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A Multi-Objective Genetic Algorithm to Find Active Modules from Multiplex Biological Networks: Supplementary Material

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A Multi-Objective Genetic Algorithm to Find Active Modules from Multiplex Biological Networks: Supplementary material Elva-Mar´ıaNovoa-del-Toro1;∗ Efr´enMezura-Montes2 Matthieu Vignes3 Fr´ed´eriqueMagdinier1 Laurent Tichit4 Ana¨ısBaudot1;∗ 1Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France 2University of Veracruz, Artificial Intelligence Research Center, Mexico 3School of Fundamental Sciences, Massey University, New Zealand 4Aix Marseille Univ, CNRS, Centale Marseille, I2M UMR 7373, Marseille, France ∗To whom correspondence should be addressed Contents Figure S1 2 Figure S2 2 Figure S3 3 Figure S4 3 Figure S5 4 Figure S6 4 Figure S7 5 Figure S8 5 Figure S9 6 Figure S10 6 Figure S11 14 Figure S12 18 Figure S13 30 Figure S14 38 Table S1 48 Table S2 48 Table S3 49 1 Figure S1: Sizes of the subnetworks identified by PinnacleZ in the experiment using the network PPI 1 and the simulated data with normal distribution Figure S2: Sizes of the subnetworks identified by PinnacleZ in the experiment using the network PPI 2 and the sampled data from RNA-Seq TCGA breast cancer dataset 2 Figure S3: Sizes of the subnetworks identified by COSINE in the experiment using the network PPI 1 and the simulated data with normal distribution Figure S4: Sizes of the subnetworks identified by COSINE in the experiment using the network PPI 2 and the sampled data from RNA-Seq TCGA breast cancer dataset 3 Figure S5: Sizes of the subnetworks identified by jActiveModules in the experiment using the network PPI 1 and the simulated data with normal distribution Figure S6: Sizes of the subnetworks identified by jActiveModules in the experiment using the network PPI 2 and the sampled data from RNA-Seq TCGA breast cancer dataset 4 Figure S7: Sizes of the subnetworks identified by all the methods in the experiment using the network PPI 1 and the simulated data with normal distribution Figure S8: Sizes of the subnetworks identified by all the methods in the experiment using the network PPI 2 and the sampled data from RNA-Seq TCGA breast cancer dataset 5 Figure S9: F1 score values of jActiveModules, COSINE, PinnacleZ, and MOGAMUN corresponding to the two experiments from the benchmark Figure S10 Eighteen active modules obtained by applying MOGAMUN on Yao's biopsies dataset [1] (see Table S1 for the list of samples). The color of the nodes represents the fold-change, where green and red nodes correspond to under- and over-expressed genes, respectively. Nodes with bold black border correspond to genes significantly differentially expressed (F DR < 0:05 and absolute log2 fold-change > 1). Blue and white nodes correspond to genes with no associated transcriptomics data and no deregulation, respectively. ORC1 MCM2 ORC6 MCM6 MCM4 FCGR3A ORC5 ORC2 CDC7 MCM5 CDC45 DBF4 FCGR3B CDK2 RPA1 MCM7 MCM3 Figure S10.1. Yao's dataset, biopsies: Active module 1 6 PRC1 POLA1 JUN CDK2 FCGR3B CDK1 MYC BARD1 CDKN1A CCNA2 ACTC1 PCDHB4 BRCA1 EP300 ABCC8 BRCA2 TP53 Figure S10.2. Yao's dataset, biopsies: Active module 2 STAT3 PLCG2 SOS1 SYK CBL SRC EGFR PIK3R1 ACTC1 PIK3CG LYN AKT1 HSPB2 CXCL14 PRKDC MAPK8 Figure S10.3. Yao's dataset, biopsies: Active module 3 ABCC8 DUSP1 VAMP2 FCGR3B HSPB2 ACTC1 FCGR3A HCK SYK OGDHL STAT3 LYN PLCG2 CBL KIT PIK3CG Figure S10.4. Yao's dataset, biopsies: Active module 4 7 EGFR PIK3R1 SYK GRB2 SHC1 PLCG2 SRC PIK3R2 CBL FYN LYN STAT3 OGDHL ERBB2 ABL1 Figure S10.5. Yao's dataset, biopsies: Active module 5 MAPK14 RB1 SOS1 EP300 GRB2 SYK PLCG1 CDKN1A AKT1 EGFR CDK1 CBL STAT3 OGDHL PIK3R1 LYN Figure S10.6. Yao's dataset, biopsies: Active module 6 PLCG1 GAB1 CBL VAV1 FYN SHC1 CRK GRB2 PIK3R1 STAT3 EGFR SRC FCGR3B ERBB2 SYK Figure S10.7. Yao's dataset, biopsies: Active module 7 8 STAT3 OGDHL CREBBP UBE2I EP300 MYC FCGR3B CKS1B JUN ATF2 FOSL1 DUSP1 JUND HSPB2 MAPK13 ACTC1 Figure S10.8. Yao's dataset, biopsies: Active module 8 SRC GRB2 EGFR PIK3R1 MET LYN SYK ERBB2 SHC1 KIT CBL FYN LCK OGDHL STAT3 Figure S10.9. Yao's dataset, biopsies: Active module 9 9 ACTC1 CDT1 RPA1 DBF4 MCM7 CDC7 MCM6 MCM4 CDK2 MCM2 CDK1 ORC6 ORC5 ORC2 CDKN1A ORC1 Figure S10.10. Yao's dataset, biopsies: Active module 10 ACTC1 PFN2 ENAH RB1 MAPK1 SP1 CDK2 TP53 SRC CCND2 CDKN1A MYC SYK SMAD2 STAT3 CREBBP EP300 Figure S10.11. Yao's dataset, biopsies: Active module 11 10 CREBBP HIF1A HDAC1 SMAD3 MAPK1 EP300 TP53 SP1 RELA MYC ESR1 JUN RB1 FCGR3B MAPK14 Figure S10.12. Yao's dataset, biopsies: Active module 12 PIK3R2 FCGR3A FCGR3B PIK3R1 HCK GRB2 CBL LILRB2 GJA4 SRC KIT SYK STAT3 CREBBP OGDHL Figure S10.13. Yao's dataset, biopsies: Active module 13 11 MYC DUSP1 EP300 MAPK14 RB1 JUN MAPK8 EGFR HSPB2 ACTC1 CDK1 STAT3 FCGR3B SYK CSF2RB PIK3CG OGDHL Figure S10.14. Yao's dataset, biopsies: Active module 14 OGDHL STAT3 RB1 JUN SMAD3 EP300 HDAC1 CDKN1A CREBBP MYC RELA ESR1 SP1 BRCA1 TP53 Figure S10.15. Yao's dataset, biopsies: Active module 15 12 ACTC1 CDH5 CDK1 BRCA1 NDC80 CDK2 RELA CDKN1A KAT5 KPNA2 UBE2I BARD1 TRRAP TP53 EP300 KAT2A MYC Figure S10.16. Yao's dataset, biopsies: Active module 16 OGDHL HIF1A STAT3 CREBBP RB1 HDAC3 MAPK14 PIK3R1 EP300 SP1 SMAD3 MYC CDKN1A KAT2B ESR1 Figure S10.17. Yao's dataset, biopsies: Active module 17 13 ABCC8 ACTC1 ITGA1 SLC5A4 HSPB2 SOX17 DAAM2 RUVBL1 CLEC14A FCGR3B ADAM33 ALYREF DDX39A CENPN KPNA2 DUSP1 APLNR LSM7 GLRX3 Figure S10.18. Yao's dataset, biopsies: Active module 18 Figure S11 Ten active modules obtained by applying MOGAMUN on Yao's myoblasts dataset [1] (see Table S1 for the list of samples). The color of the nodes represents the fold-change, where green and red nodes correspond to under- and over-expressed genes, respectively. Nodes with bold black border correspond to genes significantly differentially expressed (F DR < 0:05 and absolute log2 fold-change > 1). Blue and white nodes correspond to genes with no associated transcriptomics data and no deregulation, respectively. LRRTM4 NRXN2 SHANK3 GFRA1 DLGAP4 MDM2 UBC USP15 JUN ZG16B UBQLN1 SMAD3 SMAD2 OTUB1 EP300 Figure S11.1. Yao's dataset, myoblasts: Active module 1 14 PDLIM7 GFRA1 CLIP3 UBC FAM149A PTN TRAF2 LRRTM4 UBQLN4 NRXN1 RAI2 BRCA2 ZG16B APBA2 APP Figure S11.2. Yao's dataset, myoblasts: Active module 2 NOTCH2NL HOXA1 KRTAP10-5 LCE1B KRTAP10-3 KRTAP10-9 KRTAP9-2 KRTAP4-2 SPRY2 KRTAP10-8 KRTAP10-7 CHRD KCND3 NRXN1 GFRA1 LRRTM4 Figure S11.3. Yao's dataset, myoblasts: Active module 3 15 MCM3 ORC5 CDC45 ORC6 MCM6 MCM2 MCM7 MCM5 CDC7 CDC6 ORC1 MCM4 DBF4 RAI2 MCM10 ORC2 RHOJ GFRA1 Figure S11.4. Yao's dataset, myoblasts: Active module 4 CBL CTNNB1 RET ERBB2 LRRTM4 NRXN3 AFDN GFRA1 EGFR PIK3R1 HRAS DOK1 SRC SHC1 PTK2 Figure S11.5. Yao's dataset, myoblasts: Active module 5 16 ARAP2 ARHGEF7 APP RHOG HOMER2 PIK3R1 SHANK3 LRRTM4 GFRA1 NRXN3 SHC1 EGFR CRK GRB2 RET Figure S11.6. Yao's dataset, myoblasts: Active module 6 NRXN2 LRRTM4 NRXN3 NLGN1 NLGN2 NLGN3 NRXN1 AFDN RIT1 PTN SGTA ZG16B EPHB2 HRAS GFRA1 NRTN Figure S11.7. Yao's dataset, myoblasts: Active module 7 LRRTM4 NRXN3 NRXN1 MCM6 MCM5 KCND3 GFRA1 MCM2 CDC7 ORC6 ORC1 ORC2 MCM3 MCM7 DBF4 LTV1 ORC5 MCM4 Figure S11.8. Yao's dataset, myoblasts: Active module 8 17 GFRA1 GAB1 SRC PTN PIANP NRXN2 LRRTM4 CBL SHC1 RET CRK PIK3R1 FYN GRB2 ERBB2 Figure S11.9. Yao's dataset, myoblasts: Active module 9 LRRTM4 CDK2 BRCA1 NRXN1 MCM5 CDC7 KCND3 ORC1 MCM7 GFRA1 ORC6 MCM3 CCNA2 MCM2 MCM6 ORC2 DBF4 CDC6 ORC5 MCM4 CDT1 Figure S11.10. Yao's dataset, myoblasts: Active module 10 Figure S12 Twenty three active modules obtained by applying MOGAMUN on Yao's myotubes dataset [1] (see Table S1 for the list of samples). The color of the nodes represents the fold-change, where green and red nodes correspond to under- and over-expressed genes, respectively. Nodes with bold black border correspond to genes significantly differentially expressed (F DR < 0:05 and absolute log2 fold-change > 1). Blue and white nodes correspond to genes with no associated transcriptomics data and no deregulation, respectively. 18 TRIM49B TRIM49 TRIM49D2 TRIM49C PRAMEF12 TRIM49D1 TCEB3B ZNF705B MBD3L2 TRIM43 ARGFX ZIM3 USP29 MBD3L3 PRAMEF11 TPRX1 PRAMEF2 HNRNPCL1 Figure S12.1. Yao's dataset, myotubes: Active module 1 CCNA1 PRAMEF12 PSMD10 TRIM49D2 MDM2 CCNE1 UBE2D1 TRIM43 RB1 MBD3L2 TRIM49D1 CDKN1A PSMA3 PSMD4 TRAF2 CCNB1 Figure S12.2. Yao's dataset, myotubes: Active module 2 19 UBE2C CDK2 CCNB1 CDC20 CCNA1 SRC AR FZR1 UBE2D1 TRIM43 TRIM49B MBD3L2 TRIM49D1 PRAMEF12 TRIM49D2 Figure S12.3. Yao's dataset, myotubes: Active module 3 CCNA1 FZR1 TP53 CDC6 CDC20 CCNB1 CDK7 CDC25A CCNA2 FOXM1 CDKN1A CDK2 CDK1 ARID4A UBE2C CCNE1 Figure S12.4. Yao's dataset, myotubes: Active module 4 20 SNW1 CDKN1A CCNE1 CDC25A RB1 CCNA1 UBE2D1 CCNB1 EP300 HDAC1 TRIM43 TRIM27 CDK2 HIST1H2BA MBD3L2 Figure S12.5. Yao's dataset, myotubes: Active module 5 KLHL38 NOTCH2NL MGAT5B CREB5 KRTAP10-8 KRTAP10-3 KRTAP10-7 HOXA1 KRTAP10-9 KRTAP4-2 KRTAP5-9 KRTAP9-2 KRTAP5-6 SPRY2 LCE3E Figure S12.6. Yao's dataset, myotubes: Active module 6 21 MDM2 CCNA1 CCNA2 CCNB1 RB1 TP53 CDKN1B UBE2D1 CDK2 CDKN1A EP300 SKP2 HDAC1 BRCA1 CCNE1 Figure S12.7. Yao's dataset, myotubes: Active module 7 KRTAP5-9 KRTAP10-8 KRTAP10-7 KRTAP10-3 MEOX2 KLHL38 NOTCH2NL KRTAP10-9 PTGER3 TRIM42 TRIM43 PRAMEF12 TRIM49B MBD3L2 TRIM49D1 Figure S12.8. Yao's dataset, myotubes: Active module 8 22 PRAMEF11 ZIM3 HNRNPCL1 TPRX1 PRAMEF2 HIST2H2AC HIST1H2BA MBD3L3 RELA TRIM43 EP300 UBE2D1 HIST2H2BE CTNNB1 CREBBP LEF1 Figure S12.9.
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    PRMT5 modulates the metabolic response to fasting signals Wen-Wei Tsaia, Sherry Niessenb, Naomi Goebela, John R. Yates IIIb, Ernesto Guccionec,d, and Marc Montminya,1 aThe Clayton Foundation Laboratories for Peptide Biology, Salk Institute, La Jolla, CA 92037; bDepartment of Chemical Physiology, The Scripps Research Institute, La Jolla, CA 92037; cInstitute of Molecular and Cell Biology, Proteos, Singapore 138673; dDepartment of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074 Contributed by Marc Montminy, March 11, 2013 (sent for review February 20, 2013) Under fasting conditions, increases in circulating glucagon maintain sites. The results provide a mechanism to explain how latent cy- glucose balance by promoting hepatic gluconeogenesis. Triggering toplasmic regulators such as CRTC2 may contribute to signaling of the cAMP pathway stimulates gluconeogenic gene expression in the nucleus through their association with chromatin mod- through the PKA-mediated phosphorylation of the cAMP response ifying enzymes. element binding (CREB) protein and via the dephosphorylation of the latent cytoplasmic CREB regulated transcriptional coactivator 2 Results (CRTC2). CREB and CRTC2 activities are increased in insulin resis- In mass spectroscopy studies using epitope-tagged CRTC2 to tance, in which they promote hyperglycemia because of constitu- identify relevant interacting proteins, we recovered the protein tive induction of the gluconeogenic program. The extent to which arginine methyltransferase 5 (PRMT5) (Fig. S1 A and B). We CREB and CRTC2 are coordinately up-regulated in response to glu- confirmed the CRTC2:PRMT5 interaction in coimmunopreci- cagon, however, remains unclear. Here we show that, following its pitation studies with epitope-tagged proteins (Fig.
  • Adiponectin Suppresses Gluconeogenic Gene Expression in Mouse Hepatocytes Independent of LKB1-AMPK Signaling

    Adiponectin Suppresses Gluconeogenic Gene Expression in Mouse Hepatocytes Independent of LKB1-AMPK Signaling

    Adiponectin suppresses gluconeogenic gene expression in mouse hepatocytes independent of LKB1-AMPK signaling Russell A. Miller, … , Benoit Viollet, Morris J. Birnbaum J Clin Invest. 2011;121(6):2518-2528. https://doi.org/10.1172/JCI45942. Research Article Metabolism The adipocyte-derived hormone adiponectin signals from the fat storage depot to regulate metabolism in peripheral tissues. Inversely correlated with body fat levels, adiponectin reduction in obese individuals may play a causal role in the symptoms of metabolic syndrome. Adiponectin lowers serum glucose through suppression of hepatic glucose production, an effect attributed to activation of AMPK. Here, we investigated the signaling pathways that mediate the effects of adiponectin by studying mice with inducible hepatic deletion of LKB1, an upstream regulator of AMPK. We found that loss of LKB1 in the liver partially impaired the ability of adiponectin to lower serum glucose, though other actions of the hormone were preserved, including reduction of gluconeogenic gene expression and hepatic glucose production as assessed by euglycemic hyperinsulinemic clamp. Furthermore, in primary mouse hepatocytes, the absence of LKB1, AMPK, or the transcriptional coactivator CRTC2 did not prevent adiponectin from inhibiting glucose output or reducing gluconeogenic gene expression. These results reveal that whereas some of the hormone’s actions in vivo may be LKB1 dependent, substantial LKB1-, AMPK-, and CRTC2-independent signaling pathways also mediate effects of adiponectin. Find the latest version: https://jci.me/45942/pdf Research article Adiponectin suppresses gluconeogenic gene expression in mouse hepatocytes independent of LKB1-AMPK signaling Russell A. Miller,1 Qingwei Chu,1 John Le Lay,1 Philipp E. Scherer,2 Rexford S.
  • TDP-43 Regulates Retinoblastoma Protein Phosphorylation Through the Repression of Cyclin-Dependent Kinase 6 Expression

    TDP-43 Regulates Retinoblastoma Protein Phosphorylation Through the Repression of Cyclin-Dependent Kinase 6 Expression

    TDP-43 regulates retinoblastoma protein phosphorylation through the repression of cyclin-dependent kinase 6 expression Youhna M. Ayala*, Tom Misteli†, and Francisco E. Baralle*‡ *International Centre for Genetic Engineering and Biotechnology, Padriciano 99, 34012 Trieste, Italy; and †Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, 41 Library Drive, Building 41, Bethesda, MD 20892 Communicated by Jorge E. Allende, University of Chile, Santiago, Chile, January 21, 2008 (received for review September 3, 2007) TDP-43 (for TAR DNA binding protein) is a highly conserved we found that TDP-43 silencing alters cell cycle distribution and heterogeneous nuclear ribonucleoprotein (hnRNP) involved in spe- induces apoptosis. cific pre-mRNA splicing and transcription events. TDP-43 recently has been identified as the main component of cytoplasmic inclu- Results sions in frontotemporal lobar degeneration (FTLD) and amyotro- TDP-43 Down-Regulation Alters the Expression of pRb-Related Fac- phic lateral sclerosis (ALS), two neurodegenerative disorders. The tors. TDP-43 was depleted from HeLa cells by RNAi routinely cellular role of this protein remains to be identified. Here, we show achieving Ͼ90% silencing as measured by Western blot, 48 h that loss of TDP-43 results in dysmorphic nuclear shape, misregu- after small interfering RNA (siRNA) transfection (3, 5). RNA lation of the cell cycle, and apoptosis. Removal of TDP-43 in human microarray analysis was performed on TDP-43 depleted and cells significantly increases cyclin-dependent kinase 6 (Cdk6) pro- control treated cells. The data obtained indicated altered levels tein and transcript levels. The control of Cdk6 expression mediated of several cell proliferation factors in TDP-43-silenced cells.