Hunting complex differential gene interaction patterns across molecular contexts

Abstract

Heterogeneity in genetic networks across different signaling molecular contexts can suggest molecular regulatory mechanisms. Here we describe a comparative chi-square analysis (CPχ2) method, considerably more flexible and effective than other alternatives, to screen large gene expression data sets for conserved and differential interactions. CPχ2 decomposes interactions across conditions to assess homogeneity and heterogeneity. Theoretically, we prove an asymptotic chi-square null distribution for the interaction heterogeneity statistic. Empirically, on synthetic yeast cell cycle data, CPχ2 achieved much higher statistical power in detecting differential networks than alternative approaches. We applied CPχ2 to Drosophila melanogaster wing gene expression arrays collected under normal conditions, and conditions with overexpressed E2F and Cabut, two transcription factor complexes that promote ectopic cell cycling. The resulting differential networks suggest a mechanism by which E2F and Cabut regulate distinct gene interactions, while still sharing a small core network. Thus, CPχ2 is sensitive in detecting network rewiring, useful in comparing related biological systems.

Cabut/ dTIEG associates with the transcription factor Yorkie for growth control

“…We did not find enrichment of the proposed consensus TIEG motif 5′GGTGTG3′ , which suggests that Cbt binds to degenerated or alternative motifs or may function through its interaction with other TFs. A recent study identified a novel Mad‐like motif in promoters of Cbt‐regulated genes . However, this new motif does not coincide with previously reported Cbt binding data .…”

ChiNet uncovers rewired transcription subnetworks in tolerant yeast for advanced biofuels conversion

“…We first give an overview of the ChiNet method, which decides whether a subnetwork is conserved, or rewired in either topology or interaction strength across two conditions. In the comparative chi-square framework CPχ 2 first introduced in ( 41 ), rewired interactions were characterized by a heterogeneity chi-square. CPχ 2 can detect single strongly rewired interactions, but was not designed to detect subnetwork rewiring.…”

“…Our previous CPχ 2 work on single interaction comparative chi-square analysis ( 41 ) established interaction heterogeneity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi ^2_d(i)$\end{document} for each node in a network as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} \chi ^2_d(i) = \chi ^2_t(i) - \chi ^2_c(i) \quad (\text{Interaction heterogeneity}) \end{equation*}\end{document} which measures how far the interactions deviate from the pooled version. We proved that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi ^2_d(i)$\end{document} asymptotically follows a chi-square distribution with v d ( i ) = v t ( i )- v c ( i ) d.f.…”

“…We proved that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi ^2_d(i)$\end{document} asymptotically follows a chi-square distribution with v d ( i ) = v t ( i )- v c ( i ) d.f. when no interactions exist across the conditions ( 41 ). Here, parent/cause and child/effect variables are provided on the input subnetwork topology to ChiNet analysis.…”

“…Previously, we reported a comparative chi-square analysis (CPχ 2 ) for single-interaction rewiring ( 41 ). In this work, we present a computational method ChiNet to detect rewired subnetworks of multiple gene interactions in transcription regulation of downstream metabolic pathways for the tolerant strain NRRL Y-50049.…”