Phenotypic and Genotypic Characteristics of Shiga Toxin-Producing Escherichia coli Isolated from Surface Waters and Sediments in a Canadian Urban-Agricultural L
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
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡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? Yes. The paper's in-silico typing pipeline (Table 5: predicted O:H serotype + stx subtype, from SPAdes assemblies of the public SRA reads PRJNA287560) is clearly specified and the data are public. SCOPE: pipeline-derived in-silico results only -- in-silico serotype and stx subtype per isolate. Out of scope (the hard ~20%, not attempted): eaeA/intimin allelic variant (bespoke 24-allele scheme, no shipped reference set), Parsnp+ggtree core-genome SNP phylogeny (not a discrete checkable value), and all PCR/wet-lab phenotypic prevalences (non-pipeline). VERDICT: 1:1 on what was tested. Per P16 we reran the SAME data through current self-contained equivalents of the paper's named tools -- ECTyper for O:H (vs SerotypeFinder v1.1) and NCBI stxtyper for stx subtype (vs the paper's Blast+ BLASTn). At finalize time 4 isolates had completed assembly+typing: serotype 4/4 EXACT (O-group+H; ECTyper does not split the O128ab/ac antigen subdivision but the O-group and H agree), stx 3/4 EXACT set match. The single stx divergence (FWSEC0337) is on an isolate the AUTHORS THEMSELVES flagged ambiguous ('Mult.? stx2a, stx2d'); stxtyper, which requires intact operons, made no confident call -- not a clean discrepancy. NO fabrication signal: every reproduced value is directly derivable from the shipped SRA data. COVERAGE IS PARTIAL: the «our HPC» job (2180554) was still assembling the remaining ~41 of 47 isolates when the room was finalized on operator instruction; FWSEC0343 and FWSEC0345 have no Illumina paired-end run in the project ('no_run'). Per-isolate result.json files accumulate under «infra» «path» Engineering notes captured for reuse: compute-node $HOME is read-only (redirect conda pkgs/cache/HOME/tmp to «infra»); 'curl' absent on nodes (use wget); SPAdes 3.15 needs python<3.12 (distutils); conda shell function is not exported into xargs subshells (source conda.sh inside each tool subshell).
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 assessment Score 89assessed: 2026-06-16 ⛓ 4c1c00c062bc
✎ 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: opusWhat is the prevalence, diversity, and phenotypic and genotypic (including virulence) character of Shiga toxin-producing E. coli (STEC) in surface waters and sediments of the Lower Mainland of British Columbia, a densely populated and intensive agricultural region of Canada, to guide future risk assessment of agricultural and non-agricultural water uses?
- ★ STEC are prevalent in surface waters of four Lower Mainland BC watersheds, recovered from 21.6, 23.2, 19.5, and 9.2% of samples across sites, with seasonal variation (13.3% in fall to 34.3% in winter). finding
- ★ Surface waters of the region support highly diverse STEC populations, with 100 distinct isolates spanning 29 definitive plus 4 ambiguous/indeterminate serotypes, including Canadian priority serogroups O157, O26, O103, and O111. finding
- ★ Regional surface-water STEC include strains carrying virulence factors (stx1, stx2, eaeA intimin variants, and other acquired virulence factors) commonly associated with human pathotypes. finding
- ★ STEC were also recovered from sediments (23.8% of sediment samples at one site), indicating sediments as an additional environmental reservoir. finding
- ★ A hydrophobic grid membrane filtration–Shiga toxin immunoblot (Stx-IB) method enables enrichment-free detection and isolation of O157 and non-O157 STEC from water. method
- ★ Whole-genome sequencing of 47 isolates allows detection of stx subtypes, eae allelic variants, in silico serotyping, and core-genome SNP phylogeny relative to clinical reference strains. method
- The work characterizes the microbiological hazard implied by STEC to support future public-health risk assessments of regional water resources. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Hydrophobic grid membrane filtration–Shiga toxin immunoblot (Stx-IB) detection | Surface water and sediment from four BC Lower Mainland watersheds | none | Presence of Shiga toxin (stained spots on Stx-capture membrane) / STEC prevalence | 0.45 μm HGMF filters (Neogen); nitrocellulose Stx-capture membranes with anti-ST antibodies (PHAC NML); mTSA-VC agar |
| Sandwich ELISA for Shiga toxin confirmation | E. coli isolate broth cultures from water/sediment | none | Optical density (450/620 nm) scored vs negative controls to confirm Stx production | SpectraMax M2 Microplate Reader (MTX Lab Systems) |
| Monoplex PCR (gadA) for E. coli confirmation | Presumptive STEC isolates | none | Presence of 373 bp gadA amplicon | TopTaq DNA Polymerase (Qiagen); C1000 Touch Thermal Cycler (BioRad) |
| Serotyping (O and H antigens) | Confirmed E. coli isolates | none | Somatic (O) and flagellar (H) serotype assignment | Reference antisera (SSI Diagnostica) |
| Rep-PCR fingerprinting | E. coli isolates (multiple from same water sample) | none | BOX A1R fingerprint banding patterns to differentiate isolates | BOX A1R primer; Multiplex PCR Master Mix (Qiagen); BioRad thermal cycler |
| Multiplex PCR virulence gene profiling (eaeA, hlyA, stx1, stx2) | E. coli isolates | none | Presence/absence of virulence gene amplicons | TopTaq DNA Polymerase (Qiagen); BioRad thermal cycler |
| Whole-genome sequencing (de novo assembly, annotation, stx/eae subtyping, in silico serotyping, core-genome SNP phylogeny, virulence gene detection) | 47 STEC isolate genomes plus 15 reference genomes | none | stx1/stx2 subtypes, eae variants, in silico serotype, virulence factor presence, SNP-based phylogeny | Illumina MiSeq paired-end 250 bp (NexteraXT); SPAdes v3.1; Prokka v1.10; QUAST v2.3; Parsnp v1.2; BLAST+; SerotypeFinder v1.1 |
- – STEC recovered from surface water across the four watersheds at 21.6, 23.2, 19.5, and 9.2% of samples 21.6, 23.2, 19.5, 9.2%
- – Overall STEC prevalence showed seasonal variation, lowest in fall and highest in winter 13.3% (fall) to 34.3% (winter)
- – STEC recovered from sediment samples at one randomly selected site 23.8%
- – One hundred distinct STEC isolates recovered, distributed among 29 definitive and 4 ambiguous/indeterminate serotypes 100 isolates; 29 + 4 serotypes
- – Priority serogroup isolates recovered: O157, O26, O103, O111 O157 (3), O26 (4), O103 (5), O111 (7)
- – Forty-seven isolates characterized by whole-genome sequence analysis for stx, eaeA variants and acquired virulence factors 47 isolates
- count 100 distinct STEC isolates (Distinct STEC isolates recovered from water and sediments)
- count 29 definitive and 4 ambiguous/indeterminate serotypes (Serotype diversity among recovered isolates)
- count 47 genomes (Isolates characterized by whole genome sequencing)
- other 21.6, 23.2, 19.5, 9.2% (STEC prevalence in surface water samples across the four watersheds)
- other 13.3–34.3% (Seasonal range of overall STEC prevalence (fall to winter))
- other 23.8% (STEC prevalence in sediment samples at one site)
- count O157 (3), O26 (4), O103 (5), O111 (7) (Number of isolates from each Canadian priority serogroup)
- count 21 samples (Sediment samples collected from the Sumas River watershed site in 2012–2013)
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 cross-sectional surveillance study estimated STEC prevalence as point proportions (percentages) across four watersheds over approximately 18 months, with descriptive reporting of seasonal variation. Genomic characterization of 47 isolates used de novo whole-genome assembly, core-genome SNP phylogeny via Parsnp/FastTree2 with Shimodaira-Hasegawa local support values from 1000 resamples, and BLASTn-based virulence-gene queries with fixed identity/coverage thresholds. The statistical analyses section in the provided text is truncated before any inferential methods are described, so the specific tests applied to prevalence comparisons cannot be determined from the supplied text.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Proportion (prevalence) calculation | STEC prevalence per watershed (21.6, 23.2, 19.5, 9.2%) and per season (13.3–34.3%); sediment prevalence (23.8%) | Not individually stated per watershed in available text; 21 sediment samples from one site; statistical section truncated | not stated |
| Shimodaira-Hasegawa approximate likelihood ratio test (local support values, 1000 resamples) | Branch support in core-genome SNP phylogeny (FastTree2/Parsnp) | 47 environmental genomes plus 15 reference genomes | not stated |
| BLASTn with E-value cutoff (1×10−20) and identity/coverage thresholds (≥72% identity over ≥80% of gene length) | stx gene subtyping, eae intimin variant assignment, and virulence factor gene presence/absence across 47 environmental + 15 reference assemblies | 62 genome assemblies | not stated |
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STEC prevalence was reported as point-estimate percentages without any measure of sampling uncertainty↳ Could also: Report exact binomial 95% confidence intervals (e.g., Wilson or Clopper-Pearson) alongside each prevalence estimate — CIs would convey the precision of each estimate given the varying number of samples per watershed and season, making between-group and between-study comparisons more interpretable
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Seasonal variation in prevalence was reported descriptively (ranging from 13.3% in fall to 34.3% in winter); the inferential method for this comparison is not available in the supplied text↳ Could also: A generalized linear mixed model (GLMM) with a binomial family, site or watershed as a random effect, and season and weather covariates (temperature, precipitation) as fixed effects — A GLMM would jointly model environmental predictors while accounting for repeated sampling at the same sites over time, enabling formal inference on season and weather effects beyond descriptive proportion comparisons
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Phylogenetic branch support was quantified using Shimodaira-Hasegawa local support values under an approximate maximum-likelihood framework (FastTree2)↳ Could also: Bayesian phylogenetic inference (e.g., MrBayes or BEAST) with posterior probability branch support — Bayesian posteriors provide an alternative credibility measure under an explicit substitution model; this approach is widely used in STEC genomic epidemiology and can facilitate direct comparison with published STEC phylogenies
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Virulence gene presence/absence was determined with fixed BLASTn identity and coverage thresholds applied to in-house assemblies↳ Could also: Purpose-built tools such as ABRicate, ARIBA, or ResFinder/VirulenceFinder web services with internally validated thresholds and curated allele databases — Dedicated tools apply version-controlled databases and validated cutoffs and produce reproducible outputs that follow community standards increasingly expected in comparative E. coli genomics
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Within-sample isolate diversity was managed by Rep-PCR fingerprinting to select distinct isolates before downstream characterization↳ Could also: SNP-based pairwise distance clustering directly from WGS data to define unique strains within and across samples — WGS-based deduplication applies the same resolution used in the phylogenetic analysis to the isolate-selection step, providing a consistent and more discriminatory criterion for strain uniqueness
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Sediment prevalence (23.8%) and water prevalence were reported separately and descriptively without a formal statistical comparison between matrices↳ Could also: A chi-squared test or Fisher's exact test (given potentially small cell counts) comparing STEC positive rates between water and sediment at the shared site, with a reported odds ratio or risk ratio — A formal test with an effect-size measure would allow readers to evaluate whether the observed prevalence difference between matrices exceeds what is expected from sampling variability alone
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 — pmid-27092297 (STEC surface waters, Front Cell Infect Microbiol 2016)
DOI 10.3389/fcimb.2016.00036 · PMCID PMC4820441 · Data: SRA PRJNA287560 (samples named FWSEC####; Illumina MiSeq 250bp PE).
Pipeline-derived results in the paper (in silico, from WGS)
The paper assembled 47 sequenced STEC isolates (+ references) with SPAdes v3.1, annotated with Prokka v1.10, then ran:
- SerotypeFinder v1.1 (default params) → in silico O:H serotype → Table 5, "Predicted serotype"
- BLASTn (Blast+ v2.2.29) stx subtyping → Table 5, "stx subtype(s)"
- BLASTn eaeA/intimin variant typing → Table 5, "eaeA variant"
- VirulenceFinder DB (Blast+ v2.3.0, ≥72% id) → virulence gene presence
- Parsnp v1.2 core-genome SNP phylogeny (EDL933 ref, -x recomb filter) + ggtree → Fig (tree)
IN SCOPE (attempted — clearly specified, per-isolate checkable)
- In silico serotype (O:H) for the sequenced isolates → compare to Table 5 "Predicted serotype". Tool: ECTyper (self-contained, modern equivalent O:H caller) on SPAdes contigs. [P16: a current third-party O:H typer on the paper's own data is an equally valid reproduction.]
- stx subtype for the sequenced isolates → compare to Table 5 "stx subtype(s)". Tool: NCBI stxtyper (self-contained) on SPAdes contigs.
Ground truth = Table 5 (transcribed → original/table5_groundtruth.tsv). Isolate
NNN-... in Table 5 == SRA FWSEC0NNN; the job resolves FWSEC→SRR via the ENA
run table, so the mapping is data-derived, not hand-entered.
OUT OF SCOPE / not attempted (the hard ~20%, per 80/20 rule)
- eaeA/intimin allelic variant (β1/θ/ε/γ/ζ): a bespoke 24-allele BLASTn scheme, no shipped reference set → not cleanly reproducible. Skipped.
- Parsnp + ggtree phylogeny: exact topology / recombination-filtered SNP tree is not a discrete checkable value and depends on the full reference panel. Skipped.
- Phenotypic/PCR results (stx1/stx2/eaeA/hlyA prevalence over the 100-isolate collection; AMR phenotypes; biochemistry): wet-lab, not pipeline-derived → out of scope.
- Exact tool versions (SerotypeFinder 1.1, Blast+ 2.2.29): era-specific; we use current self-contained equivalents and document the substitution honestly.
Why this is a faithful reproduction
Table 5 is a per-isolate in silico typing table produced by a documented pipeline on public SRA data. Re-deriving O:H and stx subtype from the same reads with current standard tools and comparing call-by-call is a direct, auditable 1:1 test.
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.
On the isolates actually reproduced from the public SRA data, the paper's Table 5 in-silico typing reproduces essentially 1:1 — serotype 4/4 exact and stx subtype 3/4 exact — with no fabrication signal; all values are derivable from PRJNA287560. The two caveats are on our/technical side, not the authors': different (modern, equivalent) tools were used (ECTyper, stxtyper) so endpoints compare only indirectly, and only 4 of 48 isolates finished assembling at finalize. The single stx divergence (FWSEC0337) lands on an isolate the authors themselves flagged ambiguous ('Mult.?'), so it is not a clean discrepancy. Net: a solid, explainable, but coverage-limited reproduction → yellow.
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.