Whole Genome Sequencing based Identification of Human Tuberculosis caused by Animal-lineage <i>Mycobacterium orygis</i>
Bibliographic record
Abstract
ABSTRACT A recently described member of the Mycobacterium tuberculosis complex (MTBC) is Mycobacterium orygis, which can cause disease primarily in animals but also in humans. Although M. orygis has been reported from different geographic regions around the world, due to a lack of proper identification techniques, the contribution of this emerging pathogen to the global burden of zoonotic tuberculosis is not fully understood. In the present work, we report single nucleotide polymorphisms (SNPs) analysis using whole genome sequencing (WGS) that can accurately identify M. orygis and differentiate it from other members of MTBC species. WGS-based SNPs analysis was performed for 61 isolates from different Provinces in Canada that were identified as M. orygis . A total of 56 M. orygis sequences from the public databases were also included in the analysis. Several unique SNPs in gyrB, PPE55, Rv2042c, leuS, mmpL6, and mmpS6 genes were used to determine their effectiveness as genetic markers for the identification of M. orygis . To the best of our knowledge, five of these SNPs, viz., gyrB 277 (A→G), gyrB 1478 (T→C), leuS 1064 (A→T), mmpL6 486 (T→C), and mmpS6 334 (C→G) are reported for the first time in this study. Our results also revealed several SNPs specific to other species within MTBC. The phylogenetic analysis shows that studied genomes were genetically diverse and clustered with M. orygis sequences of human and animal origin reported from different geographic locations. Therefore, the present study provides a new insight into the high confidence identification of M. orygis from MTBC species based on WGS data, which can be useful for reference and diagnostic laboratories.
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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 | 0.000 |
| 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".