Integrating Genomic Datasets to Identify Host-Pathogen Interactions Miryala Sravan kumar1,2 Nalini Kanagarathinam1, Siva Shanmugam1, Sailu Yellaboina1 1CR Rao Advanced Institute for Mathematics, Statistics and Computer Science(AIMSCS), University of Hyderabad, India 2National Institute of Animal Biotechnology (NIAB) , Axis Clinicals Building, Miyapur, Hyderabad ,India. Abstract Figure 3 Figure 6 Summary & Ongoing Work Developed a meta-analytic approach to mine the publically available gene expression datasets Identified differentially expressed genes in infected macrophages in comparison to uninfected cells. By using RankProd(R package) generated the ranked list of differentially expressed genes. Analyzed all microarray experiments and generated the ranked list of the genes for each experiment. Ranked list from all the experiments were combined to generate a single consensus ranked list, using a novel meta-analytic method. Constructed Phylogenic profiles for human genes and phylogenic profile of macrophage genes induced upon infection. We identified more than 70 pathogens lives in macrophages. Further we will identify the interacting pairs of host and pathogen proteins by integrating the expression data with protein and domain interactions. Zoonitic diseases are the major causes of mortality and have devastating effect on livestock and human health globally. They pose a significant impact on national and international trade and welfare. Globally 60% and in India more than 75% of the human diseases come from animal. Despite their importance, we have a very little knowledge about the common pathogenic mechanisms across human and animals. The goal of our project was to collect and integrate the publically available genomic data of important pathogenic bacteria and their cellular environments to understand the host-pathogen interactions. Mycobacterium species are successful pathogens that infect almost all of the vertebrates and causes significant impact on global health of livestock and human.Presently, we focused on the adaptation mechanisms of mycobacterium pathogens . M. tuberculosis and M. Bovis and M.avium lives inside macrophages and causes tuberculosis in humans, birds and wide range of animals. We have done large scale meta-analysis of publically available gene expression data in mycobacterium species as well as infected macrophages to identify the potential interactions between host and pathogen. We have generated the ranked list of differentially expressed genes in each data of infected macrophages and finally combined the ranked list from 15 different studies and over 200 samples. Infer the host and pathogen response networks and their interactions by integrative genomics approach. The Network analysis is to identify the common and unique signatures of host-pathogen interactions and gives useful leads for identifying resistance or susceptibility loci. The gene expression signatures of host and pathogen could be potential targets for therapeutics, vaccines and biomarkers. . 605 Figure 1 Figure 4 Figure 7 Mycobacterium species Host Mycobacterium abscessus humans Mycobacterium avium birds and deer Mycobacterium tuberculosis mammals Mycobacterium intracellulare humans, cattle and sheep Mycobacterium bovis cattle, humans Mycobacterium kansasii Mycobacterium leprae Mycobacterium marinum Mycobacterium smegmatis non-pathogenic (model to study Mycobacterium species) References 1. Matteo Pellegrini, David Haynor and Jason M Johnson(2004) Protein interaction networks.Expert Review, Proteomics. 2. Dirk Koschützki and Falk Schreiber (2008) Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks. Gene Regulation and Systems Biology 2008:2 193–20. 3. Bing Zhang1, Byung-Hoon Park1, Tatiana Karpinets1 and Nagiza F. Samatova (2008) From pull-down data to protein interaction networks and complexes with biological relevance. Bioinformatics, Systems Biology. Vol. 24 no. 7, pages 979–986. 4. Lucy Skrabanek, Harpreet K, Saini Gary D, Bader Anton J. Enright (2008) Computational Prediction of Protein–Protein Interactions. Mol Biotechnol 38:1–17. 5. Sailu Yellaboina, Dawood B Dudekula and Minoru SH Ko (2008) Prediction of evolutionarily conserved interologs in Mus musculus. BMC Genomics 2008, 9:465. 6. Hartwell,L.H. et al. (1999) From molecular to modular cell biology. Nature, 402, C47–C52. 7. Raivo Kolde, Sven Laur, Priit Adler and Jaak Vilo (2012) Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics, Vol. 28 no. 4 2012, pages 573–580. P-values to z-scores conversion using inverse CDF Figure 8 Figure 2 Figure 5