A constraint-based model of Scheffersomyces stipitis for improved ethanol production
© Liu et al.; licensee BioMed Central Ltd. 2012
Received: 10 July 2012
Accepted: 13 September 2012
Published: 21 September 2012
As 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.
A 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 i TL885 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.
Systems 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.
KeywordsScheffersomyces stipitis Genome-scale metabolic model Constraint-based simulation Xylose utilization Ethanol production
Along with the increasing stress on the shortage of oil reserves and the negative ecological impacts of greenhouse gas emissions, there is a trend for searching of the renewable clean fuels to substitute the traditional fossil fuels worldwide [1–3]. Currently, bioethanol produced from lignocellulosic biomass (second generation bioethanol) has been widely recognized as one of the most attractive alternatives . However, owing to the complex components and rigid structure of plant biomass , it is particularly vital to get a robust industrial strain for the efficient bioconversion of lignocellulosic sugars to ethanol . However, none of the screened or engineered strain has been capable of highly efficient bioethanol production from lignocellulosic biomass yet . Thus the searching of an optimal microbial host is still in process. Among the diverse exploited ethanol producers, Saccharomyces cerevisiae is considered as the most suitable biocatalyst for industrial ethanol production from sugars or starch feedstocks for its well-characterized genetics, ample genetic tools, high ethanol productivity and so on . Another intensively studied microorganism possessing several appealing characteristics for ethanol production is Zymomonas mobilis. It was reported that for Z. mobilis the maximum yield of ethanol could reach 97% of the theoretical yield and the tolerance of ethanol was up to 120 g/l . However, an unnegligible drawback of the above-mentioned ethanologenic microbes is that they cannot naturally ferment pentose sugars, the main components of hemicellulose. Although many metabolic engineering strategies, typically the introduction of xylose metabolic pathway to Z. mobilis and S. cerevisiae, have been carried out to develop more efficient ethanol producers, the success is not very satisfactory [10, 11].
A naturally occurring xylose-fermenting yeast Scheffersomyces stipitis, formerly known as Pichia stiptis, was proposed as one of the potential cellulosic bioethanol strain. The most dominant feature of this unconventional yeast is that it’s capable of catabolizing glucose, mannose, galactose, rhamnose, xylose, arabinose, cellobiose, and even some lignin-related compounds . Other advantages include high production capability with a maximum ethanol yield of 0.48 g/g xylose , simple growth requirements, strong resistance to contamination and detoxification of the biomass-derived inhibitors . However, some metabolic mechanisms involved in the production of bioethanol in S. stipitis were unclear, such as the slow sugar consumption rate  and the tough control of precise oxygenation . Besides, the physiological and genetic features of S. stipitis were poorly characterized, which hinders the effective gene manipulation for strain improvement. Hence, a systematic understanding of physiological features and metabolic capacities of S. stipitis is in great need and genome-scale metabolic model (GSMM) could provide such a platform.
Up to now, there are more than 80 published genome-scale metabolic models (http://systemsbiology.ucsd.edu/InSilicoOrganisms/OtherOrganisms) and the number is still growing owing to the high-throughput genome sequence technologies. GSMMs have been successfully applied to many aspects, such as the design of the metabolic engineering strategies, the understanding of microbial physiology, the contextualization of various omics data, etc. [18, 19]. Recently, A GSMM for S. stipitis has been reconstructed to investigate some key metabolic traits . Using a different approach, a new constraint-based model i TL885 is presented. Compared with the previous model, our model captured more metabolic genes for the adoption of an integrated genome annotation way. In addition, many carbohydrate metabolic pathways were included to represent the unique characteristic of S. stipitis. Aside from the study of the physiological changes of ethanol production, the new model was mainly used to make predictions in prior to experiment validation, which is one of the most important applications of constraint-based models. In this research, the proposed model was used to predict the essentiality of the genes and evaluate the capacity of ethanol production with xylose as carbon source.
Results and discussion
Reconstruction and description of model i TL885
Gene essentiality study
Physiological characteristic of ethanol fermentation
Ethanol production from xylose
Previous study of xylose utilization in S. stipitis was mainly focused on the illustration of transport mechanism and metabolic pathway [36, 37]. In the model i TL885, seven putative high-affinity xylose transporters (XUT1-7) were annotated, and one of them (XUT1) has been biochemically characterized from S. stipitis. However, the low-affinity xylose proton symport systems could not be found just by gene annotation. So, kinetics experiment was necessary to determine the low-affinity xylose transporters. Four Sut proteins (SUT1-4) with higher affinity for glucose than xylose had been characterized in this way, indicating this low-affinity system is shared by glucose and xylose [16, 38]. As a result, the influx of xylose could be attributed to the cooperation of the two xylose transport systems. Besides, sugar sensors (SNF3, RGT2, etc.) were also identified in S. stipitis genome, which ensures the quick cellular response to the change of xylose in the environment, as the transcriptional data showed that the transcription of RGT2 with xylose could increase 65-fold .
The assimilated xylose can be metabolised via two catabolic routes: xylulokinase pathway and D-arabinose utilization pathway (Figure 2). Xylulokinase pathway is a well characterized pathway including three reactions encoded by three genes XYL1 (PICST_89614), XYL2 (PICST_86924), and XKS (PICST_68734), respectively . Based on our simulation, this pathway is thought to be the only route redirecting carbon flux from xylose to PPP in the wild-type cell. The D-arabinose utilization pathway consists of three biochemical reactions catalyzed by D-arabinitol dehydrogenase (AAD1), D-ribulose reductase (ARD2) and D-ribulokinase (RKS1), which was found due to the fact that xylose still can be metabolised in the XKS disruption strain . This pathway was also responsible for the degradation of D-arabinose, as illustrated in Figure 2.
The optimization of organic nitrogen sources had been reported to be able to enhance the bioconversion of xylose , thus the influence of specific amino acids on the production of ethanol were computationally investigated (Figure 7B). The addition of 17 of the 20 amino acids could improve both cell growth and ethanol production. Of those positive additions, the addition of glutamate had the most significantly impact, leading to the ethanol production rate increased by 27.7% compared to the control. Combined the above two strategies, i.e. gene deletion and glutamate addition, the production rate of ethanol was predicted to increase by over 20.0% for all the deletion strains, among which the addition of glutamate to the ALA2 deletion strain could improve ethanol production rate up to 1.29 fold of the control (Figure 7C). The results suggested that future work for the optimization of ethanol fermentation in S. stipitis should also consider the availability of nutrients aside from carbon source, which perhaps will be more beneficial as the cost-effective media often contain complex mixtures of nutrient derived from natural substrates.
In summary, an in silico model named i TL885 was developed representing a comprehensive knowledge of the metabolism of S. stipitis. Compared with the reported i BB814, i TL885 possessed a higher gene coverage rate in despite with a slightly smaller model size. Model-driven study of the gene essentiality validated the role of key metabolic genes in xylose metabolism and ethanol synthesis. Nevertheless, a further large-scale gene knockout study of S. stipitis is necessary for a better elucidation of its genotype-phenotype relationships. Robustness analysis demonstrated the profile of ethanol formation and FBA pointed out the impacts of oxygenation and carbon sources on the flux distribution of central metabolism. In light of the fermentation characteristic, we suggested the well-controlled oxygen concentration to achieve the maximum production of ethanol. The investigation of xylose utilization from the perspectives of sugar transport and metabolism partly accounts for the high efficient bioconversion of xylose in S. stipitis. For the overproduction of ethanol from xylose, candidate knockout targets were identified and the effects of amino acids addition were simulated, which proves that GSMM is capable of designing optimal culture conditions and metabolic engineering strategies. Therefore future work for S. stipitis can be focused on the experimental testing of strain design hypothesis generated by the computational analysis.
Model reconstruction and refinement
The availability of whole genome of P. stipitis enables us to carry out the model reconstruction following the general workflow of GSMM reconstruction described before [45, 46]. Firstly, the sequenced genome data of P. stipitis CSB 6054 was downloaded from UniProt database . Then, the functional annotation of genes was performed in two different ways. BLAST was used to conduct the sequence homology search of S. stipitis with two yeasts (S. cerevisiae and P. pastoris) and one fungus (A. niger). The thresholds of the bidirectional BLAST (BLASTp) for a functional sequence were set to have an e-value less than 1 × 10-30, an amino acid sequence identity above 40%, and a matching length at least 70% of the query sequence. To obtain a original reactions list, also called in-house model, GSMMs of S. cerevisiae i MM904 , P. pastoris i PP668  and A. niger i MA871 were selected as template frameworks to map the assigned genes. On the other hand, KAAS  was used for the functional annotation of all the query amino acid sequences. With the specific KO identifiers or Enzyme Commission (EC) numbers, particular reactions were selected from the KEGG reaction database which was then integrated to the BLAST results. Special attention was paid on the most significant components of genome-scale network, the gene-protein-reaction (GPR) associations which details the relationships between genes, proteins and reactions using the Boolean logical representations (AND and OR operators). The various isoenzyms or enzyme complexes were identified with the help of KEGG Modules  and assignments of homologous genes in S. cerevisiae. As a result, a draft model was developed and used as a start point for subsequent network refinements. With the biochemical information acquired from public databases such as KEGG , MetaCyc , BRENDA , and TCDB , manual revisions including deletion of error reactions, addition of organism-specific information, checking of mass-charge balance and filling of metabolic gaps were conducted one by one. GapFind and gapFilling in the Constraint-Based Reconsruction and Analysis (COBRA) toolbox were performed to identify and bridge the gaps in the current version of model so as to match the genotype and phenotype . A detailed model structure was accomplished when a biomass formation reaction and certain exchange reactions were added to the network. Biomass equation is an artificial linear combination of all the known biomass constituents and their defined proportions. Exchange reactions describe the uptake of nutrients from the medium and the secretion of specific metabolites to the extracellular environment, thus defining the systems boundaries.
All computational simulations were performed using COBRA toolbox  on Matlab (The MathWorks Inc., Natick, MA) with GLPK as the linear optimization solver. FBA was used as the main algorithm for network modelling and analysis. The mathematical formulation and numerous applications of FBA have been reviewed . Briefly, FBA is an effective tool for the prediction of the maximal cell growth and metabolite production when given certain constraints. The output of FBA is an optimal flux distribution for each reaction in the model and a maximum value for the objective function. Generally, the biomass equation is set as the objective function for the simulation of optimal growth and other model evaluations such as essentiality study.
All the simulations were performed on a minimal medium with limited carbon source. The uptake rates of ammonia, sulphate, phosphate, sodium, kalium, and ferrite were unconstrained with the lower and upper flux bounds of −1000 and 1000 mmol/gDW/h respectively. Xylose or glucose was set as sole carbon source with a uptake rate of 3 mmol/gDCW/h . The robustness analysis of oxygen uptake was performed by fixing the xylose uptake rate and cell growth rate (0.01 h-1) to predict the maximum ethanol production rate at each controlled oxygen uptake rate. The aerobic condition was simulated by uncostraining the oxygen uptake rate, and the semiaerobic condition by constraining the exchange reaction of oxygen to −5 mmol/gDW/h. To modelling the amino acids addition, each of amino acid was constrained to have a maximum consumption rate of 0.1 mmol/gDCW/h.
Gene deletion simulations
The computational single gene deletion was conducted to predict the important genes for the synthesis of building blocks of cellular biomass . For a given gene, the in silico knockout was performed by constraining the flux value of its corresponding reaction to zero when maximizing the growth rate. This method also works for the complex GPRs such as isoenzymes and multiplex enzymes. If the maximum growth rate of the gene knockout strain is less than 1 x 10-6 of the wild type, the deleted gene is defined as essential. Otherwise, it’s a nonessential gene. The OptKnock algorithm in COBRA toolbox identifies candidate genes that lead to the overproduction of desired metabolite [59, 60]. With ethanol as the target product, OptKonck was applied to discover target gene(s) knockouts. The scope of OptKonck was constrained to the nonessential genes in central metabolism (glycolysis, TCA, and PPP) and amino acid metabolism.
Genome-scale Metabolic Model
Basic Local Alignment Search Tool
Automatic Annotation Server (KAAS)
Constraint-Based Reconsruction and Analysis
Pentose Phosphate Pathway
- TCA cycle:
Tricarboxylic Acid Cycle
D-ribulose-5-phosphate 3- epimerase
High-affinity glucose transporter
NADP-dependent glutamate dehydrogenase.
The author would like to thank Dr. Jens Nielsen, professor at Chalmers University of Technology, for technological help on the model reconstruction. This work was supported by grants from the Outstanding Youth Foundation of Jiangsu Province (No.:BK2012002), the National Natural Science Foundation of China (31270079), the Program for Young Talents, and the national outstanding doctorate paper author special fund (No. 200962).
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