Using Machine Learning and Animal Models to Reveal Bacterial Subnetworks Essential for Development Within Complex Gut Microbiomes.

多细胞动物出现在微生物世界，许多动物，包括人类，维持肠道微生物群，这是它们正常发育和生长所需要的复杂微生物群落。包含这些微生物组的巨大物种和功能多样性使将微生物组组成与特定健康宿主表型联系起来的努力变得混乱。研究小组将把总肠道微生物组的随机子样本引入无菌宿主，并筛选那些能够解决许多与无菌相关的生长和发育缺陷的人。接下来，机器学习（ML）方法将识别与促进健康宿主结果一致相关的细菌种类。最后，该团队将构建并引入由ML模型推荐的细菌物种组成的合成微生物组，以验证其预测。最终，这项工作将确定对动物生长、发育和进化不可或缺的特定细菌谱系。这个跨学科项目利用传统和尖端技术来解决肠道微生物组组成的哪些方面对积极的宿主结果至关重要。此外，该项目将展示低成本/高重复模型系统和快速假设生成和测试的预测建模的能力。最后，研究微生物组科学的研究人员和博士后科学家将有机会从hbcu招募下一代微生物组科学家，目标是建立一支多样化的微生物组科学队伍。此外，这些参与者将获得不同职位的指导培训，使他们能够与他们的学员建立富有成效的长期专业关系。宿主相关细菌不可避免地参与了后生动物的生活史和进化。本研究项目建立在新的证据基础上，这些证据表明肠道微生物群的组成（即物种/功能多样性）对宿主动物的生长发育有很大影响，这些影响在生物组织的几个层面上实现（即基因网络表达、细胞增殖和组织分化、有机体体型和成熟）。许多动物，包括哺乳动物，拥有丰富的物种和功能复杂的肠道微生物群，在这些复杂的群落中识别细菌谱系对动物的生长和发育至关重要，这是可以通过跨学科方法解决的挑战。具体来说，机器学习方法将用于整合来自肠道微生物组及其宿主的高重复多组学数据，以及来自几个定义明确的微生物组扰动的宿主发育、生理和胃组织学数据，以推断与正常宿主生长和发育一致的细菌物种子网络。研究小组将通过在无菌幼年宿主中构建由预测物种组成的合成微生物组并跟踪它们的发育来验证这些网络支持宿主的假设。这种跨学科的方法利用分子和微生物学方法，传统（即随机森林）和前沿（即具有三重态损失的卷积神经网络）机器学习工具，以及通常含有复杂肠道微生物群的无脊椎动物模型，可以在不使用抗生素的情况下轻松饲养（即美洲大蠊），以阐明肠道微生物群在宿主生长和发育中的作用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Multicellular animals emerged into a microbial world and, many animals, including humans, maintain gut microbiomes that are complex microbial communities that they require for normal development and growth. The enormous species and functional diversity comprising these microbiomes confound efforts to link microbiome composition to specific healthy host phenotypes. The team will introduce random sub-samples of the total gut microbiome to a germ-free host and screen for those capable of resolving many of the growth and developmental deficiencies associated with being germ-free. Next, machine learning (ML) approaches will identify bacterial species that are consistently associated with promoting healthy host outcomes. Finally, the team will construct and introduce synthetic microbiomes comprised of bacterial species recommended by the ML models to germ-free animals to validate their predictions. Ultimately, this effort will identify specific bacterial lineages that are integral to animal growth, development and evolution. This interdisciplinary project leverages legacy and cutting-edge technologies to address what aspects of gut microbiome composition are essential for positive host outcomes. Additionally, this project will demonstrate the power of low-cost/high-replicate model systems and predictive modeling for rapid hypothesis generation and testing. Finally, investigators and postdoctoral scientists doing microbiome sciences will be afforded opportunities to recruit next-gen microbiome scientists from HBCUs with the goal of building a diverse microbiome science workforce. Further, these participants will obtain training in mentoring across different positionalities to enable them to build productive, long-term professional relationships with their mentees.Host-associated bacteria are inextricably involved in the life history and evolution of metazoans. This research project builds on emerging evidence that suggests composition of gut microbiota (i.e. species/functional diversity) have large effects on host animal growth and development, and these effects are realized at several levels of biological organization (i.e. gene network expression, cellular proliferation and tissue differentiation, organismal body size and maturation). Many animals, including mammals, harbor species-rich and functionally complex gut microbiomes and identifying bacterial lineages within those complex communities that are critical for animal growth and development presents challenges that can be addressed through interdisciplinary approaches. Specifically, machine learning approaches will be used to integrate high-replicate multi-omics data from the gut microbiome and its host as well as host developmental, physiological and gastric histological data from several well-defined microbiome perturbations to infer bacterial species sub-networks that are consistently associated with normal host growth and development. The research team will test the hypothesis that these networks are host-supportive by constructing synthetic microbiomes comprised of predicted species in axenic juvenile hosts and track their development. This interdisciplinary approach leverages molecular and microbiological approaches, legacy (i.e. random forest) and cutting edge (i.e. convolution neural networks with triplet loss) machine learning tools, and an invertebrate animal model that normally harbors a complex gut microbiome and can easily be reared axenically without the use of antibiotics (i.e. Periplaneta americana) to shed light on the role of gut microbiota on host growth and development.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.