Reverse engineering of regulatory networks in human B cells

Abstract

Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.

MMSET is overexpressed in cancers: Link with tumor aggressiveness

“…Overexpression of MMSET was found in glioblastoma compared to normal brain (P = 1.7E-14, P = .009, P = .002) [11; 12; 13]; in hepatocellular carcinoma compared to normal liver (P = 2.9E-7, 1.3E-6) [14; 15]; in head and neck cancer compared to the normal (P = .008, P = 2.7E-5) [16; 17]; in bladder carcinoma compared to normal bladder (P = 4.1E-11, P = 1.8E-7) [18; 19]; in primary colon cancer compared to normal adjacent mucosa (P = 9.5E-6) [20]; in esophagus adenocarcinoma compared to normal esophagus (P = .004) [21]; in breast carcinoma compared to normal breast (P = 3.8E-8) [22]; in T-cell acute lymphoblastic leukemia compared to normal bone marrow (P = 5.1E-7) [23]; in B-cell acute lymphoblastic leukaemia compared to normal bone marrow (P = .001) [23]; in lung adenocarcinoma compared to normal lung (P = .009, P = 8.4E-4, 1.7E-6) [24; 25; 26]; in lymphoma compared to normal B-cell (P = 3.5E-5, 7.3E-5) [27]; in cutaneous melanoma compared to normal melanocyte ( P = 6.46E-13, P = .01, P =.009) [28; 29; 30]; in smoldering multiple myeloma compared to normal bone marrow (P = 7.3E-4) [31]; in prostate cancer compared to normal prostate (P = 1.8E-6, P = .039, P = 5.1E-8, P = .002, P = .009, P = .006, P = .009) [32; 33; 34; 35; 36; 37; 38]; in yolk sac tumor compared to normal testis (P = .003) [39]; in ovarian carcinoma compared to normal ovary (P = .002, P = 1.1E-4) [40; 41] and in clear cell carcinoma compared to normal kidney tissue (P = .006) [42].…”

Genome-wide system analysis reveals stable yet flexible network dynamics in yeast

“…Biological networks appear to be modular in nature [32,33], i.e., they are composed of more densely connected subnetworks. To determine the degree of modularity and to identify the modules, we apply a random walk Markov CLustering algorithm (MCL) 5 [34,35] to the symmetric version, ) (s w , of the weight matrix, w. 6 The present network turns out to be highly modular (Modularity = 0.74 [37] where n is the number of modules. This reveals the global P-value of the graph theoretic modules being associated to coherent biological processes to be less than 5 10  , thus biologically validating the inferred modular architecture.…”

“…This data is often analyzed by clustering over different experiments of wholegenome expression profiles, and that technique has provided important insights into gene function [2]. However, clustering alone cannot resolve gene interactions, and progress in network identification algorithms has revealed aspects of the static wiring of gene networks [3][4][5][6][7][8][9][10][11]. A recent study by Luscombe and colleagues [8] provided a first step towards an understanding of network dynamics by describing when different sub-networks are active during different cellular conditions in Yeast.…”

Biochemical and statistical network models for systems biology

“…Thus, the relationships in the final reconstructed network are more likely to represent the direct regulatory interactions. The ARACNe algorithm was applied to 336 genome-wide expression profiles of human B cells, resulting in the identification of MYC as a major regulatory hub along with newly identified and validated MYC targets [52].…”

“…Thus, the relationships in the final reconstructed network are more likely to represent the direct regulatory interactions. The ARACNe algorithm was applied to 336 genome-wide expression profiles of human B cells, resulting in the identification of MYC as a major regulatory hub along with newly identified and validated MYC targets [52].A method related to the ARACNe algorithm, called the context likelihood of relatedness (CLR), also uses the mutual information measure but applies an adaptive background correction step to eliminate false correlations and indirect influences [53]. CLR was applied to a compendium of 445 E. coli microarray experiments collected under various conditions and compared to other inference algorithms on the same data set.…”