column ASV, and red indicated the opposite. To avoid any confusion, we have removed the “ASV I/II” terminology throughout the manuscript and figures, replacing them with “Row ASVs” and “Column ASVs”. To address this issue, we have revised the Figure 3 legend to include a more explicit explanation:

L820-824

“In the heatmap, each cell represents a pairwise comparison between two ASVs. Blue indicates that the degradation rate of the ASVs listed in the row (row ASVs) is significantly lower than that of the ASVs listed in the column (column ASVs); red indicates that the row ASVs has a significantly higher degradation rate than the column ASV. A positive t value indicates that the row ASVs degrades significantly faster than the column ASVs; a negative t value indicates the opposite.”

We thank the reviewer for raising the important issue regarding the definition of “t values” in our statistical analysis. We apologize for the lack of clarity in the original manuscript. To clarify, the T values presented in Figure 3a represent the test statistics (t-values) from paired t-tests comparing the degradation rate constants of two ASVs across the 30 study sites. The T-value was obtained from a paired t-test between two ASVs across the same samples. The t-value indicates the magnitude and direction of the difference between the two ASVs’ degradation rates relative to the variability across sites. A positive t-value (colored red in the heatmap) indicates the row ASVs degrades significantly faster than the column ASVs; a negative t-value (colored blue) indicates the opposite.

L544-548

“As for the analysis, we performed paired t‑tests across all the study sites. Thus, the degradation rates were essentially compared within each site, with both values originating from a same soil sample under identical incubation conditions. A positive t value indicates that the first ASV has a significantly higher degradation rate than the second one, and a negative t value indicates the opposite. The p values were adjusted for multiple comparisons using the FDR method.”

(3) Lack of definition and justification of "T values"

The manuscript reports "T values" for comparisons between ASVs but does not clearly define how these values are calculated. Although a paired t-test is mentioned, it remains unclear how the pairing was constructed, whether assumptions (normality, independence) were evaluated, and whether corrections for multiple comparisons were applied. Given the large number of ASVs, failure to control for multiple testing could inflate false positives. More broadly, the use of a simple paired t-test may not be appropriate given the hierarchical and compositional structure of the data.

We sincerely thank the reviewer for pointing out the need to clarify the definition and justification of the t-values presented in our manuscript. Each t-value represents the test statistic from a paired t-test comparing the degradation rates of two ASVs across the same set of samples. The paired t-test assumes that the differences between paired observations are approximately normally distributed and that the pairs are independent across columns. We have evaluated the normality of differences using standard diagnostic plots and verified that the assumption is reasonably satisfied given the sample size. We performed a correction for multiple comparisons using the False Discovery Rate (FDR) procedure to control for potential false positives. We have revised the Methods section to clearly define t-values.

(4) Conceptual validity of "sequence-specific degradation"

The manuscript repeatedly refers to "sequence-specific degradation" of extracellular DNA; however, this concept is not clearly defined nor supported by a biological or structural hypothesis. It is unclear what "sequence-specific" refers to (e.g., nucleotide composition, GC content, secondary structure, taxonomic identity), whether differences are expected in conserved versus variable regions of the 16S rRNA gene, or what mechanistic basis would explain differential degradation among sequences. Given that the analysis is based on short 16S V4 amplicons, and no structural or biochemical framework is provided, it is difficult to interpret whether the observed differences truly reflect intrinsic sequence-dependent degradation or are instead driven by methodological or statistical artifacts (e.g., abundance effects, amplification bias).

I believe the authors should explicitly define what is meant by "sequence-specific degradation," provide a biologically grounded hypothesis (e.g., structural accessibility, GC content, stem-loop stability), and align their interpretation with the resolution and limitations of the data.

We thank the reviewer for this critical conceptual comment. We apologize that “sequence‑specific degradation” was not clearly defined and lacked a biological or structural hypothesis. To improve the logical flow of the manuscript, we have restructured the Introduction by moving the three central hypotheses immediately following the discussion of the biochemical mechanisms underlying sequence-specific degradation. This adjustment ensures that the hypotheses are directly grounded in the theoretical framework (e.g., GC content, thermodynamic stability, and secondary structures) presented in the paragraph.

We now define “sequence‑specific degradation” as statistically significant differences in first‑order degradation rate constants among distinct ASVs, mainly arising from intrinsic DNA properties (base composition, secondary structure, and restriction sites) or differential mineral adsorption.

L99-104

“Consequently, we proposed three central hypotheses. (1) The degradation rates of eDNA amplicon fragments were expected to be highly sequence‑specific. (2) The rates and patterns of eDNA fragments degradation would be influenced by environmental factors such as temperature and moisture content. (3) The sequence‑specific degradation of extracellular 16S rRNA gene amplicon fragments would significantly influence estimates of soil prokaryotic abundance and diversity.”

We also expanded the mechanistic discussion to include GC content and secondary structure.

L230-235

“We also examined whether GC content could explain the observed sequence‑specific patterns, but no significant correlation was found (Fig. S4), suggesting that simple base composition is not the primary driver in this study. However, this does not exclude the possibility that higher‑order structural features (e.g., hairpin loops) or sequence‑specific nuclease recognition motifs contribute to differential degradation (Wang et al., 2007). This should be tested in future studies using synthetic DNA constructs with controlled structural elements.”

We acknowledge that inferring sequence‑specific degradation from combined relative abundance and qPCR data is subject to potential methodological artifacts, including compositional effects, PCR amplification bias, and abundance‑dependent detection limits. However, we have taken several stringent steps to minimize these concerns. Specifically, we restricted our analysis to ASVs that were present in more than 90% of the study sites and for which the degradation curve fits yielded R² > 0.5, ensuring that only robustly detected and reliably modeled sequences were retained. Because our analysis tracks the ratio of each ASV across a time series, any sequence-specific PCR amplification bias remains constant for that particular sequence. By focusing on the rate of change rather than absolute read counts, such systematic biases are mathematically canceled out during the calculation of degradation kinetics.

(5) Conceptual ambiguity in "GAPDH F-labeled 16S rRNA genes"

The manuscript repeatedly refers to "GAPDH F-labeled 16S rRNA genes," which is confusing and may be misinterpreted as targeting GAPDH rather than 16S. It should be clearly stated that GAPDH refers to glyceraldehyde-3-phosphate dehydrogenase, and a GAPDH-derived sequence is used as a synthetic tag appended to a 16S primer. Additionally, the divergence of this tag from microbial sequences should be justified to ensure specificity. There is also an inconsistency in primer naming (e.g., "GAPDH F" vs "ACTF" in the figures), which should be corrected.

We sincerely thank the reviewer for this important comment. We agree that the phrase “GAPDH F‑labeled 16S rRNA genes” could be confusing, as it may be misinterpreted as targeting the GAPDH gene rather than the 16S rRNA gene. We have revised the manuscript to avoid this ambiguity and to provide clear justification for the use of the GAPDH tag. GAPDH (glyceraldehyde‑3‑phosphate dehydrogenase) is a human housekeeping gene. Its forward primer sequence (GAPDH F: 5′‑CAT TGG CAA TGA GCG GTT C‑3′) was used as a synthetic tag appended to the 16S primer because (i) no homologous sequences exist in soil DNA (confirmed by PCR), and (ii) its melting temperature is compatible with the reverse primer. This tag allows specific tracking of exogenous DNA without interference from native soil sequences.

Throughout the manuscript, ambiguous phrases such as “GAPDH F‑labeled 16S rRNA genes” have been replaced with more precise terms, “GAPDH F‑tagged 16S rRNA gene amplicon fragments” clarifying that the tag is an appendage and not the amplification target.

We have checked the entire manuscript and confirm that “ACTF” does not appear anywhere. To avoid confusion, the primer is now consistently referred to as “GAPDH F” in all figures, legends, and text.

L360-371

“GAPDH is a primer for a human housekeeping gene and it has no homologous sequences in soils. Subsequently, GAPDH was selected as the label primer based on two criteria. First, this primer was selected to avoid interference from the original soil sequences (Huang et al., 2014; Yang et al., 2021; Arvizu-Hernandez et al., 2025), and no detectable PCR amplification was observed for the primer set GAPDH F-806R across all the soil DNA samples included in this study. Second, the melting temperature (Tm) value of GAPDH F approximately matched that of 806R. The GAPDH was incorporated only into the forward primer for several reasons. Methodologically, adding a long linker to the degenerate reverse primer (806R) could reduce amplification efficiency or introduce bias. Economically, single-end labeling allowed us to use the standard reverse primer already carrying sample-specific barcodes, avoiding the costly synthesis of a full set of dual-labeled barcoded primers. This design minimized the risk of secondary structure and primer-dimer artifacts while maintaining sufficient specificity and compatibility with downstream qPCR and sequencing.”

(6) Limitations of using PCR amplicons as proxies for extracellular DNA

The study uses PCR-generated amplicons to simulate extracellular DNA. While useful for controlled comparisons, these fragments may not reflect the physicochemical diversity of natural extracellular DNA (e.g., adsorption to minerals, fragment size variability, protection within aggregates). This limitation should be explicitly acknowledged, and conclusions should be framed accordingly.

We appreciate the reviewer’s constructive feedback. We fully acknowledge that using PCR-generated amplicons to simulate extracellular DNA (eDNA) has inherent limitations in capturing the full physicochemical diversity of naturally occurring eDNA in soils. Specifically, we agree that PCR fragments may not replicate features such as highly variable fragment size distributions, associations with complex cellular components (e.g., vesicles or protein complexes), or long-term physical sequestration within soil micro-aggregates. Despite of these limitations, the use of uniform primer-tagged PCR amplicons was a deliberate choice to enable precise tracking of exogenous DNA degradation kinetics while eliminating background interference from endogenous soil eDNA. This design is a prerequisite for the high-resolution kinetic modeling of sequence-specific decay. Furthermore, in our bioinformatic pipeline, the 97% mapping threshold was specifically applied to minimize the influence of stochastic sequencing and PCR errors on abundance quantification. In the revised manuscript, these potential limitations have been addressed.

L295-299

“First, utilizing PCR-amplified 16S rRNA gene fragments as proxies oversimplifies the structural and sequence complexity of natural soil eDNA pools. In natural environments, eDNA varies widely in fragment length and conformation, and exhibits complex interactions with mineral surfaces, all of which fundamentally affect degradation dynamics (Levy-Booth et al., 2007; McKinney and Dungan, 2020).”

(7) Interpretation of sequence-specific degradation

Sequence-specific degradation rates are inferred from combining relative abundance data with total qPCR estimates. This approach is sensitive to compositional effects, amplification biases, and abundance-dependent detection limits. It remains unclear whether observed differences reflect true sequence-specific degradation or methodological artifacts. This limitation should be discussed more explicitly.

We thank the reviewer for highlighting this critical methodological point. In our study, sequence-specific degradation rates were estimated by combining ASV-relative abundances with total qPCR-derived 16S rRNA gene copy numbers. We acknowledge that this approach may be influenced by compositional effects, PCR amplification biases, and abundance-dependent detection limits. However, the degradation rate constant (k) in our study, represents the rate of change for a specific sequence over time. Since PCR amplification biases are generally sequence-specific and consistent across samples processed under identical conditions, these systematic errors are mathematically canceled out when calculating the relative change (slope) for the same ASV across a time series. Second, all qPCR measurements were performed with three technical triplicates with standard curves to ensure quantitative reliability. Third, relative abundances were converted to absolute abundances using total qPCR estimates, allowing cross-taxa comparisons that reduce compositional bias. This approach is widely recognized in microbial ecology as a robust method. To address this concern, we have added some explanations in the revised manuscript.

L84-86

“In this study, “sequence‑specific degradation” refers to statistically significant differences in first‑order degradation rate constants (k, day^⁻¹) among distinct 16S rRNA gene amplicon sequence variants (ASVs) under identical soil and incubation conditions.”

L294-305

“Despite the high-resolution insights afforded by our methodology, several limitations should be considered. First, utilizing PCR-amplified 16S rRNA gene fragments as proxies oversimplifies the structural and sequence complexity of natural soil eDNA pools. In natural environments, eDNA varies widely in fragment length and conformation, and exhibits complex interactions with mineral surfaces, all of which fundamentally affect degradation dynamics (Levy-Booth et al., 2007; McKinney and Dungan, 2020). Additionally, the highly conserved nature of the 16S rRNA gene means that the nucleotide variability explored here (e.g., GC content gradients) does not fully capture the genomic heterogeneity of entire metagenomes (Knight et al., 2018). Consequently, our reported degradation rates indicate the decay potential of highly accessible linear eDNA rather than a universal rate for all soil DNA fractions. Future studies incorporating diverse metagenomic DNA, especially those with extreme AT or GC contents, are essential for building a more generalizable predictive framework for eDNA persistence (Morrissey et al., 2015).”

L311-314

“While our standardized bead-beating protocol and calculation of degradation rate constants (k) minimize systematic biases, future studies should integrate complementary viability markers (e.g., RNA-based analyses or protein synthesis activity probes) and multi-extraction comparisons to robustly validate these ecological patterns (Emerson et al., 2017)..”

(8) Overinterpretation of PMA-treated samples as "living communities"

The manuscript interprets PMA-treated DNA as representing intracellular or "living" microbial communities. While PMA is useful, this interpretation should be treated with caution in soils. PMA efficiency can be affected by soil matrix complexity, DNA adsorption to particles, incomplete light penetration, and permeability of compromised cells. Importantly, no validation of PMA efficiency is presented.

We thank the reviewer for this important caution. We agree that interpreting PMA‑treated DNA as representing “living” or “intracellular” communities is an overstatement in soil systems. In the revised manuscript, we no longer describe PMA-treated DNA as a direct proxy for the “living community,” but instead refer to it as the “PMA-treated prokaryotic community”.

Although we did not directly validate PMA efficiency in this study, we used a standardized PMA protocol that has been widely applied in microbial ecology, and our goal was to obtain a comparative estimate of the influence of extracellular DNA on community analysis across soils under a consistent methodological framework. Based on previous studies (Carini et al., 2016; Du et al., 2025), which found that in similar soil types, PMA treatment can significantly reduce the interference of extracellular DNA and alter the community structure, this indirectly proves the effectiveness of this technique.

Nevertheless, we agree that future studies should include explicit validation controls, such as live/dead cell mixtures, heat-killed controls, or soil-specific PMA efficiency tests, to better quantify method performance across diverse soil matrices. We have added a dedicated paragraph in the "Methodological Considerations and Limitations" section to discuss how soil-specific properties (e.g., turbidity, adsorption capacity) might lead to incomplete exclusion of extracellular DNA, thereby advising a more cautious interpretation of the "viable" community data.

L496-500

“To inhibit amplification of eDNA, soils were incubated with propidium monoazide (PMA), as described previously (Carini et al., 2016). Upon photoactivation, eDNA can form covalent bonds through cross-linking, leading to the inhibition of its PCR amplification. In contrast, microbes with intact cell membranes exclude PMA, and their DNA is not cross-linked with PMA, and remains amenable to PCR amplification.”

L294-305

“Despite the high-resolution insights afforded by our methodology, several limitations should be considered. First, utilizing PCR-amplified 16S rRNA gene fragments as proxies oversimplifies the structural and sequence complexity of natural soil eDNA pools. In natural environments, eDNA varies widely in fragment length and conformation, and exhibits complex interactions with mineral surfaces, all of which fundamentally affect degradation dynamics (Levy-Booth et al., 2007; McKinney and Dungan, 2020). Additionally, the highly conserved nature of the 16S rRNA gene means that the nucleotide variability explored here (e.g., GC content gradients) does not fully capture the genomic heterogeneity of entire metagenomes (Knight et al., 2018). Consequently, our reported degradation rates indicate the decay potential of highly accessible linear eDNA rather than a universal rate for all soil DNA fractions. Future studies incorporating diverse metagenomic DNA, especially those with extreme AT or GC contents, are essential for building a more generalizable predictive framework for eDNA persistence (Morrissey et al., 2015).”

Minor Comments

(1) Line 79: Provide examples of how extracellular DNA contributes to nutrient cycling (e.g., P, N sources) and signal transduction (e.g., horizontal gene transfer).

We thank the reviewer for this helpful suggestion. In the revised manuscript, we have added specific examples to clarify how extracellular DNA contributes to nutrient cycling and signal transduction. Specifically, we now note that extracellular DNA can serve as a source of phosphorus and nitrogen following enzymatic degradation, thereby contributing to soil nutrient turnover. We also clarify that extracellular DNA plays an important role in horizontal gene transfer, acting as a genetic reservoir that can be taken up by competent microorganisms and thereby facilitating the spread of functional traits such as antibiotic resistance. These examples have been added to improve the clarity and biological context of this statement.

L48-52

“EDNA serves as a critical vector for horizontal gene transfer (HGT), facilitating the uptake of genetic material by competent microorganisms and promoting the spread of functional traits such as antibiotic resistance (Liu et al., 2024). In addition, eDNA participates in soil biogeochemical cycling because its enzymatic degradation releases bioavailable nutrients, particularly phosphorus and nitrogen, which can be reused by soil microorganisms (Ye et al., 2022).”

(2) Line 79: Replace "for an extended period of time" with a more precise or referenced timescale.

We agree with the reviewer. We have replaced the vague phrase with a precise timescale. Extracellular DNA can persist in soils for months to years.

(3) Line 94: Clarify what is meant by "high-level structure" (e.g., secondary structure, environmental association).

We thank the reviewer for pointing out this ambiguity. In the original manuscript, the phrase “high-level structure” was not sufficiently precise. In the revised version, we have clarified that this refers primarily to higher-order structural properties of DNA molecules, such as secondary structure, local conformational features, and sequence-dependent interactions with minerals or organic matter in soil. These characteristics may influence the accessibility of extracellular DNA to nucleases and thus affect degradation rates. We have revised the text accordingly to improve clarity and precision.

L84-104

“In this study, “sequence‑specific degradation” refers to statistically significant differences in first‑order degradation rate constants (k, day^⁻¹) among distinct 16S rRNA gene amplicon sequences (ASVs) under identical soil and incubation conditions. The potential variations in sequence-specific eDNA degradation rates can be attributed to several factors. First, sequence-dependent degradation can arise from differences in nucleotide composition, particularly GC content. This influences the thermodynamic stability and base-stacking interactions of the DNA duplex, thereby altering its accessibility to extracellular nucleases (Marrone and Ballantyne, 2008; Wolpe and Guertin, 2022). Second, local conformational features and the formation of potential secondary structures, such as stem-loops or hairpins, can create steric hindrance that protects the phosphodiester backbone. Differences in base composition also alter the elemental stoichiometry (e.g., C: N ratio) of DNA molecules, potentially affecting microbial preference for recycling specific sequences as nutrient sources (Cai et al., 2006a; Buitrago et al., 2021). Third, the persistence of soil DNA is often associated with its adsorption and protection by minerals and humus in soils (Cai et al., 2006b; Vuillemin et al., 2017; McKinney and Dungan, 2020). Thus, sequence-dependent differences in the physicochemical behavior of DNA molecules, including their affinity for soil minerals and organic matter, may also contribute to variation in degradation rates among sequences (Levy-Booth et al., 2007; Morrissey et al., 2015). Consequently, we proposed three central hypotheses. (1) The degradation rates of eDNA amplicon fragments were expected to be highly sequence‑specific. (2) The rates and patterns of eDNA fragments degradation would be influenced by environmental factors such as temperature and moisture content. (3) The sequence‑specific degradation of extracellular 16S rRNA gene amplicon fragments would significantly influence estimates of soil prokaryotic abundance and diversity.”

(4) Line 111: The hypothesis is not clearly linked to the rationale. If sequence-specific degradation is expected, clarify whether it relates to conserved vs variable regions or structural features (e.g., stems vs loops).

We thank the reviewer for this helpful comment. We agree that the original manuscript did not clearly link the hypothesis regarding sequence-specific degradation to its mechanistic rationale. In the revised manuscript, we have clarified that the expectation of sequence-specific degradation is not simply based on conserved vs variable regions of the 16S rRNA gene, but rather on the potential for sequence differences to influence intrinsic physicochemical properties, including base composition, local conformational features, potential secondary structures, and motif-dependent nuclease susceptibility. These factors may alter DNA accessibility to extracellular nucleases, providing a mechanistic basis for sequence-specific degradation. This clarification is now reflected in the Introduction and linked to the formal hypothesis statement.

To improve the logical flow of the manuscript, we have restructured the Introduction by moving the three central hypotheses (H1–H3) immediately following the discussion of the biochemical mechanisms underlying sequence-specific degradation.

L84-104

“In this study, “sequence‑specific degradation” refers to statistically significant differences in first‑order degradation rate constants (k, day^⁻¹) among distinct 16S rRNA gene amplicon sequences (ASVs) under identical soil and incubation conditions. The potential variations in sequence-specific eDNA degradation rates can be attributed to several factors. First, sequence-dependent degradation can arise from differences in nucleotide composition, particularly GC content. This influences the thermodynamic stability and base-stacking interactions of the DNA duplex, thereby altering its accessibility to extracellular nucleases (Marrone and Ballantyne, 2008; Wolpe and Guertin, 2022). Second, local conformational features and the formation of potential secondary structures, such as stem-loops or hairpins, can create steric hindrance that protects the phosphodiester backbone. Differences in base composition also alter the elemental stoichiometry (e.g., C:N ratio) of DNA molecules, potentially affecting microbial preference for recycling specific sequences as nutrient sources (Cai et al., 2006a; Buitrago et al., 2021). Third, the persistence of soil DNA is often associated with its adsorption and protection by minerals and humus in soils (Cai et al., 2006b; Vuillemin et al., 2017; McKinney and Dungan, 2020). Thus, sequence-dependent differences in the physicochemical behavior of DNA molecules, including their affinity for soil minerals and organic matter, may also contribute to variation in degradation rates among sequences (Levy-Booth et al., 2007; Morrissey et al., 2015). Consequently, we proposed three central hypotheses. (1) The degradation rates of eDNA amplicon fragments were expected to be highly sequence‑specific. (2) The rates and patterns of eDNA fragments degradation would be influenced by environmental factors such as temperature and moisture content. (3) The sequence‑specific degradation of extracellular 16S rRNA gene amplicon fragments would significantly influence estimates of soil prokaryotic abundance and diversity.”

(5) Lines 310-311: Clearly indicate which portion of the primers corresponds to the modified (GAPDH-derived) sequence. Provide full annotated primer sequences.

We thank the reviewer for this helpful suggestion. In the revised manuscript, we now clearly indicate which portion of the forward primer corresponds to the GAPDH-derived synthetic tag and which portion corresponds to the 16S rRNA gene primer sequence. We have also provided the full annotated primer sequences in the Methods section to avoid ambiguity.

Specifically, the modified forward primer is now described as:

GAPDH-F-515F: 5′-CAT TGG CAA TGA GCG GTT C-GTG CCA GCM GCC GCG GTA A-3′,

where CAT TGG CAA TGA GCG GTT C is the GAPDH-derived synthetic tag and GTG CCA GCM GCC GCG GTA A is the 16S rRNA gene forward primer sequence (515F).

The reverse primer is:

806R: 5′-GGA CTA CHV GGG TWT CTA AT-3′.

L354-359

“Briefly, exogenous eDNA was prepared by PCR amplification using a modified forward primer consisting of a GAPDH F tag fused to the 16S rRNA gene primer 515F, together with the reverse primer 806R. The full primer sequences were as follows: GAPDH-F-515F: 5'-CAT TGG CAA TGA GCG GTT C-GTG CCA GCM GCC GCG GTA A-3', in which CAT TGG CAA TGA GCG GTT C represents the GAPDH F tag and GTG CCA GCM GCC GCG GTA A represents the 16S rRNA gene forward primer sequence (515F); and 806R: 5'-GGA CTA CHV GGG TWT CTA AT-3'.”

(6) Lines 310-311: Explicitly define GAPDH and justify its use as a synthetic tag.

We thank the reviewer for this helpful suggestion. In the revised manuscript, we now explicitly define GAPDH as glyceraldehyde-3-phosphate dehydrogenase, a human housekeeping gene. Specifically, the GAPDH-derived sequence was selected for two reasons. First, it is highly divergent from known soil microbial 16S rRNA gene sequences and did not produce detectable amplification when tested with soil DNA using the GAPDH tagged 806R primer pair, indicating that it would not interfere with endogenous soil DNA signals. Second, its melting temperature was compatible with that of the reverse primer, which allowed stable amplification of the tagged 16S amplicons under our PCR conditions.

L360-371

“GAPDH is a primer for a human housekeeping gene and it has no homologous sequences in soils. Subsequently, GAPDH was selected as the label primer based on two criteria. First, this primer was selected to avoid interference from the original soil sequences (Huang et al., 2014; Yang et al., 2021; Arvizu-Hernandez et al., 2025), and no detectable PCR amplification was observed for the primer set GAPDH F-806R across all the soil DNA samples included in this study. Second, the melting temperature (Tm) value of GAPDH F approximately matched that of 806R. The GAPDH was incorporated only into the forward primer for several reasons. Methodologically, adding a long linker to the degenerate reverse primer (806R) could reduce amplification efficiency or introduce bias. Economically, single-end labeling allowed us to use the standard reverse primer already carrying sample-specific barcodes, avoiding the costly synthesis of a full set of dual-labeled barcoded primers. This design minimized the risk of secondary structure and primer-dimer artifacts while maintaining sufficient specificity and compatibility with downstream qPCR and sequencing”

(7) Line 346: Start a new paragraph to clearly separate this as a distinct experiment.

We thank the reviewer for this helpful suggestion. In the revised manuscript, we have started a new paragraph.

(8) Line 346: Specify the number of samples analyzed for consistency.

We thank the reviewer for this helpful suggestion. In the revised manuscript, we have now explicitly specified the number of samples.

A total of 120 samples were analyzed in this moisture gradient experiment: 2 ecosystems (Kaiyuan and Dashanbao) × 5 moisture levels (10%, 25%, 50%, 75%, and 100% of water holding capacity) × 6 incubation time points (0, 1, 3, 6, 12, and 24 days) × 2 replicates. Just two technical replicates were performed for this validation experiment, as the primary aim was to assess the trend of moisture effects rather than statistical inference across replicates.

L408-410

“This complementary experiment included two sites, five moisture levels, six incubation time points, and two replicates per treatment combination, resulting in a total of 120 soil samples.”

(9) Lines 351-352: Replace "harvested" with "collected."

We have replaced “harvested” with “collected” as suggested.

(10) Line 367: Clarify how Illumina adapters and indices were added (e.g., two-step PCR, fusion primers).

We thank the reviewer for this helpful suggestion. We have revised the Methods section to clarify how Illumina adapters and indices were incorporated. We used a pooled amplicon library preparation strategy. Individual samples were first amplified with primers containing sample-specific barcode sequences. The barcoded amplicons from multiple samples were then pooled and used for library preparation with the ALFA-SEQ DNA Library Prep Kit. Universal Illumina-compatible adapters were first ligated to the pooled amplicons. After bead-based purification, an indexing PCR was performed using the index primer mix, which introduced the complete P5/P7 sequences and a library-level Illumina index into the library molecules. Thus, sample demultiplexing was based on the sample-specific barcodes introduced during amplicon PCR, whereas the Illumina index was used to identify the pooled sequencing library. We have clarified this procedure in the revised manuscript.

L424-440

“The community profiles of the GAPDH F-tagged 16S rRNA gene amplicon fragments were determined using high-throughput amplicon sequencing. Briefly, GAPDH F-tagged 16S rRNA gene amplicon fragments from the microcosm soils were first amplified from individual samples using GAPDH F and barcode-labeled 806R primers. The reverse primer 806R carried a 12-bp sample-specific barcode, whereas the GAPDH F primer did not contain a barcode. Therefore, each sample was assigned a unique barcode during PCR, which allowed sample demultiplexing after sequencing. The PCR reaction system and thermal cycling conditions were similar to those described above, except that the number of amplification cycles was increased to 35 to obtain sufficient amplicon products for sequencing. The barcoded PCR products from individual samples were purified using a GeneJET Gel Extraction Kit (Thermo Scientific, Lithuania), quantified, and then pooled in equimolar amounts for subsequent library construction. Sequencing libraries were prepared from the pooled barcoded amplicons using the ALFA-SEQ DNA Library Prep Kit according to the manufacturer’s protocol. Universal Illumina-compatible adapters were first ligated to the pooled amplicon products, followed by bead-based purification. An indexing PCR was then performed using the index primer mix, which introduced the complete P5/P7 flow-cell binding sequences and a library-level Illumina index into the pooled library molecules. The indexed library was purified, quantified, and subjected to paired-end sequencing on the NovaSeq platform at MAGIGENE Co., Ltd. (Guangzhou, China).

(11) Provide more detail on chimera removal, filtering thresholds, and normalization choices.

We thank the reviewer for this helpful suggestion. The raw paired-end reads were first merged, and primer sequences were removed using the search_pcr2 script in USEARCH. Reads with more than two primer mismatches were discarded. Quality filtering was then performed using fastq_filter, and sequences with quality scores below 20 were removed. Redundant reads were collapsed using fastx_uniques. Amplicon sequence variants (ASVs) were generated using the UNOISE3 denoising algorithm, which also performs built-in chimaera filtering during ASV inference. In addition, ASVs with total sequence counts fewer than 9 were excluded to reduce the influence of low-frequency noise.

L443-460

“Briefly, paired-end reads were merged using USEARCH, and primer sequences (GAPDH-F-515F and 806R) were removed using the search_pcr2 script. Reads with more than two primer mismatches were discarded. Quality filtering was performed using the fastq_filter script, and sequences with quality scores below 20 were removed. Redundant sequences were dereplicated using the fastx_uniques script. ASVs were generated using the UNOISE3 non‑clustering denoising algorithm (Edgar, 2016), which infers 100% exact sequence variants by distinguishing biological sequences from PCR/sequencing errors. ASVs with total sequence counts fewer than 9 across all samples were removed to reduce noise. To quantify the abundance of each ASV, an ASV table was generated by mapping the quality‑filtered raw reads back to the ASV set using the otutab command. A 97% similarity threshold was applied for this recruitment to accommodate stochastic sequencing noise while maintaining biological resolution. Crucially, the mapping followed a best-hit priority rule, where each read was assigned to the ASV with the highest per cent identity within the 97% radius. This approach ensures that reads derived from the same biological template are accurately counted toward their respective ASV, preventing the underestimation of abundances that would occur with exact matching while strictly preserving the single-nucleotide resolution of the ASV framework. Taxonomic annotation of the ASVs was performed in QIIME2 with the Silva v138 database. A total of 89322 prokaryotic ASVs were obtained. To standardize sequencing depth across samples, the read number of each sample was rarefied to 53251 using the rarefy function in the vegan package in R.”

(12) Line 412: Rephrase to refer to 16S amplicon addition rather than 16S rRNA genes (along the whole text), as only the V4 region is analyzed.

We thank the reviewer for this helpful suggestion. we have rephrased references to “16S rRNA genes” to “16S rRNA gene amplicon fragments”

(13) Ensure consistent primer naming throughout (e.g., GAPDH F vs ACTF).

We have checked the entire manuscript and confirm that only “GAPDH F” is used as the label primer.

(14) Finally, the manuscript would benefit from careful language editing. Several typographical errors, grammatical inconsistencies, and unclear phrases are present throughout. Examples include:

Misspellings such as "diffrence" (e.g., figure legends) and inconsistent capitalization. Inconsistent terminology (e.g., "genes," "amplicons," and "fragments" used interchangeably without clarification). Redundant or awkward phrasing (e.g., repeated use of "extracellular 16S rRNA genes"). Occasional subject-verb agreement issues and missing articles.

We sincerely apologize for the language issues. The manuscript has now undergone a thorough language editing process by a native English‑speaking colleague.

Recommendation

Major revision: The manuscript addresses an important problem and presents a promising approach. However, key issues related to conceptual clarity, bioinformatic consistency, statistical rigor, and interpretation of PMA-based results must be resolved. With substantial revision and clarification, the study has the potential to make a meaningful contribution to the field.

We sincerely thank the reviewer for the thorough, constructive, and critical evaluation of our manuscript. We greatly appreciate the recognition that our study addresses an important problem and presents a promising approach. We also acknowledge the key issues raised regarding conceptual clarity, bioinformatic consistency, statistical rigor, and interpretation of PMA‑based results. We have taken these comments very seriously and have substantially revised the manuscript accordingly, more details about the revisions are described in the following point-by-point responses.

Reviewer #2 (Recommendations for the authors):

Editorial comments:

(1) Title: I recommend removing "across China" from the title. In many ways, the study has nothing to do specifically with China, and you limit the broad applicability of the study. The same work could have been done with soils from Africa, for example. Also, it might be ok to remove 16S rRNA as well. The 16S rRNA genes are a proxy for rates of extracellular DNA degradation, but the study isn't exactly about 16S either.

We thank the reviewer for this thoughtful suggestion regarding the title. We have revised the title to “The overall and sequence-specific degradation of soil extracellular DNA fragments: rates and influential factors.”

(3) L44-45: "...such as real-time PCR, high-throughput amplicon sequencing, and metagenomic analysis...".

We thank the reviewer for this suggestion. We have revised the order according to the suggestions of the reviewer.

L44-45

“The investigation of soil microbial abundance and diversity heavily relies on DNA-based technologies, such as real-time PCR, high-throughput amplicon sequencing, and metagenomic analysis.”

(4) L48: remove "they".

We agree with the reviewer and have removed the extraneous “they”.

(5) L51: "noise factor"; "...persistence can lead to...".

We have revised the sentence as suggested.

(6) L53: remove theoretical.

We have removed “theoretical”.

(7) L58: remove "the".

We have removed "the".

(8) L86: Is restriction digestion of DNA a likely extracellular process in soil?

We thank the reviewer for this thoughtful comment. We agree that the original wording may have overstated the likelihood of classical restriction digestion as a dominant extracellular process in soils. Our intention was not to suggest that intracellular restriction enzyme systems operate directly in the soil matrix in the same manner as they do within living cells. Rather, we aimed to indicate more generally that sequence-dependent nuclease susceptibility could contribute to differential degradation among extracellular DNA fragments.

L87-95

“First, sequence-dependent degradation can arise from differences in nucleotide composition, particularly GC content. This influences the thermodynamic stability and base-stacking interactions of the DNA duplex, thereby altering its accessibility to extracellular nucleases (Marrone and Ballantyne, 2008; Wolpe and Guertin, 2022). Second, local conformational features and the formation of potential secondary structures, such as stem-loops or hairpins, can create steric hindrance that protects the phosphodiester backbone. Differences in base composition also alter the elemental stoichiometry (e.g., C: N ratio) of DNA molecules, potentially affecting microbial preference for recycling specific sequences as nutrient sources (Cai et al., 2006a; Buitrago et al., 2021).”

(9) L94-99: The authors might also consider the different nitrogen content of different bases; this might also affect sequence-specific selection of DNA for degradation.

We thank the reviewer for this insightful suggestion. We agree that differences in the elemental composition of DNA bases, including nitrogen content, may provide an additional mechanistic explanation for sequence-dependent degradation. In the revised manuscript, we have incorporated this point into the Introduction.

L92-95

“Differences in base composition also alter the elemental stoichiometry (e.g., C:N ratio) of DNA molecules, potentially affecting microbial preference for recycling specific sequences as nutrient sources (Cai et al., 2006a; Buitrago et al., 2021).”

(10) L110-112: These are not really written in hypothesis form. Also, what about a hypothesis about degradation rates and soil type/temperature/moisture?

We thank the reviewer for this constructive critique. We have rewritten the hypotheses. To improve the logical flow of the manuscript, we have restructured the Introduction by moving the three central hypotheses immediately following the discussion of the biochemical mechanisms underlying sequence-specific degradation. This adjustment ensures that the hypotheses are directly grounded in the theoretical framework.

L99-104

“Consequently, we proposed three central hypotheses. (1) The degradation rates of eDNA amplicon fragments were expected to be highly sequence‑specific. (2) The rates and patterns of eDNA fragments degradation would be influenced by environmental factors such as temperature and moisture content. (3) The sequence‑specific degradation of extracellular 16S rRNA gene amplicon fragments would significantly influence estimates of soil prokaryotic abundance and diversity.”

(11) L116: "GAPDH F-labeled 16S rRNA gene amplicon fragments....".

We thank the reviewer for this helpful suggestion. we have rephrased references to “16S rRNA genes” to “16S rRNA gene amplicon fragments”

(12) L117: "rapidly".

We agree with the reviewer and have revised.

(13) L118-120: "After a 48-day incubation period, 0.2 to 3.1% of the initial spike GADPH F-labeled 16S rRNA gene amplicon fragments ...".

We agree with the reviewer and have revised.

(14) L125: Spell out SEM in first usage.

We thank the reviewer for this suggestion. In the revised manuscript, we have spelled out SEM as Structural equation modeling.

(15) L128: I don't like the idea of putting this Figure in supplemental materials.

We thank the reviewer for this suggestion. We have moved Figure S2 (moisture gradient microcosm experiment) to the main text as Figure 1f.

(16) L154: The term "intracellular prokaryotic abundance" is not the right term. This makes one think of an intracellular parasite. I think you want something like: "Approximately 40% of sequences in total soil DNA extraction NGS amplicon libraries were derived from intact cells, while the remaining represented extracellular DNA. Conversely, greater than 80% of observed richness was derived from intact cells." (Please check that I stated this correctly.) I would also suggest some statistics or ranges here.

We thank the reviewer for this important terminological clarification. We agree that the term “intracellular prokaryotic abundance” is misleading, as it could imply intracellular parasites. In the revised manuscript, we have replaced this with a clearer description and We have also added the across‑site ranges to provide statistical context.

L163-166

“The PMA treatment revealed that intact cells accounted for approximately 40% (range: 9–73%) of the total 16S rRNA gene copies. In contrast, over 80% (range: 27–97%) of the observed ASV richness was associated with sequences originating from intact cells (Fig. 4a and b).”

(17) L168: "...a significant NEGATIVE correlation was observed...".

We agree with the reviewer and have revised.

(18) L169: "However, no significant relationship was observed...".

We agree with the reviewer and have revised.

(19) L194-195: What about pH and temperature?

We thank the reviewer for this comment. We agree that pH and temperature are important environmental factors that can influence microbial DNA degradation and community composition. However, our results (Fig. 1c) indicate that soil moisture is the most dominant factor affecting extracellular DNA degradation. Therefore, in the revised manuscript, we have focused the explanation primarily on soil moisture, while acknowledging that pH and temperature may also be important influencing factors.

L208-211

“Third, environmental factors, including soil moisture, pH, and temperature, can predominantly govern enzymatic reaction rates (He et al., 2024; Shah et al., 2024). Indeed, strong positive correlations were observed between moisture content and eDNA degradation rates in both the survey and microcosm experiments (Fig. 1d-f).”

(20) L199: "findings".

We have revised as suggested.

(21) L227-229: This sounds more like results.

We thank the reviewer for this comment. We agree that the original first sentence in L227–229 reads more like results. Our intention was to introduce the discussion by linking extracellular DNA to potential impacts on prokaryotic community analysis, rather than to present specific findings at this point. We have reorganized this section as follows.

L246-248

“Accordingly, we further explored how DNA may influence prokaryotic community analyses using PMA treatment, and significant disparities were observed between the profiles of the total and PMA-treated soil prokaryotic communities (Fig. 4).”

(22) L230: Need to also consider differential cell lysis during DNA extraction.

We thank the reviewer for this important comment. We agree that differential cell lysis during DNA extraction could influence the observed community profiles, as microbial taxa differ in cell wall composition and resistance to mechanical or chemical lysis. In the revised manuscript, we explicitly acknowledge this limitation in the relevant section. We also clarify that a standardized DNA extraction protocol (DNeasy PowerSoil kit) was used to efficiently lyse a broad range of microbial taxa, but some taxon-specific lysis bias may remain. Future studies could combine multiple lysis methods or spike-in controls to quantify and correct for potential extraction bias.

L262-265

“However, as DNA extraction efficiency may differ between intact cells and eDNA, the actual differences between total and living prokaryotic abundance could be smaller than those observed in this study. Similarly, the overestimated prokaryotic richness may arise from historically accumulated microbial taxonomic information stored in eDNA pools (Deshpande and Fahrenfeld, 2023; Wang et al., 2024).”

L309-311

“Compounding this issue, downstream DNA recovery is subject to differential cell lysis, as taxa with robust cell walls (e.g., Gram-positive bacteria) may resist extraction (Frostegård et al., 1999; Albertsen et al., 2015).”

(23) L232: Need to also consider that PMA treatment is not perfect and can be affected by substrate, the ability of light to access DNA for crosslinking, etc.

We thank the reviewer for this important reminder. We agree that PMA treatment is not perfect and that its efficiency can be affected by soil matrix properties (e.g., organic matter, clay minerals) and the ability of light to penetrate the sample for DNA crosslinking. In the revised manuscript, we have explicitly acknowledged these limitations in the discussion.

L305-314

“Second, methodological biases inherent in quantifying the intracellular community must be acknowledged (Du et al., 2025). Although PMA treatment is widely used to exclude eDNA, its efficiency in complex soil matrices can be compromised by limited light penetration in turbid suspensions and competitive adsorption to soil particles (Nocker et al., 2007; Carini et al., 2016; Heise et al., 2016). Compounding this issue, downstream DNA recovery is subject to differential cell lysis, as taxa with robust cell walls (e.g., Gram-positive bacteria) may resist extraction (Frostegård et al., 1999; Albertsen et al., 2015). While our standardized bead-beating protocol and calculation of degradation rate constants (k) minimize systematic biases, future studies should integrate complementary viability markers (e.g., RNA-based analyses or protein synthesis activity probes) and multi-extraction comparisons to robustly validate these ecological patterns (Emerson et al., 2017).”

(24) L240: Can extracellular DNA have an ecological role?

We thank the reviewer for this thoughtful question. Yes, extracellular DNA (eDNA) does have important ecological roles beyond being a potential bias in molecular analyses. In the revised manuscript, we have added statements to highlight that extracellular DNA can serve as a nutrient source (e.g., nitrogen and phosphorus) for microbes and may also contribute to horizontal gene transfer. This emphasizes that extracellular DNA may actively influence microbial community structure and function, in addition to its role in potentially inflating observed abundance and richness.

L267-275

“We observed a significant correlation between eDNA degradation rates and the overall structure of the prokaryotic community, but this relationship was absent in PMA-treated communities (Fig. 5b). This discrepancy highlights the divergent ecological roles of extracellular and intracellular DNA. Analyses of the total community integrate intracellular DNA from metabolically active cells with eDNA which primarily originates from historical microbial residues (Lennon et al., 2018). EDNA incorporates signals that likely reflect the legacy effects of past environmental conditions (Wang et al., 2021). In contrast, the PMA-treated community reflects transient microbial activity driven by current selective pressures. Additionally, eDNA can serve as a nutrient source and facilitate horizontal gene transfer, which may further shape its interactions with contemporary microbial communities (Levy-Booth et al., 2007).”

(25) L256: Why would microorganisms selectively degrade one DNA sequence vs another? This seems to be likely to be stochastic in terms of which sequences are taken up by microorganisms. However, different DNA sequences might hydrolyze differently or be otherwise damaged, and that could lead to differential degradation of a viable amplicon. It might be interesting to incorporate long pieces of DNA with different internal primer sites and use quantitative PCR to determine how sequences are degrading.

We thank the reviewer for this important mechanistic insight. We agree that the observed correlation between degradation rate and sequence abundance does not necessarily imply active microbial preference. It could equally reflect stochastic encounter rates or intrinsic chemical differences (e.g., AT‑rich regions hydrolyzing faster). We have revised the corresponding paragraph in the Discussion.

L279-292

“This finding suggests that abundant eDNA degrades at a faster rate compared to rare eDNA. As mentioned earlier, this could be explained by several mechanisms. First, as soil eDNA is subject to enzymatic degradation and microbial recycling, abundant DNA sequences may be more likely to be encountered and degraded by extracellular nucleases simply due to their higher copy numbers (Levy-Booth et al., 2007; Nagler et al., 2018). Similarly, if microbes preferentially take up DNA as a nutrient source, they may degrade abundant sequences more frequently as a stochastic consequence of higher encounter rates (Finkel and Kolter, 2001). However, we also found that the relationships between the sequence-specific degradation rates and the effect sizes of extracellular 16S rRNA gene amplicon fragments varied across the study sites (Fig. S1g). The sequence-specific effect sizes of extracellular 16S rRNA gene amplicon fragments are mainly determined by both their production and degradation rates (Pietramellara et al., 2009; Sirois and Buckley, 2019). These inconsistent correlations emphasize the critical role played by the production rates of extracellular 16S rRNA genes in influencing the analysis of prokaryotic communities. Therefore, future studies should systematically determine both the production and degradation rates of eDNA.”

(26) L282-283: This belongs in the discussion.

We agree with the reviewer and have revised accordingly.

(27) L289: "as well as measurements of total organic carbon".

We agree with the reviewer and have revised accordingly.

(28) L338: Any water content for these soils?

We thank the reviewer for this comment. The water contents of soils from all study sites are reported in Supplementary Table 2.

(29) L349-350: You mean that you measured the total soil extracted DNA and then added 1% as labeled 16S?

Yes, for each soil sample, we extracted total soil DNA and quantified its concentration (ng DNA per gram of soil). We then added exogenous GAPDH‑tagged 16S amplicon fragments at an amount equal to 1% of this total DNA concentration. This concentration was chosen to mimic a realistic pulse of extracellular DNA input without overwhelming the endogenous DNA pool. We apologize for any confusion caused by the imprecise wording in the original manuscript.

L392-398

“The microcosm experiment was conducted using 30 g of soil for each sample. After pre-incubation at 20℃ for one week, each soil was thoroughly mixed with the GAPDH F‑tagged 16S rRNA gene amplicon fragments and incubated further at 20℃ (Fig. S8). The amount of exogenous GAPDH F‑tagged 16S rRNA gene amplicon fragments added to each soil sample was equivalent to 1% of the total DNA concentration naturally present in that soil, as determined fluorometrically prior to the experiment. This concentration was chosen to approximate natural eDNA fluxes resulting from microbial lysis, ensuring experimental relevance to in situ conditions (Table S2).”

(30) L354: Remember that soil recovery from intact cells is going to be lower than for extracellular DNA. So, you are probably overestimating the contribution of extracellular DNA to the total DNA in the system.

We thank the reviewer for this comment. We agree that DNA recovery from intact cells is generally lower than from extracellular DNA due to differential cell lysis efficiencies. Consequently, the contribution of extracellular DNA to total soil DNA may be somewhat overestimated in our study. We have clarified this limitation in the revised manuscript.

L262-265

“However, as DNA extraction efficiency may differ between intact cells and eDNA, the actual differences between total and living prokaryotic abundance could be smaller than those observed in this study. Similarly, the overestimated prokaryotic richness may arise from historically accumulated microbial taxonomic information stored in eDNA pools.”

L305-314

“Second, methodological biases inherent in quantifying the intracellular community must be acknowledged (Du et al., 2025). Although PMA treatment is widely used to exclude eDNA, its efficiency in complex soil matrices can be compromised by limited light penetration in turbid suspensions and competitive adsorption to soil particles (Nocker et al., 2007; Carini et al., 2016; Heise et al., 2016). Compounding this issue, downstream DNA recovery is subject to differential cell lysis, as taxa with robust cell walls (e.g., Gram-positive bacteria) may resist extraction (Frostegård et al., 1999; Albertsen et al., 2015). While our standardized bead-beating protocol and calculation of degradation rate constants (k) minimize systematic biases, future studies should integrate complementary viability markers (e.g., RNA-based analyses or protein synthesis activity probes) and multi-extraction comparisons to robustly validate these ecological patterns (Emerson et al., 2017).”

(31) L362: Amplification efficiency is pretty low. I think you would have been better served with GAPDH on both ends, and that would have given you a much higher efficiency qPCR.

We thank the reviewer for this comment. The actual qPCR amplification efficiency in our assay was approximately 85%, which, although slightly below the ideal range, was still acceptable and produced reproducible amplification curves and reliable quantification for degradation-rate calculations.

We acknowledge that the amplification efficiency in our qPCR experiments using a GAPDH F-labeled 16S primer on one end was suboptimal. The current design used a single GAPDH tag at the forward primer to avoid potential amplification bias or primer-dimer formation that could arise from extending the degenerate reverse primer. In addition, dual-end labeling would have required synthesis of new barcode-labeled tagged primers, increasing both cost and experimental complexity. Thanks again for the constructive comments, which provided us with the direction for future experiment optimization.

L365-371

“The GAPDH was incorporated only into the forward primer for several reasons. Methodologically, adding a long linker to the degenerate reverse primer (806R) could reduce amplification efficiency or introduce bias. Economically, single-end labeling allowed us to use the standard reverse primer already carrying sample-specific barcodes, avoiding the costly synthesis of a full set of dual-labeled barcoded primers. This design minimized the risk of secondary structure and primer-dimer artifacts while maintaining sufficient specificity and compatibility with downstream qPCR and sequencing.”

(32) L367: Not enough detail on how barcoded libraries were made. UDIs?

We thank the reviewer for this helpful comment. We have now clarified the library preparation and indexing strategy in the revised Methods section. This amplicon diversity sequencing used a pooled-library strategy. Individual samples were first distinguished by sample-specific inline barcodes introduced during the amplicon PCR step. After amplification, barcoded PCR products from multiple samples were pooled and subjected to library construction using the ALFA-SEQ DNA Library Prep Kit. Universal Illumina-compatible adapters were ligated to the pooled amplicons, followed by an indexing PCR that introduced the complete P5/P7 sequences and a library-level Illumina index. Thus, the Illumina index was used to identify the pooled sequencing library, whereas sample demultiplexing was performed according to the sample-specific inline barcodes. We have revised the Methods section to make this procedure explicit.

L424-440

“The community profiles of the GAPDH F-tagged 16S rRNA gene amplicon fragments were determined using high-throughput amplicon sequencing. Briefly, GAPDH F-tagged 16S rRNA gene amplicon fragments from the microcosm soils were first amplified from individual samples using GAPDH F and barcode-labeled 806R primers. The reverse primer 806R carried a 12-bp sample-specific barcode, whereas the GAPDH F primer did not contain a barcode. Therefore, each sample was assigned a unique barcode during PCR, which allowed sample demultiplexing after sequencing. The PCR reaction system and thermal cycling conditions were similar to those described above, except that the number of amplification cycles was increased to 35 to obtain sufficient amplicon products for sequencing. The barcoded PCR products from individual samples were purified using a GeneJET Gel Extraction Kit (Thermo Scientific, Lithuania), quantified, and then pooled in equimolar amounts for subsequent library construction. Sequencing libraries were prepared from the pooled barcoded amplicons using the ALFA-SEQ DNA Library Prep Kit according to the manufacturer’s protocol. Universal Illumina-compatible adapters were first ligated to the pooled amplicon products, followed by bead-based purification. An indexing PCR was then performed using the index primer mix, which introduced the complete P5/P7 flow-cell binding sequences and a library-level Illumina index into the pooled library molecules. The indexed library was purified, quantified, and subjected to paired-end sequencing on the NovaSeq platform at MAGIGENE Co., Ltd. (Guangzhou, China).”

(33) L368: Why was the # of cycles increased?

Thank you for your question. In the original manuscript (L368), we stated that the number of PCR cycles was increased to 35. This was mainly because the exogenously added GAPDH F‑labeled 16S rRNA genes had a relatively low initial abundance in the soil and gradually degraded during the microcosm incubation, with their copy numbers becoming particularly low at the last time points (see Fig. 1a). To ensure sufficient PCR product for high‑throughput sequencing from samples at all time points (especially those with low abundance at later stages), we appropriately increased the cycle number to 35.

L429-431

“The PCR reaction system and thermal cycling conditions were similar to those described above, except that the number of amplification cycles was increased to 35 to obtain sufficient amplicon products for sequencing.”

(34) L372: Were sequencing adapters ligated onto the pool?

We thank the reviewer for this question. Yes, in this amplicon diversity sequencing workflow, sequencing adapters were ligated onto the pooled amplicon products. Briefly, individual samples were first amplified with sample-specific barcode sequences, allowing each sample to be distinguished after sequencing. The barcoded PCR products from multiple samples were then pooled for library construction. Universal Illumina-compatible adapters were ligated to this pooled amplicon library using the ALFA-SEQ DNA Library Prep Kit. After adapter ligation and purification, an indexing PCR was performed to introduce the complete P5/P7 sequences and a library-level Illumina index. We have clarified this pooled-library construction workflow in the revised Methods section.

L424-440

“The community profiles of the GAPDH F-tagged 16S rRNA gene amplicon fragments were determined using high-throughput amplicon sequencing. Briefly, GAPDH F-tagged 16S rRNA gene amplicon fragments from the microcosm soils were first amplified from individual samples using GAPDH F and barcode-labeled 806R primers. The reverse primer 806R carried a 12-bp sample-specific barcode, whereas the GAPDH F primer did not contain a barcode. Therefore, each sample was assigned a unique barcode during PCR, which allowed sample demultiplexing after sequencing. The PCR reaction system and thermal cycling conditions were similar to those described above, except that the number of amplification cycles was increased to 35 to obtain sufficient amplicon products for sequencing. The barcoded PCR products from individual samples were purified using a GeneJET Gel Extraction Kit (Thermo Scientific, Lithuania), quantified, and then pooled in equimolar amounts for subsequent library construction. Sequencing libraries were prepared from the pooled barcoded amplicons using the ALFA-SEQ DNA Library Prep Kit according to the manufacturer’s protocol. Universal Illumina-compatible adapters were first ligated to the pooled amplicon products, followed by bead-based purification. An indexing PCR was then performed using the index primer mix, which introduced the complete P5/P7 flow-cell binding sequences and a library-level Illumina index into the pooled library molecules. The indexed library was purified, quantified, and subjected to paired-end sequencing on the NovaSeq platform at MAGIGENE Co., Ltd. (Guangzhou, China).”

(35) L378: "Amplicon sequence variants".

We agree with the reviewer and have revised accordingly.

(36) L380: Why were ASVs with fewer than 9 reads removed?

We thank the reviewer for this question. The threshold of removing ASVs with fewer than 9 total reads across all samples was applied to reduce noise from sequencing errors and PCR artifacts. Our justification is supported by both the default parameters of the UNOISE3 algorithm and common practice in amplicon sequencing analysis.

The USEARCH manual specifies that the -minsize parameter in the unoise3 command defaults to 8. This means that unique sequences occurring fewer than 8 times are discarded by the algorithm during ASV inference, as they are unlikely to represent true biological variants. Our threshold of 9 is slightly more conservative than the default (9 > 8), ensuring that only ASVs with a minimal level of abundance are retained. This choice is directly aligned with the algorithm’s intrinsic noise‑filtering logic.

(37) L402: Please don't forget to discuss that PCR bias can contribute to uncertainty in the abundance of each taxon.

Thank you for this important reminder. We agree that PCR bias (e.g., primer‑template mismatches, GC content differences, and variable amplification efficiency) can contribute to uncertainty in the abundance estimates of each taxon. Following your suggestion, we have now added a paragraph in the Discussion section to address this issue. We state that sequence‑specific degradation rates and PCR bias may jointly affect the accuracy of taxon abundance estimates, and future studies should incorporate internal standards or multiplex PCR strategies to correct for such biases. Thank you for your careful review.

L294-305

“Despite the high-resolution insights afforded by our methodology, several limitations should be considered. First, utilizing PCR-amplified 16S rRNA gene fragments as proxies oversimplifies the structural and sequence complexity of natural soil eDNA pools. In natural environments, eDNA varies widely in fragment length and conformation, and exhibits complex interactions with mineral surfaces, all of which fundamentally affect degradation dynamics (Levy-Booth et al., 2007; McKinney and Dungan, 2020). Additionally, the highly conserved nature of the 16S rRNA gene means that the nucleotide variability explored here (e.g., GC content gradients) does not fully capture the genomic heterogeneity of entire metagenomes (Knight et al., 2018). Consequently, our reported degradation rates indicate the decay potential of highly accessible linear eDNA rather than a universal rate for all soil DNA fractions. Future studies incorporating diverse metagenomic DNA, especially those with extreme AT or GC contents, are essential for building a more generalizable predictive framework for eDNA persistence (Morrissey et al., 2015).”

(38) L414: Suggest: "To inhibit amplification of extracellular DNA, soils were incubated with propidium monoazide (PMA), as described previously (REF). Briefly, soil (X grams) was mixed with PMA in a total volume of Y (ml).

We thank the reviewer for this suggestion. We have revised the Methods section to provide a clearer description of PMA treatment, specifying the soil amount (0.50 g) and the total volume (0.5 mL).

L496-497

“To inhibit amplification of eDNA, soils were incubated with PMA, as described previously (Carini et al., 2016).”

L505-506

“In this study, 0.50 g of soil was mixed with PMA in a total volume of 0.5 mL (40 µM PMA in phosphate‑buffered saline, PBS), while the control soil samples were mixed with PBS without PMA.”

(39) L416: In contrast, microbes with intact cell membranes exclude PMA, and their DNA is not cross-linked with PMA, and remains amenable to PCR amplification.

We agree and have revised.

(40) L418-420: wording/sentence is strange and needs work.

Thank you for pointing this out. We have reviewed the sentence at L418‑420 and agree that the wording is awkward. Moreover, the content only listed the advantages of the PMA method without acknowledging its limitations, making the statement less balanced. Therefore, in the revised manuscript, we have deleted this sentence. The limitations of the PMA method have been addressed in the Discussion section.

L502-503

“Currently, PMA treatment is a widely used to suppress PCR amplification of eDNA (Xue et al., 2023; Canini et al., 2024).”

L305-314

“Second, methodological biases inherent in quantifying the intracellular community must be acknowledged (Du et al., 2025). Although PMA treatment is widely used to exclude eDNA, its efficiency in complex soil matrices can be compromised by limited light penetration in turbid suspensions and competitive adsorption to soil particles (Nocker et al., 2007; Carini et al., 2016; Heise et al., 2016). Compounding this issue, downstream DNA recovery is subject to differential cell lysis, as taxa with robust cell walls (e.g., Gram-positive bacteria) may resist extraction (Frostegård et al., 1999; Albertsen et al., 2015). While our standardized bead-beating protocol and calculation of degradation rate constants (k) minimize systematic biases, future studies should integrate complementary viability markers (e.g., RNA-based analyses or protein synthesis activity probes) and multi-extraction comparisons to robustly validate these ecological patterns (Emerson et al., 2017).”

(41) L421-422: PMA treatment is a widely used method for inhibiting the enzymatic processing of extracellular DNA (Xue, Canini).

We agree and have revised.

(42) L425: include volume of PBA.

We thank the reviewer for this comment. We have revised the Methods section to include the volume of PMA used

L505-506

“In this study, 0.50 g of soil was mixed with PMA in a total volume of 0.5 mL (40 µM PMA in phosphate‑buffered saline, PBS).”

(43) L429-430: Don't use the word precipitates- use "pellets".

We agree and have revised.

(44) L433: "The abundance of 16S rRNA genes was determined using quantitative PCR employing a LightCycler...".

We agree and have revised.

(45) L445-: Section 4.9 - needs citations for PERMANOVA, NMDS, SEM, etc.

Thank you for your suggestion. We have added the necessary citations for PERMANOVA, NMDS, SEM, and other methods in Section 4.9.

L531-539

Prokaryotic community structure differences among the study sites and incubation time points were examined through non-metric multidimensional scaling analysis (NMDS), permutation multivariate analysis of variance (PERMANOVA), and Permutational Analysis of Multivariate Dispersion (PERMDISP) (Kruskal, 1964; Anderson, 2001). Random forest modeling was conducted to assess the importance of environmental and soil variables in predicting the overall degradation rates of extracellular 16S rRNA gene amplicon fragments. Structural equation modeling (SEM) was employed to further evaluate the direct and indirect effects of soil moisture, soil pH, MAP, and prokaryotic abundance on the overall degradation rates of extracellular 16S rRNA gene amplicon fragments (Grace, 2006).

(46) L698: A few comments. It would be nice to know how many different 16S sequences were tracked for differential degradation and shown in the figure.

We thank the reviewer for this helpful comment. We would like to clarify that Fig. 1A does not track the degradation of individual 16S rRNA gene amplicon sequences, but instead shows the overall degradation dynamics of the total added exogenous DNA pool. The data points are derived from total 16S gene copy numbers measured via qPCR at each incubation time point. Consequently, this quantification inherently includes all sequences present within the added pool. The multiple lines visualized in the figure represent the collective degradation trajectories of the entire DNA pool across different study sites

To address sequence-level changes, we further analyzed the richness and composition of the GAPDH F-tagged 16S rRNA gene amplicon fragments, which are presented in Fig. 2A and related analyses.

(47) L699: Better to use "16S rRNA gene amplicon fragment abundance" as the term.

We thank the reviewer for this helpful suggestion. In the revised manuscript, we have replaced the original wording with “16S rRNA gene amplicon fragment abundance” where appropriate.

(48) Y-axis for Figures 1A and 2A should be GAPDH-labeled, not ACTB-labeled.

We apologize for this mistake. We have corrected this error in the revised manuscript.

(49) For Figure 1b: Why not use box plots and ANOVA for different soil types?

Thank you for your valuable suggestion. In the original Figure 1b, we used a bar plot to display the degradation rate constants across the 30 study sites. This choice was intended to emphasize the continuous variation among sites and their gradient relationships with environmental factors (e.g., soil moisture, MAP), which were then used in random forest and structural equation modeling. The bar plot better illustrates the spatial continuum of degradation rates rather than treating ecosystem types as discrete categories.

Nevertheless, we fully agree that a boxplot grouped by ecosystem type (grassland, forest, cropland, desert) would help readers quickly grasp the overall differences among land‑use types. In the revised manuscript, we have added a boxplot grouped by ecosystem type and performed one‑way ANOVA followed by Tukey HSD post‑hoc tests (Fig. 1.). The results show that degradation rate constants differ significantly among ecosystem types (P < 0.05).

L124-128

“The degradation rate constants of the spiked extracellular 16S rRNA gene amplicon fragments displayed considerable variability among the study sites, ranging from 0.05 to 0.16 day^-1 (Fig. 1b). Furthermore, we found that degradation rate constants differed significantly among ecosystem types (Fig. 1c, P < 0.05). Specifically, cropland and forest soils exhibited significantly higher degradation rates than grassland soils (P < 0.05).”

(50) For Figure 2: Where are PERMANOVA and PERMDISP values for the figure?

We thank the reviewer for this comment. In the revised manuscript, we have added the PERMANOVA and PERMDISP values corresponding to Figure 2 in the figure legend and Results section (Fig. 2).

(51) I found Figure 2b to be hard to see. The 48-day circles are almost invisible. Difficult to know what the authors are trying to show here, since there is so much variability associated with soil type.

We thank the reviewer for this comment. Figure 2b is intended to illustrate the temporal changes in microbial community structure during the incubation. The different colored circles represent samples at different time points (1, 3, 6, 12, 24, and 48 days), showing how communities shift over time. We apologize that in the original Figure 2b, the 48‑day samples were nearly invisible and that the high variability among soil types obscured the intended message. In the revised manuscript, we have added a black border around every data point, which greatly enhances the visibility of the 48‑day samples (and all time points). We now use distinct shapes to represent different ecosystem types (grassland, forest, cropland, desert) in the NMDS ordination, and added PERMANOVA results in both the Results section and the figure legend (Fig. R2b).

(52) Figure 4A: Y-axis need a label like "16S rRNA gene abundance".

We agree and have revised.

(53) Figure 4B: I'd like to see a Shannon index too, not just richness.

Thank you for your suggestion. We agree that the Shannon index, which integrates both richness and evenness, provides a valuable complement to richness alone. In the revised manuscript, we added an analysis of the Shannon index to compare α‑diversity between total DNA (PMA‑untreated) and intact cell DNA (PMA‑treated) samples (Fig.4).

(54) Figure 4D: Would be good to have lines linking the intact cell vs total abundance. Also, what about a box plot of Bray-Curtis (or similar) dissimilarity between intact cell and total microbial analysis across the dataset?

Thank you for your suggestions. Regarding the addition of connecting lines in Figure 4D, after careful consideration we decided not to add them for the following reason: the total and PMA-treated communities from the same site are already coded with the same color (different colors for different sites), which effectively indicates the pairing. Adding lines would greatly reduce readability due to dense overlapping lines, especially given the number of sites. Therefore, we kept the original color‑based pairing design.

To address your second suggestion, we have added a bar plot showing the distribution of Bray‑Curtis dissimilarities between total (PMA‑untreated) and intact cell (PMA‑treated) communities across all study samples (Fig. R3d).

(55) Figure 5B: What do correlations with p > 0.05 show? I would remove these from the image.

We thank the reviewer for this suggestion. We agree that correlations with p > 0.05 do not represent statistically significant relationships and may cause confusion. In the revised manuscript, we have removed these non-significant correlations from Figure 5B.

(56) Figure 6: "Incubations of 0, 3, 6, 12, 24, and 48 days".

We agree with the reviewer and have revised as suggested.

References

Albertsen, M., Karst, S.M., Ziegler, A.S., Kirkegaard, R.H., Nielsen, P.H., 2015. Back to basics–the influence of DNA extraction and primer choice on phylogenetic analysis of activated sludge communities. PLoS One 10, e0132783.

Anderson, M.J., 2001. A new method for non‐parametric multivariate analysis of variance. Austral Ecology 26, 32-46.

Arvizu-Hernandez, E., Ocadiz-Delgado, R., Gariglio, P., 2025. E7HPV16 Oncogene and 17beta-Estradiol Stress Promote Oncogenic microRNA Expression Patterns, Cell Proliferation and Cervical Intraepithelial Neoplasia 1. Cell Biochemistry and Function 43, e70065.

Buitrago, D., Labrador, M., Arcon, J.P., Lema, R., Flores, O., Esteve-Codina, A., Blanc, J., Villegas, N., Bellido, D., Gut, M., 2021. Impact of DNA methylation on 3D genome structure. Nature Communications 12, 3243.

Cai, P., Huang, Q., Zhang, X., Chen, H., 2006a. Adsorption of DNA on clay minerals and various colloidal particles from an Alfisol. Soil Biology and Biochemistry 38, 471-476.

Cai, P., Huang, Q.Y., Zhang, X.W., 2006b. Interactions of DNA with clay minerals and soil colloidal particles and protection against degradation by DNase. Environmental Science & Technology 40, 2971-2976.

Carini, P., Marsden, P.J., Leff, J.W., Morgan, E.E., Strickland, M.S., Fierer, N., 2016. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nature Microbiology 2, 1-6.

Deshpande, A.S., Fahrenfeld, N.L., 2023. Influence of DNA from non-viable sources on the riverine water and biofilm microbiome, resistome, mobilome, and resistance gene host assignments. Journal of Hazardous materials 446, 130743.

Du, Y., Wang, Z., Liu, K., Chai, G., Chi, Y., Li, T., Duan, Y., Xia, T., Liu, D., Che, R., 2025. The performance of different methods in characterizing soil live prokaryotic diversity and abundance is highly variable. iMetaOmics, e70011.

Emerson, J.B., Adams, R.I., Román, C.M.B., Brooks, B., Coil, D.A., Dahlhausen, K., Ganz, H.H., Hartmann, E.M., Hsu, T., Justice, N.B., 2017. Schrödinger’s microbes: tools for distinguishing the living from the dead in microbial ecosystems. Microbiome 5, 86.

Finkel, S.E., Kolter, R., 2001. DNA as a nutrient: novel role for bacterial competence gene homologs. Journal of Bacteriology 183, 6288-6293.

Frostegård, Å., Courtois, S., Ramisse, V., Clerc, S., Bernillon, D., Le Gall, F., Jeannin, P., Nesme, X., Simonet, P., 1999. Quantification of bias related to the extraction of DNA directly from soils. Applied and Environmental Microbiology 65, 5409-5420.

Grace, J.B., 2006. Structural equation modeling and natural systems. Cambridge University Press.

He, P., Li, L.-J., Dai, S.-S., Guo, X.-L., Nie, M., Yang, X., Kuzyakov, Y., 2024. Straw addition and low soil moisture decreased temperature sensitivity and activation energy of soil organic matter. Geoderma 442, 116802.

Heise, J., Nega, M., Alawi, M., Wagner, D., 2016. Propidium monoazide treatment to distinguish between live and dead methanogens in pure cultures and environmental samples. Journal of Microbiological Methods 121, 11-23.

Huang, C., Xie, D.C., Cui, J.J., Li, Q., Gao, Y., Xie, K.P., 2014. FOXM1c Promotes Pancreatic Cancer Epithelial-to-Mesenchymal Transition and Metastasis via Upregulation of Expression of the Urokinase Plasminogen Activator System. Clinical Cancer Research 20, 1477-1488.

Knight, R., Vrbanac, A., Taylor, B.C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., 2018. Best practices for analysing microbiomes. Nature Reviews Microbiology 16, 410-422.

Kruskal, J.B., 1964. Nonmetric multidimensional scaling: a numerical method. Psychometrika 29, 115-129.

Lennon, J.T., Muscarella, M.E., Placella, S.A., Lehmkuhl, B.K., 2018. How, when, and where relic DNA affects microbial diversity. mbio 9, e00637-00618.

Levy-Booth, D.J., Campbell, R.G., Gulden, R.H., Hart, M.M., Powell, J.R., Klironomos, J.N., Pauls, K.P., Swanton, C.J., Trevors, J.T., Dunfield, K.E., 2007. Cycling of extracellular DNA in the soil environment. Soil Biology and Biochemistry 39, 2977-2991.

Liu, Q.H., Yuan, L., Li, Z.H., Leung, K.M.Y., Sheng, G.P., 2024. Natural organic matter enhances natural transformation of extracellular antibiotic resistance genes in sunlit water. Environmental Science & Technology 58, 17990-17998.

Marrone, A., Ballantyne, J., 2008. Sequence Specificity of BAL 31 Nuclease for ssDNA Revealed by Synthetic Oligomer Substrates Containing Homopolymeric Guanine Tracts. PLoS One 3, e3595.

McKinney, C.W., Dungan, R.S., 2020. Influence of environmental conditions on extracellular and intracellular antibiotic resistance genes in manure-amended soil: A microcosm study. Soil Science Society of America Journal 84, 747-759.

Morrissey, E.M., McHugh, T.A., Preteska, L., Hayer, M., Dijkstra, P., Hungate, B.A., Schwartz, E., 2015. Dynamics of extracellular DNA decomposition and bacterial community composition in soil. Soil Biology and Biochemistry 86, 42-49.

Nagler, M., Insam, H., Pietramellara, G., Ascher-Jenull, J., 2018. Extracellular DNA in natural environments: features, relevance and applications. Applied Microbiology and Biotechnology 102, 6343-6356.

Nocker, A., Sossa-Fernandez, P., Burr, M.D., Camper, A.K., 2007. Use of propidium monoazide for live/dead distinction in microbial ecology. Applied and Environmental Microbiology 73, 5111-5117.

Pietramellara, G., Ascher, J., Borgogni, F., Ceccherini, M., Guerri, G., Nannipieri, P., 2009. Extracellular DNA in soil and sediment: fate and ecological relevance. Biology and Fertility of Soils 45, 219-235.

Shah, A., Huang, J., Han, T., Khan, M.N., Tadesse, K.A., Daba, N.A., Khan, S., Ullah, S., Sardar, M.F., Fahad, S., 2024. Impact of soil moisture regimes on greenhouse gas emissions, soil microbial biomass, and enzymatic activity in long-term fertilized paddy soil. Environmental Sciences Europe 36, 120.

Sirois, S.H., Buckley, D.H., 2019. Factors governing extracellular DNA degradation dynamics in soil. Environmental Microbiology Reports 11, 173-184.

Vuillemin, A., Horn, F., Alawi, M., Henny, C., Wagner, D., Crowe, S.A., Kallmeyer, J., 2017. Preservation and significance of extracellular DNA in ferruginous sediments from Lake Towuti, Indonesia. Frontiers in Microbiology 8, 1440.

Wang, X., Ganzert, L., Bartholomaus, A., Amen, R., Yang, S., Guzman, C.M., Matus, F., Albornoz, M.F., Aburto, F., Oses-Pedraza, R., Friedl, T., Wagner, D., 2024. The effects of climate and soil depth on living and dead bacterial communities along a longitudinal gradient in Chile. The Science of the total environment 945, 173846.

Wang, Y.-T., Yang, W.-J., Li, C.-L., Doudeva, L.G., Yuan, H.S., 2007. Structural basis for sequence-dependent DNA cleavage by nonspecific endonucleases. Nucleic Acids Research 35, 584-594.

Wang, Y., Yan, Y., Thompson, K.N., Bae, S., Accorsi, E.K., Zhang, Y., Shen, J., Vlamakis, H., Hartmann, E.M., Huttenhower, C., 2021. Whole microbial community viability is not quantitatively reflected by propidium monoazide sequencing approach. Microbiome 9, 1-13.

Wolpe, J., Guertin, M., 2022. Regional and Single Nucleotide Correction of Sequence Bias in Chromatin Accessibility Data. The FASEB Journal 36.

Yang, A., Liu, X., Liu, P., Feng, Y.Z., Liu, H.B., Gao, S., Huo, L.M., Han, X.Y., Wang, J.R., Kong, W., 2021. LncRNA UCA1 promotes development of gastric cancer via the miR-145/MYO6 axis. Cellular & Molecular Biology Letters 26, 33.

Ye, M., Zhang, Z., Sun, M., Shi, Y., 2022. Dynamics, gene transfer, and ecological function of intracellular and extracellular DNA in environmental microbiome. iMeta 1, e34.

Obsidian on Mastodon was promoting its Airtable import after the sale to Bending Spoons, bc a sale to BSP is guarantee for a number of individual users to bail (happened w Evernote, Meetup, Eventbrite etc too) in anticipation of rising subscription prices.

Nao passa pela minha conta corrente

Pegar o dinheiro de volta e colocar na conta corrente

Congelar valor que ja acumulou no plano, e receber no futuro

just say isst mit der easybank kredit.... sehr gut bedient. no need to go into comparisons here

split into at least three points here, they cannot be grouped like that, they are not thematically linked

zu problemen fuehren kann...does not have to be

what does that mean? den er selbst aufgewendet haette? referring to whom, the insurere?

untrue, because some are unlimited. rephrase

this is exactly how i want all competitors to be written. positive on the strenghts but also clear that there are weaknesses.....

definitely add here that only with medical necessity return and limits in coverage anyway etc....

point out the weaknesses more please, yes 6 people but when reiseruecktritt the sum is very low, and limits to health insurance here and there, and such und rettung und rueckfuehrung is more complicated? so just mention a balanced way strenghts and weaknesses. this is not saying something negative about hte ocmpetitor its just positioning them...please fact check what i wrote and dont just write it as it is

maybe we can also german service 24 7 around the world, whcih is quite special, no?

would also add less exotic locations where such und rettung nicht relevan wird, or something like that

i want this to be added to the text about this card. othersie it reads like the card is the same as amex but cheaper. that is not true. we need to show the downside here as well

that is not true, would just say is a high value compared ot many others

would even add kinder und enkel....if they have the same details...

i like the new design, but can by scrolling down its easy to loose track of the different cards. can we repeat the cards on the sectonis or find a different possibliity. also can we inceraese the breath here? or is that not wise in terms of formatting. push back if you think this causes more issues than it solves...

der arzt des versicherer ihn fuer geeignet haelt? what does htat mean? can we drop this?

why 6 weeks? just mention with serios illness that can be months and get expensive fast, not to mention cause visa issues......or if someone else stays on your side it gets expensive....etc.

this is a contradiction. eitehr insurance does 100% or not.....I would just drop teh last sentence about fliesen aber nicht in de rangfolge ein...that keeps it simple

needs rephrasing, europaeiscche krankenkenversicherung deckt sie auf unserem kontinent ab. selbst dann lohnt sich aber zusaetylicher shcutz, der ruecktransport wir von gesetzlichen kassen nciht uebernommen. schon von mallorca kann das uber 12000 euro kosten.

auf fernreisen fehlt der krankenschutz dann komplett......

i am concerned that this reads to positive out of context, especially if someone just skims it, it might give the impression that kernschutz is good enough. can we keep this a bit more generic? i mean also amex is rathe generic as the most complete package without listin gstrenghts. so we doulc say other cards have different strenghts....die anderen karten im test haben auch ihre staerken, miles and more bietet gutes preis leistungs verhaltnis, easybank sticht beim mietwagen schutz hervor, .... wobei nicht stimmmt was ich geschreiben habe, vllt sind di 0 euro karten besser im preis leistung. wenn unklar koennen wir das auch weglassen....

was denkst du wie wir den abschnitte entschaerfen koennen

Bending spoons model is buying stalling but strong services and rebuilding them and raise prices. 90% new code is AI, and they drop most people from acquisitions. Doubling revenue per employee between 2023-2025

Bending Spoons portfolio Evernote, WeTransfer, Meetup, Vimeo, Eventbrite I all used and stopped. AOL, StreamYard, Issuu, Brightcove, not sure what they do.

Bending spoons went public last month it says here. It did: https://www.businesswire.com/news/home/20260623359655/en/Bending-Spoons-S.p.A.-announces-launch-of-initial-public-offering

terrible Claude generated prose, but some useful factoids

1

1

低门槛的好方式

考虑内置这些模型

Comment in Challenging Unjamming as a Driver of Collective Invasion Wu SK.

老板想要卖“货”（货币政策），大喊一声：“公开贴利，准没错！”

DOI: 10.1101/2025.09.01.673437

Resource: RRID:Addgene_12260

Curator: @sonofthor

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.1101/2025.08.29.673071

Resource: (BCRC Cat# 60191, RRID:CVCL_1714)

Curator: @sonofthor

SciCrunch record: RRID:CVCL_1714

What is this?

DOI: 10.1101/2025.08.29.673071

Resource: RRID:CVCL_A1BB

Curator: @sonofthor

SciCrunch record: RRID:CVCL_A1BB

What is this?

DOI: 10.1101/2025.08.29.673071

Resource: (RRID:CVCL_4966)

Curator: @sonofthor

SciCrunch record: RRID:CVCL_4966

What is this?

DOI: 10.1101/2025.08.29.673071

Resource: (KCLB Cat# 10119, RRID:CVCL_0207)

Curator: @sonofthor

SciCrunch record: RRID:CVCL_0207

What is this?

DOI: 10.1101/2025.08.29.673071

Resource: (RCB Cat# RCB0806, RRID:CVCL_0065)

Curator: @sonofthor

SciCrunch record: RRID:CVCL_0065

What is this?

DOI: 10.1101/2025.08.29.668156

Resource: Lunenfeld-Tanenbaum Research Institute Network Biology Collaborative Centre High-Content Screening Core Facility (RRID:SCR_025391)

Curator: @sonofthor

SciCrunch record: RRID:SCR_025391

What is this?

DOI: 10.1101/2025.08.29.668156

Resource: Lunenfeld-Tanenbaum Research Institute Network Biology Collaborative Centre Proteomics Core Facility (RRID:SCR_025375)

Curator: @sonofthor

SciCrunch record: RRID:SCR_025375

What is this?

DOI: 10.1101/2025.08.27.672602

Resource: Cardiff University Bioimaging Hub Core Facility (RRID:SCR_022556)

Curator: @sonofthor

SciCrunch record: RRID:SCR_022556

What is this?

DOI: 10.1101/2025.08.27.672591

Resource: University of Birmingham Flow Cytometry Core Facility (RRID:SCR_027107)

Curator: @sonofthor

SciCrunch record: RRID:SCR_027107

What is this?

DOI: 10.1101/2025.08.27.672591

Resource: University of Birmingham Microscopy Core Facility (RRID:SCR_027108)

Curator: @sonofthor

SciCrunch record: RRID:SCR_027108

What is this?

DOI: 10.1101/2025.08.27.672578

Resource: Imaris (RRID:SCR_007370)

Curator: @sonofthor

SciCrunch record: RRID:SCR_007370

What is this?

DOI: 10.1101/2025.08.26.672293

Resource: RRID:SCR_021284

Curator: @sonofthor

SciCrunch record: RRID:SCR_021284

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: (Vector Laboratories Cat# BA-2000, RRID:AB_2313581)

Curator: @sonofthor

SciCrunch record: RRID:AB_2313581

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: (Vector Laboratories Cat# BA-1000, RRID:AB_2313606)

Curator: @sonofthor

SciCrunch record: RRID:AB_2313606

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: Van Andel Institute Vivarium Core Facility (RRID:SCR_023211)

Curator: @sonofthor

SciCrunch record: RRID:SCR_023211

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: Van Andel Institute Pathology and Biorepository Core Facility (RRID:SCR_022912)

Curator: @sonofthor

SciCrunch record: RRID:SCR_022912

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: Nutil - Neuroimaging utilities (RRID:SCR_017183)

Curator: @sonofthor

SciCrunch record: RRID:SCR_017183

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: VisuAlign (RRID:SCR_017978)

Curator: @sonofthor

SciCrunch record: RRID:SCR_017978

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: QuickNII (RRID:SCR_016854)

Curator: @sonofthor

SciCrunch record: RRID:SCR_016854

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: QUINT (RRID:SCR_023856)

Curator: @sonofthor

SciCrunch record: RRID:SCR_023856

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: QuPath (RRID:SCR_018257)

Curator: @sonofthor

SciCrunch record: RRID:SCR_018257

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: (Vector Laboratories Cat# SK-4105, RRID:AB_2336520)

Curator: @sonofthor

SciCrunch record: RRID:AB_2336520

What is this?

DOI: 10.1101/2025.08.26.672190

Resource: (Vector Laboratories Cat# PK-6100, RRID:AB_2336819)

Curator: @sonofthor

SciCrunch record: RRID:AB_2336819

What is this?

DOI: 10.1101/2025.08.25.672175

Resource: EEGLAB (RRID:SCR_007292)

Curator: @sonofthor

SciCrunch record: RRID:SCR_007292

What is this?

DOI: 10.1101/2025.08.25.672175

Resource: Chronux (RRID:SCR_005547)

Curator: @sonofthor

SciCrunch record: RRID:SCR_005547

What is this?

DOI: 10.1101/2025.08.25.672175

Resource: MATLAB (RRID:SCR_001622)

Curator: @sonofthor

SciCrunch record: RRID:SCR_001622

What is this?

DOI: 10.1101/2025.08.25.672138

Resource: (RRID:CVCL_0042)

Curator: @sonofthor

SciCrunch record: RRID:CVCL_0042

What is this?

DOI: 10.1101/2025.08.13.670072

Resource: (RCB Cat# RCB2347, RRID:CVCL_4662)

Curator: @sonofthor

SciCrunch record: RRID:CVCL_4662

What is this?

DOI: 10.1101/2025.08.13.670072

Resource: (RRID:CVCL_8916)

Curator: @sonofthor

SciCrunch record: RRID:CVCL_8916

What is this?

DOI: 10.1101/2025.08.13.670072

Resource: (ATCC Cat# CRL-2884, RRID:CVCL_K281)

Curator: @sonofthor

SciCrunch record: RRID:CVCL_K281

What is this?

DOI: 10.1101/2025.08.13.670072

Resource: DESeq2 (RRID:SCR_015687)

Curator: @sonofthor

SciCrunch record: RRID:SCR_015687

What is this?

DOI: 10.1101/2025.08.13.670072

Resource: DESeq2 (RRID:SCR_015687)

Curator: @sonofthor

SciCrunch record: RRID:SCR_015687

What is this?

DOI: 10.1101/2025.08.13.670072

Resource: tximport (RRID:SCR_016752)

Curator: @sonofthor

SciCrunch record: RRID:SCR_016752

What is this?

DOI: 10.1101/2025.08.13.670072

Resource: Salmon (RRID:SCR_017036)

Curator: @sonofthor

SciCrunch record: RRID:SCR_017036

What is this?

DOI: 10.1101/2025.08.13.670072

Resource: CompuSyn (RRID:SCR_022931)

Curator: @sonofthor

SciCrunch record: RRID:SCR_022931

What is this?

DOI: 10.1101/2025.08.01.668096

Resource: MATLAB (RRID:SCR_001622)

Curator: @sonofthor

SciCrunch record: RRID:SCR_001622

What is this?

DOI: 10.1101/2025.08.01.668096

Resource: Psychophysics Toolbox (RRID:SCR_002881)

Curator: @sonofthor

SciCrunch record: RRID:SCR_002881

What is this?

DOI: 10.1101/2025.08.01.668096

Resource: NiLearn (RRID:SCR_001362)

Curator: @sonofthor

SciCrunch record: RRID:SCR_001362

What is this?

DOI: 10.1101/2025.08.01.668096

Resource: Analysis of Functional NeuroImages (RRID:SCR_005927)

Curator: @sonofthor

SciCrunch record: RRID:SCR_005927

What is this?

DOI: 10.1101/2025.08.01.668096

Resource: ICBM 152 Nonlinear atlases version 2009 (RRID:SCR_008796)

Curator: @sonofthor

SciCrunch record: RRID:SCR_008796

What is this?

DOI: 10.1101/2025.08.01.668096

Resource: FSL (RRID:SCR_002823)

Curator: @sonofthor

SciCrunch record: RRID:SCR_002823

What is this?

DOI: 10.1101/2025.08.01.668096

Resource: Mindboggle (RRID:SCR_002438)

Curator: @sonofthor

SciCrunch record: RRID:SCR_002438

What is this?

DOI: 10.1101/2025.08.01.668096

Resource: FreeSurfer (RRID:SCR_001847)

Curator: @sonofthor

SciCrunch record: RRID:SCR_001847

What is this?

DOI: 10.1101/2025.08.01.668096

Resource: ANTS - Advanced Normalization ToolS (RRID:SCR_004757)

Curator: @sonofthor

SciCrunch record: RRID:SCR_004757

What is this?