Exploring halophilic environments as a source of new antibiotics
Bibliographic record
Abstract
Microbial natural products from microbes in extreme environments, including haloarchaea, and halophilic bacteria, possess a huge capacity to produce novel antibiotics. Additionally, enhanced isolation techniques and improved tools for genomic mining have expanded the efficiencies in the antibiotic discovery process. This review article provides a detailed overview of known antimicrobial compounds produced by halophiles from all three domains of life. We summarize that while halophilic bacteria, in particular actinomycetes, contribute the vast majority of these compounds the importance of understudied halophiles from other domains of life requires additional consideration. Finally, we conclude by discussing upcoming technologies- enhanced isolation and metagenomic screening, as tools that will be required to overcome the barriers to antimicrobial drug discovery. This review highlights the potential of these microbes from extreme environments, and their importance to the wider scientific community, with the hope of provoking discussion and collaborations within halophile biodiscovery. Importantly, we emphasize the importance of bioprospecting from communities of lesser-studied halophilic and halotolerant microorganisms as sources of novel therapeutically relevant chemical diversity to combat the high rediscovery rates. The complexity of halophiles will necessitate a multitude of scientific disciplines to unravel their potential and therefore this review reflects these research communities.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".