Day :
- Track 1: Structural BioinformaticsTrack 2: Systems BiologyTrack 3: Evolutionary Bioinformatics
Location: Independence B
Chair
Hugo Geerts
In Silico Biosciences, USA
Co-Chair
Gregory J Tawa
National Institutes of Health, USA
Session Introduction
Hugo Geerts
In Silico Biosciences, USA
Title: From big data to smart data in CNS disorders, developing predictive models by combining systems biology with quantitative systems pharmacology
Time : 11:10-11:40
Biography:
Hugo Geerts has spent 17 years in the (CNS) Drug Discovery Area, with Paul Janssen, probably the greatest drug hunters in history at the Janssen Research Foundation in Beerse, Belgium doing research in Alzheimer’s disease with targets in tangle and amyloid pathology. He was involved in supporting the successful preclinical, clinical and postmarketing development of galantamine. Since 2002, he became CSO of In Silico Biosciences, a company providing mathematical modeling of pathological interactions in the brain for supporting the whole process of drug discovery from target validation to clinical trial design in Psychiatry and Neurology. He is the faculty of the University of Pennsylvania, Perelman School of Medicine and Drexel Dept. of Pharmacology and has over 80 peer-reviewed publications and patents.
Abstract:
Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These “Big-Data” databases can potentially advance central nervous system (CNS) research and drug development. However, they are not sufficient and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. While these empirically-derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.
Gregory J Tawa
National Institutes of Health, USA
Title: Beyond molecular structure: Biomarker discovery and drug repurposing using gene coexpression modules
Time : 11:40-12:10
Biography:
Gregory J. Tawa completed his PhD at New YorkUniversity and postdoctoral training at the University of Minnesota. Originally a theoretical chemist working in the areas of quantum and statistical mechanics, he has over the years moved towards health sciences with emphasis on drug and biomarker discovery using molecular modeling, bioinformatics and systems biology methods. As such he has a wide variety of experience, having been a scientist in academia, small biotech, and big pharma. Currently he is Modeling and Informatics Lead of the National Institutes of Health, National Center for Advancing Translational Sciences, Therapeutics for Rare and Neglected DiseasesProgram.
Abstract:
Disease diagnosis and therapy are often ineffective if they target individual proteins. Genes and the proteins they code for work together in groups, or modules, and perturbation of these modules can lead to disease. Drugs, in addition to disease, can induce particular patterns of module activation. Identifying these patterns provides important insight into novel diagnoses and therapies. Gene module activation profiles linked to disease can be mined for diagnostic biomarkers. Drugs can be repurposed by finding diseases with gene module activation profiles anti-correlated to that of the drug, or by finding drugs with different indications but similar activation profiles. In the latter case two drugs with similar module activation profiles can be repurposed to treat each other’s disease. When using gene module activation profiles for drug discovery, it is often found that drugs effective at treating the same disease share little structural similarity. So although the link between module expression profile and disease is strong, the link between said profiles and molecular structure is not. This idea has huge implications for drug discovery, as ligand or structure-based screening and design using molecular shape or protein binding pocket complementarity, will only find a fraction of the total number of molecules effective against a disease. Gene module expression profiles, on the other hand, have the potential to identify a much larger universe of compounds potentially active against a disease. In this presentation I will illustrate these ideas with a variety of examples and then discuss implications for future research in drug discovery.
Li Liu
Arizona State University, USA
Title: Evolution-informed Biomarker Discovery for Precision Oncology
Time : 12:10-12:40
Biography:
Liu is an assistant professor of Biomedical Informatics and the director of the Bioinformatics Core Facility at ASU. She holds an M.D. degree in Medicine and an M.S. degree in Information system. As a trained clinician and a bioinformatics researcher, she fully appreciates the critical roles genomic medicine and bioinformatics play in advancing precision medicine. By integrating genomic, phylogenetic, population genetic, statistical and machine-learning techniques. Li Liu and her research team investigate clinical and molecular signatures of human diseases and develop novel computational methods to discover biomarkers for early diagnosis and accurate prediction of therapeutic responses for individual patients.
Abstract:
As one of the leading causes of death worldwide, cancer takes millions of lives each year despite of hundreds of billions of dollars spent on patient care. Major concerns have been raised on under-treatment that leads to disease progression and drug resistance, and over-treatment that exposes patients to unnecessary toxicity with little or no benefit. This situation can be significantly improved by precision oncology, in which treatment regime is tailored to each patient for best outcome. However, discovering bona fide biomarkers to help predict cancer outcomes has been a long-standing mission in cancer research. Although “omics” data generated by high-through biotechnologies offer a valuable source from which genetic markers can be identified, distinguishing spurious markers that show fortuitous statistical associations from biologically relevant markers is a grand challenge. Here, we present a novel framework that integrates evolutionary patterns and statistical association with sparse learning algorithms to assist cancer biomarker discovery. Our methods search beyond human genomes to look for signatures in cancer driver genes across mammals and vertebrates, and incorporate these patterns to discover novel drug targets and cancer biomarkers. When applied to predict therapeutic responses for patients with acute myeloid leukemia and to predict metastasis for patients with prostate cancers, this novel approach gave rise to evolution-informed models that reported lower complexity and higher accuracy than uninformed models. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets.
Mader Sylvie
Université de Montréal, Canada.
Title: Exploring tumor biology via integrated analysis of tumor transcriptome datasets with MiSTIC
Time : 13:30-14:00
Biography:
Mader Sylvie completed her PhD from Université de Strasbourg in France and postdoctoral studies from McGill University in Canada. She is a Professor at Université de Montréal and director of the Molecular targeting unit at the Insitute for Research in Immunology and Cancer. She holds the CIBC Breast cancer research chair at Université de Montréal. She has published more than 70 papers in reputed journals and is currently serving as an editorial board member of the Journal of Molecular Endocrinology. She also developed and organizes the Systems Biology Summer school in the Molecular Biology graduate program at Université de Montréal.
Abstract:
MiSTIC is a unique software package designed to visualize and annotate gene-gene correlations at different resolution levels, from global transcriptomes to gene correlation clusters to individual genes. MiSTIC compares correlation structures of large transcriptome datasets, revealing similitudes between datasets from different solid tumor types, in keeping with common gene reprogramming events in these cancers. Within datasets, MiSTIC performs systematic enrichment analysis on correlated gene clusters to explore their biological significance, using gene sets from multiple databases and lists of genes containing transcription factor binding sites or ChIP-Seq regions in their flanking sequences. This enables the rapid identification of potential causes of gene clustering, including gene amplification/deletion or transcription networks. Enrichment analysis performed at the dataset level can also directly visualize the main aspects of tumor heterogeneity targeted by each gene signature in the database, illustrating for instance that all breast cancer prognostic signatures as well as subtype classifiers are enriched in a proliferation cluster highly conserved among different cancers. Finally, patient sets defined by expression of selected biomarker genes can be visualized, compared and annotated for enrichment in clinical features. Examples will be provided to illustrate how MiSTIC greatly facilitate both the mechanistic exploration of cancer biology and tumor classification using public or in-house transcriptome datasets. A version of MiSTIC pre-loaded with public tumor transcriptome datasets and relevant gene signatures will be made accessible via web interface, and the software package will be distributed for analysis of custom datasets.
Matthias Reuss
University of Stuttgart, Germany
Title: Application of multi scale modeling of vascular tumor growth
Time : 14:00-14:30
Biography:
Matthias Reuss has studied and received his Doctorate at the Technical University of Berlin. After an academic position at the Society for Biotechnological Research Braunschweig and a Professor at the Technical University of Berlin, he was Director of the Institute of Biochemical Engineering at the University of Stuttgart from 1988 to 2009. Since 2007, he has been the Managing Director of the Center Systems Biology (CSB) of the University of Stuttgart. He is a member of the European Research Council (ERC), and since 2012 a member of the International Advisory Board in Framework 7 EU Project Infect since 2009. In 2006, he received an honorary doctorate from the Technical University of Delft.
Abstract:
Vascular tumor growth can be studied with different modeling methodologies according to the scientific questions that should be answered. The mathematical models range from systems of ordinary differential equations to partial differential equations and agent based approaches, while ordinary and partial differential equation models deal with spatially averaged quantities like tumor size and volume fractions, agent based models can discretely resolve cell populations. The multi scale modeling methodology presented in the lecture is based on a hybrid approach; a combination of continuous and discrete variables. Stochastic and deterministic processes on various temporal and spatial scales are included and coupled. Intracellular models describe the progression through the cell cycle, metabolic and signaling pathways, these processes are usually influenced by extracellular factors like nutrients, growth factors, drugs, mechanical stress etc. Cells are also able to move in the simulation domain by a biased random walk up to growth-factor gradients. Vascular sprouts can anastomosi to other sprouts or the already existing vascular network and build new perfused vessels. Within the vascular network, pressures and flows are calculated and the radii of the vessel segments evolve due to growth rules. Several applications of this model will be discussed. The examples cover translation of simulated tumor growth from xenograft models to real tumor structures such as human liver and colon tumors. The model can be also extended to simulate to the coupling of intravascular and interstitial flow caused by the high vessel permeability. The final goal of these simulations is predictions of tumor specific properties to be compared with data from perfusion imaging technology.
Ursula Klingmüller
German Cancer Research Center, Germany
Title: An integrative model to improve anemia treatment in non-small cell lung carcinoma patients
Time : 14:30-15:00
Biography:
Ursula Klingmüller has completed her PhD from Heidelberg University, Germany and Post-doctoral studies at Harvard Medical School in Boston and Whitehead Institute for Biomedical Research in Cambridge, MA, USA. She is the Head of the division “Systems Biology of Signal Transduction” at the German Cancer Research Center (DKFZ) in Heidelberg, Germany and Professor at the University of Heidelberg. She has published more than 90 papers in reputed journals and has been serving as an Editorial Board Member of repute.
Abstract:
Chemotherapy-associated anemia is a common complication affecting 40% of all cancer patients. It is particularly prevalent in lung carcinoma, with an incidence of 50-70% during disease development and reaching ≤90% at advanced stages. The anemia reduces the therapeutic response and the quality of patients’ life. Erythropoiesis Stimulating Agents (ESAs) have been widely used to re-establish normal levels of erythrocytes. However, several clinical trials reported higher mortality risk in ESA treatment and low levels of erythropoietin receptor (EpoR) protein expression has been found in tumor cell lines, rising concerns on EpoR activation in tumor context by ESA treatments. To quantitatively analyze the dynamics of interaction of the ligand erythropoietin (Epo) and the EpoR in the tumor and hematopoietic context, we combined mathematical modeling with quantitative data from pharmacokinetic and pharmacodynamic experiments of ESAs in human subjects, ESAs depletion in human erythroid progenitors and Non-Small Cell Lung Carcinoma (NSCLC) cell lines and the activation of signal transduction through the EpoR by mass spectrometry. The experimental data was used to establish an integrative mathematical model utilizing coupled ordinary differential equations (ODE) that links the cellular scale with the body scale. The ODE model was able to describe the dynamic interaction of all ESAs at molecular, cellular and systemic level in the human body. With this approach, we could estimate the binding properties of all tested ESAs and predict personalized optimal dosage protocols for each ESA to activate the EpoR in the hematopoietic context but not in the tumor context.
Anna Marabotti
University of Salerno, Italy
Title: Interactions of a novel class of beta-lactam antibiotics with their targets penicillin-binding proteins from different sources studied by covalent docking approach
Time : 15:00-15:30
Biography:
Anna Marabotti graduated in 1996 in Medicinal Chemistry, and obtained her PhD in Biochemistry in 2001. She is working as an Assistant Professor in the Universiy of Salerno, teaching Advanced Biochemistry in Master’s degree in Biology. Her main research interests are in the studies of structure-dynamics-function of proteins using a combination of bioinformatics and experimental techniques, applied to proteins of relevant biotechnological interest, or involved in genetic diseases. She is co-author of more than 70 full papers published in journals indexed by the main online resources and in journals with ISSN/ISBN code, and of about 100 communication to congresses.
Abstract:
A novel class of cephalosporin-derived antibiotics, carrying two beta lactam rings in their molecular structure, has been synthesized with the aim of overcoming problems due to the widespread antibiotic resistance. A computational approach to clarify the interactions between these antibiotics and their natural targets, penicillin binding proteins (PBPs) from either Gram + and Gram - bacteria has been carried out by means of covalent docking, a variant of the traditional docking approach, using a modified version of the popular program AutoDock. In this way, we were able to simulate the complex formed between the PBPs and these molecules, taking into account the presence of the covalent bond formed with the reactive Ser residue in the active site of the proteins. The results obtained confirmed the ability of these compounds to interact with several PBPs, and the different affinity of the two beta lactam rings for the active site. Moreover, our simulations allowed to identify the structural reasons for the different affinities showed by the two diastereoisomers of the antibiotics with respect to their target proteins. Finally, we also performed studies in order to predict the affinity of these compounds for beta lactamase, the enzyme responsible for the resistance of bacteria towards this class of therapeutic compounds. These studies will allow to improve the chemical and biological features of this class of drugs, suggesting modifications to introduce in the structure of the lead compound.
Heinz Peter Nasheuer
National University of Ireland Galway, Ireland
Title: Title: Quantitative analyses of protein-protein in living cells using Fluorescence Correlation Spectroscopy and Fluorescence Cross Correlation Spectroscopy
Time : 15:50-16:20
Biography:
Heinz Peter Nasheuer completed his PhD at MPI Göttingen, Germany, and postdoctoral studies at Stanford University School of Medicine. After academic positions at the LMU Munich and the IMB Jena, he joined NUI Galway where he was the Head of the School of Natural Sciences, the largest school in the College of Science, for 4 years. In 2013 he was appointed to a Personal Chair of Biochemistry at NUI Galway. He has published more than 70 papers in peer-reviewed journals. His research interests are mechanisms of cell cycle control and DNA replication, MAP kinase pathways, and protein-protein interactions in living cells using advanced microscopy.
Abstract:
Systems Biology requires quantitive data to computationally model pathways in living organisms. Advanced imaging techniques using fluorescent fusion proteins have the potential to deliver quantitative data from living cells and multicellular organism at the required resolution scale. Fluorescence correlation spectroscopy (FCS) and its variant fluorescence cross correlation spectroscopy (FCCS) are data-rich techniques and are able to measure quantitatively protein-protein interactions, rates of diffusion, rate constants, particle concentrations, and ligand-receptor formations in real time. The typical observation volume of confocal FCS instruments is 0.25 to 0.5 femtoliter (fl) allowing subcellular level resolutions. Correlating the fluctuation signals yields the mobility (D) and the number of molecules (N) in the observation volume. Importantly FCS needs low fluorescent protein expression levels and reaches down to the single molecule level. To reduce the levels of recombinant protein expression the Herpes Simplex Virus Thymidine Kinase (HSV TK) promoter and newly designed deletion mutants thereof were utilized in instead of the CMV promoter. The mutant promoters TK-2ST and TK-TSC containing minimal regulatory elements exhibited low fluorescent protein expression and were most suitable for FCS. Using FCCS we were able to determine the interaction of hypoxia induced factor 1 ï¡ (Hif1ï¡) with its binding partner Hif1ï¢/aryl hydrocarbon receptor after stabilisation of Hif1ï¡ by the pharmacological drug Dimethyloxaloyl-glycine (DMOG) in a concentration, time-dependent and quantitative manner. In the MAP kinase pathway, molecular brightness and FCS data analysis suggest a higher oligomeric state for h-Ras and h-Ras multiprotein complexes in living cells. The latter is also true for Raf isoforms.
Sukanta K Pradhan
Orissa University of Agriculture and Technology, India
Title: Comparative metagenomic analysis of soil microbial communities across three hexavalent chromium contamination levels isolated from chromite mine area
Time : 16:20-16:50
Biography:
S K Pradhan has graduated in Agriculture, Post-graduated in Bioinformatics and completed his PhD in Biotechnology. He is heading Post-graduate Department of Bioinformatics in Orissa University of Agriculture and Technology, Odisha (India), second oldest Agricultural University in India. Besides, he is the Coordinator of the Biotechnology Information System Network (BTISnet) funded by Department of Biotechnology, Govt. of India, New Delhi. He has 10 years of teaching and research experience in and around Computational Biology. He has published more than 16 papers in reputed journals, two book chapters and life member of several societies of repute. He has guided more than 40 post-graduate students. He has acted as organizing secretary of national level workshop and training programmer in Bioinformatics.
Abstract:
Hexavalent chromium (Cr6+) released during various industrial and mining processes leads to serious environmental problems and health hazards due to its toxic, mutagenic, teratogenic and carcinogenic properties. Microorganisms are found to be capable of converting toxic hexavalent chromium to less toxic trivalent chromium due to presence of various resistance mechanisms in them e.g. ion transport (efflux), reduction, DNA repair etc to withstand the chromate toxicity. Genes involved in chromate resistance are located either in chromosome or in plasmid of bacteria. Literature survey revealed the occurrence of chromate resistant genes among large number of bacteria across different genus and species. This paper presents the characterization of the microbial community responsible for the in situ bioremediation of hexavalent chromium. Microbial community structure was analyzed by using 16S rRNA V3 region amplicon in NextGen metagenomic sequencing method. The microbial data were generated through Illumina MiSeq platforms for three sets of soil samples which includes in situ mining site, dump site and nearby forest soil of Sukinda chromite mine of Odisha (India). Certain bacterial genera like Acinetobacter, Pseudomonas, Lactobacillus, Bacillus, Clostridium and Corynebacterium were found to be predominant in in situ mining site than dumping site and forest soil, whereas genera like (Nitrospira, DA101 and JG37-AG-70) and (Nitrospira and DA101), were found to be abundant in dumping site and forest soil respectively. Moreover, the in situ mining site exhibited a relatively higher abundance of actinomycetes than other sites. This gives an idea that actinomycetes may act as a better bioremediating agent to detoxify hexavalent chromate from chromate affected mines.
- Track 4: Computational Systems BiologyTrack 5: Bioinformatics Technologies in MedicineTrack 6: Stitching Bioinformatics Approach to Pharmacy
Location: Independence B
Chair
Heinrich Roder
Biodesix, Inc., USA
Co-Chair
Ravi Radhakrishnan
University of Pennsylvania, USA
Session Introduction
Heinrich Roder
Biodesix, USA
Title: Bridging the gap: From hypothesis-independent tests to understanding of biological mechanisms
Time : 10:45-11:15
Biography:
Heinrich Roder is an author of more than 100 publications and talks spanning the fields of theoretical physics, computational sciences, and molecular diagnostics. A Rhodes Scholar, he earned his DPhil in Theoretical Physics from Oxford University and has held positions at the Universities of Hanover and Bayreuth and Los Alamos National Laboratory. He is a Founder of Biodesix. Serving as CTO, he leads the work of the Research and Development team on sensitive, high-throughput MALDI mass-spectrometry profiling of blood-based samples and the development of molecular diagnostic tests using a machine learning platform incorporating elements of deep learning.
Abstract:
A hypothesis-independent approach to building clinically relevant tests allows the creation of multivariate classifiers that reflect the complexity of biological interactions without any bias from expectations about their mechanisms. However, once the classifier is created, it is of interest to understand the biological underpinnings of its performance. Biodesix has developed a new data analytic platform, the Diagnostic CortexTM that utilizes mass spectral data collected from patient serum samples to create clinically relevant tests without a prior hypotheses or molecular understanding of the underlying biology. It was successfully used to discover and validate a test predicting outcomes for patients with metastatic melanoma treated with the immune checkpoint inhibitor, Nivolumab. To broaden our biological understanding, we applied ideas similar to GSEA (Gene Set Enrichment Analysis) to mass spectral data (PSEA). This approach allowed us to find correlations between classification and sets of proteins associated with known biological functions, such as acute response, wound healing, and complement system. Through the association of mass-spectral features with functional sets of proteins, we constructed a biological score, calculated for each individual sample and serving as a measure of importance of a particular biological process. These scores, as well as their changes, were found to be associated with clinical outcomes of patients, at the same time providing some insights into related biological mechanisms.
Ravi Radhakrishnan
University of Pennsylvania, USA
Title: In silico oncology: Computational horizons in cancer systems biology and multiscale cancer modeling
Time : 11:15-11:45
Biography:
Ravi Radhakrishnan is a Professor of Bioengineering, Biochemistry & Biophysics and Chemical and Biomolecular Engineering at the University of Pennsylvania. His expertise is in Chemical Physics, Statistical Mechanics and Computational Biology. His laboratory focuses its research on the biophysics of single molecules and cell membranes and signaling mechanisms in cancer. Through his work, he has pioneered novel discovery platforms in in silico Oncology and in silico Pharmacology. He has authored over 100 articles in leading peer reviewed journals and serves as a Referee for over 50 leading journals, publishers and federal funding agencies. He also serves as an Editorial Board Member and Associate Editor for 5 journals and also regularly serves as a Panelist and Study Section Member for National Science Foundation, National Institutes of Health and several Federal Science Foundations’ in the EU. He is a Fellow of the American Institute of Medical and Biological Engineering.
Abstract:
We have developed a multi scale platform for the predictions of the effects of mutations on oncogene activation through a combination of molecular, biophysical and cellular models. We have combined the specificity of molecular modeling with the power of network models to predict the molecular mechanisms that lead to the activation of pathways. We have also employ spatial and stochastic models to describe how the effects of the tumor microenvironment can lead to oncogenic signals through non-canonical pathways. We will describe the applications of these models in the clinical contexts of non small cell lung cancer, neuroblastoma and hepatocellular carcinoma.
Junping Jing
GlaxoSmithKline R&D, USA
Title: Explore cancer genomic databases to support immuno-oncology drug discovery
Time : 11:45-12:15
Biography:
Junping Jing has been a researching Scientist in GlaxoSmithKline (GSK) for 18 years. He joined GSK in 1998 and his research included bioinformatics analysis, biomarker discovery and translational medicine. He is currently senior scientific investigator in Department of Computational Biology, fucusing on bioinformatic analysis supporting cancer immune therapies. He received his PhD in Biochemistry from New York University in 1997 and was an early pioneer in developing array-based DNA analysis platform.
Abstract:
Cancer genomic databases such as The Cancer Genome Atlas (TCGA) have become invaluable knowledgebases for cancer drug discover. Besides providing comprehensive genomic and genetic properties of cancer cells in a tumor, they also shed light on the profiles and distribution of tumor infiltriting lymphocytes (TILs) in the tumor micro-environment. Using markers uniqulely representing different lymphocytes such as cytotoxic and regulatory T cells, we have characterized the TIL districution of ~10,000 primary tumors from 30 different cancer types. The talk will provide concrete examples how we are using this anlaysis to identify novel targets, discover biomarkers and stratify patients.
Shijun Zhong
Dalian University of Technology, China
Title: Inhibitor design for blocking the protein-carbohydrate interactions
Time : 12:15-12:45
Biography:
Shijun Zhong has completed his PhD from Xiamen University and Post-doctoral studies from Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences. He is a Professor at Dalian University of Technology. He has published more than 40 papers in reputed journals and has been serving as an Editorial Board Member of repute.
Abstract:
Carbohydrate binding on protein is involved in many important biological processes. In this presentation, we shall report the research progresses in four cases: Inhibitors designed against glycogen phosphorylase via screening million compounds have been experimentally evaluated, leading to nine actives and two crystal complexes, helpful for controlling type 2 diabetes; screening of more than six million compounds against α-glucosidase, followed by molecular dynamics simulations and binding free energy calculations, suggested 10 hits for controlling the concentration of the postprandial blood glucose related to diabetes and the complications; molecular dynamics simulations were applied to the mannose and glucose bindings on flocculation proteins for helping design novel flocculation yeasts to improve yeast brewing production and; possible binding modes of inulin on exo-inulinase were studied using Amber, via 100 ns molecular dynamics simulations, for understanding the hydrolysis mechanism and improving the efficiency in food additives and ethanol productions.
Thomas A. McMurrough
Western Univeristy, Canada
Title: High-throughput investigation of protein-DNA substrate coevolution in Meganuclease family enzymes
Time : 13:30-14:00
Biography:
Thomas A McMurrough has completed his undergraute studies with an Honors Specialization in Genetics and major in Medical Cell Biology from Western University, Canada in 2011. He is currently completing his PhD in the Department of Biochemistry at the Schulich School of Medicine & Dentistry. He was recently awarded an Alexander Graham Bell Canada Graduate Scholarship from the Natural Sciences and Engineering Research Council (NSERC) of Canada and a Doctoral Excellence Research Award from Western Univeristy. He has authored two pubications including a first author manuscipt in PNAS (2014) and is currently exploring Post-doc. and industry opportunities for 2017.
Abstract:
Precision genome editing has applications in academia, biotechnology, agriculture and the development of novel human therapetics. Genome-editing strategies begin with the introduction of a double-strand break (DSB). Meganucleases are one class of enzyme currently used to introduce DSBs, and at highly specific 22-basepair DNA target sites. Although these enzymes create desirable 3’ singlestranded overhangs, the re-engineering of meganucleases to target desired sites is limited by a poor understanding of how cleavage specificity is regulated in the central target site region. We previously used intra-molecular covariation analyses to identify a network of coevolving amino acid residues within the meganuclease active site. We demonstrated that residues at computationally predicated positions were interdependent for catalysis, and identified novel combinations of residues that controlled enzymatic activity. Recently, we have explored a role for the coevolving amino acid residues in controlling central target site specificity. 1600 meganuclease protein variants were tested for in vivo activity against 26 central DNA target site variants using selective growth experiments. Active proteinsubstrate combinations were identified by Illumina® sequencing and compositional data analysis to identify protein variants with altered DNA specificity. Novel protein-DNA combinations were further validated using X-ray crystallography and by re-engineering specificity towards human genomic sequences that were previously untargetable. Our study provides a validated strategy for using intra-molecular covariation to identify functionally important protein networks, demonstrates the power of applying compositional analyses to high-throughput sequencing data, and will expand the genome-editing applicability of meganuclease enzymes.