Genomic analysis and immune response in a murine mastitis model of vB_EcoM-UFV13, a potential biocontrol agent for use in dairy cows
Bibliographic record
Abstract
Bovine mastitis remains the main cause of economic losses for dairy farmers. Mammary pathogenic Escherichia coli (MPEC) is related to an acute mastitis and its treatment is still based on the use of antibiotics. In the era of antimicrobial resistance (AMR), bacterial viruses (bacteriophages) present as an efficient treatment or prophylactic option. However, this makes it essential that its genetic structure, stability and interaction with the host immune system be thoroughly characterized. The present study analyzed a novel, broad host-range anti-mastitis agent, the T4virus vB_EcoM-UFV13 in genomic terms, and its activity against a MPEC strain in an experimental E. coli-induced mastitis mouse model. 4,975 Single Nucleotide Polymorphisms (SNPs) were assigned between vB_EcoM-UFV13 and E. coli phage T4 genomes with high impact on coding sequences (CDS) (37.60%) for virion proteins. Phylogenetic trees and genome analysis supported a recent infection mix between vB_EcoM-UFV13 and Shigella phage Shfl2. After a viral stability evaluation (e.g pH and temperature), intramammary administration (MOI 10) resulted in a 10-fold reduction in bacterial load. Furthermore, pro-inflammatory cytokines, such as IL-6 and TNF-α, were observed after viral treatment. This work brings the whole characterization and immune response to vB_EcoM-UFV13, a biocontrol candidate for bovine mastitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".