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

多细胞动物进入了微生物世界，包括人类在内的许多动物维持着肠道微生物群落，这是它们正常发育和生长所需的复杂微生物群落。组成这些微生物组的巨大物种和功能多样性扰乱了将微生物组组成与特定的健康宿主表型联系起来的努力。该团队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与无菌相关的许多生长和发育缺陷的微生物。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物种。最后，该团队将构建并引入由ML模型推荐的细菌种类组成的合成微生物群，以验证他们的预测。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。这一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于积极的宿主结果至关重要。此外，该项目将展示低成本/高重复的模型系统和预测性建模在快速假设生成和检验方面的能力。最后，从事微生物组科学的研究人员和博士后科学家将有机会从HBCU招聘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，这些参与者将在不同的岗位上接受指导培训，使他们能够与他们的导师建立富有成效的、长期的专业关系。寄主相关细菌与后生动物的生活史和进化密不可分。这项研究建立在新的证据基础上，表明肠道微生物区系的组成(即物种/功能多样性)对宿主动物的生长和发育有很大影响，这些影响是在几个生物组织水平上实现的(即基因网络表达、细胞增殖和组织分化、生物体大小和成熟)。许多动物，包括哺乳动物，拥有物种丰富和功能复杂的肠道微生物群，识别这些复杂群落中对动物生长和发育至关重要的细菌谱系是可以通过跨学科方法解决的挑战。具体地说，机器学习方法将被用于集成来自肠道微生物组及其宿主的高重复多组学数据，以及来自几个明确定义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种子网。研究小组将通过构建由无菌幼年宿主中预测的物种组成的合成微生物群来测试这些网络支持宿主的假设，并跟踪它们的发育。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)和尖端(即具有三联体丢失的卷积神经网络)机器学习工具，以及无脊椎动物模型，该模型通常包含复杂的肠道微生物群，并且可以很容易地在不使用抗生素的情况下进行轴向饲养(例如美洲大熊猫)，以阐明肠道微生物区系对宿主生长和发育的作用。该奖项反映了NSF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。

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.