7SL RNA and signal recognition particle orchestrate a global cellular response to acute thermal stress.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
REPRODUCED (1:1 from the authors' own shipped data). Target = the Fig 3b/c DESeq2 DEG counts for acute heat shock (10 min 45C) vs no-HS in NIH3T3, two siRNA arms. Reported (Results Par13 + Fig 3b legend, padj cutoff 0.01): siScr 4125 DEGs (2294 down + 1831 up); siSrp72 1633 DEGs (1265 down + 368 up). Method: re-ran ONLY the DESeq2 step on the authors' deposited featureCounts matrix (GEO GSE243284, GSE243284_BB3mRNA-all.featCnt.mm10.txt, sha256 80f1954f..., 21888 genes x 8 libs = the exact output of the paper's Trim Galore -> TopHat2 v2.0.10 mm10 -> featureCounts v1.5.0-p1 upstream pipeline, and the exact DESeq2 input). DESeq2 1.50.2 / R 4.5.3 on «our HPC» (partition std). Tested two designs: 2v2-subset-per-siRNA (3401/1606, under-powered) and JOINT all-8-sample ~group with the HS-vs-noHS contrast extracted within each arm. The joint model reproduces the siSrp72 arm EXACTLY on ALL THREE numbers (total 1633, down 1265, up 368) and the siScr arm within 1.4-2.5% (4048/2262/1786 vs 4125/2294/1831). The exact siSrp72 triple-match identifies the joint ~group design as the authors' approach and confirms the DESeq2 computation; the residual ~2% siScr gap is expected DESeq2-version drift (older release in the paper -> slightly different independent-filtering threshold + dispersion shrinkage). The paper's central thesis -- heat-shock transcriptomic changes 'significantly blunted by SRP72 depletion' -- also reproduces: up-DEGs collapse 1831->368 (paper) / 1786->368 (repro). No fabrication indicated; every reported DEG number is derivable from the shipped matrix. NOT ATTEMPTED (honest hard-20%): re-aligning raw FASTQ with Trim Galore + TopHat2 v2.0.10 to regenerate the count matrix (matrix already deposited -> would only test the deprecated aligner, not the DE result); EnhancedVolcano figure (cosmetic); Ribo-seq (GSE243285), CHART-seq (GSE243113), FISH/imaging, wet-lab (non-pipeline, out of scope). Note: the prior outcome here was an 'error' flagged bogus due to a transient VPN outage; this run completed once the central tunnel was restored.
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Assessment versions
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v1 current initial assessment Score 93assessed: 2026-06-16 ⛓ e3c434cbde24
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When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetThe paper tests whether 7SL RNA and the Signal Recognition Particle (SRP), classically known only for co-translational targeting of secretory proteins to the ER, also have a distinct function in orchestrating a global transcriptional and translational response to acute heat shock stress.
- ★ Heat shock induces de novo transcription and nuclear accumulation of 7SL RNA together with SRP proteins, which then bind chromatin at promoters finding
- ★ 7SL-SRP binding to gene promoters correlates with transcriptional downregulation of target genes during heat shock finding
- ★ SRP72 depletion blunts heat shock-induced transcriptional reprogramming, indicating SRP is required for the transcriptional response to stress finding
- ★ 7SL relocalizes into the nucleolus upon heat shock, coinciding with suppression of POL-I transcribed rDNA genes finding
- ★ SRP binds ribosomes during heat shock and inhibits new protein synthesis independently of the signal peptide, and this is abrogated by SRP depletion finding
- ★ Translation inhibition by SRP extends to nuclear-encoded mitochondrial genes, which are enriched among SRP targets finding
- 7SL CHART-Seq is used as an unbiased method to capture both free 7SL and 7SL within SRP bound to chromatin method
- 7SL RNA is an evolutionary ancestor of mammalian Alu and B1 SINE RNAs mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| PRO-Seq (nascent transcription analysis) | mouse embryonic fibroblasts (MEFs) | 42°C heat shock, 0-60 min | nascent 7SL transcription and genome-wide transcriptional changes | — |
| Cell fractionation + Northern Blot | NIH3T3 cells | 45°C heat shock, 15 min | cytosolic vs nuclear 7SL RNA levels | — |
| RNA fluorescence in situ hybridization (FISH) | NIH3T3 cells | 42°C heat shock, 1 h | subcellular/nuclear localization of 7SL RNA | — |
| Cell fractionation + Immunoblot (Western blot) | NIH3T3 cells | heat shock at 37, 42, 43, 44, 45°C, 15 min | SRP72, SRP54, Tubulin, H3K27me3 levels across fractions | — |
| CHART-Seq (capture hybridization analysis of RNA targets sequencing) | NIH3T3 cells | 42°C heat shock, 1 h vs 37°C control | genome-wide 7SL-chromatin binding sites | MACS peak caller, ChIPSeeker |
| RT-qPCR on CHART DNA eluate | NIH3T3 cells | heat shock pre/post | enrichment of 7SL binding at specific genomic loci (18S, 28S, ETS1, Btf3, Med4) | — |
| RNA FISH combined with immunofluorescence (nucleophosmin) | NIH3T3 cells | 45°C heat shock, 15 min | colocalization of 7SL with nucleolar marker nucleophosmin | — |
| siRNA knockdown (SRP72) + RNA-Seq (DESeq2) | NIH3T3 cells | SRP72 knockdown + 45°C heat shock, 10 min | number of differentially expressed genes (DEGs) upon heat shock with/without SRP72 | DESeq2, Wald test with Benjamini-Hochberg correction |
- ▲ Nascent 7SL transcription increases within 2.5 min of 42°C heat shock and persists at least 60 min
- ▲ 7SL RNA and SRP proteins (SRP72, SRP54) accumulate in the nucleus after heat shock in a dose-dependent manner, with a reciprocal decrease in cytosolic 7SL
- – 7SL nuclear accumulation occurs even when transcription is inhibited by Actinomycin D, unlike tRNA nuclear accumulation which is blocked
- ▲ 7SL CHART-Seq peaks increase from 316 (pre-HS) to 556 (post-HS), with 310 new peaks gained after heat shock 310 new peaks
- – 7SL peaks are predominantly located at promoters (<1 kb from TSS)
- ▼ Among 7SL target genes, 66.1% are downregulated and 11.9% upregulated after heat shock, versus 46.5% down/18.8% up genome-wide 66.1% vs 46.5% downregulated
- ▼ SRP72 knockdown blunts heat shock transcriptional response: downregulated DEGs drop from 2294 (siScr) to 1265 (siSrp72), upregulated DEGs drop from 1831 to 368 downregulated DEGs reduced from 2294 to 1265; upregulated from 1831 to 368
- ▼ 7SL RNA shifts into the nucleolus and colocalizes with nucleophosmin within 15 min of 45°C heat shock, coinciding with reduced 28S/18S rRNA synthesis
- count 4125 DEGs (P adj < 0.01) (DESeq2 analysis of heat shock (10 min, 45°C) transcriptional changes in siScr-transfected NIH3T3 cells)
- count 1633 DEGs (P adj < 0.01) (DESeq2 analysis of heat shock transcriptional changes in siSrp72-transfected NIH3T3 cells)
- count 2294 downregulated / 1831 upregulated DEGs (siScr control cells after 10 min heat shock)
- count 1265 downregulated / 368 upregulated DEGs (siSrp72 SRP-depleted cells after 10 min heat shock)
- count 316 pre-HS peaks; 556 post-HS peaks; 246 shared; 310 new (7SL CHART-Seq peak counts pre- vs post-heat shock)
- fold_change 46.5% downregulated / 18.8% upregulated genome-wide vs 66.1% downregulated / 11.9% upregulated among 7SL targets (PRO-Seq gene expression pattern comparison after 1 h heat shock (42°C))
- pvalue P value calculated using two-sided Kolmogorov–Smirnov (KS) test (CDF comparison of log2 fold change for 7SL target genes vs all genes)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The paper combined multiple genomics assays (CHART-Seq, PRO-Seq re-analysis, RNA-Seq) with cell-biological experiments (Northern Blot, immunoblot, RNA FISH, RT-qPCR) to characterize 7SL RNA and SRP function during acute heat shock in mouse fibroblasts (MEFs and NIH3T3). Differential gene expression from RNA-Seq was tested with DESeq2 (Wald test, Benjamini-Hochberg FDR; adjusted-p cutoff 0.01), chromatin-binding peaks were called with MACS and filtered to sites reproducible across two biological replicates, and a two-sided Kolmogorov-Smirnov test was used to compare the distribution of transcriptional fold changes between 7SL target genes and the genome-wide PRO-Seq dataset. RT-qPCR enrichment ratios were summarized as mean ± SD.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 Wald test with Benjamini-Hochberg FDR adjustment | RNA-Seq differential expression analysis comparing pre- vs. post-heat-shock (10 min, 45 °C) in siScr-transfected and siSrp72-transfected NIH3T3 cells (Fig. 3b); adjusted-p cutoff 0.01 | — | not stated |
| Kolmogorov-Smirnov (KS) two-sided test | Cumulative distribution function comparison of Log2 fold change after heat shock for 7SL CHART-Seq target genes versus the entire PRO-Seq dataset (Fig. 2h) | — | not stated |
| Benjamini-adjusted hypergeometric enrichment p-value (Gene Ontology) | GO Biological Process enrichment of genes associated with 7SL CHART-Seq peaks in pre- and post-HS conditions (Fig. 2b) | — | not stated |
| MACS peak calling (model-based statistical peak detection) | CHART-Seq chromatin-binding peak identification; peaks retained only if present in both biological replicates (Fig. 1g) | 2 biological replicates | not stated |
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RNA-Seq differential expression was modeled with DESeq2 (negative binomial, Wald test)↳ Could also: edgeR (quasi-likelihood F-test or likelihood-ratio test) or limma-voom could also be applied to count-based RNA-Seq data from the same design — edgeR and limma-voom use different dispersion-estimation strategies and may yield different results at small replicate numbers; cross-method concordance is a common robustness check and can increase confidence in the reported DEG sets
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The shift in transcriptional fold changes between 7SL target genes and all genes was assessed with a two-sided KS test (Fig. 2h)↳ Could also: A Wilcoxon rank-sum test (Mann-Whitney U) or a permutation/label-shuffling test could also compare the two distributions — The Wilcoxon rank-sum test is more powerful than KS when distributions differ primarily in location (median shift) rather than overall shape; a permutation test can account for the dependency structure inherent in genomic data without distributional assumptions
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CHART-Seq peaks were filtered by requiring presence in both of two biological replicates↳ Could also: The Irreproducibility Discovery Rate (IDR) framework is an alternative specifically designed to quantify peak reproducibility across replicate ChIP-Seq/CHART-Seq experiments — IDR provides a probabilistic, signal-strength-aware measure of reproducibility recommended by ENCODE; simple intersection of two replicates does not account for the uncertainty in peak calling within each replicate or for peaks of varying effect size
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RT-qPCR enrichment ratios were summarized as mean ± SD (Fig. 2f)↳ Could also: Reporting mean ± SEM or a 95% confidence interval would also be standard ways to convey central tendency and spread — A confidence interval directly expresses uncertainty about the estimated mean and facilitates inference; SD describes the spread of individual observations regardless of n, while SEM scales with n — the choice among these conveys complementary information depending on whether the goal is to describe variability or precision of the estimate
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Functional enrichment of 7SL CHART-Seq target genes was assessed by GO term over-representation analysis (Fig. 2b)↳ Could also: Gene Set Enrichment Analysis (GSEA) using a ranked list of all genes (e.g., ranked by CHART signal fold change) could also assess functional enrichment — GSEA uses the full ranked gene list rather than a binary peak/no-peak assignment, which avoids the need to choose a significance threshold to define the foreground set and can improve sensitivity for detecting moderately enriched pathways
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The effect of SRP72 depletion on heat-shock transcriptional reprogramming was assessed by comparing DEG counts between separate siScr and siSrp72 analyses (Fig. 3b, c)↳ Could also: A factorial linear model including a heat-shock × siRNA interaction term could directly test whether the transcriptional response to heat shock differs between knockdown and control conditions — Comparing the number of DEGs from two independent analyses does not propagate statistical uncertainty from each analysis; a model-based interaction term provides a single inferential test of differential response to knockdown, with appropriate error control across the full factorial design
Citation network
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Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-39952919
Paper: Bujisic et al. 2025, Nat Commun. "7SL RNA and signal recognition particle orchestrate a global cellular response to acute thermal stress." DOI 10.1038/s41467-025-56351-6.
Pipeline used by the paper (from Methods → "RNA-Seq2 analysis")
Trim Galore (https://github.com/FelixKrueger/TrimGalore) preprocessing → TopHat2 align to mm10 (default params) → featureCounts (counts) → DESeq2 for differential expression (Wald test, Benjamini-Hochberg padj); EnhancedVolcano for plots. Normalization RPKM (for display).
Data — GEO GSE243286 (SuperSeries)
- GSE243284 = RNA-seq (the one we use). Ships a processed featureCounts matrix
GSE243284_BB3mRNA-all.featCnt.mm10.txt.gzcovering all 8 RNA-seq samples + RAW.tar (FASTQ). - GSE243285 = Ribo-seq, GSE243113 = CHART-seq (out of scope here).
- 8 RNA-seq samples (NIH3T3), 2×2×2 design:
- GSM7782791 RNA nohs siCtrl rep1 | GSM7782795 nohs siCtrl rep2
- GSM7782793 RNA hs siCtrl rep1 | GSM7782797 hs siCtrl rep2
- GSM7782792 RNA nohs siSrp72 rep1| GSM7782796 nohs siSrp72 rep2
- GSM7782794 RNA hs siSrp72 rep1| GSM7782798 hs siSrp72 rep2
IN SCOPE (pipeline-derived, attempted)
Fig 3b — DESeq2 of heat-shock transcriptional changes (10 min, 45 °C):
- siScr (siCtrl) HS vs noHS: 4125 DEGs, padj cutoff = 0.01 ← primary claim
- siSrp72 HS vs noHS: 1633 DEGs, padj cutoff = 0.01 ← primary claim
Reproduction strategy (80/20): Use the authors' own deposited featureCounts matrix (output of Trim Galore→TopHat2→featureCounts) and re-run the DESeq2 step — the actual differential-expression computation behind the two reported DEG counts. This reproduces the result-generating computation 1:1 from shipped data. P16 applies (the listed "code" is the third-party Trim Galore tool; the whole pipeline is standard tools).
OUT OF SCOPE / not attempted (the hard ~20%)
- Re-running Trim Galore + TopHat2 alignment from raw FASTQ (the count matrix is already deposited; re-alignment is the expensive, low-information last 20% and would only test the aligner, not the paper's DE result). Documented, not done.
- Ribo-seq meta-analysis, CHART-seq, FISH/imaging, wet-lab assays (non-pipeline).
- EnhancedVolcano figure rendering (cosmetic; DEG counts are the quantitative claim).
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Re-running DESeq2 on the authors' own deposited featureCounts matrix (SHA256-verified, 21888×8) reproduces the Fig 3b/c DEG counts faithfully: the siSrp72 arm is exact on all three numbers (1633 / 1265 down / 368 up) and the siScr arm lands within 1.4–2.5% (4048/2262/1786 vs 4125/2294/1831). The only deviation sits on our side as expected DESeq2 version drift, not in the authors' data or logic, and is negligible. The paper's central claim — transcriptomic response blunted by SRP72 depletion (up-DEGs 1831→368 vs 1786→368) — is fully confirmed. No fabrication indicated; every reported value is derivable from the shipped data.
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Reproduction footprint
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