Bacterial‐fungal interactions and their impact on microbial pathogenesis
Bibliographic record
Abstract
Microbial communities of the human microbiota exhibit diverse effects on human health and disease. Microbial homeostasis is important for normal physiological functions and changes to the microbiota are associated with many human diseases including diabetes, cancer, and colitis. In addition, there are many microorganisms that are either commensal or acquired from environmental reservoirs that can cause diverse pathologies. Importantly, the balance between health and disease is intricately connected to how members of the microbiota interact and affect one another's growth and pathogenicity. However, the mechanisms that govern these interactions are only beginning to be understood. In this review, we outline bacterial-fungal interactions in the human body, including examining the mechanisms by which bacteria govern fungal growth and virulence, as well as how fungi regulate bacterial pathogenesis. We summarize advances in the understanding of chemical, physical, and protein-based interactions, and their role in exacerbating or impeding human disease. We focus on the three fungal species responsible for the majority of systemic fungal infections in humans: Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus. We conclude by summarizing recent studies that have mined microbes for novel antimicrobials and antivirulence factors, highlighting the potential of the human microbiota as a rich resource for small molecule discovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".