Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides

Abstract

An oleaginous yeast Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides. High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date.

Keywords: Rhodosporidium toruloides; genome-scale models; lignocellulosic biomass; metabolic networks; multi-omics.

FIGURE 1

A workflow to develop the metabolic network model of R. toruloides.

FIGURE 2

Fatty acid composition of R. toruloides in different media conditions and segmented linear regression. (A) Fatty acid composition by fatty acid methyl ester analysis in M9, YNB with C to N ratio of 120, YPD, and SD media. Colors indicate different fatty acid species and shapes indicate different media. (B) Estimation of fatty acid content in “lean” cell mass and fatty acid content in lipid body using segmented linear regression. Dashed lines are segmented linear regression for each fatty acid k using the equation y_k = m_k(x–x₀) + b_k. The inset shows the slope m_k in a black dashed line, the x-intercept x₀ in a red dotted line, and the y-intercept b_k in a blue dotted line for fatty acid k.

FIGURE 3

Pentose utilization pathway in R. toruloides. (A) Pentose sugars and alcohols are converted to D-ribulose-5-phosphate via D-arabinitol dehydrogenases before entering the pentose phosphate pathway. (B) Gene expression, protein expression, and fitness scores for pentose utilization pathway genes (exp, exponential phase; trans, transition phase; stat, stationary phase).

FIGURE 4

p-Coumarate utilization pathway in R. toruloides. (A) p-Coumarate degradation to protocatechuate by a beta-oxidation like pathway in peroxisome, protocatechuate degradation to 3-oxoadipate by the ortho-cleavage pathway in cytosol, and 3-oxoadipate degradation in mitochondria. (B) Gene expression, protein expression, and fitness scores for p-coumarate utilization pathway genes (exp, exponential phase; stat, stationary phase). (C) Intracellular and extracellular measurement of p-coumarate and intermediates in p-coumarate condition (not detected in glucose, glucose + D-xylose, D-xylose, and L-arabinose conditions).

FIGURE 5

Gene expression, protein expression, and fitness scores for fatty acid beta-oxidation and NAD biosynthesis pathway genes (exp, exponential phase; stat, stationary phase).

FIGURE 6

Model evaluation using high-throughput growth phenotype data. (A) Biolog phenotype microarray data (white indicates low growth and red indicates high growth). Comparison of model predicted growth and experimental data (B) before and (C) after manual curation to include more metabolites.

FIGURE 7

Model evaluation using high-throughput gene essentiality data. (A) RB-TDNA sequencing fitness score data for all genes in the model. Comparison of model predicted gene essentiality and experimental data for (B) all model genes and (C) conditionally essential genes in experiment.