Xing‐Xing Shen

The paper uses machine learning to identify genomic signatures of antifungal drug resistance in Saccharomycotina yeasts, finding that natural resistance patterns differ from clinical isolates.

The paper identifies genomic factors limiting Saccharomycotina fungi's ability to infect plants, finding they lack metabolic enzymes used by other phytopathogens.

The paper investigates the relationship between gene family expansions and metabolic traits in yeasts, finding that keystone gene families drive multiple metabolic innovations.

The paper analyzes gene family evolution across nearly all known Saccharomycotina yeast species, finding distinct evolutionary patterns with gene loss linked to metabolic and genomic changes.

The paper identifies three yeast lineages with reduced DNA repair genes and high evolutionary rates, showing how gene loss can lead to persistent hypermutator phenotypes.

The paper analyzes codon usage patterns across 1,154 Saccharomycotina strains, identifying biases, correlations with GC content and tRNA-ome size, and order-specific classification using machine learning.

The paper shows that the fungal gene ENB1, acquired from a bacterial operon, enables iron uptake in W/S clade yeasts and evolved through horizontal transfer and gene loss.

The paper shows that galactose metabolism genes can be horizontally transferred between yeast species, allowing for the reacquisition of a lost trait after millions of years.

The paper constructs a robust phylogenomic timetree for Aspergillus and Penicillium fungi using multiple genome datasets and phylogenetic methods to identify incongruent branches and estimate divergence times.

The paper shows that fructophilic yeasts in the W/S clade lost and reacquired alcoholic fermentation genes via horizontal gene transfer, adapting to high sugar environments.