Open AccessDrawing networks of rejection – a systems biological approach to the identification of candidate genes in heart transplantation
Author(s) -
Cadeiras Martin,
von Bayern Manuel,
Sinha Anshu,
Shahzad Khurram,
Latif Farhana,
Lim Wei Keat,
Grenett Hernan,
Tabak Esteban,
Klingler Tod,
Califano Andrea,
Deng Mario C.
Publication year - 2011
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/j.1582-4934.2010.01092.x
Subject(s) - chromatin immunoprecipitation , transcription factor , gene regulatory network , biology , gene , enhancer , creb , computational biology , candidate gene , dna microarray , gene expression , genetics , promoter
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.