The recombination-cold region as an epidemiological marker of recombinogenic opportunistic pathogen Mycobacterium avium
Bibliographic record
Abstract
BACKGROUND: The rapid identification of lineage remains a challenge in the genotyping of clinical isolates of recombinogenic pathogens. The chromosome of Mycobacterium avium subsp. hominissuis (MAH), an agent of Mycobacterium avium complex (MAC) lung disease, is often mosaic and is composed of chromosomal segments originating from different lineages. This makes it difficult to infer the MAH lineage in a simple experimental set-up. To overcome this difficulty, we sought to identify chromosomal marker genes containing lineage-specific alleles by genome data mining. RESULTS: We conducted genetic population structure analysis, phylogenetic analysis, and a survey of historical recombination using data from 125 global MAH isolates. Six MAH lineages (EA1, EA2, SC1, SC2, SC3, and SC4) were identified in the current dataset. One P-450 gene (locus_tag MAH_0788/MAV_0940) in the recombination-cold region was found to have multiple alleles that could discriminate five lineages. By combining the information about allele type from one additional gene, the six MAH lineages as well as other M. avium subspecies were distinguishable. A recombination-cold region of 116 kb contains an insertion hotspot and is flanked by a mammalian cell-entry protein operon where allelic variants have previously been reported to occur. Hence, we speculate that the acquisition of lineage- or strain-specific insertions has introduced homology breaks in the chromosome, thereby reducing the chance of interlineage recombination. CONCLUSIONS: The allele types of the newly identified marker genes can be used to predict major lineages of M. avium. The single nucleotide polymorphism typing approach targeting multiallelic loci in recombination-cold regions will facilitate the epidemiological study of MAC, and may also be useful for equivalent studies of other nontuberculous mycobacteria potentially carrying mosaic genomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".