CRISPR/Cas9 Screens Reveal Multiple Layers of B cell CD40 Regulation.
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”.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Any deviation was negligible
- 🔴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 central claim did not (fully) hold under reproduction
- 🔴Overall, the reproduction showed a material discrepancy
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. PMID 31365872 (Jiang et al. 2019, Cell Rep) is described well enough (STAR Methods name STARS v1.0.0 for the CRISPR screen, STAR v2.5.2b + DESeq2 v1.14.1 + GSEA for RNA-seq), but its pipeline-derived results are NOT independently reproducible because the required INPUT data is unavailable on two counts: (1) the genome-wide CRISPR-screen raw per-sgRNA read counts (the STARS input, ~76,000 sgRNAs x input/Fas-Hi/Fas-Lo x replicates) were never deposited - only the processed STARS OUTPUT hit tables ship as small supplements (Table S1=21KB, S2=17KB, far too small to be a count matrix); the Data Availability statement deposits only RNA-seq. (2) The RNA-seq is MIS-ACCESSIONED: the cited GEO GSE101666 authoritatively resolves to an unrelated study ('3D genome landscape of EBV oncoproteins', 3 LCL 'Donor2' samples, SRA SRP113157 / PRJNA395150), confirmed via NCBI GEO esummary and ENA - so the paper's resting/CD40L primary-B-cell RNA-seq from 3 donors cannot be retrieved. I verified the reported screen numbers ARE internally consistent with the shipped STARS output tables (85 pos hits, 57 neg hits, FAS 3;5;6;14, CD40 4;10;16;122, KIAA1429 #2 all match exactly) -> no fabrication signal in those values, but consistency with a shipped output is not a pipeline reproduction. NOT ATTEMPTED: re-running STARS (no input counts), STAR/DESeq2 RNA-seq (no correct data), GSEA, and all wet-lab assays (FACS, sgRNA KO validation, CSR, qRT-PCR, Co-IP/WB - out of scope). Auditable supplement tables + checksums + the mis-accessioning evidence are recorded for human review.
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
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessmentassessed: 2026-06-16 ⛓ d893578f34b9
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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: opusWhich B cell-autonomous factors positively and negatively regulate CD40 abundance and responses? The authors use genome-scale CRISPR/Cas9 screens in Daudi B cells to systematically identify regulators of CD40 activity.
- ★ Genome-wide CRISPR/Cas9 screens in CD40L-stimulated Daudi B cells identify known CD40/NF-κB pathway components plus many novel positive and negative CD40 regulators. finding
- ★ The nuclear ubiquitin ligase FBXO11 supports CD40 expression by targeting the repressors CTBP1 and BCL6. mechanism
- ★ FBXO11 knockout decreases primary B cell CD40 abundance and impairs class-switch recombination. finding
- ★ CELF1 is a dependency factor for CD40 expression, controlling CD40 exon splicing (inclusion of exon 6 or exons 5/6) critical for CD40 activity. mechanism
- ★ The m6A methyltransferase complex (WTAP/VIRMA/METTL14/METTL3) negatively regulates CD40 mRNA abundance via m6A modification. mechanism
- ★ ESCRT negatively regulates activated CD40 levels by routing internalized receptor for lysosomal degradation. mechanism
- The phosphatase DUSP10 provides negative feedback limiting downstream CD40 MAPK responses. mechanism
- The screen results serve as a resource and highlight potential therapeutic targets for CD40-associated autoimmunity and lymphoma. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Genome-wide pooled CRISPR/Cas9 loss-of-function screen with FACS sorting and sgRNA deep sequencing | Daudi B cells stably expressing Cas9 | Avana sgRNA library knockout | CD40L-induced plasma membrane Fas abundance (Fas Hi/Fas Low sorted populations) | Avana sgRNA library (~76,000 sgRNAs); trimeric Mega-CD40L; STARS algorithm |
| Flow cytometry (FACS) | Daudi and additional B cell lines (Akata) | FBXO11, CELF1, METTL14, CHMP5, VPS25, CTBP1 KO | PM CD40, Fas, ICAM-1, CD86, CD37 levels | — |
| Immunoblot / Western blot | Daudi B cells | FBXO11, CELF1, METTL14 KO; bortezomib proteasome inhibitor; IKK blockade | CD40 whole cell levels, IκBα turnover, p100 processing, BCL6, CTBP1, FBXO11 protein | — |
| qPCR / RT-qPCR | Daudi B cells | FBXO11, CELF1, METTL14 KO | CD40 steady-state mRNA levels | — |
| Co-immunoprecipitation | Daudi B cells | none | endogenous CTBP1–FBXO11 association | — |
| RNA immunoprecipitation (RIP) and m6A-RIP followed by RT-qPCR | Daudi B cells | METTL14 KO; anti-VIRMA/KIAA1429 IP | CD40 mRNA enrichment / CD40 mRNA m6A modification | — |
| RT-PCR + Sanger sequencing of splice products | CELF1 KO Daudi B cells | CELF1 KO | CD40 mRNA splice isoforms (exon 6 / exon 5-6 loss) | primers targeting defined CD40 splice sites |
| Primary B cell CD40 / class-switch recombination assay (FACS) | B cells from CD19/Cre-FBXO11fl/fl conditional KO mice | conditional FBXO11 KO; anti-CD40 agonist antibody + IL-4 stimulation | PM CD40, CD40-induced Fas, percentage IgG1 (CSR) | — |
- – 85 candidate positive CD40 regulators identified whose KO impaired CD40L-driven Fas upregulation 85 hits at q<0.05
- – FAS was top hit in Fas low population; CD40 also a top hit anti-FAS sgRNAs ranked 3,5,6,14; anti-CD40 ranked 4,10,16,122 of ~76,000
- – 57 candidate negative CD40 regulators identified (158 at p<0.05) 57 hits at q<0.05
- ▼ FBXO11 KO reduced CD40 PM and whole-cell levels and decreased CD40 mRNA nearly 50% decrease in CD40 mRNA
- ▼ FBXO11 KO primary B cells exhibited lower CD40 and impaired class-switch recombination significantly lower IgG1 by 72 h
- – CELF1 KO caused loss of CD40 exon 6 or exons 5/6, producing truncated messages, and reduced CD40 protein; CD40 cDNA rescued expression two truncated CD40 messages; CD40 mRNA increased
- ▲ METTL14 KO increased CD40 protein and mRNA; CD40 mRNA enriched in WTAP/VIRMA pulldown and m6A reduced by METTL14 KO ~10% of CD40 input mRNA associated with VIRMA
- ▲ CHMP5 or VPS25 KO increased CD40L-stimulated PM Fas, ICAM-1, and CD40 whole-cell levels in stimulated but not resting cells
- count 85 hits (candidate positive CD40 regulators at q<0.05)
- count 57 hits (candidate negative CD40 regulators at q<0.05)
- count 158 hits (candidate negative CD40 regulators at p<0.05)
- fold_change nearly 50% decrease (CD40 mRNA in FBXO11 KO cells by qPCR)
- other sgRNA ranks 3,5,6,14 (anti-FAS sgRNAs in Fas low population of ~76,000 Avana sgRNAs)
- other sgRNA ranks 4,10,16,122 (anti-CD40 sgRNAs in Fas low population)
- other ~10% (fraction of CD40 input mRNA associated with VIRMA in WTAP pulldown)
- other 2nd, 6th, 13th strongest hits (VIRMA/KIAA1429, METTL14, METTL3 ranking among CD40 negative regulator hits)
Statistical methods review
Model: opusA 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 is built around two genome-scale pooled CRISPR/Cas9 loss-of-function screens in Daudi B cells, using FACS-based sorting of cells with the lowest or highest plasma-membrane Fas as a readout of CD40 activity; candidate positive and negative regulators were called from enrichment/depletion of multiple independent sgRNAs per gene. Screen hits were identified with the STARS algorithm using a multiple-hypothesis-adjusted q < 0.05 (and a p < 0.05) cutoff, and pathway enrichment was assessed by GSEA. Individual mechanistic claims were validated with arrayed experiments (independent sgRNAs, cDNA rescue, immunoblot, qPCR/RT-qPCR, FACS, luciferase reporters, co-IP, and RIP), with results described as 'significant' though the specific significance tests are not stated in the available text.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| STARS algorithm for sgRNA/gene-level enrichment in pooled CRISPR screen | Identification of candidate CD40 positive regulators (Fas-low population) and negative regulators (Fas-high population), Figures 1B–1E, Tables S1–S2 | multiple independent sgRNAs per gene (e.g., 4 sgRNAs/gene in the Avana library, ~76,000 sgRNAs total) | not stated |
| Gene Set Enrichment Analysis (GSEA) | Enrichment of CD40 and NF-κB pathways among screen hits, Figure S1A | — | not stated |
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Screen hits were called with the STARS algorithm at an adjusted q < 0.05 cutoff.↳ Could also: Alternative CRISPR-screen analysis frameworks such as MAGeCK (RRA or MLE) or casTLE could also be applied to rank genes and estimate effect sizes. — Running an additional model alongside STARS would let one compare hit lists across methods and report concordance, which can add confidence that top candidates are robust to the analytic pipeline chosen.
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The negative-regulator analysis reported hits at both a q < 0.05 and a p < 0.05 cutoff.↳ Could also: Results could also be presented at a single pre-registered FDR threshold, or with the full ranked statistic distribution and an explicit count of hits at each cutoff. — Anchoring to one stated FDR level (with the looser threshold clearly labeled as exploratory) would make the false-discovery expectation transparent for readers prioritizing candidates for follow-up.
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Many validation comparisons (e.g., KO vs control for CD40/Fas levels, qPCR, RIP) are described qualitatively as 'significant' without the specific test named in the available text.↳ Could also: These comparisons could also be reported with the explicit test (e.g., two-tailed t-test or Mann-Whitney U), the n, and exact p values in each figure legend. — Stating the test, n, and exact p alongside each panel would make the arrayed validations fully reproducible and let readers gauge the evidence per comparison.
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Several validations rely on multiple independent sgRNAs targeting the same gene, treated as supporting on-target effects.↳ Could also: One could also model sgRNA as a random effect or report each guide separately with an effect size and CI, in addition to cDNA-rescue confirmation. — Distinguishing guide-to-guide variability from the gene-level effect, and pairing it with rescue, helps separate biological signal from sequence-specific or off-target contributions.
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Group differences in continuous readouts (e.g., mRNA fold-change, PM/WCL protein levels) appear to be summarized by point comparisons.↳ Could also: Effect sizes with 95% confidence intervals (and SD rather than only SEM for small-n replicates) could also be reported. — Confidence intervals and effect sizes convey the magnitude and precision of an effect, which complements a significance call and is often preferred when sample sizes are small.
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Pathway-level enrichment among hits was assessed with GSEA.↳ Could also: Complementary over-representation tests (e.g., hypergeometric/Fisher tests against curated pathway sets, or a separate gene-set tool) could also be used. — Cross-checking enrichment with an independent statistical framework can corroborate that the highlighted CD40/NF-κB enrichment is not specific to one method's assumptions.
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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METTL14 knockout increases CD40 mRNA and protein in Daudi B cells; CD40 mRNA is enriched by VIRMA/WTAP immunoprecipitation and m6A-modified in a METTL14-dependent manner, indicating m6A methylation promotes CD40 mRNA destabilization.flow-cytometry daudi up 2019×1papers★ This paper is the founder (earliest)
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Conditional FBXO11 knockout in primary mouse B cells (CD19-Cre/FBXO11fl/fl) reduces surface CD40 expression and impairs CD40-driven class-switch recombination to IgG1.flow-cytometry mouse-b-cells down 2019×1papers★ This paper is the founder (earliest)
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CELF1 knockout in Daudi B cells causes aberrant CD40 splicing with skipping of exon 6 or exons 5-6, producing truncated mRNA isoforms and reducing CD40 protein despite elevated total CD40 mRNA; CD40 cDNA rescues expression.other daudi mixed 2019×1papers★ This paper is the founder (earliest)
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CD40 knockout ranks among the top hits impairing CD40L-induced Fas upregulation in a genome-wide pooled CRISPR loss-of-function screen in Daudi B cells, confirming CD40 as a required positive regulator of this pathway.other daudi up 2019×1papers★ This paper is the founder (earliest)
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FBXO11 knockout reduces CD40 mRNA by approximately 50% in Daudi B cells, accompanied by decreased CD40 protein, identifying FBXO11 as a positive transcriptional regulator of CD40.qPCR daudi down 2019×1papers★ 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.
Downstream reach in the literature
1 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.
- The Epstein-Barr Virus Regulome in Lymphoblastoid Ce... 2017 · 114 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-31365872
Paper: Jiang C, Trudeau SJ, … Doench JG, Chiarle R, Gewurz BE. "CRISPR/Cas9 Screens Reveal Multiple Layers of B cell CD40 Regulation." Cell Reports 28(5):1307–1322.e8 (2019). PMID 31365872 · PMCID PMC6684324 · DOI 10.1016/j.celrep.2019.06.079.
Brief-listed code: https://github.com/alexdobin/STAR (the STAR aligner — a third-party tool; per rule P16 applying it to the paper's own data would be a valid reproduction). Brief-listed data: GEO GSE101666.
Pipeline-derived (in-scope) results
| # | Result | Pipeline / tool | Reported location |
|---|---|---|---|
| R1 | Genome-wide CRISPR screen for CD40 positive regulators (Fas-Low sorted vs input): gene-level hits, sgRNA ranks | STARS algorithm v1.0.0 (Doench et al. 2016) on per-sgRNA read counts from the Avana library (~76,000 sgRNAs) | Fig 1B–C; Table S1; STAR Methods |
| R2 | Genome-wide CRISPR screen for CD40 negative regulators (Fas-High sorted vs input): 57 hits at q<0.05 | STARS v1.0.0 on per-sgRNA counts | Fig 1D–E; Table S2 |
| R3 | GSEA over screen hits (CD40 / NF-κB pathway enrichment) | GSEA (preranked) | Fig S1A |
| R4 | Resting vs CD40L-stimulated primary B cell RNA-seq: alignment + differential expression | STAR v2.5.2b → DESeq2 v1.14.1 → GSEA | Fig 6/7, Fig S6/S7; Data Availability → GEO GSE101666 |
Out of scope (wet-lab / manual — not attempted)
FACS Fas/CD40/CD37 surface staining; individual-sgRNA KO validation; class-switch recombination assays; qRT-PCR (TaqMan); Co-IP / Western blots; BioPlex/BioGRID interactome lookups; GeneMANIA PPI map (Fig S7A). These are not pipeline-derived.
Data-availability findings (the blocker)
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RNA-seq raw data is mis-accessioned. The paper's Data Availability statement reads verbatim: "The accession number for all RNA-seq datasets reported in this paper is GEO: GSE101666." The Key Resources Table lists GSE101666 for "Resting and CD40L-stimulated primary B cell RNA-seq datasets … Gewurz Laboratory." However, GEO GSE101666 is a different study — "3D genome landscape of Epstein-Barr Virus oncoproteins and virus activated NF-κB in lymphoblastoid cells," with only 3 samples (all titled "Donor2 RNA-seq Rep1/2/3" in SRA SRP113157 / BioProject PRJNA395150). Verified via NCBI GEO esummary (UID 200101666, n_samples=3) and ENA filereport for SRP113157. The CD40 paper's resting/CD40L primary-B-cell RNA-seq from 3 donors is not retrievable under the cited (or any located) accession → R4 input data unavailable. A GEO search for the correct dataset (Gewurz / CD40L primary B cell / CD40-regulation CRISPR) returned no matching series.
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CRISPR-screen per-sgRNA read counts were never deposited. The Data Availability statement deposits only RNA-seq data; no GEO/SRA/figshare/Zenodo accession for the screen counts exists. The supplementary tables are far too small to hold them: PMC lists supplement-2.xlsx = 20.6 KB, -3 = 16.8 KB, -4 = 10.9 KB, -5 = 10.6 KB — these are gene-level STARS output (hit lists, sgRNA sequences), not a ~76,000-sgRNA × (input + Fas-Hi + Fas-Lo × replicates) count matrix (which would be several MB). Without the input counts, STARS (R1/R2) cannot be independently re-run → R1/R2 input data unavailable.
Consequence
Both pipeline classes (STARS screen; STAR/DESeq2 RNA-seq) require input data that
is not obtainable: the RNA-seq accession resolves to an unrelated paper, and the
screen count matrix was never deposited. The only published screen artifacts are
outputs (hit tables) — re-reading them is not a reproduction. → expected
outcome: drop / data_unavailable, with the mis-accessioning recorded as an
auditable finding. Reported values are still catalogued in original/claims.tsv
for human audit.
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 DROP / data_unavailable case: the genome-wide CRISPR screen's per-sgRNA STARS input counts were never deposited (only the small output hit tables ship), and the RNA-seq accession GSE101666 mis-resolves to an unrelated EBV 3D-genome study, so neither pipeline (STARS v1.0.0; STAR v2.5.2b + DESeq2 v1.14.1) can be independently re-run. The reported screen values (85 positive / 57 negative hits q<0.05; FAS ranks 3;5;6;14; CD40 4;10;16;122; KIAA1429 #2) match the shipped output tables exactly — internally consistent with no fabrication signal in the numbers — but consistency with a shipped output is not reproduction. The defect lies on the authors'/data-availability side (undeposited input + mis-accessioned data), not in our methodology; severity of any observed numeric deviation is negligible, but the core claims remain only consistency-checked, not confirmed.
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.