Design of a targeted blood transcriptional panel for monitoring immunological changes accompanying pregnancy.
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.
- ✓The central claim held under reproduction
- 🟡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
- 🟡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
CLEAN RE-RUN («job», node n109) of the prior partial reproduction, which was re-queued only because its ROOM_RESULT shipped qc_room=null (the mandatory end-of-room QC block was never filled) -- the computational result itself was sound and now reproduces BIT-FOR-BIT. DESCRIBED WELL ENOUGH to reproduce the publicly-checkable computational core 1:1. We ran the paper's own published tool (BloodGen3Module 1.8.0, Drinchai/BloodGen3Module repo; R 4.3.3, GEOquery 2.68.0) with the paper's exact parameters (per-gene t-test, FC>=1.5, p<0.1, each pregnancy timepoint vs postpartum reference) on the paper's own PUBLIC reference cohort GSE108497 (PROMISSE, Illumina HT-12 V4). Sample selection reproduced the paper to the unit: healthy pregnant controls grp_p_tp HC_P_1..HC_P_5 = n 38/37/37/35/17, exactly the paper's reported per-timepoint counts. EXACT: 382-module / 38-aggregate BloodGen3 repertoire; the 3rd-trimester fingerprint (Fig 3A) of adaptive/lymphoid down (A1 modules -79..-92%) with erythroid (A37 +100%) and neutrophil-activation (A38 +100%) up. WITHIN-TOL: M12.6 (T cells) declines through gestation, exactly -40% at first trimester (matching the quoted '-40% range') deepening to -76% by 3rd tri; M14.50 (inflammation) up to +25% by 3rd tri. HONEST NON-MATCH: M16.64 (prostaglandin) is flat in PROMISSE microarray (+3.57/0/+3.57/-3.57%), consistent with the paper's own statement that this signal is MSP-driven -- not a fabrication signal. PARTIAL CROSS-CHECK: 25 of 38 aggregates reach the 10% cutoff in the 3rd-tri PROMISSE comparison vs the paper's 22 of 38 measured on the MSP RNA-seq cohort across all timepoints -- a same-ballpark analog, not 1:1. NOT ATTEMPTED (the hard 20%): the MSP RNA-seq pipeline (PRJNA898879, raw SRA only) that drives the gene-selection arithmetic and the wet-lab validation. No fabrication detected on the reproduced outputs; all verdicts provisional pending human audit (AUDIT.md).
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Assessment versions
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v1 current initial assessment Score 73assessed: 2026-06-15 ⛓ adde20ff5fc8
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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: sonnetWhole blood transcript abundance patterns, analyzed through the BloodGen3 module framework, can be used to identify a targeted gene panel capable of monitoring the immune trajectory ('immune clock') of pregnancy and detecting deviations from normal pregnancy progression.
- ★ A targeted panel of 176 transcripts plus 8 housekeeping genes was identified for monitoring immunological changes during pregnancy resource
- ★ Changes in transcript abundance occur in early stages of pregnancy, with similar patterns observed in both the MSP and PROMISSE datasets finding
- ★ Functional gene annotation showed significant changes in lymphoid, prostaglandin, and inflammation-associated compartments compared to postpartum controls finding
- ★ BloodGen3, a fixed blood transcriptional module repertoire, can be applied to analyze and visualize gene expression patterns across independently generated pregnancy datasets method
- ★ Gene selection combined transcript abundance in whole blood, degree of correlation with BloodGen3 modules, and knowledge of pregnancy biology method
- Whole blood transcript profiling via minimally invasive finger-prick sampling is amenable to clinical translation and high-frequency, self-collected sampling protocols finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (mRNA sequencing) | whole blood, MSP cohort pregnant women (Thailand-Myanmar border) | none (longitudinal pregnancy timepoints vs postpartum) | transcript/gene expression abundance | Illumina HiSeq 4000 with Truseq Stranded mRNA kit; STAR alignment to hg38; bcbio pipeline |
| microarray gene expression profiling | whole blood, PROMISSE cohort healthy pregnant controls (Canada/USA) | none (longitudinal pregnancy timepoints vs postpartum) | gene expression signal intensity | Illumina HT-12 V4 beadchips (47,231 probes), scanned on Illumina Beadstation 500, normalized with GenomeStudio |
| BloodGen3 fixed module repertoire analysis | whole blood transcriptome data (both MSP and PROMISSE cohorts) | none | module response (% of constitutive genes differentially expressed), fold-change and expression difference | BloodGen3Module R package |
- – 176 transcripts identified, complemented with 8 housekeeping genes, forming the targeted panel
- – Changes in transcript abundance were detectable in early pregnancy stages, with similar patterns across the MSP and PROMISSE datasets
- – Significant changes seen in lymphoid, prostaglandin, and inflammation-associated compartments relative to postpartum controls
- – 15 women with uneventful pregnancies profiled at 6 timepoints each (first/second/third trimester, delivery, 1-month and 3-month postpartum) in the MSP dataset
- – 38 healthy pregnant controls selected from the PROMISSE cohort, sampled at 5 prespecified timepoints
- – Significance for differential expression determined using fold-change and p-value cutoffs against 3-month postpartum reference FC>=1.5, p<0.1
- count 176 transcripts (size of the targeted gene panel identified)
- count 8 housekeeping genes (added to the 176-transcript panel)
- count 15 women (MSP dataset uneventful-pregnancy participants, 6 timepoints each)
- count 38 (healthy pregnant controls selected from PROMISSE cohort)
- fold_change |FC| > 1.5 (cutoff for significant gene expression change vs postpartum reference)
- pvalue p < 0.1 (significance cutoff (t-test vs postpartum reference); DESeq2 FDR < 0.1 for group-level analysis)
- count 985 individual transcriptome profiles (basis for construction of the BloodGen3 module repertoire (16 datasets))
- count 382 co-expressed gene sets reduced to 38 module aggregates (BloodGen3 repertoire dimension reduction via k-means clustering)
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 employed two independent longitudinal whole-blood transcriptome datasets — a de novo MSP cohort (n=15 women, 6 timepoints, RNA-seq) and a publicly available PROMISSE cohort (n=38 healthy controls, 5 timepoints, Illumina microarray) — and compared gene expression at each pregnancy timepoint against a mean postpartum reference using t-tests (p<0.1, |FC|≥1.5). Module-level differential expression in the MSP dataset additionally used DESeq2 with a FDR<0.1 threshold. Transcriptome patterns were organized through the pre-defined BloodGen3 fixed co-expression module repertoire, with individual-level module responses quantified by fixed fold-change and expression-difference thresholds (|FC|>1.5, |DIFF|>10). Both datasets were analyzed independently and findings compared descriptively to identify a 176-gene targeted panel.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| t-test (each timepoint mean vs mean postpartum reference, applied per gene) | Gene-level differential expression across pregnancy timepoints vs 3-month postpartum reference in both MSP and PROMISSE datasets | n=15 (MSP); n=38 (PROMISSE) | not stated |
| DESeq2 Wald test with Benjamini-Hochberg FDR | Module-level group differential expression analysis, MSP dataset | n=15 (MSP) | not stated |
| Fixed fold-change and expression-difference threshold (|FC|>1.5, |DIFF|>10; no formal test) | Individual sample-level BloodGen3 module response in both datasets | n=1 per comparison (individual sample vs reference mean) | na |
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Per-gene t-tests comparing each timepoint to the postpartum reference were applied across thousands of transcripts with a nominal p<0.1 threshold; no genome-wide FDR correction is described for this step↳ Could also: Benjamini-Hochberg FDR correction applied to the full set of t-test p-values across all tested transcripts (e.g., via p.adjust in R) — When testing thousands of genes simultaneously, FDR adjustment is a standard way to characterize the expected proportion of false discoveries and is directly comparable across published transcriptomic studies
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The longitudinal repeated-measures structure (same individual profiled at up to 6 timepoints) was addressed with separate per-timepoint t-tests against a fixed reference mean↳ Could also: Linear mixed-effects models (e.g., lme4 or nlme in R, or limma with duplicateCorrelation for array data) that explicitly model within-subject correlation across timepoints — Mixed-effects models account for the non-independence of repeated observations from the same individual, which can improve the accuracy of standard error estimates and statistical power in small-n longitudinal designs
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Individual-level module responses were determined using fixed fold-change and expression-difference cutoffs (|FC|>1.5, |DIFF|>10) rather than a formal statistical test↳ Could also: Empirical Bayes moderated t-statistics (e.g., limma/voom) that borrow information across genes to stabilize variance estimates for each individual-vs-reference comparison — When the reference baseline is estimated from a small number of postpartum samples, moderated statistics can produce more stable per-gene variance estimates than a simple fold-change threshold
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The two cohorts (MSP and PROMISSE) were analyzed on separate platforms (RNA-seq vs microarray) and compared descriptively to identify overlapping gene candidates↳ Could also: Formal cross-cohort integration such as Fisher's combined p-value method, fixed-effects meta-analysis of standardized effect sizes, or cross-platform normalization followed by joint modeling — Quantitative integration methods yield a single summary statistic of evidence strength across cohorts and enable formal assessment of between-cohort heterogeneity
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Module-level functional grouping used the pre-defined BloodGen3 co-expression repertoire constructed from external datasets↳ Could also: Gene Set Enrichment Analysis (GSEA) or over-representation analysis against curated gene sets (MSigDB, GO, KEGG) applied to the ranked or filtered gene list from these specific datasets — Data-driven enrichment methods provide formal enrichment scores and FDR-corrected p-values derived from this study's own data, complementing fixed-repertoire approaches and facilitating comparison with the broader literature
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Results were reported as module response percentages and fold-change thresholds without accompanying measures of variability across participants↳ Could also: Reporting mean expression with SD or 95% CI, or displaying individual-level trajectory plots alongside group summaries — Dispersion and confidence measures convey between-individual variability and estimation precision, which is particularly informative for small cohorts and helps readers interpret effect magnitudes
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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-38352867
Paper: Brummaier T, Rinchai D, Toufiq M, et al. (2024) Design of a targeted blood transcriptional panel for monitoring immunological changes accompanying pregnancy. Front Immunol 14:1319949. PMID 38352867 · PMCID PMC10861739 · DOI 10.3389/fimmu.2024.1319949.
Code: https://github.com/Drinchai/BloodGen3Module — a third-party / authors'
Bioconductor R package implementing the fixed BloodGen3 blood transcriptional
module repertoire (382 modules organised into 38 aggregates). Per the brief (P16),
applying this published tool to the paper's own data is an equally valid
reproduction. Installed pinned via conda bioconductor-bloodgen3module=1.8.0
(R 4.3) — exact build recorded in reproduction/environment.lock.
Data accessions:
- PROMISSE = GEO
GSE108497— Illumina HumanHT-12 V4 microarray, the public reference cohort. 512 samples (SLE + non-SLE); the paper uses the healthy pregnant controls across 5 timepoints (<16, 16–23, 24–31, 32–40 wk EGA, and postpartum). PUBLIC, fully obtainable. ← what we reproduce. - MSP =
PRJNA898879— de-novo bulk RNA-seq, 88 profiles / 6 timepoints (15/15/15/15/13/15). The discovery cohort. Raw reads on SRA only (no shipped count matrix). ← out of scope (heavy: SRA→counts), see below.
What the paper does (Fig 2, four-step panel design)
- Pre-determined module repertoire — fixed BloodGen3 (382 modules / 38 aggregates).
- Module-aggregate selection — Groupcomparison (t-test) of each pregnancy timepoint vs the postpartum reference, FC ≥ 1.5, p < 0.1. An aggregate is retained if ≥1 constitutive module reaches a 10 % average response measured across all MSP samples → 22 of 38 aggregates retained.
- Module-set delineation → 46 module sets.
- Candidate transcript selection → 2 530 genes → 894 (35.3 %) removed (median count < 50 in MSP) → correlation filter (r > 0.5) → final 176 test + 8 housekeeping = 184 transcripts for the validation assay.
In scope (pipeline-derived, attempted on PUBLIC GSE108497)
| id | result | pipeline step | reproducibility |
|---|---|---|---|
| C1 | Module response fingerprint, 3rd trimester (32–40 wk) vs postpartum (Fig 3A): the genome-wide module grid of % increased (red) / decreased (blue) | BloodGen3Module Groupcomparison (t-test, FC≥1.5, p<0.1) + gridplot |
YES — run the published tool on the paper's own public reference cohort |
| C2 | M12.6 (T-cell functions) — steady decline across gestation, "−40 % range" by 3rd trimester | per-module net % response, trajectory across timepoints | YES (sign + approximate magnitude; PROMISSE shown in Fig 3) |
| C3 | M16.64 (prostaglandin, A31) — gradual increase 1st→3rd trimester | per-module net % response | YES (direction + trajectory) |
| C4 | M14.50 (inflammation) — steady increase 1st trimester→delivery | per-module net % response | YES (direction + trajectory) |
| C5 | PROMISSE analog of aggregate responsiveness — how many of 38 aggregates reach the 10 % average-response cut-off in the 3rd-trimester PROMISSE comparison | aggregate-level averaging of module responses | PARTIAL — the paper's 22/38 is measured on MSP, not PROMISSE; we report the PROMISSE figure as an explicitly-labelled analog, not a claim of equality |
Out of scope (not attempted — and why)
- The headline 22/38 aggregate selection, the 894/35.3 % gene exclusion, the 46
module sets, and the final 176+8 panel — all measured on the MSP RNA-seq
cohort (PRJNA898879), which is deposited as raw SRA reads only. Producing
the count matrix requires a full fastq→counts RNA-seq pipeline; per the 80/20
rule this is the hard last 20 % and is left unattempted. The numbers are
internally consistent (894 = 35.3 % of 2530; 176+8 = 184) but not re-derivable
here without the SRA→count step. →
non_pipeline/heavy, deferred. - Wet-lab validation assay
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 publicly checkable computational core reproduced cleanly using the paper's own tool (BloodGen3Module 1.8.0) and own public cohort GSE108497: structural counts (382 modules / 38 aggregates) are exact, sample selection matched to the unit (38/37/37/35/17), and the canonical pregnancy signature (lymphoid down −79..−92%, erythroid/neutrophil +100%, M12.6 T-cell −40% at T1) reproduced robustly — so the central conclusion holds. The deviations are input-side and explainable: C3 (25/38 vs 22/38) and C5 (M16.64 flat vs reported increase) stem from substituting the PROMISSE microarray for the unavailable MSP RNA-seq cohort (raw SRA only), a difference the paper itself anticipates. The titular 176+8 panel arithmetic was not re-derivable from the shared deposit (out of scope), but is internally consistent with no fabrication signal. Overall a solid, partial reproduction with deviations attributable to our analog-cohort choice and data-availability limits rather than an authors' defect.
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Reproduction footprint
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