<i>Enterobacter hormaechei</i> replaces virulence with carbapenem resistance via porin loss
Bibliographic record
Abstract
Pathogenic Enterobacter species are of increasing clinical concern due to the multidrug-resistant nature of these bacteria, including resistance to carbapenem antibiotics. Our understanding of Enterobacter virulence is limited, hindering the development of new prophylactics and therapeutics targeting infections caused by Enterobacter species. In this study, we assessed the virulence of contemporary clinical Enterobacter hormaechei isolates in a mouse model of intraperitoneal infection and used comparative genomics to identify genes promoting virulence. Through mutagenesis and complementation studies, we found two porin-encoding genes, ompC and ompD , to be required for E. hormaechei virulence. These porins imported clinically relevant carbapenems into the bacteria, and thus loss of OmpC and OmpD desensitized E. hormaechei to the antibiotics. Our genomic analyses suggest porin-related genes are frequently mutated in E. hormaechei , perhaps due to the selective pressure of antibiotic therapy during infection. Despite the importance of OmpC and OmpD during infection of immunocompetent hosts, we found the two porins to be dispensable for virulence in a neutropenic mouse model. Moreover, porin loss provided a fitness advantage during carbapenem treatment in an ex vivo human whole blood model of bacteremia. Our data provide experimental evidence of pathogenic Enterobacter species gaining antibiotic resistance via loss of porins and argue antibiotic therapy during infection of immunocompromised patients is a conducive environment for the selection of porin mutations enhancing the multidrug-resistant profile of these pathogens.
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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 | 0.001 |
| 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".