The white-knight hypothesis, or does the environment limit innovations?

Organisms often harbor latent traits that are byproducts of other adaptations. Such latent traits are not themselves adaptive but can become adaptive in the right environment. Here I discuss several examples of such traits. Their abundance suggests that environmental change rather than new mutations might often limit the origin of evolutionary adaptations and innovations. This is important, because environments can change much faster than new mutations arise. I introduce a conceptual model that distinguishes between mutation-limited and environment-limited trait origins and suggest how experiments could help discriminate between them. Wherever latent traits are plentiful, ecology rather than genetics might determine how fast new adaptations originate and thus how fast adaptive Darwinian evolution proceeds

Models in molecular evolution: the case of toyLIFE

Mención Internacional en el título de doctorThis thesis set out to contribute to the growing body of knowledge pertaining models of the genotype-phenotype map. In the process, we proposed and studied a new computational model, toyLIFE, and a new metaphor for molecular evolution —adaptive multiscapes. We also studied functional promiscuity and the evolutionary dynamics of shifting environments. The first result of this thesis was the definition of toyLIFE, a simplified model of cellular biology that incorporated toy versions of genes, proteins and regulation as well as metabolic laws. Molecules in toyLIFE interact between each other following the laws of the HP protein folding model, which endows toyLIFE with a simplified chemistry. From these laws, we saw how something reminiscent of cell-like behavior emerged, with complex regulatory and metabolic networks that grew in complexity as the genome increased. toyLIFE is, to our knowledge, the first multi-level model of the genotype- phenotype map, compared to previous models studied in the literature, such as RNA, proteins, gene regulatory networks (GRNs) or metabolic networks. All of these models either disregarded cellular context when assigning phenotype and function (RNA and proteins) or omitted genome dynamics, by defining their genotypes from high-level abstractions (GRNs and metabolic networks). toyLIFE shares the same features exhibited by all genotype-phenotype maps studied so far. There is strong degeneracy in the map, with many genotypes mapping into the same phenotype. This degeneracy translates into the existence of neutral networks, that span genotype space as soon as the genotype contains more than two genes. There is also a strong asymmetry in the size distribution of phenotypes: most phenotypes were rare, while a few of them covered most genotypes. Moreover, most common phenotypes are easily accessed from each other. We also studied the prevalence of functional promiscuity (the ability to perform more than one function) in computational models of the genotypephenotype map. In particular, we studied RNA, Boolean GRNs and toy- LIFE. Our results suggest that promiscuity is the norm, rather than the exception. These results prompt us to rethink our understanding of biology as a neatly functioning machine. One of the most interesting results of this thesis came from studying the evolutionary dynamics of shifting environments in populations showing functional promiscuity: our results show that there is an optimal frequency of change that minimizes the time to extinction of the population. Finally, we presented a new metaphor for molecular evolution: adaptive multiscapes. This framework intends to update the fitness landscape metaphor proposed by Sewall Wright in the 1930s. Adaptive multiscapes include many features that we have learned from computational studies of the genotype-phenotype map, and that have been discussed throughout the thesis. The existence of neutral networks, the asymmetry in phenotype sizes -and the concomitant asymmetry in phenotype accessibility- and the presence of functional promiscuity all alter the original fitness landscape picture.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Joshua Levy Payne.- Secretario: Saúl Arés García.- Vocal: Jacobo Aguirre Arauj

Hematopoiesis and T-cell specification as a model developmental system

The pathway to generate T cells from hematopoietic stem cells guides progenitors through a succession of fate choices while balancing differentiation progression against proliferation, stage to stage. Many elements of the regulatory system that controls this process are known, but the requirement for multiple, functionally distinct transcription factors needs clarification in terms of gene network architecture. Here, we compare the features of the T-cell specification system with the rule sets underlying two other influential types of gene network models: first, the combinatorial, hierarchical regulatory systems that generate the orderly, synchronized increases in complexity in most invertebrate embryos; second, the dueling ‘master regulator’ systems that are commonly used to explain bistability in microbial systems and in many fate choices in terminal differentiation. The T-cell specification process shares certain features with each of these prevalent models but differs from both of them in central respects. The T-cell system is highly combinatorial but also highly dose-sensitive in its use of crucial regulatory factors. The roles of these factors are not always T-lineage-specific, but they balance and modulate each other's activities long before any mutually exclusive silencing occurs. T-cell specification may provide a new hybrid model for gene networks in vertebrate developmental systems

Big Data For Microorganisms: Computational Approaches Leveraging Large-Scale Microbial Transcriptomic Compendia

Genome-wide transcriptomics data captures the molecular state of microorganisms – the expression patterns of genes in response to some condition or stimuli. With advancements in high-throughput sequencing technologies, there are thousands of microbial transcription profiles publicly available. Consequently, this data has been collected and integrated to form transcriptomic compendia, which are collections of diverse gene expression experiments. These compendia were found to be a valuable resource for studying systems level biology and hypothesis generation. We describe the construction, benefits and challenges in creating microbial transcriptomic compendia in Chapter 1. One challenge for compendia, which integrates across many different experiments, is batch effects, which are technical sources of variability that can disrupt the detection of underlying biological signals of interest. In Chapter 2, we use a generative neural network to simulate gene expression compendia with varying amounts of technical variability and assess the ability to detect the underlying biological structure in the data after noise was added and then after batch correction was applied. We define a set of principles for how batch correction should be used in the context of these large-scale compendia. In Chapter 3 and 4 we introduce computational approaches to use compendia to improve the analysis of individual experiments and analysis of genomic patterns respectively. In Chapter 3, we develop a portable framework to distinguish between common and context specific transcriptional signals using a compendium to autogenerate a null set of expression changes. This approach allows researchers to put gene expression changes from their individual experiment of interest into the context of existing compendia of experiments. In Chapter 4 we develop an approach to examine the effect of different Pseudomonas aeruginosa genomes, using two dominant strain types, on transcriptional profiles in order to understand how traits manifest. This genome-wide approach reveals a more complete picture of how different genomes affect expression, which mediates different traits present. Overall, these compendia provide a valuable resource that computational tools can leverage to extract patterns and inform research directions

Intracellular network attractor selection and the problem of cell fate decision

This project aims at understanding how cell fate decision emerges from the overall intracellular network connectivity and dynamics. To achieve this goal both small paradigmatic signalling-gene regulatory networks and their generalization to highdimensional space were tested. Particularly, we drew special attention to the importance of the effects of time varying parameters in the decision genetic switch with external stimulation. The most striking feature of our findings is the clear and crucial impact of the rate with which the time-dependent parameters are changed. In the presence of small asymmetries and fluctuations, slow passage through the critical region increases substantially specific attractor selection by external transient perturbations. This has strong implications for the cell fate decision problem since cell phenotype in stem cell differentiation, cell cycle progression, or apoptosis studies, has been successfully identified as attractors of a whole network expression process induced by signalling events. Moreover, asymmetry and noise naturally exist in any integrative intracellular decision network. To further clarify the importance of the rate of parameter sweeping, we also studied models from non-equilibrium systems theory. These are traditional in the study of phase transitions in statistical physics and stood as a fundamental tool to extrapolate key results to intracellular network dynamics. Specifically, we analysed the effects of a time-dependent asymmetry in the canonical supercritical pitchfork bifurcation model, both by numerical simulations and analytical solutions. We complemented the discussion of cell fate decision with a study of the effects of non-specific targets of drugs on the Epidermal Growth Factor Receptor pathway. Pathway output has long been correlated with qualitative cell phenotype. Cancer network multitargeting therapies were assessed in the context of whole network attractor phenotypes and the importance of parameter sweeping speed

One genome, two sexes: genomic and transcriptomic bases of sexual dimorphism in species without sexual chromosomes

Sex in the jewel wasp Nasonia vitripennis is determined by whether eggs are haploid or diploid: the radically different male and female phenotypes share the same genome, showing that their sexual dimorphism is not genetic but rather a specific case of phenotypic plasticity. As a consequence, all of Nasonia’s genes are selected for both male and female fitness. The impact of this diverging selective pressure on the evolution of its genome and whether it is comparable to organisms with sex chromosomes are questions still largely unanswered. In this thesis, I develop and apply a set of tools for the integrative analysis of different aspects of Nasonia’s biology. I characterize the improved gene set of Nasonia and identify several lineage-specific gene family expansions. I provide an algorithm for detection and comparison of splicing and transcription signal from transcriptomic data in non-model organisms. Finally, I identify the different regulatory processes that enable generation of disparate phenotypes using network analyses on Nasonia’s developmental transcriptome. Nasonia’s transcriptome shows high amounts of sex-bias not tied to linkage groups or alternative splicing. Early development shows a prevalence of sex-biased interactions between transcripts rather than single-gene upregulation, and sex-biased networks are enriched in lineage-specific regulators

Disentangling the Effects of Mutation and Selection on the Evolution of Gene Expression.

Mutation is the ultimate source of phenotypic variation. However, little is known about the effects of new mutations in the absence of natural selection and whether these effects can influence the course of evolution. This is particularly true for changes in gene expression and regulation. In this thesis I measure the effects of new cis- and trans-regulatory mutations on the expression of the Saccharomyces cerevisiae TDH3 gene. Using these measurements, I show that cis- and trans-regulatory mutations have fundamentally different effects on gene expression. In particular, I find that cis-regulatory mutations are on average larger than trans-regulatory mutations and skewed towards decreases in TDH3 expression, while trans-regulatory mutations are often, but not always, more common than cis-regulatory mutations and skewed towards increases in TDH3 expression. To determine how natural selection has acted on these differences, I generate genome sequences and genetically tractable versions of over 60 diverse S. cerevisiae strains previously isolated from a range of environments. I use these strains to determine the effects of cis- and trans-regulatory polymorphism on TDH3 expression. Comparing these effects to the effects of new mutations, I find that natural selection has acted on both cis- and trans-regulatory variants. Interestingly, the effects of selection varies between cis- and trans-regulatory changes due to differences in the effects of new mutations. Using the same approach, I also identify differences in the action of natural selection on cis- and trans-regulatory changes for the variability in expression amongst genetically identical individuals, i.e. gene expression noise. Finally, I determine the evolution of regulatory changes over long evolutionary timescales in Saccharomyces. I find widespread evidence for compensatory changes in regulation, particularly for trans-regulatory changes that act in opposite directions. Consistent with this finding, I identify hundreds of trans-acting QTL affecting TDH3 expression amongst four strains of S. cerevisiae. Together these results suggest that trans-regulatory changes are a common, but individually small, source of regulatory variation. In total, this thesis shows that understanding the effects of new mutations and comparing these effects to observed differences in natural populations can be a powerful approach for elucidating the underlying molecular mechanisms governing evolution.PHDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116646/1/bpmetz_1.pd

Deciphering Chronometabolic Dynamics Through Metabolomics, Stable Isotope Tracers, And Genome-Scale Reaction Modeling

Synchrony across environmental cues, endogenous genetic clocks, sleep/wake cycles, and metabolism evoke physiological harmony for organismal health. Perturbation of this synchrony has been recently correlated with a growing list of pathologies, which is alarming given the ubiquity of sleep deprivation, mistimed light exposure, and altered eating schedules in modern society. Deeper insights into clocks, sleep, and metabolism are necessary to understand these outcomes. In this work, extensive metabolic profiles of circadian systems were obtained from the development of new liquid chromatography mass spectrometry (LC-MS) metabolomics methods. These methods were applied to Drosophila melanogaster to discern relative influences of environmental and genetic drivers of metabolic cycles. Unique sets of metabolites oscillated with 24-hour circadian periods under light:dark (LD) and constant darkness (DD) conditions, and ultradian rhythms were noted for clock mutant flies under LD, suggesting clock-independent metabolic cycles driven by environmental inputs. However, this metabolomic analysis does not fully capture the inherently dynamic nature of circadian metabolism. These LC-MS methods were adapted to analyze isotope enrichments from a novel 13C6 glucose injection platform in Drosophila. Metabolic flux cycles were noted from glucose carbons into serine, glutamine and reduced glutathione biosynthesis, and altered under sleep deprivation, demonstrating unique energy and redox demands in perturbed sleep/wake cycles. Global isotopolome shifts were most notable in WT flies after lights-on, suggesting a catabolic rush from glucose oxidation early in the active phase. As the scope of these isotope tracer-based metabolomic analyses expand, attributing labeling patterns to specific reactions requires consideration of genome-scale metabolic networks. A new computational approach was developed, called the IsoPathFinder, which uncovered biosynthetic paths from glucose to serine, and extends to glycine and glutathione production. Carbon flux into glutamine was predicted to occur through the TCA cycle, supported by enzyme thermodynamics and circadian expression datasets. This tool is presented as a new mechanism to simulate additional isotope tracer experiments, with broad applicability beyond circadian research. Collectively, a new set of analytical and computational tools are developed to both produce dynamic metabolomic data and improve data interpretability, with applications to uncover new chronometabolic connections