Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

One of the central goals of evolutionary biology is to explain and predict the molecular basis of adaptive evolution. We studied the evolution of genetic networks in Saccharomyces cerevisiae (budding yeast) populations propagated for more than 200 generations in different nitrogen-limiting conditions. We find that rapid adaptive evolution in nitrogen-poor environments is dominated by the de novo generation and selection of copy number variants (CNVs), a large fraction of which contain genes encoding specific nitrogen transporters including PUT4, DUR3 and DAL4. The large fitness increases associated with these alleles limits the genetic heterogeneity of adapting populations even in environments with multiple nitrogen sources. Complete identification of acquired point mutations, in individual lineages and entire populations, identified heterogeneity at the level of genetic loci but common themes at the level of functional modules, including genes controlling phosphatidylinositol-3-phosphate metabolism and vacuole biogenesis. Adaptive strategies shared with other nutrient-limited environments point to selection of genetic variation in the TORC1 and Ras/PKA signaling pathways as a general mechanism underlying improved growth in nutrient-limited environments. Within a single population we observed the repeated independent selection of a multi-locus genotype, comprised of the functionally related genes GAT1, MEP2 and LST4. By studying the fitness of individual alleles, and their combination, as well as the evolutionary history of the evolving population, we find that the order in which these mutations are acquired is constrained by epistasis. The identification of repeatedly selected variation at functionally related loci that interact epistatically suggests that gene network polymorphisms (GNPs) may be a frequent outcome of adaptive evolution. Our results provide insight into the mechanistic basis by which cells adapt to nutrient-limited environments and suggest that knowledge of the selective environment and the regulatory mechanisms important for growth and survival in that environment greatly increase the predictability of adaptive evolution. We studied adaptive evolution in different nitrogen-limited environments using long-term selection of asexually reproducing Saccharomyces cerevisiae populations in chemostats. Using next generation sequencing and DNA microarrays, we identified all acquired genetic variation associated with increased fitness, in both individual lineages and entire populations. We find that amplification alleles that include nutrient transporter genes specific to the molecular form of the nitrogen present in the environment are a common mechanism underlying increased fitness. In addition, we identified a general strategy for adaptation to nitrogen-limited environments that entails remodeling of phospholipid biogenesis required for producing important cellular components including vacuoles and autophagosomes. More general strategies for adaptation to nutrient-limited environments point to a role for re-wiring of signaling pathways that coordinate cell growth with nutrient availability. We reconstructed the evolutionary dynamics of a population evolving in ammonium-limited conditions and find that a multi-locus genotype is repeatedly selected within the population and constrained by epistasis. We propose that this genotype constitutes a “gene network polymorphism (GNP),” which may be a common outcome of adaptive evolution. Our study suggests that when the selective pressure is understood the molecular basis of adaptive evolution in large microbial populations may be predicted with reasonable precision.

The abundance of a transcript is determined by its rate of synthesis and its rate of degradation; however, global methods for quantifying RNA abundance cannot distinguish variation in these two processes. Here, we introduce RNA approach to equilibrium sequencing (RATE-seq), which uses in vivo metabolic labeling of RNA and approach to equilibrium kinetics, to determine absolute RNA degradation and synthesis rates. RATE-seq does not disturb cellular physiology, uses straightforward normalization with exogenous spike-ins, and can be readily adapted for studies in most organisms. We demonstrate the use of RATE-seq to estimate genome-wide kinetic parameters for coding and noncoding transcripts in Saccharomyces cerevisiae.

Cellular responses to changing environments frequently involve rapid reprogramming of the transcriptome. Regulated changes in mRNA degradation rates can accelerate reprogramming by clearing or stabilizing extant transcripts. Here, we measured mRNA stability using 4-thiouracil labeling in the budding yeast Saccharomyces cerevisiae during a nitrogen upshift and found that 78 mRNAs are subject to destabilization. These transcripts include Nitrogen Catabolite Repression (NCR) and carbon metabolism mRNAs, suggesting that mRNA destabilization is a mechanism for targeted reprogramming of the transcriptome. To explore the molecular basis of destabilization we implemented a SortSeq approach to screen the pooled deletion collection library for trans factors that mediate rapid GAP1 mRNA repression. We combined low-input multiplexed Barcode sequencing with branched-DNA single-molecule mRNA FISH and Fluorescence-activated cell sorting (BFF) to identify the Lsm1-7p/Pat1p complex and general mRNA decay machinery as important for GAP1 mRNA clearance. We also find that the decapping modulators EDC3 and SCD6, translation factor eIF4G2, and the 5’ UTR of GAP1 are factors that mediate rapid repression of GAP1 mRNA, suggesting that translational control may impact the post-transcriptional fate of mRNAs in response to environmental changes. Budding yeast rapidly increase their rate of growth upon re-feeding of glutamine to a nitrogen-limited culture. Within the first several minutes, the transcriptome begins to reprogram. We investigated changes in mRNA stability using a 4-thiouracil label-chase experiment to track the extant transcriptome during the nitrogen upshift. We found that a functionally enriched set of mRNA are destabilized, including some members of the Nitrogen Catabolite Repression regulon. To find genetic factors that may be involved, we used GAP1 mRNA as a marker of this post-transcriptional regulation and developed a method for estimating mRNA abundance using a combination of mRNA FISH, FACS, and Barcode sequencing. We identified the Lsm1-7p/Pat1p complex as having large differences in GAP1 mRNA regulation, and further work identified that the decapping modulators EDC3 and SCD6, the translation initiation factor eIF4G2, and the 5’ UTR of GAP1 have defects in rapidly clearing GAP1 mRNA during this transition. Our study suggests that changes in the translational status of specific mRNAs may impact their stability in response to environmental signals.