Accelerated Evolution of Tissue-Specific Genes Mediates Divergence Amidst Gene Flow in European Green Lizards.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- 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
PARTIAL reproduction with a clean, auditable outcome. The named P16 tool catsequences (commit 59fd7c5) compiled and ran correctly on the paper's own data: it concatenated 2,806 single-copy-ortholog protein MSAs into a 4-taxon supermatrix (1,281,668 aa) and IQ-TREE2 (v3.1.2, JTT+F+I+G4, UFBoot=100) produced an ML species tree -- so the TOOL reproduces. However NONE of the three checkable headline numbers reproduces 1:1: (C1) the tree topology {adriatic,viridis}|{bilineata,podarcis} differs from the reported {bilineata,Adriatic} sister-pair -- but the authors' per-gene MSAs were NOT deposited so the alignments were reconstructed de novo (not like-for-like; difference most plausibly long-branch attraction of the divergent Podarcis outgroup); (C2) the deposited 'Filt' VCF is the raw 4-sample autosomal call set (79.07M records / 65.5M SNPs; 38.9M biallelic-no-missing) and no standard filter reproduces 26,985,663; (C7) the ProteinOrtho table gives 11,313 / 2,806 single-copy orthologs vs reported 1,497 (an autosomal-restricted subset not directly derivable). Root cause across all three: the Zenodo deposit sits one pipeline stage UPSTREAM of the reported figures and lacks the exact intermediate files (filtered VCF, supermatrix MSAs, autosomal gene partition). Fabrication concern LOW -- values are plausible but not independently verifiable from the shipped data, which is itself the auditable finding. Not attempted: C3 D-stat (no population panels in the 4-sample VCF), C4/C5 Ka/Ks (tissue count matrices + Z-chrom variants not deposited), C6 (out of scope).
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-24
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-24no 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 authors hypothesized that divergence among the three lineages of the Lacerta viridis species complex (L. viridis, L. bilineata, and the Adriatic lineage) occurred in the presence of gene flow between multiple lineages and involved tissue-specific gene evolution.
- ★ The Adriatic lineage is a sister taxon to L. bilineata based on mitogenome and autosomal phylogenies finding
- ★ Gene flow (admixture) occurred between L. viridis and the Adriatic lineage during divergence, in addition to previously known gene flow between L. viridis and L. bilineata finding
- ★ The Z-chromosome phylogeny is discordant with the autosomal/mitogenome phylogeny, clustering L. viridis with the Adriatic lineage instead finding
- ★ Genes highly expressed in ovaries show accelerated evolutionary rates (skewed toward fast-evolving) compared to background genes finding
- ★ Genes strongly co-expressed in the brain network are the most skewed toward faster evolutionary rates among tissue co-expression networks finding
- ★ Three Z-chromosome genes (CHRDL1, ERCC6L, MTM1) with sex-linked functions show signatures of rapid evolution (omega > 1) finding
- Whole-genome sequencing of an Adriatic lineage individual and multi-tissue (brain, heart, liver, kidney, ovary) transcriptome sequencing across the three lineages resource
- ★ Divergence of the L. viridis complex occurred amidst gene flow combined with accelerated evolution of tissue-specific genes, presumably contributing to reproductive isolation mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Whole-genome sequencing | Adriatic lineage individual (lizard) | none | genome sequence for phylogenomic/D-statistic analysis | — |
| Phylogenetic tree reconstruction (concatenated coding sequences) | mitochondrial genes, autosomal genes, and Z-chromosome genes; L. viridis, L. bilineata, Adriatic lineage, with Podarcis muralis as outgroup | none | tree topology and bootstrap support, divergence time estimates | — |
| D-statistics (ABBA-BABA admixture test) | autosomal and Z-chromosome gene sequences from L. viridis, L. bilineata, Adriatic, P. muralis | none | D-statistic and Z-score indicating gene flow between lineages | — |
| Bulk RNA-seq / transcriptome sequencing | brain, heart, liver, kidney, ovary tissues from L. viridis, L. bilineata, and Adriatic lineage | none | gene expression levels, tissue-specific differential expression | — |
| Differential gene expression / PCA clustering (DESeq2) | multi-tissue transcriptome samples | none | variance-stabilized expression clustering by tissue, log fold change, number of tissue-specific genes | DESeq2 |
| GO over-representation analysis | tissue-specific gene sets (brain, heart, liver, kidney, ovary) | none | enriched biological processes per tissue | — |
| Molecular evolutionary rate analysis (Ka/Ks, omega) | orthologous genes between L. viridis and L. bilineata, stratified by tissue-specific expression | none | nonsynonymous/synonymous substitution ratio, MWU test comparing tissue-specific vs background genes | — |
| Gene co-expression network analysis (weighted topological overlap, wTO) | tissue-specific transcriptomes (brain, heart, liver, kidney, ovary) | none | network connectivity, tissue-specific links, node evolutionary rate (Ka/Ks) mapped onto network | — |
- – Mitogenome and autosomal trees cluster Adriatic lineage with L. bilineata (bootstrap 64 and 100, respectively)
- – Z-chromosome tree shows L. viridis and Adriatic clustering together instead, with 100 support
- – D-statistics show significant gene flow between L. viridis and Adriatic on both autosomes and Z-chromosome Autosomes D=-0.242 (Z=-79.326); Z-chromosome D=-0.311 (Z=-13.794)
- ▲ Ovaries have by far the highest number of tissue-specific genes, almost twice that of the second-highest tissue (brain) 1066 genes
- ▲ Evolutionary rates (Ka/Ks) between L. viridis and L. bilineata are significantly skewed toward faster evolution for ovary-, liver-, and kidney-specific genes, but not brain or heart Ovaries p<1e-06, q<1e-06; Liver p=1.5e-05, q=6e-04; Kidneys p=0.0141, q=0.0235
- ▲ Three genes on the Z-chromosome (CHRDL1, ERCC6L, MTM1) show signatures of rapid evolution and sex-linked functions omega > 1
- ▲ Brain gene co-expression network has the highest weighted topological overlap and connectivity, and brain co-expressed genes are most skewed toward faster evolutionary rates
- – Autosomal divergence time estimates: L. viridis vs L. bilineata/Adriatic ~4.9 Ma; L. bilineata vs Adriatic ~2.27 Ma; Lacerta vs Podarcis ~33.5 Ma 4.9(±2.7) Ma; 2.27(±1.3) Ma; 33.5(±18.9) Ma
- other D-statistic autosomes = -0.242, Z-score = -79.326 (gene flow test between L. viridis and Adriatic lineage on autosomes)
- other D-statistic Z-chromosome = -0.311, Z-score = -13.794 (gene flow test between L. viridis and Adriatic lineage on Z-chromosome)
- other 4.9(±2.7) Ma (divergence time between L. viridis and L. bilineata/Adriatic clade)
- other 2.27(±1.3) Ma (divergence time between L. bilineata and Adriatic lineage)
- pvalue <1e-06 (MWU test), q<1e-06 (BH) (ovary tissue-specific genes vs background evolutionary rate comparison)
- count 1066 genes (number of ovary-specific expressed genes, highest among tissues tested)
- count 12,100 genes (genes with significant tissue-specific network integration (co-expression links) across tissues)
- other sterility of 4% in F1 females and <1% in males (hybridization experiments between L. bilineata and L. viridis showing higher female hybrid sterility (Rykena 1991))
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 study combined comparative genomics (whole-genome and multi-tissue transcriptome sequencing across three lizard lineages) with phylogenetic reconstruction (mitochondrial, autosomal, and Z-chromosome gene trees with bootstrap support), a multi-species-coalescent species-tree analysis, and four-population D-statistics with Z-scores to test for gene flow. For gene expression, DESeq2-based differential expression/tissue-specificity calling was paired with one-sided Mann–Whitney U tests comparing evolutionary-rate (Ka/Ks) distributions between tissue-specific and background gene sets across five tissues, with Benjamini–Hochberg correction applied across those five tests. Weighted topological overlap (wTO) co-expression networks were built per tissue, and GO over-representation was used to characterize gene sets. Results were reported via a summary table with p-values and BH q-values, divergence-time point estimates with a ± value, and descriptive/enrichment statistics rather than a single global model.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Four-population D-statistic (ABBA-BABA admixture test) with Z-scores | gene flow testing among L. viridis, L. bilineata, and the Adriatic lineage, using P. muralis as outgroup, separately for autosomes and Z-chromosome (table 1) | — | not stated |
| Multi-species coalescent species-tree analysis (accounting for incomplete lineage sorting) | reconstruction of the unrooted species tree, compared against the autosomal gene tree topology | — | not stated |
| One-sided (alternative = greater) Mann–Whitney U test | comparing evolutionary rates (Ka/Ks between L. viridis and L. bilineata) of tissue-specific genes versus background genes, per tissue (table 2) | gene counts per tissue as given in table 2 (e.g., brain: 316 tissue genes vs 9524 background; ovaries: 1066 vs 8774; kidneys: 27 vs 9813) | not stated |
| Benjamini–Hochberg (BH) FDR correction | applied to the five per-tissue MWU p-values in table 2 | five tests, one per tissue | na |
| DESeq2-based differential expression / tissue-specific gene calling, with variance-stabilizing transformation for PCA | sample clustering (fig. 2) and identification of genes with significantly higher expression in one tissue versus others | — | not stated |
| Gene Ontology (GO) over-representation analysis | functional enrichment of tissue-specific gene sets (supplementary table S3) | — | not stated |
| Bootstrap support values for phylogenetic trees | mitochondrial, autosomal, and Z-chromosome gene trees (fig. 1C–E) | — | na |
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Evolutionary-rate distributions were compared between tissue-specific and background gene sets using five separate one-sided Mann–Whitney U tests (one per tissue), corrected jointly with Benjamini–Hochberg FDR.↳ Could also: A single linear or rank-based model with tissue as a covariate (e.g., a Kruskal-Wallis test followed by pairwise post-hoc contrasts, or a mixed model with tissue as a factor) could also be used to compare rate distributions across all tissues jointly. — Pooling the comparisons into one model can borrow information across tissues and provides a unified framework for the family-wise correction alongside pairwise contrasts.
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Divergence times between lineages are reported as a point estimate with a ± value (e.g., 4.9(±2.7) Ma) without specifying whether this reflects a standard deviation, standard error, or credible interval.↳ Could also: Reporting an explicit 95% highest posterior density (HPD) interval or confidence interval, as commonly output by molecular-clock/coalescent dating software, would also convey the uncertainty range in a directly interpretable probabilistic form. — An explicitly labeled interval removes ambiguity about what the reported spread represents, which can aid comparison with divergence estimates from other studies.
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Gene flow among the three lineages was assessed using four-population D-statistics (ABBA-BABA) with Z-scores.↳ Could also: Complementary approaches such as f-branch statistics, TreeMix, or explicit demographic model fitting (e.g., with dadi or fastsimcoal2) could also be applied. — These methods can help distinguish among alternative admixture scenarios and estimate migration rates or timing, beyond establishing the presence of gene flow.
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Tissue-specific genes were defined via a DESeq2-based differential expression workflow comparing each tissue against the others.↳ Could also: A module-based approach such as WGCNA, correlating co-expression modules with tissue identity, could also be used to characterize tissue specificity. — A module-trait correlation framework can capture coordinated multi-gene expression patterns that a per-gene one-tissue-vs-rest test may not directly summarize.
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The D-statistic results in table 1 are reported with Z-scores but without an explicit multiple-comparison adjustment across the tested lineage quartets.↳ Could also: Block-jackknife-derived standard errors (a standard companion to D-statistics) combined with a multiple-comparison adjustment, if additional quartets were tested, could also be reported. — This would make explicit how the significance threshold accounts for the number of admixture configurations examined.
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Weighted topological overlap (wTO) co-expression networks were built per tissue to characterize gene-gene relationships.↳ Could also: A permutation-based null model or FDR-adjusted threshold for wTO edge significance (as implemented in permutation-based wTO frameworks) could also be used to define network links. — An explicit null-model-based threshold can help control the false discovery rate among the many pairwise gene links being evaluated.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
scope.md — pmid-33988711
Paper: Kolora et al. 2021, Genome Biol Evol 13(8):evab109. "Accelerated Evolution of Tissue-Specific Genes Mediates Divergence Amidst Gene Flow in European Green Lizards." PMID 33988711 · PMCID PMC8382678 · DOI 10.1093/gbe/evab109.
Linked code (P16, third-party tool): github.com/ChrisCreevey/catsequences — a FASTA-alignment concatenation utility (builds a supermatrix + partition file for IQ-TREE2). NOT the authors' own analysis code; it is one step of the phylogenomics pipeline. Per brief P16, running it (or any pipeline tool) on the paper's own data is an equally valid reproduction.
Data deposited:
- ENA PRJEB38736 — raw Illumina reads (WGS of 1 Adriatic female; RNA-seq of 5 tissues x 3 lineages).
- Zenodo 10.5281/zenodo.3900517 (9 files, 4.2 GB): nuclear+mito genome assemblies, 3 transcriptome assemblies, orthology.tar.gz (38 MB), variants_lizards.Filt.Chr1-18.vcf.gz (3.6 GB) = filtered autosomal SNP calls.
Pipeline-derived results (candidate reproduction targets)
| # | Result (reported) | Pipeline | Inputs available? | In scope? |
|---|---|---|---|---|
| C1 | Concatenated supermatrix + species-tree topology (IQ-TREE2) | catsequences → IQ-TREE2 | orthology.tar.gz (per-gene alignments) | YES — core, exercises the named repo |
| C2 | Autosomal SNP count = 26,985,663 | SNP calling (BWA/GATK-like) → filtered VCF | deposited VCF (3.6 GB) | YES — direct count vs VCF (clean QC) |
| C3 | D-statistic (Adriatic vs L.viridis, autosomes) = -0.242, Z = -79.326 | AdmixTools D-stat on SNPs | deposited VCF + population labels | YES if pop assignment derivable |
| C4 | Ka/Ks: ovary-specific & brain-coexpressed genes evolve faster (MWU p<1e-6) | KaKs_Calculator on SCO alignments + tissue sets | orthology + tissue assignment | STRETCH — needs tissue-gene sets |
| C5 | Z-chrom genes w/ omega>1: CHRDL1, ERCC6L, MTM1 | per-gene dN/dS | orthology Z-chrom alignments | STRETCH |
| C6 | Tissue-specific gene counts (ovary 1066, brain 316, ...) | DESeq2 on counts | count matrices NOT in Zenodo list | OUT — counts not deposited |
Out of scope (wet-lab / not deposited): dissection/RNA extraction; divergence-time BPP MCMC (needs full alignment set + calibration, heavy); co-expression wTO/CoDiNA networks (need expression count matrices, not in the Zenodo file list).
Plan: C1 (catsequences→tree) + C2 (SNP count) are the quick floor; C3 (D-stat) and C4/C5 (Ka/Ks) are the harder push. All compute on «our HPC».
BLOCKER (2026-06-18): «infra» per-user quota exceeded
The shared account's «infra» quota is full (write of even a 3-byte file fails with "Disk quota exceeded"; account holds ~14 TB of the operator's real research data in projects/scratch/etc., which must NOT be deleted). Freed ~5 GB of my own regenerable conda package caches; quota accounting is async and had not caught up at last check. Retrying periodically; downloads + compute resume once headroom is available.
Assessments & scoring basis
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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.
The P16 tool catsequences (commit 59fd7c5) compiles and runs correctly on the paper's own data, building the 2,806-SCO / 1,281,668-aa supermatrix and a fully-supported IQ-TREE2 tree — so the tool reproduces. However none of the three checkable headline values reproduces 1:1: the tree topology flips sister pairs (on de-novo-reconstructed alignments, not like-for-like), the SNP count (26,985,663) is not reachable by any standard filter on the deposited 79.07M-record VCF, and the 1,497 autosomal SCO count is an undeposited autosomal subset of the 11,313/2,806 ProteinOrtho counts. The root cause is on the data-availability/authors' side — the open Zenodo deposit sits one pipeline stage upstream and lacks the filtered VCF, supermatrix MSAs and gene→chromosome mapping needed to derive the figures — and the central biological claims (C3–C6) could not be tested at all. Fabrication concern is LOW; the values are plausible but not independently verifiable, which is itself the auditable finding.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.