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

Abstract

Background: mRNA 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 usersettable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features. Results: In 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 coexpression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56 Fe 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. Conclusions: We 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.

Effects of Low Dose Space Radiation Exposures on the Splenic Metabolome

“…A schematic of the experimental processing is shown in Figure 1 . The radiation scheme described here was designed for a parent study on hepatocellular carcinoma [ 34 , 35 ], with spleen analysis emerging to maximize experimental analysis from a single animal study.…”

Comparative RNA-Seq transcriptome analyses reveal dynamic time-dependent effects of 56Fe, 16O, and 28Si irradiation on the induction of murine hepatocellular carcinoma

“…Facilities at both BNL and UTMB used for animal housing are fully AAALAC accredited. Selection of animals for sacrifice at each of the 5 time points and preparation of the left love of livers were performed as previously described [64].…”

“…Total RNA was isolated from the liver slices using RNA-queous™ Total RNA Isolation Kit (ThermoFisher Scientific, Waltham, MA), and rRNA was removed using the Ribo-Zero™ rRNA Removal Kit (Illumina, San Diego, CA). Library preparation and sequencing were performed, as previously described [64]. CLC Genomics Workbench v12.0.3 was used for bioinformatical quality control and mapping of the RNA-Seq data.…”

“…Raw abundance counts of 107,319 mRNAs from 15 non-irradiated control, 15 56 Fe irradiated, 15 16 O irradiated, and 15 28 Si irradiated C3H/HeNCrl male mice liver tissue samples were subjected to differential transcript expression analysis. Differential transcript expression analysis was performed as previously described using edgeR [64][65][66]. Statistical tests were then conducted at every time point, to compare between 56 Fe irradiated and non-irradiated control, 16 O irradiated and non-irradiated control, and 28 Si irradiated and non-irradiated control samples using a quasi-likelihood negative binomial generalized loglinear model for count data [67][68][69].…”

“…The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (Nia et al, 2020) and are accessible through GEO Series accession number GSE146254 through https://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=GSE146254. GRCm38.97 reference genome was obtained through ftp://ftp.ensembl.org/pub/release-97/fasta/mus_musculus/.…”