SIVmac239 infection dysregulates anti-mycobacterial immunity at the granuloma level and contributes to severe tuberculosis in cynomolgus macaques
Bibliographic record
Abstract
Abstract Tuberculosis (TB) is a global pandemic caused by Mycobacterium tuberculosis (Mtb) infection that is characterized by formation of lung lesions called granulomas. TB has a synergistic relationship with HIV and TB often occurs in HIV-infected individuals with near normal CD4+ T cell counts. Despite the importance of this co-infection to public health, interactions between HIV and lesion-level immunity remain poorly understood. To address this issue, we used SIVmac239-infected or SIV-negative Mauritian cynomolgus macaques with active TB to identify which cell types are infected in granulomas and how SIV modulates local immunity. Using RNAscope and immunohistochemistry, we found that CD11c+ macrophages were the most-commonly infected cell type in granulomas. To identify how viral infection changes the immune landscape in granulomas, we performed a transcriptional analysis on RNA isolated from lung granulomas. We found that SIV infection downregulated large numbers of macrophage-activating cytokines and receptors, toll-like receptors, and T cell activation markers. In contrast, anti-viral responses including type 1 interferons and receptors expressed by NK cells were upregulated. IL-10 was among the genes that were not differentially regulated. Pathway analysis indicated that many pathways were downregulated, including pathways associated with Th1-, Th2-, and NFkB-mediated responses. These data suggest that SIV infection deactivates protective responses in granulomas but activates antiviral responses that are counterproductive in TB while leaving detrimental IL-10 expression intact. We hypothesize that this disequilibrium leads to an inability to kill bacteria and restrict dissemination that exacerbates TB.
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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) | 0.001 | 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".