Quantitative proteomics reveals that dormancy-related proteins mediate the attenuation in mycobacterium strains
Bibliographic record
Abstract
Although members of the Mycobacterium tuberculosis complex (MTBC) exhibit high similarity, they are characterized by differences with respect to virulence, immune response, and transmissibility. To understand the virulence of these bacteria and identify potential novel therapeutic targets, we systemically investigated the total cell protein contents of virulent H37Rv, attenuated H37Ra, and avirulent M. bovis BCG vaccine strains at the log and stationary phases, based on tandem mass tag (TMT) quantitative proteomics. Data analysis revealed that we obtained deep-coverage protein identification and high quantification. Although 272 genetic variations were reported in H37Ra and H37Rv, they showed very little expression difference in log and stationary phase. Quantitative comparison revealed H37Ra and H37Rv had significantly dysregulation in log phase (227) compared with stationary phase (61). While BCG and H37Rv, and BCG and H37Ra showed notable differences in stationary phase (1171 and 1124) with respect to log phase (381 and 414). In the log phase, similar patterns of protein abundance were observed between H37Ra and BCG, whereas a more similar expression pattern was observed between H37Rv and H37Ra in the stationary phase. Bioinformatic analysis revealed that the upregulated proteins detected for H37Rv and H37Ra in log phase were virulence-related factors. In both log and stationary phases, the dysregulated proteins detected for BCG, which have also been identified as M. tuberculosis response proteins under dormancy conditions. We accordingly describe the proteomic profiles of H37Rv, H37Ra, and BCG, which we believe will potentially provide a better understanding of H37Rv pathogenesis, H37Ra attenuation, and BCG immuno protection.
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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 | 0.000 |
| 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) | 0.000 | 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".