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Record W3181748795 · doi:10.3168/jds.2021-20305

CRISPR-Cas9–mediated knockout of TLR4 modulates Mycobacterium avium ssp. paratuberculosis cell lysate–induced inflammation in bovine mammary epithelial cells

2021· article· en· W3181748795 on OpenAlexafffund
Umesh K. Shandilya, Ankita Sharma, S. Mallikarjunappa, Jiale Guo, Mao Yong Jiang, Kieran G. Meade, Niel A. Karrow

Bibliographic record

VenueJournal of Dairy Science · 2021
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaTeagasc
KeywordsBiologyTLR4ParatuberculosisInflammationChemokineTumor necrosis factor alphaCytokineMicrobiologyMolecular biologyImmunologyMycobacteriumBacteria

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.017
GPT teacher head0.281
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2021
Admission routes2
Has abstractyes

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