Mice infected with <i>Mycobacterium tuberculosis</i> are resistant to secondary infection with SARS-CoV-2
Bibliographic record
Abstract
Abstract Mycobacterium tuberculosis (Mtb)and SARS-CoV-2 (CoV2) are the leading causes of death due to infectious disease; however, little is known regarding the immunological environment in the Mtb/CoV2 coinfected lung. The goal of this study was to use a mouse model of Mtb/CoV2 coinfection to determine if CoV2 affects Mtb bacterial burden and/or alters the lung immune profile. Using lung collected from human ACE2 transgenic (K18-hACE2) mice infected with both pathogens (Mtb only, SARS-CoV-2 only, and Mtb/SARS-CoV-2 co-infected), we evaluated immune gene expression, cytokine production, and bacterial burden. Surprisingly, these data show that Mtb suppresses CoV2 -related weight loss and lung viral burden in the human ACE2 transgenic mouse model. We also report a reduction in lung interferon gamma production and expression in coinfected mice compared to the Mtb only infected group, suggesting a possible altered T cell profile in the coinfected group. To determine whether Mtb-induced resistance to CoV2 was specific to the ACE2 transgenic model of COVID19, we performed the same set of experiments using a second mouse model of COVID19: Mouse Adapted SARS-CoV-2 (MACoV2) infection of C57BL/6 (B6) mice. In both model systems, Mtb-infected mice were resistant to secondary CoV2 infection and its pathological consequences, and CoV2 infection did not affect Mtb burdens. Single cell RNA sequencing of coinfected and monoinfected lungs demonstrated the resistance of Mtb-infected mice is associated with expansion of T and B cell subsets upon viral challenge. Collectively, these data demonstrate that Mtb infection conditions the lung environment in a manner that is not conducive to CoV2 survival. Supported by OSU Advancing Research in Infection and Immunity Fellowship Award
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".