Genome-wide whole blood microRNAome and transcriptome analyses reveal miRNA-mRNA regulated host response to foodborne pathogen Salmonella infection in swine
Bibliographic record
Abstract
To understand the role of miRNAs in regulating genes involved in host response to bacterial infection and shedding of foodborne pathogens, a systematic profiling of miRNAs and mRNAs from the whole blood of pigs upon Salmonella challenge was performed. A total of 62 miRNAs were differentially expressed post infection (false discovery rate <0.1). An integrative analysis of both the differentially expressed miRNAs and mRNAs using sequence-based miRNA target prediction and negative correlation of miRNA-mRNA profiles helped identify miRNA-mRNA networks that may potentially regulate host response to Salmonella infection. From these networks, miR-214 and miR-331-3p were identified as new candidates potentially associated with Salmonella infection. An miRNA seed sequence analysis suggested that these miRNAs regulate several critical immune-related genes including SLC11A1, PIGE-108A11.3 and VAV2. We showed that challenged pigs had reduced miR-214 expression and increased miR-331-3p expression in the whole blood. Furthermore, the expression of the proposed targets of miR-214 (SLC11A1 and PIGE-108A11.3) increased while that of the proposed target of miR-331-3p (VAV2) decreased following challenge (expression changes confirmed by in vitro assays). Based on these observations, we propose potential roles for miR-214 and miR-331-3p in regulation of immune responses to Salmonella infection.
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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".