Recent advances in genomic technologies have changed the landscape of medicine and pharmacology. Instead of focusing on a small number of traditional “druggable targets”, complex diseases are now being studied at a systems-level to develop new therapeutic strategies that involve drug combinations. In addition to monitoring whole genomes and transcriptomes, these advanced technologies are allowing many mechanisms of gene regulation to be analyzed at the genomic scale. The new biological data sets generated by these methods require novel computational approaches to analyze and integrate. In the Department of Pharmacology of UNC, we use genomic profiling approaches to identify new druggable pathways and apply computational modeling to dissect disease networks. We also develop high-throughput screens and bioengineer tools to identify new drug targets for complex diseases such as cancer and psychiatric disorders. This research is allowing the integration of genomic information into the diagnosis and treatment of disease.
|Long noncoding RNA function||Data integration and analysis||Antibiotic resistance mechanisms|
|Alternative splicing regulation||Pancreatic cancer|
Quantitative genomic models for dissecting long noncoding RNA function
Long noncoding RNAs (lncRNAs) are a functionally diverse class of biomolecules whose regulatory capacities, if leveraged appropriately, have tremendous potential to impact human health. The human genome encodes approximately 10,000 lncRNA genes, many of which appear to be conserved, and most of which have yet to be studied in any context. Early works suggest lncRNAs control a range of cellular processes, most notably the regulation of gene expression. Defects in lncRNA-mediated gene regulation underlie many human diseases and are particularly notable in cancer, where they affect metastasis, cell division, and the control of cell death. Quantitative genomic models help to discover principles of lncRNA-mediated gene regulation, focusing on X-chromosome inactivation and autosomal imprinting, two conserved, gene regulatory processes that depend on lncRNAs.
Data Integration and Analysis
With continued improvements in technology, there are ever increasing challenges in data management, integration, analysis and interpretation. To address such challenges, we are continually developing and applying both novel and existing computational tools and methodologies so as to enable insight into biological function. Methods focus extensively on network (re)construction, representation and analysis, with applications in areas such as cancer signaling networks, gene splicing, metabolomics data interpretation, linking deep-sequencing data to network representations and target identification in infectious disease.
Bioinformatics helps to identify the evolution of protein features that are critical to antibiotic interactions. High-level penicillin-resistant strains of Neisseria gonorrhoeae have an altered penA gene encoding a peptidoglycan transpeptidase (penicillin-binding protein or PBP) that is the lethal target of β-lactam antibiotics such as penicillin. Penicillin-resistant strains of N. gonorrhoeae harbor a penA gene with an Asp345a insertion that is a major determinant for decreased inhibition by penicillin. In this study we identified, using bioinformatics and structures of PBPs within the PDB database, a conserved interaction between a variable residue within an active site motif and an amino acid on a loop directly above the active site (this loop is the site of the Asp345a insertion). We showed that there are 4 classes of PBPs in the family of cell division PBPs, each of which has a specific and crucial interaction with the active site motif. In the figure below, we show the 4 classes of PBPs showing conserved residues, with the conserved interaction between the x amino acid of the SxN motif (far right) and a conserved amino acid in the preceding loop shown by the arrows.
Alternative splicing regulation and modulation
The majority of human genes undergo alternative splicing to generate multiple isoforms with distinct function, and this process is tightly controlled in different tissues and developmental stages. We seek to solve the “splicing code” (i.e. a set of rules for splicing regulation by cis-elements and trans-factors) that direct the splicing regulation. We also study the splicing regulation in the context of human cancer, whrere the splicing is deregulated in a genomic scale.
It was found that cancer cells have extensive mis-regulation of alternative splicing, but the detailed mechanism for splicing dysregulation in largely unclear. We have identified several splicing factors that function as oncogenes or tumor suppressors, and are using genomic approach to study how these splicing factors affect splicing of entire transcriptome. We also use massive amount of RNA-seq data generated from the cancer genome atlas (TCGA) to infer how splicing is regulated/deregulated in cancers.
Pancreatic cancer is uniquely characterized by abundant desmoplastic stroma, with neoplastic cells often representing only a minor population within the tumor mass. The stromal compartment is composed of both cellular and extracellular components, including pancreatic stellate cells, fibroblasts, blood vessels, nerves, extracellular matrix and soluble proteins such as growth factors. Accumulating evidence suggests that the stroma is a dynamic microenvironment that promotes tumor growth and invasion, protects cancer cells from apoptosis and is thought to create barriers to effective drug delivery. Because of this abundant desmoplasia, the study of pancreatic tumors continues to be hampered by low cellularity. In order to better understand and study both the stroma and tumor contributions to pancreatic cancer, we have taken a bioinformatics approach to deconvolute or virtually microdissect the stroma and tumor compartments using non-negative matrix factorization of gene expression data from 381 primary and metastatic tumor samples with variable cellularity.
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Midland AA, Whittle MC, Duncan JS, Abell AN, Nakamura K, Zawistowski JS, Carey LA, Earp HS 3rd, Graves LM, Gomez SM and Johnson GL. Defining the expressed breast cancer kinome. Cell Research. 2012 Feb 7. Doi:10.1038/cr.2012.25.
Doolittle JM and Gomez SM. Mapping protein interactions between Dengue virus and its human and insect hosts. PLoS Neglected Tropical Diseases. 2011 Feb 15;5(2):e954.
Staab J, O’Connell TM, Gomez SM. Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS). BMC Bioinformatics. 2010, Mar 9;11:123.
Choi K and Gomez SM. Comparison of phylogenetic trees through alignment of embedded evolutionary distances. BMC Bioinformatics. 2009 Dec 15;10:423.
Yang W, Johnson GL, Gomez SM. Data-driven modeling of cellular stimulation, signaling and output response in RAW 263.7 cells. J Mol Signal. 2008 May 22;3:11.
Gomez SM, Noble WS, Rzhetsky A. Learning to predict protein-protein interactions from protein sequences. Bioinformatics. 2003 Oct 12;19(15):1875-81.