Survival proteomes: the emerging proteotype of antimicrobial resistance
Bibliographic record
Abstract
Antimicrobial resistance is one of the greatest challenges in modern medicine. Infectious diseases that have historically been eliminated with routine antibiotic therapy are now re-emerging as life threatening illnesses. A better understanding of the specific mechanisms that contribute to resistance are required to optimize the treatment of infectious microorganisms and limit the survival of recalcitrant populations. This challenging area of research is made more problematic by the observation that multiple, overlapping, and/or compensatory resistance mechanism are often present within a single bacterial species. High-resolution proteomics has emerged as an effective tool to study antimicrobial resistance as it allows for the quantitative investigation of multiple systems concurrently. Furthermore, the ability to examine extracellular mechanisms of resistance and important post-translational modifications make this research tool well suited for the challenge. This review discusses how proteomics has contributed to the understanding of antimicrobial resistance and focuses on advances afforded by the more recent development of technologies that produce quantitative high-resolution proteomic information. We discuss current strategies for studying resistance, including comparative analysis of resistant and susceptible strains and protein-based responses to antimicrobial challenge. Lastly, we suggest specific experimental approaches aimed at advancing our understanding of protein-based resistance mechanisms and maximizing therapeutic outcomes in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".