Enhancers of host immune tolerance to bacterial infection discovered using linked computational and experimental approaches
Bibliographic record
Abstract
Abstract Current therapeutic strategies against bacterial infections focus on reduction of pathogen load through antibiotics; however, stimulation of host tolerance to infection might offer an alternative approach. Here we used computational transcriptomics and a Xenopus embryo infection model to rapidly discover infection response pathways, identify potential tolerance inducer drugs, and validate their ability to induce broad tolerance. Xenopus embryos exhibit natural tolerance to A. baumanii, K. pneumoniae, S. aureus , and S. pneumoniae bacteria, whereas A. hydrophila and P. aeruginosa produce infection that leads to death. Transcriptional profiling led to definition of a 20-gene signature that allows for discrimination between tolerant and susceptible states, as well as identification of active and passive tolerance responses based on the degree of engagement of gene transcription modulation. Upregulation of metal ion transport and hypoxia pathways reminiscent of responses observed in primate and mouse infection models were identified as tolerance mediators, and drug screening in the susceptible A. hydrophila infection model confirmed that a metal chelator (deferoxamine) and HIF-1α agonist (1,4-DPCA) increase embryo survival despite high pathogen load. These data demonstrate the value of combining the Xenopus embryo infection model with multi-omics analyses for mechanistic discovery and drug repurposing to induce host tolerance to bacterial infection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".