MasterPATH: network analysis of functional genomics screening data

Abstract

Background: Functional genomics employs several experimental approaches to investigate gene functions. High-throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). Several computational methods exist to analyze and interpret such lists, the most widespread of which aim either at investigating of significantly enriched biological processes, or at extracting significantly represented subnetworks.

Results: Here we propose a novel network analysis method and corresponding computational software that employs the shortest path approach and centrality measure to discover members of molecular pathways leading to the studied phenotype, based on functional genomics screening data. The method works on integrated interactomes that consist of both directed and undirected networks - HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase. The method finds nodes and short simple paths with significant high centrality in subnetworks induced by the hit genes and by so-called final implementers - the genes that are involved in molecular events responsible for final phenotypic realization of the biological processes of interest. We present the application of the method to the data from miRNA loss-of-function screen and transcriptome profiling of terminal human muscle differentiation process and to the gene loss-of-function screen exploring the genes that regulates human oxidative DNA damage recognition. The analysis highlighted the possible role of several known myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets (AR, NR3C1, ARRB1, ITSN1, VAV3, TDGF1), as well as linked two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, to their previously known muscle-related targets (TGFB1, CDC42, CTCF) and also to a number of proteins such as C-KIT that have not been previously studied in the context of muscle differentiation. The analysis also showed the role of the interaction between H3 and SETDB1 proteins for oxidative DNA damage recognition.

Conclusion: The current work provides a systematic methodology to discover members of molecular pathways in integrated networks using functional genomics screening data. It also offers a valuable instrument to explain the appearance of a set of genes, previously not associated with the process of interest, in the hit list of each particular functional genomics screening.

Keywords: Centrality; DNA repair; Loss-of-function screening; Molecular pathway; Muscle differentiation; Network analysis.

Fig. 1

Subnetworks for human muscle differentiation process. Hit genes in miRNA loss-of-function screen are in dark blue, hit genes in transcriptome profiling are in orange, final implementers are in pink, intermediate genes and proteins are in white. a SMAD3-hsa-mir-145 subnetwork. b SMAD3, MYOD1 subnetwork. c MDM2-TCAP subnetwork

Fig. 2

Histones H3-SETDB1 subnetwork for oxidative DNA damage recognition screening. Hit genes are in orange, the intermediate proteins are in white or blue depending on the centrality score. Grey arrows show the direction of interactions that were taken from literature

Fig. 3

a Undirected interactions are not included into the integrated network during merging of databases in case directed interactions exist between the same nodes. b Main steps of the MasterPath. Detailed description is presented in the Method section. c Direction of interactions is taken into account when paths are found using breadth-first algorithm. Only the first two paths will be considered between nodes e and f by the method. It should be noted that the arrow represents here only the direction of the interaction but not the effect (e.g. activation or inhibition)