Metabolic engineering under uncertainty--II: analysis of yeast metabolism

Yeast metabolism has been used extensively in scientific investigations and industrial applications. Understanding the properties of the yeast metabolic network is crucial, yet unaccomplished due to its high complexity, the different culture conditions, and the uncertainty associated with kinetic parameters. We recently developed a computational and mathematical framework using Monte Carlo method in which parameter uncertainty can be addressed through large-scale sampling procedure. This framework was applied on the compartmentalized central carbon pathways of Saccharomyces cerevisiae metabolism considering the growth environment of batch and chemostat reactor and integrating information from metabolic flux analysis. Statistical analysis of the results indicates that yeast cells growing in batch culture condition exhibit dramatically different control schemes from those growing in a chemostat. The difference is mainly due to the feedback introduced by the constraints of the chemostat. The control of the enzymes on the rates of the substrate uptake, product excretion, and cell growth and its practical implication are discussed. Clustering of the reaction steps according to the similarity of their responses to enzyme activity perturbations reveals functional coupling of metabolic reactions.     

Inferring Metabolic States in Uncharacterized Environments Using Gene-Expression Measurements

The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production.     

The yeast Zygosaccharomyces bailii: a new host for heterologous protein production, secretion and for metabolic engineering applications

Molecular tools for the production of heterologous proteins and metabolic engineering applications of the non-conventional yeast Zygosaccharomyces bailii were developed. The combination of Z. bailii's resistance to relatively high temperature, osmotic pressure and low pH values, with a high specific growth rate renders this yeast potentially interesting for exploitation for biotechnological purposes as well as for the understanding of the biological phenomena and mechanisms underlying the respective resistances. Looking forward to these potential applications, here we present the tools required for the production and the secretion of different heterologous proteins, and one example of a metabolic engineering application of this non-conventional yeast, employing the newly developed molecular tools.     

酵母生物多样性开发及工业应用

酵母是一类包括酿酒酵母和非常规酵母在内的多种单细胞真菌的总称,其中酿酒酵母是应用较多的重要工业微生物,广泛应用于生物医药、食品、轻工和生物燃料生产等不同生物制造领域.近年来,研究者从不同生态环境中分离了大量的酵母菌株,鉴定了多个新种,也发现了抗逆性不同以及具有多种活性产物合成能力的菌株,证明天然酵母资源具有丰富的生物多...     

Genetics and molecular physiology of the yeast Kluyveromyces lactis

With the recent development of powerful molecular genetic tools, Kluyveromyces lactis has become an excellent alternative yeast model organism for studying the relationships between genetics and physiology. In particular, comparative yeast research has been providing insights into the strikingly different physiological strategies that are reflected by dominance of respiration over fermentation in K. lactis versus Saccharomyces cerevisiae. Other than S. cerevisiae, whose physiology is exceptionally affected by the so-called glucose effect, K. lactis is adapted to aerobiosis and its respiratory system does not underlie glucose repression. As a consequence, K. lactis has been successfully established in biomass-directed industrial applications and large-scale expression of biotechnically relevant gene products. In addition, K. lactis maintains species-specific phenomena such as the "DNA-killer system, " analyses of which are promising to extend our knowledge about microbial competition and the fundamentals of plasmid biology.     

非常规酵母的分子遗传学及合成生物学研究进展

先进的合成生物学技术与传统的分子遗传学技术的结合更有助于实现酵母底盘细胞的快速改造和优化。酵母合成生物学研究最早开始于常规酵母——酿酒酵母(Saccharomyces cerevisiae),近些年来又迅速扩展至一些非常规酵母,包括巴斯德毕赤酵母(Pichiapastoris)、解脂耶氏酵母(Yarrowialipolytica)、乳酸克鲁维酵母(Kluyveromyces lactis)和多形汉逊酵母(Hansenula polymorpha)等。借助合成生物学技术与工具,目前科学家们已经成功开发出了能够高效生产生物材料、生物燃料、生物基化学品、蛋白质制剂、食品添加剂和药物等工业产品的重组非常规酵母工程菌株。本文系统总结了合成生物学工具(主要是基因组编辑工具)、合成生物学组件(主要是启动子和终止子)和相关分子遗传学方法在上述非常规酵母系统(底盘细胞)中的最新研究进展和应用情况,并讨论了其他合成生物学技术在这些非常规酵母表达系统中的潜在适用性和应用前景。这为研究人员利用合成生物学方法在这一新型非模式微生物底盘细胞中设计和构建各种高附加值工业产品的异源合成模块并最终实现目标化合物的高效生物合成提供了科学的理论指导。     

Searching for process information in the aroma of cell cultures

Aroma emissions from living cells can provide valuable information about the metabolic and physiological condition of those cells. Electronic noses are chemical gas-sensor arrays that use artificial neural network models to evaluate aromas. They can interpret the complex aroma information emitted from cultures of bacteria, yeast cells and animal cells. Potential applications for electronic noses range from medical diagnosis to industrial bioprocessing.     

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.     

Comparative Analysis of Yeast Metabolic Network Models Highlights Progress,Opportunities for Metabolic Reconstruction

We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.     

From the bakery to the brain business: developing inducible yeast models of human neurodegenerative disorders

In the last decade, the budding yeast Saccharomyces cerevisiae has been used as a model system to study the mechanisms of the human aging process and of age-associated neurodegenerative disorders such as Parkinson's, Huntington's, Alzheimer's, and amyotrophic lateral sclerosis. S. cerevisiae is a facultative aerobic, unicellular yeast, and despite their simplicity, yeast cells possess most of the same basic cellular machinery as neurons in the brain, including pathways required for protein homeostasis and energy metabolism. The power of yeast genetics and the use of high-throughput screening technologies have provided important clues concerning the pathophysiology of these disorders and the identification of candidate therapeutic targets and drugs. The yeast models are based on the expression of human disease proteins in yeast and recapitulate some of the cytotoxic features observed in patients. However, the currently available models mostly suffer from high-level protein expression that results in acute cytotoxicity, and from metabolic constraints when the models are based on extensively used, strong, galactose-inducible promoters. The models would increase their significance if they were based on continuous and tightly regulated gene expression systems for both activation and levels of expression. This would allow for more chronic cytotoxicity that better simulates the timing of events that occur during disease progression. Additionally, the use of metabolism-independent inducers would allow for the study of cell toxicities under conditions where the cells are forced to exclusively respire, thus more reliably modeling the highly oxidative neuronal metabolism. Here we have constructed yeast models of Huntington's disease based on the expression, under the control of different promoters, of the first exon of the huntingtin-containing polyglutamine tracts of both wild-type and mutant lengths. The different models are compared and evaluated.     

应用代谢网络模型解析工业微生物胞内代谢

为了快速、高效地理解工业微生物胞内代谢特征,寻找潜在的代谢工程改造靶点,基因组规模代谢网络模型(GSMM)作为一种系统生物学工具越来越受到人们的关注。文中在回顾GSMM 20年发展历程的基础上,分析了当前GSMM的研究现状,总结了GSMM的构建及分析方法,从预测细胞表型和指导代谢工程两个方面阐述了GSMM在解析工业微生物胞内代谢中的应用,并展望了GSMM未来的发展趋势。     

Recent advances in reconstruction and applications of genome-scale metabolic models

In the last decade, reconstruction and applications of genome-scale metabolic models have greatly influenced the field of systems biology by providing a platform on which high-throughput computational analysis of metabolic networks can be performed. The last two years have seen an increase in volume of more than 33% in the number of published genome-scale metabolic models, signifying a high demand for these metabolic models in studying specific organisms. The diversity in modeling different types of cells, from photosynthetic microorganisms to human cell types, also demonstrates their growing influence in biology. Here we review the recent advances and current state of genome-scale metabolic models, the methods employed towards ensuring high quality models, their biotechnological applications, and the progress towards the automated reconstruction of genome-scale metabolic models.     

Genome –Scale Reconstruction of Metabolic Networks of Lactobacillus casei ATCC 334 and 12A

Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is strain dependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains for specific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotype relationships and to compare different organisms. To assist in the selection and development of strains with enhanced industrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate, were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications.     

利用酵母菌生产有机酸的研究进展

有机酸是含有一种或多种低分子量酸性基团(如羧基、磺酸基)的可生物合成的有机化合物,广泛应用于食品、农业、医药、生物基材料工业等领域。酵母菌具有生物安全、抗逆性强、底物谱广泛、方便遗传改造,以及大规模培养技术成熟等独特优点,因此利用酵母菌生产有机酸的研究日益受到国内外学者的关注。目前利用酵母生产有机酸还存在浓度低、副产物多,以及发酵效率低等缺陷。随着酵母菌代谢工程和合成生物学技术的发展,利用酵母菌生产有机酸取得了快速进展。本文总结了利用酵母合成11种有机酸的研究,包括内源和异源合成的大宗羧酸和高价值有机酸,并对该领域的未来研究方向进行了展望。     

Metabolism of ricinoleic acid into gamma-decalactone: beta-oxidation and long chain acyl intermediates of ricinoleic acid in the genus Sporidiobolus sp

In order to study differences in gamma-decalactone production in yeast, four species of Sporidiobolus were cultivated with 5% of methyl ricinoleate as the lactone substrate. In vivo studies showed different time courses of intermediates of ricinoleic acid breakdown between the four species. In vitro studies of the beta-oxidation system were conducted with crude cell extracts of Sporidiobolus spp. and with ricinoleyl-CoA (RCoA) as substrate. The beta-oxidation was detected by measuring acyl-CoA oxidase, 3-hydroxyacyl-CoA dehydrogenase activities, and acetyl-CoA production. The time courses of the CoA esters resulting from RCoA breakdown by crude extract of Sporidiobolus spp. permit the proposal of different metabolic models in the yeast. These models explained the differences observed during in vivo studies.     

Improving the <Emphasis Type="Italic">i</Emphasis>MM904 <Emphasis Type="Italic">S. cerevisiae</Emphasis> metabolic model using essentiality and synthetic lethality data

Background  

Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., i MM904) remains significantly less predictive than the latest E. coli model (i.e., i AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model.     

Reduction of a biochemical model with preservation of its basic dynamic properties

The complexity of full-scale metabolic models is a major obstacle for their effective use in computational systems biology. The aim of model reduction is to circumvent this problem by eliminating parts of a model that are unimportant for the properties of interest. The choice of reduction method is influenced both by the type of model complexity and by the objective of the reduction; therefore, no single method is superior in all cases. In this study we present a comparative study of two different methods applied to a 20D model of yeast glycolytic oscillations. Our objective is to obtain biochemically meaningful reduced models, which reproduce the dynamic properties of the 20D model. The first method uses lumping and subsequent constrained parameter optimization. The second method is a novel approach that eliminates variables not essential for the dynamics. The applications of the two methods result in models of eight (lumping), six (elimination) and three (lumping followed by elimination) dimensions. All models have similar dynamic properties and pin-point the same interactions as being crucial for generation of the oscillations. The advantage of the novel method is that it is algorithmic, and does not require input in the form of biochemical knowledge. The lumping approach, however, is better at preserving biochemical properties, as we show through extensive analyses of the models.     

A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles

Background  

In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally, non-linear curve-fitting algorithms have been used for modelling, because of the non-linear properties of parameter estimation from time series. However, the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by linearization of S-systems.