Phylogenetic detection of horizontal gene transfer during the step-wise genesis of Mycobacterium tuberculosis
Bibliographic record
Abstract
BACKGROUND: In the past decade, the availability of complete genome sequence data has greatly facilitated comparative genomic research aimed at addressing genetic variability within species. More recently, analysis across species has become feasible, especially in genera where genome sequencing projects of multiple species have been initiated. To understand the genesis of the pathogen Mycobacterium tuberculosis within a genus where the majority of species are harmless environmental organisms, we have used genome sequence data from 16 mycobacteria to look for evidence of horizontal gene transfer (HGT) associated with the emergence of pathogenesis. First, using multi-locus sequence analysis (MLSA) of 20 housekeeping genes across these species, we derived a phylogeny that serves as the basis for HGT assignments. Next, we performed alignment searches for the 3989 proteins of M. tuberculosis H37Rv against 15 other mycobacterial genomes, generating a matrix of 59835 comparisons, to look for genetic elements that were uniquely found in M. tuberculosis and closely-related pathogenic mycobacteria. To assign when foreign genes were likely acquired, we designed a bioinformatic program called mycoHIT (mycobacterial homologue investigation tool) to analyze these data in conjunction with the MLSA-based phylogeny. RESULTS: The bioinformatic screen predicted that 137 genes had been acquired by HGT at different phylogenetic strata; these included genes coding for metabolic functions and modification of mycobacterial lipids. For the majority of these genes, corroborating evidence of HGT was obtained, such as presence of phage or plasmid, and an aberrant GC%. CONCLUSION: M. tuberculosis emerged through vertical inheritance along with the step-wise addition of genes acquired via HGT events, a process that may more generally describe the evolution of other pathogens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".