Chemical genomics informs antibiotic and essential gene function in Acinetobacter baumannii.
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.
- ✓Reported values were directly comparable
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡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, with one material data gap. The paper's headline Fig 1C edgeR->CG-score pipeline was run on «our HPC» using the authors' exact Calculate-LFC-FDRs-EdgeR.R (edgeR 4.4.0 QL, robust) on the shipped minimally-processed counts. The gene universe reproduces EXACTLY (406). Genes with >=1 significant chemical-gene interaction reproduce as 376/406 = 92.61% vs the reported 378/406 = 93% (within-tol), and median interactions/gene as 12-13.5 vs reported 14 (partial). The small shortfalls are fully explained by 5 of 45 contrasts being unreproducible because the shipped count matrix omits the batch-1 (1AbJT*) samples and the harmonizing recode() rules were redacted from the published script. This is a faithful PARTIAL reproduction; the close near-match strongly corroborates the published numbers. NOT attempted: raw-read guide counting (barcoder), fDOG conservation, STRING/limma-voom/network secondary analyses, wet-lab.
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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 assessment Score 71assessed: 2026-06-20 ⛓ 21339106d1a5
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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
- 2026-06-20
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-20no 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 hypothesize that combining a CRISPRi essential-gene knockdown library with a diverse panel of chemical stressors (chemical genomics) in Acinetobacter baumannii will reveal chemical-gene interactions that inform both essential gene function and antibiotic/inhibitor mechanism of action.
- ★ The vast majority of A. baumannii essential genes show significant chemical-gene interactions upon knockdown finding
- ★ Lipooligosaccharide (LOS) transport (Lpt) knockdown broadly sensitizes cells to chemicals via increased outer/inner membrane permeability finding
- ★ lptA (LOS transport) knockdown causes greater antibiotic sensitization than lpxC (LOS synthesis) knockdown, indicating LOS transport is more critical than LOS synthesis for chemical resistance finding
- ★ Chemical-gene interaction phenotypes can be used to construct an essential gene network linking poorly characterized genes to known processes such as cell division finding
- ★ Phenotype-structure analysis distinguishes structurally similar antibiotics by their distinct cellular/physiological impacts and suggests targets for underexplored inhibitors finding
- ★ Genes such as lptC, gtrOC1, and wzx are highly divergent or rare outside A. baumannii/Acinetobacter, marking taxon-specific determinants of chemical susceptibility finding
- A pooled CRISPRi knockdown library (406 essential genes, perfect-match and mismatch sgRNAs, 1000 non-targeting controls) screened against 45 chemicals via competition fitness assays method
- ★ The resulting chemical-gene interaction dataset serves as a resource for mechanistic studies, therapeutic strategy design, and antibiotic target identification resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Pooled CRISPRi competition fitness screen (sgRNA amplicon sequencing) | A. baumannii ATCC19606 CRISPRi essential-gene knockdown library | CRISPRi knockdown of 406 essential genes + 45 chemical treatments (sublethal) | sgRNA abundance / chemical-gene (CG) score (median log2 fold change) | — |
| STRING functional enrichment analysis | Ranked gene list from CG score screen | none | GO term enrichment score and FDR | STRING database |
| MIC test strip (Etest-type) assay | A. baumannii 19606 lptA knockdown, lpxC knockdown, non-targeting control | IPTG-induced CRISPRi knockdown + antibiotic gradient (tigecycline, azithromycin, colistin, levofloxacin) | minimum inhibitory concentration (μg/mL) | — |
| Antibiotic sensitivity comparison | 19606 lptA/lpxC knockdowns and 19606 lpxC(S106R) LOS-deficient (LOS-) mutant background | CRISPRi knockdown in LOS- background + colistin/levofloxacin | MIC / growth sensitivity | — |
| Ethidium bromide (EtBr) permeability assay | 19606 lptA knockdown and non-targeting control, in wild-type or LOS- background | CRISPRi induction | fluorescence over time (membrane permeability) | — |
| Phylogenetic profiling / orthology analysis | Representative Gammaproteobacteria and non-A. baumannii Acinetobacter species genomes | none | ortholog presence ratio across isolates | — |
- – 93% (378/406) of essential genes exhibited at least one significant CG score (medL2FC ≥|1|, p<0.05), median 14 significant interactions per gene 378/406 (93%); median 14/gene
- ▼ ~73% of significant CG scores were negative (reduced growth) rather than positive 3895/5345 (73%)
- ▼ Lpt system genes were significantly enriched among negative interactors, with negative CG scores in 70% of screen conditions enrichment score 8.95, FDR=1.04e-05; 70% of conditions
- ▼ lptA knockdown sensitized cells to several antibiotics (e.g., tigecycline, azithromycin) while lpxC knockdown was not sensitized
- ▼ lptA knockdown was sensitized to levofloxacin similarly to fully LOS-deficient (LOS-) strains
- ▲ 19606 lptA knockdown showed substantially higher membrane permeability (EtBr fluorescence) than LOS- and LOS- lptA knockdown strains
- – 15 candidate genes were identified as rare in Gammaproteobacteria outside the order containing A. baumannii yet highly responsive to chemicals when knocked down 15 genes; significant CG scores in >10% of chemicals
- – lptC orthologs are considerably rare among Gammaproteobacteria; gtrOC1, wzx, and GO593_15125 identified as specific/divergent relative to other Acinetobacter species
- count 93% (378/406) (essential genes with ≥1 significant CG score)
- count median 14 significant chemical interactions per gene (chemical-gene interaction screen)
- fold_change 73% (3895/5345) negative CG scores (distribution of significant CG scores)
- other enrichment score = 8.95, FDR = 1.04e-05 (STRING GO:0015920 (Lpt system) enrichment)
- correlation r > 0.95 (DMSO-only vs mock treatment library composition correlation)
- count n=4 (replicates in ethidium bromide permeability assay)
- fold_change medL2FC ≥ |1|, p < 0.05 (significance threshold for CG score)
- count 15 candidate genes; <38% ortholog presence (taxon-restricted genes rare outside A. baumannii-related Gammaproteobacteria)
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 describes a pooled CRISPRi chemical-genomics screen in Acinetobacter baumannii in which chemical-gene (CG) interaction scores were calculated as the median log2 fold change (medL2FC) of perfect-match sgRNA guides under chemical treatment versus induction alone, with significance called using Stouffer's combined p-value method (medL2FC ≥|1| and p<0.05). Functional enrichment of gene clusters was assessed using the STRING database (with an FDR-corrected enrichment score reported for one pathway), and CG-score patterns were visualized using hierarchical clustering (Ward method, Canberra distance). Individual validation experiments (MIC strip assays, ethidium bromide permeability assays) were reported descriptively, with variability summarized as standard deviation across replicates (n=4) in at least one assay; the excerpt provided does not include a full Materials and Methods statistics section, so some procedural details may not be captured here.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Stouffer's method (combined p-value across perfect-match sgRNA guides) | Calling significant chemical-gene (CG) interaction scores across screen chemicals and genes (Fig 1C, 1D, 2B–2C, 3A–3C) | four perfect-match sgRNA guides per gene, as stated | not stated |
| STRING functional enrichment analysis with FDR-corrected enrichment score | Enrichment of the lipooligosaccharide (Lpt) transport pathway among genes with negative CG scores (GO:0015920, enrichment score = 8.95, FDR = 1.04e-05) | — | not stated |
| Pearson correlation (r) | Quality-control comparison of library composition between DMSO-only and mock-treatment samples (S1C Fig) | — | not stated |
| Hierarchical clustering (Ward method, Canberra distance) | Clustering of CG scores across knockdowns for the lpt gene cluster heatmap (Fig 2C) | — | na |
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Significant chemical-gene interactions were called using Stouffer's method to combine p-values across four perfect-match guides per gene, with a fixed threshold (|medL2FC|≥1, p<0.05).↳ Could also: Count-based screen-analysis frameworks such as MAGeCK or casTLE, or a mixed-effects/linear model directly on guide-level counts — These approaches model guide-to-guide and replicate variance directly from count data, which can also account for differences in guide efficiency without relying on combining independently computed p-values.
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Across the many thousands of gene-by-chemical CG-score comparisons, significance was called using a fixed p<0.05 and effect-size cutoff rather than a stated family-wise or false-discovery correction spanning the whole dataset.↳ Could also: A Benjamini-Hochberg FDR correction applied across the full set of CG-score tests — Applying FDR correction across all simultaneous comparisons would also help control the expected proportion of false positives given the very large number of tests performed.
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Variability in the ethidium bromide permeability assay (n=4) was summarized using standard deviation ribbons.↳ Could also: Standard error of the mean (SEM) or a 95% confidence interval — With a small n, SEM or a CI can also communicate the precision of the estimated mean trajectory, which some readers find more directly interpretable than SD for comparing group means.
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Replicate concordance for DMSO-only versus mock-treatment samples was assessed with a Pearson correlation coefficient (r>0.95).↳ Could also: A Spearman rank correlation — A rank-based correlation could also be used if fold-change or count data show a non-normal or skewed distribution, since it does not assume a linear relationship.
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CG-score patterns were visualized using Ward-method hierarchical clustering with Canberra distance.↳ Could also: Alternative distance metrics (e.g., Euclidean or correlation-based distance) or model-based clustering approaches — Different distance/linkage choices can also be explored as a complementary view of cluster structure, since clustering results can be sensitive to the chosen metric.
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Gene-set enrichment for the Lpt pathway was assessed via the STRING database with an FDR-corrected enrichment score.↳ Could also: Gene Set Enrichment Analysis (GSEA) or a hypergeometric/Fisher's exact test with Bonferroni correction — These are commonly used alternative enrichment frameworks that could also be applied to cross-check pathway-level signal using a different statistical model of enrichment.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40153700
Paper: Tran JS, Ward RD, Iruegas-López R, Ebersberger I, Peters JM. Chemical genomics informs antibiotic and essential gene function in Acinetobacter baumannii. PLoS Genet 2025. PMID 40153700 · PMCID PMC11975115 · DOI 10.1371/journal.pgen.1011642
Code-link correction (important provenance note)
- The RU was harvested with code URL
https://github.com/BIONF/fDOGf— this is a 404 (typo / harvest false-positive). The real BIONF tool isBIONF/fDOG("Feature-aware Directed OrtholoG search"; paper used fDOG v0.1.26), used only for the conservation/phyloprofile sub-analysis (Ebersberger-lab collaboration). - The paper's own Data/Code Availability statement points to the lab org
https://github.com/jasonpeterslab. The actual analysis repo for THIS paper isjasonpeterslab/Abau_chemical_genomics("Minimally processed data and code for PLOS Genetics manuscript", main branch, pushed 2025-02-25, ~118 MB, no LICENSE file). This repo ships the minimally-processed count data + the R scripts that regenerate the figures. It is the reproduction target.
In scope (pipeline-derived, attempted) — 80/20 target
The pipeline is fully self-contained in R on shipped minimally-processed data
(sequencing processing/count_by_position.tsv.gz + guide key + design + QC lists):
- edgeR fitness pipeline (
sequencing processing/Calculate-LFC-FDRs-EdgeR.R): guide×sample count matrix → CPM filter (rowSums(cpm>0.5)>=10) →calcNormFactors→estimateDisp→glmQLFit(robust=TRUE)→ 46 drug-vs-control contrasts → guide-level logFC + FDR. - Gene-level CG scores: median guide-level logFC for
type=="perfect"guides (control-median-adjusted) + Stouffer combined p of guide FDRs (poolr::stouffer). - Fig 1C headline count (
CG scores and relative fitness/Counting significant phenotypes.R): genes with ≥1 significant chemical-gene interaction (medL2FC ≥ |1| & FDR < 0.05), over 406 essential genes, and the median number of significant interactions per gene.
Reproduced claims (see claims.tsv):
- C1: "93% (378/406) of the genes investigated exhibited at least one significant CG score (medL2FC ≥ |1|, p<0.05)" — Fig 1C / Results.
- C2: "a median of 14 significant chemical interactions per gene" — Results.
These are deterministic (edgeR QL fit is deterministic) and depend only on shipped data.
Out of scope (NOT attempted) — the hard ~20%, with reasons
- barcoder guide counting from raw SRA reads (PRJNA1190454): upstream of the
shipped counts; uses a separate third-party tool (
ryandward/barcoder). The minimally-processed counts are shipped, so we take them as the pipeline input (standard for this repo). Re-deriving counts from FASTQ is a separate, large job. - fDOG conservation analysis (fDOG v0.1.26 over 2792 Gammaproteobacteria isolates,
474 genera, NCBI RefSeq r213) + PhyloProfile visualization: very large compute, needs
a specific genome database snapshot; phyloprofile output (
J_tran-essentials.phyloprofile.gz) is shipped. Not 80/20. - STRING-db v11.5 enrichment, limma-voom gene-set testing, essential-gene network, cheminformatics clustering: secondary analyses; not the headline claim.
- All wet-lab results (ethidium-bromide permeability, MIC assays, growth curves): out of scope.
No completeness claim
We reproduce only the headline Fig 1C counts from the shipped edgeR→CG-score pipeline. Everything else is explicitly not attempted (reasons above).
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 faithful partial reproduction that closely corroborates the paper: the gene universe matches exactly (406/406) and the headline 93% (378/406) reproduces as 92.61% (376/406) using the authors' own edgeR→CG-score script on the shipped counts. The only deviations (median 14→12/13.5, 46→40 contrasts) are small, expected, and fully explained by a data-deposit gap — the batch-1 1AbJT* samples are absent from the deposited count matrix and the harmonizing recode() was redacted from the published script. The problem is on the authors'/curation side (incomplete deposit + redacted code), not our methodology, and shows no sign of fabrication — the near-exact match argues the published numbers are real. Overall yellow: solid and confirmatory, but not a clean 1:1 because of the explainable input gap.
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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.