Involvement of CRISPR-Cas Systems in <i>Salmonella</i> Immune Response, Genome Editing, and Pathogen Typing in Diagnosis and Surveillance
Bibliographic record
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR) and their associated cas genes (CRISPR-Cas) provide acquired immunity in prokaryotes and protect microbial cells against infection by foreign organisms. CRISPR regions are found in bacterial genomes including Salmonella which is one of the primary causes of bacterial foodborne illness worldwide. The CRISPR array is composed of a succession duplicate sequences (repeats) which are separated by similar sized variable sequences (spacers). This chapter will first focus on the CRISPR-Cas involved in Salmonella immune response. With the emergence of whole genome sequencing (WGS) in recent years, more Salmonella genome sequences are available, and various genomic tools for CRISPR arrays identification have been developed. Second, through the analysis of 115 Salmonella isolates with complete genome sequences, significant diversity of spacer profiles in CRISPR arrays. Finally, some applications of CRISPR-Cas systems in Salmonella are illustrated, which mainly includes genome editing, CRISPR closely relating to antimicrobial resistance (AMR), CRISPR typing and subtyping as improved laboratory diagnostic tools. In summary, this chapter provides a brief review of the CRISPR-Cas system in Salmonella, which enhances the current knowledge of Salmonella genomics, and hold promise for developing new diagnostics methods in improving laboratory diagnosis and surveillance endeavors in food safety.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".