Single-cell phenomics reveals intra-species variation of phenotypic noise in yeast

BACKGROUND: Most quantitative measures of phenotypic traits represent macroscopic contributions of large numbers of cells. Yet, cells of a tissue do not behave similarly, and molecular studies on several organisms have shown that regulations can be highly stochastic, sometimes generating diversified cellular phenotypes within tissues. Phenotypic noise, defined here as trait variability among isogenic cells of the same type and sharing a common environment, has therefore received a lot of attention. Given the potential fitness advantage provided by phenotypic noise in fluctuating environments, the possibility that it is directly subjected to evolutionary selection is being considered. For selection to act, phenotypic noise must differ between contemporary genotypes. Whether this is the case or not remains, however, unclear because phenotypic noise has very rarely been quantified in natural populations. RESULTS: Using automated image analysis, we describe here the phenotypic diversity of S. cerevisiae morphology at single-cell resolution. We profiled hundreds of quantitative traits in more than 1,000 cells of 37 natural strains, which represent various geographical and ecological origins of the species. We observed abundant trait variation between strains, with no correlation with their ecological origin or population history. Phenotypic noise strongly depended on the strain background. Noise variation was largely trait-specific (specific strains showing elevated noise for subset of traits) but also global (a few strains displaying elevated noise for many unrelated traits). CONCLUSIONS: Our results demonstrate that phenotypic noise does differ quantitatively between natural populations. This supports the possibility that, if noise is adaptive, microevolution may tune it in the wild. This tuning may happen on specific traits or by varying the degree of global phenotypic buffering

How does evolution tune biological noise?

International audiencePart of molecular and phenotypic differences between individual cells, between body parts, or between individuals can result from biological noise. This source of variation is becoming more and more apparent thanks to the recent advances in dynamic imaging and single-cell analysis. Some of these studies showed that the link between genotype and phenotype is not strictly deterministic. Mutations can change various statistical properties of a biochemical reaction, and thereby the probability of a trait outcome. The fact that they can modulate phenotypic noise brings up an intriguing question: how may selection act on these mutations? In this review, we approach this question by first covering the evidence that biological noise is under genetic control and therefore a substrate for evolution. We then sequentially inspect the possibilities of negative, neutral, and positive selection for mutations increasing biological noise. Finally, we hypothesize on the specific case of H2A.Z, which was shown to both buffer phenotypic noise and modulate transcriptional efficiency. The recent advances in dynamic imaging and single-cell studies have revealed the stochastic nature of biochemical reactions. Numerous factors are known to affect the degree of noise in these reactions, including temperature (Jo et al., 2005), drug treatment (Dar et al., 2014), age (Bahar et al., 2006) and, very importantly, genotypes (Raser and O'Shea, 2004; Levy and Siegal, 2008; Ansel et al., 2008; Hornung et al., 2012). If mutations can modulate a reaction without necessarily changing the average concentration of its product, then they do not fit in the traditional (often deterministic) view of genotype–phenotype control. Such mutations can change the probabilistic laws of single-cell traits, such as phenotypic noise, which may have important consequences at the multicellular level (Yvert, 2014). Noise has the property to increase disorder. In contrast, living systems are highly organized, developmental processes are under many constrains, and numerous phenotypic traits display robustness to stochastic variation. It is therefore unclear how optimization and control of noise can affect both fidelity and diversity. One way to apprehend this is to examine the mutations that were shown to increase or decrease noise levels. In this review, we first present evidence that noise is under genetic control. We then speculate on the ways by which natural selection acts on it. Finally, we hypothesize on the contribution of histone variant H2A.Z to noise evolution

The Genotype-Phenotype Map: Origins, Properties, and Evolutionary Consequences

Describing and understanding the relationship between genotypes and phenotypes, or the genotype-phenotype map, is of long-lasting interest in genetics and evolutionary biology. My dissertation focuses on understanding the origins, properties, and evolutionary consequences of genotype-phenotype maps. In Chapter 2, using yeast morphological traits, I showed that most traits are affected by a small proportion of genes, many of which have small effects while a few have large effects. To explain why many phenotypic effects are small, in the rest of Chapter 2 as well as in Chapter 3, I studied yeast morphological traits, yeast gene expression traits, and E. coli reaction flux traits and found evidence supporting the hypothesis of adaptive genetic robustness. In Chapter 4, by comparing the evolutionary rates of phenotypic traits of varying importance, I found evidence for that yeast morphological traits have evolved generally by adaptation while yeast gene expression traits have evolved largely neutrally. In Chapter 5, using yeast morphological traits, I found that increasing mutational correlation generally facilitates phenotypic evolution when the correlation is low, but constrains it when the correlation become very high. Thus, an intermediate level of mutation correlation is most conducive to phenotype evolution. In Chapter 6, using E. coli gene expression level traits and E. coli reaction flux traits, I found that genetic changes tend to reverse plastic changes when a population adapts to a new environment, suggesting that phenotypic plasticity does not generally serve as a steppingstone to genetic adaption. To sum up, this dissertation highlights the importance of incorporating genotype-phenotype maps into the study of evolution, identifies influential factors in phenotypic evolution, and thus deepens our understanding of general principles of evolution.PHDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138643/1/weichho_1.pd

Epigenetisk og genetisk tilpasning i eksperimentelle gjærpopulasjoner

Evolution is a highly complex process where life change over time by the influence of relatively simple forces. Bringing evolution into the lab is a valuable asset to answer questions about processes that shape evolution. What determines how populations adapt to changing environments? To what degree can we predict evolutionary outcomes? How fast can populations adapt to a novel environment? I have utilized and contributed to the develop- ment of an experimental evolution platform using a combination of modern tools to answer fundamental questions about adaptive evolution using the baker’s yeast Saccharomyces cerevisiae as a model. In Paper I, we showed how the concerted improvement of genetically independent fitness components can be used as a signature of natural selection, disclosing selection pressures to which adaptation has occurred. Accordingly, we reported adaptation of natural yeast lineages to diverse nitrogen niches and pinpoint the mutations underlying this diversity. In Paper II, we let yeast evolve in the lab in order to study if fast adaptation can be explained without invoking epigenetics. By comparing our results to data-driven simulations we found that fast adapta- tion to arsenic could be explained by pleiotropy of fitness components and slightly elevated mutation rate without invoking epigenetic explanations. In paper III we performed massively parallell experimental evolution in order to probe the stability of adaptation and identified reversible adaption to oxidative stress, through mitochondrial DNA erosion. Chronic stress led to genetic assimilation of adaptation by complete degradation of mitochondrial DNA. Overall, these findings illustrate the power of experimental evolution as a tool to understand evolution.Evolusjon er en svært kompleks prosess der levende organismer endres over tid som følge av relativt enkle krefter. Evolusjon inn i laboratoriet er et et verdifullt verktøy i arbeidt med å gi svar på noen grunnleggende spørsmål rundt evolusjon. Hva bestemmer hvordan populasjoner tilpasser seg endrede miljøbetingelser? I hvilken grad kan vi forutsi resultatet av evolusjon? Hvor fort kan populasjonener tilpasse seg et helt nytt miljø? Jeg har anvendt og videreutviklet en plattform for eksperimentell evolusjon som utnytter moderne labmetoder for å svare på grunnleggende spørsmål om adaptiv evolusjon i modellorganismen gjær (Saccharomyces cerevisiae). I artikkel I viste vi hvordan samvariasjon i genetisk uavhengige fitnesskomponenter kan brukes som en signatur på naturlig seleksjon for å avdekke hvilke se- leksjonstrykk som har ført til adaptasjon. Vi avdekket adatasjon av naturlig forekommende gjærstammer til ulike nitrogenkilder og finkartla de kausale mutasjonene som har gitt opphav til den diversiteten vi ser. I artikkel II brukte vi labevolusjon av gjær til å studere om hurtig tilpassning kan forklares uten å ty til epigenetiske mekanisker. Vi sammenholdt eksperimentelle resultater med datadrevne simuleringer og fant at rask adaptasjon til arsenikk kunne forklares uten epigenetikk, dersom man forutsetter pleiotropi for fitnesskom- ponetner og noe forhøyet mutasjonsrate. I artikkell III gjorde vi stor-skala paralell eksperimentell evolusjon for undersøke hvor stabile tilpasningene var og avdekket reversibel adaptasjon til oksidativt stress, gjennom delvis nedbryting av mitokondrielt DNA. Vedvarendende stress førte til genetisk assimilering ved fullstending nedbryting av mitokondrielt DNA. Disse resul- tatene illustrere at eksperimentell evolusjon er et kraftfullt verktøy for å forstå evolusjon

On metabolic and phenotypic diversity in yeast

This thesis explores metabolic and phenotypic diversity in the two model yeasts Schizosaccharomyces pombe and Saccharomyces cerevisiae. Colony screens are a classical and powerful technique for investigating these topics, but there is a lack of modern, scalable bioinformatics tools. To address this need, I have developed pyphe which greatly facilitates colony screen data acquisition and statistical analysis. I explore optimal experimental designs, especially regarding the usefulness of timecourse imaging and colony viability analysis. Pyphe is used in a functional genomics screen, aiming to find functions for a set of largely uncharacterised lincRNAs. We identify hundreds of new lincRNA-associated phenotypes across numerous conditions and compare lincRNA phenotype profiles to those of codinggene mutants. Next, I have used pyphe to investigate the respiration/fermentation balance of wild S. pombe isolates. Contrary to the expectation that glucose completely represses respiration in this Crabtree-positive species, I find that strains generally strike a balance and that individual strains differ significantly in their residual respiration activity. This is associated with an unusual miss-sense variant in S. pombe’s sole pyruvate kinase gene. Its impact is dissected in detail, revealing a change in flux through pyruvate kinase and associated changes in gene expression, metabolism, growth and stress resistance. Finally, I explore how extracellular amino acids interact with cellular metabolism, with the aim of answering the important question whether or not clonal yeast cultures segregate into heterogeneous producer/consumer populations that exchange amino acids. I develop a novel proteomics-based method that characterises amino acid labelling patterns in peptides. I find that the supplementation of some, but not all amino acids completely suppresses selfsynthesis. However, I find no evidence for heterogeneous responses of our laboratory S. cerevisiae strain, but the functionality of the method is demonstrated clearly. Overall, this work represents several advancements to our understanding of yeast metabolism and physiology, as well as new experimental and computational methods

On Computable Protein Functions

Proteins are biological machines that perform the majority of functions necessary for life. Nature has evolved many different proteins, each of which perform a subset of an organism’s functional repertoire. One aim of biology is to solve the sparse high dimensional problem of annotating all proteins with their true functions. Experimental characterisation remains the gold standard for assigning function, but is a major bottleneck due to resource scarcity. In this thesis, we develop a variety of computational methods to predict protein function, reduce the functional search space for proteins, and guide the design of experimental studies. Our methods take two distinct approaches: protein-centric methods that predict the functions of a given protein, and function-centric methods that predict which proteins perform a given function. We applied our methods to help solve a number of open problems in biology. First, we identified new proteins involved in the progression of Alzheimer’s disease using proteomics data of brains from a fly model of the disease. Second, we predicted novel plastic hydrolase enzymes in a large data set of 1.1 billion protein sequences from metagenomes. Finally, we optimised a neural network method that extracts a small number of informative features from protein networks, which we used to predict functions of fission yeast proteins

Genetic influences on translation in yeast

Heritable differences in gene expression between individuals are an important source of phenotypic variation. The question of how closely the effects of genetic variation on protein levels mirror those on mRNA levels remains open. Here, we addressed this question by using ribosome profiling to examine how genetic differences between two strains of the yeast S. cerevisiae affect translation. Strain differences in translation were observed for hundreds of genes. Allele specific measurements in the diploid hybrid between the two strains revealed roughly half as many cis-acting effects on translation as were observed for mRNA levels. In both the parents and the hybrid, most effects on translation were of small magnitude, such that the direction of an mRNA difference was typically reflected in a concordant footprint difference. The relative importance of cis and trans acting variation on footprint levels was similar to that for mRNA levels. There was a tendency for translation to cause larger footprint differences than expected given the respective mRNA differences. This is in contrast to translational differences between yeast species that have been reported to more often oppose than reinforce mRNA differences. Finally, we catalogued instances of premature translation termination in the two yeast strains and also found several instances where erroneous reference gene annotations lead to apparent nonsense mutations that in fact reside outside of the translated gene body. Overall, genetic influences on translation subtly modulate gene expression differences, and translation does not create strong discrepancies between genetic influences on mRNA and protein levels

Cancer proteogenomics : connecting genotype to molecular phenotype

The central dogma of molecular biology describes the one-way road from DNA to RNA and finally to protein. Yet, how this flow of information encoded in DNA as genes (genotype) is regulated in order to produce the observable traits of an individual (phenotype) remains unanswered. Recent advances in high-throughput data, i.e., ‘omics’, have allowed the quantification of DNA, RNA and protein levels leading to integrative analyses that essentially probe the central dogma along all of its constituent molecules. Evidence from these analyses suggest that mRNA abundances are at best a moderate proxy for proteins which are the main functional units of cells and thus closer to the phenotype. Cancer proteogenomic studies consider the ensemble of proteins, the so-called proteome, as the readout of the functional molecular phenotype to investigate its influence by upstream events, for example DNA copy number alterations. In typical proteogenomic studies, however, the identified proteome is a simplification of its actual composition, as they methodologically disregard events such as splicing, proteolytic cleavage and post-translational modifications that generate unique protein species – proteoforms. The scope of this thesis is to study the proteome diversity in terms of: a) the complex genetic background of three tumor types, i.e. breast cancer, childhood acute lymphoblastic leukemia and lung cancer, and b) the proteoform composition, describing a computational method for detecting protein species based on their distinct quantitative profiles. In Paper I, we present a proteogenomic landscape of 45 breast cancer samples representative of the five PAM50 intrinsic subtypes. We studied the effect of copy number alterations (CNA) on mRNA and protein levels, overlaying a public dataset of drug- perturbed protein degradation. In Paper II, we describe a proteogenomic analysis of 27 B-cell precursor acute lymphoblastic leukemia clinical samples that compares high hyperdiploid versus ETV6/RUNX1-positive cases. We examined the impact of the amplified chromosomes on mRNA and protein abundance, specifically the linear trend between the amplification level and the dosage effect. Moreover, we investigated mRNA-protein quantitative discrepancies with regard to post-transcriptional and post-translational effects such as mRNA/protein stability and miRNA targeting. In Paper III, we describe a proteogenomic cohort of 141 non-small cell lung cancer clinical samples. We used clustering methods to identify six distinct proteome-based subtypes. We integrated the protein abundances in pathways using protein-protein correlation networks, bioinformatically deconvoluted the immune composition and characterized the neoantigen burden. In Paper IV, we developed a pipeline for proteoform detection from bottom-up mass- spectrometry-based proteomics. Using an in-depth proteomics dataset of 18 cancer cell lines, we identified proteoforms related to splice variant peptides supported by RNA-seq data. This thesis adds on the previous literature of proteogenomic studies by analyzing the tumor proteome and its regulation along the flow of the central dogma of molecular biology. It is anticipated that some of these findings would lead to novel insights about tumor biology and set the stage for clinical applications to improve the current cancer patient care