The ability to label fractionally more of the sialoglycoRNAs in the cell opened the possibility that we may be detecting novel sialoglycoRNAs in addition to the forms found initially with Ac4ManNAz. For example, while we noticed clear and significant loss of rPAL signal after NGI-1 and Kif treatment of HeLa cells, the effects were less than what we had previously seen only with Ac4ManNAz-labeling
Very cool!
Finally, to establish the high MW signal is indeed sialoglycoRNA, we evaluated its sensitivity to enzymatic digestion. Incubation of purified RNA with RNase or sialidase and subsequent rPAL-labeling results in total loss of all rPAL signal after RNase but selective and complete loss of the high MW signal after sialidase treatment (Figure 1E), consistent with this rPAL-based method labeling sialic acids in sialoglycoRNAs.
Great and convincing specificity control. This would be a better way of confirming Neu5Ac-specific labeling than my suggestion above because it can be used in a culture-independent fashion.
While rPAL improves sensitivity of apparent high molecular weight (MW) glycoRNA species, it also induces background labeling; most notably the 18S rRNA and the small RNA pool (Figure 1C and elsewhere).
Do you think combining Ac4ManNAz and rPAL labeling could be a good way to both specifically identify Neu5Ac-ligated RNA and amplify that signal using orthogonal labels (perhaps Biotin and a FLAG tag) with different fluorophores?
Extracting RNA from Ac4ManNAz labeled HeLa cells with TRIzol and either performing copper-free click1 or rPAL, we found that rPAL generates approximately 150x the amount of signal (Figure 1C, lanes 1-3 vs 7-9).
Would this difference in signal intensity be smaller if the copper-free click reaction were incubated longer or if a higher concentration of DBCO-biotin were used?
sialoglycoRNA
This approach seems to work well for carbohydrates susceptible to periodate oxidation like Neu5Ac, but do you think there are strategies that could target non-NeuAc-containing RNA-glycoconjugates?
The single homology model (HM0) was evaluated in its ability to discriminate agonists and antagonists from decoys, obtaining EF value for agonists discrimination slightly better than AF0 and IT0 (Fig. 2) and the top AUC registered within the models of this study. On the other hand, poor results are obtained for the antagonist model.
This is interesting and highlights the utility of this work.
Superposition between IT3+ in complex with flufenamic acid (both in green licorice) and IT3-in complex with the flufenamic acid derivative antagonist LW131 (both in orange licorice). The two ligands overlap within the binding site. The H-bond interaction observed only in the IT3+ between 3.36 and 6.48 is shown as a yellow dotted line and it is potentially affected by the presence of the para substituent on ring B (orange transparent circle) of the antagonist.
Just a minor suggestion: the 4-chloro substituent in the antagonist molecule is a little difficult to discern in figure 3b because the 4-chloro group is a similar color to the carbon skeleton of the overlapping flufenamic acid agonist. Changing the 4-chloro group to a more distinct color would be helpful.
In an extensive TAS2R14 mutagenesis study, Nowak et al suggested that flufenamic acid and aristolochic acid bind differently in the receptor-binding pocket
Would it be possible to generate hypothetical models for aristolochic acid binding using techniques employed in this study?
Among the antagonists with novel scaffolds discovered through the computational study, LF1, LF14, and LF22 were able to reduce the activity of the receptor and block flufenamic acid induced TAS2R14 activity with a half-maximal inhibitory concentration of 6.8±3.2, 22±16 and 7.2±3μM, respectively
Being able to tolerate a two atom bridge between the two key aromatic rings would increase antagonist diversity (LF1, LF14). LF22 is also interesting as there doesn't seem to be a strict requirement that there are two closely bridged aromatic rings in the inhibitor backbone. Was LF22 tested in racemic form? If so, is one isomer expected to be a better inhibitor than the other?
(Table 2
Can you provide some commentary about why LW209 and LW145 appear to be similarly effective in the antagonist assay but LW209 appears to be an order of magnitude more effective (as an inhibitor) in the agonist assay? Is this just an assay artifact?
further confirming our hypothesis that these cells can be differentiated by their unique lipid signatures serving as molecular ‘barcodes’.
This is such an interesting revelation! It begs atleast three additional questions for future studies. (1) Do these cell types maintain the same lipid composition outside the tonsils, (2) does differential lipid compositioning between the same cell type indicate anything about the state of a cell, and (3) does lipid composition track with the upregulation of specific enzymes involved in the syntheses of these particular lipids (would rummaging through existing transcriptomics or proteomics datasets be helpful?)
Tonsils were anonymized discarded pathologic specimens obtained from Children’s National Medical Center (CNMC) under the auspices of the Basic Science Core of the District of Columbia Developmental Center for AIDS Research
Were these tonsils extracted from patients undergoing treatment for similar conditions? Would you expect to find more or less concordance in lipid composition between cell types if healthy patients' tonsils were under examination? The answer to these questions may extend beyond the scope of this investigation so please feel free to ignore; these are such cool results that these questions had to be asked.
Since MSI is performed in situ, the only currently available instruments that can separate isobaric ions prior to the mass analyzer are hybrid ion mobility spectrometer-mass spectrometers
MALDI imaging on a timstof flex could yield some interesting additional insights in future investigations! Alternatively, a less preferable approach to resolving isobars could involve applying an ozonolysis approach to cleave lipids at sites of unsaturation. This could potentially resolve some PC and SM species differing by acyl-proximal unsaturation points. Setting this up on a MALDI-ready slide may not be trivial but performing this on bulk-extracted samples should be approachable. Bulk extracted samples might also be amenable to structural characterization by UVPD (https://pubs.acs.org/doi/10.1021/acs.analchem.6b03353).
Relevant scripts and intermediate files can be found in our Github repository https://github.com/LiZhaoLab/utORF_mass_spec.
Github link may be broken or moved?
N-terminal acetylation as variable modifications
This isn't critical but curious how many N-terminally modified peptides were observed overall and how that compares to the overall number of overall PSMs observed without this variable modification?
using MaxQuant v. 1.6.1.0
What were your criteria for positive protein/ORF-product identification by MS? Where applicable, were utORFs identified by more than one unique peptide? Each additional unique peptide could add a lot of confidence to utORF discovery.
OutlookTogether, our results show that evolution of young proteins may progress along different, distinct trajectories in Drosophila. Whether similarly distinct trajectories are apparent in other model species such as yeast or mammals remains to be seen. Of note, Drosophila is a taxon of multicellular organisms with a large effective population size, so selective processes are more efficient; mammals – especially primates and Homo – are evolutionarily young and have a smaller effective population size, while yeasts are unicellular. If these factors affect general evolutionary properties, such as the selective cost of translation of lowly functional proteins and the probability of fixation by drift, it is possible that they may affect the evolution of de novo proteins. In the case of Homo, all these factors may be more favorable to the fixation of new de novo proteins, and the availability of broad and varied -omics data is unparalleled. It would therefore be an obvious extension to employ a similar approach to investigate possible utORFs and de novo proteins in humans.
This is an interesting approach to utORF discovery and a useful reexamination of publicly available biological data resources.
Accordingly, to improve total sensitivity while maintaining an acceptable FDR, we used two rounds of analysis
Large protein sequence databases are common in metaproteomics and this paper describes a 2-step approach similar to the one used here: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201200352
While this approach has generated some criticism with respect to false discovery rate estimation, it can still be a useful tool for discovery.