Joint Effects of Host Genetic Background and Mycobacterial Pathogen on Susceptibility to Infection
Bibliographic record
Abstract
The present study examined the differential contribution of host genetic background and mycobacterial pathogen variability to biological and mechanistic phenotypes of infection. For this purpose, A/J and C57BL/6J mice were infected intravenously with a low dose of Mycobacterium tuberculosis H37Rv or the Russia, Japan, and Pasteur substrains of Mycobacterium bovis bacille Calmette-Guérin (BCG). The pulmonary bacterial counts (number of CFU) and transcript levels of select cytokines (e.g., Ifng, Il12b, and Il4) at 1, 3, and 6 weeks postinfection were measured as biological and mechanistic phenotypes, respectively. The individual and combined impact of the host and mycobacteria on these phenotypes was assessed using three-way analysis of variance (ANOVA), which partitions phenotypic variation into host, pathogen, time, and interaction effects. All phenotypes, except pulmonary Il4 transcript levels, displayed evidence for host-mycobacterium specificity by means of significant interaction terms. Pulmonary expression profiles of 34 chemokines and chemokine-related genes were compared across the hosts and mycobacteria. The differences in induction of these immune messenger genes between A/J and C57BL/6J mice were modest and generally failed to reach significance. In contrast, the mycobacteria induced significant variance in a subset of the immune messenger genes, which was more evident in A/J mice relative to that in C57BL/6J mice. Overall, the results demonstrated the importance of considering the joint effects of the mycobacterial and host genetic backgrounds on susceptibility to mycobacterial infections.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".