Transcriptomic and Epigenetic Profiling of the Lung of Influenza-Infected Pigs: A Comparison of Different Birth Weight and Susceptibility Groups
Bibliographic record
Abstract
Influenza viruses are a common cause of respiratory disease in swine. Infections range in severity from asymptomatic to causing significant morbidity. The main objective of this study was to compare lung transcriptomic and epigenetic responses to influenza infection in pigs from high or low birth weight litters. The latter is a potential indicator of intrauterine growth restriction, a significant risk factor for prenatal programming effects. Individual pigs from high (HBW) or low birth weight (LBW) litters (n = 17) were inoculated with influenza A virus and euthanized 48 hours later. Lesion severity and viral loads were assessed as previously described. The transcriptional response to infection in LBW and HBW groups (n = 16) was assessed by microarray. A separate analysis of pigs classified as 'Resilient' (RES) or 'Susceptible' (SUS) (n = 6) on the basis of severity of lung pathology was also conducted. Eight genes were confirmed as differentially expressed for the birth weight comparison, including three antiviral genes with lower expression in LBW: ISG15, OAS1, and OAS2 (P<0.05). The promoter region methylation status of these three genes was assessed for each birth weight group, and no differences were found. These expression data are consistent with our previous finding that LBW pigs had less severe lesion scores and a trend towards lower viral titres in lung than the HBW cohort. The SUS v RES comparison identified 91 differentially expressed genes (FDR<0.05) that were enriched with functional annotation terms and pathways associated with inflammation. The cytokine genes IL6, IL8, and CCL2 were all upregulated in SUS pigs, and may have driven disease severity in these animals. In conclusion, this study found no evidence that the transcriptional immune response to influenza was adversely affected by low litter birth weight, but did identify several candidate genes for driving disease pathology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".