Genomic epidemiology of emerging terbinafine-resistant Trichophyton indotineae
Bibliographic record
Abstract
Abstract Dermatophyte skin infections affect around a quarter of the world’s population and are a growing public health concern due to increasing incidence of novel species causing severe infections that are resistant to antifungal treatments. Trichophyton species cause the greatest burden of dermatophytosis worldwide, with the T. mentagrophytes species complex being particularly associated with the emergence of new aggressive infections. One emerging species, T. indotineae (originally T. mentagrophytes genotype VIII) is notable for the extensive nature of the often inflammatory infection, its clinical resistance to terbinafine antifungal treatment, and its rapid global spread. To better understand the epidemiology of this disease, we sourced isolates from severe cases of dermatophytosis in the United Kingdom, Ireland, France, Canada and India for the period 2018-2023, including the type strain from Japan. We used whole-genome sequencing to confirm 90 isolates were T. indotineae, and antifungal susceptibility testing indicated that over half of these (62%) were resistant to terbinafine (MIC ≥1 mg/L). Pairwise genetic distances showed very high identity with only 147 (1-414) SNPs separating isolates that were nested within a monophyletic phylogeny, supporting a single evolutionary origin of T. indotineae. That no clear geographic clustering of isolates was observed confirms the rapid transcontinental spread of T. indotineae from its likely centre of diversity in Asia. Genome-wide analyses identified multiple non-synonymous SNPs in SQLE (ERG1), the squalene epoxidase target of terbinafine, that were associated with terbinafine in vitro resistance ≥1 mg/L. However, five isolates exhibited high MIC values without SQLE mutations, suggesting the presence of alternative resistance mechanisms. Our findings highlight the importance of better genomic surveillance to understand and manage this severe and rapidly emerging terbinafine-resistant dermatophyte.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".