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

智力上的功绩。大多数原核生物和真核生物基因组编码数百种未知功能的酶；找到它们的功能是后基因组生物学的关键任务。越来越多的证据表明，这些酶中许多功能未知的酶参与了“代谢物修复”，即逆转不必要的酶副反应或化学降解对代谢物造成的损害。由于代谢物不断受到化学攻击(例如，通过氧化或水解)，而酶会产生浪费和有毒的催化错误，因此代谢网络的有效运作需要一个专门用于代谢岩修复的支持系统。这个系统已经被经典的生物化学、遗传学和代谢组学一瞥，但其中大部分仍然隐藏着。因此，该项目将结合化学生物学、比较基因组学和使用细菌模型和植物的代谢组学来剖析代谢物修复系统。具体目标是：(A)通过化学信息学、基因组规模的代谢重建和数据挖掘，确定30-50种高度容易受到化学或酶损伤(即需要修复)的目标代谢物；(B)利用比较基因组学预测目标代谢物编码保守修复酶的基因，并预测修复反应的化学成分；(C)通过敲除模型生物体中的修复基因，分析正常和应激条件下的代谢组谱，并通过化学信息学鉴定结构，测试20种修复预测；(D)通过结构的质谱学鉴定、重组蛋白质的生化分析以及细菌或植物突变体的功能互补来验证修复反应；以及(E)将经过验证的修复功能纳入下一代基因组规模的新陈代谢模型。该项目以两种方式集成了建模。首先，它创新地利用建模来预测最有可能需要修复的代谢物。其次，将有效的修复功能添加到基因组规模的细菌模型中，建立了一个预测的良性循环--进一步的预测，以推动代谢物修复方面的发现。它还开创了一项基本的建模开发：捕捉不受控制的形成和降解有害代谢物的成本的模型。在新兴的代谢物修复领域的研究有可能取代目前以完美精度运行的代谢路线范例，其中，完美系统的幻觉由一组不引人注目但关键的修复功能维持。此外，几乎可以肯定，代谢物修复对于压力适应、衰老和代谢工程至关重要。更广泛的影响。该项目将为两名博士生和三名博士后提供比较基因组学、代谢组学、化学生物学和综合建模方面的跨学科培训，他们每年将离开自己的机构在另一个合作机构工作。本科生将参加。此外，还将有一个由三个方面组成的培训外联部分：(A)在不同的大学举办8个为期两天的实践讲习班(每年2个)，利用种子数据库和工具对博士生、博士后和教师进行比较基因组学方面的培训。至少有三家工作场所将设在为少数族裔服务的机构。每个工作坊将培训10-12人。(B)开发一个网页，供远程学习使用讲习班的教学内容。(C)指导项目博士后和学生如何组织和举办讲习班，首先是担任助教，最后是自学。

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.