Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials

Abstract

BackgroundPichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research.ResultsIn this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed.ConclusionsIn conclusion the first GEM of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials.

Bioconversion of degraded husked sorghum grains to ethanol

“…This may be a reflection of the poor stress tolerance of P. stipitis compared with S. cerevisiae. Batch-2 substrates with higher sugars level tend to exert osmotic stress on cells than Batch-1 substrates [24,27,29]. Furthermore, the additional presence of lignin-derived phenolic compounds liberated during mashing (predominantly from husks materials) may further exacerbate environmental stress on P. stipitis cells [16,27,29,30].…”

“…It is conceivable that as ethanol concentration approached 30 g/L (Fig. 3b), P. stipitis ability to biosynthesize maltase enzymes diminishes [24,27,29].…”

“…However, Pichia (Scheffersomyces) stipitis are able to efficiently ferment both xylose and glucose [14]. Nevertheless, while S. cerevisiae cells exhibit tolerance for high alcohol concentration during fermentation, P. stipitis growth is restricted at alcohol levels beyond 33 g/L [14,24]. Thomas and Ingledew [25] suggested a minimum of 150 mg/L of FAN is required for efficient yeast metabolism during fermentation.…”

New insight into the gut microbiome through metagenomics

“…133 The manual reconstruction of genome-scale metabolic models is complex and timeconsuming, but numerous tools (eg, Model SEED, 134 Pathway Tools, 135 RAVEN 136 ) that utilize the biological knowledge from different databases (such as KEGG, 49 Reactome, 137 BioCyc, 138 BRENDA, 139 and PubMed 140 ) have been developed to perform automatic or semiautomatic reconstruction. The RAVEN toolbox 136 was developed recently and has been used to reconstruct metabolic models for Pichia stipitis, 141 Pichia pastoris, 141 Saccharomyces cerevisiae, 142 and Penicillium chrysogenum. 136 This toolbox reconstructs draft models based on the homology to the reference organism and performs automatic gap-filling to ensure that the the different parts of metabolic network are connected.…”

Modelling and multiobjective optimization for simulation of cyanobacterial metabolism

“…FBA has been satisfactorily applied to predict growth rates and pathway usage of both natural and genetically modified strains of different organisms of interest, as well as to assess the effect of different growth conditions and media compositions (see Famili et al (2003); Teusink et al (2009); Lewis et al (2010);Caspeta et al (2012) for examples).…”