Genetic Control of Host–Pathogen Interactions in Mice
Bibliographic record
Abstract
The onset, progression and outcome of infections are determined by performance of host defence mechanisms and expression of pathogen virulence determinants. Genetic analysis in mouse can identify host genes that play critical roles at the interface of host-pathogen interactions. Genetic effects detected as variations in susceptibility in inbred, recombinant and mutant strains of mice can be mapped as simple traits or quantitative trait loci followed by identification by positional cloning. We have used mouse models of infection with bacterial (Mycobacterium, Legionella) and parasitic pathogens (Plasmodium) to discover genes and proteins that are important for macrophage function against such infectious agents. These studies have identified Nrampl-mediated exclusion of divalent metals from the phagosomal space as a key regulator of intracellular replication of Mycobacteria. Also, intracellular sensing of Legionella by functional Birc1e/Naip5 protein is essential to prevent replication of this bacterium in macrophages. Finally, we have identified two new loci that affect blood-stage replication of Plasmodium chabaudi AS in mice, and have cloned the corresponding genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".