A Tale of Two Genomes: The Complex Interplay Between the Mitochondrial and the Nuclear Genomes

Mitochondria, the product of an ancient endosymbiotic event are pivotal to eukaryotic cells by synthesizing the majority of the cell’s ATP output. However, modern- day mitochondria are completely dependent on more than one thousand nuclear-encoded products for their function and the maintenance of their genomes. The fundamentally different ways in which the mitochondrial (mtDNA) and the nuclear (nucDNA) genomes are replicated and inherited lead to captivating coevolutionary dynamics between them. The aims of this dissertation are to investigate the coevolutionary dynamics between the mitochondrial and nuclear genomes at three distinct biological scales. At the organismal level, we use a Drosophila strain with a characterized mitochondrial-nuclear incompatibility to test for the functional effects of mitochondrial-nuclear interactions on male reproductive fitness, in the context of both gene-environment interactions and the female-specific selective sieve that operates on mtDNA. We find that the mitochondrial- nuclear incompatibility negatively affects male fertility, although these effects are largely context-dependent. At the molecular level, using sequence and structural comparisons, we classify and characterize mutations associated with human mitochondrial disorders in the mtDNA and nucDNA as compensated or uncompensated based on whether the mutant amino acid is observed in a non-human species. We find that mtDNA, relative to nucDNA harbors a higher proportion of compensated mutations and this pattern is likely driven by the higher mtDNA background substitution rate. At the phylogenomic level, we estimate rates of evolutionary change for mtDNA- and nucDNA-encoded genes and compare correlations between the rates of mtDNA-encoded genes and three nuclear- encoded gene sets with differing extent of overlapping interactions with the mtDNA genes. We find that the patterns of rate correlations are consistent with the extent of overlap between the mtDNA and nucDNA genes with nucDNA genes that directly interact with mtDNA exhibiting the strongest correlations. In summary, we find that the higher rate of mutation in mtDNA appears to be driving mitochondrial-nuclear coevolutionary dynamics with the effects of mitochondrial-nuclear interactions being largely context-dependent. Advisors: Kristi L. Montooth & Colin D. Meiklejoh

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Modelling the structural, functional and phenotypic consequences of protein coding mutations

Proteins are integral to all cellular processes and underpin the function of all extant organisms, meaning variants impacting them are a primary cause of phenotypic variation. Protein coding variants are a key area of study in biology, with relevance from structural and molecular biology to population genetics. They are also medically important, impacting inherited genetic diseases, cancer and response to pathogens. Recent advances in highthroughput experimental techniques have opened the door to many new approaches in biology, and protein variants are no exception. Deep mutational scanning experiments exhaustively measure the fitness of variants in a protein, which gives us more experimentally validated mutational consequence measurements than ever before. Such advances, together with ever larger sequence and structure databases, have created an opportunity to apply large scale analyses to coding variation, studying the effect on protein structure, function and phenotype. In this thesis I perform three large scale variant analyses. First, I use the consequences of variation to learn about protein structure and function. I compile a dataset from 28 deep mutational scanning studies, covering 6291 positions in 30 proteins, and use the consequences of mutation at each position to define a mutational landscape. I show rich biophysical relationships in this landscape and identify functionally distinct positional subtypes of each amino acid. In the second analysis, I explore genotype to phenotype prediction using a dataset of 1011 S. cerevisiae strains, with genotypes, transcriptomics, proteomics and measured phenotypes, and comprehensive gene deletions in four strains. I show knowledge-based models of mutational consequences and pathway function can be used to associate genes with phenotypes and predict growth phenotypes across 34 growth conditions. However, genetic background is found to have a large effect on variant consequences, to such an extent that the same deletion can be highly significant in one strain and have no effect in another. Finally, I analyse computational variant effect prediction, benchmarking current predictors using deep mutational scanning data. I then develop a new end-to-end deep convolutional neural network predictor that predicts consequences directly from sequence and structure and show it improves on current methods. Together these projects advance our knowledge of protein coding variation and enhance our capacity to link variation to impacts on structure, function and phenotype

The role of visual adaptation in cichlid fish speciation

D. Shane Wright (1) , Ole Seehausen (2), Ton G.G. Groothuis (1), Martine E. Maan (1) (1) University of Groningen; GELIFES; EGDB(2) Department of Fish Ecology & Evolution, EAWAG Centre for Ecology, Evolution and Biogeochemistry, Kastanienbaum AND Institute of Ecology and Evolution, Aquatic Ecology, University of Bern.In less than 15,000 years, Lake Victoria cichlid fishes have radiated into as many as 500 different species. Ecological and sexual sel ection are thought to contribute to this ongoing speciation process, but genetic differentiation remains low. However, recent work in visual pigment genes, opsins, has shown more diversity. Unlike neighboring Lakes Malawi and Tanganyika, Lake Victoria is highly turbid, resulting in a long wavelength shift in the light spectrum with increasing depth, providing an environmental gradient for exploring divergent coevolution in sensory systems and colour signals via sensory drive. Pundamilia pundamila and Pundamilia nyererei are two sympatric species found at rocky islands across southern portions of Lake Victoria, differing in male colouration and the depth they reside. Previous work has shown species differentiation in colour discrimination, corresponding to divergent female preferences for conspecific male colouration. A mechanistic link between colour vision and preference would provide a rapid route to reproductive isolation between divergently adapting populations. This link is tested by experimental manip ulation of colour vision - raising both species and their hybrids under light conditions mimicking shallow and deep habitats. We quantify the expression of retinal opsins and test behaviours important for speciation: mate choice, habitat preference, and fo raging performance

Evolvability and organismal architecture:The blind watchmaker and the reminiscent architect

Organisms are constantly faced with the challenge of adapting to new circumstances. In this thesis, I argue that the ability to adapt to new circumstances, “evolvability”, is deeply ingrained in the genetic, developmental, morphological, and physiological architecture of organisms. Using a blend of conceptual research, theoretical modelling, and multidisciplinary studies, I demonstrate how organismal architecture can evolve so that organisms can cope better and better with future environmental challenges. As a first step, I systematically classify the many factors contributing to evolvability. Then I use a simulation approach to show how evolvability-enhancing structures can readily evolve in gene-regulatory networks. This happens via the evolution of "mutational transformers" - structural elements that convert random mutations at the genetic level into adaptation-enhancing mutations at the phenotypic level. In another thesis chapter, I demonstrate that even if selection acts only sporadically, complex adaptations can evolve and persist over long time periods. In other words, complex adaptations do not require constant selection pressure. In an interdisciplinary contribution, I apply biological insights regarding the properties of an evolvability-enhancing mutation structure to the design of algorithms used in Artificial Intelligence. The result is the “Facilitated Mutation” method which enhances the performance of the algorithms in various respects, highlighting the potential for leveraging biological principles in computational sciences. Finally, I embed my research findings in a philosophical context. I emphasise the importance of organismal architecture in retaining evolutionary memories and suggest future research directions to further enhance our understanding of evolvability