Non-canonical D-xylose and L-arabinose metabolism via D-arabitol in the oleaginous yeast Rhodosporidium toruloides.

R. toruloides is an oleaginous yeast, with diverse metabolic capacities and high tolerance for inhibitory compounds abundant in plant biomass hydrolysates. While R. toruloides grows on several pentose sugars and alcohols, further engineering of the native pathway is required for efficient conversion of biomass-derived sugars to higher value bioproducts. A previous high-throughput study inferred that R. toruloides possesses a non-canonical L-arabinose and D-xylose metabolism proceeding through D-arabitol and D-ribulose. In this study, we present a combination of genetic and metabolite data that refine and extend that model. Chiral separations definitively illustrate that D-arabitol is the enantiomer that accumulates under pentose metabolism. Deletion of putative D-arabitol-2-dehydrogenase (RTO4_9990) results in > 75% conversion of D-xylose to D-arabitol, and is growth-complemented on pentoses by heterologous xylulose kinase expression. Deletion of putative D-ribulose kinase (RTO4_14368) arrests all growth on any pentose tested. Analysis of several pentose dehydrogenase mutants elucidates a complex pathway with multiple enzymes mediating multiple different reactions in differing combinations, from which we also inferred a putative L-ribulose utilization pathway. Our results suggest that we have identified enzymes responsible for the majority of pathway flux, with additional unknown enzymes providing accessory activity at multiple steps. Further biochemical characterization of the enzymes described here will enable a more complete and quantitative understanding of R. toruloides pentose metabolism. These findings add to a growing understanding of the diversity and complexity of microbial pentose metabolism.

Engineering transcriptional regulation of pentose metabolism in Rhodosporidium toruloides for improved conversion of xylose to bioproducts.

Efficient conversion of pentose sugars remains a significant barrier to the replacement of petroleum-derived chemicals with plant biomass-derived bioproducts. While the oleaginous yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) has a relatively robust native metabolism of pentose sugars compared to other wild yeasts, faster assimilation of those sugars will be required for industrial utilization of pentoses. To increase the rate of pentose assimilation in R. toruloides, we leveraged previously reported high-throughput fitness data to identify potential regulators of pentose catabolism. Two genes were selected for further investigation, a putative transcription factor (RTO4_12978, Pnt1) and a homolog of a glucose transceptor involved in carbon catabolite repression (RTO4_11990). Overexpression of Pnt1 increased the specific growth rate approximately twofold early in cultures on xylose and increased the maximum specific growth by 18% while decreasing accumulation of arabitol and xylitol in fast-growing cultures. Improved growth dynamics on xylose translated to a 120% increase in the overall rate of xylose conversion to fatty alcohols in batch culture. Proteomic analysis confirmed that Pnt1 is a major regulator of pentose catabolism in R. toruloides. Deletion of RTO4_11990 increased the growth rate on xylose, but did not relieve carbon catabolite repression in the presence of glucose. Carbon catabolite repression signaling networks remain poorly characterized in R. toruloides and likely comprise a different set of proteins than those mainly characterized in ascomycete fungi.

Active and machine learning-based approaches to rapidly enhance microbial chemical production.

In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)-requiring many experimental datasets for their parameterization-while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.

2H and 13C metabolic flux analysis elucidates in vivo thermodynamics of the ED pathway in Zymomonas mobilis.

Zymomonas mobilis is an industrially relevant bacterium notable for its ability to rapidly ferment simple sugars to ethanol using the Entner-Doudoroff (ED) glycolytic pathway, an alternative to the well-known Embden-Meyerhof-Parnas (EMP) pathway used by most organisms. Recent computational studies have predicted that the ED pathway is substantially more thermodynamically favorable than the EMP pathway, a potential factor explaining the high glycolytic rate in Z. mobilis. Here, to investigate the in vivo thermodynamics of the ED pathway and central carbon metabolism in Z. mobilis, we implemented a network-level approach that integrates quantitative metabolomics with 2H and 13C metabolic flux analysis to estimate reversibility and Gibbs free energy (ΔG) of metabolic reactions. This analysis revealed a strongly thermodynamically favorable ED pathway in Z. mobilis that is nearly twice as favorable as the EMP pathway in E. coli or S. cerevisiae. The in vivo step-by-step thermodynamic profile of the ED pathway was highly similar to previous in silico predictions, indicating that maximizing ΔG for each pathway step likely constitutes a cellular objective in Z. mobilis. Our analysis also revealed novel features of Z. mobilis metabolism, including phosphofructokinase-like enzyme activity, tricarboxylic acid cycle anaplerosis via PEP carboxylase, and a metabolic imbalance in the pentose phosphate pathway resulting in excretion of shikimate pathway intermediates. The integrated approach we present here for in vivo ΔG quantitation may be applied to the thermodynamic profiling of pathways and metabolic networks in other microorganisms and will contribute to the development of quantitative models of metabolism.

Model-enabled gene search (MEGS) allows fast and direct discovery of enzymatic and transport gene functions in the marine bacterium Vibrio fischeri.

Whereas genomes can be rapidly sequenced, the functions of many genes are incompletely or erroneously annotated because of a lack of experimental evidence or prior functional knowledge in sequence databases. To address this weakness, we describe here a model-enabled gene search (MEGS) approach that (i) identifies metabolic functions either missing from an organism's genome annotation or incorrectly assigned to an ORF by using discrepancies between metabolic model predictions and experimental culturing data; (ii) designs functional selection experiments for these specific metabolic functions; and (iii) selects a candidate gene(s) responsible for these functions from a genomic library and directly interrogates this gene's function experimentally. To discover gene functions, MEGS uses genomic functional selections instead of relying on correlations across large experimental datasets or sequence similarity as do other approaches. When applied to the bioluminescent marine bacterium Vibrio fischeri, MEGS successfully identified five genes that are responsible for four metabolic and transport reactions whose absence from a draft metabolic model of V. fischeri caused inaccurate modeling of high-throughput experimental data. This work demonstrates that MEGS provides a rapid and efficient integrated computational and experimental approach for annotating metabolic genes, including those that have previously been uncharacterized or misannotated.