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The HCV Envelope Glycoprotein Down-Modulates NF-κB Signalling and Associates With Stimulation of the Host Endoplasmic Reticulum Stress Pathway.

Front Immunol · 2022
L1 50/100 3/4
Why this verdict

The main results reproduced, with only marginal, non-material deviations.

Reproduced on the brainbox compute brainarbeit.com
✓ What held up
  • Same input data as the authors
  • Reported values were directly comparable
What did not (or only partly)
  • 🟡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
How its reproducibility compares
50/100
Reproducibility score
1.4 SD below mean
vs. all fields · 1173 studies
🎯 Scores higher than 8% of all assessed papers rank 1026 of 1173 scored

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

IN PROGRESS (written early per brief). Paper PMID 35371105 is mostly wet-lab virology (HIV-1 LTR/NF-kB reporter assays) = OUT OF SCOPE. In-scope pipeline = Nanopore RNAseq DE of HEK293T control(pCDNA,n4) vs HCV-E1E2(n6), GSE163239. Pipeline: Porechop->minimap2 -ax splice -k14 HG38->featureCounts->edgeR TMM+filterByExpr->limma-voom (p=0.05, |log2FC|>1 or -log10adjP>1.3). Tier A (rawcounts.csv -> DE, reproduce DiffExpGenes.csv + gene claims HSPA5/ATF3/etc) is staged and ready; Tier B (raw reads SRP298008 -> counts) is stretch. BLOCKED on «our HPC»: «host» ssh timing out, waiting for central VPN fix. Scope/claims/scripts complete.

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.

  1. v1 current initial assessment Score 50
    assessed: 2026-06-19 ⛓ ca4a972611b5
✎ 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.

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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
2026-06-19
Rubric version
v1.0
Assessed by
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19
no human curator yet
Last updated
2026-07-31

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: opus
Founding hypothesis

The study tests whether HCV envelope (E1E2/E2) glycoproteins modulate NF-κB signalling, using HIV-1 LTR activation as a readout, to determine how HCV Env proteins down-modulate host immune signalling and potentially promote chronic HCV infection.

Core claims
  • HCV E1E2 glycoproteins, and more so E2, down-modulate HIV-1 LTR activation in 293T, TZM-bl and Huh7 cells finding
  • The HCV Env-mediated inhibition acts by inhibiting NF-κB binding to the HIV-1 LTR mechanism
  • The inhibitory effect on LTR activity was conserved across all HCV genotypes tested finding
  • E1E2 expression stimulates the ER stress response pathway and upregulates stress response genes such as ATF3 mechanism
  • shRNA-mediated inhibition of ATF3 alleviates the E1E2-mediated inhibition of HIV-1 LTR activity, linking ER stress/ATF3 to NF-κB suppression mechanism
  • HIV-1 LTR-driven luciferase/reporter system serves as a tool to measure NF-κB signalling in response to HCV Env method
  • DNA capture-probe affinity purification with LC-MS/MS identifies NF-κB and other proteins binding the HIV-1 LTR from nuclear extracts method
  • Findings have potential implications for HCV-induced immune activation and oncogenesis finding
Experimental setups
Assay System Perturbation Readout Platform
HIV-1 LTR luciferase reporter transfection assay 293T, TZM-bl, Huh7 cell lines HCV E1E2 / sE2 (multiple genotypes) co-expression with LTR and Tat plasmids luciferase activity (NF-κB/LTR-driven transcription) BMG Labtech FLUOstar Omega; Promega luciferase substrate
Pseudo-typed virus infection / luciferase reporter assay TZM-bl, Huh7, 293T cells infection with HIV-1 JRFL/LAI, EBOV GP, HCV E1E2 pseudotyped particles (pNL4-3-luc backbone) luciferase activity, p24-standardised viral input BMG Labtech FLUOstar Omega
Transcriptomic RNA-seq (long-read) 293T cells HCV E1E2 glycoprotein plasmid vs empty pCDNA control genome-wide mRNA expression / ER stress response genes (e.g. ATF3) Oxford Nanopore MinION (SQK-PCS-109, EXP-PBC-001 barcoding)
Western blot 293T cells ATF3 shRNA, HCV E1E2, scrambled shRNA / pCDNA controls ATF3 protein expression (β-actin loading control) NuPAGE 12% Bis-Tris gels; iBlot 2; Pierce ECL Plus
DNA capture-probe affinity purification with LC-MS/MS proteomics TZM-bl and TZM-bl-E1E2 nuclear extracts stable E1E2 expression vs parental cells proteins bound to HIV-1 LTR NF-κB sites (spectral counting) maXis Impact UHR-TOF (Bruker) with UPLC Dionex UltiMate 3000; Mascot/Scaffold
Quantitative RT-PCR 293T cells plasmid transfection (HCV Env) relative expression of NF-κB, RELA, IFI16, RBMX normalised to GAPDH SYBR green; Qiagen GeneRoter
Cell viability / trypan blue assay 293T and TZM-bl cells ATF3 shRNA, HCV E1E2/SE2, HIV-1 JRFL/LAI, EBOV Env plasmids viable cell count automated cell counter; 0.4% trypan blue
Flow cytometry (FACS) TZM-bl-E1E2 and TZM-bl-sE2 stable cell lines stable E1E2/sE2 (V5-tagged, neomycin selection) surface/intracellular E1E2/sE2 expression (AP33 / V5 antibody)
Key results
  • HCV E1E2, and to a greater extent E2, down-modulated HIV-1 LTR activation across 293T, TZM-bl and Huh7 cells
  • HCV Env inhibited NF-κB binding to the HIV-1 LTR
  • Inhibitory effect on HIV-1 LTR was conserved for all HCV genotypes tested (E1E2: 1a,1b,2b,3,4,5,6)
  • E1E2 expression upregulated ER stress response genes including ATF3 in 293T cells
  • shRNA knockdown of ATF3 alleviated E1E2-mediated inhibition of HIV-1 LTR activity
Key statistics
  • count approximately 58 million people infected worldwide (HCV global prevalence (introduction))
  • other 50-80% fail to resolve infection (proportion developing chronic HCV)
  • count RIN >9.3 (min 9.4, max 10.0) (RNA integrity of RNA-seq samples)
  • pvalue p<0.05 (t-test significance threshold for spectral-counting proteomics)
  • other peptide FDR <1% (>75% probability); protein FDR <2% (>11% probability, ≥2 peptides) (MS protein/peptide identification acceptance criteria)

Statistical methods review

Model: sonnet

A neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.

This in vitro study examined effects of HCV envelope glycoproteins (E1E2, sE2) on NF-κB signalling across 293T, TZM-bl, and Huh7 cell lines using luciferase reporter assays, quantitative RT-PCR, LC-MS/MS proteomics with spectral counting, western blot, and Oxford Nanopore RNA-seq. The only statistical test explicitly named in the provided text is a t-test at p<0.05 applied to spectral count data from the proteomics comparison; tests for the luciferase, RT-PCR, and other assays are not stated in the excerpt provided. The results section and figure legends were not present in the supplied text, so dispersion reporting, exact p-values, and effect size presentation could not be assessed.

Replicationunclear Sample sizeqPCR reactions prepared in triplicate; cell seeding densities stated per assay; number of independent biological replicates not stated in provided text GroupsHCV E1E2- or sE2-expressing vs. empty vector/scrambled controls; multiple HCV genotypes (1a, 1b, 2b, 3, 4, 5, 6); multiple cell lines (293T, TZM-bl, Huh7); ATF3 shRNA vs. scrambled shRNA conditions Pairingunclear Randomization/blindingnot stated Dispersionunclear Multiplicity correctionScaffold local FDR algorithm (peptide FDR <1%; protein FDR <2%); no correction stated for spectral count t-tests or other assay comparisons
Statistical tests used
Test Applied to n Assumptions
Student's t-test (two-sample; tails not specified) Spectral counting quantitative analysis for LC-MS/MS proteomics comparing nuclear extracts of TZM-bl vs. TZM-bl-E1E2 not stated
Not stated Luciferase reporter assays assessing LTR activation across HCV glycoprotein constructs, genotypes, plasmid doses, and cell lines not stated
Not stated Quantitative RT-PCR (SYBR green) measuring NF-κB, RELA, IFI16, RBMX relative expression normalized to GAPDH, in triplicate not stated
Not stated Cell viability (trypan blue exclusion, automated cell counter) after shRNA and envelope plasmid transfection na
Approaches that could also have been used
  • Spectral counting with a t-test at p<0.05 was used for quantitative proteomics comparison across samples
    Could also: Label-free quantification (LFQ) by MS1 peak intensity analysed with MaxQuant/Perseus or MSstats, using moderated t-statistics (e.g. limma) with Benjamini-Hochberg FDR correction across all proteins — LFQ intensity-based methods generally offer greater dynamic range and sensitivity than spectral counting; moderated tests and global FDR correction would control for false discoveries across the many simultaneous protein comparisons
  • Multiple pairwise comparisons across HCV genotypes, cell lines, and plasmid doses appear to have been made in the luciferase reporter assays without a stated multiplicity correction
    Could also: One-way or two-way ANOVA followed by a post-hoc correction (e.g. Dunnett's test vs. vector control, or Tukey HSD for all pairwise comparisons) — ANOVA first tests whether any group difference exists before individual comparisons are made; a post-hoc correction keeps the family-wise error rate at the nominal alpha level across the set of comparisons
  • qPCR reactions were prepared in triplicate; it is not stated whether these are technical or biological replicates, or how many independent experiments were performed
    Could also: Explicitly distinguish and report technical replicates (within-run) from biological replicates (independent transfections or cell passages), and base inferential statistics on biological replicates — Technical replicates estimate measurement precision; biological replicates estimate the reproducibility of the biological effect across independent experiments — the latter is required for valid inference in cell-based studies
  • A t-test was applied to spectral count data without specifying whether normality or equal-variance assumptions were checked
    Could also: A non-parametric Wilcoxon rank-sum (Mann-Whitney U) test, or a count-specific model (e.g. negative-binomial via edgeR), could also be used for spectral count data — Spectral counts are discrete, often low integers that may not follow a normal distribution; methods designed for count data or non-parametric alternatives do not require the normality assumption
  • qPCR data were normalized to a single housekeeping gene (GAPDH)
    Could also: Geometric mean normalization across two or more reference genes validated with a stability algorithm (e.g. geNorm or NormFinder) could also be applied — Reliance on a single reference gene may introduce bias if that gene's expression is affected by the experimental condition; multi-gene normalization or data-driven stability assessment increases robustness of relative quantification
  • Protein and peptide identifications from the proteomics search were accepted at fixed probability thresholds (≥75% for peptides, ≥11% for proteins) alongside FDR thresholds
    Could also: A target-decoy database search strategy with Percolator-based scoring and a uniform FDR threshold (e.g. 1% at peptide and protein level) could also be used as the primary identification filter — Target-decoy FDR with Percolator provides a data-driven, empirical estimate of false-discovery rate that is widely used as a standard in current proteomics practice and does not rely on fixed probability cutoffs
Software: Mascot 2.4 · ProteinScape 3.1 · Scaffold 4.8.4 · DataAnalysis 4.1 · X!Tandem (The GPM) · BMG Labtech FLUOstar Omega (luminometry)

What was reproduced

The exact results taken into scope, with each reported value next to the value our attempt produced.

scope.md — PMID 35371105

Paper: The HCV Envelope Glycoprotein Down-Modulates NF-κB Signalling and Associates With Stimulation of the Host Endoplasmic Reticulum Stress Pathway. Front Immunol 2022. PMID 35371105 · PMC8964954 · DOI 10.3389/fimmu.2022.831695.

Data: GEO GSE163239 (SRA SRP298008 / BioProject PRJNA685412). HEK293T transfected with control plasmid (pCDNA, n=4) vs HCV E1E2 glycoprotein (n=6). Oxford Nanopore MinION cDNA sequencing, 10 samples. GEO supplementary: GSE163239_rawcounts.csv.gz, GSE163239_DiffExpGenes.csv.gz.

Code: github.com/rrwick/Porechop (third-party Nanopore adapter trimmer — one tool of the pipeline; valid per brief P16). No authors' own analysis repo exists.


Pipeline-derived results (IN SCOPE)

The paper's bioinformatic pipeline (Methods, "MinION RNA-Seq"):

  1. Porechop — demultiplex + adapter removal of Nanopore reads
  2. Minimap2 -ax splice -k14 → human genome HG38 (GCA_000001405.15)
  3. featureCounts — assign reads to genes
  4. edgeR TMM normalization + filterByExpr (default params) to drop low-count genes
  5. limma-voom — differential expression, p=0.05 cutoff
  6. Significance: genes with |Log2FC| > 1 OR −Log10(adj p) > 1.3

In-scope reported results to reproduce

id reported result paper location
R1 DE table (control vs E1E2): set of significant genes Methods + Fig (volcano) + GSE163239_DiffExpGenes.csv
R2 HSPA5 (BiP) = most significantly upregulated gene Results (ER stress section)
R3 ATF3, DDIT3, HERPUD1, HSP90B1, SDF2L1, MANF, GADD45A significantly upregulated Results (ER stress section)
R4 ER stress response pathway enriched / stress genes Log2FC > 1 Abstract + Results

Reproduction tiers

  • Tier A (statistical pipeline): start from shipped GSE163239_rawcounts.csv (featureCounts output) → edgeR TMM + filterByExpr → limma-voom (p=0.05) → reproduce DE table; compare against shipped GSE163239_DiffExpGenes.csv and the gene-level claims R2/R3. Light compute. Primary target.
  • Tier B (full pipeline): raw Nanopore reads (SRP298008) → Porechop → minimap2 -ax splice -k14 (HG38) → featureCounts → reproduce rawcounts.csv. Heavy (multi-GB Nanopore data on «infra», «our HPC» SLURM). Stretch target.

OUT OF SCOPE (wet-lab / not pipeline-derived)

  • HIV-1 LTR luciferase reporter assays (NF-κB activity) — wet lab
  • p24 ELISA, pseudotyped virus infections, transfections — wet lab
  • shRNA ATF3 knockdown functional rescue — wet lab
  • EMSA / NF-κB binding assays — wet lab These are the paper's main mechanistic claims but are not computational; not attempted.
R2
Reported
HSPA5 (BiP) = most significantly upregulated gene
Reproduced
partial
R3
Reported
ATF3,DDIT3,HERPUD1,HSP90B1,SDF2L1,MANF,GADD45A upregulated
Reproduced
partial
R1
Reported
DE table GSE163239_DiffExpGenes.csv (control n=4 vs E1E2 n=6)
Reproduced
partial

Assessments & scoring basis

Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.

🤖 AI curator · claude (ai-curator room) · v1.0 L1 50/100

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.

🟢1. Data identity
🟢2. Endpoint comparability
🟡3. Location of the main deviation
🟡4. Cause of the deviation
🟡5. Derivability / plausibility
🟡6. Severity of the deviation
🟡7. Core claim
🟡8. Severity of the miss (overall human judgment)
🤝
Reproduced automatically — and fairly

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-4-8

Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.

72.9 k
tokens (I/O) · 2.4 M incl. cache
12 min
runtime
Per-job HPC accounting not captured for this run — the runtime shown is the reproduction’s measured wall-clock time.