Genome-wide mapping using new AFLP markers to explore intraspecific variation among pathogenic Sporothrix species
Bibliographic record
Abstract
Sporotrichosis is a chronic subcutaneous mycosis caused by Sporothrix species, of which the main aetiological agents are S. brasiliensis, S. schenckii, and S. globosa. Infection occurs after a traumatic inoculation of Sporothrix propagules in mammals' skin and can follow either a classic route through traumatic inoculation by plant debris (e.g., S. schenckii and S. globosa) or an alternative route through zoonotic transmission from animals (e.g., S. brasiliensis). Epizootics followed by a zoonotic route occur in Brazil, with Rio de Janeiro as the epicenter of a recent cat-transmitted epidemic. DNA-based markers are needed to explore the epidemiology of these Sporothrix expansions using molecular methods. This paper reports the use of amplified-fragment-length polymorphisms (AFLP) to assess the degree of intraspecific variability among Sporothrix species. We used whole-genome sequences from Sporothrix species to generate 2,304 virtual AFLP fingerprints. In silico screening highlighted 6 primer pair combinations to be tested in vitro. The protocol was used to genotype 27 medically relevant Sporothrix. Based on the overall scored AFLP markers (97-137 fragments), the values of polymorphism information content (PIC = 0.2552-0.3113), marker index (MI = 0.002-0.0039), effective multiplex ratio (E = 17.8519-35.2222), resolving power (Rp = 33.6296-63.1852), discriminating power (D = 0.9291-0.9662), expected heterozygosity (H = 0.3003-0.3857), and mean heterozygosity (Havp = 0.0001) demonstrated the utility of these primer combinations for discriminating Sporothrix. AFLP markers revealed cryptic diversity in species previously thought to be the most prevalent clonal type, such as S. brasiliensis, responsible for cat-transmitted sporotrichosis, and S. globosa responsible for large sapronosis outbreaks in Asia. Three combinations (#3 EcoRI-FAM-GA/MseI-TT, #5 EcoRI-FAM-GA/MseI-AG, and #6 EcoRI-FAM-TA/MseI-AA) provide the best diversity indices and lowest error rates. These methods make it easier to track routes of disease transmission during epizooties and zoonosis, and our DNA fingerprint assay can be further transferred between laboratories to give insights into the ecology and evolution of pathogenic Sporothrix species and to inform management and mitigation strategies to tackle the advance of sporotrichosis.
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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 | 0.001 |
| 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".