Joel S. Bader - US grants

[unreadable] DESCRIPTION (provided by applicant): While the human genome sequence is known, medical advances will require knowledge of how individual genes and proteins assemble into functional biological networks. These networks are built in part through physical interactions between proteins. Protein interaction networks can now be probed using highthroughput experimental methods, yet challenges remain: data can be noisy and incomplete, and methods developed for simpler model organisms can be difficult to scale up to human. [unreadable] [unreadable] This proposal aims to determine the feasibility of mapping human networks relevant to disease by developing a joint computational / experimental approach to meeting these challenges. Specific aims include developing a statistical metric for confidence in proteomic data from high-throughput two-hybrid screens; developing computational methods for mapping networks cross-species; and generating experimental data with network exploration guided by the computational predictions. [unreadable] [unreadable] If this feasibility study is successful, work in Phase II will focus on building proof-of-principle networks for two specific disease areas. Exemplary areas are cancer, to suggest potential targets for small-molecule and antibody drugs, and infectious disease, to identify host-pathogen interaction. Success in Phase I of this project will result in improved methods for mapping disease-relevant human biological networks. Success in Phase II will result in targets for therapeutic intervention. [unreadable] [unreadable] [unreadable]

Obtaining the genome sequence of an organism provides a blueprint, but not one that we yet know how to read. The instructions encoded by regulatory DNA remain particularly obscure. This CAREER project will advance the mapping of biological networks. Specific interactions between protein transcription factors and DNA regulatory elements will be predicted purely from genome sequence and inferred protein structure using all-atom, explicit solvent simulations of transcription factors bound to DNA. Advances in molecular force-fields and simulation algorithms, increases in computer speed, and homologous binding modes within transcription factor families make this a feasible goal. New algorithms will be developed for protein-DNA simulations and binding site predictions. Binding sites for members of selected gene families will be determined, with the vision of enabling a prediction for every family member within a genome. In a complementary effort, the human protein-protein interaction network will be analyzed at the domain level and the topological organization of protein subunits within protein complexes will be predicted. This project will have broader impact through dissemination of algorithms and data sets generated by the research plan, including public databases of protein-DNA and protein-protein interactions. Scientific outgrowths of this project include improved priors for Bayesian prediction of transcriptional regulatory networks, synergy with experimental methods for binding site analysis, anticipated advances in organism-specific research, and release of a large-scale human protein interaction network. Undergraduate, graduate and postdoctoral researchers will be mentored and trained; new course material will be developed at the undergraduate and graduate levels, essential for the rapidly evolving area of computational biology and bioinformatics; and outreach programs will provide science enrichment and mentoring to public high school students and professional development opportunities for their teachers. All results will be disseminated through appropriate channels, including peer-reviewed publications, conferences, workshops, freely available software and databases, and on-line course material.

[unreadable] DESCRIPTION (provided by applicant): This proposal will lead to identification of gene variants that increase the risk of developing cardiovascular disease. The underlying hypothesis is that systematic genetics conducted in model organisms enables the identification of disease-specific gene modules. This hypothesis will be tested in Phase I through the following Aims: (1) Create a database of phenotypes observed in systematic genetic screens conducted in yeast, worm, fly, zebrafish, and other model organisms. (2) Develop and apply data mining algorithms to identify modules of genes whose deletion or silencing phentoypes show similar patterns across model organisms. (3) Identify modules that are enriched for genes with known variants relevant to cardiovascular disease. Other genes in these modules will then be candidate genes for variants conferring cardiovascular disease risk or can be used to improve priors for whole-genome association tests. In Phase II, variants of candidate genes will be genotyped across a cardiovascular patient population with the goal of identifying genetic markers to assist in diagnosis and treatment of cardiovascular disease. If successful, this proposal will lead to genetic tests that will improve the clinical treatment of patients at risk for cardiovascular disease. The comparative systematic genetics platform developed will be broadly applicable to identifying gene modules relevant to other human diseases and will extend to systematic genetics data collected for a growing set of model organisms. [unreadable] [unreadable] [unreadable] [unreadable]

[unreadable] DESCRIPTION (provided by applicant): This proposal requests funds to support the Gordon Research Conference on Structural, Functional & Evolutionary Genomics, July 29 - August 3, 2007, at the Wellcome Trust Sanger Institute Genome Center in Hinxton, UK, and for the subsequent meeting in 2009. The GRC on Structural, Functional & Evolutionary Genomics is devoted to ambitious analysis of biological systems at the broadest scale possible: full genomes and proteomes; full examination of gene and protein function in the context of biological networks; and full use of the power of comparative genomics and evolutionary theory. This GRC is unique in bringing together experts in both the experimental and computational / theoretical aspects of this research. Experimental talks introduce computational scientists to new, promising data sets and empirical results. Computational talks provide experimentalists with new methods and algorithms for revealing the significance of their findings and also offer the theoretical framework that is needed to integrate different types of data. [unreadable] [unreadable] The broad themes for the 2007 meeting are the evolution of gene regulation, non-protein-coding DNA, and regulatory networks; evolution of networks in the presence of noise; comparative genomics of gene expression; biodiversity and metagenomics; functional genomics; evolution of synthetic life; and contribution of comparative genomics to our understanding of the major transitions in life's evolution. The topics discussed in the meeting have broad relevance to understanding basic human biology. Specific topics including the function of non-coding DNA and the genomic architecture of complex traits are directly relevant to human disease, especially as technologies first developed for model organisms are applied to human. [unreadable] [unreadable] In addition to the scientific objectives, this GRC, in line with the broad goals of Gordon Conferences, strives to encourage mixing and interactions among a diverse scientific body. We aim to have representation from senior scientists and those just starting their scientific careers and to include, as much as possible, women and minorities. The GRC has an additional goal of encouraging scientific interchange between the US and Canada, Europe, Asia, and other scientific centers. In that regard, this GRC stands a chance to be particularly fruitful because it will be the first meeting at a new GRC site, Hinxton (UK), a major center of Genomics, Systems Biology, and Bioinformatics for the UK and Europe. [unreadable] [unreadable] [unreadable]

The design of a synthetic form based on Saccharomyces cerevisiae will be used to answer a wide variety of profound biological questions, including the minimum gene set compatible with free-living eukaryotic life and the fundamental requirements for genome and chromosome stability. Ultimately, an entirely new version of S. cerevisiae is planned. As a first step a medium sized chromosome, IX, about 440 kb long, will be synthesized in this project. Planned alterations to native chromosome sequences are potentially infinite in number and so much thought must be given to the specific alterations to be incorporated into the synthetic chromosome. This project will use an iterative recombinational approach in which segments of ~30 to 100 kb sequentially replace wild-type segments and with the benefit that the investment made in the project at any given stage is limited in scope. If any particular segment is inviable, it can be resynthesized after the nature of the growth defect is mapped and diagnosed. Specific genomic features to be deleted or relocated in the genome include telomeric regions, repeats such as transposon sequences, tRNA genes, introns, silenced regions, and certain nonessential genes. Most importantly, an internal genome reshuffling mechanism will be built into the synthetic yeast chromosome, and tested. This will be accomplished by including symmetric loxP sites in the 3' UTRs of all nonessential genes. The ability of these chromosomes to recombine and rearrange in the presence of Cre recombinase expressed at very low levels will be tested. This process will generate "genome swarms" differing in gene content and order on the synthetic chromosome. Analysis of these swarms will provide information on minimal gene sets as well as probing underlying gene (or other feature) adjacency rules and other genome structural requirements.

This project will allow for the first time, deep questions to be asked about fundamental properties of chromosomes, genome organization, gene content, the function of RNA splicing, the distinction between prokaryotes and eukaryotes, and numerous other questions relating to evolution. In fact the availability of a fully synthetic chromosome (and ultimately a fully synthetic genome) allows for direct testing of evolutionary questions that cannot be addressed in any other way. The "synthetic yeast" that will eventually be designed and refined is likely to play an important role in practical applications. Notably, yeast is the preeminent organism used for industrial fermentations, with a wide variety of practical uses, including ethanol production from agricultural products and by-products. Numerous educational opportunities, both within Johns Hopkins University and outside its walls will spring from the project. In addition to new course content and a new course overall, course content will be created in a developing country (India) and activities involving talented local high school students, high school teachers, and the Maryland Science Center, who have taken a keen interest in the project, will be initiated. The new course will focus on involving undergraduates directly in a large scale functional genomic project.

DESCRIPTION (provided by applicant): A major challenge facing global tuberculosis (TB) eradication efforts is the fact that two billion people are latently infected with Mycobacterium tuberculosis (Mtb), many of whom, especially in the setting of HIV co-infection, will develop reactivation disease. Efforts to gain insight into the molecular mechanisms by which Mtb persists in the host have been impeded by the lack of adequate research models and molecular tools. Current research has focused on identifying individual Mtb genes or host factors required for TB latency and reactivation in specific models and inferring their relevance for TB latency and reactivation in humans. The central hypothesis of this proposal is that no single host or microbial pathway is responsible for Mtb entry into or emergence from latency, but rather, that these complex phenomena are attributable to multiple interdependent host and mycobacterial molecular networks, which cannot be deduced from any one particular model. Using a systems biology approach, including several novel animal models of latent TB infection in combination with transcriptional, proteomic, genetic, imaging, and computational techniques, followed by experimental verification of the data using human samples, we will identify host cytokine networks responsible for immunological control of Mtb growth, as well as Mtb regulatory and metabolic pathways required for bacillary growth restriction and reactivation. Our data are expected to yield: 1) Novel potential drug targets for nonreplicating bacilli, with the goal of shortening the duration of TB chemotherapy; 2) Novel diagnostic markers specific to the latent stage of infection and to reactivation disease; and 3) Novel attenuated vaccine candidates with an inability to reactivate, which would be particularly important in the setting of HIV/AIDS.

DESCRIPTION (provided by applicant): Genetic Hotspots: The goal of this Phase 1 SBIR project is to develop and apply new methods to identify genes that harbor genetic variants that affect disease risk. The long-term objective of this work is to identify genes and gene variants that can improve human health by providing greater information about individual susceptibility to disease risk. The hypothesis driving this work is that multiple variants within a single gene may contribute independently to the risk of a single disease. This genetic heterogeneity is known to exist for familial diseases, and is anticipated for complex disorders. When individuals in a study may have one of a number of risk-enhancing alleles, this genetic heterogeneity decreases the power to detect such variants. The proposed work aims to increase the power of genome wide association studies (GWAS) to detect such genetic hotspots by developing new types of gene-based tests of association. Specific Aim 1 is to develop Bayesian regularization strategies that can reliably identify the correct model for a gene: the number of independent disease-linked variants it contains (0 for most genes), and the genotyped marker most highly correlated with each effect. The power of these methods to detect real associations will be compared to traditional SNP-based tests. Specific Aim 2 is to generalize these methods for application to actual, published genotype data sets where personal information has been censored for privacy concerns (such as the SHARe dataset from the Framingham Heart Study), leaving only summary p-values or regression statistics available to the public domain for analysis. Specific Aim 3 is to test the proposed gene-based methods in real data sets. If the proposed work in Phase 1 is successful, the Phase 2 aims will be to increase the computational efficiency, to develop related methods for genetic studies using ultra-high- throughput sequencing (also called Nextgen-sequencing methods) to analyze genetic variation, and to integrate these gene-based tests with pathway-based tests that require gene-specific p- values as input. The proposed methods have the potential to increase the ability to link specific genetic variants with disease risk, a critical step in predicting individual disease risk especially for new complete genome sequence data. In Phase 2 the developed methods will be integrated into the Genome Interpretation System, a commercial workflow software suite developed at Omicia. As such, it will serve as licensable commercial technology for the company by helping other biotechnology companies to develop their genetic biomarkers for diagnostic and therapeutic developments (theranostics). In addition, any novel variants drawn from this Phase 1 study will be licensable intellectual property, useful both as the basis for future products in our internal pipeline, as well as potentially valuable additions to our patent portfolio. PUBLIC HEALTH RELEVANCE: Genetic Hotspots: Project Narrative A single gene can have multiple independent variants that all contribute to risk for cardiovascular disease, cancer, or other complex disorders. Current genetic analysis methods focus on individual markers, usually single-nucleotide polymorphisms (SNPs), and are not designed to detect gene-based patterns. This proposal will develop new methods that are able to detect the presence of multiple independent risk-enhancing alleles within a gene, increasing the ability to predict individual risk for disease susceptibility. In Aim3 we will be testing the methods with respect to performance in "known" datasets with the focus in the area of cardiovascular disease (CVD). The improved methods will be used as part of the Omicia/s Genome Interpretation System (GIS) product pipeline, and can be licensed to third parties. In addition, any novel genetic markers identified as part of the Aim3 study will themselves be valuable additions to the Omicia product and IP portfolio. Omicia's goal is to provide content and analysis tools for molecular diagnostic tests for cardiovascular conditions, with the promise of identifying patients at high risk to enable them to begin preventive care before symptoms appear. Given the prevalence of CVD in the developed world, these products are potentially a great boon to public health, as well as being significant commercial opportunities.

Mendelian traits are governed by single genes, and methods to identify
these genes have been remarkably successful. In stark contrast,
complex traits result from multiple genetic variants that are
individually neither necessary nor sufficient, often interacting with
each other and the environment. Indeed, collectively, genetic
variants identified to date for complex traits typically explain less
than 10% of the phenotype variance. No unified computational and
statistical framework has been advanced for organizing the discovery
process. To date, a single strategy has dominated: static
variant-by-variant analysis. In contrast, the investigators propose a
new coarse-to-fine statistical framework motivated by the biomedical
hypothesis that mutations contributing to a specific disease cluster
in specific pathways, and in genes within these pathways. Simulations
demonstrate that multi-scale, hierarchical coarse-to-fine sequential
tests have greater power than conventional methods under this
hypothesis. The researchers convert these heuristics into mathematics
and provide a comprehensive analysis, both empirical and theoretical,
of the trade-offs resulting from the introduction of carefully chosen
biases about the distribution of active variants within genes and
pathways. The new methods are applied to data from real genome-wide
association studies (GWAS) with large cohorts to validate their
utility.

Knowing the genetic variants that contribute to cardiovascular
disease, diabetes, autism, and other prevalent disorders would have
great value in identifying drug targets, predicting people at risk,
and suggesting personalized therapies. These diseases are not caused
by mutations in single genes, however, but by multiple mutations that
combine to disrupt multi-gene biological pathways. The investigators
therefore develop a new statistical framework that begins the search
for disease-risk genes at the pathway level, then sequentially narrows
the search to genes within pathways and alleles within genes.
Successful applications to ongoing human genetic studies involving
tens to hundreds of thousands of people identify genes contributing to
cardiovascular disease. More generally, the coarse-to-fine
statistical framework has great value in the current era of "big
data", with increasingly large data volumes calling for innovative
statistical methods.

The objective of this SAVI is to create a "Virtual Institute" that will support the synthesis of an entire eukaryotic genome. Writing a complete eukaryotic genome remains daunting and beyond the proven DNA synthesis capabilities of any individual lab. In analogy to the Human Genome Project, these first efforts will require the coordinated activities of many individual centers. This proposal will put in place the research coordination infrastructure to enable the complete synthesis of the Saccharomyces cerevisiae genome within 5 years at sites spanning the USA, Asia, and Europe, at institutions ranging from four-year liberal arts colleges to major research institutions, thereby capturing the imagination of undergraduate students, fellows, staff and faculty members worldwide.

The specific objectives of this SAVI initiative are to solidify current collaborations between researchers at Johns Hopkins University, Loyola University of Maryland, and international partners in China, India and Europe. The strategic outsourcing of the synthesis of individual chromosomes guarantees that the entire yeast genome is completed within a five-year period with leveraged funds and resources from the US and international participating partners and funding agencies.

Broader Impacts - The broader impacts of this SAVI project are the first-ever ability to ask and answer deep questions about fundamental properties of chromosomes, genome organization, gene content, the function of RNA splicing, the distinction between prokaryotes and eukaryotes, and numerous other aspects of genomes and evolution. The "synthetic yeast" will play an important role in practical applications, ranging from a test-bed for focused biological problems to a chassis for future synthetic biology projects. Notably, yeast is the preeminent organism used for industrial fermentations, with a wide variety of practical uses, including ethanol production from agricultural products and by-products. As the only effort of its kind outside industry, this project provides a unique and very important service in keeping the knowledge in the Synthetic Biology field public through open source availability of all reagents and protocols without intellectual property restrictions. This SAVI effort is unique also in training researchers in genome-scale synthetic Biology. An innovative "Build A Genome" course was developed and implemented at Johns Hopkins University, which has since been offered at Loyola Univ. of Maryland, and two universities in China. The SAVI imitative will also offer opportunities for multi-disciplinary training of students, postdocs, and researchers, and will encourage international exchange.

This award is designated as a Science Across Virtual Institutes (SAVI) award and is being co-funded by NSF's Directorate for Biological Sciences, Directorate for Engineering, and Office of International Science and Engineering.

PROJECT SUMMARY (See instructions): Protein modification on histone lysines is critical for controlling gene expression, which itself controls the variable and plastic expression of the proteome in diverse cell types. Modifications on lysine are chemically diverse and include acetylation, methylation, ubiquitylation and sumoylation. We and others have discovered acetyl- and methyl-lysines in many other proteins, and only some directly control gene expression; many are critical regulatory metabolic enzymes. Ubiquitylation controls the life and death of most proteins, and other protein functions. The pathways regulating diverse modifications on lysines are remarkably complex; much remains to be learned. The network of and dynamic interactions among these modification pathways is even more complex; many lysine-modifying proteins are encoded by multi-gene families, have redundant activities, and multiple substrates, only some of which are known. Cross-talk between modifications provides an extra layer of regulation. We have developed genetic, protein chip, chemical, microfiuidic and computational approaches to decrypt and abstract the complex networks defined by these signaling pathways and monitor how they change over time. This proposal extends many unique technologies developed in the last budget period, with a special focus on adapting these technologies to monitoring dynamic proteomic changes occurring in response to a range of biological stimuli. These newer approaches are complemented in this Technology Center for Networks and Pathways by application of innovative mass spectrometry technologies, including sensitive and diverse technologies for quantifying dynamics of lysine modification in cells. The yeast metabolic cycle, integrated with cell cycling and DNA integrity is a fascinating dynamic cycle that will be studied in detail with several of the technologies. Diverse Driving Biological Projects centered on lysine acetylation, methylation, ubiquitylation and SUMOylation, as well as advanced Training efforts. Including an internship for students in Puerto Rico, are integrated with the Technology Development aspects of the proposal. Technologies and resources are actively disseminated via multiple routes; both static and dynamic proteomics datasets will be centrally warehoused/disseminated.

This project, funded by the Systems and Synthetic Biology Program in MCB and the Biotechnology, Biochemical and Biomass Engineering Program in CBET, is part of a larger ERASynBio funded collaborative. The researchers have previously developed the capability to design and synthesize entire yeast chromosomes, and have inserted into the chromosomes sites that make it easy to modify or recombine pieces of the chromosome. In this work, they leverage this capability to evolve yeast strains that will be particularly well suited for production of chemicals or fuels or other biomanufacturing tasks. In addition, they will collect data that will help them address questions about how chromosomes evolve in laboratory or manufacturing settings, and what characteristics give rise to more stable chromosomes and better growing yeast strains. At the same time, the researchers will develop on line courses to teach ethics related to the field of synthetic biology, and expand their laboratory classes that enable undergraduates to synthesize parts of the yeast chromosome and learn the tools needed to enter into this research field.

Technical Description: Induced Evolution of Synthetic Yeast genomes (IESY) will use the first synthetic eukaryote, Saccharomyces cerevisiae 2.0 (Sc2.0), as a platform for metabolic engineering and genome minimization, and more importantly for generating and understanding industrially high-value phenotypes. Synthetic chromosomes in Sc2.0 permit rapid and comprehensive genome evolution through synthetic chromosome rearrangement and modification by loxP-mediated evolution (SCRaMbLE). SCRaMbLE will be exploited here to evolve strains selected for high-value phenotypes for biofuels and biotechnology, using both chemostats and batch transfer methods in the USA and Europe. Technologies for neochromosomes and orthogonal SCRaMbLE of gene classes will be developed. Evolutionary trajectories will be analyzed to relate genome structure with genome function: DNA sequencing will reveal the genome sequence, rearrangements, and copy number changes in the evolved strains; chromosome conformation capture will show how massive rearrangements affect 3D structure; and deep sequencing technologies will relate sequence and structure to gene expression and isoform abundance. Computational analysis will identify the evolutionary drivers for high fitness, with the potential for further optimization. IESY builds on resources uniquely available from the international Sc2.0 consortium and will be an international resource for efficient evolution of high-value phenotypes. This project represents a new paradigm in synthetic biology in which a genome is pre-programmed to explore combinatorial diversity space to evolve new and useful function.

PROJECT SUMMARY The overwhelming majority of deaths from cancer are attributable to metastasis, rather than growth of the primary tumor. In breast cancer, metastatic recurrence can occur years to decades after apparently successful surgery. Current methods do not allow individualized assessment of metastatic recurrence risk nor do they offer effective therapies for metastatic breast cancer patients. Breast cancer presents a unique research opportunity because the long interval between surgery and recurrence offers the potential to improve patient outcomes if effective anti-metastatic therapies could be developed. However, few drug discovery efforts to date have focused on the metastatic process specifically. The challenges we address are developing and applying methods to identify the basic mechanisms of metastasis, then prioritizing and validating genes and proteins as potential therapeutic targets. Our approach combines advances in experimental (Ewald) and computational (Bader) methods that we have developed to interrogate the metastatic process and to systematically dissect the genetic basis of human disease. Experimentally, we will use a pipeline that relies on organoids from primary human breast cancer tissue to model several distinct steps of metastasis: invasion into the surrounding matrix, dissemination of cancer cell clusters, and outgrowth of these clusters molecular models of distant organs. Computationally, we have developed and applied powerful methods to connect quantitative traits to their genetic basis across multiple complex human disease. We will now apply these computational methods to dissect the molecular basis of breast cancer metastasis. The central insight of our proposal is that the known heterogeneity of breast tumors, while confounding to other methods, enables our quantitative trait loci approach. We will exploit this heterogeneity with computational methods that have the potential to identify the molecular differences between primary human breast tumor organoids that demonstrate metastatic vs. non-metastatic cell behaviors (Aim 1). We will use network analysis techniques to prioritize these as targets, and then use a combination of mammalian genetic engineering and small molecule perturbations to validate targets first in the organoid system and then in accepted mouse PDX models for metastatic growth (Aim 2). Finally, we will combine our novel target based approaches with chemical and genetic perturbagens from the CTD2 Network and broader drug discovery efforts (Aim 3). In this way, we can build on existing knowledge to accelerate our progress towards improved patient outcomes. Success of this program will provide clinically actionable targets for preventing metastatic recurrence or treating patients with established breast cancer metastases. Importantly, our approaches can provide a general platform for dissecting metastasis across epithelial cancers.