NetBioSIG2014-Talk by Salvatore Loguercio

<ul><li> Network-augmented Genomic Analysis (NAGA) Applied to Cystic Fibrosis studies Salvatore Loguercio, Ph.D. loguerci@scripps.edu @sal99k http://sulab.org July 11, 2014 Network Biology SIG ISMB 2014 </li> <li> Cystic fibrosis overview inherited recessive chronic disease - chest infection, lung damage, and bowel obstruction. 30,000 children and adults in the US (70,000 worldwide); 1,000 new cases diagnosed each year. Predicted median age of survival for a person with CF: late 30s. Primary therapy: airway clearance techniques (ACT) Source: Cystic Fibrosis Foundation </li> <li> CFTR and mucous flow 3 Source: http://www.flickr.com/photos/ajc1/3737955649 Mutation cause the body to produce unusually thick, sticky mucus Clogs the lungs and leads to life-threatening lung infections Obstructs the pancreas and stops natural enzymes from helping the body break down and absorb food </li> <li> Golgi ER Lysosome WT CFTR WT chloride conductance B C SDS-PAGE endosomes Apical surface degradation DF508 CFTR cannot exit the ER DF508 X Credit: Bill Balch CFTR mutations affect protein folding and export </li> <li> A systematic approach to CF correction Cell line: CFBE Functional: siRNA screen F508 CFTR against PN library* 368 siRNAs that significantly rescue CFTR function *Collection of 2500 siRNA targeting proteins involved in protein homeostasis (proteostasis) Biochemical: MudPIT proteomics 775 differentially interacting proteins (WT/ F508-CFTR) </li> <li> A systematic approach to CF correction Functional: siRNA screen F508 CFTR against PN library* 368 siRNAs that significantly rescue CFTR function Biochemical: MudPIT proteomics 775 differentially interacting proteins (WT/ F508-CFTR) (368) </li> <li> Connect Functional with Biochemical data </li> <li> Target 1 2 3 I) Compute all shortest paths from siRNA hits to the target through a weighted protein interaction network (Dijstra algorithm) II) Prioritize connecting proteins specific to the set of high-scoring siRNA hits considered. Connect siRNA hits to a target through the Human Interactome 2 2 </li> <li> I. Build integrated PPI network II. Run Shortest Path analysis III. Control for unrelated protein hubs </li> <li> Publicly available interaction data: From 10 source databases and 11 studies 14796 proteins 169625 interactions Quality score [0:1] for each interaction, based on experimental evidences* *Source: Human Integrated Protein- Protein Interaction reference (HIPPIE) d = 9 Average path length: 3.6 I. Build a weighted protein interaction network include MS data + Experimental interactome (nodes + edges) Updated scores, based on databases and experimental interactome S(u,v) = 2 Sexp Sdb Sexp= 1 if e(u,v) in exp 0 </li> <li> Target 1 2 3 2 2 I. Build integrated PPI network II. Run Shortest Path analysis </li> <li> Target 1 2 3 2 2 I. Build integrated PPI network II. Run Shortest Path analysis III. Control for unrelated protein hubs </li> <li> siRNA library Randomly select a subset of the same size of the target set shortest path analysis Repeat n times Randomized hubness For each connecting node Target Randomization select proteins specific for the set of siRNA hits For each protein connecting siRNA hits to the target, compute: Nsp: number of distinct siRNA hits that utilize the protein on its shortest path to the target Nrnd: randomized Nsp p-value = ( ) ( ) Nsp, Nrnd and the associated p-value are used to prioritize connecting proteins specific to the set of siRNA hits considered </li> <li> CFTR PN connectors first degree real vs. randomized Nsp 3Select: Nsp 3 Nsp /Nrnd2 (12 proteins) </li> <li> Assessing candidate regulators 15 42 candidate regulators 31 previously screened 11 novel genes 22 (71%) previously identified as hits 8 (73%) validate in de novo experiments </li> <li> Validation of predicted protein targets siRNA screen CFTR rescue of function 8/11 (73%) novel candidate regulators validate x x x </li> <li> Gene Symbol Solo vs. MudPit Vx809 vs. MudPit SRRM1 x CDC5L x NDKB x TPR x AIFM1 x 2ABB x KPCD2 x PLSCR1 x MAP3K14 x TFG x x XRCC5 x x CTNB1 x XPO1 x MCM7 x WDR61 x PP2AB x H2AFX x MYC x Validation of predicted targets - Specificity X: predicted : validated siRNA screen CFTR rescue of function New condition: Vx-809 drug </li> <li> X: predicted : validated siRNA screen CFTR rescue of function Validation of predicted targets - Coverage Restrain flow through a subset of direct interactors Gene Symbol Solo vs. MudPIT (partial) Solo vs. MudPIT (full) Vx809 vs. MudPIT (full) SRRM1 x x EIF3L x STAU1 x CAN2 x SNRPA x AUP1 x Good specificity Sub-optimal coverage </li> <li> Summary NAGA is a network-based method to integrate functional genomics data (e.g. siRNA screens) with interactomics datasets (e.g. AP-MS, MudPIT) Useful for prioritizing novel functional targets and for identifying relevant network modules It leverages publicly available information on protein-protein interactions and thus is readily applicable to many scenarios where a connection between functional and biochemical data is sought Good specificity, coverage to be improved </li> <li> Contact loguerci@scripps.edu @sal99k http://sulab.org Andrew Su Su Lab William Balch Darren Hutt Daniela Roth Chao Wang Anita Pottekat Sumit Chanda Stephen Soon Dieter Wolf Trey Ideker Anne Carvunis Jean Wang Daniel Quan Travel funding to ISMB 2014 was generously provided by NSF and the NetBio SIG committee NetBio SIG </li> </ul>