Under-dominance constrains the evolution of negative autoregulation in diploids

Regulatory networks have evolved to allow gene expression to rapidly track changes in the environment as well as to buffer perturbations and maintain cellular homeostasis in the absence of change. Theoretical work and empirical investigation in Escherichia coli have shown that negative autoregulation confers both rapid response times and reduced intrinsic noise, which is reflected in the fact that almost half of Escherichia coli transcription factors are negatively autoregulated. However, negative autoregulation is rare amongst the transcription factors of Saccharomyces cerevisiae. This difference is surprising because E. coli and S. cerevisiae otherwise have similar profiles of network motifs. In this study we investigate regulatory interactions amongst the transcription factors of Drosophila melanogaster and humans, and show that they have a similar dearth of negative autoregulation to that seen in S. cerevisiae. We then present a model demonstrating that this stiking difference in the noise reduction strategies used amongst species can be explained by constraints on the evolution of negative autoregulation in diploids. We show that regulatory interactions between pairs of homologous genes within the same cell can lead to under-dominance - mutations which result in stronger autoregulation, and decrease noise in homozygotes, paradoxically can cause increased noise in heterozygotes. This severely limits a diploid's ability to evolve negative autoregulation as a noise reduction mechanism. Our work offers a simple and general explanation for a previously unexplained difference between the regulatory architectures of E. coli and yeast, Drosophila and humans. It also demonstrates that the effects of diploidy in gene networks can have counter-intuitive consequences that may profoundly influence the course of evolution

Under-dominance constrains the evolution of negative autoregulation in diploids

Regulatory networks have evolved to allow gene expression to rapidly track changes in the environment as well as to buffer perturbations and maintain cellular homeostasis in the absence of change. Theoretical work and empirical investigation in Escherichia coli have shown that negative autoregulation confers both rapid response times and reduced intrinsic noise, which is reflected in the fact that almost half of Escherichia coli transcription factors are negatively autoregulated. However, negative autoregulation is exceedingly rare amongst the transcription factors of Saccharomyces cerevisiae. This difference is all the more surprising because E. coli and S. cerevisiae otherwise have remarkably similar profiles of network motifs. In this study we first show that regulatory interactions amongst the transcription factors of Drosophila melanogaster and humans have a similar dearth of negative autoregulation to that seen in S. cerevisiae. We then present a model demonstrating that this fundamental difference in the noise reduction strategies used amongst species can be explained by constraints on the evolution of negative autoregulation in diploids. We show that regulatory interactions between pairs of homologous genes within the same cell can lead to under-dominance - mutations which result in stronger autoregulation, and decrease noise in homozygotes, paradoxically can cause increased noise in heterozygotes. This severely limits a diploid's ability to evolve negative autoregulation as a noise reduction mechanism. Our work offers a simple and general explanation for a previously unexplained difference between the regulatory architectures of E. coli and yeast, Drosophila and humans. It also demonstrates that the effects of diploidy in gene networks can have counter-intuitive consequences that may profoundly influence the course of evolution

The construction of transcription factor networks through natural selection

Transcription regulation plays a key role in determining cellular function, response to external stimuli and development. Regulatory proteins orchestrate gene expression through thousands of interactions resulting in large, complex networks. Understanding the principles on which these networks are constructed can provide insight into the way the expression patterns of different genes co-evolve. One method by which this question can be addressed is to focus on the evolution of the structure of transcription factor networks (TFNs). In order to do this, a model for their evolution through cis mutation, trans mutation, gene duplication and gene deletion is constructed. This model is used to determine the circumstances under which the asymmetrical in and out degree distributions observed in real networks are reproduced. In this way it is possible to draw conclusions about the contributions of these different evolutionary processes to the evolution of TFNs. Conclusions are also drawn on the way rates of evolution vary with the position of gene in the network. Following this, the contributions of cis mutations, which occur in the promoters of regulated genes, and trans mutations, which occur in the coding reign of transcription factors, to the evolution of TFNs are investigated. A space of neutral genotypes is constructed, and the evolution of TFNs through cis and trans mutations in this space is characterised. The results are then used to account for large scale rewiring observed in the yeast sex determination network. Finally the principles governing the evolution of autoregulatory motifs are investigated. It is shown that negative autoregulation, which functions as a noise reduction mechanism in haploid TFNs, is not evolvable in diploid TFNs. This is attributed to the effects of dominance in diploid TFNs. The fate of duplicates of autoregulating genes in haploid networks is also investigated. It is shown that such duplicates are especially prone to loss of function mutations. This is used to account for the lack of observed autoregulatory duplicates participating in network motifs. From this work, it is concluded that the relative rates of different evolutionary processes are responsible for shaping the global statistical properties of TFN structure. However, the more detailed TFN structure, such as network motif distribution, is strongly influenced by the population genetic details of the system being considered. In addition, extensive neutral evolution is shown to be possible in TFNs. However, the effects of neutral evolution on network structure are shown to depend strongly on the structure of the space on neutral genotypes in which the TFN is evolving

Parallelism, constraint, and functional genome evolution in experimentally evolving populations of Saccharomyces cerevisiae

From sticklebacks to insects to mice, closely related populations encountering similar selective pressures tend to evolve parallel phenotypes. The era of genomics has revealed that some instances of parallel phenotypes are caused by similar mutations in similar genes. Others cases of parallel adaptation are shown to be driven by mutations in distinct genes. These observations spark an open question in evolutionary biology; given identical starting points and selective pressures, how repeatable should we expect molecular adaptation to be? Laboratory evolution of model organisms presents an ideally suitable system for beginning to answer this question. This work uses experimental evolution of Saccharomyces cerevisiae combined with genomics and functional genetics to ask questions about the repeatability of evolution. We focus on identifying and examining the adaptive consequence of molecular events that arise more often than expected by chance. We find extensive parallelism in ploidy evolution when genome duplication proves adaptive. We use whole genome sequencing to identify loci targeted by selection multiple independent times across populations. We use one of these loci, STE4, to examine how dominance constrains mutation and adaptation. We then leverage the extensive gene-level parallelism we observe to detect genetic interactions and measure the effect of epistasis on genotype evolution

Modeling the "microbial chassis effect" on the performance of a genetic switch

Escherichia coli is one of the most established bacterial hosts for genetic devices, partially due to the available knowledge and tools for ease of manipulation. However, there is an incentive to increase the current number of available recipients. For instance, marine bacteria are being recognized for their potential as microbial "chassis" due to their rich genetic and metabolic diversity. With this in mind, tools for simulating the behavior of genetic devices would help synthetic biology expand its reach to untraditional hosts. Hence, here in this study, the effect of recipient on the performance of a genetic switch, the "chassis" effect, was estimated across different bacteria with an aim to indicate the possibilities of marine microorganisms as hosts. The device considered in this study was assembled from two sub-parts 1) L-arabinose-inducible PBAD promoter that expresses tetR and gfp genes encoding production of the TetR repressor and GFP fluorescent protein; and 2) anhydrotetracycline (aTc)-inducible PTet promoter that controls araC and mKate expression, which codes for the AraC repressor and mKate fluorescent protein. AraC protein is a repressor of the PBAD promoter, while TeR represses PTet. The dynamic behavior and stability of this device was simulated by a mathematical model based on a system of ordinary differential equations (ODEs) that predicted possible "chassis" effect and compared its strength across selected bacterial hosts. To further our understanding of the performance of a genetic switch, a dynamic modeling framework was established, and a behavior was simulated for a set of marine bacteria and E. coli. This was done by building a mathematical model that included system of already parametrized non-linear ODEs which were solved using the R programming language. The parametrization of ODEs by a non-linear model resulted in the Hill (n) and activation (K) coefficient estimates. The non-linear regression was performed on a GFP fluorescence data collected from the induction study with E. coli. This assay estimated GFP-, GFP/OD600 signals and GFP rates from the cells induced with L-arabinose. The simulated dynamic response was quantified by a response time, a limiting factor for designing efficient gene circuits. The simulation estimated the fastest response of Vibrio natriegens and the slowest of Pseudomonas oceani. This outcome has indicated high potentials of V. natriegens for future applications in the synthetic biology. The "chassis" effect predicted by the model was estimated as a direct consequence of the specific growth rate

Darwin throws dice: modelling stochastic processes of molecular evolution

The availability of protein and DNA sequences in the second half of the 20th century revolutionised evolutionary biology. For the first time, it was possible to quantify genetic variation among individuals and populations. Using molecular data to understand past demography and natural selection became an attainable goal. In the era of whole-genome sequences, application of early theoretical results proved to be challenging. The stochastic nature of evolutionary processes acting on DNA sequences makes it hard to distinguish signal from noise. Although progress has been made, models of molecular evolution are still lagging behind the availability of sequence data. In this thesis I contribute to bridging this gap, even if slightly. My main result is the development of the integrated sequentially Markovian coalescent (iSMC) – a novel framework that jointly models the effects of ancestral demography, recombination heterogeneity (Chapter 1) and mutation heterogeneity (Chapter 2) in shaping genetic diversity along the genome. This principled approach represents a step towards more realistic models of Population Genetics. The consequences of intracellular stochasticity extend beyond DNA sequences, however. Due to randomness in the diffusion of key molecules, isogenic cells differ in their gene expression patterns – hence in their phenotypes – even in homogeneous environments. To avoid chaos, intracellular stochasticity must be tamed by natural selection. In Chapter 3, I leverage single-cell transcriptomics data to disentangle the factors that constrain gene expression noise. Although selection against elevated noise acts at different levels of organisation, I show that it responds primarily to the architecture of molecular networks. This result may impact our understanding of the genotype-phenotype-fitness map

Under-dominance constrains the evolution of negative autoregulation in diploids

Regulatory networks have evolved to allow gene expression to rapidly track changes in the environment as well as to buffer perturbations and maintain cellular homeostasis in the absence of change. Theoretical work and empirical investigation in Escherichia coli have shown that negative autoregulation confers both rapid response times and reduced intrinsic noise, which is reflected in the fact that almost half of Escherichia coli transcription factors are negatively autoregulated. However, negative autoregulation is rare amongst the transcription factors of Saccharomyces cerevisiae. This difference is surprising because E. coli and S. cerevisiae otherwise have similar profiles of network motifs. In this study we investigate regulatory interactions amongst the transcription factors of Drosophila melanogaster and humans, and show that they have a similar dearth of negative autoregulation to that seen in S. cerevisiae. We then present a model demonstrating that this striking difference in the noise reduction strategies used amongst species can be explained by constraints on the evolution of negative autoregulation in diploids. We show that regulatory interactions between pairs of homologous genes within the same cell can lead to under-dominance--mutations which result in stronger autoregulation, and decrease noise in homozygotes, paradoxically can cause increased noise in heterozygotes. This severely limits a diploid's ability to evolve negative autoregulation as a noise reduction mechanism. Our work offers a simple and general explanation for a previously unexplained difference between the regulatory architectures of E. coli and yeast, Drosophila and humans. It also demonstrates that the effects of diploidy in gene networks can have counter-intuitive consequences that may profoundly influence the course of evolution.Publisher PDFPeer reviewe

The Aims and Structures of Research Projects That Use Gene Regulatory Information with Evolutionary Genetic Models

abstract: At the interface of developmental biology and evolutionary biology, the very criteria of scientific knowledge are up for grabs. A central issue is the status of evolutionary genetics models, which some argue cannot coherently be used with complex gene regulatory network (GRN) models to explain the same evolutionary phenomena. Despite those claims, many researchers use evolutionary genetics models jointly with GRN models to study evolutionary phenomena. How do those researchers deploy those two kinds of models so that they are consistent and compatible with each other? To address that question, this dissertation closely examines, dissects, and compares two recent research projects in which researchers jointly use the two kinds of models. To identify, select, reconstruct, describe, and compare those cases, I use methods from the empirical social sciences, such as digital corpus analysis, content analysis, and structured case analysis. From those analyses, I infer three primary conclusions about projects of the kind studied. First, they employ an implicit concept of gene that enables the joint use of both kinds of models. Second, they pursue more epistemic aims besides mechanistic explanation of phenomena. Third, they don’t work to create and export broad synthesized theories. Rather, they focus on phenomena too complex to be understood by a common general theory, they distinguish parts of the phenomena, and they apply models from different theories to the different parts. For such projects, seemingly incompatible models are synthesized largely through mediated representations of complex phenomena. The dissertation closes by proposing how developmental evolution, a field traditionally focused on macroevolution, might fruitfully expand its research agenda to include projects that study microevolution.Dissertation/ThesisDoctoral Dissertation Biology 201

INSIGHTS INTO NUCLEAR ARCHITECTURE AND STOCHASTIC GENE EXPRESSION THROUGH THE STUDY OF HOMOLOGOUS CHROMOSOME PAIRING AND TRANSVECTION

Chromosomes are organized in a complex manner within the nucleus. Their localization relative to activating and repressing nuclear compartments and to other chromosomes can have major effects on gene expression. One important aspect of nuclear architecture involves the physical colocalization, or “pairing” of different alleles of the same gene. Pairing is involved in mammalian processes including genomic imprinting and X-inactivation, but the most well-studied example occurs in Drosophila melanogaster, where homologous chromosomes are paired along their entire lengths throughout interphase. Homologous chromosome pairing in fruit flies facilitates a gene-regulatory phenomenon known as transvection, in which DNA elements on one mutant allele of a gene act between chromosomes on another mutant allele to rescue expression. While homologous chromosome pairing and transvection were first described over 60 years ago, the mechanisms that drive homologous chromosome pairing and transvection across the genome are still unclear. Here, we develop a DNA FISH-based approach to identify button regions across the fly genome that drive pairing. These buttons allow “reconstitution” of genes that are split apart by chromosome rearrangements, suggesting that buttons play a role in maintaining the structural integrity of the genome. Buttons are enriched for topologically associated domains (TADs), indicating that TADs are responsible for homologous chromosome interactions. Using the stochastically expressed spineless (ss) locus as a paradigm, we gain deeper insight into the mechanisms that control transvection. We find that pairing is necessary but not sufficient for transvection, and that pairing and transvection are cell-type specific. We also identify the DNA elements required for the separable mechanisms of activation and repression between chromosomes. Furthermore, we find a biological role for transvection in regulating the expression of naturally occurring ss alleles to control photoreceptor patterning. Our work suggests a model in which specialized TADs drive homologous chromosome pairing to facilitate cell-type-specific interchromosomal gene regulation. This work has important implications for our understanding of nuclear architecture-linked diseases including Prader-Willi and Angelman syndromes, breast, renal, and pancreatic cancers, and limb malformations