Combined metagenomic- and culture-based approaches to investigate bacterial strain–level associations with medication-controlled mild-moderate atopic dermatitis
Bibliographic record
Abstract
Background The skin microbiome is disrupted in atopic dermatitis (AD). Existing research focuses on moderate to severe, unmedicated disease. Objective We sought to investigate metagenomic- and culture-based bacterial strain–level differences in mild, medicated AD and the effects these have on human keratinocytes (HKs). Methods Skin swabs from anterior forearms were collected from 20 pediatric participants (11 participants with AD sampled at lesional and nonlesional sites and 9 age- and sex-matched controls). Participants had primarily mild to moderate AD and maintained medication use. Samples were processed for microbial metagenomic sequencing and bacterial isolation. Isolates identified as Staphylococcus aureus were tested for enterotoxin production. HK cultures were treated with cell-free conditioned media from representative Staphylococcus species to measure barrier effects. Results Metagenomic sequencing identified significant differences in microbiome composition between AD and control groups. Differences were seen at the species and strain levels for Staphylococci, with S aureus found only in participants with AD and differences in Staphylococcus epidermidis strains between control and AD swabs. These strains showed differences in toxin gene presence, which was confirmed in vitro for S aureus enterotoxins. The strain from the participant with the most severe AD produced enterotoxin B levels more than 100-fold higher than the other strains ( P < .001). Strains also displayed differential effects on HK metabolism and barrier function. Conclusions Strain-level differences in toxin genes from Staphylococcus strains may explain varying effects on HK, with S aureus and non –aureus strains negatively affecting viability and barrier function. These differences are likely important in AD pathogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".