The Drosophila estrogen-related receptor promotes triglyceride storage within the larval fat body.
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
- ✓Any deviation was negligible
- ✓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
- 🟡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 result 1:1, via the paper's own named tool. The repo (P16 third-party tool) is PCAtools; the paper used PCAtools 2.14.0 for a PCA of its 12-sample RNA-seq (GSE273774). GEO ships the exact PCAtools input, the DESeq2 rlog count matrix (GSE273774_annotated_rlog_counts.csv.gz), so the PCA is directly reproducible without re-running the aligner. On «our HPC» (SLURM 2175761, R 4.3.3 + PCAtools 2.14.0 / DESeq2 1.42.0 / apeglm 1.24.0 = the paper's exact versions) the PCA reproduced EXACTLY: PC1 = 84.14% of variance and separates the two tissues (fat body vs whole animal; eta2=0.996), PC2 = 4.26% and separates genotype (control vs ERR mutant; eta2=0.822) — matching the paper's '>84%' (PC1, tissue) and '<5%' (PC2, genotype) statements. C1 (12 samples) also exact. NOT attempted / not reproducible: C4, the '40 genes altered in both tissues' DESeq2/apeglm overlap, because the raw integer count matrix is not in the public supplementary (only rlog + SOFT metadata) and reconstructing it requires Salmon re-quantification from SRA FASTQ (PRJNA1143037) per the upstream described parameters - the deliberately-skipped hard 20%. Also out of scope: all wet-lab (TAG assays, microscopy, genetics) and narrative pathway/GO statements with no single pinnable number. No fabrication concern: the PCA variance values are directly derivable from the shipped data via the cited tool and match the paper; C4 is a data-completeness gap, not a discrepancy. A minor in-job labeling bug (tissue regex also matched 'whole_body') did not affect the PCAtools variance output and was corrected for the tissue assignment on «host»; see AUDIT.md.
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 88assessed: 2026-06-14 ⛓ 4469badbe0c1
✎ 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: opusDoes the Drosophila estrogen-related receptor (dERR) act cell-autonomously within the larval fat body to promote triglyceride (TAG) storage and coordinate lipid metabolism with carbohydrate metabolism and developmental growth?
- ★ dERR autonomously promotes TAG accumulation within larval fat body cells finding
- ★ Fat body-specific restoration of dERR expression in dERR mutants rescues the lean phenotype, and fat body-specific dERR-RNAi reduces TAG accumulation finding
- ★ dERR mutant fat bodies show altered expression of genes involved in glycolysis, fatty acid β-oxidation, lipid synthesis/catabolism, and isoprenoid metabolism finding
- ★ Loss of dERR causes downregulation of β-oxidation genes, a result not previously seen in whole-animal RNA-seq studies finding
- ★ dERR mutant fat bodies exhibit decreased expression of known dHNF4 target genes, and dHNF4 activity is decreased in dERR mutants mechanism
- ★ Tissue-specific (fat body) lipidomic, transcriptomic, and genetic approaches reveal dERR functions masked by prior whole-animal analyses method
- dERR coordinates lipid storage with carbohydrate metabolism and developmental growth in the larval fat body mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Quantitative lipidomics (LC-MS/MS, untargeted) | Drosophila larval fat body / larval extracts | dERR loss-of-function mutant (ERR1/2) | Lipid species abundance / lipid class quantification | Agilent 6545 Q-TOF dual AJS-ESI MS with Acquity UPLC CSH C18 column; Agilent 1290 Infinity |
| Bulk RNA-seq | Drosophila larval fat body (dERR1/2 vs dERR1/+ L2 larvae, 66-68 h) | dERR mutant (ERR1/2) | Gene expression / transcript abundance | BDGP6.46 assembly, v110 annotation, FastQC 0.12.1 |
| TAG biochemical assay (colorimetric) | mid-L2 whole larval extracts (25 larvae) | dERR mutant, fat body-specific dERR rescue, fat body-specific dERR-RNAi | Triglyceride content (absorbance 540 nm) | Sigma TAG reagent T2449, free glycerol reagent F6428 |
| Trehalose biochemical assay (colorimetric) | mid-L2 whole larval extracts (25 larvae) | dERR mutant | Trehalose/circulating sugar levels (absorbance 540 nm) | Sigma trehalase T8778, glucose oxidase reagent GAGO-20 |
| Bradford protein assay | mid-L2 larval homogenate | none (normalization) | Soluble protein content | — |
| Nile Red and Solvent Black 3 (SB3) lipid staining + confocal/quantification | L2 larval fat body | dERR mutant / dERR-RNAi | Neutral lipid droplet stain intensity | Leica SP8 Confocal; EVOS FL Auto; ImageJ |
| Immunofluorescence (anti-FLAG, anti-HNF4) | Drosophila larval fat body | dERR-GFP-StrepII-FLAG reporter; dHNF4 detection in dERR mutant | Protein localization/expression | Leica SP8 Confocal |
| dHNF4-LBD ligand-sensor (GAL4-LBD/UAS-nlacZ X-Gal) activation assay | mid-L2 larval tissue | dERR1 mutant vs dERR1/+ control vs UAS-nlacZ control | β-galactosidase (X-Gal) staining intensity as readout of dHNF4 activity | EVOS FL Auto; ImageJ v1.53t |
- ▲ Fat body-specific dERR rescue in dERR mutants restores TAG (rescues lean phenotype)
- ▼ Fat body-specific dERR-RNAi reduces TAG accumulation within fat body cells
- ▼ dERR mutant fat bodies show significant decrease in glycolytic gene expression
- ▼ dERR mutant fat bodies show significant downregulation of fatty acid β-oxidation genes
- – dERR mutant fat bodies show changes in lipid synthesis and lipid catabolism gene expression
- ▼ Known dHNF4 target genes are downregulated in dERR mutant fat bodies
- ▼ dHNF4 activity is decreased in dERR mutants (reduced LBD activation)
- – Prior whole-animal study showed dERR mutants die near end of L2 with elevated circulating sugars and decreased TAG
- fold_change 1.5 (FC cutoff for volcano plot) (Lipidomic statistical analysis threshold in MetaboAnalyst)
- pvalue adjusted P value cutoff 0.05 (FDR correction) (Lipidomic volcano plot significance threshold)
- count n = 8 pooled QC; n = 4 process blank (Lipidomics QC/blank injections)
- other relative standard deviation <30% in QC; blank AUC <30% of QC (Lipid filtering criteria)
- count 15 larvae dissected, three technical replicates per genotype (RNA-seq sample collection)
- count 25 mid-L2 larvae per sample (TAG/trehalose/protein assays)
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.
This study combined colorimetric biochemical assays (TAG, trehalose, protein), fluorescence and transmitted-light imaging with ImageJ-based intensity quantification, quantitative LC-MS/MS lipidomics, and RNA-seq to examine dERR function specifically within the Drosophila larval fat body. Lipidomic data were normalized to class-specific internal standards and tissue mass, log10-transformed and Pareto-scaled in MetaboAnalyst 6.0, QC-filtered by RSD and background thresholds, and compared via volcano plots with FDR-adjusted P values (cutoff 0.05) and a fold-change threshold of ≥1.5. RNA-seq was performed on fat bodies pooled from L2 larvae in three technical replicates per genotype; the differential expression statistical framework is described in processing scripts referenced but not reproduced in the provided text excerpt.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Volcano plot with FDR multiple-testing correction (underlying pairwise test not explicitly named; performed in MetaboAnalyst 6.0 on log10-transformed, Pareto-scaled normalized data) | Lipidomics comparison of lipid species between dERR mutant and control fat bodies | — | not stated |
| Colorimetric plate-reader absorbance at 540 nm (inferential statistical test not named in provided text) | TAG, trehalose, and protein quantification from pooled mid-L2 larvae across genotypes | 25 mid-L2 larvae pooled per sample; number of independent biological replicates not stated | not stated |
| ImageJ measure function on inverted ROI selections (descriptive intensity quantification; inferential test not named in provided text) | SB3 neutral-lipid staining intensity and HNF4 LBD X-Gal staining intensity across genotypes | — | na |
| RNA-seq differential expression analysis (specific tool and test not stated in provided text, which is truncated mid-description of the processing pipeline) | Fat body transcriptome comparison of dERR1/2 mutants vs dERR1/+ heterozygous controls | 3 technical replicates per genotype (5 larvae dissected per replicate, 15 larvae total per genotype) | not stated |
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RNA-seq replication used 3 technical replicates per genotype, each from a separate dissection of 5 pooled larvae drawn from the same larval cohort, explicitly described as technical replicates in the Methods↳ Could also: True biological replicates — independently raised larval cohorts dissected on separate occasions — could also have been used as the primary unit of replication — Biological replicates are the standard unit for RNA-seq differential expression with tools such as DESeq2 or edgeR, as they capture between-experiment biological variance; variance estimates derived from technical replicates alone reflect extraction and library-preparation noise and may not accurately represent the biological confidence interval around the observed effect
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Lipidomic group differences were evaluated using a volcano plot with a binary FC ≥ 1.5 threshold alongside FDR-adjusted P < 0.05, without naming the underlying pairwise statistical test↳ Could also: A linear model (e.g., limma or an ANOVA-based approach within MetaboAnalyst) that explicitly names the test and incorporates run order or batch as a covariate could also have been applied — Named linear models provide transparent, reproducible test specifications; modeling run order as a covariate can remove residual systematic variation that randomization alone may not fully eliminate, and linear models also yield effect-size confidence intervals rather than only binary pass/fail thresholds
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Biochemical assay comparisons (TAG, trehalose, SB3 staining, X-Gal staining) do not name a specific inferential statistical test and do not report the number of independent biological replicates in the provided Methods text↳ Could also: A two-sample t-test or Mann-Whitney U test (for two-group comparisons) or a one-way ANOVA with Tukey or Dunnett post-hoc correction (for multi-group comparisons), with explicit reporting of the biological replicate n, could also have been stated — Naming the inferential test, distinguishing the number of independent experiments from the number of larvae pooled per sample, and reporting a dispersion measure (SD or 95% CI) allows readers to independently evaluate reproducibility and statistical power
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Lipidomic features were QC-filtered by requiring RSD < 30% in pooled QC samples and a background/QC area ratio < 30% before statistical analysis↳ Could also: A D-ratio filter (ratio of within-QC variance to within-study-sample variance) or a minimum detection-frequency threshold across samples could also serve as complementary or alternative QC criteria — The D-ratio directly benchmarks analytical precision against biological variance, retaining features where biological signal meaningfully exceeds measurement noise; it is recommended by consensus lipidomics reporting guidelines (e.g., the Lipidomics Standards Initiative) as a complement to RSD-based filtering, which does not account for the magnitude of biological variability
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Lipidomic data were Pareto-scaled prior to multivariate analysis in MetaboAnalyst↳ Could also: Auto (unit-variance) scaling, or log transformation alone without additional scaling, could also have been applied before PCA or PLS-DA — Pareto scaling moderates but does not equalize the influence of high-abundance lipid features; auto-scaling assigns each feature equal analytic weight regardless of absolute concentration, which may be preferable when biological interest extends equally to minor and major lipid classes; explicitly stating the rationale for the chosen scaling method aids interpretation of multivariate component scores
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A fixed fold-change threshold of 1.5 was applied as a secondary binary filter alongside FDR-adjusted P < 0.05 in the lipidomics volcano plot↳ Could also: A moderated effect-size estimate with a 95% confidence interval (e.g., from a limma or similar linear model output) could also be reported alongside, or in place of, a pre-specified binary FC cutoff — Confidence intervals around the fold change communicate uncertainty in the magnitude of difference and do not require a threshold to be pre-specified; this is particularly informative in small-n lipidomic experiments where point estimates of fold change can be imprecise and a binary threshold may include or exclude features arbitrarily near the boundary
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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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-40288680 (Drosophila dERR fat-body RNA-seq)
Paper: Fasteen TD, Hernandez MR, Policastro RA, Sterrett MC, Zentner GE, Tennessen JM. The Drosophila estrogen-related receptor promotes triglyceride storage within the larval fat body. J Lipid Res 2025. PMID 40288680 · PMCID PMC12155637 · DOI 10.1016/j.jlr.2025.100815 Code: https://github.com/kevinblighe/PCAtools (PCAtools, Bioconductor; the paper used PCAtools 2.14.0 for the PCA of the RNA-seq samples). Data: GEO GSE273774 — 12 RNA-seq samples (Salmon→tximport→DESeq2), Drosophila melanogaster, mid-L2 larvae.
Nature of the paper
Mixed wet-lab (TAG assays, microscopy, genetics) + a bulk RNA-seq pipeline.
Only the RNA-seq computational results are in scope. The named code repo is the
third-party tool PCAtools (Kevin Blighe). Per the brief (P16), applying a
third-party tool to the paper's own data is a fully valid reproduction. The
shipped data (GEO supplementary GSE273774_annotated_rlog_counts.csv.gz) is the
rlog-normalized count matrix — exactly the input PCAtools consumed — so the
PCA is directly reproducible without re-running the upstream aligner.
Sample design (from GEO GSM titles)
12 samples = 2 tissues × 2 genotypes × 3 reps:
- whole animal: control (GSM…726–728), ERR mutant (729–731)
- fat body: control (732–734), ERR mutant (735–737)
Methods (verbatim from paper Methods)
- Aligner: Salmon 1.10.2 (
--seqBias --gcBias --posBias --softclip softclipOverhangs --numGibbsSamples 100); genome BDGP6.46, annotation v110. - tximport 1.30.0 → gene counts.
- DESeq2 1.42.0, design
~ condition + source + condition:source,blind=FALSE. - PCAtools 2.14.0 on
rlog()-normalized counts. - apeglm 1.24.0 LFC shrinkage; significance
s-value < 0.005AND|log2FC| > 1.
In scope (pipeline-derived, attempted)
- PRIMARY — PCAtools PCA (matches the named repo exactly). Reproduce on the
shipped rlog counts:
- C2 PC1 separates by tissue and accounts for >84% of variance.
- C3 PC2 separates by genotype and accounts for <5% of variance.
- C1 dataset = 12 samples × N genes (rlog matrix dimensions).
- SECONDARY (harder 20%) — DESeq2/apeglm DE overlap
- C4 "only 40 genes exhibited significantly altered expression in both
datasets" (fat body AND whole animal), at
s-value<0.005 & |log2FC|>1. - Attempt only if raw counts (
GSE273774.txt.gz) are usable; the exact overlap depends on per-tissue model details not fully specified, so this is expected to bepartialat best.
- C4 "only 40 genes exhibited significantly altered expression in both
datasets" (fat body AND whole animal), at
Out of scope / not attempted (with reason)
- TAG biochemical assays, microscopy, lipid droplet quantification, genetics —
wet-lab, no pipeline (
non_pipeline). - Pathway/GO statements ("all glycolytic genes down", "isoprenoid up") — narrative
gene-set claims without a single pinnable printed number →
no_expected_result. - Salmon re-quantification from raw FASTQ — the upstream step; rlog counts are shipped, so re-running the aligner is the unnecessary hard 20% (80/20 rule).
- dHNF4 target-gene analysis — secondary, no pinnable RNA-seq number.
Heavy-compute note
Compute is light (12 samples). Runs on «our HPC» in a conda env built on the compute node (internet there). Data fetched inside the job onto «infra».
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.
The PCA core (C1–C3) reproduced 1:1 — PC1=84.14% (paper '>84%', tissue, eta2=0.996) and PC2=4.26% (paper '<5%', genotype, eta2=0.822) — using the paper's own PCAtools 2.14.0 and exact DESeq2/apeglm versions on the shipped rlog matrix, with no fabrication concern since the values are directly derivable from shared data. The only gap is C4 (40 DEGs in both tissues), which is unverified because GEO ships only the rlog matrix + SOFT metadata, not the raw integer counts DESeq2 needs. That gap is a data-completeness/scope issue on our and the deposit side — the FASTQ are public in SRA but the Salmon re-quant was the deliberately-skipped hard-20% — not an authors' defect. Overall: a strong, clean reproduction of the targeted result with one explainable out-of-scope gap → yellow.
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
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.