RNA-sequence analysis of primary alveolar macrophages after in vitro infection with porcine reproductive and respiratory syndrome virus strains of differing vir
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 result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
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 reproduction. Upstream pipeline steps (sickle trimming, TopHat2 alignment, Cufflinks/Cuffmerge/Cuffcompare assembly) reproduce within/near the paper's reported ranges for 7 of 8 samples, with the 8th (SRR1205844) a well-explained outlier (extreme viral load). Condition labels (Mock/LV/Lena) are not present in SRA metadata; they were inferred via viral-reference alignment, cleanly separating 3 LV + 3 Lena + 2 Mock samples -- which explains the paper's stated 9 libraries vs 8 SRA runs as a missing 3rd Mock replicate. Downstream differential-expression results (edgeR gene-level, Cuffdiff isoform/TSS/promoter-level) show the correct DIRECTION and RANKING (LV/Lena vs Mock >> LV vs Lena) but substantially lower significant-hit COUNTS than reported: edgeR DE genes are ~50-100x lower (6/2/1 vs 446/153/241); Cuffdiff isoform and TSS counts are ~30-65% of reported magnitude; Cuffdiff promoter-switching is nearly null (0/0/1 vs 14/6/7). Diagnostics (BCV=0.25, reasonable nominal p<0.05 hit counts before FDR correction, no MDS structure suggesting recoverable pig identity) indicate this is a genuine, well-substantiated STATISTICAL POWER shortfall -- not a pipeline execution failure -- stemming from two structural SRA-metadata gaps that cannot be recovered from the public deposition: (1) the original paired per-animal (3 pigs x 3 conditions) design removes inter-animal variance via blocking, which is unrecoverable without pig-identity labels, forcing an unpaired analysis here; (2) the Mock group has only 2 of the presumed 3 replicates. Wet-lab or purely descriptive results (e.g. clinical/histopathology findings) were out of scope and not attempted. All pipeline mechanics were verified sound at each stage (mapping rates, cuffcompare Sn/Sp>=92%, HTSeq counts, edgeR dispersion diagnostics) before attributing the DE-count gap to study design/power rather than execution.
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When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-08-03
- Rubric version
- not recorded
- Assessed by
- —
- Last updated
- 2026-08-03
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 study asks how primary porcine alveolar macrophages (PAMs) transcriptionally respond in vitro to European PRRSV strains of differing virulence, testing whether the low-virulence Lelystad (LV, subtype 1) and high-virulence Lena (subtype 3) strains induce distinct gene, isoform, transcription start site and promoter usage programs at 12 h post-infection.
- ★ Infection of PAMs with either LV or Lena affects signaling pathways directly linked to the innate immune response, including IRF activation, RIG1-like receptors, TLRs and PKR pathways. finding
- ★ Interferon signaling is the pathway most strongly modulated during PAM infection and is crucial for transcriptional regulation upon PRRSV infection. finding
- ★ IFN-β1 and IFN-αω, but not IFN-α, are up-regulated following infection with either the LV or Lena strain. finding
- ★ Canonical pathways including the interplay between innate and adaptive immune responses, cell death, and TLR3/TLR7 signaling are down-regulated by both strains, with Lena triggering stronger down-regulation than LV. finding
- ★ PRRSV infection of PAMs produces a complex pattern of transcriptional and post-transcriptional regulation detectable as changes in gene expression, isoforms, alternative transcription start sites and differential promoter usage. finding
- RNA-Seq applied to primary PAMs allows simultaneous characterization of gene expression, splice variants, TSSs and differential promoter usage, unlike the microarray studies used previously in this system. method
- The RNA-Seq dataset from LV-, Lena- and mock-infected PAMs is deposited in the NCBI Sequence Read Archive (SRX352447) as a community resource. resource
- The 'Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses' pathway response involved IRF7 but not IRF3. mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-Seq (2×100 bp paired-end) | Primary pulmonary alveolar macrophages from three 3-week-old piglets (Landrace x Large White sows × Pietrain boars), cultured in vitro | Infection at MOI 0.5 with PRRSV LV strain or Lena strain vs. mock inoculation; cells collected 12 h post-infection | Transcript abundance (counts/FPKM) for genes, isoforms, transcription start sites and promoter usage | Illumina HiSeq 2000; TruSeq Sample Prep Kits (Illumina) |
| Immunoperoxidase staining (viral antigen detection) | PRRSV-infected primary PAM cultures (duplicate of each infection/control) | LV or Lena infection at MOI 0.5; fixed in acetone-100% methanol at −20°C at 12 h post-infection | Percentage of viral antigen-positive cells (3 microscopic fields, minimum 200 cells per field) | Monoclonal anti-nucleocapsid antibody P3/27, HRP-labeled goat anti-mouse secondary, 3-amino-9-ethylcarbazole substrate; Olympus light microscope |
| Quantitative PCR | LV- and Lena-infected primary PAMs | LV or Lena infection | Threshold cycle (Ct) values confirming infection | — |
| Immunoperoxidase monolayer assay (IPMA) | 3-week-old piglets from a PRRS-negative herd | none (screening) | PRRSV-negative status of donor piglets | — |
| PCR | 3-week-old piglets from a PRRS-negative herd | none (screening) | PCV2-negative status of donor piglets | — |
| Microcapillary electrophoresis (RNA quality control) | Total RNA from LV-, Lena- and mock-infected PAMs | none | RNA integrity/quality | Agilent 2001 Bioanalyzer with RNA 6000 Nanochips |
| Spectrophotometric RNA quantification | Total RNA from PAMs (TRIzol + RNeasy column extraction) | none | RNA concentration | NanoDrop ND-1000 |
| Bioinformatic differential expression and pathway analysis | Nine cDNA libraries (3 LV, 3 Lena, 3 mock) mapped to Sus scrofa genome Sscrofa10.2.71 | Pairwise comparisons LV vs mock, Lena vs mock, LV vs Lena (paired design by pig) | Differentially expressed genes/isoforms/TSSs/promoters (FDR<0.05, FC≥1.5) and enriched canonical pathways | FASTQC, Sickle, TopHat v2.0.8, Cufflinks v2.1.1/Cuffmerge/Cuffcompare/Cuffdiff, HTSeq-count, edgeR, Ingenuity Pathway Analysis (IPA) |
- – 446 genes were differentially expressed between LV-infected and mock-infected PAMs; 153 between Lena and mock; 241 between LV and Lena. 446 / 153 / 241 genes
- ▲ 'Interferon Signaling' was the top canonical pathway in LV vs mock and included 12 up-regulated genes (IFIT1, IFIT3, IFITM1, IFNβ1, IRF1, JAK2, MX1, OAS1, PSMB8, SOCS1, STAT1, STAT2). −log(p-value) = 1.08E+01; ratio = 3.87E-01; 12 genes
- ▲ 14 up-regulated genes (ADAR, DDX58, DHX58, IFIH1, IFIT2, IFNβ1, IL10, IRF7, ISG15, NFKBIA, STAT1, STAT2, TNF, ZBP1) were involved in the 'Activation of IRF by Cytosolic Pattern Recognition Receptors' pathway in LV vs mock. −log(p-value) = 9.97E+00; ratio = 2.64E-01; 14 genes
- ▲ IFN-β1 and IFN-αω were up-regulated after infection with either LV or Lena, whereas IFN-α was not.
- ▼ Pathways covering innate–adaptive immune interplay, cell death and TLR3/TLR7 signaling were down-regulated by both strains, more strongly by Lena than by LV.
- – Differentially affected isoforms, TSSs and promoters differed between comparisons: isoforms 187 (LV vs mock), 72 (Lena vs mock), 34 (LV vs Lena); TSSs 240 / 93 / 50; promoters 14 / 6 / 7. 187/72/34 isoforms; 240/93/50 TSSs; 14/6/7 promoters
- – The percentage of PAMs infected was 21% for LV and 16% for Lena on average, corroborated by qPCR Ct values. 21% vs 16%
- ▲ The 'Role of PKR in Interferon Induction and Antiviral Response' and 'Retinoic Acid Mediated Apoptosis Signaling' pathways were up-regulated and shared four genes (CASP8, BID, IFNβ1, IRF1). −log(p-value) = 7.03E+00; ratio = 2.5E-01 (PKR pathway)
- count 446 differentially expressed genes (LV vs mock), 153 (Lena vs mock), 241 (LV vs Lena) (Significance threshold FDR<0.05 and fold change ≥1.5)
- pvalue −log(p-value) = 1.08E+01, ratio 3.87E-01 (Interferon Signaling, top IPA canonical pathway, LV vs mock (right-tailed Fisher's Exact Test))
- pvalue −log(p-value) = 9.97E+00, ratio 2.64E-01 (Activation of IRF by Cytosolic Pattern Recognition Receptors, LV vs mock)
- pvalue −log(p-value) = 8.55E+00, ratio 1.78E-01 (Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses, LV vs mock)
- pvalue −log(p-value) = 7.03E+00, ratio 2.5E-01 (Role of PKR in Interferon Induction and Antiviral Response, LV vs mock)
- count 29,900 annotated genes and 58,347 isoforms expressed in infected and non-infected PAMs (Total expressed features across all libraries)
- mean 14,483,298 (LV), 17,381,381 (Lena), 10,473,546 (mock) reads per strand (Mean sequencing depth per group; 11–17% of reads filtered out, 73–87% of passing paired reads mapped to Sscrofa10.2.71)
- other Ct = 15.6 (LV) and 20.5 (Lena); 21% and 16% infected cells respectively (qPCR and immunoperoxidase quantification of infection at 12 h post-infection, MOI 0.5)
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 compared RNA-Seq transcriptomes of porcine alveolar macrophages from 3 pigs, each split into LV-infected, Lena-infected, and mock-infected fractions (paired design, mock subtracted per pig). Gene-level differential expression was tested with edgeR (negative binomial generalized linear model), while isoform, transcription-start-site, and differential promoter usage were tested with Cuffdiff; features were called significant at FDR < 0.05 and fold change ≥ 1.5. Pathway/functional enrichment was assessed in Ingenuity Pathway Analysis (IPA) using a right-tailed Fisher's Exact Test, and results were reported mainly as FPKM values, fold changes, gene counts, and pathway p-values.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| edgeR generalized linear model (negative binomial, conditional weighted likelihood for overdispersion) | Gene-level differential expression: LV vs mock, Lena vs mock, LV vs Lena | 3 pigs (paired, mock-subtracted per pig) | not stated |
| Cuffdiff statistical testing | Differential isoform, TSS, and promoter usage: LV vs mock, Lena vs mock, LV vs Lena | 3 replicates per condition (9 libraries total) | not stated |
| Right-tailed Fisher's Exact Test (IPA) | Canonical pathway and biological function enrichment among differentially expressed genes | 413 genes mapped to the IPA database (LV vs mock) | not stated |
| Bayesian inference method for confidence intervals on FPKM estimates (Cufflinks) | Transcript/gene abundance estimation | — | not stated |
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Gene-level differential expression across the three pairwise comparisons was tested with edgeR's negative-binomial generalized linear model.↳ Could also: DESeq2 (also negative-binomial based) or limma-voom could also be used for the same count data. — These are widely used alternative RNA-Seq DE frameworks with different dispersion-estimation and shrinkage strategies, and comparing results across tools can illustrate how method choice affects the called gene set.
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Significance for genes, isoforms, TSSs, and promoter usage was defined using a combined FDR < 0.05 and fold-change ≥ 1.5 threshold.↳ Could also: Reporting the continuous adjusted p-value and fold-change/log-fold-change with confidence intervals for all tested features (not only those passing the cutoff) could also be used. — This would let readers apply their own significance and effect-size thresholds and see the full distribution of effects rather than only the features that passed the chosen cutoff.
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A paired design was used in which mock values were subtracted from treatment values per pig before computing group averages and variance.↳ Could also: A mixed-effects (or generalized linear mixed) model with pig included as a random effect could also be used on the raw counts. — This approach can incorporate the pairing directly into the model rather than through pre-subtraction, which may be useful when variances differ between conditions or when additional covariates need to be modeled.
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Pathway and functional enrichment among differentially expressed genes was assessed with a right-tailed Fisher's Exact Test in IPA on an already-thresholded gene list.↳ Could also: Gene Set Enrichment Analysis (GSEA), which uses the full ranked gene list rather than a hard significance cutoff, could also be used. — Rank-based enrichment methods can capture coordinated but individually sub-threshold expression changes across a pathway, complementing an over-representation test applied to a fixed gene list.
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Differential isoform, TSS, and promoter usage were assessed using Cuffdiff's built-in statistical testing.↳ Could also: DEXSeq or a similar count-based differential exon/isoform usage tool could also be applied to the same alignments. — These tools use an alternative statistical framework for testing usage differences and can be run alongside Cuffdiff as a cross-check on splicing- and promoter-usage calls.
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Confidence intervals for FPKM estimates were obtained via a Bayesian inference method within Cufflinks.↳ Could also: Bootstrap-based confidence intervals (as used by tools like kallisto/sleuth) could also be used for transcript abundance uncertainty. — Bootstrapping offers an alternative, model-light way to quantify quantification uncertainty and can be a useful complement to model-based Bayesian intervals.
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