Lipidomics and colistin resistance in non-human isolates of <i>Acinetobacter seifertii</i>
Bibliographic record
Abstract
Abstract Acinetobacter baumannii is most well known for its role as a human pathogen and as a member of the Acinetobacter calcoaceticus-baumannii (ACB) complex. However, lesser characterised members of the ACB complex, have also been implicated in hospital-acquired infections. Once mainly considered opportunistic pathogens, many A. baumannii and non- baumannii strains are being isolated from agricultural, water and food sources. The surveillance and tracking of Acinetobacter spp. have been recently suggested to be part of the One Health consortium, to understand and prevent the spread of antimicrobial resistance. Here, we isolated four Acinetobacter strains from tank milk in Bogor, Indonesia and using ANI and dDDH techniques have identified them as Acinetobacter seifertii . MLST methods assigned these A. seifertii strains to a novel Sequence Types (ST), highlighting the diversity not only within the ACB complex but also in non-human Acinetobacter spp. These four A. seifertii strains are colistin-resistant and while they do not harbour any known mechanism of colistin resistance, they do share amino acid substitutions in regulatory proteins, AdeS, PmrAB, H-NS, and the membrane associated proteins, LpxACD, MlaD, PldA, LpsB and EptA that may contribute to this phenotype. Furthermore, down-regulation of the RND efflux pump AdeAB, may also be a key factor in colistin resistance in these non-human A. seifertii strains. Lipidomics revealed an acyl-homoserine lactone (AHL) molecule, and lyso-phosphatidylethanolamine (lyso-PE) in significant abundance compared to colistin-sensitive A. baumannii ATCC17978 revealing lipidomic differences between species. Finally, these four tank milk A. seifertii strains are avirulent in an insect model of virulence. It is possible that A. seifertii strains are intrinsically resistant to colistin and require further study. By investigating these less understood Acinetobacter spp. from non-human sources, our study supports the One Health approach to combatting antibiotic resistance.
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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 | 0.001 | 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) | 0.001 | 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".