Arnould, L. | bioRxiv Science

bioRxiv2026

Genotypic and phenotypic diversity of Maudiozyma humilis: the multiple evolutionary trajectories of a domesticated yeast

Maudiozyma humilis is the second most frequently encountered yeast species in sourdough bread. Despite its ecological and food relevance, little is known about its evolutionary trajectories and phenotype traits of interest. Here we investigated the genomic and phenotypic diversity of a world-wide collection of 55 M. humilis strains, including 52 from sourdough, by combining genomic analysis, flow cytometry and high-throughput phenotyping of fermentation kinetics and fitness. Population genomic analysis revealed six genetically distinct clades, three diploid and three triploid, with no geographical or substrate-specific structuring. Phylogenetic, loss of heterozygosity (LOH) distribution and allele specific analysis indicated that triploid strains originated from both recent and more ancient hybridization events involving multiple diploid lineages. The absence of the HO gene, and mating-type silent cassettes, revealed that M. humilis is not able to carry mating-type switching. In addition, high linkage disequilibrium (LD) and variable LOH accumulation were observed consistent with a predominantly clonal reproduction. Last, an exceptionally high level of heterozygosity was detected, suggesting that occasional hybridization is the major driver of genetic diversity. Phenotypic characterization in a synthetic sourdough medium revealed variation in fermentation kinetics and fitness statistically associated with the genetic clades. Interestingly, genetic distance between clades and strains better explain the phenotypic variation than difference in ploidy. Altogether, our findings highlight the complex evolutionary history of M. humilis, shaped by hybridization and ploidy variation and reveal that historical contingency, more than ploidy, shapes the phenotypic landscape of this species. Beyond providing the first analysis of M. humilis evolution, our result challenges the hypothesis that increases in ploidy are necessarily beneficial in domesticated species.

genomics↗

bioRxiv2022

A Deep Learning solution for triaging patient with cancer from their predicted mutational status using histopathological images

The presence of mutation in cancer can be associated with a response to a targeted cancer therapy. Therefore it has become an important information while it helps giving a more specialized and more efficient treatment for each patient. Detection of mutation is routinely made by DNA-sequencing diagnostic tests. Recently, a new strategy of deep learning based method of prediction of mutation from histopathological images has shown promising results. However it is still unknown whether these methods can be useful as screening tests aside from sequencing methods for efficient population diagnosis. Here we developed our own deep learning based prediction pipeline for the detection of mutation in breast, lung, colon and ovarian cancer and find clinically relevant genes that have easy-to-detect mutational status, and especially TP53 which can be detected in these four tumors. We then propose 3 potential screening strategies and show the effectiveness of our deep learning pipeline for optimizing diagnosis in the patient population.

bioinformatics↗