Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden.
BMC Genomics. 2010 Dec 22;11:723. doi: 10.1186/1471-2164-11-723.
The need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering.
In this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function.
With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at http://www.sysbio.se/cenpk.
通过使能技术代谢工程，能够快速有效地设计和构建微生物细胞工厂，而系统生物学方法现在正在促进代谢工程的发展。代谢工程通常与定向进化相辅相成，在定向进化中，对部分基因工程菌株施加选择性压力，以赋予理想的表型。导致改善表型的精确遗传修饰或产生的基因型通常无法识别或理解，从而无法进行进一步的代谢工程。
在这项工作中，我们进行了全基因组高通量测序和注释，可用于鉴定酿酒酵母 S288c 和 CEN.PK113-7D 菌株之间的单核苷酸多态性（SNP）。酵母菌株 S288c 是第一个被测序的真核生物，作为酿酒酵母基因组数据库的参考基因组，而 CEN.PK113-7D 是工业生物技术研究中首选的实验室菌株。在这两个菌株（参考菌株：S288c）之间共检测到 13787 个高质量的 SNP。仅考虑代谢基因（5596 个注释基因中的 782 个），总共分布在 158 个代谢基因中的 219 个代谢特异性 SNP，其中 85 个 SNP 是非同义的（例如，编码氨基酸修饰）。在检测到的代谢 SNP 中，半乳糖摄取途径（GAL1、GAL10）和麦角固醇生物合成途径（ERG8、ERG9）存在途径富集。生理特性证实 S288c 对半乳糖摄取和代谢的能力明显弱于 CEN.PK113-7D，同样，在葡萄糖和半乳糖补充培养物中，CEN.PK113-7D 的麦角固醇含量明显高于 S288c。此外，在葡萄糖和半乳糖分批培养物中对 S288c 和 CEN.PK113-7D 的 DNA 微阵列分析并没有为观察到的主要表型提供明确的假说，这表明表型与基因型之间的相关性是在转录后或翻译后表现出来的，要么通过蛋白质浓度和/或功能。
随着对生产各种目标化合物的微生物细胞工厂的需求日益加剧，全基因组高通量测序和 SNP 检测注释可帮助更好地简化和定义代谢景观。这项工作证明了基因型与表型之间的直接相关性，为代谢工程提供了明确且高成功率的目标。CEN.PK113-7D 的基因组序列、注释和 SNP 查看器已存放在 http://www.sysbio.se/cenpk。
