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NetBioSIG2014-Talk by Ashwini Patil

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NetBioSIG2014 at ISMB in Boston, MA, USA on July 11, 2014 …

NetBioSIG2014 at ISMB in Boston, MA, USA on July 11, 2014

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  • Using a large molecular interaction and regulatory network
    Using time-course gene expression profiles on activation
    Identify novel candidate genes and their time-dependent sub-networks
  • Primary host response to invading pathogens
    Characterized by pattern-recognition receptors (PRRs) eg. Toll-like receptors Tlr1, Tlr2 … Tlr10
    PRRs recognize specific microbial components – pathogen associated patterns (PAMPs)
    PAMPs bind to PRRs and trigger downstream signaling cascades, resulting in expression of pro-inflammatory cytokines and systemic inflammation

    MyD88 dependent pathway
    early response
    expression of proinflammatory cytokines

    TRIF dependent pathway
    late response
    Expression of interferons (IFNs) and IFN-inducible genes

  • Comparatively small network of high confidence interactions connecting genes showing large changes in expression over time
    2681 interactions among 1225 proteins
    Each edge and node is assigned a flow – indicative of its connectivity (importance?) in the network
    Genes showing no significant change in expression form a substantial part of the network
  • Jun, Fos, Chemokines, kinases, Stats, Sp3
  • Akt serine threonine kinases

    Dual specificity phosphatases – responsible for dephosphorylating the Map kinases to repress the immune response

    Xiap – anti-apoptotic inhibitor

    Ppp2ca- Protein phosphatase 2a catalytic subunit alpha
    Important role in regulation of endotoxin tolerance through the regulation of MyD88 activity (Xie et al., Cell Reports 2013)
    Dephosphorylates 20S proteasome subunit
    Affects ability of the proteasome to degrade substrates in concert with Protein Kinase A (Zong et la., Circulation Res 2006)
    Network suggests a similar regulation of the immunoproteasome by ppp2ca

  • GO term enrichment: immune response, regulation of programmed cell death
    KEGG pathways enriched: TLR signaling pathway, Jak-STAT signaling pathway, pathways in cancer, chemokine signaling pathway
  • Transcript

    • 1. TimeXNet: Identifying active gene sub-networks using time- course gene expression profiles Ashwini Patil Institute of Medical Science University of Tokyo NetBio SIG, ISMB 2014
    • 2. Goal • Comprehensive computational analysis of the innate immune response Mouse Interaction network 103218 protein-protein, protein-DNA, post-translational modifications Time-course gene expression RNA-seq expression levels in dendritic cells on LPS stimulus at 8 time points
    • 3. Innate immune system Kawai & Akira, Nat. Immunology, 2010
    • 4. Method - TimeXNet Partition differentially expressed genes into 3 time-based groups Identify most probable paths in the network connecting the three groups Patil et al., PLOS Comp. Biol., 2013
    • 5. Minimum cost flow optimization • ResponseNet • Identifies paths between two groups of genes (genetic hits and differentially expressed genes in yeast) - Yeger-Lotem et l., Nat. Genetics, 2009
    • 6. TimeXNet methodology • Edge cost: inversely proportional to edge reliability • Edge capacity: directly proportional to • Fold change in expression of adjacent gene(s) • Absolute tag counts of adjacent gene(s) • Objective function Minimize cost of flow through the network from T1 to T3 genes • Constraint Flow must pass through intermediate nodes (T2 genes) Most probable paths connecting T1->T2->T3 genes 2681 scored interactions among 1225 proteins
    • 7. Candidate genes Early genes (0.5-1 hour) Intermediate genes (2-4 hours) Late genes (6-8 hours) Genes with no change in expression Gene Flow Gene Flow Gene Flow Gene Flow Jun 13.68 Socs3 85.85 Cxcl10 10.91 Stat1 8.74 Fos 10.34 Nfκb1 76.87 Ddx58 9.33 Mapk8 8.72 Il1b 9.86 Jak2 54.44 Stat2 8.65 Irf5 7.60 Tnf 9.36 Src 38.30 Atf3 8.29 Adcy5 7.43 Cxcl2 7.59 Pik3r5 27.86 Isg15 8.15 Mapk1 7.40 Il1a 7.40 Rela 23.35 Irf7 7.30 Sp1 7.37 Akt1 6.43 Stat5a 20.40 Nos2 6.91 Stat6 7.17 Atf4 5.49 Met 18.94 Ifnar2 5.20 Sp3 7.13
    • 8. Candidate networks Gm13305 Ifnar1 Il12rb1 Il13ra2 Ifngr2 Gm2002 Il13ra1 Il11ra1 Stat5b Stat4 Irs3 Irs4 Lifr Jak2 Cxcr4 Stat6 Il9r Nck1 Il20ra Il22ra11 Il22ra2 Il7r Il2rg Il4ra2 Il28raa Il2ra Il6ra Ifnar2 Il21r Stat2 Il3ra Crlf2 Ifngr1 Il15ra Ddx58 Fos Rela Nfkb1 Stat5a Bcl10 Il10rb JunStat1 Sp3 CR974586.2 Socs3 Foxo3 CT868723.4 Csf2rb Gfi1b CT868723.4CT868723.4CT868723.4 Csf2rb2 Cntfr bb Creb1 • Socs3 • Suppressor of cytokine signaling 3 • Induced by Nfkb and inhibits a large number of proteins, specifically the interleukin receptors
    • 9. Candidate networks
    • 10. Method evaluation • Comparison with experimentally identified regulators • Amit et al., Science 2009: 49.6% previously unknown genes identified • Chevrier et al., Cell 2011: 69.8% regulators (novel and known) and 54.9% TLR target genes identified • Overlap with KEGG pathways • Directed paths of 3 to 7 edges identified in 13 KEGG pathways • Jak-STAT signaling pathway, Chemokine signaling pathway, Toll-like receptor pathway, MAPK signaling pathway
    • 11. Noise in the interaction network
    • 12. Comparison with other methods Method Experimentally confirmed regulators (3 datasets) KEGG Pathways with predicted paths (max length) Execution time (4 CPUs, 2.4Ghz, 12Gb RAM) Prior knowledge required Time- course data TimeXNet 49.6%1 69.8%2 54.9%3 13 (7 edges) 3 min None Yes ResponseNet* 39.2%1 53.5%2 39.2%3 0 (3 edges) 1 min None No SDREM 12.0%1 32.6%2 11.8%3 2 (4 edges) ~10 days Initial genes Yes 1 Regulatory genes from Amit et al., Science, 2009 2 Regulatory genes from Chevrier et al., Cell, 2011 3 Target genes from Chevrier et al., Cell, 2011 *Local implementation using GLPK
    • 13. Yeast osmotic stress response • Time-course gene expression (min) in yeast on hyperosmotic stress - Romero-Santacreu et al., RNA 2009 • Previously used to evaluate SDREM and ResponseNet - Gitter et al., Genome Research 2013 • Genes with 1.5 fold change in expression • Initial response genes: 2-4 min • Intermediate regulators: 6-8 min • Final effectors: 10-15 min
    • 14. Predicted osmotic stress response network • 2-4 min • 6-8 min • 10-15 min • Predicted Method Gold Standard* TFs* Hog1 Runtime TimeXNet 19 5 Yes 5 sec SDREM* 10 4 Yes - ResponseNet* 3 2 No -*Taken from Gitter et al., Genome Research 2013
    • 15. Circadian regulation of metabolism in mouse liver cells - Unpublished • Paths connecting genes showing rhythmic patterns of expression in 24 hours • Network predicted by TimeXNet contains Sphk2, Pld1, Pld2, Glud1
    • 16. TimeXNet Availability: http://timexnet.hgc.jp/
    • 17. • Input • 3 sets of genes with scores • Weighted interaction network • Parameters gamma1 and 2 • Location of glpsol executable from the GLPK • Directory where results will be storedCytoscape Running TimeXNet • Standalone application • Command line version • Iterative command line version to identify optimal parameters Patil & Nakai, under review
    • 18. Conclusion • TimeXNet: A method to predict active gene sub-networks using time- course gene expression profiles • Advantages • Accurate and fast • Independent of biological system: Innate immune response, circadian regulation of metabolism in mouse, yeast osmotic stress response • Amenable to incorporation of other time-course data types: phosphorylation levels, protein levels, epigenetic information • Issues to be addressed • Allowing path prediction between more than 3 groups of genes while maintaining speed and accuracy • Incorporating other forms of time-course information • Enhancements: Automatic install of GLPK, allowing users to enter non-numeric gene IDs Patil et al., PLOS Comp. Biol., 2013
    • 19. Acknowledgements • Innate immune response • Prof. Kenta Nakai - University of Tokyo • Dr. Yutaro Kumagai – Osaka University • Dr. Kuo-ching Liang – University of Tokyo • Prof. Yutaka Suzuki – University of Tokyo • Dr. Tomonao Inobe – Toyama University • Yeast osmotic stress response • Dr. Anthony Gitter – Microsoft Research • Circadian regulation of metabolism • Dr. Craig Jolley – RIKEN Center for Developmental Biology, Kobe • Funding • Japan Society for the Promotion of Science (JSPS) FIRST Program • JSPS Grant-in-Aid for Young Scientists • Takeda Science Foundation (with Dr. Tomonao Inobe) • Computational resources • Supercomputer at the Human Genome Center, Institute of Medical Science, University of Tokyo
    • 20. Edge Capacities For edges between the auxiliary source, S, and the initial response genes GT1, 2 1log / / imax i Si T imax ii i fc e C i G fc N e N       (3) For edges connected to the intermediate regulators GT2, 2 2 2log , / / imax i ij T T imax ii i fc e C i G j G fc N e N        (4) 2 2 2 log log / // / , 2 jmax jimax i imax jmaxi ji ji j ij T fc efc e fc N fc Ne N e N C i j G                         (5) For edges between the late effectors, GT3, and the auxiliary sink T, 2 3log / / imax i iT T imax ii i fc e C i G fc N e N       (6) 2 2 2 log log / // / , 2 jmax jimax i imax jmaxi ji ji j ij T fc efc e fc N fc Ne N e N C i j G                         (5 For edges between the late effectors, GT3, and the auxiliary sink T, 2 3log / / imax i iT T imax ii i fc e C i G fc N e N       (6 For edges between the auxiliary source, S, and the initial response genes GT1, 2 1log / / imax i Si T imax ii i fc e C i G fc N e N       (3) For edges connected to the intermediate regulators GT2, 2 2 2log , / / imax i ij T T imax ii i fc e C i G j G fc N e N        (4) 2 2 2 log log / // / , 2 jmax jimax i imax jmaxi ji ji j ij T fc efc e fc N fc Ne N e N C i j G                         (5) For edges between the late effectors, GT3, and the auxiliary sink T, For edges connected to the intermediate regulators GT2, • Graph G = (V, E) with E edges and V nodes (containing S – auxiliary source, T – auxiliary sink) • fc = fold change • 𝑒 = average expression level at all time points • N = number of genes with expression values • S = auxiliary source node • T = auxiliary sink node • GT1, GT2, GT3 = genes having maximal fold change at times T1, T2 and T3 For all other edges, not connected to the intermediate regulators or the auxiliary source and s 21 ,ij TC i j S G T  
    • 21. Edge costs 1Si Si Tw C i G   (8) 2ij ij Tw C i G   (9) 3iT iT Tw C i G   (10)   2,ij ij Tw f s i j S G T   , as per equation (2) The edge costs were calculated as: Where ()f = scaling function  likelihood ratio , HitPredictijs i j   ; 0.163 999ijs   999 , Innatedb, KEGGijs i j    , TRANSFACijs Transfacscore i j   ; 1 6ijs  3iT iT Tw C i G     2,ij ij Tw f s i j S G T   , as per equation (2) The edge costs were calculated as:  10log ,ij ijA w i j E    2ij ij Tw C i G   3iT iT Tw C i G     2,ij ij Tw f s i j S G T   , as per equation (2) The edge costs were calculated as:  log ,A w i j E    2ij ij Tw C i G   3iT iT Tw C i G     2,ij ij Tw f s i j S G T   , as per equation (2) The edge costs were calculated as:  10log ,ij ijA w i j E   
    • 22. Optimization problem

    ×