Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice

Brooke L Barnette,Tianlong Chen,Mark R Emmett,Anna M Nia,Robert L Ullrich,Suresh K Bhavnani,Kamil Khanipov

doi:10.1186/s12859-020-3446-5

Abstract

BackgroundmRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on user-settable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features.ResultsIn this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay.ConclusionsWe present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.

Highlights

MRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions
Differential gene expression analysis Results of differential gene expression analysis are shown in Table 1, which includes the total number of differentially expressed genes at each time point, as well as whether genes are up/down-regulated
Weighted Gene Coexpression Network Analysis (WGCNA) with Modularity Maximization To optimize the number and size of identified modules as well as reduce the number of unassigned genes (~ 12–14%), we exploited the concept of Modularity Maximization, to assist in finding community structures, as an alternative to utilizing the Dynamic Tree Cut algorithm employed in the standard WGCNA pipeline

Summary

Introduction

MRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. RNA-Seq, an approach to genome profiling that uses deepsequencing technologies, has become an increasingly common technique to understand biological phenomena at the molecular level This method generates quantitative count data on thousands of different mRNAs within each experiment. Even though differential gene expression analysis is one of the most common methods for identifying disease pathways in various experimental conditions, it does not take into consideration the interactions of genes that work as a system to coordinate cellular functions. MRNAs never act in isolation, but rather in concert with each other and other signaling molecules to define a particular biological pathway and function Interactions of these signaling molecules can be viewed as networks of interconnected genes and their partners, that are up/down regulated under certain chemical or environmental conditions

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