AI-powered multi-scale modeling of microbiome-host interactions

人工智能驱动的微生物与宿主相互作用的多尺度建模

• 批准号: 2226183
• 负责人: Lei Xie
• 金额: $ 78.23万
• 依托单位: CUNY Hunter College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226183&HistoricalAwards=false
• 关键词: AI; powered; multi; scale; modeling

Prediction of organismal characteristics based on genetic signatures of an individualorganism is a fundamental problem in biology. Recent advancements in the field indicate that microbiome, a community of microorganisms found in all environments on earth from humanbodies to soils, plays an essential role in determining the function of ecosystems. Microbiomegenerates a variety of metabolites that serve as messengers for the microbiome to communicatewith its surroundings. It remains unclear how these messengers communicate. This researchintends to decipher the molecular language of microbiome-environment interactions, therebyresolving a major enigma in life sciences. Understanding the fundamental rules governingmicrobiome-environment interactions will facilitate the solution of pressing problems inagriculture, environment, energy, and human health/well-being. By modulating the function of themicrobiome in plants, for instance, agriculture could become more productive while requiring lesspesticides and fertilizers. Utilizing the microbiome's photosynthetic energy is a promisingalternative clean energy solution. Maintaining gut microbiome stability may improve humanimmunity. This multidisciplinary research involving biology, chemistry, and computerscience will create opportunities for underrepresented students in life sciences to pursuecareers in computer science and bioinformatics, fields in which racial and ethnic minorities arewoefully underrepresented.Metabolite-protein interactions (MPIs) play a major mechanistic role in maintaining microbiomecommunities and mediating microbiome-host interactions. Exploration of the global landscape ofMPIs and the host gene expressions regulated by the MPI would fill critical knowledge gaps incausal environment-genotype-phenotype associations. However, our understanding of MPIs andtheir regulatory pathways is limited due to the transient and low-affinity nature of MPIs. Existingexperimental techniques for determining MPIs and their functional roles are time-consuming,biased to certain molecules, or applicable on a relatively small scale. The rapid growth of multipleomics data provides us with new opportunities to predict MPIs across entire microbiomes and hostgenomes. However, due to noisiness, biasness, incompleteness, and heterogeneity of omicsdata sets, their potential has not been fully appreciated for developing accurate, robust, andinterpretable predictive models for MPIs and their functions. The project will develop aninnovative AI-powered multi-scale modeling framework to predict biological functions ofmicrobiome metabolites by integrating diverse data from chemical genomics, structural genomics,and functional genomics. Specifically, this research will develop a novel end-to-end meta-learningmethod following a sequence-structure-function paradigm to predict genome-wide microbiomemetabolite-host protein interactions, and innovative domain adaption methods to translate cell linescreens to host systemic responses to microbiome metabolites by disentangling intrinsic biologicalsignals from both biological and technical confounders. An immediate outcome will be a bespokeplatform to infer novel biological relationships from noisy, biased, and incomplete data, whichwill have broad applications in addressing many fundamental biological problems. Another far-reachingbenefit will be the discovery of novel genetic, molecular, and cellular mechanisms ofbiological processes. Completing this project will provide a powerful computational framework tocorrelate molecular interactions to physiological processes, and establish causal environment microbiome-genotype-phenotype associations. The results of the project can be found athttps://github.com/XieResearchGroup/.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.

根据生物个体的遗传特征来预测生物特征是生物学中的一个基本问题。该领域的最新进展表明，微生物群落是一种微生物群落，存在于从人体到土壤的所有环境中，在决定生态系统功能方面发挥着至关重要的作用。微生物产生各种代谢物，这些代谢物作为微生物组与周围环境交流的信使。目前尚不清楚这些信使如何沟通。这项研究旨在破译微生物-环境相互作用的分子语言，从而解开生命科学中的一个重大谜团。了解微生物-环境相互作用的基本规则将有助于解决农业、环境、能源和人类健康/福祉方面的紧迫问题。例如，通过调节植物中微生物的功能，农业可以在需要杀虫剂和化肥的同时变得更有生产力。利用微生物的光合能是一种很有前途的替代清洁能源解决方案。维持肠道微生物群的稳定可能会提高人体免疫力。这项涉及生物学、化学和计算机科学的多学科研究将为生命科学中代表性不足的学生创造机会，从事计算机科学和生物信息学领域的研究，而在这些领域，少数民族和少数民族的代表性严重不足。代谢-蛋白质相互作用(MPIs)在维持微生物群落和调节微生物群-宿主相互作用方面发挥着重要的机械作用。探索MPI的全球格局和受MPI调控的宿主基因表达将填补因果环境-基因型-表型关联的关键知识空白。然而，由于MPI的暂时性和低亲和力的性质，我们对MPI及其调控途径的理解是有限的。现有的确定MPI及其功能作用的实验技术是耗时的、对某些分子有偏见的，或者适用于相对较小的规模。多重组学数据的快速增长为我们提供了新的机会来预测整个微生物群和宿主基因组的MPI。然而，由于OMICS数据集的噪声、偏见、不完备性和异质性，它们在开发准确、稳健和可解释的MPI及其功能预测模型方面的潜力尚未得到充分认识。该项目将开发一个创新的人工智能支持的多尺度建模框架，通过整合来自化学基因组学、结构基因组学和功能基因组学的各种数据来预测微生物组代谢物的生物学功能。具体地说，这项研究将开发一种新的端到端元学习方法，遵循序列-结构-功能范式来预测全基因组微生物代谢产物-宿主蛋白的相互作用，以及创新的结构域适应方法，通过从生物学和技术混杂因素中分离出内在的生物信号，将细胞系筛选转化为宿主对微生物组代谢物的系统反应。一个直接的结果将是一个定制的平台，可以从嘈杂、有偏见和不完整的数据中推断出新的生物关系，这将在解决许多基本的生物学问题方面有广泛的应用。另一个深远的好处将是发现生物过程的新的遗传、分子和细胞机制。完成这个项目将提供一个强大的计算框架，将分子相互作用与生理过程相关联，并建立因果环境微生物组-基因型-表型关联。该项目的结果可以在https://github.com/XieResearchGroup/.This上找到，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。