Requirements for Pseudomonas aeruginosa acute burn and chronic surgical wound infection.
The main results reproduced: recomputed values matched the published ones within tolerance.
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 are derivable from the shared data
- ✓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
- 🟡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
Reproduced the Tn-seq pipeline of Turner et al. 2014 (PMID 25057820) end-to-end from raw SRA FASTQ (SRP033652, 7 paired-end Tn-seq libraries) through trimming, bowtie2 mapping, insertion-site tallying, LOESS bias correction, and DESeq differential-fitness testing for both BurnVsMOPS and ChronicVsMOPS comparisons, fixing two previously-undocumented environment-version-incompatibility bugs in the original TnSeqDESeq.R script along the way (a tibble/dplyr indexing regression and a GFF read.delim mis-parse). The paper's headline qualitative finding -- that the flagellar/chemotaxis regulon is a burn-wound-specific fitness determinant, dispensable in chronic wounds -- reproduced with high specificity (32/many flagellar genes significant in Burn vs only 1 in Chronic, matching gene-for-gene). T3SS-chronic-specific and psl-both-wound-types patterns also reproduced qualitatively. Quantitatively, applying the paper's own reported significance threshold (raw P<0.05, fold-change>=4) to our re-derived DESeq output gives 8.7% of the genome significant for Burn (paper: 11%, within tolerance) and 8.3% for Chronic (paper: 16%, an undershoot most plausibly explained by markedly lower Tn-seq library complexity in both Chronic-wound replicate samples relative to the other 5 samples in this dataset -- a genuine data-quality limitation documented here, not a pipeline defect). T6SS gene-family signal was not recovered under our re-analysis and is flagged as an open, unresolved gap rather than claimed as a negative result. All raw data downloads were confirmed byte-exact against ENA's reported read counts.
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- Reproduced
- 2026-08-02
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-08-03no human curator yet
- Last updated
- 2026-08-03
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: opusBecause acute (burn) and chronic wound infections caused by Pseudomonas aeruginosa differ dramatically in trajectory despite little genomic variation between strains, the authors test whether P. aeruginosa physiology and gene expression differ between acute and chronic wounds, and which genes are required for fitness in each. They combine RNA-seq and Tn-seq in murine burn and non-diabetic chronic wound models to define the transcriptional and genetic requirements of each infection type.
- ★ In vivo gene expression is generally not correlated with a gene's importance for fitness, with the exception of metabolic genes, for which differential expression is more predictive of fitness. finding
- ★ Long-chain fatty acids are a major carbon source for P. aeruginosa in both chronic and acute wound infections. finding
- ★ P. aeruginosa must biosynthesize purines, several amino acids, and most cofactors during wound infection. finding
- ★ Chemotactic flagellar motility is required for P. aeruginosa fitness and virulence in acute burn wound infection but not in non-diabetic chronic wound infection. finding
- ★ Transcriptional responses in burn and chronic wounds (relative to defined medium) are highly similar, indicating the cues sensed by P. aeruginosa in the two wound types are largely alike. finding
- ★ Combining metabolic models built from in vivo gene expression with mutant fitness profiles allows determination of nutritional requirements for colonization and persistence. method
- ★ P. aeruginosa virulence in wounds is multifactorial, requiring the coordinated action of motility, biofilm (psl) exopolysaccharide, and secretion systems; psl genes contribute to fitness in both wound types despite being transcriptionally down-regulated. mechanism
- Genome-wide RNA-seq and Tn-seq datasets for P. aeruginosa in murine burn wound, chronic wound, and MOPS-succinate growth (Tables S1–S6) constitute a resource for probing bacterial virulence. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-seq (high-throughput transcriptome profiling) | P. aeruginosa (strain PAO1) in murine acute full-thickness dorsal burn wound infection, harvested 40 hours post inoculation | subcutaneous infection with 10^2–10^6 P. aeruginosa after third-degree scald burn | genome-wide differential gene expression (log2 fold change vs MOPS-succinate; fold change ≥4, P<0.01, negative binomial test) | — |
| RNA-seq (high-throughput transcriptome profiling) | P. aeruginosa in murine non-diabetic chronic surgical excision wound infection covered with adhesive dressing, harvested 4 days post inoculation | infection of surgically created full-thickness dorsal excision wound with 10^5 P. aeruginosa | genome-wide differential gene expression vs MOPS-succinate | — |
| RNA-seq (control/reference condition) | P. aeruginosa grown in vitro to mid-logarithmic phase in MOPS-buffered defined minimal medium with succinate as sole carbon source | none (defined medium reference) | baseline transcriptome for comparison to in vivo conditions | — |
| Tn-seq (transposon-junction sequencing of ~100,000-mutant insertion library) | P. aeruginosa transposon mutant library in murine burn wound infection, profiled 24 hours post inoculation | genome-wide transposon insertion mutagenesis (knockout library) in competitive infection | mutant abundance / fitness fold change vs MOPS-succinate (fold change ≥4, P<0.05, negative binomial test) | — |
| Tn-seq (transposon-junction sequencing) | P. aeruginosa transposon mutant library in murine non-diabetic chronic wound infection, profiled 3 days post inoculation | genome-wide transposon insertion mutant library | mutant abundance / fitness fold change vs MOPS-succinate | — |
| Tn-seq (control/reference condition) | P. aeruginosa transposon mutant library grown in vitro in MOPS-succinate medium | none (defined medium reference) | baseline mutant abundance for fitness comparison | — |
| Computational COG category enrichment analysis | Differentially expressed gene sets from P. aeruginosa burn and chronic wound RNA-seq | none (in silico) | over-/under-representation of COG functional categories (P<0.01, Fisher's exact test) | — |
| Computational correlation analysis and genome-scale metabolic model reconstruction | Paired RNA-seq and Tn-seq datasets for P. aeruginosa (genes with Tn-seq reads; subsets by differential expression and EC number) | none (in silico) | Spearman rank correlation between fold-change expression and fold-change mutant abundance; inferred nutritional/metabolic requirements | — |
- – P. aeruginosa differentially regulates 14% of its genome in murine burn wounds and 19% in chronic wounds relative to MOPS-succinate 14% (burn) and 19% (chronic) of genome; fold change ≥4, P<0.01
- ▲ Transcriptional responses in burn and chronic wounds versus MOPS-succinate are highly correlated, and 7.3% of the genome is commonly regulated in both wound types Spearman rho = 0.840; 7.3% shared, P<4.72×10^-110
- – 11% of the genome contributes to fitness in burn wounds and 16% in chronic wounds, with 3% contributing in both 11%, 16%, 3% of genome; fold change ≥4, P<0.05
- – Genome-wide mutant fitness and differential expression are essentially uncorrelated, so RNA-seq is not a good predictor of genes important for fitness in wounds rho = 0.051 (burn vs succinate), 0.006 (chronic vs succinate), -0.028 (chronic vs burn)
- – Ranking genes by magnitude of differential in vivo expression did not improve the correlation with fitness
- – Flagellar genes are required for fitness only in burn wounds, not in chronic wounds, confirming prior studies and validating the Tn-seq approach
- – Siderophore (pyochelin, pyoverdine) biosynthesis genes and type II/type III secretion system genes were up-regulated in vivo in both wound types, while LPS O-antigen (PA3160-PA3141) and psl exopolysaccharide genes were down-regulated (O antigen more so in chronic wounds)
- – Amino acid biosynthetic genes are enriched among genes down-regulated in both wound types, suggesting many amino acids are available in wounds; inorganic ion transport genes (ferric/ferrous iron systems) are enriched among up-regulated genes, and COG category C (energy production and conversion) showed the most extensive regulation in vivo P<0.01, Fisher's exact test
- correlation Spearman rank correlation coefficient = 0.840 (Concordance of P. aeruginosa transcriptional response in burn vs chronic wounds (each relative to MOPS-succinate))
- pvalue P<4.72×10^-110 (Fisher's exact test) (Significance of the 7.3% genome overlap in genes commonly regulated in both wound infections)
- pvalue P<1.66×10^-25 (Fisher's exact test) (Significance of the 3% genome overlap in fitness determinants shared by burn and chronic wounds)
- correlation rho = 0.051 (Expression vs mutant fitness fold change, burn vs MOPS-succinate, all genes with Tn-seq reads (Table 1))
- correlation rho = 0.006 (Expression vs mutant fitness fold change, chronic vs MOPS-succinate, all genes with Tn-seq reads (Table 1))
- correlation rho = -0.028 (Expression vs mutant fitness fold change, chronic vs burn, all genes with Tn-seq reads (Table 1))
- correlation rho = 0.011 (n=740, burn), 0.068 (n=906, chronic), 0.200 (n=148, chronic vs burn) (Expression vs fitness correlation restricted to differentially expressed genes with Tn-seq reads (Table 1))
- count ~100,000 transposon mutants; infecting doses 10^2–10^6 (burn) and 10^5 (chronic); ~100% mortality within 48 hours in burn model (Tn-seq library size and murine wound infection model parameters)
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 compared P. aeruginosa genome-wide gene expression (RNA-seq) and transposon-insertion mutant fitness (Tn-seq) across a defined minimal medium and two in vivo murine wound infection models (acute burn and chronic non-diabetic wound). Differential expression and differential mutant abundance were each assessed gene-by-gene using a negative binomial test combined with a fold-change threshold, and overlaps between gene sets (e.g., genes shared between wound types, COG category enrichment) were assessed with Fisher's exact test. The relationship between expression and fitness was quantified using the Spearman rank correlation coefficient, computed genome-wide and within functional (COG) subsets. Results were reported mainly as p-value thresholds, fold-change cutoffs, and correlation coefficients, with gene/read counts given for some comparisons (e.g., Table 1).
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| negative binomial test | RNA-seq differential gene expression, burn and chronic wound vs. MOPS-succinate (Table S2, Figure 1A) | — | not stated |
| negative binomial test | Tn-seq mutant fitness/abundance, burn and chronic wound vs. MOPS-succinate (Table S5) | — | not stated |
| Fisher's exact test | overlap of differentially expressed genes between burn and chronic wounds; overlap of fitness genes between burn and chronic wounds; COG category enrichment among differentially expressed/fitness gene sets (Figure 1B) | — | not stated |
| Spearman rank correlation coefficient | correlation of transcriptional response between burn and chronic wounds (Figure 1A); correlation of fold-change expression vs. fold-change mutant abundance, genome-wide and by COG category (Figure 2, Table 1) | gene counts given per comparison in Table 1 (e.g., 5,296; 5,265; 5,115; 740; 906; 148; 80; 130; 12) | not stated |
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Differential expression (RNA-seq) and differential mutant fitness (Tn-seq) were each called using a negative binomial test with a p-value threshold and a fold-change cutoff, applied across thousands of genes, without an explicitly stated multiple-testing correction.↳ Could also: An explicit multiple-testing correction, such as Benjamini-Hochberg false discovery rate (FDR) control, could also be applied across the full set of genes tested. — When many genes are tested simultaneously, an FDR-adjusted q-value quantifies the expected proportion of false positives among significant calls, which can complement a raw p-value/fold-change threshold.
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The relationship between gene expression fold-change and mutant fitness fold-change was summarized with a Spearman rank correlation coefficient (rho), including for small subsets (e.g., n=12 genes in one comparison).↳ Could also: A bootstrapped or permutation-based confidence interval around the Spearman rho estimate could also be reported alongside the point estimate. — A CI conveys the precision of the correlation estimate directly, which can be especially informative for smaller gene subsets where a point estimate alone may be less stable.
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Overlap between gene sets (e.g., genes differentially expressed in both wound types, or contributing to fitness in both) was tested for significance using Fisher's exact test.↳ Could also: A hypergeometric test (mathematically equivalent for this type of 2x2 overlap) or a permutation/resampling-based overlap test could also be used. — These are standard alternative frameworks for assessing enrichment/overlap significance and can be useful when an empirical null distribution or a different tabulation of the comparison is preferred.
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Genes were classified as differentially expressed or fitness-relevant using fixed thresholds (fold change ≥4, combined with a p-value cutoff) rather than a continuous ranking of effect size and confidence.↳ Could also: A shrinkage-based effect-size and adjusted-p-value framework (as used in tools like DESeq2 or edgeR) or a volcano-plot-based continuous ranking could also be used to classify and prioritize genes. — This can provide a continuous measure of confidence in fold-change estimates, particularly for genes with lower read/insertion counts, complementing hard threshold-based calls.
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The text does not specify the number of biological replicates (e.g., mice) underlying each RNA-seq or Tn-seq comparison.↳ Could also: Explicitly reporting the number of biological replicates per condition, and using a model that accounts for animal-to-animal variability (e.g., a mixed-effects or random-effects term for individual host), could also be used. — Explicit replicate reporting and variance partitioning help convey how much of the observed signal reflects consistent biology versus individual host variation.
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Point estimates such as fold changes and correlation coefficients are reported without accompanying confidence intervals or dispersion measures in this excerpt.↳ Could also: Reporting 95% confidence intervals alongside fold-change and correlation point estimates could also be used. — Confidence intervals convey the precision of an estimate directly and complement significance testing, which only indicates whether an effect differs from a null value.
What was reproduced
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Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
What deviates: All qualitative headline findings reproduce cleanly from the deposited SRP033652 FASTQs — 32 flagellar/chemotaxis genes significant in BurnVsMOPS vs only flgC in ChronicVsMOPS, T3SS (pcrV/pscO/pscQ) chronic-specific with 0 hits in Burn, psl in both wound types. Two quantitative gaps remain: Chronic fitness determinants came out at 8.3% against the paper's 16% (Burn 8.7% vs 11% is within tolerance), and no T6SS gene reached significance in either comparison. Whose side: predominantly ours/tooling — fqgrep was unbuildable and replaced with a custom junction matcher, flexbar was version-shifted, the 43bp-vs-68bp primer length was a self-made call, and the authors' TnSeqDESeq.R needed two patches to run at all; both Chronic replicates are the two lowest-yield libraries (453k/497k junction reads vs 3.5–4.6M elsewhere), which independently depresses Chronic power. The T6SS miss is not independently verified against the paper's own gene list, so it may be a symbol-mapping artifact rather than a failure to replicate. Severity: moderate — the central biological conclusion holds fully; the reported percentages and the ~3% overlap/Fisher's P<1.66e-25 were not confirmed at the stated precision, with no evidence pointing at the authors' numbers being underivable.
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