Transcriptomic Adjustments of Staphylococcus aureus COL (MRSA) Forming Biofilms Under Acidic and Alkaline Conditions.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
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
- ✓Reported values were directly 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
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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 the DOWNSTREAM analysis 1:1. The 'code' link is the third-party TM4/MeV tool (P16), not author code; GEO ships the authors' own normalised value matrix (GSE138075 series matrix, 3887 probesets x 12 arrays) which is exactly the input to their Excel step. Replaying the described computation (log2 of mean expression ratio + two-tailed paired t-test, p<0.05) on that matrix reproduces EVERY reported per-gene number: all 92 log2FC values in Tables 1A-4B match a probeset to <=0.05 (most <=0.01) and 84/92 also match the printed p to <=0.001; the named genes (codY, mecA, ctsR, sceD, femA, agrB, sarA, hfq) match their annotated probeset on BOTH log2FC and p exactly. => the reported values are genuine, NO fabrication. What does NOT reproduce is the DEG COUNTS: the stated thresholds (log2FC>~3.1 & p<0.05) actually pass 143-196 probesets per comparison (e.g. 177 up / 167 down for pH9 biofilm, only 2 AFFX controls), not the reported 8/11/16/16 up or 4/18/11/12 down. The reported short lists are a heavily curated, annotation-driven subset (reported genes have variance ranks 191-564, not top-50; significant probesets up to log2FC 23.6 go unreported; even band+presence filtering leaves 154 candidates). The 'MeV variance filter value 50' + manual KEGG/Aureowiki curation that shrinks ~175 to ~8-18 is under-specified and not algorithmically reproducible -- selective reporting, not fabrication. NOT attempted: independent RMA re-normalisation from raw CEL (blocked -- no Bioconductor CDF for GPL1339, would need Thermo S_aureus.CDF + makecdfenv; low value since deposited values already reproduce the numbers), and all wet-lab results (growth/CFU/biofilm assays/RT-PCR/microscopy, out of scope).
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Assessment versions
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v1 current initial assessment Score 73assessed: 2026-06-16 ⛓ 917f77eda1c2
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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
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-18no 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: opusTo detect genes differentially expressed in Staphylococcus aureus COL (MRSA) biofilm-associated and planktonic cells under acidic (pH5) and alkaline (pH9) conditions using DNA microarrays, in order to understand the molecular mechanisms linking pH-related stress response with biofilm formation and pathogenicity.
- ★ S. aureus COL (MRSA) can survive and grow under acidic (pH5) and alkaline (pH9) conditions both planktonically and as a biofilm. finding
- ★ The pathogen is possibly more tolerant to highly alkaline than acidic environments. finding
- ★ Genes encoding transcription regulators, ion transporters, cell wall biosynthetic enzymes, autolytic enzymes, adhesion proteins and antibiotic resistance factors are differentially regulated by pH and growth mode, most associated with biofilm formation. finding
- ★ Microarray-based transcriptomic profiling of planktonic and biofilm cells at acidic and alkaline pH identifies pH/growth-mode-specific gene expression adjustments. method
- Eight genes were over-expressed in biofilm cells at alkaline pH9, including transcriptional regulators CodY, MecA, CtsR and capsule enzyme CapC. finding
- Eleven genes were over-expressed in biofilm cells at acidic pH5, including cell surface proteins MapW, Efb/FnbA and secreted VWbp. finding
- FemA (factor essential for methicillin resistance) was over-expressed in planktonic cells at both pH9 and pH5. finding
- These results facilitate development of new treatment or disinfection strategies against biofilm-associated MRSA. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Growth/viability assay (CFU enumeration by serial dilution) | S. aureus COL (MRSA) planktonic cells in liquid TSB | acidic/neutral/alkaline pH (pH5, 7, 9) adjusted with HCl/NaOH | colony forming units per 10 mL | — |
| Growth/viability assay (CFU enumeration by serial dilution) | S. aureus COL (MRSA) biofilm on nitrocellulose membrane on solid TSA | acidic/neutral/alkaline pH (pH5, 7, 9) | colony forming units per nitrocellulose disk | nitrocellulose membrane 0.45 μm (Sartorius) |
| DNA microarray (transcriptomics) | S. aureus COL planktonic cells in liquid TSB | acidic (pH5) and alkaline (pH9) vs neutral (pH7) | gene expression (Log2 fold change) | GeneChip S. aureus Genome Array (Affymetrix Cat. No. 900514) |
| DNA microarray (transcriptomics) | S. aureus COL biofilm cells on nitrocellulose on solid TSA | acidic (pH5) and alkaline (pH9) vs neutral (pH7) | gene expression (Log2 fold change) | GeneChip S. aureus Genome Array (Affymetrix Cat. No. 900514) |
| Total RNA extraction and first-strand cDNA synthesis | S. aureus COL biomass (planktonic and biofilm) | none | RNA quality, cDNA for hybridization | Nucleospin RNA II kit (Macherey-Nagel); PrimeScript 1st strand cDNA Synthesis Kit (Takara) |
- – Total planktonic growth reached 10^9 CFU/10 mL in acidic and 10^10 CFU/10 mL in neutral and alkaline media, indicating greater tolerance to alkaline conditions 10^9 vs 10^10 CFU/10 mL
- ▲ Eight genes over-expressed in biofilm cells at pH9 log2 fold-change > 3.16
- ▲ Eleven genes over-expressed in biofilm cells at pH5 log2 fold-change > 3.12
- ▲ Sixteen genes over-expressed in planktonic cells at pH9 log2 fold-change 3.22–4.14
- ▲ Sixteen genes over-expressed in planktonic cells at pH5 log2 fold-change 2.74–4.12
- ▼ Four genes down-regulated in biofilm cells at pH9 (AirR, NreB, PhoU-related, MapW) log2 fold-change -3.24 to -3.34
- ▼ Eighteen genes down-regulated in biofilm cells at pH5 log2 fold-change -2.37 to -4.05
- ▲ FemA over-expressed in planktonic cells at both pH9 and pH5 4.12 log2 fold-change
- pvalue 0.0009 (p-value for total planktonic/biofilm growth difference across pH)
- fold_change log2 > 3.16, p < 0.0061 (8 upregulated genes in biofilm cells at pH9)
- fold_change log2 > 3.12, p < 0.0316 (11 upregulated genes in biofilm cells at pH5)
- fold_change log2 3.22–4.14, p < 0.0356 (16 upregulated genes in planktonic cells at pH9)
- fold_change log2 2.74–4.12, p < 0.0354 (16 upregulated genes in planktonic cells at pH5)
- fold_change log2 -3.24 to -3.34, p < 0.0042 (down-regulated genes in biofilm cells at pH9)
- count over 3,300 ORFs (open reading frame genes on the microarray)
- count 72,444 (MRSA infection cases reported in US in 2014 (morbidity 11.8%))
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 study used Affymetrix GeneChip microarrays to profile genome-wide transcriptional changes in S. aureus COL cells growing as biofilm or planktonically at pH 5, 7, and 9, with biological duplicates (n = 2) for microarray experiments and at least three biological replicates for colony-forming unit (CFU) counts. Raw microarray data were normalized in R (TM4 protocol), variance-filtered in MeV, and differential expression was assessed by computing Log2 fold-change ratios of averaged expression values and applying a two-tailed paired t-test in Excel. Genes were reported as significantly differentially expressed when Log2 fold-change exceeded approximately 3.1 and p < 0.05; no correction for multiple testing across the >3,300 array features was stated.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-tailed paired t-test | Microarray gene expression comparisons between pH conditions (pH9 vs pH7; pH5 vs pH7) in both biofilm and planktonic cells | n = 2 biological replicates per condition | not stated |
| Unspecified test (p-value = 0.0009 reported) | Comparison of total growth (log CFU) across pH conditions in liquid and solid media (Figure 1) | n = 3 biological replicates | not stated |
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Differential expression across >3,300 microarray features was assessed with a paired t-test at p < 0.05 with no correction for multiple comparisons.↳ Could also: Apply a false discovery rate procedure (e.g., Benjamini-Hochberg FDR) across all tested probes simultaneously, as implemented in limma (R) or similar microarray-specific tools. — When thousands of features are tested simultaneously, the expected number of false positives at an uncorrected α = 0.05 can be large; FDR control provides a principled way to interpret the resulting gene list and is the current standard in transcriptomics
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Microarray experiments used n = 2 biological replicates per condition.↳ Could also: Use three or more biological replicates per condition, which is the commonly cited minimum for microarray and RNA-seq studies. — Additional replicates improve variance estimation and statistical power; with n = 2, degrees of freedom for the t-test are minimal, limiting the ability to detect true differences and increasing sensitivity to outliers
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Differential expression was assessed using a standard paired t-test computed in Excel.↳ Could also: Use a purpose-built microarray analysis package such as limma (R), which implements moderated t-statistics (empirical Bayes shrinkage of variance estimates across genes). — Moderated statistics borrow information across the full gene set to stabilize per-gene variance estimates, which is particularly beneficial when n is small; this approach is widely used and better calibrated for microarray data than the standard t-test
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The statistical test used to compare CFU counts across pH conditions (Figure 1, p = 0.0009) was not identified in the text.↳ Could also: Name the specific test (e.g., one-way ANOVA followed by Tukey HSD, or Kruskal-Wallis followed by Dunn's test) and report the test statistic alongside the p-value. — Identifying the test and reporting the statistic (e.g., F or H value) allows readers to assess whether model assumptions were met and enables independent reproduction of the analysis; for three-group comparisons, an omnibus test with post-hoc correction also accounts for multiple pairwise contrasts
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Results were reported with exact p-values and Log2 fold-change ratios but without confidence intervals.↳ Could also: Report 95% confidence intervals for Log2 fold-change estimates alongside p-values. — Confidence intervals convey both the magnitude and precision of an estimated difference; with n = 2 replicates, intervals would be wide and informative about the uncertainty in each estimate, complementing the point estimate and p-value
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A fixed Log2 fold-change threshold (~3.1) was applied as a co-criterion for significance alongside p < 0.05, but the basis for this threshold was not explained.↳ Could also: Combine a statistically derived adjusted p-value threshold (e.g., FDR < 0.05 or 0.10) with a biologically motivated fold-change cutoff stated with explicit justification, or report all genes passing the statistical threshold and use fold-change to rank or annotate them. — Transparently justifying both thresholds—statistical and biological—helps readers understand the sensitivity/specificity trade-off in the gene list and allows comparison across studies using different cutoff conventions
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.
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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.md — pmid-31681245
Paper: Efthimiou, Tsiamis, Typas, Pappas (2019) Front Microbiol 10:2393. "Transcriptomic Adjustments of Staphylococcus aureus COL (MRSA) Forming Biofilms Under Acidic and Alkaline Conditions." DOI 10.3389/fmicb.2019.02393.
Experiment
Affymetrix GeneChip S. aureus Genome Array (GPL1339), one-colour. S. aureus COL grown at pH 5 / 7 / 9, as biofilm (BF) on nitrocellulose membranes and as planktonic (PL) cells. Biological duplicates (n=2). 12 arrays total (GSE138075). pH 7 is the control in every comparison.
Pipeline as described (Methods + GEO data_processing field)
- Raw CEL normalised in R, "TM4 protocol" (http://www.tm4.org/normalizing.html).
- Data filtering in MeV (MultiExperiment Viewer Quickstart Guide v4.2), "variance filter value = 50" (under-specified — count? percentile?).
- Filtered data exported to Excel; per gene Log2(avg Expr_cond1 / avg Expr_cond2).
- Two-tailed paired t-test (Excel) on the per-gene expression values of the two conditions; significant if p < 0.05.
- Up/down lists thresholded at log2FC ≈ ±3.1 (KEGG/Aureowiki only for annotation).
The "code" link in the registry is github.com/dfci-cccb/www.tm4.org = the TM4/MeV suite itself, i.e. a third-party tool (P16 case), not author analysis code. There is no author script; the analysis is the generic TM4→MeV→Excel workflow above.
IN SCOPE (pipeline-derived, reproduced here)
The reported result is a set of differentially expressed gene counts and
per-gene log2FC + p-values (Tables 1–4) produced by steps 3–4 applied to the
normalised data. GEO ships the authors' normalised value matrix
(GSE138075_series_matrix.txt.gz, 3887 probesets × 12 arrays) — this is exactly the
input to their Excel step. So the faithful 1:1 reproduction is:
- R1 (core). Recompute, on the authors' deposited normalised values, for each of the 4 comparisons (BF/PL × pH9/pH5 vs pH7): per-probeset log2(mean ratio) + two-tailed paired t-test, then apply the paper's thresholds and the MeV variance filter (tested under several interpretations of "value 50"), and compare the resulting up/down DEG counts to C1–C8.
- R2. Map the named genes (codY, mecA, sceD, femA, sarA, hfq, …) to array probesets via the GPL1339 annotation and compare per-gene log2FC + p (C9–C14).
- R3 (independent cross-check, harder). Re-normalise the 12 raw CEL files from
scratch (RMA/affy) and repeat R1, to test robustness to the normalisation choice.
Blocker: no Bioconductor CDF package exists for this array (
saureuscdfabsent); needs the AffymetrixS_aureus.CDF(Thermo, registration) +makecdfenv. Attempted as a bonus; documented if blocked.
OUT OF SCOPE (not pipeline / not attempted)
- Wet-lab: bacterial growth, CFU counts, biofilm crystal-violet assays, RT-PCR validation, microscopy (Figs of biofilm morphology). Manual/experimental.
- KEGG/Aureowiki functional interpretation narrative (manual annotation).
- The MeV variance-filter exact semantics are not fully specified by the paper; R1 brackets the plausible interpretations rather than guessing one.
Data / compute
- Data + all intermediates on «infra»:
«path» - Compute on «our HPC» via SLURM. Analysis is light (t-tests on 3887×12) but run as a job for provenance. «host» holds only small result tables + this scope.
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.
Every reported per-gene number reproduces essentially 1:1 from the authors' own GEO-deposited normalised matrix (GSE138075): all 92 log2FC values match a probeset to <=0.05 and the named genes (codY, mecA, ctsR, sceD, femA, agrB, sarA, hfq) match on both log2FC and p — so no fabrication, and the input data is identical. The substantive deviation is in the DEG counts: the stated thresholds (log2FC>~3.1 & p<0.05) pass 143-196 probesets per comparison (177 up / 167 down for pH9 biofilm) versus the reported 8/4, because an under-specified MeV variance filter + manual KEGG/Aureowiki curation shrinks the list. This is authors'-side selective reporting / under-specified method, not a computation error on our side — the listed values are genuine but the lists are a hand-picked subset. Overall yellow: reliable gene-level results with an explainable, authors-side discrepancy in the reported gene-set sizes.
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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.