Apoptotic brown adipocytes enhance energy expenditure via extracellular inosine
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
- 🔴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 IDENTIFICATION COUNT, PARTIALLY FOR THE STATISTICS. The single pipeline-derived dataset (phosphoproteomics, PRIDE PXD032153) was re-fetched to «infra» and analysed on «our HPC» («job»). C1 (38,451 phosphopeptides) reproduces to within 0.8% directly from the shipped MaxQuant Phospho (STY)Sites.txt (38,157) — clean. C2-C4 (regulated-site counts 7,875/8,613/2,535) reproduce only PARTIALLY: a faithful EasyPhos/Perseus two-sample permutation-FDR test, run at BOTH the collapsed-site and the canonical multiplicity-expanded levels across a full valid-value x S0 x FDR-method sweep, yields ~36-58% of the reported counts in EVERY configuration (best: FORSK 3,769 / inosine 5,018 / both 919). The qualitative pattern IS reproduced (inosine regulates more sites than FORSK; large overlap). The Methods do not publish the Perseus parameters and none are deposited, so the reported counts are not derivable from the public data as-is — flagged for human review (agreement.json possible_fabrication_notes), not asserted as fabrication. C5 (PKA over-representation) reproduces for the forskolin/cAMP/PKA arm (OR 1.8-2.5, p<=1e-17) but not for the inosine arm. NOT ATTEMPTED (out of scope): untargeted metabolomics (Compound Discoverer/MetaboAnalyst, no public accession), RNA-seq of Slc29a1/a2 (no resolvable accession), and all wet-lab/in-vivo/human-cohort results (energy expenditure, KO phenotypes, ENT1 Ile216Thr-BMI association).
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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 59assessed: 2026-06-19 ⛓ 34c11c5c1c15
✎ 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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-20no 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 paper tests whether apoptotic brown adipocytes release signaling metabolites (particularly purines) that promote replacement/activation of the thermogenic program in surrounding brown/beige adipocytes, and whether the transporter ENT1 (SLC29A1) regulates this process by controlling extracellular inosine levels to influence energy expenditure and obesity.
- ★ Apoptotic brown adipocytes release a specific secretome enriched in purine metabolites (including inosine, AMP, hypoxanthine, ATP) finding
- ★ The apoptotic secretome enhances thermogenic and adipogenic gene expression in healthy brown adipocytes finding
- ★ Inosine activates the cAMP-PKA signaling pathway (via p38, Sik2, Crtc3, Creb) to drive the thermogenic program mechanism
- ★ Inosine treatment increases BAT-dependent energy expenditure in vivo, induces browning of white adipose tissue, and counteracts diet-induced obesity finding
- ★ ENT1 (SLC29A1) regulates extracellular inosine levels in BAT; ENT1 deficiency increases extracellular inosine and enhances thermogenic adipocyte differentiation mechanism
- ★ Pharmacological inhibition or genetic ablation of ENT1 in mice enhances BAT activity and counteracts diet-induced obesity finding
- ★ In human brown adipocytes, ENT1 knockdown/blockade increases extracellular inosine and thermogenic capacity; high ENT1 expression correlates with lower UCP1 in human adipose tissue finding
- ★ The ENT1 Ile216Thr loss-of-function variant is associated with significantly lower BMI and 59% lower odds of obesity in humans finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| untargeted metabolomics | murine brown adipocytes (differentiated SVF) | nutlin-3 (MDM2 inhibitor, p53-mediated apoptosis) | secreted metabolite levels | — |
| untargeted metabolomics | murine brown adipocytes | UV irradiation (caspase-dependent apoptosis) | secreted metabolite levels | — |
| targeted purine metabolite profiling | murine brown adipocytes, endothelial cells, fibroblasts | UV irradiation / nutlin-3 | extracellular ATP, ADP, AMP, adenosine, inosine, hypoxanthine concentrations | — |
| cAMP assay | murine brown adipocytes | inosine, AMP, hypoxanthine, or forskolin treatment | intracellular cAMP levels | — |
| phosphoproteomics (high-sensitivity) | murine brown adipocytes | inosine or forskolin treatment | phospho-site regulation across phosphoproteome | — |
| indirect calorimetry / metabolic cages | mice (WT, A2A-KO, A2B-KO; DIO/HFD models) | inosine injection or micro-osmotic pump infusion | oxygen consumption, energy expenditure, body weight, body composition | — |
| 3H-inosine uptake assay | murine brown adipocytes (WT vs ENT1-KO) | genetic ENT1 knockout | radiolabeled inosine uptake and extracellular inosine accumulation | — |
| human genetic association study | human population (ENT1/SLC29A1 gene) | Ile216Thr loss-of-function variant (natural genetic variation) | BMI and odds of obesity | — |
- – Nutlin-3-induced apoptosis significantly enriched 84 metabolites and reduced 13 metabolites, with purine metabolism the most significantly altered pathway 84 up / 13 down
- – UV-induced apoptosis significantly altered the secretome with 50 metabolites up and 19 down, purine pathway again significantly involved 50 up / 19 down
- ▲ UV-induced apoptosis increased extracellular ATP, AMP, inosine and hypoxanthine, with AMP, inosine and hypoxanthine reaching the highest concentrations
- ▲ Supernatant from apoptotic brown adipocytes significantly increased Ucp1, Ppargc1a, Pparg and Fabp4 expression in healthy brown adipocytes
- ▲ Inosine, but not AMP or hypoxanthine, significantly increased intracellular cAMP in brown adipocytes
- ▲ Inosine injection increased oxygen consumption in WT mice; this effect was suppressed in A2A-KO and A2B-KO mice
- – Chronic inosine delivery (micro-osmotic pump) during HFD reduced body weight gain, increased oxygen consumption and cold-induced thermogenesis, and increased UCP1 expression in BAT and WATi
- – ENT1-KO brown adipocytes showed significantly reduced 3H-inosine uptake and correspondingly increased extracellular inosine accumulation
- count 84 metabolites significantly increased, 13 significantly reduced (nutlin-3-induced apoptosis metabolomics in brown adipocytes)
- count 50 metabolites up, 19 down (UV-induced apoptosis metabolomics in brown adipocytes)
- count 330 compounds detected in supernatant (targeted purinergic metabolite screen)
- count 38,451 phosphopeptides identified; 7,875 (FORSK) and 8,613 (inosine) phospho-sites regulated (FDR<0.05); 2,535 overlapping (phosphoproteomic analysis of inosine vs forskolin treatment)
- pvalue P < 0.05 (increased TUNEL-positive apoptotic cells in BAT after thermoneutrality (30°C, 3 and 7 days))
- pvalue P = 0.0592 (trend for reduced fasting blood glucose after 25 days of inosine injection during HFD)
- other 59% lower odds of obesity (human carriers of ENT1 Ile216Thr Thr variant vs Ile)
- other 100 µg kg-1 inosine dose (acute inosine injection used to increase oxygen consumption in mice)
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 uses primarily murine and cell-culture experiments with small group sizes (n typically 3–16), comparing treatment vs. control (e.g., inosine vs. vehicle, knockout vs. wild-type) using two-tailed Student's t-tests for most pairwise comparisons and one-way ANOVA with Tukey's post-hoc test for comparisons among more than two groups; an ANCOVA was used for one energy-expenditure/body-weight covariate analysis, and FDR-based thresholds were applied to phosphoproteomic site-level statistics. Results are reported as mean ± s.e.m. with significance stars and a note that exact P values are available in source data.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-tailed Student's t-test | Fig. 1 panels d, e, h–m, o–q (e.g., extracellular purine levels, Ucp1 expression, oxygen consumption, lipolysis, body weight, thermogenic gene expression) | varies by panel, stated as n=3 to n=16 | not stated |
| One-way ANOVA with Tukey's post hoc test | Fig. 1 panels a–c, f (TUNEL quantification, metabolomics volcano/enrichment comparisons, intracellular cAMP across treatments) | varies by panel (e.g., n=3, n=6) | not stated |
| ANCOVA (non-linear fit) on area under the curve | Fig. 1n, oxygen consumption/body weight AUC at 23 °C | n=5 | not stated |
| Multiple t-tests | Fig. 2f, 14C-triolein lipid uptake across organs in WT vs ENT1-KO mice | n=5 | not stated |
| FDR thresholding (FDR<0.05) on regulated phosphopeptides | Phosphoproteomic comparison of inosine vs forskolin treatment (Extended Data Fig. 2a) | 38,451 phosphopeptides detected; group n not stated in this excerpt | not stated |
| Untargeted metabolomics differential abundance testing (volcano plot) | Fig. 1b, nutlin-3-induced apoptosis metabolite changes | n=6 | not stated |
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Many independent panels each use a two-tailed t-test for a single pairwise comparison (e.g., treatment vs. vehicle) across dozens of separate figures/panels.↳ Could also: A two-way or repeated-measures ANOVA (or linear mixed model) with a post-hoc correction (e.g., Sidak or Tukey) across related comparisons — Jointly modelling related comparisons (e.g., across time points or genotypes) can account for shared variance structure and also provide a built-in adjustment for multiple comparisons within a related family of tests.
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Variability is summarized as mean ± s.e.m. throughout, including for panels with small group sizes (e.g., n=3).↳ Could also: Reporting standard deviation (SD) or a 95% confidence interval alongside or instead of SEM — SD or CIs directly convey the spread of the underlying data rather than the precision of the mean estimate, which can be informative when group sizes are small.
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Fig. 2f uses 'multiple t-tests' to compare lipid uptake across several organs between WT and ENT1-KO mice.↳ Could also: A two-way ANOVA (organ × genotype) with a post-hoc multiple-comparison correction (e.g., Holm-Sidak or Benjamini-Hochberg) — This approach would test for an organ-by-genotype interaction directly and control the family-wise error rate across the multiple organ comparisons in one framework.
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Body weight and body composition are compared at discrete time points (e.g., days −1 and 25, or from day 7 onward) using t-tests.↳ Could also: A repeated-measures or mixed-effects model that treats the full longitudinal trajectory within the same animals over time — This would use the correlation between repeated measurements on the same animals and could capture the trajectory of weight change over the full study period, not just isolated time points.
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An explicit multiple-testing correction (FDR) is described for the phosphoproteomics dataset but is not explicitly stated for the metabolomics volcano-plot comparisons (Fig. 1b) despite testing hundreds of metabolites.↳ Could also: Applying an explicit false-discovery-rate procedure (e.g., Benjamini-Hochberg) to the metabolomics comparisons as well — Given the large number of metabolites tested simultaneously, an FDR-based approach applied consistently across both omics datasets would extend the same false-positive control already used for the phosphoproteomics data.
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The human ENT1 variant association with BMI and obesity odds is reported as a significant association without further statistical detail in this excerpt.↳ Could also: A covariate-adjusted regression model (e.g., logistic regression adjusted for age, sex, and population structure) — Adjusting for common confounders and stratification variables is a standard complementary approach that can help characterize the robustness of a genetic association across subgroups.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35790189
Paper: Niemann et al. 2022, Nature 609:361–368. "Apoptotic brown adipocytes enhance energy expenditure via extracellular inosine." DOI 10.1038/s41586-022-05041-0, PMCID PMC9452294.
How this paper is structured
Predominantly a wet-lab / in-vivo physiology paper (mouse BAT thermogenesis, energy expenditure, ENT1/SLC29A1 pharmacology & genetics, human cohort genotype– phenotype association). The single substantial pipeline-derived computational dataset with a public accession is the phosphoproteomics (PRIDE PXD032153).
In scope (pipeline-derived, attempted)
| result | pipeline | data | status |
|---|---|---|---|
| C1 — 38,451 phosphopeptides identified | MaxQuant v2.0.1.0 (EasyPhos, label-free, MBR) | PXD032153 MaxQuant output tables | attempt from shipped tables |
| C2 — 7,875 phospho-sites regulated on FORSK (FDR<0.05) | Perseus two-sample test on MaxQuant Phospho (STY)Sites | PXD032153 | attempt (Perseus-equivalent in Python) |
| C3 — 8,613 phospho-sites regulated on inosine (FDR<0.05) | Perseus two-sample test | PXD032153 | attempt |
| C4 — 2,535 sites regulated in BOTH | intersection of C2 & C3 | PXD032153 | attempt |
| C5 — PKA target sites over-represented | motif/kinase enrichment (qualitative) | PXD032153 | attempt (qualitative) |
Because PRIDE ships the MaxQuant result tables (Phospho (STY)Sites.txt,
proteinGroups.txt, peptides.txt, …), the 80/20 quick win is to recompute the
identification count and the differential phospho-site counts directly from those
tables — no MaxQuant re-search required (re-search of the 44 GB raw is the
optional harder extension).
Out of scope (not attempted; reason)
- Untargeted metabolomics (Compound Discoverer v3.2, MetaboAnalyst v5.0): no
public accession (MetaboLights/Metabolomics Workbench) found in the text or via
search →
no_data_accessionfor that modality. Not attempted. - RNA-seq of Slc29a1/Slc29a2 expression: mentioned in text; no resolvable GEO accession found → not attempted.
- All wet-lab / in-vivo / human-cohort results (energy expenditure, qPCR, histology, mouse KO phenotypes, ENT1 Ile216Thr–BMI association): manual/ experimental, not a bioinformatic pipeline → out of scope by brief rule 2.
Key reproducibility caveat
The paper's main-text Methods (as available via PMC/EuropePMC) do not specify the Perseus parameters that drive the regulated-site counts: valid-value filter, imputation width/downshift, t-test S0, and permutation-FDR randomization count. These choices materially affect C2–C4. The identification count (C1) is deterministic from the MaxQuant output and is the cleanest target.
Third-party-tool note (brief rule P16)
The "code" here is the standard MaxQuant + Perseus (EasyPhos) stack, not a bespoke authors' repo. Per the brief this is an equally valid reproduction: run the described tools on the paper's own deposited data with the described parameters.
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.
Identification-level claim C1 reproduced cleanly (38,157 vs 38,451, 0.8% below) from the public, complete PXD032153 raw data, so input identity and comparability are sound. The deviation lives entirely in the downstream statistics: regulated-site counts (C2–C4) come out ~2.5x lower with EasyPhos/Perseus defaults because the paper never publishes the Perseus permutation-FDR parameters, and the reproduction is still preliminary (sweep running). This is best read as a methodology/underspecification gap on the analysis side rather than a fabrication concern — the qualitative conclusion of broad PKA-linked phospho-regulation likely survives, but exact counts remain unconfirmed pending the sweep.
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.