Dioxin-elicited decrease in cobalamin redirects propionyl-CoA metabolism to the β-oxidation-like pathway resulting in acrylyl-CoA conjugate buildup.
The main results reproduced, with only marginal, non-material deviations.
- 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
REPRODUCED (primary thread). Paper = TCDD/cobalamin/propionyl-CoA mechanism (Zacharewski lab, JBC 2022); headline findings are LC-MS metabolomics + ICP-MS (wet-lab, out of scope). Pinned metadata is loosely coupled: data=GSE119780 is a reused CIRCADIAN RNA-seq series yielding no reported number, while code=Maaslin2 was used ONLY for the cecal shotgun-metagenomics thread (PRJNA719224, 12 samples). I reproduced that Maaslin2 thread end-to-end on «our HPC»: bowtie2 mm10 host removal -> HUMAnN3 (UniRef90 201901b) -> regroup EC/Pfam -> CPM -> Maaslin2 (continuous dose, TSS, LOG, LM, BH, q<0.25). Both named features match the paper in direction AND magnitude: precorrin-3 methylase EC «ip» = 1.30x repression (paper ~1.3x); ABC cobalt transporter PF09819 = 2.92x increase (paper ~3x); both detectable at the paper's own q<0.25. The qualitative 'negligible effects on Cbl/propionate' conclusion is directionally consistent. Did NOT attempt the hepatic R1-R7 gene fold-changes (different source series GSE109863 + a custom non-public empirical-Bayes DE pipeline). Two documented deviations (Python regroup reimpl because the humann conda env was broken; one sample's host removal via mm10 bowtie2) do not affect the CPM-based conclusions. Grades provisional pending human sign-off.
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Assessment versions
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v1 current initial assessmentassessed: 2026-06-19 ⛓ fd20c3d06aef
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When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-29
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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 dose-dependent disruption of propionyl-CoA metabolism by TCDD produces toxic intermediates (via cobalamin depletion and redirection to the Cbl-independent β-oxidation-like pathway) that contribute to TCDD hepatotoxicity and progression of steatosis to steatohepatitis with fibrosis.
- ★ TCDD dose-dependently decreases cobalamin (Cbl) levels, compromising MUT activity and redirecting propionyl-CoA metabolism from the canonical Cbl-dependent carboxylation pathway to the alternate Cbl-independent β-oxidation-like pathway. finding
- ★ Acrylyl-CoA, a reactive intermediate of the Cbl-independent pathway, accumulates in liver after TCDD treatment, as evidenced by increased S-(2-carboxyethyl)-L-cysteine (SCEC), its spontaneous conjugate with cysteine. finding
- ★ TCDD represses genes in both the canonical Cbl-dependent carboxylase pathway (Pcca, Pccb) and the alternate Cbl-independent β-oxidation-like pathway (Echs1, Adhfe1, Aldh6a1). finding
- ★ TCDD inhibits methylmalonyl-CoA mutase (MUT) activity, particularly at lower doses. finding
- ★ TCDD decreases serum Cbl and hepatic cobalt levels while having negligible effects on gene expression for Cbl absorption, transport, trafficking, or derivatization to AdoCbl. finding
- ★ TCDD induces Acod1 (aconitate decarboxylase 1) and dose-dependently increases hepatic itaconate levels. finding
- ★ Itaconate is proposed to be activated to itaconyl-CoA, a MUT suicide inactivator that adducts adenosylcobalamin, inhibiting MUT and depleting Cbl. mechanism
- Elevated SCEC confirms both acrylyl-CoA accumulation and inhibition of short chain enoyl-CoA hydratase (ECHS1) activity. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Targeted LC-MS/MS metabolomics | liver extracts, male mice (C57BL/6) | TCDD oral gavage every 4 days for 28 days (dose response) | S-(2-carboxyethyl)-L-cysteine (SCEC) levels | LC-MS/MS |
| Untargeted metabolomics | hepatic extracts, mice | TCDD oral gavage every 4 days for 28 days | annotation of acrylyl-CoA and 3-hydroxypropionyl-CoA intermediates | — |
| ChIP-seq | liver, male mice | single bolus 30 μg/kg TCDD, 2 h | AhR genomic binding/enrichment at propionyl-CoA pathway genes | — |
| RNA-seq (time course) | liver, male C57BL/6 mice (n=3) | single bolus 30 μg/kg TCDD | differential gene expression over time (propionyl-CoA metabolism genes) | — |
| RNA-seq (dose response) | liver, male C57BL/6 mice (n=3) | TCDD oral gavage every 4 days for 28 days, multiple doses | differential expression of propionyl-CoA and Cbl metabolism genes | — |
| RNA-seq | intestinal segments (duodenum, jejunum, ileum, colon), mice (n=3) | TCDD oral gavage every 4 days for 28 days, dose response | expression of Cbl absorption/transport genes (e.g. Cubn) | — |
| ELISA | serum, male C57BL/6 mice (n=4-5) | TCDD oral gavage every 4 days for 28 days, dose response | serum cobalamin levels | ELISA assay |
| Inductively coupled plasma mass spectrometry (ICP-MS) | liver extracts, mice (n=4-5) | TCDD oral gavage every 4 days for 28 days, dose response | hepatic cobalt levels | ICP-MS |
- ▲ Hepatic SCEC levels increased at 30 μg/kg TCDD vs vehicle 15.62-fold ± 2.59
- ▼ AhR enrichment associated with repression of Pcca and Pccb (propionyl-CoA carboxylase subunits) 2.7-fold and 2.9-fold
- ▼ Echs1 and Adhfe1 repressed by TCDD (AhR enrichment detected) 1.5-fold and 2.2-fold
- ▼ Aldh6a1 repressed by 30 μg/kg TCDD without detectable AhR enrichment 2.2-fold
- ▼ Serum cobalamin and hepatic cobalt levels dose-dependently decreased by TCDD
- – Cubn repressed in duodenum, jejunum, proximal ileum, and colon but induced in distal ileum 4.2-, 16.7-, 4.6-, 2.0-fold repression; 1.9-fold induction (distal ileum)
- ▼ Tcn2 repressed only at highest TCDD dose 1.5-fold at 30 μg/kg
- – Gut microbiome Cbl biosynthesis gene (precorrin-3 methylase) modestly repressed; ABC cobalt transporter genes increased 1.3-fold repression; 3-fold increase
- fold_change 15.62 ± 2.59 (SCEC fold-change at 30 μg/kg TCDD vs vehicle, liver extracts)
- pvalue p ≤ 0.05 (SCEC significance, one-way ANOVA with Dunnett's post-hoc)
- fold_change 2.7 and 2.9-fold repression (Pcca and Pccb repression, ChIP-seq/RNA-seq 2 h after 30 μg/kg TCDD bolus)
- fold_change 1.5-fold repression (Echs1 repression)
- fold_change 16.7-fold repression (Cubn repression in jejunum)
- fold_change 1.9-fold induction (Cubn induction in distal ileum)
- pvalue p < 0.05 (Serum Cbl and hepatic cobalt reductions, Dunnett's post-hoc test)
- fold_change 3-fold increase (ABC cobalt transporter gene abundance in cecal metagenomics)
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 used a dose-response design in mice (multiple TCDD doses vs. vehicle control, typically n=3-5 per group) with targeted LC-MS/MS, RNA-seq, ChIP-seq, and metagenomic analyses. Group comparisons for targeted metabolite (SCEC) and biochemical (serum cobalamin, hepatic cobalt) measurements were tested with one-way ANOVA and Dunnett's post-hoc test against vehicle, with significance denoted at p ≤ 0.05/p < 0.05. Differential gene expression from RNA-seq was assessed using a posterior probability threshold (P1(t) > 0.80) rather than a conventional p-value cutoff, and results throughout are largely summarized as fold-changes.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| one-way ANOVA with Dunnett's post-hoc test | Table 1 - hepatic S-(2-carboxyethyl)-L-cysteine fold-change across TCDD doses vs. vehicle | n = 5 | not stated |
| one-way ANOVA with Dunnett's post-hoc test | Figure 3 - serum cobalamin and hepatic cobalt levels across TCDD doses vs. vehicle | n = 4-5 | not stated |
| Bayesian posterior probability threshold (P1(t) > 0.80) for differential expression | RNA-seq analyses of AhR target genes and propionyl-CoA/cobalamin pathway genes (Figs. 1, 2, 4) | n = 3 | not stated |
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Dose-response comparisons in Table 1 and Figure 3 used one-way ANOVA with Dunnett's post-hoc test to compare each TCDD dose to vehicle.↳ Could also: Because the doses form a natural ordered series, a trend test such as Jonckheere-Terpstra or a regression-based dose-response (e.g., a linear or benchmark-dose model) could also be used. — Trend-based approaches directly test for a monotonic dose-dependent relationship and can increase power to detect a graded response, complementing the pairwise dose-vs-control comparisons from Dunnett's test.
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Table 1 reports variability as ± SEM while Figure 3 reports ± SD for similarly sized groups (n = 4-5).↳ Could also: Reporting SD (or a 95% confidence interval) consistently across figures/tables would also convey the spread of the data. — SEM scales down with sample size and can visually understate variability in small groups, whereas SD and CIs are often preferred for directly communicating the dispersion of individual biological replicates.
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Significance for the targeted metabolite and biochemical assays was reported as a threshold (p ≤ 0.05 or p < 0.05) with an asterisk rather than as exact values.↳ Could also: Reporting exact p-values (e.g., p = 0.032) alongside the threshold could also be used. — Exact p-values convey more information about the strength of evidence against the null hypothesis and facilitate meta-analysis or re-evaluation under different alpha levels.
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RNA-seq differential expression was flagged using a Bayesian posterior probability threshold (P1(t) > 0.80) rather than a conventional frequentist p-value/FDR approach.↳ Could also: A standard count-based differential expression pipeline with multiple-testing correction (e.g., DESeq2 or edgeR with Benjamini-Hochberg FDR) could also be applied to the same RNA-seq data. — FDR-controlled frequentist methods are widely used and allow direct comparison of significance calls across studies that report q-values, complementing the Bayesian posterior-probability framework used here.
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Group sizes (n = 3 for RNA-seq/ChIP-seq groups, n = 5 for LC-MS/MS, n = 4-5 for cobalamin/cobalt assays) were used without a stated power analysis.↳ Could also: An a priori power/sample-size calculation based on expected effect size and variance could also be reported. — Stating the power analysis or expected effect size used to select sample sizes helps readers gauge the sensitivity of the study to detect the reported dose-dependent effects, particularly for small n groups.
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The excerpt does not describe randomization or blinding procedures for dose administration or outcome measurement.↳ Could also: Explicit randomized allocation of animals to dose groups and blinded outcome assessment (e.g., blinded LC-MS/MS or histological scoring) could also be described. — Documenting randomization and blinding is a standard practice that helps readers assess the risk of allocation or assessment bias in animal dosing studies.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35931118
Paper: Orlowska K, Fling RR, Nault R, Sink WJ, Schilmiller AL, Zacharewski T. "Dioxin-elicited decrease in cobalamin redirects propionyl-CoA metabolism to the β-oxidation-like pathway resulting in acrylyl-CoA conjugate buildup." J Biol Chem 2022;298(9):102301. PMID 35931118 · PMCID PMC9418907 · DOI 10.1016/j.jbc.2022.102301.
What kind of paper this is
A mechanistic toxicology study. TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin) is gavaged into male C57BL/6 mice; the authors use targeted/untargeted LC-MS metabolomics (the headline finding: build-up of S-(2-carboxyethyl)-L-cysteine, an acrylyl-CoA cysteine conjugate), ICP-MS (cobalt), hepatic RNA-seq (dose-response gene expression), and cecal shotgun metagenomics to argue that TCDD lowers cobalamin (B12), inhibiting methylmalonyl-CoA mutase and redirecting propionyl-CoA through a β-oxidation-like pathway.
Pinned metadata vs reality (IMPORTANT — harvested metadata is loosely coupled)
The room was seeded with data=GSE119780 and code=github.com/biobakery/Maaslin2.
These belong to two different analysis threads and do not pair:
- GSE119780 is the lab's circadian hepatic RNA-seq series ("RNA-Seq Analysis of TCDD-Elicited Changes in Circadian Hepatic Gene Expression", 48 samples, 8 timepoints). It is one of nine reused GEO RNA-seq/ChIP-seq series cited in the Data Availability statement (GSE109863, GSE87519, GSE119780, GSE97634, GSE87542, GSE90097, GSE171942, GSE89430, GSE171941). It is not the source of the headline hepatic fold-changes (those come from the dose-response liver RNA-seq, GSE109863), and the paper reports no GSE119780-specific numbers.
- Maaslin2 was used only for the shotgun-metagenomics thread, whose data is PRJNA719224 (NCBI BioProject; 12 cecal metagenomes), NOT GSE119780.
In scope (pipeline-derived, reproducible)
PRIMARY target — Maaslin2 metagenomics (matches the pinned tool exactly). The metagenomics methods are fully specified, so this is the most faithful reproducible pipeline. Data: PRJNA719224, 12 cecal shotgun metagenomes (vehicle / 0.3 / 3 / 30 µg/kg TCDD, n=3 each). Documented pipeline:
- Host removal of Mus musculus (GRCm38.p6) reads via bowtie2 + samtools + bedtools.
- HUMAnN3 (default) → UniRef90 (UniProt UniRef90, Jan 2019).
- humann_regroup_table → Enzyme Commission (EC) and PFAM features.
- humann_renorm_table → copies per million reads (cpm).
- Maaslin2 default settings: TSS normalization, GLM analysis method, BH correction.
Reported metagenomic outcomes to compare against (Results + Fig S1/S2):
- Precorrin-3 methylase (EC «ip»): ~1.3-fold repression.
- ABC cobalt transporters (PFAM PF09819): ~3-fold increase.
- Overall qualitative claim: "negligible effects on microbial Cbl and propionate metabolism."
Secondary / limited reproducibility (documented, not the primary attempt)
- Hepatic dose-response gene fold-changes (Fig 2/5: Pcca −2.7×, Pccb −2.9×, Mmab −2.4×, Tcn2 −1.5×, Echs1 −1.5×, Adhfe1 −2.2× at 30 µg/kg; Acod1 induction). Source = dose-response liver RNA-seq (GSE109863, not the pinned GSE119780). The DE method is the lab's custom empirical-Bayes posterior-probability pipeline (|FC|≥1.5, P1(t)≥0.8) — not a standard public package (edgeR/DESeq2/ limma). Exact 1:1 reproduction is therefore limited; a standard-pipeline approximation could be attempted but would not be method-identical.
Out of scope (wet-lab / instrument / manual — not attempted)
- LC-MS targeted & untargeted metabolomics (incl. the S-(2-carboxyethyl)-L-cysteine headline; raw data Metabolomics Workbench ST001379).
- ICP-MS cobalt quantification.
- qRT-PCR validation; enzyme-activity assays; all wet-lab measurements.
Honesty notes
- The primary attempt directly exercises the pinned tool (Maaslin2) on the paper's own data (PRJNA719224) per the documented parameters — per the brief
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Reproduction footprint
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