Uncovering the underlying immune perturbations that determine long-term severity of chronic virus and <i>Mycobacterium tuberculosis</i> coinfection
Bibliographic record
Abstract
Abstract Chronic viral infections increase severity of Mycobacterium tuberculosis (Mtb) coinfection, yet how they alter the pulmonary microenvironment to foster coinfection and worsen disease severity is unclear. We developed a coinfection model in mice with chronic lymphocytic choriomeningitis virus and Mtb coinfection that recapitulated the central clinical manifestations of coinfection, including increased Mtb burden, extra-pulmonary dissemination and heightened mortality. These long-term disease consequences were not due to chronic virus-induced immunosuppression or exhaustion, but instead were determined by early alterations in immune surveillance of Mtb coinfection. Mechanistically, increased chronic virus induced TNFα production initially arrested pulmonary Mtb growth, impeding dendritic cell mediated antigen transportation to the lung-draining lymph nodes (LNs) and allowing bacterial sanctuary. The inhibited antigen arrival to LNs delayed CD4 T cell priming, allowing Mtb to replicate to higher set-points before T cell mediated control could be initiated. Once primed, Mtb-specific CD4 T cell differentiation skewed away from the Th1 responses associated with Mtb control, and instead toward Th17 differentiation. The elevated IL17 increased pulmonary neutrophil influx that decreased the long-term survival of coinfected mice. Therapeutically correcting the timing of CD4 T cell priming re-established CD4 Th1 over Th17 dominance, diminished pulmonary neutrophilia, and enabled enhanced Mtb control in the presence of chronic viral coinfection. Thus, Mtb co-opts TNFα from the chronic inflammatory environment to subvert immune-surveillance, avert early immune function and foster long-term coinfection.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".