Analysis of the molecular evolution of histone variant H2A.Z using a linker-mediated complex strategy and yeast genetic complementation

The histone variant H2A.Z is deposited into chromatin by specific machinery and is required for genome functions. Using a linker-mediated complex strategy combined with yeast genetic complementation, we demonstrate evolutionary conservation of H2A.Z together with its chromatin incorporation and functions. This approach is applicable to the evolutionary analyses of proteins that form complexes with interactors.

Engineering of Synthetic Transcriptional Switches in Yeast

Genetic switches can be utilized for many purposes in synthetic biology including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although genetic switches have mainly been developed in prokaryotes to date, eukaryotic genetic switches are increasingly being reported as both rational and irrational engineering technologies mature. In this review, we describe genetic switches in yeast based on synthetic transcription factors and/or synthetic promoters. We also discuss directed evolution technologies for the rapid and robust construction of yeast genetic switches.

Data integration uncovers the metabolic bases of phenotypic variation in yeast

The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map. Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccharomyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes. Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (Jmax) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high Jmax was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results. This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.

Robust and flexible platform for directed evolution of yeast genetic switches

A wide repertoire of genetic switches has accelerated prokaryotic synthetic biology, while eukaryotic synthetic biology has lagged in the model organism Saccharomyces cerevisiae. Eukaryotic genetic switches are larger and more complex than prokaryotic ones, complicating the rational design and evolution of them. Here, we present a robust workflow for the creation and evolution of yeast genetic switches. The selector system was designed so that both ON- and OFF-state selection of genetic switches is completed solely by liquid handling, and it enabled parallel screen/selection of different motifs with different selection conditions. Because selection threshold of both ON- and OFF-state selection can be flexibly tuned, the desired selection conditions can be rapidly pinned down for individual directed evolution experiments without a prior knowledge either on the library population. The system’s utility was demonstrated using 20 independent directed evolution experiments, yielding genetic switches with elevated inducer sensitivities, inverted switching behaviours, sensory functions, and improved signal-to-noise ratio (>100-fold induction). The resulting yeast genetic switches were readily integrated, in a plug-and-play manner, into an AND-gated carotenoid biosynthesis pathway.

H/ACA Small Ribonucleoproteins: Structural and Functional Comparison Between Archaea and Eukaryotes

During ribosome synthesis, ribosomal RNA is modified through the formation of many pseudouridines and methylations which contribute to ribosome function across all domains of life. In archaea and eukaryotes, pseudouridylation of rRNA is catalyzed by H/ACA small ribonucleoproteins (sRNPs) utilizing different H/ACA guide RNAs to identify target uridines for modification. H/ACA sRNPs are conserved in archaea and eukaryotes, as they share a common general architecture and function, but there are also several notable differences between archaeal and eukaryotic H/ACA sRNPs. Due to the higher protein stability in archaea, we have more information on the structure of archaeal H/ACA sRNPs compared to eukaryotic counterparts. However, based on the long history of yeast genetic and other cellular studies, the biological role of H/ACA sRNPs during ribosome biogenesis is better understood in eukaryotes than archaea. Therefore, this review provides an overview of the current knowledge on H/ACA sRNPs from archaea, in particular their structure and function, and relates it to our understanding of the roles of eukaryotic H/ACA sRNP during eukaryotic ribosome synthesis and beyond. Based on this comparison of our current insights into archaeal and eukaryotic H/ACA sRNPs, we discuss what role archaeal H/ACA sRNPs may play in the formation of ribosomes.

Engineered yeast genomes accurately assembled from pure and mixed samples

Yeast whole genome sequencing (WGS) lacks end-to-end workflows that identify genetic engineering. Here we present Prymetime, a tool that assembles yeast plasmids and chromosomes and annotates genetic engineering sequences. It is a hybrid workflow—it uses short and long reads as inputs to perform separate linear and circular assembly steps. This structure is necessary to accurately resolve genetic engineering sequences in plasmids and the genome. We show this by assembling diverse engineered yeasts, in some cases revealing unintended deletions and integrations. Furthermore, the resulting whole genomes are high quality, although the underlying assembly software does not consistently resolve highly repetitive genome features. Finally, we assemble plasmids and genome integrations from metagenomic sequencing, even with 1 engineered cell in 1000. This work is a blueprint for building WGS workflows and establishes WGS-based identification of yeast genetic engineering.

Exploring a local genetic interaction network using evolutionary replay experiments

ABSTRACTUnderstanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally-evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using “evolutionary replay” experiments we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, and sheds light on a previously under-explored region of the yeast genetic interaction network.

CiBER-seq dissects genetic networks by quantitative CRISPRi profiling of expression phenotypes

To realize the promise of CRISPR-Cas9–based genetics, approaches are needed to quantify a specific, molecular phenotype across genome-wide libraries of genetic perturbations. We addressed this challenge by profiling transcriptional, translational, and posttranslational reporters using CRISPR interference (CRISPRi) with barcoded expression reporter sequencing (CiBER-seq). Our barcoding approach allowed us to connect an entire library of guides to their individual phenotypic consequences using pooled sequencing. CiBER-seq profiling fully recapitulated the integrated stress response (ISR) pathway in yeast. Genetic perturbations causing uncharged transfer RNA (tRNA) accumulation activated ISR reporter transcription. Notably, tRNA insufficiency also activated the reporter, independent of the uncharged tRNA sensor. By uncovering alternate triggers for ISR activation, we illustrate how precise, comprehensive CiBER-seq profiling provides a powerful and broadly applicable tool for dissecting genetic networks.

Proteomics Answers Which Yeast Genes Are Specific for Baking, Brewing, and Ethanol Production

Yeast strains are convenient models for studying domestication processes. The ability of yeast to ferment carbon sources from various substrates and to produce ethanol and carbon dioxide is the core of brewing, winemaking, and ethanol production technologies. The present study reveals the differences among yeast strains used in various industries. To understand this, we performed a proteomic study of industrial Saccharomyces cerevisiae strains followed by a comparative analysis of available yeast genetic data. Individual protein expression levels in domesticated strains from different industries indicated modulation resulting from response to technological environments. The innovative nature of this research was the discovery of genes overexpressed in yeast strains adapted to brewing, baking, and ethanol production, typical genes for specific domestication were found. We discovered a gene set typical for brewer’s yeast strains. Baker’s yeast had a specific gene adapted to osmotic stress. Toxic stress was typical for yeast used for ethanol production. The data obtained can be applied for targeted improvement of industrial strains.

Data integration uncovers the metabolic bases of phenotypic variation in yeast

The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map.Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccha-romyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes.Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (Jmax) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high Jmax was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results.This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.