title: Drawing networks of rejection - a systems biological approach to the identification of candidate genes in heart transplantation.
abstract: Technological development led to an increased interest in systems biological approaches to characterize disease mechanisms and candidate genes relevant to specific diseases. We suggested that the human peripheral blood mononuclear cells (PBMC) network can be delineated by cellular reconstruction to guide identification of candidate genes. Based on 285 microarrays (7370 genes) from 98 heart transplant patients enrolled in the Cardiac Allograft Rejection Gene Expression Observational study, we used an information-theoretic, reverse-engineering algorithm called ARACNe (algorithm for the reconstruction of accurate cellular networks) and chromatin immunoprecipitation assay to reconstruct and validate a putative gene PBMC interaction network. We focused our analysis on transcription factor (TF) genes and developed a priority score to incorporate aspects of network dynamics and information from published literature to supervise gene discovery. ARACNe generated a cellular network and predicted interactions for each TF during rejection and quiescence. Genes ranked highest by priority score included those related to apoptosis, humoural and cellular immune response such as GA binding protein transcription factor (GABP), nuclear factor of κ light polypeptide gene enhancer in B-cells (NFκB), Fas (TNFRSF6)-associated via death domain (FADD) and c-AMP response element binding protein. We used the TF CREB to validate our network. ARACNe predicted 29 putative first-neighbour genes of CREB. Eleven of these (37%) were previously reported. Out of the 18 unknown predicted interactions, 14 primers were identified and 11 could be immunoprecipitated (78.6%). Overall, 75% (n= 22) inferred CREB targets were validated, a significantly higher fraction than randomly expected (P < 0.001, Fisher's exact test). Our results confirm the accuracy of ARACNe to reconstruct the PBMC transcriptional network and show the utility of systems biological approaches to identify possible molecular targets and biomarkers.
authors.affiliation: Department of Medicine, Columbia University, New York, NY, USA.
authors.forename: Martin
authors.initials: M
authors.lastname: Cadeiras
authors.forename: Manuel
authors.initials: M
authors.lastname: von Bayern
authors.forename: Anshu
authors.initials: A
authors.lastname: Sinha
authors.forename: Khurram
authors.initials: K
authors.lastname: Shahzad
authors.forename: Farhana
authors.initials: F
authors.lastname: Latif
authors.forename: Wei Keat
authors.initials: WK
authors.lastname: Lim
authors.forename: Hernan
authors.initials: H
authors.lastname: Grenett
authors.forename: Esteban
authors.initials: E
authors.lastname: Tabak
authors.forename: Tod
authors.initials: T
authors.lastname: Klingler
authors.forename: Andrea
authors.initials: A
authors.lastname: Califano
authors.forename: Mario C
authors.initials: MC
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies / Q000379:methods*; D006084:Graft Rejection / Q000235:genetics*; D016027:Heart Transplantation; D006801:Humans; D015203:Reproducibility of Results; D049490:Systems Biology / Q000379:methods*; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors