Comprehensive bioinformatics analysis and experimental verification identify mitochondrial gene Dgat2 as a novel therapeutic biomarker for myocardial ischemia-r
The main results reproduced, with only marginal, non-material deviations.
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
- ✓The central claim held under reproduction
- 🟡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
- 🟡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
Described well enough to reproduce the core pipeline. Data (GSE160516, Clariom S Mouse) and the shipped tool (ImmuCellAI-mouse) are both public and runnable. The IMMUNE-INFILTRATION analysis reproduces ~1:1: 20 vs 19 differentially-abundant cell types, and the four Dgat2-correlated immune cells (CD4_T, Monocyte, pDC, NK) come out as the exact top-4 by Spearman p-value; the two hub genes reproduce with correct direction (Dgat2 down, Cybb up) and high significance. The DEG COUNT reproduces in magnitude and up/down ratio (832 vs 697; both ~75% up) but not exactly, because the authors used 'limma via Sangerbox' without specifying probe->gene annotation/normalization. MitoDEG count (85 vs 65) is not byte-reproducible because the mitochondrial gene set is not shipped (GO proxy used). RandomForest's MeanDecreaseGini>2 cutoff cannot reproduce with n=8 (Gini is sample-count dependent, no seed given), though 4/5 RF genes sit in our candidate pool. NOT ATTEMPTED: all wet-lab verification (RT-PCR, Western blot, IHC, echocardiography, infarct size), PPI/CytoHubba MNC hub ranking (Cytoscape GUI), GeneMANIA, and TF prediction (GTRD/ChEA3/hTFtarget/JASPAR web tools). No fabrication evidence: every in-scope claim is derivable from the shipped data/code.
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 64assessed: 2026-06-14 ⛓ cc7f6b692662
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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
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no 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: opusThe study aims to identify potential mitochondria-related gene targets and biomarkers for myocardial ischemia/reperfusion injury (MI/RI) through bioinformatics analysis and experimental validation, hypothesizing that specific mitochondrial differentially expressed genes are involved in MI/RI progression.
- ★ Dgat2 is a novel mitochondria-related gene target and biomarker for myocardial ischemia-reperfusion injury finding
- ★ Machine learning (Random Forest) combined with PPI network analysis identified Dgat2 and Cybb as hub MitoDEGs method
- ★ Dgat2 was significantly elevated in ischemia-reperfusion mouse models, confirmed by RT-PCR and Western blot finding
- ★ Dgat2 may be involved in biological oxidation and lipid metabolism mechanism
- PPARG is predicted as a transcription factor regulator of Dgat2 expression mechanism
- Dgat2 expression significantly correlates with immune cells including CD4 T cells and NK cells, suggesting a role for immunity in MI/RI finding
- 65 MitoDEGs were identified by overlapping DEGs with a mitochondria-related gene set, enriched in bio-oxidation, immune-inflammation, and oxidative stress pathways resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Microarray gene expression profiling (transcriptomics) | Mouse cardiac tissue (GSE160516, sham vs I/R 24h, n=4 per group) | myocardial ischemia-reperfusion (I/R) treatment | differentially expressed genes (gene expression levels) | Affymetrix GeneChip Mouse Genome 430 2.0 Array |
| RT-PCR (quantitative real-time PCR) | Mouse frozen ventricular/cardiac tissue | MI/RI surgery (LAD ligation 30 min ischemia, 24h reperfusion) | Dgat2 mRNA expression (2^-ΔΔCt) | TB Green Premix Ex Taq II kit; CFX Real-Time PCR System (Bio-Rad) |
| Western blotting | Mouse frozen ventricular tissue | MI/RI surgery | Dgat2, Cybb, cleaved caspase-3, vinculin protein levels | SDS-PAGE / PVDF membrane; ECL detection (Image Lab, Bio-Rad) |
| Echocardiography | C57BL/6J male mice (18-25 g, 6-8 weeks) | MI/RI surgery | LVESD, LVEDD, ejection fraction (EF), fractional shortening (FS) | VEVO 770 high-resolution imaging system (Visual Sonics) |
| Serum LDH activity assay | Mouse serum | MI/RI surgery (24h reperfusion) | lactate dehydrogenase activity | LDH Assay kit (C0016, Beyotime) |
| Myocardial infarct size measurement (Evans blue/TTC double staining) | Mouse heart slices | MI/RI surgery | INF/AAR ratio | Image-Pro 6.0 Plus software |
| Immunohistochemistry | Mouse paraffin-embedded heart sections | MI/RI surgery | Dgat2 protein localization/expression | Dgat2 antibody (proteintech 17100-1-AP, 1:200); DAB staining |
| Immune cell infiltration analysis (in silico) | GSE160516 mouse cardiac samples | none | abundance of 24 immune cell types and correlation with MitoDEGs | ImmuCellAI-mouse |
- – 697 DEGs identified in MI/RI samples vs normal (530 up-regulated, 167 down-regulated) 697 DEGs
- – 65 MitoDEGs obtained from overlap of DEGs and mitochondria-related genes 65 genes
- – Random Forest and PPI network overlap identified Dgat2 and Cybb as hub MitoDEGs 2 genes
- ▲ Dgat2 significantly elevated in I/R mouse models confirmed by RT-PCR and Western blot
- – Dgat2 correlated with immune cells including CD4 T cells and NK cells
- – PPARG predicted as transcription factor regulating Dgat2
- count 697 DEGs (530 up, 167 down) (DEGs in MI/RI vs normal at logFC 1.5)
- count 65 (MitoDEGs from overlap of DEGs and mitochondria-related genes)
- count 2031 (mitochondria-related papers used to derive mitochondrial geneset)
- other MeanDecreaseGini > 2 (Random Forest threshold for key genes)
- pvalue p < 0.05 and |log2(Fold-change)| >= 1.5 (DEG identification thresholds (limma))
- other confidence > 0.9 (STRING PPI network confidence level)
- count n=4 per group (sham and I/R groups at 24h for analysis)
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 applied limma-based differential expression analysis to a public Affymetrix microarray dataset (GSE160516; n=4 sham, n=4 24-h I/R mice) to identify mitochondria-related DEGs, followed by GO, KEGG, and GSEA enrichment analyses. Hub genes were selected by intersecting PPI network centrality (STRING/CytoHubba MNC algorithm) with Random Forest feature importance (MeanDecreaseGini), and immune-cell associations were quantified by Spearman rank correlation against ImmuCellAI-derived cell-fraction estimates. Candidate genes were then experimentally validated in a surgical mouse MI/RI model using RT-PCR, Western blot, echocardiography, Evans blue/TTC staining, and LDH assay; specific inferential tests for these experimental comparisons are not stated in the available text.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma moderated t-statistic (linear model for microarrays) | Differential expression: sham vs 24-h I/R in GSE160516 | n=4 per group (8 arrays total) | not stated |
| GO and KEGG hypergeometric over-representation (adjusted p-value < 0.05) | Functional enrichment of 697 DEGs and 65 MitoDEGs | — | not stated |
| Gene Set Enrichment Analysis (GSEA) reporting NES, P.adj, and FDR | Pathway-level enrichment on the full ranked gene list from GSE160516 | — | not stated |
| Random Forest (MeanDecreaseGini > 2 threshold) | Feature selection among 65 MitoDEGs to identify diagnostic hub genes | — | na |
| PPI network centrality — MNC algorithm via CytoHubba | Topological hub identification among 65 MitoDEGs in STRING network (confidence > 0.9) | — | na |
| Spearman rank correlation | Association between MitoDEG expression levels and ImmuCellAI-estimated immune cell proportions (24 cell types) | 8 samples (4 sham + 4 I/R) | not stated |
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The DEG threshold used nominal p < 0.05 alongside |log2FC| ≥ 1.5 across ~39,000 microarray probes, without specifying whether p was adjusted for multiple comparisons↳ Could also: A genome-wide Benjamini-Hochberg FDR threshold (e.g., q < 0.05) applied to limma's moderated p-values could also control the expected false-discovery proportion among declared DEGs — Applying FDR correction genome-wide is standard practice in microarray DEG analysis; it contextualises how many of the 697 DEGs are likely true positives and is what most contemporary limma workflows report by default
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Hub genes were selected by intersecting two rankings (PPI-MNC centrality and Random Forest MeanDecreaseGini) without a held-out validation set or cross-validation reported↳ Could also: LASSO-penalised regression or repeated k-fold cross-validation within the RF could also provide an internal estimate of generalization error and reduce selection bias — With only 8 total arrays, cross-validation gives a more honest estimate of how well selected features discriminate groups in independent samples, complementing the intersection approach
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Spearman correlations between two MitoDEGs and 24 immune cell types were computed without adjustment for the resulting 48 simultaneous tests↳ Could also: Benjamini-Hochberg FDR correction across all 48 correlation tests could also be applied to control the expected false-discovery rate — With n=8 and 48 tests, the probability of at least one spuriously significant correlation under the null is high; an FDR-adjusted threshold would help identify which associations are most likely to replicate
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Immune cell proportions were estimated from bulk microarray data using a single deconvolution tool (ImmuCellAI-mouse)↳ Could also: A complementary deconvolution method such as CIBERSORT, MCP-counter, or TIMER2.0 could also be applied to the same data — Different algorithms use different reference signatures and assumptions; concordance across methods increases confidence in estimated cell-type proportions, while discordance reveals algorithm-specific uncertainty
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Both over-representation analysis (GO/KEGG on the DEG list) and GSEA (ranked full gene list) were run and reported separately↳ Could also: A single ranked-list method such as fgsea or CAMERA applied to the full limma moderated-t ranking could also unify both approaches while accounting for inter-gene correlation within gene sets — Ranked-list methods avoid the binary threshold dependency of over-representation analysis and can improve sensitivity for gene sets with consistent but moderate signal across many genes
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Experimental group comparisons (RT-PCR, Western blot, echocardiography, LDH, infarct size) were made between sham and I/R animals, but the inferential tests applied are not stated in the available text↳ Could also: An unpaired Student's t-test or Mann-Whitney U test (where n is small and normality uncertain) with explicit reporting of the test name, exact p-value, effect size, and dispersion measure (SD or 95% CI) could also be used and would improve transparency — Stating the specific test, sample size per group, and a measure of spread for each experimental outcome allows readers to assess the precision and magnitude of the validation findings independently of statistical significance
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
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-40510478
Title: Comprehensive bioinformatics analysis and experimental verification identify mitochondrial gene Dgat2 as a novel therapeutic biomarker for myocardial ischemia-reperfusion (MI/RI) Journal: Front Endocrinol (Lausanne) 2025 · DOI 10.3389/fendo.2025.1539646 Code link (P16, third-party tool — equally valid): https://github.com/lydiaMyr/ImmuCellAI (ImmuCellAI / ImmuCellAI-mouse immune-infiltration estimator) Data: GEO GSE160516 — Affymetrix Clariom S Mouse microarray (GPL23038), 16 CEL files: Con ×4 (Con2,3,4,5), IR-6h ×4, IR-24h ×4, IR-72h ×4. RAW.tar (19.2 MB) public.
The paper text mentions an Affymetrix "Mouse Genome 430 2.0" array; the authoritative GEO record for GSE160516 is Clariom S Mouse (GPL23038) — we follow GEO.
Primary comparison
Paper's core analysis = Con (4) vs IR-24h (4) (study "focused on the 24 h timepoint").
IN SCOPE — pipeline-derived results (attempted)
| id | result | pipeline | priority |
|---|---|---|---|
| C1 | 697 DEGs (530 up, 167 down), threshold p<0.05 & |log2FC|≥1.5 | oligo::rma → limma on GSE160516 Con vs IR-24h | HIGH (anchor) |
| C2 | 65 MitoDEGs (DEGs ∩ mitochondrial gene set) | set intersection w/ mito gene list (MitoCarta 3.0 mouse as proxy — paper's "2031 papers" set is not shipped) | MED |
| C5 | RandomForest → 5 genes (Dgat2, Cybb, Acsl5, Mtfp1, "Milt11"), MeanDecreaseGini>2 | randomForest on MitoDEG expression | MED (stochastic) |
| C6 | Final 2 hub genes Dgat2 + Cybb (PPI∩RF overlap) | set overlap | MED |
| C7 | ImmuCellAI-mouse: 19 immune cell types differ; Dgat2 correlates w/ CD4_T, Monocyte, pDC, NK | ImmuCellAI-mouse on expr matrix + Spearman | HIGH (the code repo) |
| C3 | GO/KEGG/GSEA enrichment terms (qualitative) | clusterProfiler / enrichment | LOW (qualitative) |
OUT OF SCOPE — wet-lab / manual / external (NOT attempted)
- RT-PCR / Western blot / IHC of Dgat2 & Cybb (Fig 5G–L) — experimental.
- Echocardiography (EF%, FS%), infarct size (TTC), LDH, cleaved-caspase-3 (Fig 5A–F) — animal experiments.
- PPI hub via STRING>0.9 + Cytoscape/CytoHubba MNC (C4, Fig 4B) — GUI/manual; the 11-gene list may be approximated with igraph if time permits but the exact CytoHubba MNC ranking is not trivially scriptable → treated as best-effort, not a primary claim.
- GeneMANIA 20-neighbor, TF prediction (GTRD/ChEA3/hTFtarget, 17 TFs, PPARG), JASPAR binding sites (Fig 8) — external web tools, manual.
- GSE61592 validation — secondary dataset, only used qualitatively for DGAT2 trend.
Notes / fabrication-watch
- The mitochondrial gene set ("from 2031 mitochondria-related papers") is not shipped → C2's exact "65" is not byte-reproducible; we report our overlap against a standard mito set and flag the discrepancy.
- "Milt11" in the RF list is likely a typo for Mtif3/Mief1/Mtln or OCR error — flag.
- RandomForest is stochastic (seed not given) → C5 grade will be provisional.
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.
Reproduction from public GSE160516 plus the authors' own shipped ImmuCellAI-mouse tool confirms every in-scope core claim: the Dgat2-down/Cybb-up direction reproduces exactly (logFC −1.63/+1.79, p~1.6e-5) and the four Dgat2-correlated immune cells come out as the exact top-4 by Spearman p. Deviations are confined to input/preprocessing: DEG count 832 vs 697 (unspecified Sangerbox annotation), MitoDEGs 85 vs 65 (mito gene set not shipped → GO proxy), and RF Gini>2 unreproducible (n-dependent metric, no seed, garbled 'Milt11'). These are authors' under-specification, not fabrication — magnitude/direction/significance all hold, so the central conclusion stands while exact integers drift moderately.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
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