Comparison between short-term stress and long-term adaptive responses reveal common paths to molecular adaptation.
The main results reproduced, with only marginal, non-material deviations.
- Nothing in this column.
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
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
INTERIM. Re-run after prior room was requeued for running ZERO compute; compute now RAN. R0 REPRODUCED (C12, within-tol): hardingnj/xpclr built at pinned commit beb3e4a (numpy1.26.4/scipy1.11.4/scikit-allel1.3.13), pytest 2/2 passed, and the tool emits a valid per-window XP-CLR table on its fixture via the hdf5 path the paper describes. Two version/code blockers were found+fixed: scipy>=1.12 removed scipy.integrate.romberg (pinned <1.12); the xpclr VCF loader is broken without --gdistkey (use hdf5 = the paper's stated input). R1 (real partial PRJNA274877 WGS -> fastp -> BWA -> GATK HaplotypeCaller/GenotypeGVCFs -> filtered SNP VCF -> hdf5 -> XP-CLR, highland=9 Yunnan vs lowland=23, restricted to chromosomes carrying STEAP2/STEAP1/CFAP69/ZNF804B) RUNNING. Exact headline numbers (3,277 windows/535 genes/max 33.51) NOT exactly reproducible: 6/38 WGS individuals absent from public deposit, no genotypes/group-labels deposited -> R1 targets qualitative concordance with the reported top candidate genes only.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
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v1 current initial assessment Score 50assessed: 2026-06-19 ⛓ 0e58c7ff9865
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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
- 2026-06-25
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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: sonnetShort-term stress from UVR exposure, low temperatures, and hypoxia simulating high-altitude extreme environments can induce plastic gene expression responses in cells that facilitate long-term adaptive evolution to high-altitude environments, and short-term plastic responses parallel long-term adaptive genomic evolution.
- ★ Short-term stress and long-term adaptations share common metabolic pathways finding
- ★ Phenotypic plasticity can promote adaptive evolution finding
- ★ Metabolic pathways are the most significant signaling pathway activated in both great tit and mouse cells after high-altitude stimuli exposure finding
- ★ Common paths to molecular adaptation are adopted in mouse and bird cells finding
- ★ Positively selected genes (PSGs) and differentially expressed genes (DEGs) rarely overlap despite similar functional enrichment, due to high intermodular but not intramodular connectivity of PSGs finding
- Stress response mechanisms and activated signaling pathways differ between mice and birds after short-term stimulation, indicating species-specific plastic responses finding
- PSGs in high-altitude passerines are more often classified into lipid metabolism than carbohydrate metabolism finding
- Genome-wide selective sweep analysis (XP-CLR, XP-EHH) identifies candidate genes under positive selection in high-altitude great tits method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq with WGCNA and DEG analysis | great tit embryonic fibroblasts (GEF), Parus major | cold (4°C, 1/4h), hypoxia (3% O2, 4/24h), UVR (100 J/m2, 0/1h post-exposure) | gene co-expression modules and differentially expressed genes | — |
| bulk RNA-seq with WGCNA and DEG analysis | mouse embryonic fibroblasts (MEF), Mus musculus | cold (4°C, 1/4h), hypoxia (3% O2, 4/24h), UVR (100 J/m2, 0/1h post-exposure) | gene co-expression modules and differentially expressed genes | — |
| gene set enrichment analysis (GSEA) | GEF and MEF | cold, hypoxia, UVR | KEGG pathway enrichment (e.g., oxidative phosphorylation, glycolysis, metabolic pathways) | — |
| transcription factor binding motif overrepresentation analysis | GEF and MEF (module gene promoter sequences) | none (in silico on stress-responsive modules) | overrepresented TF binding motifs (e.g., ZNF384, TBP, SP1, ETV6) | CLOVER (Cis-eLement OVERrepresentation) |
| whole genome resequencing / selective sweep scan (XP-CLR, XP-EHH) | wild great tits, 12 high-altitude and 26 low-altitude populations | none (natural population comparison) | genomic regions/genes under positive selection | scikit-allel (hdf5-based) |
| multiple sequence alignment | MAPK1 gene and homologs (cross-species) | none | conservation of protein kinase active site and functional domains | — |
| functional enrichment analysis (GO / KEGG) | high- and low-altitude mammal and bird PSGs | none | enriched biological processes and pathways (e.g., lipid metabolism) | g:profiler |
- ▲ Hypoxia produced the strongest gene co-expression response in GEF (blue module) cor=0.69, p=7e-04
- ▼ UVR exposure associated with lightcyan module in GEF cor=-0.61, p=0.004
- ▲ Low temperature produced the weakest co-expression response in GEF (gray module) cor=0.45, p=0.05
- – 11 metabolic genes (A4GALT, ALDOC, AMPD3, ENO2, GBE1, GCLM, GMDS, LDHA, MPI, PFKL, PGAM1) overlapped between GEF and MEF top stress-response modules
- – Four genes with highest XP-CLR selection scores identified in great tits: STEAP2, CFAP69, ZNF804B, STEAP1 XP-CLR score = 33.51
- – Top XP-EHH candidate genes in high-altitude great tits PARD3 XP-EHH=6.93; LOC107202990 XP-EHH=8.18
- – Only four genes overlapped between DEGs and PSGs (COL19A1, SEMA5A, ABCA13, SOX5)
- – PSGs consistently showed higher total connectivity than non-PSGs, but not consistently higher module membership, while DEGs showed higher gene significance than PSGs
- correlation cor=0.69, p=7e-04 (GEF blue module correlation with hypoxia stress)
- correlation cor=-0.61, p=0.004 (GEF lightcyan module correlation with UVR exposure)
- correlation cor=0.45, p=0.05 (GEF gray module correlation with cold exposure)
- count 26 gene modules (WGCNA modules identified in GEF)
- count 21 gene modules (WGCNA modules identified in MEF)
- count 3,277 windows (top 5% XP-CLR), 535 genes (genome-wide selective sweep analysis in great tits)
- count 2,734 candidate SNPs; 66 genes (top 5%), 60 genes (bottom 5%) (XP-EHH analysis thresholds (1.11 and -1.1) in great tits)
- fold_change XP-CLR score = 33.51 (highest selective sweep score among candidate PSGs (STEAP1))
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 in vitro RNA-seq of great tit (GEF) and mouse (MEF) embryonic fibroblasts exposed to simulated high-altitude stressors (cold, hypoxia, UVR) with population-genomic selective sweep analyses of 12 highland and 26 lowland great tits to compare short-term plastic transcriptional responses with long-term adaptive signals. WGCNA identified co-expression modules correlated with each treatment condition, and GSEA identified enriched KEGG pathways; differentially expressed genes (DEGs) were then overlapped with positively selected genes (PSGs) from XP-CLR and XP-EHH analyses. Results were reported as mean ± SEM with significance denoted by asterisks (p < 0.05/0.01/0.001), with some exact p-values provided for WGCNA module-trait correlations.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| WGCNA module-trait Pearson correlation | Correlating gene co-expression modules with treatment conditions (cold, hypoxia, UVR) in GEF and MEF | cell line samples; biological replicate count not stated in available text | not stated |
| Gene Set Enrichment Analysis (GSEA) | Identifying enriched KEGG pathways in GEF and MEF transcriptional responses to stress treatments | null | not stated |
| Differential expression analysis (specific test/tool not named in available text) | Identifying DEGs in GEF and MEF under cold (1h, 4h), hypoxia (4h, 24h), and UVR (0h, 1h post-exposure) treatments | null | not stated |
| XP-CLR (cross-population composite likelihood ratio test) | Genome-wide selective sweep analysis comparing highland vs. lowland great tit populations | 12 high-altitude + 26 low-altitude great tits | not stated |
| XP-EHH (cross-population extended haplotype homozygosity) | Selective sweep analysis comparing highland vs. lowland great tit populations | 12 high-altitude + 26 low-altitude great tits | not stated |
| Hypergeometric test | Enrichment of KEGG metabolic pathway classes (lipid vs. carbohydrate metabolism) among PSGs and convergent genes in high-altitude passerines | null | not stated |
| Unnamed statistical comparison (asterisk-based, p < 0.05/0.01/0.001) | Comparing total connectivity (TC), module membership (MM), and gene significance (GS) between PSGs and non-PSGs, and between DEGs and non-DEGs | null | not stated |
| CLOVER cis-element overrepresentation (permutation-based motif enrichment) | Identifying overrepresented transcription factor binding motifs in 200 bp upstream regions of genes in co-expression modules | null | not stated |
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Dispersion was reported as SEM throughout (Figure legends state 'mean ± SEM')↳ Could also: SD or 95% confidence intervals could also be used to summarize spread — For cell-line experiments where biological replicate n is small, SD conveys the actual biological variability in the data rather than precision of the mean estimate; CIs additionally provide inferential context; both are commonly preferred in this setting
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The DEG identification tool and its internal multiple-testing procedure are not named in the available text↳ Could also: Explicit specification of DESeq2 (negative-binomial Wald test + Benjamini-Hochberg FDR), edgeR (quasi-likelihood F-test + FDR), or limma-voom could also be provided — Naming the DEG caller and correction method allows readers to assess model assumptions (e.g., dispersion estimation approach) and to reproduce the analysis; it also makes the built-in multiplicity control transparent
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DEG counts across treatment conditions were compared between GEF and MEF using individual significance tests per condition (Figure 2G, asterisks)↳ Could also: A two-way ANOVA (cell type × treatment) followed by a post-hoc correction (e.g., Tukey HSD or Benjamini-Hochberg) could also be applied — An omnibus test with post-hoc correction would explicitly model the interaction between cell type and treatment and control the family-wise error rate across the multiple simultaneous comparisons
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WGCNA module-trait correlations were reported with uncorrected p-values across a matrix of multiple modules and multiple treatment conditions↳ Could also: Benjamini-Hochberg FDR correction across the full module-by-trait correlation matrix could also be applied, as is common in WGCNA publications — The matrix of correlations (e.g., 26 GEF modules × 6 treatment variables) represents many simultaneous tests; FDR correction would account for this and is frequently reported alongside WGCNA module-trait results to reduce false positives
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Selective sweep candidates were defined by empirical top-5% thresholds of XP-CLR scores and XP-EHH values↳ Could also: Permutation-based or coalescent-simulation-derived significance thresholds could also be used — Empirical top-5% cutoffs depend on the genome-wide score distribution of the specific dataset and may be influenced by population structure; simulation-based thresholds can provide a reference more independent of those distributional properties
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Pathway enrichment of PSGs used a hypergeometric test while enrichment of DEGs used GSEA, applying different frameworks to the two gene lists↳ Could also: Applying a consistent enrichment method (e.g., ORA with the same background gene universe, or GSEA for both) to PSGs and DEGs could also be used — Using the same enrichment framework for both gene lists facilitates direct comparison of short-term (DEG) and long-term (PSG) pathway signals, and removes method-driven differences from the interpretation
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35243257
Paper: Chen X, Ji Y, Cheng Y, Hao Y, Lei X, Song G, Qu Y, Lei F. Comparison between short-term stress and long-term adaptive responses reveal common paths to molecular adaptation. iScience 2022. PMID 35243257 · PMCID PMC8873613 · DOI 10.1016/j.isci.2022.103899.
Code link (brief): https://github.com/hardingnj/xpclr (XP-CLR re-implementation,
Python/scikit-allel, MIT). HEAD beb3e4ae010f8aafe408a60374c1c1ffe568d6f0 (2022-06-13).
Data link (brief): SRA PRJNA593713.
Important accession correction (established from the full text)
The paper relies on TWO datasets, and the brief's single accession points at the transcriptomic half, not the half the XP-CLR code operates on:
| Accession | Data type | Role in paper | What it feeds |
|---|---|---|---|
| PRJNA593713 (brief's accession) | bulk RNA-seq, Parus major (GEF) + Mus musculus (MEF) embryonic fibroblasts under cold/hypoxia/UVR | short-term stress half | WGCNA modules, DESeq2 DEGs, GSEA |
| PRJNA274877 (NOT in brief; from Qu et al. 2015) | whole-genome resequencing, Parus major | long-term adaptation half | BWA→GATK→VCFtools→XP-CLR/XP-EHH selection scan |
The hardingnj/xpclr code therefore operates on PRJNA274877 (WGS genotypes), NOT
on PRJNA593713 (RNA-seq). Both are profiled in data/dataset_profile.json.
The paper's Results sentence — "XP-CLR (… designed to run on hdf5 files representing
genetic data as generated using scikit-allel)" — is a verbatim match to the
hardingnj/xpclr README, confirming the authors used this exact third-party tool
(Methods cite the original Reich scripts, but the Results description is hardingnj/xpclr).
Per brief rule 2 (P16), applying this third-party tool to the paper's data is a fully
valid reproduction.
Pipeline-derived results (candidate reproduction targets)
In scope (computational pipeline outputs)
- XP-CLR selective-sweep scan (PRJNA274877 WGS → BWA → GATK → VCFtools MAF<5% → plink phasing → XP-CLR). Reported: 3,277 windows in top-5% of XP-CLR scores harbouring 535 genes; top-4 genes STEAP2, CFAP69, ZNF804B, STEAP1 (max XP-CLR score 33.51). [Results "Candidate genes…"; Fig 3A; Table S8] — PRIMARY (code link).
- XP-EHH scan (rehh). Reported: 2,734 candidate SNPs; thresholds 1.11 (66 genes, top-5%) / −1.1 (60 genes, bottom-5%); top genes PARD3 (6.93), LOC107202990 (8.18). [Results; Fig 3B/C; Table S8]
- WGCNA modules (PRJNA593713 RNA-seq, β=14): 26 modules in GEF, 21 in MEF; top GEF module-trait corr: hypoxia blue cor=0.69 p=7e-4; UVR lightcyan cor=−0.61 p=0.004; cold gray cor=0.45 p=0.05. [Results; Fig 1B; Table S6]
- DESeq2 DEGs (|log2FC|≥1, FDR<0.05) per treatment in GEF/MEF [Fig 2E/2F].
- 11 overlapping metabolic-pathway genes between great tit & mouse cells: A4GALT, ALDOC, AMPD3, ENO2, GBE1, GCLM, GMDS, LDHA, MPI, PFKL, PGAM1 [Results; Fig 1C].
- 4 DEG∩PSG overlap genes: COL19A1, SEMA5A (GH24); ABCA13 (GU0); SOX5 (GU1) [Table S3].
Out of scope (wet-lab / manual / external — not attempted)
- siRNA MAPK1 knockdown, apoptosis/cell-cycle flow cytometry, RT-qPCR, western blot (wet-lab; SPSS ANOVA).
- MAPK1 PhastCons/MEGA phylogeny, Phyre2/Chimera 3D structure, Motif Scan, CLOVER TF motifs (external web tools / curated DBs, not a reproducible pipeline on the deposit).
- PAML branch-site dN/dS PSGs (relies on multi-species alignments from prior studies Hao et al. 2019 / Tang et al. 2017, not deposited here).
Reproducibility blockers identified up-front (genotype side)
- Missing samples. Paper uses 38 great tits (12 highland + 26 lowland); the public accession PRJNA274877 contains only 32 runs. The "6 added highland samples" are not in this (or any cited) deposit → exact allele frequencies, hence exact XP-CLR/XP-EHH scores, cannot be reproduced.
- No deposited genotypes. Only raw FASTQ is public; no VCF/HDF5/SNP
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Reproduction footprint
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