Identification of essential genes in Mycobacterium avium subsp. paratuberculosis genome for persistence in dairy calves
Bibliographic record
Abstract
To cause disease Mycobacterium avium subsp. paratuberculosis needs to enter mammalian cells, arrest phagosomal maturation and manipulate the host immune system. The genetic basis of the bacterial capacity to achieve these outcomes remains largely unknown. Identifying these genes would allow us to gain a deeper understanding of MAP’s pathogenesis and potentially develop a live attenuated Johne’s disease vaccine by knocking out these genes. MAP genes demonstrated to be essential for colonization in the natural host, ruminants, are unknown. Genome-wide transposon mutagenesis and high-throughput sequencing were combined to evaluate the essentiality of each coding region in the bacterial genome to survive in dairy calves. A saturated library of 3,852 MAP Tn mutants, with insertions in 56% of TA sites, interrupting 88% of genes, was created using a MycoMarT7 phagemid containing a mariner transposon. Six calves were inoculated with a high dose of a library of MAP mutants, 1011 CFUs, (input) at 2 weeks of age. Following 2 months of incubation, MAP cells were isolated from the ileum, jejunum, and their associated lymph nodes of calves, resulting in approximately 100,000 colonies grown on solid media across 6 animals (output). Targeted next-generation sequencing was used to identify the disrupted genes in all the mutants in the input pool and the output pool recovered from the tissues to identify in vivo essential genes. Statistical analysis for the determination of essential genes was performed by a Hidden Markov Model (HMM), categorizing genes into essential genes that are devoid of insertions and growth-defect genes whose disruption impairs the growth of the organism. Sequence analysis identified 430 in vivo essential and 260 in vivo growth-defect genes. Gene ontology enrichment analysis of the in vivo essential and growth-defect genes with the highest reduction in the tissues revealed a high representation of genes involved in metabolism and respiration, cell wall and cell processing, virulence, and information pathway processes. This study has systematically identified essential genes for the growth and persistence of MAP in the natural host body.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".