Sweat and Sebum Preferences of the Human Skin Microbiota
Bibliographic record
Abstract
ABSTRACT The microorganisms that inhabit human skin, collectively termed the skin microbiome, must overcome numerous challenges that typically impede microbial growth, including low pH, osmotic pressure, and low nutrient availability. Yet, the skin microbiota thrive on the skin and have adapted to these stressful conditions. Limited skin nutrients are available for microbial use in this unique niche, including those from host-derived sweat, sebum, and corneocytes. Here, we have developed physiologically-relevant, skin-like growth media that is composed of compounds present in human sweat and sebum. We find that skin-associated bacterial species exhibit unique growth profiles in different concentrations of sweat and sebum. The majority of strains evaluated demonstrate a preference for high sweat concentrations, while sebum preference is highly variable, suggesting that the capacity for sebum utilization may be an important driver of skin microbial community structure. Furthermore, these findings provide experimental rationale for why different skin microenvironments harbor distinct microbiome communities. In all, our study further emphasizes the importance of studying microorganisms in an ecologically-relevant context, which is critical for our understanding of their physiology, ecology, and function on the skin.
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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 | 0.000 |
| 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".