Collaborative Research: Metabolite repair - Uncovering the hidden support system for metabolic networks

合作研究：代谢修复——揭示代谢网络隐藏的支持系统

• 批准号: 1153491
• 负责人: Oliver Fiehn
• 金额: $ 58.74万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-15 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1153491&HistoricalAwards=false
• 关键词: Collaborative; Research; Metabolite; repair; Uncovering

Intellectual Merit. Most pro- and eukaryote genomes encode hundreds of enzymes of unknown function; finding what they do is a critical task for post-genomic biology. Mounting evidence implicates many of these enzymes of unknown function in "metabolite repair", i.e. in reversing damage done to metabolites by unwanted enzymatic side-reactions or chemical degradation. Because metabolites are under constant chemical attack (e.g. by oxidation or hydrolysis) and enzymes make wasteful and toxic catalytic errors, it follows that efficient functioning of meta¬bolic networks demands a support system dedicated to meta¬bolite repair. This system has been glimpsed by classical biochemistry, genetics, and metabolomics but most of it remains hidden. This project will therefore dissect the metabolite repair system by combining chemical biology, comparative genomics, and metabolomics using bacterial models and plants. Specific aims are to: (a) identify 30-50 target metabolites that are highly prone to chemical or enzymatic damage (i.e. that need repair) by cheminformatics, genome-scale metabolic reconstruction, and data mining; (b) predict genes encoding conserved repair enzymes for target metabolites using comparative genomics, and predict chemistries for the repair reactions; (c) test 20 repair predictions by knocking out the repair gene in a model organism, analyzing metabolomic profiles in normal and stress conditions, and identifying structures by cheminformatics; (d) validate repair reactions by mass spectrometric authentication of structures, by biochemical assays of recombinant proteins, and by functional complementation of bacterial or plant mutants; and (e) incorporate validated repair functions in next-generation genome-scale metabolic models. This project integrates modeling in two ways. First, it makes innovative use of modeling to predict a priori the metabolites most likely to need repair. Second, adding validated repair functions to genome-scale bacterial models sets up a virtuous cycle of prediction 'experiment' further prediction to drive discovery in metabolite repair. It also pioneers an essential modeling development: Models that capture the cost of uncontrolled formation and degradation of unwanted metabolites. Research in the emerging field of metabolite repair has the potential to displace a current paradigm of metabolic routes operating with perfect precision by one where the illusion of a flawless system is maintained by a battery of unobtrusive but critical repair functions. Moreover, metabolite repair is almost surely crucial to stress adaptation, to aging, and to metabolic engineering. Broader Impacts. This project will provide interdisciplinary training in comparative genomics, metabolomics, chemical biology, and integrative modeling to two PhD students and three postdoctorals who will spend time away from their own institution each year at another collaborating institution. Un-dergraduates will participate. In addition, there will be a training outreach component with three facets: (a) Eight two-day hands-on workshops (2 per year) at different universities to train PhD students, post¬doctorals, and faculty in comparative genomics using SEED databases and tools. At least three work¬shops will be at minority-serving institutions. Each workshop will train 10-12 people. (b) Development of a web page in which the instructional content of the workshop will be available for distance learning. (c) Instruction of project postdoctorals and students in how to organize and present workshops, culminating first in their acting as teaching assistants, and ultimately in them teaching themselves.

智力优势。 大多数原核生物和真核生物基因组编码数百种功能未知的酶;找到它们的作用是后基因组生物学的关键任务。越来越多的证据表明，这些酶中有许多在“代谢物修复”方面的功能未知，即在逆转不需要的酶副反应或化学降解对代谢物造成的损害方面。因为代谢物处于恒定的化学攻击下（例如通过氧化或水解）并且酶产生浪费和有毒的催化错误，所以Meta网络的有效功能需要专用于代谢物修复的支持系统。这个系统已经被经典的生物化学、遗传学和代谢组学瞥见，但大部分仍然隐藏着。因此，本项目将通过结合化学生物学，比较基因组学和代谢组学，使用细菌模型和植物来剖析代谢物修复系统。具体目标是：（a）确定30-50种极易受到化学或酶损害的目标代谢物（B）使用比较基因组学预测编码靶代谢物的保守修复酶的基因，并预测修复反应的化学性质;（c）通过敲除模式生物体中的修复基因、分析正常和应激条件下的代谢组学谱以及通过化学信息学鉴定结构来测试20个修复预测;（d）通过结构的质谱鉴定、重组蛋白的生物化学测定和细菌或植物突变体的功能互补来验证修复反应;以及（e）在下一代基因组规模的代谢模型中结合经验证的修复功能。 该项目以两种方式集成建模。首先，它创新性地使用建模来预测最有可能需要修复的代谢物。其次，将经过验证的修复功能添加到基因组规模的细菌模型中，建立了一个预测“实验”的良性循环，进一步预测，以推动代谢物修复的发现。它还开创了一个重要的建模发展：捕捉不受控制的形成和不需要的代谢物降解的成本的模型。代谢物修复这一新兴领域的研究有可能取代目前精确运作的代谢途径范式，取而代之的是一种由一系列不显眼但关键的修复功能维持的完美系统的错觉。此外，代谢物修复几乎可以肯定对压力适应、衰老和代谢工程至关重要。更广泛的影响。 该项目将提供比较基因组学，代谢组学，化学生物学和综合建模的跨学科培训，两名博士生和三名博士后，他们每年将在另一个合作机构度过一段时间。本科生将参加。此外，还将有一个培训推广部分，包括三个方面：（a）在不同大学举办八次为期两天的实践讲习班（每年两次），利用SEED数据库和工具对博士生、博士后和教职员工进行比较基因组学培训。至少有三个讲习班将设在为少数群体服务的机构。每个培训班将培训10-12人。(b)开发一个网页，将讲习班的教学内容提供给远程学习。(c)指导项目博士后和学生如何组织和举办研讨会，首先是作为助教，最终是自学。