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Enhanced Understanding of Molecular Interactions and Function Underlying Pain Processes Through Networks of Transcript Isoforms, Genes, and Gene Families

Authors Zhang P, Southey BR, Sweedler JV, Pradhan A, Rodriguez-Zas SL

Received 5 October 2020

Accepted for publication 5 January 2021

Published 18 February 2021 Volume 2021:14 Pages 49—69


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Juan Fernandez-Recio

Pan Zhang,1 Bruce R Southey,2 Jonathan V Sweedler,3 Amynah Pradhan,4 Sandra L Rodriguez-Zas1,2,5

1Illinois Informatics Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA; 2Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA; 3Department of Chemistry and the Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA; 4Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA; 5Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Correspondence: Sandra L Rodriguez-Zas
University of Illinois at Urbana-Champaign, 1207 W Gregory Dr., Urbana, Illinois 61801, USA
Tel +1 217 333 8810
Fax +1 217 333 8286

Introduction: Molecular networks based on the abundance of mRNA at the gene level and pathway networks that relate families or groups of paralog genes have supported the understanding of interactions between molecules. However, multiple molecular mechanisms underlying health and behavior, such as pain signal processing, are modulated by the abundances of the transcript isoforms that originate from alternative splicing, in addition to gene abundances. Alternative splice variants of growth factors, ion channels, and G-protein-coupled receptors can code for proteoforms that can have different effects on pain and nociception. Therefore, networks inferred using abundance from more agglomerative molecular units (eg, gene family, or gene) have limitations in capturing interactions at a more granular level (eg, gene, or transcript isoform, respectively) do not account for changes in the abundance at the transcript isoform level.
Objective: The objective of this study was to evaluate the relative benefits of network inference using abundance patterns at various aggregate levels.
Methods: Sparse networks were inferred using Gaussian Markov random fields and a novel aggregation criterion was used to aggregate network edges. The relative advantages of network aggregation were evaluated on two molecular systems that have different dimensions and connectivity, circadian rhythm and Toll-like receptor pathways, using RNA-sequencing data from mice representing two pain level groups, opioid-induced hyperalgesia and control, and two central nervous system regions, the nucleus accumbens and the trigeminal ganglia.
Results: The inferred networks were benchmarked against the Kyoto Encyclopedia of Genes and Genomes reference pathways using multiple criteria. Networks inferred using more granular information performed better than networks inferred using more aggregate information. The advantage of granular inference varied with the pathway and data set used.
Discussion: The differences in inferred network structure between data sets highlight the differences in OIH effect between central nervous system regions. Our findings suggest that inference of networks using alternative splicing variants can offer complementary insights into the relationship between genes and gene paralog groups.

Keywords: Gaussian Markov random fields, pain, alternative splicing, pathway, RNA-seq, transcript isoform network

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