Finding New Cell Wall Regulatory Genes in Populus trichocarpa Using Multiple Lines of Evidence

Abstract

Understanding the regulatory network controlling cell wall biosynthesis is of great interest in Populus trichocarpa, both because of its status as a model woody perennial and its importance for lignocellulosic products. We searched for genes with putatively unknown roles in regulating cell wall biosynthesis using an extended network-based Lines of Evidence (LOE) pipeline to combine multiple omics data sets in P. trichocarpa, including gene coexpression, gene comethylation, population level pairwise SNP correlations, and two distinct SNP-metabolite Genome Wide Association Study (GWAS) layers. By incorporating validation, ranking, and filtering approaches we produced a list of nine high priority gene candidates for involvement in the regulation of cell wall biosynthesis. We subsequently performed a detailed investigation of candidate gene GROWTH-REGULATING FACTOR 9 (PtGRF9). To investigate the role of PtGRF9 in regulating cell wall biosynthesis, we assessed the genome-wide connections of PtGRF9 and a paralog across data layers with functional enrichment analyses, predictive transcription factor binding site analysis, and an independent comparison to eQTN data. Our findings indicate that PtGRF9 likely affects the cell wall by directly repressing genes involved in cell wall biosynthesis, such as PtCCoAOMT and PtMYB.41, and indirectly by regulating homeobox genes. Furthermore, evidence suggests that PtGRF9 paralogs may act as transcriptional co-regulators that direct the global energy usage of the plant. Using our extended pipeline, we show multiple lines of evidence implicating the involvement of these genes in cell wall regulatory functions and demonstrate the value of this method for prioritizing candidate genes for experimental validation.

Genome-Wide Association Study of Wood Anatomical and Morphological Traits in Populus trichocarpa

“…A correlation value threshold of 0.85 was set as criteria for retention of gene pairs in the coexpression network. As previously demonstrated, this threshold minimized false positives, and the resulting network was significantly different from random (Furches et al, 2019).…”

“…Read mapping, calculation of transcripts per million reads, Spearman correlation analysis, and network construction were conducted as previously described ( Weighill et al, 2018 ). Gene pairs with correlation values greater than or equal to 0.95 were retained in the comethylation network ( Furches et al, 2019 ).…”

“…A network was created using five data layers generated from P. trichocarpa genetic and phenotypic analyses: pairwise gene coexpression, pairwise gene comethylation, metabolite GWAS, rare variant regional metabolite GWAS, and py-MBMS GWAS. The first four of these layers were previously described (Furches et al, 2019). The py-MBMS GWAS analysis was performed for this study.…”

The Phylogenetic Roots of Addiction: Compulsive Drug Seeking, Natural and Drug-Sensitive Reward, and the Acquisition of Learned Habits

“…In addition, multiplex networks can be constructed from the vast array of omics data publically available for Drosophila. These networks can be used with lines of evidence (LOE) algorithms in order to filter GWAS/GWES results to remove false positives and capture false negatives [Weighill et al, 2018[Weighill et al, , 2019Furches et al, 2019;Chhetri et al, 2020]. The use of algorithms that can reveal interactions amongst genes and across defined phenotypes collectively offer strong predictors of addiction outcomes.…”

A High-Performance Computing Implementation of Iterative Random Forest for the Creation of Predictive Expression Networks

“…Reads were aligned to the Populus trichocarpa v.3.0 reference [17]. Transcript per million (TPM) counts were then obtained for each genotype, resulting in a genotype-transcript matrix, as referenced in [18]. The adjacency matrix resulting from iRF-LOOP represents a Predictive Expression Network (PEN) where a directed edge (AB) between and two genes (A and B) is weighted according to the importance of gene A's expression in predicting gene B's expression, conditional on all other genes in the iRF model.…”