Vertex labels swapping Edges swapping Pathway activity levels with ratio Abstract Metabolic pathway activity estimation from RNA-Seq data Yvette Temate-Tiagueu, Qiong Cheng, Meril Mathew, Igor Mandric, Olga Glebova, Nicole Beth Lopanik, Ion Mandoiu and Alex Zelikovsky Department of Computer Science, Department of Biology, Georgia State University Computer Science and Engineering, University of Connecticut Our Contribution Using Kegg: database resource for understanding high-level functions and utilities of the biological system from molecular-level information. [Kanehisa M., and Goto S., 2000] (1)A novel graph-based approach to analyze pathways significance (2)Representing a pathway as a set an inferring activity from the information extracted from those sets (3)Validating the two approaches through differential expression analysis at the transcripts and genes level and also through qPCR experiment Objectives Methods Results 1. Moran NA: Symbiosis. Curr Biol 2006, 16:R866–R McFall-Ngai M, Hadfield MG, Bosch TCG, Carey HV, Domazet-Loso T, Douglas AE, Dubilier N, Eberl G, Fukami T, Gilbert SF et al: Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci USA 2013, 110(9): Haine ER: Symbiont-mediated protection. Proc R Soc B-Biol Sci 2008, 275(1633): Lopanik NB: Chemical defensive symbioses in the marine environment. Funct Ecol 2013, 28: Cragg GM, Newman DJ: Natural products: A continuing source of novel drug leads. Biochimica Et Biophysica Acta-General Subjects 2013, 1830(6): Piel J: Metabolites from symbiotic bacteria. Natural Product Reports 2009, 26(3): Gerwick WH, Moore BS: Lessons from the past and charting the future of marine natural products drug discovery and chemical biology. Chem Biol 2012, 19(1): Our experimental studies on Bugula neritina RNA-seq data (mutualistic symbiosis data vs none) show that, by analyzing metabolic pathways using our tool XPathway, we can effectively locate pathways which activities level significantly differ. This result is been validated through qPCR. This project is supported in part by the Molecular Basis of Disease fellowship of GSU Conclusions and Future Work The application of RNA-Seq has allowed various differential analysis studies including differential expression for pathways. A standard approach to study the metabolic differences between species is metabolic pathway. In this study, we introduce a novel approach to characterize pathways activity levels of two samples. We present XPathway, a set of pathways activity analysis tools based on Kegg-Kaas mapping of proteins to pathways. We applied our proposed methods on RNA-Seq Bugula neritina metagenomics data. We successfully identified several pathways with differential activity levels using our novel computational approaches implemented in XPathway. Further validation of initial results is conducted through qPCR.  Develop efficient algorithms for reliable estimation of pathway activity level  Identify pathways which activities significantly differ between two conditions Validation Experimental studies: Bugula neritina In United States - Three sibling species: 1.Deep-water (West coast of United States) 2.Shallow-water (West and Southern East coasts) 3.Northern Atlantic (Northern East coast) Illumina sequence paired-end reads: Sample 1: Bugula with symbiont Sample 2: Bugula without symbiont  50bp paired-end reads  200bp mean fragment length  Assembly into contigs by Trinity  BLAST with Swissprot database Sample 1 Sample 2  Topology-based estimation of pathway significance  EM-based estimation of pathway activity  Selected pathways for qPCR validation  qPCR Model 1: permutation of labels a e b c c a d e b d Model 2: permutation of edges a c b c d a b d RNA-seq reads 2 Samples Trinity Binary EM Contigs IsoDE Contigs validation KEGG, SEED Ortholog groups K00161 K00162 K00163 KEGG, SEED Ortholog groups K00161 K00162 K00163 Graph-based Pathway significance Pathway activity Differentially expressed pathways Experimental validation Proteins MAFSAED VLK EYD RRMEAL BLAST Bootstrapping: -Repeat 1000 times 1. Randomly switch edges 2. Compute density of the largest component -Sort wrt to density -Find the rank of the observed induced subgraph  For gene expression analyses: -Select pathways with significantly different activity -Select DE transcripts from these pathways -Select the genes from these transcripts -Primers are created to test genes per condition  Preliminary results  More primers ordered References In induced graph: # nodes N # edges M # green connected components # 0 in- & out-degrees Density of the induced graph: M/(N-1)