RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells.
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
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
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
Reproduced the paper's core Tuxedo-pipeline (TopHat2 -> Cufflinks -> Cuffdiff) differential-expression analysis of GSE52778's 8-sample untreated-vs-Dex airway smooth muscle RNA-seq subset. 16 of 17 spot-checked Table 1 genes (including the paper's flagship finding, CRISPLD2) reproduce with closely matching fold-change magnitude, direction, and significance (exact or within-tolerance); 1 gene (C13orf15) is absent from this run's reference annotation and could not be evaluated. The paper's headline '316 differentially expressed genes' count reproduces at 290 (~8% lower), the same order of magnitude and consistent with expected drift from reference genome/GTF build and tool-version differences accumulated over the ~12 years since original publication. Noted but not force-resolved: the paper's Table 1 column is labeled 'Ln[Fold Change]' but the reproduced values match Cuffdiff's native log2(fold_change) output almost exactly -- most likely a labeling artifact in the original paper, not a real natural-log transform; flagged transparently rather than silently unit-converted or silently treated as a mismatch. NOT attempted: wet-lab/functional assays (cytokine secretion, siRNA knockdown, qPCR validation) as these are out of pipeline scope by design. The RU record's 'geo:GSE34313' field is a metadata mismatch (belongs to a different, unrelated paper) and was not used. Process note: this run required 3 SLURM job submissions to complete (2 quick bugfix jobs to patch a broken TopHat2/bowtie2 version-string parser and a bad gtf_to_fasta reference symlink in the third-party repo's pipeline scripts, then a 4h TIMEOUT on 7/8 samples' alignment followed by a clean 8h resubmission that finished all remaining work including Cuffdiff), plus a ~32h SLURM backfill queue wait (account-wide std-partition contention, not job-specific) and a ~3.5h transient VPN tunnel outage (operator-2FA-gated, resolved by the owner, cluster-side SLURM job continued running unaffected throughout).
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.
✎ 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
- 2026-08-01
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-08-01no human curator yet
- Last updated
- 2026-08-01
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: opusThe paper asks how glucocorticoids act on the airway smooth muscle (ASM) transcriptome to suppress inflammation in asthma, hypothesizing that comprehensive RNA-Seq profiling of dexamethasone-treated primary human ASM cells will identify novel glucocorticoid-responsive genes that mediate anti-inflammatory effects and contribute to asthma drug response.
- ★ Dexamethasone treatment (1 µM, 18 h) of primary human ASM cells differentially regulates 316 genes, including both known and previously uninvestigated glucocorticoid-responsive genes. finding
- ★ CRISPLD2 is a glucocorticoid-responsive gene in ASM cells, with DEX increasing both its mRNA and protein levels. finding
- ★ CRISPLD2 acts as an inhibitory modulator of the immune response in ASM: siRNA knockdown increases IL1β-induced IL6 and IL8 expression. mechanism
- ★ CRISPLD2 harbors SNPs nominally associated with inhaled corticosteroid resistance and with bronchodilator response in asthma GWAS cohorts, making it an asthma pharmacogenetics candidate gene. finding
- ★ CRISPLD2 is also induced by the proinflammatory cytokine IL1β at both mRNA and protein level, indicating it is immuno-responsive as well as GC-inducible. finding
- RNA-Seq provides a comprehensive, less biased view of the ASM glucocorticoid transcriptome than prior microarray studies, quantifying baseline expression and a wider dynamic range. method
- The 316 DEX-regulated genes are enriched for glycoprotein/extracellular matrix, vasculature and lung development, regulation of cell migration, and extracellular matrix organization categories. finding
- Reanalysis of public microarray datasets GSE34313 and GSE13168 independently supports significant CRISPLD2 induction by glucocorticoids in ASM cells. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-Seq transcriptome profiling | Primary human airway smooth muscle cells from four white male donors | 1 µM dexamethasone vs. control vehicle, 18 h | Gene/transcript expression (FPKM), differential expression with Benjamini-Hochberg FDR | Illumina iGenomes hg19 reference; alignment plus Cufflinks quantification; ERCC spike-ins used for QC |
| Quantitative real-time RT-PCR (qRT-PCR) | Primary human ASM cell lines (three, plus a fourth donor from the RNA-Seq set) | 1 µM DEX, 18 h | mRNA fold change of DUSP1, FKBP5, KLF15, PER1, TSC22D3 and of CRISPLD2, C13orf15, KCTD12, SERPINA3, PTX3 | — |
| qRT-PCR | Most GC-sensitive primary human ASM cell line | DEX (100 nM range per Figure 3 legend) treatment | CRISPLD2 mRNA fold induction | — |
| Western blot / immuno-blotting with densitometry | Primary human ASM cells | DEX treatment, 24 h | CRISPLD2 protein level, normalized densitometry | — |
| qRT-PCR and immuno-blotting | Single primary human ASM cell line | IL1β 5 ng/mL, 24 h | CRISPLD2 mRNA and protein levels | — |
| siRNA knockdown with qRT-PCR and immuno-blot readout | Single primary human ASM cell line | CRISPLD2-specific siRNA vs. non-targeting siRNA, 72 h, then 24 h of 5 ng/mL IL1β, 100 nM DEX, or both | CRISPLD2 knockdown efficiency and IL1β-induced IL6 and IL8 mRNA expression | — |
| qRT-PCR (time- and dose-response; cell-type specificity control) | Single-donor primary ASM cells and A549 pulmonary epithelial carcinoma cells | DEX across time points and doses | CRISPLD2 mRNA expression | — |
| Genome-wide association study reanalysis (ICS resistance and bronchodilator response), plus replication | Human asthma clinical trial cohorts; replication in 552 white subjects from the Severe Asthma Research Program (SARP) | Inhaled corticosteroid therapy 4–8 weeks; albuterol administration | SNP association P-values for variants within genes ±50 kb; change in lung function/FEV1 | — |
- – 316 genes were significantly differentially expressed in ASM cells after DEX treatment (BH-adjusted p<0.05) 316 genes
- ▲ CRISPLD2 was significantly induced by DEX in RNA-Seq (7.89 to 51.17 mean FPKM) Ln[fold change] = 2.70
- ▲ DEX increased CRISPLD2 mRNA in the most GC-sensitive ASM line 8.1-fold
- ▲ DEX increased CRISPLD2 protein in ASM cells at 24 hours 1.7-fold
- ▲ IL1β (5 ng/mL, 24 h) increased CRISPLD2 mRNA and protein 10.4-fold mRNA; 1.9-fold protein
- ▼ CRISPLD2-specific siRNA reduced CRISPLD2 mRNA and protein 74% mRNA decrease; 60% protein decrease
- ▲ IL1β induced significantly higher IL6 expression in CRISPLD2-knockdown cells than in NT siRNA controls; IL8 induction by IL1β was likewise enhanced, while baseline IL6 was unchanged by knockdown alone
- ▼ DEX decreased CRISPLD2 mRNA in A549 pulmonary epithelial carcinoma cells, opposite to the induction seen in ASM
- count 316 differentially expressed genes (Benjamini-Hochberg adjusted p-value <0.05) (RNA-Seq of DEX- vs. vehicle-treated ASM cells from four donors)
- fold_change Ln[Fold Change] 2.70; mean FPKM 7.89 (control) vs. 51.17 (DEX); P<6.7E-16, Q = 6.9E-13 (CRISPLD2 differential expression in RNA-Seq)
- fold_change 8.1-fold mRNA increase; 1.7-fold protein increase at 24 h (CRISPLD2 induction by DEX in the most GC-sensitive ASM line)
- fold_change 10.4-fold mRNA increase; 1.9-fold protein increase (CRISPLD2 induction by IL1β (5 ng/mL, 24 h) in a single ASM line)
- pvalue rs8047416 primary P = 4.5E-04 (listed as 4.4E-04 in Table 2), SARP replication P = 0.038, overall P = 9.0E-05 (CRISPLD2 SNP association with bronchodilator response, replication in SARP white subjects)
- pvalue ICS resistance: rs67343076 P = 3.3E-04; rs7188498 P = 5.1E-04; rs9928433 P = 5.8E-04; rs8061778 P = 7.5E-04; rs7189551 P = 7.8E-04 (CRISPLD2 SNPs nominally associated with inhaled corticosteroid resistance)
- fold_change GSE34313: 1.85 (4 hr DEX, adj. P = 5.1E-05) and 1.95 (24 hr DEX, adj. P = 8.3E-06); GSE13168: 6.09 (fluticasone vs. basal, adj. P = 3.9E-07), 4.33 (fluticasone+EGF vs. EGF), 2.78 (fluticasone+IL1β vs. IL1β), 2.13 (fluticasone+EGF+IL1β) (CRISPLD2 differential expression in two prior public microarray studies)
- mean 58.9 million raw reads per sample (range 44.2–71.3M); 83.36% aligned to hg19 (range 81.94%–84.34%); 26.43% of mapped reads spanned junctions; >98% of bases corresponded to mRNA (RNA-Seq sequencing and alignment quality metrics across samples)
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 combines a genome-wide RNA-Seq differential expression analysis of four dexamethasone-treated versus vehicle-treated primary airway smooth muscle (ASM) cell lines (transcripts quantified with Cufflinks; genes called significant using a Benjamini-Hochberg FDR-corrected p-value <0.05) with qRT-PCR/Western blot validation experiments analyzed by t-tests, and follow-up look-ups of SNP-phenotype associations from previously conducted GWAS datasets and reanalysis of two public microarray datasets. Results are reported mainly as fold-change with SEM error bars and significance thresholds (*, **) in the validation figures, alongside exact or bounded p-values and q-values in the RNA-Seq and GWAS tables.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| t-test (paired/unpaired not specified) | qRT-PCR/Western blot validation of DEX-, IL1β-, and siRNA-induced fold-changes in individual genes/proteins (Figures 1B, 2, 3, 4) | Triplicate technical replicates per experiment; 3-4 donor ASM cell lines for Figures 1B/S2 and 2, a single donor cell line with triplicate/repeated independent experiments for Figures 3 and 4 | not stated |
| Differential expression test underlying Cufflinks-based quantification (specific statistical model not named), with Benjamini-Hochberg FDR correction | Genome-wide comparison of DEX- vs vehicle-treated ASM transcriptomes (Figure 1A, Table S3, Table 1) | 4 primary ASM donor cell lines | not stated |
| SNP-phenotype association test (specific model not named; results combined across cohorts as an 'overall P-value') | Association of SNPs in differentially expressed genes with inhaled corticosteroid resistance and bronchodilator response (Table 2), including SARP replication of rs8047416 | Previously conducted GWAS cohorts (sizes not given in this text); 552 white subjects for the SARP replication | not stated |
| Differential expression test (specific model not named) applied in reanalysis of public microarray data | CRISPLD2 expression across comparisons in datasets GSE34313 and GSE13168 (Table 3) | — | not stated |
-
RNA-Seq differential expression significance was assessed via Cufflinks-based (FPKM) quantification with Benjamini-Hochberg FDR correction, without stating the underlying statistical model for calling differential expression.↳ Could also: Count-based differential expression tools such as DESeq2 or edgeR, which use negative binomial generalized linear models — These are also widely used for RNA-Seq differential expression and can offer a different approach to variance/dispersion estimation across biological replicates compared to FPKM-based methods.
-
Validation of individual genes across multiple donors and conditions (Figures 1B, 2, 3, 4) used separate t-tests for each gene/comparison without a stated multiplicity correction across the panel tested.↳ Could also: One-way or two-way ANOVA with a post-hoc multiple-comparison correction (e.g., Tukey HSD, Holm-Sidak) — This would also control the family-wise error rate when several genes or treatment conditions are compared within the same figure or experiment.
-
Variability in qRT-PCR and protein data is presented using SEM (error bars described as SE values).↳ Could also: Reporting SD or a 95% confidence interval — Either would also convey the spread of the underlying replicate data, which some readers find more directly interpretable than SEM, particularly given the small sample sizes (3-4 donors or triplicate replicates) involved.
-
Several functional follow-up experiments (Figures 3 and 4) used a single ASM cell line with triplicate technical replicates or 'three independent experiments.'↳ Could also: Extending these assays across additional independent donor cell lines — Additional biological replicates would also allow donor-to-donor variability to be estimated and incorporated into the statistical comparison, complementing the technical-replicate-based approach used here.
-
GWAS SNP-phenotype associations for genes in Table 1 were reported using a nominal significance threshold (P<1E-03) without an explicit multiple-testing correction stated for the number of SNPs and genes examined.↳ Could also: A permutation-based, Bonferroni, or FDR correction applied across the tested SNP set — This would also provide a formal control of the false-positive rate when screening many SNPs across multiple candidate genes, complementing the nominal-threshold screening approach used here.
-
DEX-treated vs vehicle-treated (and IL1β-treated vs untreated) comparisons are drawn from the same donor cell lines, but the t-tests used are not described as paired.↳ Could also: An explicitly paired t-test or a repeated-measures/mixed model treating donor as a random effect — This would also make direct use of the within-donor pairing structure, which can increase statistical power and allows donor-level variability to be modeled explicitly.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
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.
This is a strong, near-1:1 reproduction from raw public data: the authors' own Tuxedo pipeline was re-run end-to-end on the 8 untreated-vs-Dex GSE52778 samples, and 16 of 17 spot-checked Table 1 genes match in magnitude, direction and significance — including the flagship CRISPLD2 (reported 2.7 vs reproduced log2FC 2.72142, q=2.54e-13). The two blemishes are on opposite sides and both minor: the paper mislabels its Table 1 column 'Ln[Fold Change]' when the values are plainly log2 (authors' side, cosmetic/units), and C13orf15 is missing from the modern GTF plus the DE-gene count comes in at 290 vs the reported 316 (~8.2% lower) (technical/annotation drift over ~12 years, neither side's fault). Nothing here touches the scientific conclusion — the glucocorticoid-response signature (FKBP5, TSC22D3, PER1, KLF15) and the CRISPLD2 finding hold cleanly. Only q2 and q3 are yellow, reflecting the labeling ambiguity and the input-side annotation drift rather than any defect in the reported results.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.