Evolution's footsteps : reconstructing in vitro and in vivo evolutionary trajectories via massively parallel sequencing and profiling

Thesis (Ph. D. in Computational and Systems Biology)--Massachusetts Institute of Technology, Dept. of Biology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references.Understanding how phenotypes evolve through natural selection is a fundamental question of biology. Microbial evolution studies provide the rare opportunity to experimentally elucidate the changes that allow an organism to adapt to novel conditions. In an in vitro experimental evolution system, cells evolve in response to a lab-controlled selective environment. In such experiments, the evolved strains may have no fitness-gain in non-stressed conditions, but outperform their progenitors in the selective growth conditions. A complementary in vivo system is monitoring the evolution of drug resistance in microbial pathogens. Identifying the mutations underlying such evolved phenotypes have typically been limited to the identification of regions of interest by low-resolution techniques such as classical genetics or microarray mapping followed by sequencing, and many relevant genes may remain undetected. The recent development of technologies for cost-effective whole-genome resequencing offers the opportunity to comprehensively study evolution in action. Here, I present a combined experimental and computational strategy to detect and study recurrent genetic aberrations accompanying adaptive evolution in Saccharomyces cerevisiae and Candida albicans by whole-genome re-sequencing of evolved strains using Illumina technology. We sequence parental and evolved strains from multiple evolutionary trajectories under the same selective pressure. Our computational approach focuses on the detection of recurrent aberrations - ranging from SNPs to larger variations. We remove variants present in parental strains as background and catalogue subsequent aberrations that persist and co-occur with phenotypic changes. Likely functional changes are identified by recurrence across independent evolutionary time courses. In S. cerevisiae we identify those mutations that are responsible for evolved, adaptive phenotypes, as well as demonstrate that independently arising adaptive alleles, when in the same genetic background, reduce hybrid viability. In C. albicans, we show both large and small recurrent variations that are highly associated with acquisition of fluconazole resistance. Our approach elucidates the function and evolution of key systems in a key model organism and an human pathogen. More generally, our methodology is applicable to a broad range of species, allowing us to trace phenotypic evolution from bacteria to human cancers.by Jason Michael Funt.Ph.D.in Computational and Systems Biolog

Leveraging existing data sets to generate new insights into Alzheimer’s disease biology in specific patient subsets

To generate new insights into the biology of Alzheimer’s Disease (AD), we developed methods to combine and reuse a wide variety of existing data sets in new ways. We first identified genes consistently associated with AD in each of four separate expression studies, and confirmed this result using a fifth study. We next developed algorithms to search hundreds of thousands of Gene Expression Omnibus (GEO) data sets, identifying a link between an AD-associated gene (NEUROD6) and gender. We therefore stratified patients by gender along with APOE4 status, and analyzed multiple SNP data sets to identify variants associated with AD. SNPs in either the region of NEUROD6 or SNAP25 were significantly associated with AD, in APOE4+ females and APOE4+ males, respectively. We developed algorithms to search Connectivity Map (CMAP) data for medicines that modulate AD-associated genes, identifying hypotheses that warrant further investigation for treating specific AD patient subsets. In contrast to other methods, this approach focused on integrating multiple gene expression datasets across platforms in order to achieve a robust intersection of disease-affected genes, and then leveraging these results in combination with genetic studies in order to prioritize potential genes for targeted therapy

The evolution of drug resistance in clinical isolates of Candida albicans

Candida albicans is both a member of the healthy human microbiome and a major pathogen in immunocompromised individuals. Infections are typically treated with azole inhibitors of ergosterol biosynthesis often leading to drug resistance. Studies in clinical isolates have implicated multiple mechanisms in resistance, but have focused on large-scale aberrations or candidate genes, and do not comprehensively chart the genetic basis of adaptation. Here, we leveraged next-generation sequencing to analyze 43 isolates from 11 oral candidiasis patients. We detected newly selected mutations, including single-nucleotide polymorphisms (SNPs), copy-number variations and loss-of-heterozygosity (LOH) events. LOH events were commonly associated with acquired resistance, and SNPs in 240 genes may be related to host adaptation. Conversely, most aneuploidies were transient and did not correlate with drug resistance. Our analysis also shows that isolates also varied in adherence, filamentation, and virulence. Our work reveals new molecular mechanisms underlying the evolution of drug resistance and host adaptation.National Science Foundation (U.S.). Graduate Research Fellowship ProgramHoward Hughes Medical InstituteHelen Hay Whitney Foundation (Postdoctoral Fellowship)Alfred P. Sloan FoundationNational Institutes of Health (U.S.) (Grant 8DP1CA174427)National Institutes of Health (U.S.) (Grant 2R01CA119176-01

Advanced bioinformatics rapidly identifies existing therapeutics for patients with coronavirus disease-2019 (COVID-19)

BACKGROUND: The recent global pandemic has placed a high priority on identifying drugs to prevent or lessen clinical infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by Coronavirus disease-2019 (COVID-19). METHODS: We applied two computational approaches to identify potential therapeutics. First, we sought to identify existing FDA approved drugs that could block coronaviruses from entering cells by binding to ACE2 or TMPRSS2 using a high-throughput AI-based binding affinity prediction platform. Second, we sought to identify FDA approved drugs that could attenuate the gene expression patterns induced by coronaviruses, using our Disease Cancelling Technology (DCT) platform. RESULTS: Top results for ACE2 binding iincluded several ACE inhibitors, a beta-lactam antibiotic, two antiviral agents (Fosamprenavir and Emricasan) and glutathione. The platform also assessed specificity for ACE2 over ACE1, important for avoiding counterregulatory effects. Further studies are needed to weigh the benefit of blocking virus entry against potential counterregulatory effects and possible protective effects of ACE2. However, the data herein suggest readily available drugs that warrant experimental evaluation to assess potential benefit. DCT was run on an animal model of SARS-CoV, and ranked compounds by their ability to induce gene expression signals that counteract disease-associated signals. Top hits included Vitamin E, ruxolitinib, and glutamine. Glutathione and its precursor glutamine were highly ranked by two independent methods, suggesting both warrant further investigation for potential benefit against SARS-CoV-2. CONCLUSIONS: While these findings are not yet ready for clinical translation, this report highlights the potential use of two bioinformatics technologies to rapidly discover existing therapeutic agents that warrant further investigation for established and emerging disease processes

Advanced Bioinformatics Rapidly Identifies Existing Therapeutics for Patients with Coronavirus Disease - 2019 (COVID-19)

The recent global pandemic has placed a high priority on identifying drugs to prevent or lessen clinical infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by Coronavirus disease – 2019 (COVID-19). We applied two computational approaches to identify potential therapeutics. First, we sought to identify existing FDA approved drugs that could block coronaviruses from entering cells by binding to ACE2 or TMPRSS2 using a high-throughput AI-based binding affinity prediction platform. Top results included several ACE inhibitors, a beta-lactam antibiotic, two antiviral agents (Fosamprenavir and Emricasan) and glutathione. The platform also assessed specificity for ACE2 over ACE1, important for avoiding counterregulatory effects. Further studies are needed to weigh the benefit of blocking virus entry against potential counterregulatory effects and possible protective effects of ACE2. However, the data herein suggest readily available drugs that warrant experimental evaluation to assess potential benefit. Second, we sought to identify FDA approved drugs that could attenuate the gene expression patterns induced by coronaviruses, using our Disease Cancelling Technology (DCT) platform. DCT was run on an animal model of SARS-CoV, and ranked compounds by their ability to induce gene expression signals that counteract disease-associated signals. Top hits included Vitamin E, ruxolitinib, and glutamine. Glutathione and its precursor glutamine were highly ranked by two independent methods, suggesting both warrant further investigation for potential benefit against SARS-CoV-2. While these findings are not yet ready for clinical translation, this report highlights the potential use of two bioinformatics technologies to rapidly discover existing therapeutic agents that warrant further investigation for established and emerging disease processes.</p

Y[subscript MAP]: a pipeline for visualization of copy number variation and loss of heterozygosity in eukaryotic pathogens

The design of effective antimicrobial therapies for serious eukaryotic pathogens requires a clear understanding of their highly variable genomes. To facilitate analysis of copy number variations, single nucleotide polymorphisms and loss of heterozygosity events in these pathogens, we developed a pipeline for analyzing diverse genome-scale datasets from microarray, deep sequencing, and restriction site associated DNA sequence experiments for clinical and laboratory strains of Candida albicans, the most prevalent human fungal pathogen. The Y[subscript MAP] pipeline (http://lovelace.cs.umn.edu/Ymap/) automatically illustrates genome-wide information in a single intuitive figure and is readily modified for the analysis of other pathogens with small genomes.Howard Hughes Medical InstituteBurroughs Wellcome Fund (Career Award at the Scientific Interface)National Institutes of Health (U.S.) (PIONEER Award)Alfred P. Sloan Foundation (Fellowship)National Institute of Allergy and Infectious Diseases (U.S.) (R01 AI-0624273

Determinants of Divergent Adaptation and Dobzhansky-Muller Interaction in Experimental Yeast Populations

Divergent adaptation can be associated with reproductive isolation in speciation [1]. We recently demonstrated the link between divergent adaptation and the onset of reproductive isolation in experimental populations of the yeast Saccharomyces cerevisiae evolved from a single progenitor in either a high-salt or a low-glucose environment [2]. Here, whole-genome resequencing and comparative genome hybridization of representatives of three populations revealed 17 mutations, six of which explained the adaptive increases in mitotic fitness. In two populations evolved in high salt, two different mutations occurred in the proton efflux pump gene PMA1 and the global transcriptional repressor gene CYC8; the ENA genes encoding sodium efflux pumps were overexpressed once through expansion of this gene cluster and once because of mutation in the regulator CYC8. In the population from low glucose, one mutation occurred in MDS3, which modulates growth at high pH, and one in MKT1, a global regulator of mRNAs encoding mitochondrial proteins, the latter recapitulating a naturally occurring variant. A Dobzhansky-Muller (DM) incompatibility between the evolved alleles of PMA1 and MKT1 strongly depressed fitness in the low-glucose environment. This DM interaction is the first reported between experimentally evolved alleles of known genes and shows how reproductive isolation can arise rapidly when divergent selection is strong.National Science Foundation (U.S.). Graduate Research Fellowship ProgramAlfred P. Sloan Foundation (Fellowship)National Institutes of Health (U.S.). Pioneer AwardBurroughs Wellcome Fund (Career Award at the Scientific Interface)Howard Hughes Medical Institut

The evolution of drug resistance in clinical isolates of Candida albicans

Candida albicans is both a member of the healthy human microbiome and a major pathogen in immunocompromised individuals. Infections are typically treated with azole inhibitors of ergosterol biosynthesis often leading to drug resistance. Studies in clinical isolates have implicated multiple mechanisms in resistance, but have focused on large-scale aberrations or candidate genes, and do not comprehensively chart the genetic basis of adaptation. Here, we leveraged next-generation sequencing to analyze 43 isolates from 11 oral candidiasis patients. We detected newly selected mutations, including single-nucleotide polymorphisms (SNPs), copy-number variations and loss-of-heterozygosity (LOH) events. LOH events were commonly associated with acquired resistance, and SNPs in 240 genes may be related to host adaptation. Conversely, most aneuploidies were transient and did not correlate with drug resistance. Our analysis also shows that isolates also varied in adherence, filamentation, and virulence. Our work reveals new molecular mechanisms underlying the evolution of drug resistance and host adaptation