Screening under infection-relevant conditions reveals chemical sensitivity in multidrug resistant invasive non-typhoidal <i>Salmonella</i> (iNTS)
Bibliographic record
Abstract
ABSTRACT Bloodstream infections caused by invasive, non-typhoidal salmonellae (iNTS) are a major global health concern. These infections are especially problematic in sub-Saharan Africa, where the sequence type (ST) 313 of invasive non-typhoidal Salmonella Typhimurium (iNTS) is dominant. Unlike S . Typhimurium strains that cause mild gastroenteritis, iNTS strains are resistant to multiple first-line antibiotics and have higher extraintestinal invasiveness, limiting current treatment options. Here, we performed multiple small molecule screens under infection-relevant conditions to reveal chemical sensitivities in ST313 as entry points to drug discovery to combat the clinical burden of iNTS. By screening the invasive ST313 sequence type under host-mimicking conditions, we identified the antimicrobial activity of the nucleoside analog 3’-azido-3’-deoxythymidine, which required bacterial thymidine kinase activity for its antimicrobial activity. In a parallel macrophage-based screening platform, we also identified three host-directed compounds (amodiaquine, berbamine, and indatraline) that significantly restricted intracellular replication of ST313 in macrophages without directly impacting bacterial viability. This work provides evidence that despite elevated invasiveness and multidrug resistance, iNTS S . Typhimurium remains susceptible to unconventional drug discovery approaches.
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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 | 0.000 |
| 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".