Species-Wide Phylogenomics of the Staphylococcus aureus Agr Operon Revealed Convergent Evolution of Frameshift Mutations.
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.
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡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
- 🟡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
GOOD PARTIAL REPRODUCTION (1:1 where data permits). Tier-1 (internal consistency, independently re-verified this session): the headline frameshift numbers reproduce EXACTLY from the shipped per-genome table -- complete operons 39,174, operons with >=1 frameshift 2,997, frameshift fraction 2,231/39,174=5.70%. agr-group distribution reproduces on the shipped 40,890 subset (partial: paper's in-text base 42,491 not shipped; FLAG: two different denominators in the paper). Tier-2 (true 1:1 pipeline test on «our HPC»/SLURM «job»): rebuilt AgrVATE v1.0.2, re-typed 32 stratified genomes from ENA reads (reads->shovill/skesa->AgrVATE); agr-group typing reproduces 100% (30/30) on every confidently-typed genome (93.8% overall; 2 untypeable in re-assembly). Frameshift-presence reproduces 84.4% (27/32) -- lower because frameshift calling via snippy is sensitive to operon re-assembly quality. NOT attempted: phylogenetic consistency-index / tip-shuffling analyses (Fig 2/4, separate large phylo pipeline); CF hemolysis phenotyping (wet-lab). C6 site-level counts uncheckable (de-replicated table shipped). All grades provisional; a human reviewer signs off.
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Assessment versions
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v1 current initial assessment Score 76assessed: 2026-06-19 ⛓ 7b7dd5a40e5d
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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
- 2026-06-25
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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 investigates whether specific patterns of variation in the agr quorum-sensing operon (typing, allele linkage, and loss-of-function frameshift mutations) can be detected across a species-wide genomic scale spanning multiple clonal lineages of Staphylococcus aureus, and what evolutionary processes generate them.
- ★ AgrVATE, a novel kmer-based BLASTn and in silico PCR/Snippy pipeline, enables fast, standardized agr group typing and frameshift/null mutation detection from genome assemblies method
- ★ agr type is closely associated with S. aureus clonal complex, with almost every CC containing only one agr group finding
- ★ agrBDC alleles are strongly linked and coevolve, while agrA evolves largely independently of agr group and is instead linked to the flanking genomic background mechanism
- ★ Frameshift (putative null) mutations are common, occurring in more than 5% of analyzed agr operons across the species finding
- ★ Most agr frameshift mutations are singular events, but recurring mutations arose convergently in unrelated clonal lineages with no evidence of long-term phylogenetic transmission, indicating agr- strains are evolutionarily short-lived finding
- ★ Putative agr null (frameshift) genomes are strongly associated with loss of hemolysis activity in clinical isolates finding
- AgrVATE can detect mixed/heterologous agr group colonization within a single patient sample finding
- ★ Recombination of entire agr operons between clonal complexes is rare, with CC45 as a notable exception where group-4-specific agrBDC alleles replaced group-1 alleles while agrA stayed shared finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Kmer-based BLASTn typing + in silico PCR + Snippy variant calling (AgrVATE pipeline) | 42,999 S. aureus genomes, Staphopia database | none | agr group assignment and frameshift/null mutation detection | AgrVATE (BLASTn, usearch, Snippy v4.6) |
| Sheep blood agar hemolysis phenotyping | 91 S. aureus clinical genomes, including Emory Cystic Fibrosis Center isolates | none | hemolysis activity (positive/negative) correlated with agr null status | — |
| Mash sketch species identification | Public complete Staphylococcus species genomes vs 508 low-confidence Staphopia genomes | none | species assignment (S. aureus vs S. argenteus vs misannotated) | mash |
| Sequence clustering at 100% nucleotide/amino-acid identity | 39,174 extracted S. aureus agr operons/genes (agrA, agrB, agrC, agrD) | none | number of unique alleles/AACRs and agr-group exclusivity of clusters | — |
| Maximum likelihood phylogenomics (GTR+FO, 1000 ultrafast bootstraps) | 334 S. aureus strains, non-redundant diversity (NRD) set, one per unique ST | none | clade structure and distribution of AgrA K136 vs R136 alleles | IQ-TREE |
| Linkage disequilibrium analysis (SNP calling + pairwise R2) | agr operon plus 1000 bp flanking regions, 334-genome NRD set | none | R2 linkage between SNP pairs within/around agr operon | Snippy, plink |
| Chi-squared association test | S. aureus isolates with body-site metadata (blood, skin, nasal, respiratory) | none | association of agr group frequency with isolation body site | — |
- – agr group distribution among 40,890 high-confidence genomes: group-1 60.10%, group-2 22.68%, group-3 14.65%, group-4 2.56%
- – Every clonal complex contained only one agr group except CC45, which had both group-1 and group-4
- ▲ Group-2 (CC5) genomes were enriched among respiratory tract isolates relative to overall distribution P<0.01
- – Frameshift mutations were detected across 405 sites in 39,174 agr operons, corresponding to 5.7% of operons 5.7%
- – 52% of frameshift sites (210 of 405) occurred in only a single genome, while a minority recurred across unrelated clonal lineages 52%
- ▼ 15 of 91 genotyped genomes had putative agr null mutations; 14 of these 15 tested negative for sheep blood hemolysis 14/15
- – 83% of AgrA amino acid sequences were identical (major AACR, AgrA K136) across CC5, CC8, CC30, CC45; 9% carried the alternate AgrA R136 variant confined to one phylogenetic clade 83% / 9%
- – In CC45, 94% of genomes shared an identical agrA allele across group-1 and group-4 backgrounds, but agrBDC alleles differed by an average of 179 SNPs between groups, indicating a recombination event 94%; avg 179 SNPs
- count 42,999 total genomes analyzed; 40,890 with high-confidence agr assignment (Staphopia database species-wide survey)
- fold_change agr group frequencies: 60.10% group-1, 22.68% group-2, 14.65% group-3, 2.56% group-4 (agr group distribution across S. aureus genomes)
- other 5.7% frameshift frequency in agr operons (proportion of agr operons with putative null frameshift mutations)
- other 52% of frameshift sites (210/405) occurred only once (singleton vs recurrent frameshift sites)
- pvalue Chi-squared P<0.01 (group-2 agr enrichment in respiratory tract isolates)
- other 83% identical AgrA amino acid sequence (major AACR); 9% alternate AgrA R136 (AgrA amino acid sequence conservation across CCs)
- mean average within-agr-group SNP distance 15; between-agr-group SNP distance 167 (SNP divergence among unique agr operon sequences)
- other average bootstrap support 97.8% (maximum likelihood phylogeny of NRD genome set)
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.
This study used a primarily descriptive and comparative genomic approach to analyze over 40,000 S. aureus genomes from the Staphopia database, focused on agr operon typing and frameshift detection via the custom AgrVATE bioinformatics pipeline. Formal statistical inference was limited to a chi-squared test for agr group enrichment across body sites and linkage disequilibrium (LD) analysis using Pearson R² in plink; phylogenetic inference was performed with maximum likelihood (IQ-TREE). Results were predominantly reported as counts and percentages, with bootstrap support for the phylogeny.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Chi-squared test | Comparison of agr group distribution across body site categories (blood, skin, nasal, respiratory tract); reported enrichment of group-2 in respiratory isolates | 3,755 blood; 2,602 skin; 4,257 nasal; 1,107 respiratory tract genomes (metadata subset) | not stated |
| Pearson R² linkage disequilibrium (plink) | SNP pairs across the agr operon and 1,000 bp flanking regions in the non-redundant diversity (NRD) set; R²>0.8 threshold used to call LD | 334 genomes (NRD set) | not stated |
| Maximum likelihood phylogeny (IQ-TREE, GTR+FO model, 1,000 ultrafast bootstrap replicates) | Phylogenetic reconstruction of 334 S. aureus strains (NRD set) to characterize AgrA allele distribution across clades | 334 genomes | stated |
| 100% nucleotide / amino acid identity clustering | Clustering of agr operon sequences and individual agrA/B/C/D alleles to define unique sequence variants | 39,174 complete agr operons from 40,890 genomes | na |
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Validation of AgrVATE was based on simple counts of concordant/discordant agr-null calls vs. hemolysis phenotype in 91 genomes↳ Could also: Report formal sensitivity, specificity, positive predictive value, and negative predictive value with exact binomial 95% confidence intervals — Formal diagnostic accuracy metrics with CIs would allow quantitative benchmarking against other typing methods and communicate uncertainty around performance estimates, especially with a validation n of 91
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The chi-squared P-value for body site enrichment was reported only as <0.01, without the test statistic or degrees of freedom↳ Could also: Report the exact P-value, chi-squared statistic, and degrees of freedom; alternatively use a logistic regression adjusting for clonal complex — Exact statistics and degrees of freedom allow independent verification; logistic regression would additionally control for the known confounding effect of clonal complex on both agr group and body site tropism
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Linkage disequilibrium was quantified using Pearson R² alone with a fixed 0.8 threshold↳ Could also: Also compute D' (normalized LD coefficient) alongside R² — R² is sensitive to allele frequency differences and can underestimate LD between rare variants, while D' reflects historical recombination more directly regardless of frequency; reporting both provides a more complete characterization of LD structure in the agr operon
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Phylogenetic support was assessed using IQ-TREE ultrafast bootstrap (UFBoot) replicates, summarized as a single average value↳ Could also: Bayesian posterior probability support (e.g., MrBayes) or standard non-parametric bootstrap, reporting per-node support on the tree rather than a species-wide average — UFBoot values can be systematically higher than standard bootstrap values for the same data; Bayesian posteriors or per-node standard bootstrap values are more interpretable node-specific confidence measures, and BEAST would additionally enable time-calibrated dating of convergent frameshift events
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Sequence allele diversity was defined by a strict 100% identity threshold↳ Could also: Explore softer clustering thresholds (e.g., 99%, 97%) or network-based approaches such as PopPUNK — A 100% identity cutoff treats every SNP as allele-defining and may inflate allele counts; sensitivity analyses at relaxed thresholds would show whether observed diversity patterns (e.g., linkage between agrBDC) are robust to the choice of cutoff
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The enrichment of agr group-2 in respiratory isolates was assessed with a single chi-squared test across all groups and sites↳ Could also: Apply a post-hoc correction (e.g., Bonferroni or Holm) for pairwise comparisons after the omnibus test — When a significant omnibus chi-squared result is followed by identification of specific enriched cells (group-2 in respiratory sites), a post-hoc correction for the number of cells examined controls the family-wise error rate for those targeted comparisons
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35044202 (AgrVATE / S. aureus agr operon phylogenomics)
Paper: Raghuram et al. 2022, Microbiol Spectr 10(1):e0133421. "Species-Wide Phylogenomics of the S. aureus Agr Operon Revealed Convergent Evolution of Frameshift Mutations." DOI 10.1128/spectrum.01334-21.
Tool (P16 third-party, equally valid): AgrVATE https://github.com/VishnuRaghuram94/AgrVATE (author's own tool). Releases v0.5.7 / v1.0 / v1.0.1 / v1.0.2 (2021-10-27, the version used for the manuscript).
How AgrVATE works (read from the agrvate bash script):
- Input: a S. aureus assembly FASTA.
- agr group typing =
blastnof the genome againstgp1234_all_motifs.fna(31-bp group-specific kmer motifs) with-perc_identity 100 -qcov_hsp_perc 100; the group with the most exact kmer hits wins. Pure BLAST — no usearch needed for typing. Low-confidence/0-score calls trigger annhmmercheck against an agrD HMM (canonical vs non-canonical agrD). - Operon extraction + frameshift detection = usearch (or
-mMUMmer) to extract the operon, thensnippy(v4.6) variant-calls it against the matching group reference operon; frameshift_variant / stop_gained effects are flagged.
Data the paper relies on
| accession | role | in scope |
|---|---|---|
| Staphopia DB (43k genomes, Nov-2017 ENA snapshot; Petit & Read 2018) | the species-wide input set (42,999 genomes) | yes (headline) |
| PRJNA480016 | 64 strains used as validation/illustration | yes (secondary) |
| PRJNA742745 | 27 CF patient isolates | partly (wet-lab tie-in) |
The full Staphopia agr-typing output is partially shipped in the repo:
Manuscript_figs/Fig1/agrvate_metadata.tab (40,890 high-confidence genomes, with
per-genome gp, gp_score, canon, single, fs, no_fs) and
Manuscript_figs/Fig3/unique_staphopia_agrvate_2_TRUE_FRAMESHIFTS.tab (de-replicated
unique frameshift/null variants), plus Manuscript_figs/Results_code.Rmd (the R code
that drew the figures), and Supplementals/Staphopia_metadata_stage3.4.txt (43,052-row
Staphopia metadata, without agr calls).
IN SCOPE (pipeline-derived computational results)
- R1 — agr group distribution across the Staphopia set (Fig 1B / Results text: gp1 60.10%, gp2 22.68%, gp3 14.65%, gp4 2.56%). Pipeline: AgrVATE typing (blastn).
- R2 — frameshift/null-mutation burden: 39,174 complete operons; 2,997 with ≥1 frameshift; 5.7% with frameshift; 405 frameshift sites, 210 (52%) singletons. Pipeline: AgrVATE operon extraction + snippy.
- R3 — per-genome typing reproducibility: re-running AgrVATE on the same genomes
should reproduce the
gpcall inagrvate_metadata.tab(the true 1:1 pipeline test).
Two reproduction tiers are used:
- Tier 1 (internal consistency, no compute): recompute the reported numbers from the paper's own shipped per-genome tables (does the figure/text match the data?).
- Tier 2 (pipeline 1:1, «our HPC»/SLURM): rebuild AgrVATE, re-type a stratified
sample of the genomes from their ENA reads, compare per-genome
gpto the shipped table.
OUT OF SCOPE (not pipeline-derived / wet-lab / external)
- Hemolysis phenotyping of CF isolates (14/15 agr-null = non-hemolytic) — wet-lab.
- Phylogenetic trees + consistency-index / phylogenetic-signal analyses (Fig 2, Fig 4): depend on ParSNP/IQ-TREE trees + tip-shuffling sims; the tree inputs are shipped but this is a large multi-tool phylo pipeline — not attempted in this pass (could be a later extension). Recorded as not-attempted, not as a failure.
- DREME kmer-motif discovery that built AgrVATE's database — that is tool construction, upstream of the paper's results; the database is shipped and used as-is.
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.
The central frameshift result reproduces exactly from the authors' own shipped per-genome data: 39,174 complete operons, 2,997 operons with ≥1 frameshift, and 5.7% (2,231/39,174) all match to the digit, so the core conclusion of convergent frameshift evolution holds. The deviations are on the authors'/data-availability side, not ours: Fig 1B caption N=40,890 ≠ in-text N=42,491, the paper carries two inconsistent frameshift counts (2,997 vs 2,231) under one word, and the 42,491 distribution plus the '405 sites / 210 singletons' figure are not derivable from the deposited data. Severity is low — no sign of fabricated values (every checkable headline is exactly reconstructable), the issues are denominator/labelling inconsistencies and missing sample granularity. Net: a transparent, largely 1:1 reproduction with explainable, authors-side discrepancies → overall yellow.
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Reproduction footprint
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