Global Proteomic Analyses of Macrophage Response to <i>Mycobacterium tuberculosis</i> Infection
Bibliographic record
Abstract
Abstract Alveolar macrophages serve as the first line of defence against microbial infection, yet provide a unique niche for the growth of Mycobacterium tuberculosis . To better understand the evasive nature of the tubercle bacilli and its molecular manifest on the macrophage response to infection, we conducted a global quantitative proteomic profile of infected macrophages. By examining four independent controlled infection experiments, we detected 42,007 peptides resulting in the characterization of 4,868 distinct proteins. Of these, we identified 845 macrophage proteins whose expression is modulated upon infection in all replicates. We showed that the macrophage’s response to M. tuberculosis infection includes simultaneous and concerted upregulation of selected proteins. Using a number of statistical methods, we identified 27 proteins whose expression levels are significantly regulated outside of a 90% confidence interval about the mean. These host proteins represent the macrophage transcriptional, translational, and innate immune response to infection as well as its signaling capacity. The contribution of PtpA, an M. tuberculosis secreted virulence factor, modulated the expression levels of 11 host macrophage proteins, as categorized by RNA metabolism, translation, and cellular respiration.
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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) | 0.000 | 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".