Collaborative Research: MTM 1: Decoding the genomic rules of denitrification in bacterial communities

微生物在土壤、海洋甚至我们的身体中发挥着化学作用。他们在由许多不同成员组成的社区中集体实现这一目标，每个成员都有自己的基因编码能力，所有成员都参与复杂的代谢贸易网络。生物体和生态系统的健康和稳健取决于微生物的新陈代谢。深入了解预测微生物群落集体代谢的原理将使我们能够控制和改造这些群落，以改善人类健康和环境健康。 DNA 测序是我们表征微生物最强大的工具之一。不幸的是，将 DNA 序列与微生物群落的代谢能力联系起来仍然具有挑战性。鉴于细胞生理学的复杂性以及不同物种之间许多复杂的相互作用，这个问题具有挑战性。研究人员将利用进行反硝化作用的天然细菌分离物（反硝化作用是氮循环的重要组成部分）、数学建模和机器学习，证明可以从基因组序列预测群落代谢。这项工作具有开创性，因为它意味着通过对复杂群落的 DNA 进行测序，人们现在可以“读出”它们的代谢功能。这一见解将使设计具有预定代谢功能的微生物群落成为可能。这项研究的其他更广泛的影响包括对本科生研究人员进行生物化学、统计和计算研究方面的培训，以及向家长和儿童宣传有关微生物组和反硝化的知识。 微生物群落的集体代谢功能通过基因组编码过程的层次结构出现，从亚细胞信息处理和基因表达到细胞外代谢物、非生物因素和集体现象介导的相互作用。了解这种集体代谢功能如何在联合体的基因组结构中编码是微生物生态学的核心挑战。这项研究提出利用反硝化作用（通过无氧呼吸减少氧化氮化合物的一系列反应）作为代谢功能模型，在微生物群落的基因组结构和代谢功能之间架起一座桥梁。通过对测序的反硝化细菌分离株库进行代谢物测量，研究人员将确定每个菌株所拥有的反硝化基因与代谢动力学之间的预测定量关系。然后，群落组装实验将用于根据个体种群的动态来预测群落代谢动态。如果仅从个体种群的动态无法预测群落动态，研究人员将研究行为背后的基因组预测因子和分子机制。一旦在一个实验系统中建立了基因组结构和代谢功能之间的桥梁，研究人员将尝试在更复杂的系统中做同样的事情，以更好地接近自然环境。其结果将是从基因组结构到代谢功能的普遍映射，从而能够合理设计微生物群落，并为功能基因内容提供新的定量解释。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Microbes perform chemistry in the soils, oceans, and even our bodies. They accomplish this collectively in communities composed of many distinct members, each with their genetically-encoded capabilities, all participating in an intricate metabolic trade network. The health and robustness of organisms and ecosystems alike depend on the metabolism of microbes. A deep understanding of the principles that predict the collective metabolism of microbial communities would grant us the ability to control and engineer these communities for the betterment of human health, and the health of our environment. DNA sequencing is one of our most powerful tools for characterizing microbes. Unfortunately, it remains challenging to relate the DNA sequences to the metabolic capabilities of a microbial community. This problem is challenging given the complexity of cellular physiology, and the many complex interactions between different species. Using natural bacterial isolates that perform denitrification, a process that makes up an important part of the nitrogen cycle, mathematical modeling, and machine learning, the researchers will show that it is possible to predict community metabolism from genomic sequences. The work is groundbreaking because it means that by sequencing the DNA of complex communities one can now "readout" their metabolic function. The insight will enable the design of microbial communities with predefined metabolic function. Other broader impacts of this research include training of undergraduate researchers in biochemical, statistical, and computational research and outreach to parents and children regarding microbiomes and denitrification. The collective metabolic function of microbial communities emerges through a hierarchy of genomically-encoded processes, from sub-cellular information processing and gene expression to interactions mediated by extracellular metabolites, abiotic factors, and collective phenomena. Understanding how this collective metabolic function is encoded in the genomic structure of the consortium is a core challenge for microbial ecology. This research proposed leverages denitrification, a cascade of reactions that reduce oxidized nitrogen compounds via anaerobic respiration, as a model metabolic function to build a bridge between genomic structure and metabolic function in microbial communities. By employing metabolite measurements on a library of sequenced denitrifying bacterial isolates, the researchers will determine the predictive quantitative relationship between the denitrification genes that each strain possesses and metabolic dynamics. Community assembly experiments will then be used to predict community metabolic dynamics from the dynamics of individual populations. In cases where community dynamics are not predictable from the dynamics of individual populations alone, the researchers will investigate genomic predictors and molecular mechanisms underlying the behavior. Once the bridge between genomic structure and metabolic function is built in one experimental system, the researchers will attempt to do the same in a more complex system that better approximates a natural context. The result will be a generalizable mapping from genomic structure to metabolic function that enables the rational design of microbial communities and provides a new quantitative interpretation of functional gene content.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.