Genetic interaction networks: better understand to better predict

Abstract

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.

Predicting protein function and other biomedical characteristics with heterogeneous ensembles

“…Since these interactions represent cases of functional buffering and interconnections, their knowledge is very useful for understanding cellular pathways and their interactions, and predicting gene function (Horn et al (2011); Boucher and Jenna (2013); Kelley and Ideker (2005); Mohammadi et al (2012); Michaut and Bader (2012)). However, despite their utility, and substantial progress in experimental mapping of GIs in model organisms (Horn et al (2011); Costanzo et al (2010)), there is a general paucity of GI data for several organisms important for biomedical research.…”

“…This comparison is carried out in the context of the prediction of protein function, as well as that of genetic interactions (Boucher and Jenna (2013); Pandey et al (2010)), a problem intimately related to PFP. In addition to assessing the overall performance of these methods on these problems, we will investigate the following critical aspects of heterogeneous ensembles that have not been investigated before:…”

Chemical Genetic Screens for TDP-43 Modifiers and ALS Drug Discovery

“…With the increasing number of genes linked to ALS along with the diverse functions of these genes, it is essential to identify common pathological pathways relevant to ALS. Genetic interactions amongst genes can refer to functional relationships amongst a group of genes (Boucher and Jenna, 2013). However, genetic interactions are not always easy to interpret and do not necessarily point toward genes that function in the same pathway but rather identify functional similarity between genes that could be in the same pathway or in compensatory pathways (Boucher and Jenna, 2013).…”

“…Genetic interactions amongst genes can refer to functional relationships amongst a group of genes (Boucher and Jenna, 2013). However, genetic interactions are not always easy to interpret and do not necessarily point toward genes that function in the same pathway but rather identify functional similarity between genes that could be in the same pathway or in compensatory pathways (Boucher and Jenna, 2013). Therefore, identification of genetic interactions between ALS genes could point toward potential therapeutic avenues for ALS patients (Figure 1).…”

Deciphering genetic interactions between ALS genes usingC. elegans

“…Genetic interactions are not always easy to determine, but if genes interact it may mean they function in the same pathway or in a compensating cellular pathway. 15 To study genetic pathways, model organisms are essential. Analyzing genetic interactions requires models that can be easily genetically modified and in which phenotypes can be quickly and easily observed.…”