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Record W4283741416 · doi:10.1038/s41467-022-30351-2

Reply to: Pitfalls in using phenanthroline to study the causal relationship between promoter nucleosome acetylation and transcription

2022· letter· en· W4283741416 on OpenAlexafffund
Benjamín Martín, LeAnn J. Howe

Bibliographic record

VenueNature Communications · 2022
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsNucleosomeAcetylationTranscription (linguistics)Computational biologyGeneticsPromoterHistoneTranscription factorBiologyDNAGeneGene expression

Abstract

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We appreciate the comments of Zencir et al. regarding our manuscript on the impact of transcription on genome-wide histone acetylation patterns 1 . We agree that our paper shifts our understanding of how acetylation is targeted to transcribed genes and thus the more scrutiny the results receive, the better. With this being said, we respectfully disagree that the loss of histone acetylation in cells treated with the transcription inhibitor, 1,10-phenanthroline (1,10-pt), is due to the disruption of histone acetyltransferase (HAT) binding to promoters. Although we do not dispute that the binding of some transcription activators, and thus HATs, may be impacted by this drug, the NuA4/piccolo HAT complex subunit, Epl1, is not lost at most promoters following 1,10-pt treatment. Using DESeq2’s binomial test 2 with a lenient cutoff ( p -value < 0.1, 1.5 fold-change), we observe a statistically significant loss of Epl1 binding at only 3.6% (198/5542) of promoters (Fig. 1a ). This re-analysis of our data is completely consistent with the heatmap in our original manuscript (Figure S5A) 1 , which shows that upon transcription inhibition: (1) Epl1 binding is stable at most promoters, albeit with a small fraction showing decreased binding, and (2) Epl1 is ubiquitously lost from 5′ gene body regions, consistent with our interpretation of transcription-dependent targeting of Epl1 to gene bodies. While the binding of Epl1 to both promoters and gene bodies is difficult to differentiate in the gene-dense yeast genome, the contrasting impact of Epl1 mutation and 1,10-pt treatment on Epl1 binding to these features confirms the specificity of our ChIP-seq experiments and strengthens our finding that Epl1 binding is stable to 1,10-pt at the vast majority of promoters. While Zencir et al. raise good points about the pitfals of metaplots, the heatmaps we included in our original paper address many of the points they now claim to uncover. Despite the limited impact of 1,10-pt on Epl1 occupancy, we observed loss of H4K8ac and H4K12ac at the majority of adjacent +1 nucleosomes (Fig. 1b, c ). We do observe Epl1 loss at a larger portion of promoters with Epl1-peaks (126/562 promoters, Fig. 1d ), but Fig. 1e, f show that acetylation is lost at nucleosomes adjacent to all promoters in this class, even those exhibiting increased Epl1 occupancy, suggesting that loss of acetylation cannot be simply explained by loss of HAT recruitment. These data provide indirect support that the binding of transcription factors to most promoters is undisturbed upon 1,10-pt treatment, consistent with the results of Poramba-Liyanage et al., which characterized the impact of 1,10-pt on the occupancy of general transcription factors at a transcribed locus 3 . Interestingly, Fig. 1a, b, d in Zencir et al. also reinforce this point. At the genes shown, Rpb3 binding to promoters, presumably via recruitment by transcription activators, is not impacted by 1,10-pt. Regardless, the key point is that Epl1 occupancy at most genes is largely unaffected by 1,10-pt, which is inconsistent with the Zencir et al. statement that this drug has profound genome-wide effects on the localization and activity of transcription activators, as well as HAT and HDAC complexes. Again, we do not dispute that some promoters might be impacted, such as those of genes encoding ribosomal proteins and proteins involved in ribosome biogenesis, however, although these genes, represent a large portion of RNAPII initiation events, they are a small fraction of total genes (~350 in total), and thus account for a very small percentage of acetylated histones in the cell. Zencir et al. also speculate that increased targeting of the HDAC, Rpd3, could explain the decrease in acetylation following 1,10-pt treatment. However, we tested this possibility in our original paper (Supplementary Fig. 2e) 1 , finding that the decrease in bulk histone acetylation was not due to increased HDAC activity. Fig. 1: Transcription inhibition by 1,10-phenanthroline (1,10-pt) does not impact Epl1 occupancy at most promoters. Scatter plots comparing the effect of 1,10-pt on fragment midpoint counts from Epl1 ( a , d ), H4K8ac ( b , e ), or H4K12ac ( c , f ) ChIP-seq, over promoters ( a and d ) or associated +1 nucleosomes ( b , c , e , and f ) of all (5542) genes ( a , b , c ) or of 562 genes with NDR Epl1 peaks identified in MNase ChIP-seq in control cells ( d , e , f ). Promoters with >1.5 fold-change in Epl1 using an adjusted p -value < 0.1 (DESeq2’s binomial test 2 ) are shown in red (increased) or blue (decreased). In b , c , e , and f , the line is: y = x + 0. Full size image

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.046
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0460.050
Insufficient payload (model declined to judge)0.0050.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.349
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2022
Admission routes2
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