Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein

Epistatic interactions between residues determine a protein's adaptability and shape its evolutionary trajectory. When a protein experiences a changed environment, it is under strong selection to find a peak in the new fitness landscape. It has been shown that strong selection increases epistatic interactions as well as the ruggedness of the fitness landscape, but little is known about how the epistatic interactions change under selection in the long-term evolution of a protein. Here we analyze the evolution of epistasis in the protease of the human immunodeficiency virus type 1 (HIV-1) using protease sequences collected for almost a decade from both treated and untreated patients, to understand how epistasis changes and how those changes impact the long-term evolvability of a protein. We use an information-theoretic proxy for epistasis that quantifies the co-variation between sites, and show that positive information is a necessary (but not sufficient) condition that detects epistasis in most cases. We analyze the "fossils" of the evolutionary trajectories of the protein contained in the sequence data, and show that epistasis continues to enrich under strong selection, but not for proteins whose environment is unchanged. The increase in epistasis compensates for the information loss due to sequence variability brought about by treatment, and facilitates adaptation in the increasingly rugged fitness landscape of treatment. While epistasis is thought to enhance evolvability via valley-crossing early-on in adaptation, it can hinder adaptation later when the landscape has turned rugged. However, we find no evidence that the HIV-1 protease has reached its potential for evolution after 9 years of adapting to a drug environment that itself is constantly changing.Comment: 25 pages, 9 figures, plus Supplementary Material including Supplementary Text S1-S7, Supplementary Tables S1-S2, and Supplementary Figures S1-2. Version that appears in PLoS Genetic

Adaptation in tunably rugged fitness landscapes: The Rough Mount Fuji Model

Much of the current theory of adaptation is based on Gillespie's mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by the MLM. In the present article we propose and analyse a simple fitness landscape model with tunable ruggedness based on the Rough Mount Fuji (RMF) model originally introduced by Aita et al. [Biopolymers 54:64-79 (2000)] in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulae for the expected number of local fitness maxima, the location of the global peak, and the fitness correlation function. The statistics of single and multiple adaptive steps on the RMF landscape are explored mainly through simulations, and the results are compared to the known behavior in the MLM model. Finally, we show that the RMF model can explain the large number of second-step mutations observed on a highly-fit first step backgound in a recent evolution experiment with a microvirid bacteriophage [Miller et al., Genetics 187:185-202 (2011)].Comment: 43 pages, 12 figures; revised version with new results on the number of fitness maxim

Sign Epistatis Networks

In this master thesis I consider primarily the NK model for fitness landscapes and adaptive walks on such landscapes. Specifically a notation for a low-dimensional representation of (reciprocal) sign epistasis on the space of genetic loci is introduced for these models and used to derive sign-dependent properties, in particular the so called accessibility property of the landscape

The role of clonal interference across genetic backgrounds and environments

The study of adaptation in microorganisms has led to a significant expansion in knowledge at many biological levels, ranging from biochemistry and genetics, to ecology and demography. Experimental evolution, in particular, has been invaluable at elucidating how complex the adaptive dynamics in microbial populations can be. One of the most fundamental characteristics of these dynamics is the distribution of beneficial mutations driving the adaptive process. How often do microorganisms acquire these mutations? And what are their expected effects? These questions have been at the heart of evolutionary biology from the very beginning, and the studies that have tackled these difficult issues have been tremendously enlightening about adaptive processes. However, the increasing awareness of the complexity of the environment where microorganisms live requires constant development of new approaches to answer these fundamental questions about their evolution. Large population sizes lead to increased levels of clonal interference, and thus to a deviation from the expected outcome in classical regimes of periodic selection. Genetic variation within an evolving population, which is now easily detected by sequencing technologies, can create complex interactions between phenotypes. Environments with antagonistic biotic interactions, pose very different selective pressures from the ones experienced when a species grows alone. All these factors influence adaptation in microorganisms and, importantly, drive the pathogenicity traits that create severe clinical and epidemiological problems

The maintenance of genetic variation by balancing selection

Adaptive evolution occurs when selection acts on genetic variation for phenotypic traits. In doing so, selection is expected to remove fitness variation in the population. Contrary to this expectation, DNA sequencing has shown that populations harbour high levels of standing genetic variation for fitness. This paradox results in a long-standing question: what maintains genetic variation? One possible mechanism is ‘balancing selection’, where selection actively maintains polymorphism. Once considered unlikely, studies using genomic and phenotypic approaches have recently given new support for balancing selection and have provided evidence of balancing selection in several species. However, it is often difficult to connect genetic and phenotypic evidence for balancing selection with evidence of the action of selection in real time. This limits our understanding of how balancing selection occurs and its contribution to maintaining genetic variation. To address these knowledge gaps, I first assayed the fitness effects of a polymorphism in the Drosophila melanogaster gene fruitless, which shows a signature of balancing selection in wild populations. I show that this polymorphism displays antagonistic pleiotropy, a possible mechanism for balancing selection at this locus (Chapter 2). I next used experimental evolution and pool-sequencing to track the frequency of the fruitless polymorphism over time in laboratory populations (Chapter 3). I was able to demonstrate that the fruitless polymorphism is probably evolving under balancing selection in these populations, although this result is complicated by 44% of putatively neutral SNPs also being diagnosed as under balancing selection. I next expanded this approach to diagnose selection at 397 candidate sexually antagonistic SNPs. 60% appeared to be under balancing selection (Chapter 4). The equilibrium allele frequency of these SNPs was positively related to that in two wild populations, illustrating that the short-term evolution in the cages is correlated to long-term evolution in wild populations. That shows that selection is consistent and supports the inference of balancing selection. Overall, this thesis describes the action of balancing selection in maintaining fitness influencing polymorphisms in D. melanogaster and develops methods to diagnose active balancing selection at the population level

Genetic Interactions and Gene-by-Environment Interactions in Evolution

The phenotypic effect of a mutation depends on both genetic interactions (G×G) and gene-by-environment interactions (G×E). G×G and G×E can distort the additive relationship between genotypes and phenotypes and complicate biological and biomedical studies. Understanding the patterns and mechanisms of these interactions is important for predicting evolutionary trajectories, designing plant and animal breeding strategies, detecting “missing heritability”, and guiding “personalized medicine”. In this thesis, I study how G×G and G×E affect mutational effects, including developing new methods and new models. Recent advancements in high-throughput DNA sequencing and high-throughput phenotyping provide powerful tools to study the relationships among genotypes, phenotypes, and the environment at unprecedented scales. Therefore, I take advantage of several published large datasets in my study, each containing hundreds to thousands of different genotypes of model organisms and their corresponding phenotypes in tens of environments. In Chapter 2, I report some general patterns of G×E and demonstrate the importance of considering potential environmental variations in mapping quantitative trait loci. In Chapter 3, I report how the environment affects diminishing returns epistasis and propose a modular life model to explain the patterns of diminishing returns. In Chapter 4, I propose and demonstrate that genetic dominance is a special case of diminishing returns epistasis. In Chapter 5, I report how and why the relationship between growth rate (r) and carrying capacity (K) in density-dependent population growth varies across environments. In Chapter 6, I demonstrate the existence of an intermediate optimal mating distance for hybrid performance in three model organisms. Overall, I find that large genomic and phenomic data are useful resources to address classical genetic questions, such as the origin of dominance (Chapter 4), the relationship between r and K (Chapter 5), and presence of an optimal mating distance (Chapter 6). The environment is a key player in the phenotypic effects of mutations, but it is also a high-dimension complex system that is hard to quantify. In this thesis, I define environment quality (Q) as the average fitness of many different genotypes measured in the environment. I demonstrate that Q is useful in studying how the environment affects additive (Chapter 3), interactive (Chapters 3 and 4), and pleiotropic mutational effects (Chapter 5). Many classical theories and models were developed based on observations made in a single environment, and they are often insufficient to explain across-environment observations. Studying across-environment effects provides valuable information for testing old models and for designing new models when old models fail. I conclude that studying G×G and G×E shed light on underlying biological mechanisms.PHDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144160/1/xinzhuw_1.pd

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

Maintenance of quantitative genetic variance in complex, multitrait phenotypes:the contribution of rare, large effect variants in 2 Drosophila species

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits

Of Single Nucleotides and Single Cells: Charting the Genotype-Phenotype Map at High Resolution Using \u3ci\u3eDrosophila melanogaster\u3c/i\u3e

Understanding the mechanisms by which genetic variation brings about phenotypic variation is essential for understanding variation in complex traits. Drosophila melanogaster is a powerful model organism for such studies. Flies are easy to raise in the laboratory under controlled genetic and environmental conditions and many genetic tools are widely available. To chart the genotype-phenotype map, we need to study how individual genetic variants contribute to phenotypic variation, as well as how environmental perturbations influence gene expression. Regarding the former, I generated single nucleotide substitutions in Obp56h in a common genetic background. Obp56h, a member of the Odorant binding protein multigene family, is a small gene in a favorable genomic location for CRISPR-Cas9 mediated deletion. After deletion, I reinserted the gene at the endogenous locus with individual allelic variants chosen from those segregating in a wild-derived inbred population to produce five lines varying at single nucleotides in a common genetic background. Different alleles, both within and near the gene (potentially regulatory) and both common and rare, have different, large effects on organismal fitness traits as well as on genome-wide coregulated ensembles of transcripts. These effects are at the level of mean and microenvironmental variance in both fitness traits and the transcriptome. However, these alleles have only small effects on fitness traits in the wild-derived inbred population indicating that the effects of individual alleles can be context-specific and are perhaps suppressed in natural populations via epistatic interactions. Next, I studied how acute cocaine consumption and developmental alcohol exposure affect the transcriptome at single-cell resolution. The Drosophila brain is small, allowing for comprehensive whole-brain studies. Further, previous studies have characterized effects of acute cocaine consumption and developmental alcohol exposure on flies, which resemble those in humans. Single-cell RNA sequencing revealed that the transcriptomes of cells in the fly brain are affected in a cell-type and sex-dependent manner after the flies consumed fixed amounts of cocaine or are exposed to developmental alcohol exposure. These effects are sexually dimorphic, with males showing a greater degree of differential expression and are particularly prominent in glial and mushroom body cells. Developmental alcohol exposure leads to a similar, but different, sexually dimorphic and cell-type dependent pattern of differential expression as cocaine consumption. Some mechanisms are shared between the experimental paradigms indicating common processes. The strategies used in the studies described in this dissertation can be generally applied to explore genotype-phenotype relationships at high resolution