Modeling the function and evolution of metabolic networks across hypersaline-adapted Archaea

该奖项旨在寻求对微生物如何在地球上最极端的条件下收集营养并保持活力的基本理解。这些生物中有许多生活在富含盐的湖泊和池塘中，这些湖泊和池塘中营养物质含量低，氧气含量有限。这些“嗜盐菌”，古生菌领域的成员，可以告诉我们能量代谢的起源，因为它们利用广泛的代谢策略在同一个稀缺资源池中生存。这些嗜盐菌自然合成独特的化学物质，比如类似于喷气燃料和可生物降解塑料的化学物质。关于亲盐菌代谢功能的知识的巨大差距阻碍了将亲盐菌用于替代能源解决方案。该项目将通过生成和比较80种嗜盐菌能量产生途径的计算模型来填补这些空白。从两个测试物种代谢产物的经验测量将用于完善模型预测。每年，PI的研究小组将教授两个为期一周的科学浸入式研讨会，一个针对高中生，另一个针对波多黎各大学的本科生。在这两个工作坊中，学生将测量和模拟极端压力下的嗜盐菌活力。这项研究将提供有关能源生产演变的基础知识，实现未来的替代能源战略，并在STEM领域的早期职业生涯中吸引不同的学生群体。该项目的长期目标是增加关于古细菌微生物的代谢途径是如何根据环境中不同的营养物质进行调节的基础知识。高盐适应古细菌，或嗜盐菌，为研究转录调控和代谢网络的共同进化提供了一个独特的模型。成员物种共享一个共同的高盐栖息地，但在如何产生能量方面表现出广泛的多样性。在季节变化期间，高盐湖的营养物质是间歇性的。作为回应，嗜盐菌已经获得了广泛的可能的代谢解决方案，以在同一稀缺资源池中生存。最近来自PI实验室的转录组学和代谢组学证据表明，嗜盐菌利用转录调节作为一种主要机制来动态调节代谢网络，以响应营养波动。基于这一证据，工作假设是在营养波动过程中，调节网络以多种方式与代谢网络共同进化。然而，迄今为止，由于缺乏可处理的模式生物，古细菌的代谢多样性在很大程度上尚未被探索。最近，80个嗜盐菌的基因组序列已经成为可能，这在古细菌中是前所未有的。利用这些基因组序列数据，将进行以下目标来验证中心假设：(a)使用自动计算方法构建和比较80种嗜盐菌的代谢网络模型；(b)在两种密切相关的、遗传上易于处理的嗜盐模式物种中测试关于营养和遗传扰动的模型预测；(c)利用转录组和代谢组数据作为附加约束，完善代谢模型。

This award, seeks fundamental understanding of how microorganisms gather nutrients and remain viable while facing the most extreme conditions on Earth. Many of these organisms reside in salt saturated lakes and ponds that are low in nutrients and limited for oxygen. These "halophiles", members of the domain Archaea, can teach us about the origins of energy metabolism since they exploit a wide array of metabolic strategies to survive on the same pool of scarce resources. These halophiles naturally synthesize unique chemicals, such as those resembling jet fuel and biodegradable plastic. Large gaps in knowledge regarding halophile metabolic functions have prevented the use of halophiles for alternative energy solutions. This project will fill these gaps by generating and comparing computational models of energy production pathways across 80 species of halophiles. Empirical measurements of metabolic products from two test species will be used to refine the model predictions. Each year, the PI's research group will teach two weeklong science immersion workshops, one for high school students and the other for undergraduates at University of Puerto Rico. In both workshops, students will measure and model halophile viability during extreme stress. This research will provide fundamental knowledge regarding the evolution of energy production, enable future alternative energy strategies, and engage a diverse population of students in STEM fields early in their careers.The long-term goal of this project is to increase fundamental knowledge regarding how metabolic pathways of archaeal microorganisms are regulated in response to varying nutrients in the environment. Hypersaline-adapted Archaea, or halophiles, provide a unique model for investigating the co-evolution of the transcription regulatory and metabolic networks. Member species share a common hypersaline habitat, but exhibit extensive diversity in how they generate energy. Nutrients are intermittently available in hypersaline lakes during seasonal variations. In response, halophiles have acquired a wide array of possible metabolic solutions to survive on the same pool of scarce resources. Recent transcriptomic and metabolomics evidence from the PI's lab suggest that halophiles use transcriptional regulation as a primary mechanism to tune the metabolic network dynamically in response to nutrient fluctuations. Based on this evidence, the working hypothesis is that the regulatory network co-evolves with the metabolic network in diverse ways during nutrient fluctuation. However, to date, archaeal metabolic diversity has been largely unexplored due to the scarcity of tractable model organisms. Recently, 80 genome sequences of halophiles have become available, a scale that is unprecedented among Archaea. Using these genomic sequence data, the following objectives will be carried out to test the central hypothesis: (a) Construct and compare metabolic network models for 80 species of halophiles using automated computational methods; (b) Test model predictions regarding nutrient and genetic perturbations in two closely related, genetically tractable halophile model species; (c) Refine metabolic models using transcriptome and metabolome data as additional constraints.