Functional analysis of bovine interleukin-10 receptor alpha in response to Mycobacterium avium subsp. paratuberculosis lysate using CRISPR/Cas9
Bibliographic record
Abstract
BACKGROUND: The interleukin-10 receptor alpha (IL10RA) gene codes for the alpha chain of the IL-10 receptor which binds the cytokine IL-10. IL-10 is an anti-inflammatory cytokine with immunoregulatory function during the pathogenesis of many inflammatory disorders in livestock, including Johne's disease (JD). JD is a chronic enteritis in cattle caused by Mycobacterium avium subsp. paratuberculosis (MAP) and is responsible for significant economic losses to the dairy industry. Several candidate genes including IL10RA have been found to be associated with JD. The aim of this study was to better understand the functional significance of IL10RA in the context of immune stimulation with MAP cell wall lysate. RESULTS: An IL10RA knock out (KO) bovine mammary epithelial cell (MAC-T) line was generated using the CRISPR/cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9) gene editing system. These IL10RA KO cells were stimulated with the immune stimulant MAP lysate +/- IL-10, or with LPS as a positive control. In comparison to unedited cells, relative quantification of immune-related genes after stimulation revealed that knocking out IL10RA resulted in upregulation of pro-inflammatory cytokine gene expression (TNFA, IL1A, IL1B and IL6) and downregulation of suppressor of cytokine signaling 3 (SOCS3), a negative regulator of pro-inflammatory cytokine signaling. At the protein level knocking out IL10RA also resulted in upregulation of inflammatory cytokines - TNF-α and IL-6 and chemokines - IL-8, CCL2 and CCL4, relative to unedited cells. CONCLUSIONS: The findings of this study illustrate the broad and significant effects of knocking out the IL10RA gene in enhancing pro-inflammatory cytokine expression and further support the immunoregulatory role of IL10RA in eliciting an anti-inflammatory response as well as its potential functional involvement during the immune response associated with JD.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".