Comparison of Phylogenetic Tree Topologies for Nitrogen Associated Genes Partially Reconstruct the Evolutionary History of Saccharomyces cerevisiae

Phenotyping of a new yeast mapping population reveals differences in the activation of the TORC1 signalling pathway between wild and domesticated yeast strains

Domestication can be understood as a symbiotic relationship that benefits both domesticator and domesticated species, involving multiple genetic changes that configure the phenotype of the domesticated species. One of the most important domesticated species is the yeast Saccharomyces cerevisiae, with both domesticated strains used for different fermentations processes for thousands of years and wild strains existing only in environments without human intervention; however, little is known about the phenotypic effects associated with its domestication. In the present work, we studied the effect of domestication on yeast TORC1 activation, a pleiotropic signalling pathway conserved across the eukaryotic domain. To achieve this goal, we improved a previously generated methodology to assess TORC1 activation, which turned out to be as effective as the original one but also presents several practical advantages for its application (such as facilitating confirmation of transformants and putting the Luc reporter gene under the control of the same PRPL26A promoter for each transformed strain). We then generated a mapping population, the so-called TOMAN-G population, derived from the "1002 Yeast Genomes Project" population, the most comprehensive catalogue of the genetic variation in yeasts. Finally, strains belonging to the TOMAN-G population were phenotyped for TORC1 activation, and then we compared the results obtained between yeast strains with different ecological origins, finding differences in TORC1 activation between wild and domesticated strains, particularly wine strains. These results are indicative of the effect of domestication on TORC1 activation, specifically that the different evolutionary trajectories of wild and domesticated strains have in fact caused differences in the activation of this pathway; furthermore, the phenotypic data obtained in this work could be used to continue underlying the genetic bases of TORC1 activation, a process that is still not fully understood, using techniques such as GWAS to search for specific genetic variants underlying the observed phenotypic variability and phylogenetic tree inferences to gain insight into the evolutionary relationships between these genetic variants.

Quartet Based Gene Tree Imputation Using Deep Learning Improves Phylogenomic Analyses Despite Missing Data

Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, for a combination of reasons (ranging from sampling biases to more biological causes, as in gene birth and loss), gene trees are often incomplete, meaning that not all species of interest have a common set of genes. Incomplete gene trees can potentially impact the accuracy of phylogenomic inference. We, for the first time, introduce the problem of imputing the quartet distribution induced by a set of incomplete gene trees, which involves adding the missing quartets back to the quartet distribution. We present Quartet based Gene tree Imputation using Deep Learning (QT-GILD), an automated and specially tailored unsupervised deep learning technique, accompanied by cues from natural language processing, which learns the quartet distribution in a given set of incomplete gene trees and generates a complete set of quartets accordingly. QT-GILD is a general-purpose technique needing no explicit modeling of the subject system or reasons for missing data or gene tree heterogeneity. Experimental studies on a collection of simulated and empirical datasets suggest that QT-GILD can effectively impute the quartet distribution, which results in a dramatic improvement in the species tree accuracy. Remarkably, QT-GILD not only imputes the missing quartets but can also account for gene tree estimation error. Therefore, QT-GILD advances the state-of-the-art in species tree estimation from gene trees in the face of missing data.

Differential Gene Expression and Allele Frequency Changes Favour Adaptation of a Heterogeneous Yeast Population to Nitrogen-Limited Fermentations

Alcoholic fermentation is fundamentally an adaptation process, in which the yeast Saccharomyces cerevisiae outperforms its competitors and takes over the fermentation process itself. Although wine yeast strains appear to be adapted to the stressful conditions of alcoholic fermentation, nitrogen limitations in grape must cause stuck or slow fermentations, generating significant economic losses for the wine industry. One way to discover the genetic bases that promote yeast adaptation to nitrogen-deficient environments are selection experiments, where a yeast population undergoes selection under conditions of nitrogen restriction for a number of generations, to then identify by sequencing the molecular characteristics that promote this adaptation. In this work, we carried out selection experiments in bioreactors imitating wine fermentation under nitrogen-limited fermentation conditions (SM60), using the heterogeneous SGRP-4X yeast population, to then sequence the transcriptome and the genome of the population at different time points of the selection process. The transcriptomic results showed an overexpression of genes from the NA strain (North American/YPS128), a wild, non-domesticated isolate. In addition, genome sequencing and allele frequency results allowed several QTLs to be mapped for adaptation to nitrogen-limited fermentation. Finally, we validated the ECM38 allele of NA strain as responsible for higher growth efficiency under nitrogen-limited conditions. Taken together, our results revealed a complex pattern of molecular signatures favouring adaptation of the yeast population to nitrogen-limited fermentations, including differential gene expression, allele frequency changes and loss of the mitochondrial genome. Finally, the results suggest that wild alleles from a non-domesticated isolate (NA) may have a relevant role in the adaptation to the assayed fermentation conditions, with the consequent potential of these alleles for the genetic improvement of wine yeast strains.