Biology subjects

Chavez, C. M.

Publications and source records attributed to Chavez, C. M..

3 recordsLinked to original sources

bioRxiv2025

The Macroevolution of Filamentation Morphology Across the Saccharomycotina Yeast Subphylum

Saccharomycotina yeasts are a highly diverse and widely distributed subphylum of ascomycete fungi that exhibit diversity in their asexual growth morphologies; the human commensal yeast Candida albicans exhibits both unicellular and filamentous growth, whereas the bakers yeast Saccharomyces cerevisiae grows unicellularly by budding. Filamentous growth (and the production of hyphae) can comprise linear and branched budding cells that do not undergo cell separation, termed pseudohyphae, or of tubular filaments with septa that perforate allowing movement of organelles, termed true hyphae. We integrated phenotypic, genomic, metabolic, and isolation environment data from 1,154 strains from 1,051 Saccharomycotina species to examine the variation and evolutionary history of filamentation across Saccharomycotina and determine the extent to which genes, metabolic traits, and environments can predict filamentation types. We found that 63.37% of strains can form filaments; 6.56% can form true hyphae, 42.40% can form pseudohyphae, and 14.39% can form both true hyphae and pseudohyphae. We found that the distributions of species that can produce true hyphae or that can filament were more strongly correlated with the yeast phylogeny than the distribution of species with pseudohyphae. Ancestral state reconstruction suggested that the ability to form true hyphae and/or pseudohyphae evolved several times in yeast evolution. We infer that most yeast ancestors likely produced pseudohyphae or lacked filaments, while the Saccharomycotina last common ancestor likely produced pseudohyphae but not true hyphae. Machine learning models trained on genomic and metabolic features predicted filament morphologies with about 70% accuracy. Connecting the evolution of Saccharomycotina morphologies to their genomic, metabolic, and ecological characteristics will enrich our understanding of the evolution of the diversity of lifestyles observed in this lineage.

evolutionary biology↗

bioRxiv2025

A trait syndrome ties cell morphology to glycolysis across the yeast subphylum

Traits that co-vary across species can provide fundamental insights into the trade-offs and constraints that govern their evolution. It was recently reported that glucose uptake rates (GUR) are inversely correlated with the cell surface area-to-volume (SA:V) ratio across 11 yeast species. Here we substantially expand this analysis to 282 species to test whether the GUR-SA:V correlation generalizes across the ancient Saccharomycotina yeast subphylum and to determine the contribution of shared evolutionary history to the co-variation of these two traits. Using regression models that account for co-variation due to phylogeny, we found that extracellular acidification rates (ECAR, which we used as a scalable proxy for GUR) had a weak, but significant, correlation with SA:V across Saccharomycotina. We found additional weak, but significant, correlations between ECAR with genome sizes and growth rates. Our findings largely agree with the recently reported correlations between GUR and SA:V ratio, but they also show that there are likely several other associated variables, including genome size. Specifically, yeasts that consume glucose faster tend to have lower SA:V, faster growth rates, and larger genomes. Our results suggest that a trait syndrome governs several metabolic, genomic, and morphological traits across the yeast subphylum.

evolutionary biology↗

bioRxiv2021

BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data

Bioinformatic analysis--such as genome assembly quality assessment, alignment summary statistics, relative synonymous codon usage, paired-end aware quality trimming and filtering of sequencing reads, file format conversion, and processing and analysis--is integrated into diverse disciplines in the biological sciences. Several command-line pieces of software have been developed to conduct some of these individual analyses; however, the lack of a unified toolkit that conducts all these analyses can be a barrier in workflows. To address this obstacle, we introduce BioKIT, a versatile toolkit for the UNIX shell environment with 40 functions, several of which were community-sourced, that conduct routine and novel processing and analysis of genome assemblies, multiple sequence alignments, coding sequences, sequencing data, and more. To demonstrate the utility of BioKIT, we assessed the quality and characteristics of 901 eukaryotic genome assemblies, calculated alignment summary statistics for 10 phylogenomic data matrices, determined relative synonymous codon usage across 171 fungal genomes including those that use alternative genetic codes, and demonstrate that a novel metric, gene-wise relative synonymous codon usage, can accurately estimate gene-wise codon optimization. BioKIT will be helpful in facilitating and streamlining sequence analysis workflows. BioKIT is freely available under the MIT license from GitHub (https://github.com/JLSteenwyk/BioKIT), PyPi (https://pypi.org/project/jlsteenwykbiokit/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/jlsteenwyk-biokit). Documentation, user tutorials, and instructions for requesting new features are available online (https://jlsteenwyk.com/BioKIT).

bioinformatics↗