Genome scale metabolic model driven strategy to delineate host response to <i>Mycobacterium tuberculosis</i> infection
Bibliographic record
Abstract
We analyze high throughput proteomics data reflecting the response of the Mφ-like THP1 cell line to Mycobacterium tuberculosis (M. tuberculosis) infection. M. tuberculosis's engagement with the host's metabolic pathways is a known strategy employed by the pathogen to shift the balance in its favour. Our study revisits this strategy through the integration of the temporal proteomics data in the genome-scale metabolic model (GSMM) giving context-specific GSMMs. THP1 cells were infected with H37Ra, H37Rv, BND433 and JAL2287 strains of M. tuberculosis and the host response was studied at 6, 18, 30 and 42 hours after infection. We have developed a modified flux balance analysis (FBA), which does not use an objective function, to find the fluxes of metabolic reactions in different strains and stages of infection and have revealed different functional modules. Hence, we have established a method of rewiring using GSMMs to explore potential strategies to change the flux state of virulent M. tuberculosis infected macrophages as against their avirulent counterparts. Our methodology gives a correlation between different flux states, the extent of which was interpreted as the extent of rewiring. The accuracy of the results from the proposed methodology was validated with gene knockout experimental data. We found that more than one reaction has to be rewired simultaneously to alter virulent to an avirulent response. The identified modules showed influence across the investigated strains and time points suggesting that these reactions could be therapeutically targeted. This novel methodology is now available for use in other systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".