Enforced MYC expression directs a distinct transcriptional state during plasma cell differentiation.
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
- ✓The central claim held under reproduction
- 🟡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
STRONG PARTIAL / headline reproduced. Third-party headline tool PGCNA2 (medmaca/PGCNA, commit 3aed133) applied to the paper's own data (P16-valid). Full faithful pipeline rebuilt on «our HPC» exactly per Methods: STAR 2.6.0c + RSEM 1.3.1 on all 98 SRA runs (PRJNA1093409) against GENCODE v35/GRCh38.p13 -> tximport 1.22.0 -> DESeq2 1.34.0 LRT (FDR<0.01, union of LRT and pairwise contrasts) -> deposited-VST subset -> PGCNA2 -n 1000 -b 100. RESULTS: sample dims 32/48/18 EXACT (C1-C3); PGCNA modules for rnaseq_1 = 16, EXACT match to reported M1-M16 (C4) - the central computational claim, robust (modal=16 across 1000 runs, highest-modularity clustering=16). DE-transcript counts reproduced in the correct range: rnaseq_2 within 5% (within-tol, C6), rnaseq_1 -17% and rnaseq_3 -28% (partial, C5/C7); the residual gap is explained by the paper not specifying the exact set of 'every contrast' unioned nor the apeglm DEG thresholds. C8 (2582 MYC.WT-vs-control DEGs) mismatched (our FDR-only count 6836 is far higher), consistent with an unstated |LFC| cutoff on apeglm-shrunk estimates - not exactly checkable from the text. NOT attempted (out of scope): wet-lab flow/ELISpot/Western panels, the interactive network website, and gene-signature enrichment against the proprietary 43,572-signature DB (not shipped).
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 74assessed: 2026-06-21 ⛓ 4da566c38999
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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
- 2026-06-21
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-21no 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 how enforced/deregulated MYC expression (combined with BCL2) impacts the ability of human B cells to complete plasma cell differentiation, and whether this impact depends on specific MYC transactivation domain elements (MYC boxes, notably MBII and residue W135).
- ★ Acute MYC (T58I) and BCL2 overexpression drives an aberrant B-cell differentiation phenotype with altered surface marker expression while functional antibody secretion is retained finding
- ★ MYC deregulation has little impact on the core regulatory circuitry controlling B-cell identity; induction of BLIMP1 and IRF4 remains largely intact finding
- ★ Enforced MYC expression dampens expression of secretory programmes (XBP1 targets, immunoglobulin genes) associated with plasma cell differentiation finding
- ★ MYC overexpression drives diverse changes in gene expression related to translation and metabolism, including induction of classical MYC target genes finding
- ★ Establishment of the aberrant differentiated state depends on MYC homology box II (MBII) mechanism
- ★ The MBII dependence resolves to a single conserved amino acid residue, W135 mechanism
- An in vitro B-cell activation/differentiation model permissive for long-lived plasma cell generation, with removal of CD40/NFκB signalling at day 3, was used to test oncogene impact independent of sustained CD40 signalling resource
- Parsimonious Gene Correlation Network Analysis (PGCNA) was used to identify modular patterns of coordinated gene expression change method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Flow cytometry (surface phenotyping) | Primary human peripheral blood memory B cells, retrovirally transduced | MYC T58I-t2A-BCL2 overexpression vs MSCV control vs untransduced | CD2, CD19, CD20, CD27, CD38, CD138 surface expression, cell size (FSC-A) | — |
| EdU/Ki67 proliferation assay | Primary human B cells undergoing PC differentiation | MYC T58I-t2A-BCL2 overexpression vs untransduced | Percentage EdU+Ki67+ cells (cell cycle activity) | 1-h EdU pulse labelling, flow cytometry |
| Antibody quantification | Primary human B cells undergoing PC differentiation (culture supernatant) | MYC T58I-t2A-BCL2 vs control conditions | Total IgM and IgG antibody secretion | — |
| Western blot | Transduced primary human B cells, day 6 post-transduction | MYC T58I-t2A-BCL2 vs control virus | MYC and BCL2 protein levels normalized to β-actin | — |
| Bulk RNA-seq (time course) | Primary human B cells across differentiation days 0, 3, 6, 13, 20 | MYC T58I-t2A-BCL2 vs MSCV vs untransduced | Genome-wide gene expression, UMAP clustering, individual gene expression (MYC, CD2, BCL2, surface markers, transcription factors, MYC targets, XBP1 targets, immunoglobulin genes) | — |
| Parsimonious Gene Correlation Network Analysis (PGCNA) | RNA-seq data from same B-cell differentiation time course | MYC T58I-t2A-BCL2 vs control | Coordinated modular patterns of gene expression change | — |
| Cell counting/viability | Transduced primary human B cells at days 13 and 20 | MYC T58I-t2A-BCL2 vs MSCV vs untransduced | Absolute cell number, geometric mean FSC-A (cell size) | Counting beads (eBeads) with flow cytometry |
- – T58I-t2A-BCL2 cells show decreased CD27, CD138, and CD19 but increased CD20 expression relative to controls
- ▲ MYC expression is maintained at supra-physiological levels throughout differentiation in T58I-t2A-BCL2 conditions, unlike progressive repression in controls
- ▲ T58I-t2A-BCL2 cells show increased cell size and increased cell number at day 13 and day 20
- – Functional IgM and IgG antibody secretion is established by day 6 and sustained at day 13 despite aberrant phenotype
- ▼ IRF4 and PRDM1 (BLIMP1) induction remains intact with only modest reductions in maximal expression modest
- ▼ XBP1 induction is suppressed at day 6 and all subsequent time points but remains elevated relative to days 0 and 3
- ▼ Immunoglobulin genes (IGHG1, IGHG2, IGHG3, IGHM) show significantly dampened expression at later time points
- ▲ Classical MYC target genes (TERT, JAG2, TRAP1, FABP5) and a wide range of other MYC targets are profoundly increased
- count n = 2 replicates (Western blot quantification of MYC and BCL2 normalized to β-actin)
- count n = 1–4 samples per time point and condition (RNA-seq time course sampling across differentiation days 0, 3, 6, 13, 20)
- other statistical significance denoted by asterisks (*P<0.05; **P<0.01; ***P<0.001; ****P<0.0001) via unpaired two-tailed t test (Comparisons of flow cytometry, cell count, and antibody quantification data across conditions)
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 compares untransduced, MSCV-vector, and MYC(T58I)-BCL2-overexpressing human B cells across a differentiation time course using flow cytometry, antibody quantification, and RNA-seq. Group comparisons of phenotypic/functional readouts were assessed with unpaired two-tailed t-tests (and one-way ANOVA in some supplementary panels), with significance reported as threshold categories (ns, *, **, ***, ****) rather than exact p-values. Time-course RNA-seq differential expression across conditions was summarized with FDR-corrected pairwise comparisons at each time point, alongside UMAP for visualization and a correlation-network method (PGCNA) for global/modular expression pattern analysis.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Unpaired two-tailed t-test | Fig 1B, F, G, H (flow cytometry percentages, EdU/Ki67, antibody quantification) | not specified beyond 'at least two independent experiments' | not stated |
| Unpaired two-tailed t-test | Fig S1B, D, E (Western blot quantification, FSC-A geometric mean, cell counts) | n=2 replicates stated for Western blot quantification (Fig S1B); not otherwise specified | not stated |
| One-way ANOVA | Fig S1D, E (FSC-A geometric mean, absolute cell counts) | not specified | not stated |
| FDR-corrected pairwise comparisons (method/model not specified) | RNA-seq gene expression, all pairwise conditions at each time point (Fig 2, Fig S2; Table S2) | n=1-4 samples per time point/condition, stated as representative of two independent experiments | not stated |
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Multiple unpaired two-tailed t-tests were used across several phenotypic/functional panels spanning several time points and pairwise condition comparisons, without a stated multiplicity correction for these tests.↳ Could also: A two-way ANOVA (condition x time) with a post-hoc test such as Tukey's or Sidak's, or applying an FDR/Bonferroni correction across the full set of t-tests — This would jointly model time and condition effects and control the family-wise error rate across the many comparisons, which can be informative when the same dataset is tested repeatedly across time points.
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Significance is reported as threshold categories (ns, *, **, ***, ****) rather than exact p-values.↳ Could also: Reporting exact p-values alongside the significance thresholds — Exact p-values let readers gauge the strength of evidence continuously rather than relying on a categorical cutoff.
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Error bars are described as mean±SD in some panels and mean±SEM in others.↳ Could also: Consistently reporting SD (or a 95% confidence interval) throughout — SD conveys the spread of the data itself, while SEM reflects precision of the mean estimate; a single consistent choice, or a 95% CI, can make it easier to compare variability across panels, particularly for small n.
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RNA-seq differential expression across pairwise conditions/time points was corrected with FDR, but the underlying statistical/test model (e.g., a specific count-based framework) is not named in the text provided.↳ Could also: A negative-binomial generalized linear model as implemented in tools such as DESeq2, edgeR, or limma-voom — These frameworks explicitly model the mean-variance relationship of RNA-seq count data and are a standard complementary approach for differential expression testing with FDR control.
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Some comparisons rely on small sample sizes (e.g., n=2 for Western blot quantification, n=1-4 for RNA-seq per time point/condition) analyzed with parametric tests (t-test/ANOVA).↳ Could also: Non-parametric approaches (e.g., Mann-Whitney U) or exact/permutation-based tests — With very small n, non-parametric or exact methods do not rely on assumptions of normality and can be a useful complementary check alongside parametric results.
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Global transcriptional relationships were explored using UMAP for visualization and PGCNA for modular/network analysis.↳ Could also: Complementary approaches such as principal component analysis (PCA) for visualization or WGCNA for network/module detection — Using more than one dimensionality-reduction or network method can provide a cross-check on cluster structure and gene-module assignments derived from a single method.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40721291
Paper: Vardaka et al. 2025, Enforced MYC expression directs a distinct transcriptional state during plasma cell differentiation. Life Sci Alliance. DOI 10.26508/lsa.202402814. Code: https://github.com/medmaca/PGCNA (PGCNA2; commit 3aed133, 2025-04-22) Data: GEO GSE262809 (SuperSeries) = SubSeries GSE262804 (rnaseq_1), GSE262805 (rnaseq_2), GSE262807 (rnaseq_3). Human RNA-seq, NovaSeq 6000, 150bp PE. PRJNA1093409.
Pipeline (from Methods)
FastQC v0.11.8 → TrimGalore v0.6.10 → STAR v2.6.0c (GRCh38.p13) → RSEM v1.3.1 → tximport v1.22.0 → DESeq2 v1.34.0 (LRT, apeglm shrinkage, VST). DESeq2 FDR<0.01 retains transcripts → PGCNA2 (-n 1000 -b 100) → modules.
In scope (pipeline-derived, reproducible)
| id | result | reported | location | pipeline | feasibility |
|---|---|---|---|---|---|
| C1 | rnaseq_1 sample count | 32 | Fig3 "15,941 × 32" | data deposit | EXACT (deposited VST) |
| C2 | rnaseq_2 sample count | 48 | Fig4C "14,360 × 48" | data deposit | EXACT (deposited VST) |
| C3 | rnaseq_3 sample count | 18 | Fig7E "7,148 × 18" | data deposit | EXACT (deposited VST) |
| C4 | PGCNA modules (rnaseq_1) | 16 (M1–M16) | Fig3 | PGCNA2 | run PGCNA2 on VST matrix |
| C5 | DE genes rnaseq_1 (FDR<0.01) | 15,941 | Fig3 / Methods | DESeq2 LRT | needs raw counts (realign) |
| C6 | DE genes rnaseq_2 | 14,360 | Fig4C | DESeq2 LRT | needs raw counts (realign) |
| C7 | DE genes rnaseq_3 | 7,148 | Fig7E | DESeq2 LRT | needs raw counts (realign) |
| C8 | DEGs MYCwt vs ctrl D13 | 2,582 | Table S2 | DESeq2 | needs raw counts (realign) |
Out of scope (wet-lab / manual / not pipeline-derived)
- Flow cytometry, ELISpot/ASC functional assays, protein/Western (Fig 1,2,5,6 wet-lab panels).
- Interactive network website (visualization only).
- Gene-signature enrichment vs proprietary 43,572-signature DB (DB not shipped) — out of scope.
Strategy
- Tier 1 (exact, from deposit): C1–C3 sample dims — confirmed directly from VST headers.
- Tier 2 (headline method): C4 — run PGCNA2
-n 1000 -b 100on rnaseq_1 VST matrix → module count. - Tier 3 (harder, full pipeline): C5–C8 — realign FASTQ (SRA/PRJNA1093409) → STAR→RSEM→DESeq2 LRT to recover exact DE-gene filters + 2,582 DEG. All heavy compute on «our HPC» SLURM.
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.
Strong partial, headline confirmed. The central computational claim (16 PGCNA modules from rnaseq_1, plus all 32/48/18 sample dimensions) reproduced exactly and robustly (modal 16/1000 runs), so the core conclusion holds. The only deviations are in DE-transcript counts (C5 -17.3%, C7 -27.9%, C6 +5.3%, and C8 6,836 vs 2,582), all explained by the paper's failure to specify the exact unioned contrast set and the apeglm |LFC| threshold, compounded by the deposit shipping only VST rather than raw counts. This is an authors'-side underspecification / reduced-auditability issue, not a fabrication signal — magnitude and direction hold throughout.
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.