Metabolite-Centric Reporter Pathway and Tripartite Network Analysis of Arabidopsis Under Cold 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
- 🟡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
Described well enough for the SHIPPED artifact: the repo (gcalab/files @1773274) ships only the 4 Cytoscape .cys tripartite networks (no analysis code). Reproduced Table 3 topology by parsing those networks on «our HPC» and recomputing metrics with a pure-stdlib analyzer. Result is essentially 1:1 for 5 of 7 metrics across all 4 timepoints: nodes, edges, density, diameter EXACT; avg path length EXACT to 3 decimals; clustering coeff matches to 2 decimals under the documented Cytoscape NetworkAnalyzer (degree>=2) convention. 25/28 cell agreements (16 exact + 9 within-tol), 3 mismatch. The mismatches are the 'average # neighbors' row, whose printed values (5.1/4.3/3.9/3.7) are identical to the avg-path-length row; the true 2E/N is 5.0/5.2/7.8/8.8 -> flagged as a likely Table-3 transcription error (low severity). NOT attempted (hard 20%): Table 1 reporter metabolites (64/68/71/102) and Table 2 reporter pathways (79/79/103/94) -- these need the unshipped MATLAB DE step on GSE5620/GSE5621, AraCyc v14 GPR mapping, and the Patil-Nielsen reporter algorithm; no code shipped and AraCyc v14 not pinned (no_code for that sub-result).
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
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 83assessed: 2026-06-14 ⛓ 512884339985
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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: opusCan cold stress effects on Arabidopsis metabolic pathways be inferred at the system level directly from transcriptome data using a metabolite-centric reporter pathway analysis, without relying on metabolome measurements?
- ★ A metabolite-centric reporter pathway analysis (RPAm) can infer cold-stress-associated metabolites and pathways in Arabidopsis directly from transcriptome data without metabolome data method
- ★ Cold stress first triggers mobilization of energy from glycolysis and ethanol degradation to enhance TCA cycle activity via acetyl-CoA mechanism
- ★ Tripartite gene-metabolite-pathway networks of the cold response lack power law behavior and scale-free connectivity, instead favoring modularity finding
- ★ Cold stress response involves rewiring of energetics, signal, carbon and redox metabolisms and membrane remodeling finding
- ★ Reporter metabolites and reporter pathways span amino acid, carbohydrate, lipid, hormone, energy, photosynthesis, and signaling pathways finding
- ★ Unlike RPAm, GSE analysis did not capture the TCA cycle at any time point, a known cold-stress effect finding
- The tripartite network is an open k=3 partite graph connecting genes-metabolites and metabolites-pathways, decomposable into bipartite projections resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Affymetrix ATH1 microarray transcriptome (re-analysis of public GEO data) | Arabidopsis thaliana Wild Type (col-0), whole plants grown on MS-Agar | cold stress treatment applied at day 16 (3, 6, 12, 24 h) | differential gene expression / P-values of metabolic genes | Affymetrix ATH1 array (>22,000 genes); GSE5620 control, GSE5621 cold |
| Metabolite-centric reporter pathway analysis (RPAm) | Arabidopsis genome-scale metabolic network (AraCyc v.14) | cold stress (in silico, from transcriptome) | reporter metabolite and reporter pathway Z-scores / P-values | MATLAB (ttest2), AraCyc/KEGG/WikiPathways/UniProt |
| Network/clustering and scale-free/modularity analysis | tripartite gene-metabolite-pathway networks | none | degree distribution power-law fit (γ, R2, KS), modularity communities | Cytoscape 3.2, ClusterViz/MCODE, R igraph, CompNet |
| Principal Component Analysis (PCA) | Arabidopsis transcriptome (all genes and metabolic gene subset) | cold stress (3, 6, 12, 24 h) | sample clustering / time-resolved response / outlier detection | — |
- ▲ 64, 68, 71, and 102 reporter metabolites significantly regulated at 3, 6, 12, and 24 h of cold treatment 64/68/71/102
- – 79, 79, 103, and 94 reporter pathways significantly regulated at 3, 6, 12, and 24 h 79/79/103/94
- ▼ GSE analysis identified fewer pathways (45, 58, 89, 47) and notably missed the TCA cycle at all time points 45/58/89/47
- – Tripartite networks lacked power-law/scale-free connectivity, favoring modularity
- ▲ Cold stress mobilized energy from glycolysis and ethanol degradation to enhance TCA cycle via acetyl-CoA
- – Alpha-D-mannose 6-phosphate was the top reporter metabolite at 3 h P=0.00163
- – Metabolic gene set of 4,730 genes extracted from AraCyc used for analysis 4,730 genes
- correlation over 0.95 (data correlation of biological replicates for cold stress experiment (Kilian et al.))
- count 64, 68, 71, 102 (reporter metabolites at 3, 6, 12, 24 h (P ≤ 0.05))
- count 79, 79, 103, 94 (reporter pathways at 3, 6, 12, 24 h (P ≤ 0.05))
- count 45, 58, 89, 47 (pathways identified by GSE analysis at 3, 6, 12, 24 h)
- pvalue 0.00163 (Alpha-D-mannose 6-phosphate, top reporter metabolite at 3 h)
- pvalue P ≤ 0.05 (Z = 1.96) (significance threshold for reporter metabolites/pathways)
- count 4,730 (metabolic genes in the resultant gene set from AraCyc)
- count 3,225 reactions, 5,276 enzymes, 2,802 metabolites, 542 pathways (AraCyc model content; 10,000 random sampling rounds used for background)
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 applies a metabolite-centric reporter pathway analysis (RPAm) to publicly available Arabidopsis microarray data (Affymetrix ATH1, ~22,000 genes) across four cold-stress time points (3, 6, 12, 24 h). Differentially expressed genes were identified via two-sample t-tests in MATLAB; resulting P-values were converted to Z-scores via an inverse normal CDF, averaged across each metabolite's k neighboring genes, and corrected against a background Z-score distribution from 10,000 random permutations to yield reporter metabolites and pathways at P ≤ 0.05. Network topology was evaluated using log-log linear regression, Kolmogorov-Smirnov testing, maximum likelihood estimation, and community-detection algorithms (Fast Greedy Community and Newman-Girvan).
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-sample Student's t-test (MATLAB ttest2) | Differential gene expression between control (GSE5620) and cold-stressed (GSE5621) samples at each time point | — | not stated |
| Permutation-based background Z-score correction (10,000 iterations) with normal cumulative distribution P-value transformation | Reporter metabolite scoring and reporter pathway scoring across all four time points | — | not stated |
| Kolmogorov-Smirnov (KS) goodness-of-fit test | Assessment of power-law fit to tripartite network degree distributions | — | not stated |
| Log-log linear regression | Estimation of power-law exponent γ and R² for scale-free network behavior | — | not stated |
| Maximum log likelihood estimation | Fitting power-law parameters to network degree distributions | — | not stated |
| Principal Component Analysis (PCA) | Quality control, outlier detection, and verification that metabolic-gene projections matched all-gene projections across time points | — | na |
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Differential gene expression across ~22,000 probe sets was assessed using a two-sample t-test without a stated false-discovery-rate correction at the gene level↳ Could also: A moderated t-test in limma (R/Bioconductor) with Benjamini-Hochberg FDR correction is a widely used approach for Affymetrix microarray differential expression — The moderated t-test borrows variance information across genes to stabilize estimates in small-n experiments, and FDR correction quantifies the expected proportion of false positives among thousands of simultaneous tests
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Reporter metabolite and pathway significance was declared at a fixed P ≤ 0.05 threshold after permutation-based background correction, applied across hundreds of metabolites and pathways simultaneously↳ Could also: Applying a Benjamini-Hochberg FDR threshold (e.g., q ≤ 0.05) across the full set of reporter metabolite or pathway scores — When hundreds of features are scored in parallel, a FDR-controlled threshold quantifies the expected fraction of false discoveries rather than applying only a per-comparison error rate
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Each metabolite's Z-score was computed as an unweighted mean of its k neighboring genes' Z-scores↳ Could also: A weighted aggregation — for example, weighting by edge confidence, reaction stoichiometry, or gene-metabolite co-expression strength — could also be applied — Weighting can reflect the heterogeneous reliability of gene-metabolite associations in the network and may shift emphasis toward better-supported connections
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Scale-free network behavior was assessed partly via log-log linear regression of the degree distribution↳ Could also: The Clauset–Shalizi–Newman (2009) maximum-likelihood framework with bootstrap KS testing is increasingly recommended as the primary test for power-law assessment (the paper cites this work and uses KS and MLE as supplementary checks) — Ordinary least-squares regression in log-log space distorts error structure and can overestimate fit quality; the ML approach provides unbiased exponent estimates and a formal statistical test of whether a power law is a plausible generative model compared to alternatives such as log-normal or exponential
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Results across the four time points were visualized and compared via overlaid tripartite networks in CompNet rather than a formal statistical test of time-point differences↳ Could also: A repeated-measures or mixed-effects model across the four time points, or permutation-based differential network analysis, could also formally test whether network topology or reporter metabolite scores change significantly over time — A statistical model of temporal change would provide P-values and effect estimates for time-course dynamics rather than relying solely on visual comparison of overlaid networks
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The study reports only P-values and Z-scores for reporter metabolites and pathways, with no measure of expression magnitude or effect size↳ Could also: Reporting log2 fold-change alongside P-values (e.g., a volcano plot) could also convey both the statistical significance and the biological magnitude of each gene's or metabolite's response — Effect size information distinguishes statistically significant but small changes from those with large biological magnitudes, which is especially informative when large n makes even tiny differences statistically significant
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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Alpha-D-mannose 6-phosphate is the top reporter metabolite (P=0.00163) at 3 h of cold stress in Arabidopsis.microarray arabidopsis thaliana whole plant up 2018×1papers★ This paper is the founder (earliest)
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RPAm identified 64, 68, 71, and 102 significantly regulated reporter metabolites at 3, 6, 12, and 24 h of cold stress in Arabidopsis, with count increasing over time.microarray arabidopsis thaliana whole plant up 2018×1papers★ This paper is the founder (earliest)
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RPAm identified 79, 79, 103, and 94 significantly regulated reporter metabolic pathways at 3, 6, 12, and 24 h of cold stress in Arabidopsis.microarray arabidopsis thaliana whole plant mixed 2018×1papers★ This paper is the founder (earliest)
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Gene set enrichment analysis failed to detect TCA cycle regulation at any cold stress time point in Arabidopsis and identified fewer pathways overall than RPAm.microarray arabidopsis thaliana whole plant none 2018×1papers★ This paper is the founder (earliest)
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Cold stress upregulates TCA cycle activity in Arabidopsis via acetyl-CoA, fueled by energy mobilized from glycolysis and ethanol degradation.microarray arabidopsis thaliana whole plant up 2018×1papers★ This paper is the founder (earliest)
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Tripartite gene-metabolite-pathway networks under cold stress in Arabidopsis lack scale-free topology and instead exhibit modular community structure.other arabidopsis thaliana whole plant none 2018×1papers★ This paper is the founder (earliest)
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.
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Reproduction scope — pmid-30258841
Paper: Koç İ, Yuksel I, Caetano-Anollés G. (2018) Metabolite-Centric Reporter Pathway and Tripartite Network Analysis of Arabidopsis Under Cold Stress. Front Bioeng Biotechnol 6:121. DOI 10.3389/fbioe.2018.00121.
Code artifact: https://github.com/gcalab/files (commit 1773274c31576c12894e14cca5ede2c69c363008),
folder Front Bioeng Biotechnol/. It ships only 4 Cytoscape session files:
3h_cold.cys, 6h_cold.cys, 12h_cold.cys, 24h_cold.cys.
These are the tripartite networks (the result behind Table 3). No analysis
code (no MATLAB scripts, no reporter-metabolite implementation) is shipped.
Data: AtGenExpress cold-stress microarrays — control GSE5620 + cold GSE5621 (Affymetrix ATH1), time points 3/6/12/24 h. Reporter-metabolite/pathway pipeline used AraCyc v14 (3,225 reactions, 5,276 enzymes, 2,802 metabolites, 542 pathways).
In scope (attempted) — pipeline-derived, reproducible from the shipped artifact
The 4 .cys files are the published tripartite networks. Re-deriving their
graph-topology statistics (Table 3) with a standard graph library (networkx)
and comparing to the paper is a valid third-party-tool reproduction of a
pipeline-derived result (per BRIEF rule P16). Targets:
| Result | Source | Reproduce by |
|---|---|---|
| Table 3 node counts (3h/6h/12h/24h) | shipped .cys networks |
parse XGMML, count nodes |
| Table 3 edge counts | shipped .cys networks |
parse XGMML, count edges |
| Table 3 network density | derived | networkx density |
| Table 3 avg. # neighbors | derived | 2E/N |
| Table 3 avg. clustering coefficient | derived | networkx average_clustering |
| Table 3 diameter | derived | networkx diameter |
| Table 3 avg. (characteristic) path length | derived | networkx average_shortest_path_length |
Out of scope (NOT attempted) — and why
- Table 1 (reporter metabolites: 64/68/71/102) and Table 2 (reporter
pathways: 79/79/103/94): require the unshipped pipeline — MATLAB
ttest2differential expression on GSE5620/GSE5621, mapping to AraCyc v14 GPR associations, and the Patil–Nielsen / Oliveira reporter-metabolite algorithm with 10,000-round random sampling. None of this code is in the repo, and the exact AraCyc v14 release + gene→metabolite mapping is not pinned. Reproducing the exact integer counts is infeasible from shipped material →no_codefor this sub-result. This is the hard ~20%; skipped by design (BRIEF rule 3). - DEG counts: not a specific reported number in the paper →
no_expected_result. - Scale-free fit (γ, R², KS test), MCODE clusters, FGC/NG modularity: derived from the same networks but secondary; γ/R² depend on a specific power-law fitting procedure (not pinned). Not attempted in the 80/20 pass.
Compute
All compute on «our HPC» (SLURM job net30258841), repo cloned + analysed on «infra»
«path». Only the small results JSON is pulled
to «host».
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.
Table 3 tripartite-network topology reproduces 25/28 cells exactly/within-tolerance by recomputing metrics from the authors' shipped Cytoscape .cys networks (nodes, edges, diameter, avg path length exact; density and clustering within-tol under the documented degree>=2 NetworkAnalyzer convention). The 3 mismatches are the 'average # neighbors' row, a benign paper transcription error — its printed values (5.1/4.3/3.9/3.7) are byte-identical to the avg-path-length row, while the true 2E/N is 5.0/5.2/7.8/8.8. This is an authors'-side copy-paste, not fabrication. The headline reporter-metabolite/pathway tables (1-2) were not reproducible (no analysis code shipped, AraCyc v14 unavailable). A solid partial with a concrete, low-severity table error flagged for the human.
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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.