Ramana V. Davuluri, Ph.D.

Associate Professor
Molecular and Cellular Oncogenesis Program
215-495-6903, Office

Introduction

The laboratory of Ramana V. Davuluri, Ph.D., focuses on two closely related fundamental aspects of mammalian genomics and cancer: (i) transcriptional regulation, a system of controls governing gene activation, and (ii) epigenetic modifications, which relate to a system of controls governing gene activation. His research program is computationally driven and interdisciplinary in nature with a complement of collaborative experimental investigation. Davuluri’s group develops computational models and bioinformatics tools for systems biology research to study cancer and other diseases, which eventually help in biomarker identification and drug discovery.

Research Summary

We are beginning to appreciate the increasing complexity of mammalian gene structure. A phenomenon that adds an important dimension to this complexity is the use of alternative gene promoters that drive widespread cell type, tissue type and/or developmental gene regulation. To determine the cellular state, genes require guidance cues that enable them to express precise isoforms in right cell types at appropriate times. Such cues are partly provided by the use of alternative promoters and chromatin state of the corresponding genomic regions, which are altered in disease settings.

Recent annotations of the mammalian genomes suggest that almost half of the protein-coding genes contain alternative promoters, including those of many disease-associated genes. Aberrant use of one promoter over another has been found to be associated with various diseases, including cancer. Whether the alternative promoters are normally regulated by different pathways or whether their expression is tightly linked to one another is an important aspect that needs to be fully explored. Therefore, determining the activity of alternative promoters in different cellular conditions and dissecting the genetic and epigenetic regulatory mechanisms across alternative promoters is imperative to understanding a diversity of developmental processes in both the normal and diseased states. Computational modeling coupled with recent high-throughput technologies, such as chromatin immunoprecipitation (ChIP) followed by microarray analysis (ChIP-chip) and ChIP coupled with massive parallel sequencing (ChIP-seq), enable the genome-wide identification of alternative promoters and associated chromatin modifications. These integrative approaches will lead to better understanding of the role of alternative promoters in a wide variety of cell types, developmental stages and disease conditions, and help to address the key outstanding issues in mammalian genome research.

Recent Scientific Advances

Genome-wide analysis of alternative promoters of human genes using a custom promoter tiling array and ChIP-chip technology: The Davuluri group developed a custom microarray platform that tiles roughly 35,000 alternative putative promoters from nearly 7,000 genes in the human genome. By collaborating with the Huang laboratory at Ohio State University, Columbus, ChIP-chip experiments using antibody against RNA Pol II, were performed to demonstrate the utility of this array platform. The patterns of promoter usage in 17β-estradiol (E2)-treated and untreated MCF7 cells were analyzed, showing widespread usage of alternative promoters. Bioinformatics tools to analyze the ChIP-chip data were developed by Davuluri group.

Genome-wide analysis of alternative promoters in different mouse tissues using ChIP-Seq technology: The Davuluri laboratory has collaborated with the Huang laboratory to perform chromatinimmunoprecipitation (ChIP) using antibody against RNA Pol II to pull-down the RNA polymerase II pre-initiation complex (PIC) bound genomic regions. This was followed by massive parallel sequencing of PIC-bound DNA sequences by Illumina solexa high-throughput sequencer, which produced millions of 35 mer sequence tags. By computationally mapping the 35mer sequence tags to the mouse genome, the Davuluri laboratory built a genome-wide map of active promoters and alternative promoter usage in five mouse tissues (brain, liver, lung, spleen and kidney). Features of the map show the global promoter activities in different mouse tissues, identification of novel alternative promoters that are selectively used in different mouse tissues. Davuluri laboratory is developing bioinformatics tools and pipeline to analyze the high-throughput sequence data.

Development of computational models to infer Transcription Factor Binding Site (TFBS) modules: Transcription initiation in eukaryotic cells is a complex process that typically involves multiple transcription factors binding to the DNA and interacting both with each other and with the RNA polymerase II complex. Because of their short, highly degenerate recognition sites, computational methods for identifying TFBS in the genome suffer from high error rates. However, when combined with some experimental data (either expression data to identify co-regulated genes or ChIP-chip data to identify real TFBS), the accuracy of these predictions increases substantially. The Davuluri group has evaluated two machine learning algorithms—classification trees and random forests—in their ability to identify related promoters in the human genome and to predict sets of TFBS that apparently work in concert. These algorithms are being implemented as GenePattern modules that can be easily integrated into an analysis pipeline. In addition, a web interface is available.

Single nucleotide polymorphisms inside micro-RNA target sites influence tumor susceptibility: MicroRNAs (miRNAs) are small, non-coding RNAs that base pair imperfectly to complementary sequences in target mRNAs (mRNAs) and negatively control the gene expression. Single nucleotide polymorphisms (SNPs) are the most common genetic variants in the human genome, and an immense source of information for localizing and identifying disease susceptible genes. Dr. Hao Sun, a senior scientist in the Davuluri group, is investigating how the SNPs located in transcribed regions of protein coding genes will affect the miR-mRNA interaction by altering the Minimum Free Energy (MFE) of the miR-mRNA duplex, thus destroying the existing miR target sites or creating the new target sites. They are collaborating with the George Calin Laboratory at MD Anderson Cancer Center, Houston, TX to investigate how single nucleotide polymorphisms (SNPs) within miRNA target sites perturb RNA duplex minimum free energy (MFE), miRNA binding and, consequently influence the gene expression/activity. A bioinformatics pipeline was developed to predict the target SNPs, which can potentially influence the miR-mRNA interaction, based on the SNPs’ ability to alter the MFE of the miR-mRNA duplex. The annotations of target SNPs, miRs, and the target gene annotation information was integrated into a database, called miR-SNPDB (http://bioinformatics.wistar.upenn.edu/mir-snpdb) as a public resource for research community. The Davuluri team is currently expanding this study in collaboration with the Calin laboratory to breast cancer, suggesting that a disruption of miRNA gene regulation by SNPs may contribute to tumor susceptibility.

Modeling SMAD Regulatory Modules by Integrative Data-mining: While the molecular mechanisms of TGF-β/SMAD signaling pathway have been studied in detail, the global networks downstream of SMAD remain largely unknown. To address this question, the Davuluri and Huang (OSU) laboratories simultaneously performed chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) and mRNA expression profiling to identify TGF-β/SMAD regulated and synchronously coexpressed gene sets in ovarian surface epithelium. Intersecting the ChIP-chip and gene expression data yielded 150 direct targets, of which 141 were grouped into 3 co-expressed gene sets (sustained up-regulated, transient up-regulated and down-regulated), based on their temporal changes in expression after TGF-ß activation. The Davuluri group developed a data-mining method driven by the Random Forest algorithm to model SMAD transcriptional modules in the target sequences. The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1). Together, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules.

Mammalian promoter databases (http://bioinformatics.med.ohio-state.edu/databases/index.html): The Davuluri laboratory is developing mammalian promoter databases, MPromDb and OMGProm, by integrating UCSC known gene annotations and full length cDNA data from Fantom project. The two databases currently contain 85,843 promoters (35,977 of human, 49,322 of mouse and 850 of rat protein coding genes respectively). The computationally identified transcription factor binding sites (TFBS) are mapped to all the annotated promoter regions. A set of Perl modules and APIs (Application Programming Interface) that can automatically retrieve the data from MPromDb and OMGProm are implemented to study, i) the over represented TFBSs in the list of genes, ii) to find the list of genes that are regulated by a specific transcription factor. The publicly available alternative promoter annotations, methylation status data from ChIP-Seq experiments for mouse embryonic stem cells, neural progenitor cells, embryonic fibroblasts, and human CD4+ T cell are regularly updated in in MPromDb. The ChIP-Seq data from 5 different mouse tissues (brain, kidney, liver, lung and spleen) is also added to MPromDb. Further, MPromDb is also updated with computationally annotated miRNA PolII promoters for nearly 400 human and 300 mouse miRs.

Selected Publications

Cheng, A.S., V.X. Jin, M. Fan, L.T. Smith, S. Liyanarachchi, P.S. Yan, Y.W. Leu, M.W. Chan, C. Plass, K.P. Nephew, R.V. Davuluri, and T.H. Huang. 2006.
Combinatorial Analysis of Transcription Factor Partners Reveals Recruitment of c-MYC to Estrogen Receptor-alpha Responsive Promoters. Mol Cell 21: 393-404.

R.V. Davuluri, Y. Suzuki, S. Sugano, C. Plass, and T.H. Huang. 2008. The functional consequences of alternative promoter use in mammalian genomes. Trends Genet 24: 167-177.

R.V. Davuluri, 2007. Bioinformatics tools for modeling transcription factor target genes and epigenetic changes. Methods Mol Biol 408: 129-151.

R.V. Davuluri I. Grosse, and M.Q. Zhang. 2001. Computational identification of promoters and first exons in the human genome. Nat Genet 29: 412-417.

R.V. Davuluri, Y. Suzuki, S. Sugano, and M.Q. Zhang. 2000. CART classification of human 5' UTR sequences. Genome Res 10: 1807-1816.

Jin, V.X., Y.W. Leu, S. Liyanarachchi, H. Sun, M. Fan, K.P. Nephew, T.H. Huang, and R.V. Davuluri. 2004. Identifying estrogen receptor alpha target genes using integrated computational genomics and chromatin immunoprecipitation microarray. Nucleic Acids Res 32: 6627-6635.

Jin, V.X., G.A. Singer, F.J. Agosto-Perez, S. Liyanarachchi, and R.V. Davuluri. 2006. Genome-wide analysis of core promoter elements from conserved human and mouse orthologous pairs. BMC Bioinformatics 7: 114.

Jin, V.X., H. Sun, T.T. Pohar, S. Liyanarachchi, S.K. Palaniswamy, T.H. Huang, and R.V. Davuluri. 2005. ERTargetDB: an integral information resource of transcription regulation of estrogen receptor target genes. J Mol Endocrinol 35: 225-230.

Kulshreshtha, R., M. Ferracin, M. Negrini, G.A. Calin, R.V. Davuluri, and M. Ivan. 2007. Regulation of microRNA expression: the hypoxic component. Cell Cycle 6: 1426-1431.

Palaniswamy, S.K., V.X. Jin, H. Sun, and R.V. Davuluri. 2005. OMGProm: a database of orthologous mammalian gene promoters. Bioinformatics 21: 835-836.

Singer, G.A., J. Wu, P.S. Yan, C. Plass, T.H. Huang, and R.V. Davuluri. 2008. Genome-wide analysis of alternative promoters of human genes using a custom promoter tiling array. (Accepted).

Sun, H. and R.V. Davuluri. 2004. Java-based application framework for visualization of gene regulatory region annotations. Bioinformatics 20: 727-734.

Sun, H., S.K. Palaniswamy, T.T. Pohar, V.X. Jin, T.H. Huang, and R.V. Davuluri. 2006. MPromDb: an integrated resource for annotation and visualization of mammalian gene promoters and ChIP-chip experimental data. Nucleic Acids Res 34: D98-103.