<i>Mycobacterium tuberculosis</i>infection decreases macrophage Tpl-2 protein levels over time which alters responses to TLR agonists (MPF4P.730)
Bibliographic record
Abstract
Abstract Mycobacterium tuberculosis(M.tb), the causative agent of tuberculosis, is a host-adapted intracellular pathogen of macrophages. A complex, evolutionally adaptive relationship exists between M.tb and the host during which M.tb evades and subverts the immune response mounted by the macrophage. Understanding the interactions during primary infection between the macrophage and M.tb is important because it can impact the outcomes of infection and course of disease. We show that expression of the MAP3K Tpl-2, which was previously identified as a host defense molecule against M.tb in a murine knockout model, is down-regulated during M.tb infection of human primary macrophages. We observed downstream signaling effects of M.tb-induced suppression of Tpl-2 by studying ERK and MEK activation after stimulation with Toll-like receptor (TLR) ligands. M.tb lipomannan (ErdLM) stimulation of M.tb-infected macrophages showed decreased activation of ERK1/2 and MEK, congruent with decreased Tpl-2 levels after M.tb infection. However, ERK1/2 activation did not decrease significantly in M.tb-infected macrophages after treatment with the pure TLR agonists LPS, Pam3CysSK4, and CpG ODN, although decreased MEK phosphorylation was observed, suggesting that TLR ligands activate ERK1/2-mediated pathways that differ from those activated by ErdLM. These data indicate that M.tb has the capacity to suppress Tpl-2 over time and alter MAPK signaling to promote a survival advantage within the macrophage.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".