<i>Salmonella enterica</i> serovar Typhimurium ST313 sublineage 2.2 has emerged in Malawi with a characteristic gene expression signature and a fitness advantage
Bibliographic record
Abstract
Abstract Invasive non-typhoidal Salmonella (iNTS) disease is a serious bloodstream infection that targets immune-compromised individuals, and causes significant mortality in sub-Saharan Africa. Salmonella enterica serovar Typhimurium ST313 causes the majority of iNTS in Malawi. We performed an intensive comparative genomic analysis of 608 S. Typhimurium ST313 isolates dating between 1996 and 2018 from Blantyre, Malawi. We discovered that following the arrival of the well-characterized S. Typhimurium ST313 lineage 2 in 1999, two multidrug-resistant variants emerged in Malawi in 2006 and 2008, designated sublineages 2.2 and 2.3, respectively. The majority of S. Typhimurium isolates from human bloodstream infections in Malawi now belong to sublineages 2.2 or 2.3. To understand the emergence of the prevalent ST313 sublineage 2.2, we studied two representative strains, D23580 (lineage 2) and D37712 (sublineage 2.2). The chromosome of ST313 lineage 2 and sublineage 2.2 only differed by 29 SNPs/small indels and a 3 kb deletion of a Gifsy-2 prophage region including the sseI pseudogene. Lineage 2 and sublineage 2.2 had distinctive plasmid profiles. The transcriptome was investigated in 15 infection-relevant in vitro conditions and within macrophages. During growth in physiological conditions that do not usually trigger S. Typhimurium SPI2 gene expression, the SPI2 genes of D37712 were transcriptionally active. We identified down-regulation of flagellar genes in D37712 compared with D23580. Following phenotypic confirmation of transcriptomic differences, we discovered that sublineage 2.2 had increased fitness compared with lineage 2 during mixed growth in minimal media. We speculate that this competitive advantage is contributing to the emergence of sublineage 2.2 in Malawi.
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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".