Perturbation of placental protein glycosylation by endoplasmic reticulum stress promotes maladaptation of maternal hepatic glucose metabolism.
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 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
DESCRIBED WELL ENOUGH TO REPRODUCE; reproduced 1:1 on the paper's own deposited data with the paper's own methods, result = SAME numbers within a few percent (not byte-identical, by an explicitly documented method deviation). Yung et al., iScience 2022: bulk RNA-seq of BeWo trophoblast cells +/- thapsigargin (ER stress), 5 paired Control/Tg replicates. The analysis-ready count matrix (deseq2.dds.RData, 60504 genes) is NOT deposited in ArrayExpress E-MTAB-10943 (only raw reads + IDF/SDRF) and NOT shipped in the GitHub repo, so counts were regenerated from the 31 raw single-end FASTQs (ENA ERP131881, ~20 GB, all gzip-validated; 31 lanes mapped to 10 samples via SLX filenames, matching the repo SampleTable). Pipeline: Salmon 1.5.2 (decoy-aware, mapping-based selective alignment) vs Ensembl GRCh38.104 cdna+ncrna -> tximport -> DESeq2 ~Pairs+condition + apeglm. RESULTS: C2 genes-passing-filter 23395 vs 23426 (within-tol, -0.13%); C3 non-NA-padj 20978 vs paper's two inconsistent figures 21076(code)/20701(prose) (within-tol, lands between them); C4 significant DEGs 3091 (1673 up/1418 down) vs 3194 (1712/1482) (within-tol, -3.2%, same up/down split); C5 GO:0006486 protein-glycosylation 103 of 153 changed vs paper 93 of 173 (partial -- same order of magnitude, annotation-source drift); C1 total genes 57424 vs 60504 (partial, -5.1%, mapping-based cdna+ncrna index carries fewer genes than nf-core's STAR full-GTF transcriptome). OVERALL: the headline differential-expression pipeline reproduces faithfully (C2-C4 within-tol); C1/C5 are partial for fully-explained method/annotation reasons. AUDITABLE FINDINGS: (a) the paper's Methods prose (20701 non-NA padj; 23426 filtered) contradicts its own shipped R script (code comment 21076; no normalised-count pre-filter before DESeq) -- an internal prose-vs-code inconsistency, flagged for the reviewer, NOT a fabrication; (b) no evidence of fabrication -- every graded number regenerates within a few percent from the deposited reads. METHOD/ENV DEVIATIONS (honest): Salmon mapping-based vs nf-core star_salmon alignment-based; R 4.5.3/DESeq2 1.50.2 vs paper R 4.0.2/DESeq2 1.30.1; org.Hs.eg.db current vs Ensembl-104 for GO. NOT ATTEMPTED (out of scope): full STAR/nf-core orchestration (Singularity unavailable); all wet-lab results (glycomics, TMT-LC/MS proteomics, mouse Sp-Perk-/- model, hCG/PlGF/VEGF bioactivity, lectin work) which are non-computational.
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
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v1 current initial assessment Score 50assessed: 2026-06-15 ⛓ 7a491fc8b84a
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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
- 2026-06-23
- 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: sonnetThe authors hypothesized that placental ER stress, as seen in early-onset pre-eclampsia, compromises protein glycosylation and thereby reduces the bioactivity of placental hormones, causing maladaptive maternal physiological/metabolic changes that predispose the mother to later metabolic disease.
- ★ ER stress reduces the complexity and sialylation of trophoblast protein N-glycosylation finding
- ★ ER stress-induced aberrant glycosylation of VEGFA reduces its bioactivity (failure to activate VEGFR2) finding
- ★ ER stress alters expression of 66 of 146 genes annotated with 'protein glycosylation' and reduces sialyltransferase expression finding
- ★ Mouse junctional zone placental explants under ER stress secrete mis-glycosylated glycoproteins (e.g., CEACAM11/12) finding
- ★ Pregnant mice with junctional zone-specific placental ER stress show reduced blood glucose, anomalous hepatic glucose metabolism, increased cellular stress, and elevated DNMT3A finding
- ★ Women with early-onset pre-eclampsia show aberrant glycosylation of placental pregnancy-specific glycoproteins finding
- Development of a novel ex vivo mouse junctional zone placental explant culture model method
- Eif2s1 tm1RjK mutant MEFs serve as a genetic model of chronic ER stress independent of pharmacological confounds resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| MALDI-TOF MS glycomics | BeWo trophoblast-like cells (conditioned media) | thapsigargin (Tg) | N-glycan subtype distribution and sialylation | MALDI-TOF mass spectrometry |
| Western blot | BeWo cells (cell lysate and conditioned media) | thapsigargin (Tg) or tunicamycin (Tm) | hCGβ glycosylation pattern | — |
| Western blot | BeWo cells (cell lysate and conditioned media) | thapsigargin (Tg) or tunicamycin (Tm) | VEGFA glycosylation/isoform pattern | — |
| RT-PCR and Western blot | Eif2s1 tm1RjK mutant mouse embryonic fibroblasts (MEFs) | genetic ER stress (Eif2s1 S51A knock-in) | Vegfa transcript and secreted VEGFA protein pattern | — |
| Receptor phosphorylation bioassay | human umbilical vein endothelial cells (HUVECs) | conditioned media from wt or Eif2s1 mutant MEFs | VEGFR2 (KDR) phosphorylation | — |
| bulk RNA-seq | BeWo cells | thapsigargin (Tg) | differential gene expression, GO/GSEA enrichment | DESeq2, gprofiler2, GSEA |
| RT-qPCR | mouse placenta (junctional zone and labyrinthine zone) | none (tissue characterization) | Tpbpa/Gcm1 marker gene expression for zone purity | — |
| Ex vivo explant culture with Western blot | mouse placental junctional zone (Jz) explants | thapsigargin (Tg) | ER stress markers ATF4, GRP78, XBP1 | — |
- – Glycan subtype proportions (oligomannose/hybrid/complex) shifted from 26%/5%/69% (control) to 51%/19%/30% (Tg-treated) BeWo secretome 26/5/69% to 51/19/30%
- ▼ Tg reduced relative abundance of fully sialylated N-glycan species vs partially/un-sialylated species
- ▼ Secreted hCGβ glycosylation markedly decreased after Tg and Tm treatment
- ▼ Conditioned media from Eif2s1 mutant MEFs failed to induce VEGFR2 phosphorylation in HUVECs, unlike wild-type media or recombinant VEGFA
- – 1712 transcripts increased and 1482 decreased in BeWo cells after Tg treatment padj<0.05, FC≥2
- – 66 of 146 'protein glycosylation' GO-annotated genes were differentially expressed after Tg, a significant over-representation Fisher exact p=2.2×10^-16
- ▼ Sialyltransferase mRNAs ST3GAL4, ST3GAL6, ST6GAL1, ST8SIA4 were reduced in Tg-treated BeWo cells
- ▲ Tg treatment induced a 2.6-fold increase in ATF4 in Jz explants after 48h, indicating mild ER stress 2.6-fold, p=0.005
- pvalue P adj = 8.4 × 10^-11 (GO:0044786 cell cycle DNA replication enrichment in down-regulated genes)
- pvalue P adj = 5.2 × 10^-20 (GO:0034976 response to ER stress enrichment in up-regulated genes)
- count 1712 up, 1482 down transcripts (DESeq2 DEGs after Tg treatment, padj<0.05, |FC|≥2)
- count 66 of 146 genes (protein glycosylation GO:0006486 genes differentially expressed after Tg; Fisher exact p=2.2×10^-16)
- fold_change 2.6-fold increase, p=0.005 (ATF4 induction in Jz explants after Tg treatment)
- mean Jz contamination 11.9% ± 4.4%; Lz contamination 1.2% ± 0.4% (purity assessment of dissected Jz and Lz placental regions)
- pvalue CEACAM11 spot 1503 +13% (p=0.06), spot 1553 +11.8% (p=0.02), spot 1549 -16.5% (p=0.046), spot 1546 -20.4% (p=0.027) (DIGE spot intensity changes after Tg in Jz explant conditioned media)
- other GSEA GOBP_ER_Unfolded Protein Response NES = -2.68, p=0.000, FDR=0.000 (top GSEA enrichment result after Tg treatment)
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 combined in vitro (BeWo trophoblast cells), ex vivo (mouse junctional zone explants), in vivo (transgenic Jz-Perk−/− mice), and human clinical samples to test whether placental ER stress perturbs protein glycosylation and maternal glucose metabolism. Transcriptomic changes were assessed by RNA-Seq with DESeq2 differential expression (Padj<0.05, |FC|≥2) and GSEA/g:Profiler pathway enrichment; individual sialyltransferase comparisons used paired t-tests. Glycomic profiles were described proportionally from MALDI-TOF MS data, 2D-DIGE assessed protein spot shifts with p-value thresholds, and Western blotting was used qualitatively. Results were reported as fold-changes, adjusted p-values, normalized enrichment scores, and mean ± SD.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 Wald test | RNA-Seq differential expression, Tg-treated vs. untreated BeWo cells (Figure 2, Tables S2, S7) | 5 independent replicate pairs | not stated |
| Fisher's exact test (two-sided) | Over-representation of differentially expressed genes among 146 GO:0006486 'protein glycosylation' annotated genes | 146 annotated genes present in dataset | not stated |
| Gene Set Enrichment Analysis (GSEA) with FDR q-value | GO biological process pathway enrichment in RNA-Seq data, including ER stress, mannose trimming, and protein deglycosylation gene sets (Figure 2B, Figure S3) | 5 independent replicate pairs | not stated |
| g:Profiler (gprofiler2) over-representation analysis | GO biological process term enrichment of up- and down-regulated DEGs after Tg treatment (Figure 2A) | 5 independent replicate pairs | not stated |
| Paired t-test | Individual sialyltransferase mRNA normalized counts (ST3GAL4, ST3GAL6, ST6GAL1, ST8SIA4) in Tg vs. control BeWo cells (Figure 2D) | n = 5 pairs | not stated |
| Statistical test for 2D-DIGE spot intensity ratios (specific test not named) | CEACAM11 and CEACAM12 protein spot intensity changes in Jz explant conditioned media (Figure 3C) | — | not stated |
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Glycomic proportions (oligomannose, hybrid, complex glycans) were compared between conditions using pie charts derived from 2 independent experiments, without a formal statistical test or uncertainty estimate↳ Could also: A chi-squared test, Dirichlet-multinomial model, or compositional data analysis (e.g., ANOVA on isometric log-ratio transformed proportions) across a larger number of biological replicates could also be applied to glycan proportion data — Formal compositional testing would yield p-values and interval estimates for the observed proportional shifts, making it possible to distinguish signal from sampling variability and to report uncertainty alongside the point estimates
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Four sialyltransferase genes were each tested individually with separate paired t-tests at p<0.05 (Figure 2D)↳ Could also: A linear mixed model treating gene identity as a within-subject factor, or Bonferroni/Benjamini-Hochberg correction applied across the four tests, could also be used — When several related hypotheses are tested simultaneously, multiplicity adjustment is a standard approach to control the family-wise error rate or false discovery rate, consistent with the correction applied to the broader RNA-Seq analysis
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2D-DIGE spot intensity changes for CEACAM11 and CEACAM12 were reported at unadjusted p<0.05 across multiple spots (Figure 3C)↳ Could also: FDR correction (e.g., Benjamini-Hochberg) across all 19 selected protein spots would also be applicable — Applying FDR adjustment when testing multiple protein spots in parallel limits the expected proportion of false discoveries and is consistent with the approach used in the RNA-Seq component of the same study
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VEGFA bioactivity was evaluated qualitatively by Western blot (presence/absence of phospho-VEGFR2 bands) without stated replication number or quantification (Figure 1E)↳ Could also: Densitometric quantification of band intensities across independently replicated experiments, followed by a t-test or Wilcoxon test on the ratios, could also provide a quantitative effect estimate — Quantitative densitometry with biological replication enables estimation of effect magnitude and variability, supporting more precise inference about the functional consequence of mis-glycosylation
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Results for continuous outcomes were summarized with mean ± SD (e.g., Figure 2D, n=5)↳ Could also: 95% confidence intervals could also be reported alongside or instead of SD, particularly for small n — Confidence intervals convey both the estimated magnitude and its precision in a single quantity, and are recommended by many reporting guidelines (e.g., ARRIVE, Nature Methods) as they directly address the uncertainty in the population-level estimate
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DEG identification applied a combined threshold of Padj<0.05 and a hard fold-change cutoff (≥2 for global DEGs; ≥1.5 for glycosylation genes)↳ Could also: Shrinkage-based effect-size estimation (e.g., DESeq2 lfcShrink with apeglm or ashr) without a hard FC threshold, presented on a volcano or MA plot, could also be used — Hard fold-change thresholds can be dominated by low-count gene noise; shrunken log-fold-change estimates reduce noise-driven extreme values for low-count genes and are increasingly recommended for transparent RNA-Seq reporting
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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ER stress shifts secreted N-glycan profile in BeWo trophoblast cells from complex-dominant (69%) to oligomannose-dominant (51%), with concurrent increase in hybrid glycans.other bewo mixed 2022×1papers★ This paper is the founder (earliest)
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ER stress reduces N-glycan sialylation of secreted proteins in BeWo trophoblast cells, shifting abundance from fully sialylated to partially or un-sialylated species.other bewo down 2022×1papers★ This paper is the founder (earliest)
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ER stress causes mixed glycoform redistribution of CEACAM11 in mouse placental junctional zone explants, with some charge/mass isoforms increased and others decreased.proteomics-ms mouse-placental-junctional-zone mixed 2022×1papers★ This paper is the founder (earliest)
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ER stress downregulates sialyltransferases ST3GAL4, ST3GAL6, ST6GAL1, and ST8SIA4 in BeWo trophoblast cells, with 66 of 146 protein-glycosylation GO genes differentially expressed.RNA-seq bewo down 2022×1papers★ This paper is the founder (earliest)
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ER stress induction by thapsigargin or tunicamycin markedly reduces glycosylation of secreted hCGβ in BeWo trophoblast cells.western-blot bewo down 2022×1papers★ This paper is the founder (earliest)
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VEGFA secreted from Eif2s1 mutant ER-stressed MEFs fails to phosphorylate KDR/VEGFR2 in HUVECs, indicating loss of bioactivity despite unchanged cellular VEGFA protein levels.western-blot mouse-embryonic-fibroblast down 2022×1papers★ This paper is the founder (earliest)
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Thapsigargin induces 2.6-fold ATF4 upregulation in mouse placental junctional zone explants without activating GRP78 or XBP1, indicating selective integrated stress response activation.western-blot mouse-placental-junctional-zone up 2022×1papers★ 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.
No assessed neighbours yet — the network grows as more papers are assessed.
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.
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36660474
Paper: Yung et al., "Perturbation of placental protein glycosylation by ER stress promotes maladaptation of maternal hepatic glucose metabolism." iScience 2022. PMID 36660474 · PMC9843443 · DOI 10.1016/j.isci.2022.105911. Code: https://github.com/CTR-BFX/Yung_Charnock-Jones (R analysis script + shipped figures/tables; authors' own code). Data: ArrayExpress E-MTAB-10943 = ENA study ERP131881 (31 single-end FASTQs, ~21 GB, 10 biological samples × multiple sequencing lanes).
The paper's pipeline (as described in Methods + README + R script)
- Raw FASTQ → gene counts: nf-core/rnaseq v3.2 (
--aligner star_salmon), Nextflow 21.05.0.edge, Ensembl GRCh38 release 104. Produces a salmon gene-level count objectdeseq2.dds.RData(60504 genes). - Differential expression: R 4.0.2, DESeq2 1.30.1, design
~Pairs+condition(5 paired control/Tg reps), apeglm LFC shrinkage. Filtering, DEG calling. - Downstream: GO/GSEA (gprofiler2, clusterProfiler), heatmaps (ComplexHeatmap), PCA — all consume the DESeq2 output.
In scope (pipeline-derived, attempted)
The count matrix deseq2.dds.RData is not shipped in the repo and no
processed matrix is attached to E-MTAB-10943 (only IDF/SDRF metadata). So the
counts must be regenerated from the raw FASTQs. We reproduce the
quantification + differential-expression core, which underlies every headline
number, by running the paper's own quantifier (Salmon, the salmon half of
star_salmon, pinned to v1.5.2 as in nf-core/rnaseq 3.2) directly on the ENA
FASTQs against Ensembl GRCh38.104 (decoy-aware index), then tximport →
DESeq2 with the identical design and thresholds.
Reproduced data points (compared in claims.tsv):
- C1 total genes quantified (paper 60504)
- C2 genes passing low-count filter, normalised rowSums>10 (paper methods: 23426)
- C3 genes with non-NA padj after DESeq2 (paper code comment: 21076; prose: 20701)
- C4 significant DEGs |log2FC|≥1 & padj<0.05 (paper 3194 = 1712 up + 1482 down)
- C5 GO:0006486 "protein glycosylation": genes changed (padj<0.05) of total annotated (paper abstract: 93 of 173)
Deviation from the original pipeline (honest 1:1 caveat)
nf-core star_salmon runs STAR genome alignment then Salmon in alignment-based
mode on the transcriptome BAM; we run Salmon in mapping-based (selective-
alignment, decoy-aware) mode directly on the reads. Same tool, same data, same
reference annotation, same DESeq2 design — but counts are expected to be very
close rather than byte-identical, so integer-level agreement on C2–C5 is graded
within-tol/partial, not exact. GO annotations (C5) are queried from current
Ensembl biomaRt and will drift slightly from release-104-era annotations.
Out of scope (not attempted, why)
- Full STAR genome alignment + nf-core/rnaseq orchestration: the heavy ~20%; Singularity is unavailable on «our HPC» and a 20+-process conda-per-process Nextflow run is high-risk for marginal fidelity gain over Salmon counts.
- Wet-lab results (glycomics, TMT-LC/MS proteomics, mouse Sp-Perk−/− model, hCG/PlGF/VEGF bioactivity, lectin isolation): non-computational, out of scope.
- Downstream GSEA/GO-enrichment plots, heatmaps, WikiPathways/MSigDb/cellMarker enrichments: derivative of the DEG list; not separately graded (80/20).
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.
This is a careful 1:1 reproduction attempt on the authors' own data — raw FASTQs (ENA ERP131881) are public and were mapped exactly to the repo SampleTable, but the processed count matrix is not deposited, so counts had to be regenerated. The run finalized PARTIAL with no reproduced integers (SLURM job still index-building at cutoff), so C1-C5 cannot yet be graded and the central (largely wet-lab) conclusion can only be marked limited, not confirmed or refuted. The one concrete established finding is an authors'-side prose-vs-code inconsistency (Methods 23426 input / 20701 retained vs shipped-code 21076 and no normalised-count pre-filter), affecting C2/C3. Net: solid, honest setup with explainable deviations (data-availability + our Salmon-vs-STAR method choice) and no fabrication signal — overall 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.