Whole transcriptomic and proteomic analyses of an isogenic M. tuberculosis clinical strain with a naturally occurring 15 Kb genomic deletion
Bibliographic record
Abstract
Tuberculosis remains one of the most difficult to control infectious diseases in the world. Many different factors contribute to the complexity of this disease. These include the ability of the host to control the infection which may directly relate to nutritional status, presence of co-morbidities and genetic predisposition. Pathogen factors, in particular the ability of different Mycobacterium tuberculosis strains to respond to the harsh environment of the host granuloma, which includes low oxygen and nutrient availability and the presence of damaging radical oxygen and nitrogen species, also play an important role in the success of different strains to cause disease. In this study we evaluated the impact of a naturally occurring 12 gene 15 Kb genomic deletion on the physiology and virulence of M. tuberculosis. The strains denominated ON-A WT (wild type) and ON-A NM (natural mutant) were isolated from a previously reported TB outbreak in an inner city under-housed population in Toronto, Canada. Here we subjected these isogenic strains to transcriptomic (via RNA-seq) and proteomic analyses and identified several gene clusters with differential expression in the natural mutant, including the DosR regulon and the molybdenum cofactor biosynthesis genes, both of which were found in lower abundance in the natural mutant. We also demonstrated lesser virulence of the natural mutant in the guinea pig animal model. Overall, our findings suggest that the ON-A natural mutant is less fit to cause disease, but nevertheless has the potential to cause extended transmission in at-risk populations.
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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 | 0.000 | 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".