Phylogenetic Analysis of Phenotypically Characterized Cryptococcus laurentii Isolates Reveals High Frequency of Cryptic Species
Bibliographic record
Abstract
BACKGROUND: Although Cryptococcus laurentii has been considered saprophytic and its taxonomy is still being described, several cases of human infections have already reported. This study aimed to evaluate molecular aspects of C. laurentii isolates from Brazil, Botswana, Canada, and the United States. METHODS: In this study, 100 phenotypically identified C. laurentii isolates were evaluated by sequencing the 18S nuclear ribosomal small subunit rRNA gene (18S-SSU), D1/D2 region of 28S nuclear ribosomal large subunit rRNA gene (28S-LSU), and the internal transcribed spacer (ITS) of the ribosomal region. RESULTS: BLAST searches using 550-bp, 650-bp, and 550-bp sequenced amplicons obtained from the 18S-SSU, 28S-LSU, and the ITS region led to the identification of 75 C. laurentii strains that shared 99-100% identity with C. laurentii CBS 139. A total of nine isolates shared 99% identity with both Bullera sp. VY-68 and C. laurentii RY1. One isolate shared 99% identity with Cryptococcus rajasthanensis CBS 10406, and eight isolates shared 100% identity with Cryptococcus sp. APSS 862 according to the 28S-LSU and ITS regions and designated as Cryptococcus aspenensis sp. nov. (CBS 13867). While 16 isolates shared 99% identity with Cryptococcus flavescens CBS 942 according to the 18S-SSU sequence, only six were confirmed using the 28S-LSU and ITS region sequences. The remaining 10 shared 99% identity with Cryptococcus terrestris CBS 10810, which was recently described in Brazil. Through concatenated sequence analyses, seven sequence types in C. laurentii, three in C. flavescens, one in C. terrestris, and one in the C. aspenensis sp. nov. were identified. CONCLUSIONS: Sequencing permitted the characterization of 75% of the environmental C. laurentii isolates from different geographical areas and the identification of seven haplotypes of this species. Among sequenced regions, the increased variability of the ITS region in comparison to the 18S-SSU and 28S-LSU regions reinforces its applicability as a DNA barcode.
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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 | 0.001 |
| 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 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".