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Record W4296515891 · doi:10.1093/mmy/myac072.s8.5c

S8.5c MLST genotyping and phylogenetics of AD-hybrids

2022· article· en· W4296515891 on OpenAlexaff
Massimo Cogliati, Min Chen, Jianping Xu, Megan Hitchcock, June Kwon Chung, Dong-Hoon Yang, Volker Rickerts, Marie Desnos Ollivier, Joao Inacio Silva, Wieland Meyer, Magdalena Florek, Urszula Nawrot, Patricia Escandón, Andrés Puime, Frédéric Roger, Sébastien Bertout

Bibliographic record

VenueMedical Mycology · 2022
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMultilocus sequence typingGenotypingBiologyGenotypeAlleleGeneticsCryptococcus neoformansLocus (genetics)TypingPopulationHaplotypeHybridGeneMedicine

Abstract

fetched live from OpenAlex

Abstract S8.5 Genotyping of Cryptococcus neoformans and C. gattii, September 23, 2022, 3:00 PM - 4:30 PM Objectives In a previous study a set of new molecular-type specific primers were designed to apply the standard ISHAM consensus multi-locus sequence typing (MLST) scheme to Cryptococcus neoformans AD hybrids. In the present study, we report the preliminary results of the investigation by MLST of a large number of AD hybrids with the aim to identify the circulating genotypes, their phylogenesis, and population genetics. Methods A total of 50 AD-hybrid isolates from different parts of the world and from different sources were genotyped by MLST. Minimum spanning trees using GoeBurst algorithm were generated by comparing hybrid genotypes and by comparing separately either allele-A and allele-D portions of the hybrid genotypes to the haplotypes recorded in the MLST global database. Results Analysis identified 32 hybrid genotypes grouped in three distinct main clusters (CC12, CC21, and CC30) including 12 isolates each. Both CC12 and CC21 clusters included isolates from different countries and continents but the former grouped only isolates with mating type aADalpha whereas the latter those with mating type alphaADa. Cluster CC30 included only isolates from Ivory Coasts. Heterozygous allelic combinations in each of the seven MLST loci presented two or three combinations more frequent than the other ones. In some isolates, one or more alleles were not amplified after multiple attempts, and therefore, they were considered as lacking. A total of 22 MLST profiles were identified by analyzing separately the allele-A combinations of the hybrids. Comparison with all MLST profiles of VNI, VNII, and VNB included in the MLST global database showed that the allele-A portion of the hybrid genotypes was grouped in few VNI or VNB clusters. In none of the investigated hybrids, the allele-A portion originated from VNII genotypes. Similarly, when the MLST profile of allele-D portion of hybrids was compared to all VNIV genotypes present in the global MLST database, few clusters were identified but, in this case, mostly originated from genotypes not yet found among VNIV haplotypes. Conclusions These preliminary results suggest that the AD hybrids here investigated originated from the mating of A haploids very common in both clinical and environmental isolates and D haploids that are not circulating at present or are very rare. Therefore, it is likely that hybrids originated in the environment where VNIV genotypic diversity is higher and suitable AD combinations can occur. Sequencing of further AD hybrids is in progress to confirm these results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.272
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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