Bioinformatics and Genomics
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|
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.
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.
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.
Calabrese, J.M., Sun, W. Song, L., Mugford, J.W., Williams, L., Yee, D., Starmer, J., Mieczkowski, P., Crawford, G.E., Magnuson, T. 2012. Site-specific silencing of regulatory elements as a mechanism of X-inactivation. Cell 151(5): 951-63. PMC3511858
Tomberg J, Temple B, Fedarovich A, Davies C, Nicholas RA. 2012. A highly conserved interaction involving the middle residue of the SXN active-site motif is crucial for function of class B penicillin-binding proteins: mutational and computational analysis of PBP 2 from N. gonorrhoeae. Biochemistry. 2012 Apr 3;51(13):2775-84.
Choudhury R, Ghose Roy S, Tsai YS, Tripathy A , Graves LM and Wang Z. The splicing activator DAZAP1 integrates splicing control into MEK/Erk regulated cell proliferation and migration. Nature Communications In press
Wang Y and Wang Z Systematical identification of splicing regulatory cis-elements and cognate trans-factors (2013), Methods. Aug 22. doi:pii: S1046-2023(13)00326-5.
Wang Y, Xiao X, Zhang J, Choudhury R, Robertson A, Li K, Ma M, Burge CB and Wang Z. A complex network of factors with overlapping affinities control splicing repression by intronic elements. (2013) Nature Structure Molecular Biology Jan;20(1):36-45. doi: 10.1038/nsmb.2459. Epub 2012 Dec 16.
Wang Y, Ma M, Xiao XS and Wang Z. Intronic splicing enhancers, cognate splicing factors and context dependent regulation rules. (2012) Nature Structure Molecular Biology,19(10):1044-52. doi: 10.1038/nsmb.2377. Epub 2012 Sep 16
Wang Y, Cheong CG, Hall TM, Wang Z. Engineering splicing factors with designed specificities (2009) Nature Methods, 6(11):825-30. Epub 2009 Oct 4.
Xiao X, Wang Z, Jang M, Nutiu R, Wang ET, Burge CB. Splice site strength-dependent activity and genetic buffering by poly-G runs. (2009) Nature Struct Mol Biol, 16(10):1094-100. Epub 2009 Sep 13.
Wang Z, Rolish M, Yeo G, Tung V, Mawson M and Burge CB. Systematic identification and analysis of exonic splicing silencers. (2004) Cell 119 (6): 831-845.
Torphy RJ, Tigananelli CJ, Kamande JW, Moffitt RA, Herrera Loeza SG, Soper SA, Yeh JJ. Circulating Tumor Cells as a Biomarker of Response to Treatment in Patient-Derived Xenograft Mouse Models of Pancreatic Adenocarcinoma. PLoS One 2014 Feb 19; 9)2:389474.
Neel NF, Stratford JK, Shinde V, Ecsedy JA, Martin TD, Der CJ, Yeh JJ. Response to MLN8237 in pancreatic cancer is not dependent on RalA phosphorylation. Mol Cancer Ther. 2014 Jan;13(1):122- 33.
Stratford JK, Bentrem DJ, Anderson JM, Fan C, Volmar KA, MarronJS, Routh ED, Caskey LS, Samuel JC, Der CJ, Thorne LB, Calvo BF, Kim HJ, Talamonti MS, Iacobuzio-Donahue CA, Hollingsworth MA, Perou CM, Yeh JJ. A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma. PLoS Medicine. 2010 June 13;7(7):e1000307.
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.