Modeling cross-regulatory influences on monolignol transcripts and proteins under single and combinatorial gene knockdowns in Populus trichocarpa

Abstract

Accurate manipulation of metabolites in monolignol biosynthesis is a key step for controlling lignin content, structure, and other wood properties important to the bioenergy and biomaterial industries. A crucial component of this strategy is predicting how single and combinatorial knockdowns of monolignol specific gene transcripts influence the abundance of monolignol proteins, which are the driving mechanisms of monolignol biosynthesis. Computational models have been developed to estimate protein abundances from transcript perturbations of monolignol specific genes. The accuracy of these models, however, is hindered by their inability to capture indirect regulatory influences on other pathway genes. Here, we examine the manifestation of these indirect influences on transgenic transcript and protein abundances, identifying putative indirect regulatory influences that occur when one or more specific monolignol pathway genes are perturbed. We created a computational model using sparse maximum likelihood to estimate the resulting monolignol transcript and protein abundances in transgenic Populus trichocarpa based on targeted knockdowns of specific monolignol genes. Using in-silico simulations of this model and root mean square error, we showed that our model more accurately estimated transcript and protein abundances, in comparison to previous models, when individual and families of monolignol genes were perturbed. We leveraged insight from the inferred network structure obtained from our model to identify potential genes, including PtrHCT, PtrCAD, and Ptr4CL, involved in post-transcriptional and/or post-translational regulation. Our model provides a useful computational tool for exploring the cascaded impact of single and combinatorial modifications of monolignol specific genes on lignin and other wood properties.

Identification of biological pathway and process regulators using sparse partial least squares and triple-gene mutual interaction

“…According to the verified lignin biosynthesis genes and the annotation information of the genes collected from Phytozome ( https://phytozome.jgi.doe.gov/) 36 , 37 (Table S4 ). We identified 40 P. trichocarpa lignin pathway genes whose expression patterns in Data Set 3 as represented by a heatmap are shown in Fig.…”

Genome editing of wood for sustainable pulping

“…The biosynthesis of lignin is a complex process governed by at least 11 gene families and hundreds of genetic and metabolic regulatory elements [7]. The starting point for Sulis and colleagues work to select the best candidate gene targets was a previously proposed computational predictive model for monolignol biosynthesis in poplar [8,9], which had been developed on the basis of integrative transcriptomic, proteomic, fluxomic and phenomic data from ~2000 transgenic poplar lines. Because the model predicts the transduction of quantitative relationships from gene transcript abundances to absolute enzyme abundances, pathway metabolic fluxes, and 25 wood physicochemical properties, the model revealed how individual wood properties could be modified through multiplex editing of monolignol genes.…”

Multiscale plant modeling: from genome to phenome and beyond

“…This multiscale model was used to explore potential gene engineering strategies for producing trees with improved bioenergy traits while mitigating negative impacts on tree growth. This model was later expanded by incorporating the impact of cross-regulatory influences between the monolignol gene transcripts and proteins, capturing the effect of regulatory mechanisms that occur after transcription, such as potential post-transcriptional and post-translational modifications on predicted monolignol protein abundances [34,35].…”