CRISPR-Cas9–mediated knockout of TLR4 modulates Mycobacterium avium ssp. paratuberculosis cell lysate–induced inflammation in bovine mammary epithelial cells
Bibliographic record
Abstract
Toll-like receptor 4 (TLR4) is a pattern-recognition receptor involved in the recognition of microbial pathogens and host alarmins. Ligation to TLR4 initiates a signaling cascade that leads to inflammation. Polymorphisms in bovine TLR4 have been associated with Mycobacterium avium ssp. paratuberculosis (MAP) susceptibility and resistance, the cause of Johne's disease, and milk somatic cell score, a biomarker of mastitis. Although the contribution of TLR4 to recognition of bacterial lipopolysaccharide (LPS) has been well characterized, its role in MAP recognition is less certain. Clustered regularly interspaced short palindromic repeats-Cas9 mediated gene editing was performed to generate TLR4 knockout (KO) mammary epithelial cells to determine if TLR4 expression is involved in the initiation of the host inflammatory response to MAP cell lysate (5 and 10 µg/mL) and Escherichia coli LPS (5 µg/mL). The absence of TLR4 in KO cells resulted in enhanced expression of key inflammatory genes (TNFA and IL6), anti-inflammatory genes (IL10 and SOCS3), and supernatant cytokine and chemokine levels (TNF-α, IL-6, IL-10, CCL3) in response to the MAP cell lysate (10 µg/mL). However, in response to LPS, the KO cells showed reduced expression of key inflammatory genes (TNFA, IL1A, IL1B, and IL6) and supernatant cytokine levels (TNF-α, IL-6, CCL2, IL-8) as compared with unedited cells. Overall, these results confirm that TLR4 is essential for eliciting inflammation in response to LPS; however, exacerbated gene and protein expression in TLR4 KO cells in response to MAP cell lysate suggests a different mechanism of infection and host response for MAP, at least in terms of how it interacts with TLR4. These novel findings show potential divergent roles for TLR4 in mycobacterial infections, and this may have important consequences for the therapeutic control of inflammation in cattle.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".