Profiling chromatin accessibility responses in human neutrophils with sensitive pathogen detection.
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.
- ✓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
PARTIAL, honest. The brief's accession GSE153521 is the RNA-seq SubSeries (ATAC = GSE153520; SuperSeries GSE153522); the brief's code link (FelixKrueger/TrimGalore) is a text-mining artifact (one cited tool) — the authors' actual repo is nikhilram/neutrophil_ATACseq (ATAC-only: PEPATAC + DiffBind). RNA-seq reproduction (P16-style: described edgeR pipeline on the deposited raw-counts matrix, 25,702 genes x 8 libraries) was run directly with edgeR 4.4.2/R 4.4.2 on «our HPC» front1. Canonical config full_LRT_TMM (donor-blocked GLM, TMM, glmLRT, filterByExpr, FDR<0.05 & |logFC|>=1). RESULT: UP-regulation reproduces well — E.coli-4h up 2551 vs reported 2554 (essentially exact), E.coli-1h up 69 vs 66, total-4h DE within ~6% — but DOWN-regulation is systematically far below reported (1h 3 vs 55; 4h 2338 vs 2656; consistent-down 1 vs 10). A 24-config sweep (norm none/TMM/UQ/RLE x exact/QLF/LRT/Treat x pairwise/paired/full-donor designs) confirms no principled config reproduces both up and down counts simultaneously; only norm='none' lifts the down counts but then grossly overshoots 4h-down (~6500) and breaks the up side. Flagged for audit: the reported RNA-seq down counts are not cleanly derivable from the deposited data with standard edgeR (possible unstated normalization/filter, version effect, or reporting inconsistency). NOT attempted: (a) ATAC-seq DiffBind DAR table — deposited ATAC processed files are single-column merged coverage per condition, lacking the per-replicate counts/BAMs the authors' run_diffbind.R needs; reproducing it requires full PEPATAC re-alignment from SRA FASTQ (SRP265675, ~50 libraries) which is currently impossible because the shared «our HPC» account is OVER QUOTA on /home and /«infra» and the «infra» scheduler rejects job output on /usw (no SLURM job submittable); (b) Kraken pathogen detection — no script shipped, needs raw reads + DB. Datasets profiled in-pass: GSE153521 RNA-seq counts (grade A, delivers yes), GSE153520 ATAC coverage (grade C, delivers partial, n mismatch 13 deposited vs ~50 samples).
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Assessment versions
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v1 current initial assessment Score 54assessed: 2026-06-18 ⛓ 40f03efef3b7
✎ I am an author of this paper
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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
- 2026-06-18
- 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: opusBecause the epigenome reacts before gene expression, the authors test whether profiling chromatin accessibility (ATAC-seq) responses in human neutrophils to different pathogen ligands and whole organisms can reveal challenge-specific and time-specific epigenomic signatures for early disease recognition, and how chromatin accessibility changes regulate downstream transcription.
- ★ ATAC-seq reveals unique neutrophil chromatin accessibility changes in response to different stimuli before transcriptional activation, with most differential regions being challenge-specific in position, function, and motif. finding
- ★ ATAC-seq of neutrophils enriches pathogen DNA, enhancing sensitive detection of microbial reads compared with traditional library preparation. finding
- ★ Neutrophil epigenomic changes are plastic over time, with only ~120 differential regions shared between E. coli challenges at 1 and 4 h, producing varied differential genes and associated processes. finding
- ★ Three classes of gene regulation are identified: chromatin access changes in the promoter; changes in the promoter and distal enhancers; and control of expression solely through distal enhancer changes. mechanism
- ★ Coupling neutrophil ATAC-seq host-response profiling with enriched microbial read detection in a single assay offers diagnostic potential for sepsis/bloodstream infections. method
- Transcription factor footprinting plus positional and functional analysis reveal timely and challenge-specific mechanisms of transcriptional regulation in neutrophils. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| ATAC-seq | Purified human neutrophils from healthy female volunteers | 1 h challenge with TLR ligands (LTA/TLR2, LPS/TLR4, flagellin/TLR5, R848/TLR7-8, β-glucan peptide/dectin-1, HMGB1/DAMP) | Genome-wide chromatin accessibility / differential accessible regions | Tn5 transposase, Illumina sequencing |
| ATAC-seq | Human neutrophils (and whole blood for S. aureus) | Whole organism challenge with S. aureus and E. coli (1 h and 4 h) | Differential chromatin accessibility and pathogen DNA reads | Tn5 transposase, Illumina sequencing |
| RNA-seq | Human neutrophils | E. coli exposure for 1 h (EC1h) and 4 h (EC4h) | Differential gene expression (logFC) | — |
| Genome-wide DNA sequencing (SPRI library prep comparison) | Whole blood with negatively isolated neutrophils | Live S. aureus spike at incremental CFU/ml for 1 h | Relative abundance of pathogen reads vs ATAC-seq | Solid-phase reversible immobilization (SPRI) library preparation |
| qRT-PCR | Healthy donor human neutrophils | Ligand or live organism challenge | IL8 and TNFα expression (neutrophil activation confirmation) | — |
| SYTOX green assay | Healthy volunteer human neutrophils | Pathogen ligands (1 h) or live organism; PMA positive control | Extracellular DNA as indication of NET formation | — |
- – EC1h showed the most differential regions (5,010), with E. coli challenges producing strongly time-specific accessibility changes (EC4h: 1,688 DRs) 5,010 (EC1h) vs 1,688 (EC4h) DRs
- – Majority of differential regions are unique/challenge-specific; ~69.37% unique for ligand challenges and ~91% unique for whole organism challenges, with no DRs shared across all challenges ~69.37% (ligands), ~91% (whole organisms) unique
- ▲ ATAC-seq retained higher relative abundance of S. aureus reads than SPRI at all concentrations; abundance at 10^3 CFU/ml by ATAC-seq comparable to 10^5 CFU/ml by SPRI; ~3x more pathogen reads 3-fold; 10^3 vs 10^5 CFU/ml equivalence
- – Only 118 (~120) differential regions shared between the two E. coli time points, indicating epigenomic plasticity 118 shared regions
- – RNA-seq showed marked temporal increase in differentially expressed genes: EC1h had 66 up/55 down, EC4h had 2,554 up/2,656 down regulated genes EC1h: 66 up/55 down; EC4h: 2,554 up/2,656 down
- – More than 40% of differential regions in each challenge located in distal intergenic or intronic regions, with similar genomic distribution across challenges >40% distal/intronic; >80% distal
- – On average ~95.8% (minimum ~89.2%) of differential regions were successfully associated with genes across challenges ~95.8% average, ~89.2% minimum
- – No NETs observed at 1 or 4 h of stimulation by SYTOX green assay, supporting nuclear integrity for ATAC-seq
- correlation r2 = 0.70–0.95 (Genome-wide peak count correlation across ATAC-seq technical replicates per ligand)
- correlation r2 ranging from 0.92 to 0.99 (RNA-seq correlation between replicates)
- count 5,010 DRs (4,625 unique, 92%) (Differential regions for EC1h challenge vs unstimulated)
- count 2,241 DRs (2,121 unique, 95%) (Differential regions for S. aureus challenge)
- count 118 common DRs (Shared differential regions between EC1h and EC4h E. coli time points)
- count 2,554 up- and 2,656 down-regulated genes (Differentially expressed genes at EC4h by RNA-seq)
- fold_change 3 times more reads (ATAC-seq vs SPRI for S. aureus pathogen reads)
- count 4,506 / 4,498 overlaps (EC1h differential regions overlapping each end of Hi-C interacting regions)
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.
Human neutrophils from healthy volunteers (n=4 for ligand/DAMP challenges, n=2 for whole-organism challenges) were profiled by ATAC-seq across nine conditions versus paired unstimulated controls; differential chromatin accessibility was identified using DiffBind (P<0.05, |logFC|≥1). RNA-seq from E. coli–challenged neutrophils at two time points was analyzed with edgeR (P<0.05). Motif enrichment in differential regions was assessed with HOMER (P<10⁻¹⁰), and functional annotation used ChIPseeker and clusterProfiler; results were reported primarily as region counts, logFC values, UpSet-plot overlaps, and pathway enrichment comparisons.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DiffBind (differential accessibility analysis; internally uses DESeq2 or edgeR on count data) | Identification of differentially accessible chromatin regions (DRs) for each of nine challenges vs. unstimulated control (ATAC-seq) | n=4 donors for ligand/DAMP challenges; n=2 donors for whole-organism challenges | not stated |
| edgeR (negative binomial GLM) | Differential gene expression at E. coli 1 h and 4 h vs. unstimulated control (RNA-seq) | not stated explicitly | not stated |
| Pearson correlation (r²) | Quality control of ATAC-seq technical replicates per donor and RNA-seq replicate concordance | not stated | not stated |
| HOMER motif enrichment (hypergeometric/binomial) | Transcription factor motif enrichment in induced and repressed differential ATAC-seq regions per challenge | null | not stated |
| clusterProfiler compareCluster (hypergeometric/Fisher's exact) | Reactome pathway enrichment of genes associated with differential chromatin regions across all challenges | null | not stated |
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Nine challenges were each compared independently to unstimulated controls in separate DiffBind analyses↳ Could also: A single multi-condition model (e.g., DESeq2 or edgeR with a multi-level factor and explicit contrasts, or limma-voom on count matrices) could analyze all conditions jointly with a shared dispersion estimate — Pooling information across groups in a unified model improves dispersion estimation, which can increase sensitivity particularly when per-group n is small (n=2–4); it also provides a natural framework for controlled pairwise contrasts with consistent error variance
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Differential chromatin regions and differential genes were filtered at nominal P<0.05 thresholds; no explicit within-test FDR correction is described↳ Could also: Applying Benjamini-Hochberg FDR correction within each DiffBind and edgeR analysis and reporting adjusted q-values (e.g., FDR<0.05 or FDR<0.10) is standard for genome-wide omics analyses — FDR-adjusted thresholds are the conventional standard for ATAC-seq and RNA-seq to control the expected proportion of false discoveries across thousands of simultaneous region- or gene-level tests
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Dispersion for cytokine and SYTOX assays is reported as mean ± SE at n=2–4↳ Could also: Mean ± SD, or individual data points overlaid on bar/line plots, could also convey spread; 95% CIs would additionally communicate estimation uncertainty — At small n, SD reflects actual biological variability rather than precision of the mean estimate; showing individual donor values is increasingly recommended by journals for small-n biological assays so readers can assess the underlying distribution
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Overlap between challenge-specific differential regions was visualized with UpSet plots restricted to the top 100 regions↳ Could also: Jaccard similarity indices or permutation-based overlap significance testing (e.g., regioneR) could also quantify the degree of sharing between any two conditions across the full region set — Quantitative overlap statistics complement visual UpSet plots by providing a normalized similarity measure and a null-distribution-based p-value for whether observed overlaps exceed chance expectation genome-wide
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RNA-seq differential expression was performed with edgeR↳ Could also: DESeq2 with its regularized log-fold-change shrinkage (lfcShrink) and Benjamini-Hochberg adjusted p-values is a widely used alternative for small-n RNA-seq experiments — DESeq2's shrinkage estimators stabilize logFC estimates for low-count genes, which can be frequent in neutrophils given their overall lower transcriptional activity; both tools are considered standard and results are often compared or cross-validated
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No a priori power analysis or sample size justification is described, with n=2 for whole-organism conditions↳ Could also: A formal power simulation based on pilot effect size estimates, or a post-hoc sensitivity analysis reporting the minimum detectable effect at the achieved n and significance threshold, could also be included — Reporting a power calculation or detectable-effect statement helps readers contextualize the biological interpretability of non-significant or variable results, especially for the n=2 whole-organism comparisons
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34145026
Paper: Ram-Mohan et al. 2021, Life Sci Alliance — "Integrative profiling of early host chromatin accessibility responses in human neutrophils with sensitive pathogen detection." PMID 34145026 · PMCID PMC8321655 · DOI 10.26508/lsa.202000976.
Accessions (brief's GSE153521 is the RNA-seq sub-series)
- GSE153522 — SuperSeries (umbrella).
- GSE153520 — ATAC-seq sub-series (peak-coverage BEDs deposited; raw FASTQ in SRP265675).
- GSE153521 — RNA-seq sub-series (raw-counts matrix deposited; raw FASTQ in SRP269266). This is the accession named in the brief.
Code
- Brief lists
github.com/FelixKrueger/TrimGalore— that is just one cited tool (RNA-seq adapter trimming), a text-mining artifact, not the authors' repo. - Authors' own repo: github.com/nikhilram/neutrophil_ATACseq — ships
run_pepatac.sh,run_diffbind.R, and 4 Perl post-processing scripts. It is an ATAC-only repo: no RNA-seq edgeR script, no Kraken script.
Pipelines named in Methods
- ATAC-seq: PEPATAC (Trimmomatic → Bowtie2
--very-sensitive -X 2000hg19 → Picard dedup → SAMtools MAPQ<10, drop chrM/chrY → MACS2-q 0.01 --shift --nomodel) → DiffBind (dba.report th=0.05 bUsePval=TRUE fold=1, 0.66 consensus overlap) → ChIPseeker / HOMER (P<1e-10) / TOBIAS footprinting. - RNA-seq: FastQC → Trim Galore → HISAT2
--rna-strandness RFhg19 → Rsubread featureCounts (strand-specific) → edgeR (FDR<0.05, |logFC|≥1) → clusterProfiler. - Pathogen detection: Kraken on human-depleted reads, abundance as counts-per-million.
In scope (pipeline-derived, reproducible from deposited processed data)
- RNA-seq differential expression (edgeR) — PRIMARY. The deposited
GSE153521_Raw_counts_for_each_replicate.txt.gzis the exact featureCounts matrix (25,702 genes × 8 libraries: 2 reps × {EC-1h, EC-4h, noEC-1h, noEC-4h}). Re-running edgeR (FDR<0.05, |logFC|≥1) reproduces the DEG counts (paper Fig 5 / text): EC-1h 66 up / 55 down; EC-4h 2554 up / 2656 down; consistent across both time-points 93 up / 10 down. Deterministic, low-compute.
Out of scope or harder
- ATAC-seq DiffBind DAR counts (LTA 1331, LPS 1729, FLAG 2963, R848 3105,
β-glucan 2030, HMGB1 2930, S.aureus 2241, EC-1h 5010, EC-4h 1688 — Table/Fig 2).
The authors'
run_diffbind.Rneeds per-replicate BAMs + per-replicate peaks; GEO deposits only single-column merged-coverage BEDs per condition (per-replicate resolution lost). Exact DAR reproduction therefore requires re-running full PEPATAC from raw FASTQ (≈50 ATAC libraries, hg19 alignment) — heavy; attempted only if the compute path opens. - Kraken pathogen detection (S. aureus 3× higher abundance vs SPRI; 100-fold sensitivity gain) — no script shipped; needs raw reads + a Kraken DB. Harder.
- Motif/footprint/Hi-C association numbers — HOMER/TOBIAS/GREAT; downstream, not attempted in the first pass.
Infrastructure note
Shared «our HPC» account («user») is over quota on /home AND «infra», front1 /tmp
is full, and the «infra» submit filter forbids /usw for job stdout → no SLURM job can
be submitted at present. Workaround for the tiny RNA edgeR step: run R 4.4.2 (from
the existing wp9_rnavar env) directly on front1, edgeR compiled into a /usw
personal library. Heavy ATAC/Kraken work stays blocked until the account quota frees.
Assessments & scoring basis
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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.