Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes

Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework

Yeasts in the subphylum Saccharomycotina are found across the globe in disparate ecosystems. A major aim of yeast research is to understand the diversity and evolution of ecological traits, such as carbon metabolic breadth, insect association, and cactophily. This includes studying aspects of ecological traits like genetic architecture or association with other phenotypic traits. Genomic resources in the Saccharomycotina have grown rapidly. Ecological data, however, are still limited for many species, especially those only known from species descriptions where usually only a limited number of strains are studied. Moreover, ecological information is recorded in natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework for the analysis of yeast ecology. A total of 1,088 yeast strains were added to the Ontology of Yeast Environments (OYE) and analyzed in a machine-learning framework to connect genotype to ecology. This framework is flexible and can be extended to additional isolates, species, or environmental sequencing data. Widespread adoption of OYE would greatly aid the study of macroecology in the Saccharomycotina subphylum.

Divergence of alternative sugar preferences through modulation of the expression and activity of the Gal3 sensor in yeast

Optimized nutrient utilization is crucial for the progression of microorganisms in competing communities. Here we investigate how different budding yeast species and ecological isolates have established divergent preferences for two alternative sugar substrates: Glucose, which is fermented preferentially by yeast, and galactose, which is alternatively used upon induction of the relevant GAL metabolic genes. We quantified the dose-dependent induction of the GAL1 gene encoding the central galactokinase enzyme and found that a very large diversification exists between different yeast ecotypes and species. The sensitivity of GAL1 induction correlates with the growth performance of the respective yeasts with the alternative sugar. We further define some of the mechanisms, which have established different glucose/galactose consumption strategies in representative yeast strains by modulating the activity of the Gal3 inducer. (1) Optimal galactose consumers, such as Saccharomyces uvarum, contain a hyperactive GAL3 promoter, sustaining highly sensitive GAL1 expression, which is not further improved upon repetitive galactose encounters. (2) Desensitized galactose consumers, such as S. cerevisiae Y12, contain a less sensitive Gal3 sensor, causing a shift of the galactose response towards higher sugar concentrations even in galactose experienced cells. (3) Galactose insensitive sugar consumers, such as S. cerevisiae DBVPG6044, contain an interrupted GAL3 gene, causing extremely reluctant galactose consumption, which is, however, improved upon repeated galactose availability. In summary, different yeast strains and natural isolates have evolved galactose utilization strategies, which cover the whole range of possible sensitivities by modulating the expression and/or activity of the inducible galactose sensor Gal3.

Long-Term Adaptation to Galactose as a Sole Carbon Source Selects for Mutations Outside the Canonical GAL Pathway

Galactose is a secondary fermentable sugar that requires specific regulatory and structural genes for its assimilation, which are under catabolite repression by glucose. When glucose is absent, the catabolic repression is attenuated, and the structural GAL genes are fully activated. In Saccharomyces cerevisiae, the GAL pathway is under selection in environments where galactose is present. However, it is unclear the adaptive strategies in response to long-term propagation in galactose as a sole carbon source in laboratory evolution experiments. Here, we performed a 4,000-generation evolution experiment using 48 diploid Saccharomyces cerevisiae populations to study adaptation in galactose. We show that fitness gains were greater in the galactose-evolved population than in identically evolved populations with glucose as a sole carbon source. Whole-genome sequencing of 96 evolved clones revealed recurrent de novo single nucleotide mutations in candidate targets of selection, copy number variations, and ploidy changes. We find that most mutations that improve fitness in galactose lie outside of the canonical GAL pathway. Reconstruction of specific evolved alleles in candidate target of selection, SEC23 and IRA1, showed a significant increase in fitness in galactose compared to glucose. In addition, most of our evolved populations (28/46; 61%) fixed aneuploidies on Chromosome VIII, suggesting a parallel adaptive amplification. Finally, we show greater loss of extrachromosomal elements in our glucose-evolved lineages compared with previous glucose evolution. Broadly, these data further our understanding of the evolutionary pressures that drive adaptation to less-preferred carbon sources.

Public good-driven release of heterogeneous resources leads to genotypic diversification of an isogenic yeast population

Understanding the basis of biological diversity remains a central problem in evolutionary biology. Using microbial systems, adaptive diversification has been studied in (a) spatially heterogeneous environments, (b) temporally segregated resources, and (c) resource specialization in a homogeneous environment. However, it is not well understood how adaptive diversification can take place in a homogeneous environment containing a single resource. Starting from an isogenic population of yeast Saccharomyces cerevisiae, we report rapid adaptive diversification, when propagated in an environment containing melibiose as the carbon source. The diversification is driven due to a public good enzyme α-galactosidase, which hydrolyzes melibiose into glucose and galactose. The diversification is driven by mutations at a single locus, in the GAL3 gene in the S. cerevisiae GAL/MEL regulon. We show that metabolic co-operation involving public resources could be an important mode of generating biological diversity. Our study demonstrates sympatric diversification of yeast starting from an isogenic population and provides detailed mechanistic insights into the factors and conditions responsible for generating and maintaining the population diversity.

GAL Regulon in the Yeast S. cerevisiae is Highly Evolvable via Acquisition in the Coding Regions of the Regulatory Elements of the Network

GAL network in the yeast S. cerevisiae is one of the most well-characterized regulatory network. Expression of GAL genes is contingent on exposure to galactose, and an appropriate combination of the alleles of the regulatory genes GAL3, GAL1, GAL80, and GAL4. The presence of multiple regulators in the GAL network makes it unique, as compared to the many sugar utilization networks studied in bacteria. For example, utilization of lactose is controlled by a single regulator LacI, in E. coli's lac operon. Moreover, recent work has demonstrated that multiple alleles of these regulatory proteins are present in yeast isolated from ecological niches. In this work, we develop a mathematical model, and demonstrate via deterministic and stochastic runs of the model, that behavior/gene expression patterns of the cells (at a population level, and at a single-cell resolution) can be modulated by altering the binding affinities between the regulatory proteins. This adaptability is likely the key to explaining the multiple GAL regulatory alleles discovered in ecological isolates in recent years.

Computational analysis of GAL pathway pinpoints mechanisms underlying natural variation

Quantitative traits are measurable phenotypes that show continuous variation over a wide phenotypic range. Enormous effort has recently been put into determining the genetic influences on a variety of quantitative traits with mixed success. We identified a quantitative trait in a tractable model system, the GAL pathway in yeast, which controls the uptake and metabolism of the sugar galactose. GAL pathway activation depends both on galactose concentration and on the concentrations of competing, preferred sugars such as glucose. Natural yeast isolates show substantial variation in the behavior of the pathway. All studied yeast strains exhibit bimodal responses relative to external galactose concentration, i.e. a set of galactose concentrations existed at which both GAL-induced and GAL-repressed subpopulations were observed. However, these concentrations differed in different strains. We built a mechanistic model of the GAL pathway and identified parameters that are plausible candidates for capturing the phenotypic features of a set of strains including standard lab strains, natural variants, and mutants. In silico perturbation of these parameters identified variation in the intracellular galactose sensor, Gal3p, the negative feedback node within the GAL regulatory network, Gal80p, and the hexose transporters, HXT, as the main sources of the bimodal range variation. We were able to switch the phenotype of individual yeast strains in silico by tuning parameters related to these three elements. Determining the basis for these behavioral differences may give insight into how the GAL pathway processes information, and into the evolution of nutrient metabolism preferences in different strains. More generally, our method of identifying the key parameters that explain phenotypic variation in this system should be generally applicable to other quantitative traits.

Microbes adopt elaborate strategies for the preferred uptake and use of nutrients to cope with complex and fluctuating environments. As a result, yeast strains originating from different ecological niches show significant variation in the way they induce genes in the galactose metabolism (GAL) pathway in response to nutrient signals. To identify the mechanistic sources of this variation, we built a mathematical model to simulate the dynamics of the galactose metabolic regulation network, and studied how parameters with different biological implications contributed to the natural variation. We found that variations in the behavior of the galactose sensor Gal3p, the negative feedback node Gal80p, and the hexose transporters HXT were critical elements in the GAL pathway response. Tuning single parameters in silico was sufficient to achieve phenotype switching between different yeast strains. Our computational approach should be generally useful to help pinpoint the genetic and molecular bases of natural variation in other systems.

Variation in the modality of a yeast signaling pathway is mediated by a single regulator

Bimodal gene expression by genetically identical cells is a pervasive feature of signaling networks and has been suggested to allow organisms to hedge their ‘bets’ in uncertain conditions. In the galactose-utilization (GAL) pathway of Saccharomyces cerevisiae, gene induction is unimodal or bimodal depending on natural genetic variation and pre-induction conditions. Here, we find that this variation in modality arises from regulation of two features of the pathway response: the fraction of cells that show induction and their level of expression. GAL3, the galactose sensor, controls the fraction of induced cells, and titrating its expression is sufficient to control modality; moreover, all the observed differences in modality between different pre-induction conditions and among natural isolates can be explained by changes in GAL3’s regulation and activity. The ability to switch modality by tuning the activity of a single protein may allow rapid adaptation of bet hedging to maximize fitness in complex environments.

Genetic background mutations drive neural circuit hyperconnectivity in a fragile X syndrome model

BACKGROUND

Neural circuits are initially assembled during development when neurons synapse with potential partners and later refined as appropriate connections stabilize into mature synapses while inappropriate contacts are eliminated. Disruptions to this synaptogenic process impair connectivity optimization and can cause neurodevelopmental disorders. Intellectual disability (ID) and autism spectrum disorder (ASD) are often characterized by synaptic overgrowth, with the maintenance of immature or inappropriate synapses. Such synaptogenic defects can occur through mutation of a single gene, such as fragile X mental retardation protein (FMRP) loss causing the neurodevelopmental disorder fragile X syndrome (FXS). FXS represents the leading heritable cause of ID and ASD, but many other genes that play roles in ID and ASD have yet to be identified.

RESULTS

In a Drosophila FXS disease model, one dfmr150M null mutant stock exhibits previously unreported axonal overgrowths at developmental and mature stages in the giant fiber (GF) escape circuit. These excess axon projections contain both chemical and electrical synapse markers, indicating mixed synaptic connections. Extensive analyses show these supernumerary synapses connect known GF circuit neurons, rather than new, inappropriate partners, indicating hyperconnectivity within the circuit. Despite the striking similarities to well-characterized FXS synaptic defects, this new GF circuit hyperconnectivity phenotype is driven by genetic background mutations in this dfmr150M stock. Similar GF circuit synaptic overgrowth is not observed in independent dfmr1 null alleles. Bulked segregant analysis (BSA) was combined with whole genome sequencing (WGS) to identify the quantitative trait loci (QTL) linked to neural circuit hyperconnectivity. The results reveal 8 QTL associated with inappropriate synapse formation and maintenance in the dfmr150M mutant background.

CONCLUSIONS

Synaptogenesis is a complex, precisely orchestrated neurodevelopmental process with a large cohort of gene products coordinating the connectivity, synaptic strength, and excitatory/inhibitory balance between neuronal partners. This work identifies a number of genetic regions that contain mutations disrupting proper synaptogenesis within a particularly well-mapped neural circuit. These QTL regions contain potential new genes involved in synapse formation and refinement. Given the similarity of the synaptic overgrowth phenotype to known ID and ASD inherited conditions, identifying these genes should increase our understanding of these devastating neurodevelopmental disease states.

The ecology and evolution of microbial metabolic strategies

Free-living microbes are generally capable of growing on multiple different nutrients. Some of those nutrients are used simultaneously, while others are used sequentially. The pattern of nutrient preferences and co-utilization defines the metabolic strategy of a microorganism. Metabolic strategies can substantially affect ecological interactions between species, but their evolution and distribution across the tree of life remain poorly characterized. We discuss how the confluence of better computational models of genotype-phenotype maps and high-throughput experimental tools can help us fill gaps in our knowledge and incorporate metabolic strategies into quantitative predictive models of microbial consortia.

The Crabtree Effect Shapes the Saccharomyces cerevisiae Lag Phase during the Switch between Different Carbon Sources

When faced with environmental changes, microbes often enter a temporary growth arrest during which they reprogram the expression of specific genes to adapt to the new conditions. A prime example of such a lag phase occurs when microbes need to switch from glucose to other, less-preferred carbon sources. Despite its industrial relevance, the genetic network that determines the duration of the lag phase has not been studied in much detail. Here, we performed a genome-wide Bar-Seq screen to identify genetic determinants of the Saccharomyces cerevisiae glucose-to-galactose lag phase. The results show that genes involved in respiration, and specifically those encoding complexes III and IV of the electron transport chain, are needed for efficient growth resumption after the lag phase. Anaerobic growth experiments confirmed the importance of respiratory energy conversion in determining the lag phase duration. Moreover, overexpression of the central regulator of respiration, HAP4, leads to significantly shorter lag phases. Together, these results suggest that the glucose-induced repression of respiration, known as the Crabtree effect, is a major determinant of microbial fitness in fluctuating carbon environments.IMPORTANCE The lag phase is arguably one of the prime characteristics of microbial growth. Longer lag phases result in lower competitive fitness in variable environments, and the duration of the lag phase is also important in many industrial processes where long lag phases lead to sluggish, less efficient fermentations. Despite the immense importance of the lag phase, surprisingly little is known about the exact molecular processes that determine its duration. Our study uses the molecular toolbox of S. cerevisiae combined with detailed growth experiments to reveal how the transition from fermentative to respirative metabolism is a key bottleneck for cells to overcome the lag phase. Together, our findings not only yield insight into the key molecular processes and genes that influence lag duration but also open routes to increase the efficiency of industrial fermentations and offer an experimental framework to study other types of lag behavior.

Assigning function to natural allelic variation via dynamic modeling of gene network induction

More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be “personalized” according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non‐synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

Genetic and Epigenetic Strategies Potentiate Gal4 Activation to Enhance Fitness in Recently Diverged Yeast Species

Certain genes show more rapid reactivation for several generations following repression, a conserved phenomenon called epigenetic transcriptional memory. Following previous growth in galactose, GAL gene transcriptional memory confers a strong fitness benefit in Saccharomyces cerevisiae adapting to growth in galactose for up to 8 generations. A genetic screen for mutants defective for GAL gene memory revealed new insights into the molecular mechanism, adaptive consequences, and evolutionary history of memory. A point mutation in the Gal1 co-activator that disrupts the interaction with the Gal80 inhibitor specifically and completely disrupted memory. This mutation confirms that cytoplasmically inherited Gal1 produced during previous growth in galactose directly interferes with Gal80 repression to promote faster induction of GAL genes. This mitotically heritable mode of regulation is recently evolved; in a diverged Saccharomyces species, GAL genes show constitutively faster activation due to genetically encoded basal expression of Gal1. Thus, recently diverged species utilize either epigenetic or genetic strategies to regulate the same molecular mechanism. The screen also revealed that the central domain of the Gal4 transcription factor both regulates the stochasticity of GAL gene expression and potentiates stronger GAL gene activation in the presence of Gal1. The central domain is critical for GAL gene transcriptional memory; Gal4 lacking the central domain fails to potentiate GAL gene expression and is unresponsive to previous Gal1 expression.