Systems Analysis of the Unfolded Protein response in Saccharomyces cerevisiae

酿酒酵母中未折叠蛋白反应的系统分析

• 批准号: 7650722
• 负责人: Keith Edward Jaggard Tyo
• 金额: $ 0.79万
• 依托单位: CHALMERS UNIVERSITY OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-30 至 2010-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7650722
• 关键词: Accounting; Affinity; Alzheimer' s Disease; Amino Acids; Apoptosis; Appearance; Binding; Biological; Biological Process; Cells; Classification; Codon Nucleotides; Collaborations; Complex; Computer software; Consensus; Coupled; DNA-Protein Interaction; Data; Disease; Endoplasmic Reticulum; Enzymes; Equilibrium; Eukaryota; Eukaryotic Cell; Facility Construction Funding Category; Gene Dosage; Gene Proteins; Genes; Genetic; Genome; Graph; Human; Hybrids; Knowledge; Laboratories; Lead; Lipids; Literature; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Measurement; Membrane; Metabolic; Microarray Analysis; Modeling; Modification; Network-based; Non-Insulin-Dependent Diabetes Mellitus; Oxidation-Reduction; Pathway Analysis; Pathway interactions; Phosphorylation; Prevention; Production; Productivity; Protein Biosynthesis; Protein Secretion; Proteins; Proteomics; Recombinant Proteins; Recombinants; Resistance; Resources; Saccharomyces cerevisiae; Score; Systems Analysis; Transcript; United States National Institutes of Health; Work; Yeasts; analytical method; base; biological adaptation to stress; follow-up; glycosylation; improved; intracellular protein transport; metabolomics; prevent; promoter; protein aggregate; protein expression; protein folding; protein metabolite; protein misfolding; protein protein interaction; protein transport; research study; response; scaffold; size; therapeutic protein

The unfolded protein response (UPR) is a large-scale, coordinated response to correct misfolded proteins in eukaryotes. The diverse set of actions a cell coordinates in parallel (approx. 380 genes in yeast) to alleviate the various problems that can occur in protein synthesis, folding, glycosylation, and translocation of membrane-bound and secreted proteins has a large impact on therapeutic protein production and aggregated protein-related disease. The proposed work will map the biological information flow through the many genes involved in the response to achieve an understanding of the UPR and identify targets for altering the response. A biological interaction network of the UPR will be constructed from available biological interaction data for yeast. Transcriptional, proteomic, metabolomic, and metabolic flux data will be collected for recombinant yeast producing preinsulin and TNFa with modulated UPR induction. UPRassociated changes in biological molecule levels detected from the X-omics data will be superimposed onto the interaction network, revealing the flow of biological information in the UPR. This combination of information from biological interaction networks and X-omics-scale data will lead to a better understanding of secreted protein production. In particular, metabolite measurements can indicate possible limitations in glycosylation pathways, amino acid supply, energetics, redox balance, or lipid synthesis. Transcript and protein levels may reveal pathways that benefit or detract from high level secretion of protein. From this information, important biological molecules and interactions can be targeted to improve protein production and, in humans, identify targets to prevent/treat aggregated-protein related disease. The findings of the proposed work will be very helpful in understanding the molecules and their interconnections involved in stress response. This strategy represents a multifaceted approach to understanding a complex biological response and is consistent with the NIH Building Blocks, Biological Pathways, and Networks roadmap initiative. Yeast is an attractive commercial producer of therapeutic proteins, but can be limited by low product yields or poor quality that could be improved by this work. A thorough understanding of the UPR could have consequences for prevention and treatment of disease known to be related to aggregated protein, such as Alzheimer's, Type 2 diabetes, and apoptosis-resistant, uncontrolled protein production in certain cancers.

展开的蛋白质反应（UPR）是一种大规模的协调反应，以纠正错误折叠的蛋白质 真核生物。各种动作集合均衡（约380个基因）来减轻细胞的坐标 蛋白质合成，折叠，糖基化和易位的各种问题 膜结合和分泌的蛋白质对治疗性蛋白质的产生和 蛋白质相关疾病的骨骼。拟议的工作将绘制通过 许多基因参与响应，以实现对UPR的理解并确定目标 改变响应。 UPR的生物相互作用网络将由可用 酵母的生物相互作用数据。转录，蛋白质组学，代谢组和代谢通量数据将是 用于重组酵母菌的收集，并产生具有调节式UPR诱导的前胰岛素和TNFA。起义 从X-omics数据中检测到的生物分子水平的变化将叠加到 相互作用网络，揭示了UPR中生物学信息的流动。这种组合 来自生物互动网络和X派尺度数据的信息将使人们更好地理解 分泌的蛋白质产生。特别是，代谢物测量可能表明 糖基化途径，氨基酸供应，能量，氧化还原平衡或脂质合成。成绩单和 蛋白质水平可能揭示出受益于蛋白质高水平分泌的途径。由此 信息，重要的生物分子和相互作用可以针对改善蛋白质的产生 并且，在人类中，识别预防/治疗蛋白质相关疾病的靶标。的发现 拟议的工作将非常有助于理解分子及其涉及的互连 压力反应。该策略代表了一种理解复杂生物学的多方面方法 响应，与NIH的构建块，生物途径和网络路线图一致 倡议。酵母是一种有吸引力的治疗蛋白的商业生产商，但可以受到低限制 这项工作可以提高产品的产量或质量差。对UPR的透彻理解 可能对已知与综合有关的疾病的预防和治疗产生后果 蛋白质，例如阿尔茨海默氏症，2型糖尿病，抗凋亡的抗凋亡，不受控制的蛋白质产生 某些癌症。