Characterization of the bovine salivary gland transcriptome associated with Mycobacterium avium subsp. paratuberculosis experimental challenge
Bibliographic record
Abstract
BACKGROUND: Mycobacterium avium subsp. paratuberculosis (MAP), the etiologic agent of Johne's disease is spread between cattle via the fecal-oral route, yet the functional changes in the salivary gland associated with infection remain uncharacterized. In this study, we hypothesized that experimental challenge with MAP would induce stable changes in gene expression patterns in the salivary gland that may shed light on the mucosal immune response as well as the regional variation in immune capacity of this extensive gland. Holstein-Friesian cattle were euthanized 33 months' post oral challenge with MAP strain CIT003 and both the parotid and mandibular salivary glands were collected from healthy control (n = 5) and MAP exposed cattle (n = 5) for histopathological and transcriptomic analysis. RESULTS: A total of 205, 21, 61, and 135 genes were significantly differentially expressed between control and MAP exposed cattle in dorsal mandibular (M1), ventral mandibular (M2), dorsal parotid (P1) and ventral parotid salivary glands (P2), respectively. Expression profiles varied between the structurally divergent parotid and mandibular gland sections which was also reflected in the enriched biological pathways identified. Changes in gene expression associated with MAP exposure were detected with significantly elevated expression of BoLA DR-ALPHA, BOLA-DRB3 and complement factors in MAP exposed cattle. In contrast, reduced expression of genes such as polymeric immunoglobin receptor (PIGR), TNFSF13, and the antimicrobial genes lactoferrin (LF) and lactoperoxidase (LPO) was detected in MAP exposed animals. CONCLUSIONS: This first analysis of the transcriptomic profile of salivary glands in cattle adds an important layer to our understanding of salivary gland immune function. Transcriptomic changes associated with MAP exposure have been identified including reduced LF and LPO. These critical antimicrobial and immunoregulatory proteins are known to be secreted into saliva and their downregulation may contribute to disease susceptibility. Future work will focus on the validation of their expression levels in saliva from additional cattle of known infection status as a potential strategy to augment disease diagnosis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".