A constraint-based model of Scheffersomyces stipitis for improved ethanol production

Abstract

BackgroundAs one of the best xylose utilization microorganisms, Scheffersomyces stipitis exhibits great potential for the efficient lignocellulosic biomass fermentation. Therefore, a comprehensive understanding of its unique physiological and metabolic characteristics is required to further improve its performance on cellulosic ethanol production.ResultsA constraint-based genome-scale metabolic model for S. stipitis CBS 6054 was developed on the basis of its genomic, transcriptomic and literature information. The model iTL885 consists of 885 genes, 870 metabolites, and 1240 reactions. During the reconstruction process, 36 putative sugar transporters were reannotated and the metabolisms of 7 sugars were illuminated. Essentiality study was conducted to predict essential genes on different growth media. Key factors affecting cell growth and ethanol formation were investigated by the use of constraint-based analysis. Furthermore, the uptake systems and metabolic routes of xylose were elucidated, and the optimization strategies for the overproduction of ethanol were proposed from both genetic and environmental perspectives.ConclusionsSystems biology modelling has proven to be a powerful tool for targeting metabolic changes. Thus, this systematic investigation of the metabolism of S. stipitis could be used as a starting point for future experiment designs aimed at identifying the metabolic bottlenecks of this important yeast.

Evaluation of a kinetic model for computer simulation of growth and fermentation by Scheffersomyces (Pichia) stipitis fed D‐xylose

“…However, there is still strong interest in applying S. stipitis to whole hydrolyzates of lignocellulosic biomass since it has been considered the native pentose‐fermenting yeast with most promise for commercial application (Agbogbo et al, , 2008; Agbogbo and Wenger, ; Lin et al, ). Advancements are being made toward application of S. stipitis to lignocellulosic hydrolyzate fermentation, including strain improvement (Bajwa et al, , ; Caspeta and Nielsen, ; Hughes et al, ; Jeffries et al, ; Liu et al, , , ; Slininger et al, ), new chemical and enzyme hydrolysis technologies (Balan et al, ; Chundawat et al, ), and new understandings of nutritional requirements for hydrolyzate fermentation (Slininger et al, ; Wang et al, ). For the future, the model represents a good basic framework for addition of mathematical expressions to accommodate the prediction of fermentation of mixed sugars, particularly glucose and xylose, and the impacts of limiting or inhibitory factors in a hydrolyzate environment.…”

Flux balance analysis predicts NADP phosphatase and NADH kinase are critical to balancing redox during xylose fermentation inScheffersomyces stipitis

“…Genome-scale network reconstruction and analysis. The S. stipitis GENRE was obtained from a panfungal GENRE that combined the GENRE's of S. stipitis (Balagurunathan et al, 2012;Caspeta et al, 2012;Li, 2012;Liu et al, 2012), Schizosaccharomyces pombe (Sohn et al, 2012), Aspergillus niger (Andersen et al, 2008), Yarrowia lipolytica (Pan and Hua, 2012; Loira et al, 2012), Komagataella phaffii (Caspeta 3/28 et al, 2012), Kluyveromyces lactis (Dias et al, 2014), and S. cerevisiae (Heavner et al, 2013). Model simulations were carried out using COnstraints Based Reconstruction and Analysis for Python version 0.9.1 (COBRApy) (Ebrahim et al, 2013).…”

“…Flux balance analysis (FBA) is a computational method often used to gain insight into metabolism (Österlund et al, 2013;McCloskey et al, 2013), and is well suited to study redox metabolism in yeasts (Pereira et al, 2016). To date, there are four genome-scale network reconstructions (GENRE's) of S. stipitis, the most widely studied xylose fermenting yeast: iBB814 (Balagurunathan et al, 2012), iSS884 (Caspeta et al, 2012), iTL885 (Liu et al, 2012), and iPL912 (Li, 2012). Our xylose fermentation simulations with the four models led to xylitol accumulation when we forced the in vitro XR cofactor selectivity (60% NADPH), removed cytosolic NADP-dependent acetaldehyde dehydrogenase from the metabolic models, which is not encoded in S. stipitis' genome (Correia et al, 2017), prevented flux 2/28 through degradation pathways and considered alternative optima.…”

Pan-genome-scale network reconstruction: a framework to increase the quantity and quality of metabolic network reconstructions throughout the tree of life

“…stipitis (Balagurunathan et al, 2012;Caspeta et al, 2012;Li, 2012;Liu et al, 2012), Eremothecium gossypii (Ledesma-Amaro et al, 2014), and S. cerevisiae (Aung et al, 2013). Additional reactions were added to the pan-GENRE by reviewing enzyme assays, and reactions inferred from growth assays and Biolog data; these are referenced in the notes and reference section of the GENRE.…”