Metagenomic and Untargeted Metabolomic Analysis of the Effect of Sporisorium reilianum Polysaccharide on Improving Obesity.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
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- 🔴Could not use the authors’ exact input data
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- 🟡A deviation arose in the data or preprocessing
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This paper has a computational component, but its primary data is legally or ethically access-restricted — identifiable patient cohorts, rare-disease genomes, or controlled-access biobanks that cannot be openly shared. The reproduction therefore could not be attempted. That is a neutral verdict: it does not mean the result is wrong or that the authors fell short — only that, for legitimate privacy reasons, it cannot be independently checked from public data. We deliberately do NOT assign a 0–100 score here, because a low number would wrongly read as a failed reproduction.
▸Reproduction agent’s raw note
DROP (data_unavailable). Code = github.com/OpenGene/fastp (third-party read-QC tool, P16-valid). Data = SRA BioProject PRJNA946687. The accession resolves but as METADATA ONLY ('Fecal metagenome / Rat feces', Changchun University of Chinese Medicine): it exposes ZERO public runs/samples. ENA read_run count=0, sample count=0; read_run/submitted/analysis filereports return headers only; NCBI esearch db=sra term=PRJNA946687 -> Count 0. fastp operates on raw FASTQ, which are not downloadable, so the single deterministic in-scope target (reads_before == 2x SRA spot-count per run) cannot be reproduced. No «our HPC» compute was spent (nothing to download or run). NOT attempted, by design: metagenomic taxonomic/functional downstream (F/B ratio, Lactobacillus/Bacteroides species shifts, KEGG) -- under-specified (no assembler/profiler/db/version named) AND data unavailable; untargeted metabolomics (36 metabolites, KEGG pathways) -- no accession, proprietary instrument software, out of scope; all wet-lab biology -- out of scope. Fabrication not assessable: sequencing values cannot be checked against shipped data because none was deposited.
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Assessment versions
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v1 current initial assessmentassessed: 2026-06-15 ⛓ ed944fbdcc73
✎ I am an author of this paper
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Provenance — full disclosure
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- Reproduced
- 2026-06-15
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15👤 1 human curator(s) · Level L2 2026-06-15
- 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 authors hypothesize that polysaccharides from Sporisorium reilianum (SRP) alleviate high-fat-diet-induced obesity in rats by altering the gut microbiota and associated fecal metabolites, and aim to clarify this 'gut microbiota metabolism' mechanism using metagenomics and untargeted metabolomics.
- ★ SRP intervention reduces obesity, serum lipid levels, hepatic lipid accumulation, and adipocyte hypertrophy in high-fat-diet rats, especially at high dose. finding
- ★ SRP improves gut microbiota composition and function, decreasing the Firmicutes/Bacteroidetes ratio. finding
- ★ SRP increases Lactobacillus and decreases Bacteroides abundance, with species-level shifts (increased L. crispatus, L. helveticus, L. acidophilus; decreased L. reuteri and Staphylococcus xylosus). finding
- ★ 36 metabolites are related to the anti-obesity effect of SRP, acting through linoleic acid metabolism, phenylalanine/tyrosine/tryptophan biosynthesis, and phenylalanine metabolism pathways. mechanism
- ★ SRP alleviates obesity via gut-microbiota-related metabolic pathways and could be used for prevention/treatment of obesity. finding
- Combined metagenomics and untargeted fecal metabolomics can elucidate the gut-microbiota-metabolism mechanism of a fungal polysaccharide. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Body weight and obesity phenotype measurement | Male SD rats on high-fat diet | SRP oral 100/200/400 mg/kg/day vs HFD/NC | weekly body weight and weight gain | — |
| Serum biochemistry (lipids and liver enzymes) | Male SD rat serum | SRP 100/200/400 mg/kg/day vs HFD/NC | TG, TC, HDL-c, LDL-c, ALT, AST levels | Beyotime assay kits |
| Histology (HE staining) | Rat liver and white adipose tissue (perirenal/epididymal) | SRP vs HFD/NC | hepatocyte lipid degeneration and adipocyte size/morphology | — |
| Shotgun metagenomic sequencing | Rat fecal samples | SRP (HFD-SRPH) vs HFD/NC | taxonomic composition (phylum/genus/species) and functional (KEGG/eggNOG) gene abundance | Illumina HiSeq4000; E.Z.N.A. Stool DNA Kit |
| Untargeted metabolomics (LC-HRMS) | Rat fecal samples | SRP vs HFD/NC | differential metabolites and enriched pathways | Thermo Ultimate 3000 UHPLC + Orbitrap-MS (ESI) |
| FTIR characterization | SRP polysaccharide extract | none | characteristic polysaccharide absorption peaks / β-pyranose structure | Infrared spectrometer |
- ▼ Weight gain decreased dose-dependently with SRP vs HFD (HFD-SRPL/M/H reduced by 16.07%, 26.10%, 38.25%) 38.25% (high dose)
- ▼ Serum TG decreased with SRP vs HFD 23.05%/28.53%/29.68% (L/M/H)
- ▼ Serum TC decreased with SRP vs HFD 31.81%/47.73%/54.02% (L/M/H)
- ▼ Serum LDL-c decreased with SRP vs HFD (significant in M/H) 0.56%/19.34%/35.45% (L/M/H)
- ▲ Serum HDL-c increased with SRP vs HFD 18.09%/28.78%/35.10% (L/M/H)
- ▼ ALT and AST decreased with SRP vs HFD ALT up to 52.19%; AST up to 45.85%
- ▼ Firmicutes/Bacteroidetes ratio reduced by high-dose SRP NC 2.16, HFD 7.65, HFD-SRPH 4.28
- – 36 metabolites associated with SRP anti-obesity effect via linoleic acid and phenylalanine-related pathways 36 metabolites
- fold_change 1.44 times (TG) and 2.35 times (TC) higher in HFD vs NC (HFD serum lipids vs NC)
- mean TG 2.24 ± 0.15; TC 3.89 ± 0.26 (HFD) (HFD serum lipid means)
- mean LDL-c 0.77 ± 0.06 (HFD), 2.03x NC; HDL-c 0.44 ± 0.05 (HFD) (HFD lipoprotein means)
- mean ALT 99.70 ± 8.63 (2.25x NC); AST 245.60 ± 16.51 (1.75x NC) (HFD liver enzymes)
- count weight gain: NC 406.6, HFD 612.6, SRPL 527.8, SRPM 485.8, SRPH 443.1 g/rat (per-group weight gain (n=10/group))
- fold_change F/B ratio 2.16 (NC), 7.65 (HFD), 4.28 (HFD-SRPH) (Firmicutes/Bacteroidetes ratio)
- other VIP > 1 and p < 0.05 (differential metabolite screening criteria)
- count average raw reads 51,539,591; average ORF 563,992 (metagenomic sequencing QC)
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 randomized, five-group rat model (NC, HFD, HFD-SRPL, HFD-SRPM, HFD-SRPH; n=10 per group) with 8-week HFD induction followed by 8-week SRP intervention. Continuous physiological and biochemical outcomes were compared using Student's t-test for two-group contrasts and one-way ANOVA with Tukey post-hoc for multi-group contrasts. Gut microbiota community structure was visualized with Bray–Curtis PCoA, and untargeted fecal metabolomics used unsupervised PCA, supervised PLS-DA, and t-tests with combined VIP>1 and p<0.05 thresholds to identify differential metabolites, followed by KEGG/MetaboAnalyst pathway enrichment. Results were reported as mean ± SEM with percentage-change descriptors.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Student's t-test (two-tailed implied, via GraphPad Prism 8.0) | Pairwise two-group comparisons (e.g., NC vs. HFD) for body weight, serum lipids, liver enzymes | n=10 per group | not stated |
| One-way ANOVA | Multi-group comparisons across NC, HFD, HFD-SRPL, HFD-SRPM, HFD-SRPH for all physiological/biochemical endpoints (Figures 1b–g) | n=10 per group, 5 groups | not stated |
| Tukey HSD multiple comparison (post-hoc to one-way ANOVA) | Pairwise group separations following ANOVA for serum lipids, liver enzymes, and body weight | n=10 per group | na |
| PLS-DA (supervised partial least-squares discriminant analysis) combined with t-test; VIP>1 and p<0.05 threshold | Identification of differential fecal metabolites in untargeted metabolomics | null | not stated |
| Unsupervised PCA | Dimensionality reduction and clustering of untargeted metabolomics data (SIMCA-P 13.0) | null | na |
| PCoA (principal coordinate analysis, Bray–Curtis dissimilarity) | Gut microbiota community composition at phylum, genus, and species levels comparing NC, HFD, HFD-SRPH | null | na |
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Dispersion of group means reported as SEM throughout↳ Could also: Report SD, or 95% confidence intervals, alongside or instead of SEM — With n=10 per group, SEM is notably smaller than SD and can visually compress group spread; SD directly describes sample variability and is often preferred for biological characterization, while 95% CIs additionally convey estimation uncertainty in a way that supports effect-size interpretation
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Bray–Curtis PCoA was used to visualize gut microbiota community separation between groups↳ Could also: Complement PCoA with a formal PERMANOVA (adonis) or ANOSIM permutation test — PCoA is a visualization tool; PERMANOVA/ANOSIM would provide a formal significance test for whether group membership explains a statistically meaningful proportion of compositional variance, supplying a p-value and R² to accompany the ordination plot
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Differential metabolites were screened using an uncorrected t-test (p<0.05) combined with a VIP>1 threshold from PLS-DA↳ Could also: Apply Benjamini–Hochberg FDR correction across all metabolite features tested — Untargeted metabolomics datasets typically involve hundreds to thousands of features; nominal p<0.05 without FDR control inflates the expected false discovery count; BH-FDR at q<0.05 or q<0.20 is a widely adopted standard that would contextualize how many of the 36 reported metabolites are likely true discoveries
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PLS-DA model performance was not reported with cross-validation metrics or permutation testing↳ Could also: Report R²Y, Q² (cross-validated R²), and a permutation test p-value for the PLS-DA model — PLS-DA can overfit, particularly with small n; Q² and permutation tests (e.g., 999 permutations) are standard practice for assessing whether the model generalizes beyond the training data, helping distinguish real group separation from chance fitting
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Multiple Student's t-tests were used for selected two-group contrasts in addition to the one-way ANOVA↳ Could also: Pre-specify all pairwise contrasts as planned comparisons within the ANOVA framework, or use a single ANOVA with orthogonal contrasts — Mixing standalone t-tests with ANOVA can create ambiguity about which family of comparisons was intended; pre-planned contrasts within ANOVA maintain a consistent error structure and make the multiplicity scope explicit
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Pathway enrichment analysis was performed with MetaboAnalyst using the 36 differential metabolites as input↳ Could also: Additionally apply a rank-based enrichment approach (e.g., GSEA-style using all metabolite fold-changes as a ranked list) rather than a binary hit/miss enrichment — Threshold-based enrichment depends on the chosen VIP and p-value cut-offs; a rank-based method uses the full gradient of effect sizes across all detected metabolites and can detect pathway-level signals even when no individual metabolite clears a discrete threshold
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.
Downstream reach in the literature
0 downstream papers · 1 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-37107373
Paper: Guo et al. 2023, "Metagenomic and Untargeted Metabolomic Analysis of the Effect of Sporisorium reilianum Polysaccharide on Improving Obesity." Foods 12(8):1578. PMID 37107373 · PMCID PMC10137368 · DOI 10.3390/foods12081578.
Design: Male Sprague-Dawley rats on high-fat diet ± SRP (100/200/400 mg/kg/day, 8 weeks). Readouts: obesity/serum-lipid indices (wet-lab), shotgun metagenomics of rat feces, and untargeted metabolomics of serum.
- Nominated code:
https://github.com/OpenGene/fastp— fastp, a third-party read-QC/trimming tool (NOT the authors' own pipeline). P16-valid: running it on the paper's own data would be a legitimate reproduction (same pattern as pmid-41494533, pmid-36618608). - Nominated data: SRA BioProject PRJNA946687 ("Fecal metagenome — Rat feces", Changchun University of Chinese Medicine).
In scope (pipeline-derived) — IF the data existed
- fastp QC of the fecal metagenomic FASTQ: per-run read counts before/after
filtering, Q20/Q30, GC%, adapter/low-quality removal. The standard integrity
check (
reads_before == 2 × SRA spot-countper run) is the only clearly specified, deterministic, reference-free pipeline output here.
Out of scope
- All wet-lab biology: body weight, serum lipids, liver histology, adipocyte size.
- Metagenomic taxonomic/functional profiling downstream of fastp (assembly, binning, species abundance, KEGG/F-to-B ratio): the paper names no assembler, profiler, database, version, or threshold for these — not reproducible as specified, and not covered by the nominated code (fastp only).
- Untargeted metabolomics (36 differential metabolites, KEGG pathways): instrument data + proprietary software (typical LC-MS / Compound Discoverer / MetaboAnalyst); no accession, no code, no parameters → out of scope.
BLOCKER — data unavailable (drop)
PRJNA946687 is registered as metadata only; it contains no public sequencing runs or samples:
| check | result |
|---|---|
ENA read_run count for study PRJNA946687 |
0 |
ENA sample count for study PRJNA946687 |
0 |
ENA read_run / submitted / analysis filereport |
empty (headers only) |
NCBI esearch db=sra term=PRJNA946687 |
Count 0 (PhraseNotFound) |
| ENA project XML | resolves: PROJECT "Fecal metagenome / Rat feces", no run/sample/experiment children |
The project accession resolves but the raw FASTQ were never released (or were withdrawn). fastp operates on those reads; with no downloadable reads, the only in-scope pipeline-derived result cannot be reproduced.
→ Outcome: drop, drop_reason = data_unavailable
(controlled vocab: "accession no longer resolves / dataset withdrawn — GEO/SRA/ENA
lookup empty"). No «our HPC» compute was spent: there is nothing to download or run.
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