Inferring gene function and network organization in Drosophila signaling by combined analysis of pleiotropy and epistasis

Abstract

High-throughput genetic interaction screens have enabled functional genomics on a network scale. Groups of cofunctional genes commonly exhibit similar interaction patterns across a large network, leading to novel functional inferences for a minority of previously uncharacterized genes within a group. However, such analyses are often unsuited to cases with a few relevant gene variants or sparse annotation. Here we describe an alternative analysis of cell growth signaling using a computational strategy that integrates patterns of pleiotropy and epistasis to infer how gene knockdowns enhance or suppress the effects of other knockdowns. We analyzed the interaction network for RNAi knockdowns of a set of 93 incompletely annotated genes in a Drosophila melanogaster model of cellular signaling. We inferred novel functional relationships between genes by modeling genetic interactions in terms of knockdown-to-knockdown influences. The method simultaneously analyzes the effects of partially pleiotropic genes on multiple quantitative phenotypes to infer a consistent model of each genetic interaction. From these models we proposed novel candidate Ras inhibitors and their Ras signaling interaction partners, and each of these hypotheses can be inferred independent of network-wide patterns. At the same time, the network-scale interaction patterns consistently mapped pathway organization. The analysis therefore assigns functional relevance to individual genetic interactions while also revealing global genetic architecture.

Keywords: epistasis; genetic interaction; genetic network; pleiotropy; signaling network.

Figure 1

Eigentrait compositions in terms of measured phenotypes. Eigentrait 1 (ET1) is the signal common to all three phenotypes, whereas eigentrait 2 (ET2) encodes the differences.

Figure 2

Genetic interactions between drk and Rho1 knockdowns. (A) Inferred main and interaction effects of drk and Rho1 mutations on eigentraits ET1 and ET2. (B) Significant main and interaction effects shown in terms of positive (green) and negative (red) influences. Edge width represents interaction strength. (C) Interaction model consistent with both ET1 and ET2, in which drk knockdown suppresses Rho1 knockdown. (D) The same model expressed in terms of the original phenotypes of total cell number and nuclear area (total fluorescent intensity is similar to cell number and not shown).

Figure 3

Adjacency matrix of significant interactions for knockdowns of Ras (blue labels), Ras inhibitors (red labels), and JNK (green labels) genes. Previously uncharacterized gene names are in italics. Color denotes interaction sign and intensity of directional interactions. Overall, within-pathway interactions are overwhelmingly suppressive (blue squares), whereas interactions across antagonistic pathways are enhancing (yellow squares). Ras knockdowns generally reduce cellular phenotypes, while Ras inhibitors and JNK knockdowns increase phenotypes. Empty squares did not meet the significance threshold of P < 0.01 (see text).

Figure 4

Network of novel candidate Ras inhibitor phosphatases and Ras kinases inferred from interaction patterns. Knockdowns of phosphatase genes (orange nodes) enhance the effects of Ras kinase knockdowns (blue nodes), similar to known Ras inhibitors and JNK signaling genes (Figure 3). Edge width denotes relative strength of enhancement.