Mark L. Siegal, Ph.D. - US grants

The long-term objective of the proposed research is to understand the functional divergence and conservation of the sex-determination pathway in flies and other taxa. The first specific aim is to test the hypothesis that those components of the pathway that act last in the hierarchy are most likely to be conserved. The second specific aim is to study the evolutionary acquisition of molecular function. The third specific aim is to test competing hypothesis about the expected level of within species variation ingenues acting near the beginning of the sex-determination hierarchy, as compared with the level of within species variation in genes acting near the end of the hierarchy. The value of this research is that it will illuminate the process by which genetic pathways involve, and thereby produce a conceptual framework for applying knowledge of model systems like Drosophila to the development of the other organisms, including humans. The first two specific aims will be achieved by cloning and characterizing homologies of the intersex and determining mechanisms. To achieve the third specific aim, a mutation in hermaphrodite will be introgressed into a diverse set of wildtype fly strains, to uncover genetic variation in interacting genes involved in sex determination.

Mark L. Siegal
IOB-0642999
CAREER: Phenotypic Robustness and Diversity: Integrating Theory and Experiment in Genomics Research and Teaching

Living things develop and function reliably, despite experiencing a range of environmental conditions, and despite genetic differences caused by mutations. Understanding how organisms achieve this robustness is an important goal of modern biology. A key related goal is to understand how robustness affects evolution. The central aim of the PI's project is to identify genes that buffer environmental and genetic variation, and that might therefore affect how novel traits evolve. This project builds on the PI's previous work using computer simulations, which led to the prediction that a large number of such genes would exist. The project takes advantage of a comprehensive collection of single-gene mutants in the yeast, Saccharomyces cerevisiae, as well as a method for measuring physical traits of individual yeast cells using fluorescence microscopy and automated image analysis. Analysis of data from 4700 mutant strains will identify genes whose impairment causes greater physical variation. Such genes are inferred to contribute to robustness. In a complementary experiment, the PI will analyze physical variation in progeny from a cross between laboratory and wild yeast. This will permit identification of genes that modulate robustness in nature. The merging of computational and experimental approaches in biological research is increasing in importance. The educational goal of this project is to develop two new courses that use computers to enhance learning of difficult quantitative concepts. For the first course, the PI will develop intensive, hands-on computer exercises to teach advanced biology students how to analyze large data sets. For the second, the PI will develop multimedia learning tools to convey quantitative concepts to non-science majors. The broader impacts of these efforts will be to prepare the next generation of biologists to tackle increasingly complex problems, and to enable non-scientists to evaluate technological advances that will have increasing importance in their lives.

DESCRIPTION (provided by applicant): The molecular systems underlying complex traits are in general poorly understood. Even less well understood is how genetic and environmental differences between individuals translate into phenotypic differences. A general feature of this mapping from genotype to phenotype is robustness, or the buffering of the phenotype against genetic and environmental variation. Complex human diseases can be viewed as failures of robust systems, with phenotypic variation manifesting as individual differences in clinical presentation and in disease outcome. Phenotypic variation is a product of and provides a window into the evolutionary processes that have shaped regulatory networks. The long-term goal of this project is to understand at a mechanistic level the sources and the consequences of variation in complex phenotypes. Specifically, this project will study variation in single-cell morphology of the yeast Saccharomyces cerevisiae in different genetic backgrounds. Yeast is an established model organism for understanding basic cellular processes, and also an important model for human disease, particularly the cell-cycle defects and chromosome instability (CIN) associated with cancer. Specific Aim 1 is to determine the congruence between mechanisms that buffer complex phenotypes against environmental variation and against genetic variation. Previous experiments identified hundreds of deletion mutations that increase morphological variation in isogenic cells. This disrupted buffering of environmental differences will be compared to that of genetic differences by introducing a subset of these mutations into diverse yeast strains. Because of the importance of transcriptional networks in robustness, particular focus will be on mutations in genes that encode transcriptional regulators. Genes that act in chromosome organization are also disproportionately found to be required for buffering. One such gene, HTZ1, encodes H2A.Z, a histone variant that is required for proper transcriptional regulation and also proper chromosome segregation. Specific Aim 2 is to determine the relative contributions to phenotypic variation of impaired buffering and CIN, using engineered mutations in HTZ1 that separate these two sources of variation. Whereas the genetic variability that accompanies CIN in cancer has been a long-recognized potential source of phenotypic heterogeneity, impaired buffering of regulatory networks has not been. Specific Aim 3 is to test whether partial loss-of-function mutations in essential genes impair buffering. Nonessential genes that contribute to robustness share properties with essential genes, such as participation in core cellular processes and high connectivity in genetic networks. This observation raises the possibility that essential genes are major contributors to robustness. A comprehensive collection of strains containing hypomorphic mutations in essential genes will be used to determine the extent to which these genes buffer morphological phenotypes. The project will test key hypotheses about the genetic architecture of robustness and may reveal an underappreciated mechanism generating heterogeneity in human disease.

DESCRIPTION (provided by applicant): Spontaneous mutations are the ultimate cause of genetic differences between individuals, and are therefore key to understanding the evolutionary process and many human diseases. However, the rates and patterns of mutation are difficult to measure because mutations are rare and are immediately subjected to natural selection. Recent advances in DNA sequencing technology, combined with the development of high- throughput assays of components of fitness, such as growth rate, present a unique opportunity to obtain a dramatically more precise and comprehensive view of the spectrum of spontaneous mutations. Mutation rates and patterns can be measured directly using mutation-accumulation (MA) lines, which are constructed in the laboratory by many generations of repeated population bottlenecking. The bottlenecks keep effective population size low and therefore prevent natural selection from purging deleterious mutations. In this project, a collection of 149 diploid MA lines of the genetically well-characterized yeast species, Saccharomyces cerevisiae, will be used. The lines were passaged for 2100 generations and therefore collectively capture over 300,000 cell divisions (600,000 replications of a haploid genome). Haploids have been used previously to estimate mutational spectra in yeast, but diploidy has several critical advantages, including better shielding of deleterious mutations, avoidance of genomic instability, and facilitation of downstream genetic analyses. In Aim 1, the complete genome sequences of all 149 MA lines and their common ancestral line will be obtained using next-generation (Illumina) technology. This will yield almost two orders of magnitude more direct data on spontaneous mutations than previously achieved. In Aim 2, genetic analysis will be performed to identify each diploid MA line that carries a highly deleterious mutation. For each such line, high-coverage sequencing of pooled haploid progeny will identify the highly deleterious mutation molecularly. In Aim 3, high-throughput growth-rate assays will be performed on haploid progeny from each diploid MA line. The growth-rate assays will provide an estimate of the distribution of marginal fitness effects of spontaneous mutations. This will be a major advance because the rate of deleterious mutations is typically inaccessible to direct measurement, yet is a fundamental parameter in theoretical models of evolution. Moderately deleterious mutations will be identified molecularly by high-coverage sequencing of pooled haploid progeny. In Aim 4, a collection of 96 haploid lines of mating-type a, each derived from a different diploid MA line, will be established, as a community resource for studying the effects of mutations on complex traits. The complete genotype of each line will be obtained by sequencing. For maximum utility, two sets of lines derived from these 96 lines will also be made: haploids of mating-type a and homozygous a/a diploids. This project will have an immediate and major impact on research in quantitative genetics, systems biology and evolutionary genetics and genomics, by accelerating future investigations of the links between mutations and their phenotypic effects.

Some genetic differences cause differences in observable traits. For example, differences in genes controlling eye-pigment production lead to differences in eye color. Other genetic differences have no consequence, because they do not affect any biological process in any meaningful way. A third type of genetic difference, called cryptic genetic variation, is the focus of this project. A cryptic genetic variant is inconsequential under normal circumstances, but causes an observable difference under altered circumstances, such as when the environment dramatically changes. Cryptic genetic variation has not been well studied, but it has been proposed to be important in the adaptation of organisms to environmental stress and climate change. The aim of this project is to gain a better understanding of cryptic genetic variation, by studying it in a more systematic, quantitative way than has been done before. The project will use the fly Drosophila melanogaster, the study of which has advanced genetics research for over 100 years. Cryptic genetic variation will be revealed in a diverse collection of flies, and the induced variation in the shapes, sizes and numbers of relevant body parts will be quantified. The underlying cause of the induced variation will be investigated by using a powerful technology to measure, in sets of different flies, the activities of thousands of genes. The expected outcome of the project is an increased understanding of both the extent of cryptic genetic variation and the molecular processes that convert inconsequential genetic variants into ones that cause individuals to appear different. The project will also contribute to the training of young scientists in several ways. The PI is Director of Undergraduate studies for Biology at New York University and is actively involved in placing undergraduate students in research laboratories, including members of groups underrepresented in science. In addition, the PI participates in an Open Education Project, and his lectures on Genomes and Diversity for non-science majors are freely available online. Finally, the PI will participate in public outreach efforts to highlight the relevance of cryptic genetic variation to climate change, in part through an online module on these topics.

? DESCRIPTION (provided by applicant): The long-term goal of this research program is to understand the mechanistic and evolutionary causes of variation in complex traits. The current focus is on mechanisms that appear to either suppress or promote variation. The primary experimental approach is to perform large-scale analyses of single-cell traits of the budding yeast, Saccharomyces cerevisiae. One line of work joins others in showing that cryptic genetic variation, kept suppressed until a perturbation reveals its phenotypic effects, is pervasive. This observation suggests that genetic interactions (epistasis) might be a major determinant of complex-trait variation. A second line of work joins others in suggesting that some clonal populations generate heterogeneity in order to hedge their bets against environmental uncertainty. The research program will follow these two lines of work. One set of projects aims to understand how epistasis contributes to natural variation in complex traits. Understanding the sources of variation in complex traits is a major goal in biomedical research because this knowledge impinges directly on the prospect of personalized medicine, for example the prediction of disease risk from an individual's genotype. If not taken into account, epistasis can confound such predictions. Epistasis is also important because it can constrain evolutionary adaptation to follow particular paths, making adaptation more predictable. This predictability could be valuable in the treatment of diseases that have a strong evolutionary component, such as microbial infections and cancer. Although epistasis has been well studied using lab- derived mutations, it has not been well studied in nature because most experimental designs have insufficient power to detect interacting loci. A key aim of this research program is to perform studies with dramatically increased power to detect interactions, for a large number of independent phenotypes, to gain a far richer view of the underlying causes of differences in complex traits. These studies will leverage recent progress in developing high-throughput, microscopy-based methods of quantifying many independent phenotypes, and they will create and use strains of S. cerevisiae that make searching for epistasis much more powerful. The other set of projects aims to understand the molecular mechanisms underlying a newly discovered bet-hedging phenomenon whereby clonal populations of S. cerevisiae contain fast-growing cells that are sensitive to acute stress and slow-growing cells that are tolerant of acute stress. Molecular mechanisms of this kind of adaptive heterogeneity are poorly understood, especially in eukaryotes, so the opportunity to study such a system in a model eukaryote with powerful genetic, molecular and cell-biological tools could lead to major advances. A candidate pathway for controlling the heterogeneity in growth and stress resistance will be studied. In addition, natural variation in growth-rate distributions between S. cerevisiae strains will be mapped, in an effort to understand how ecological pressures shape bet-hedging mechanisms. The two lines of work converge here because epistatic interactions appear to dominate the genetic basis of differences in growth-rate variance.