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中，包括人类在内的许多动物都维持着肠道微生物组，这些微生物组是它们正常发育和生长所需的复杂微生物群落。组成这些微生物群的巨大物种和功能多样性混淆了将微生物群组成与特定健康宿主表型联系起来的努力。该团队将向无菌宿主引入总肠道微生物组的随机子样本，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宿主。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模型推荐的细菌物种组成的合成微生物组，以验证他们的预测。最终，这项工作将确定特定的细菌谱系是不可或缺的动物生长，发育和进化。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/高重复模型系统和预测建模的能力，以快速生成假设和测试。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工作队伍。此外，这些参与者将获得不同职位的指导培训，使他们能够与学员建立富有成效的长期专业关系。宿主相关细菌与后生动物的生活史和进化密不可分。该研究项目建立在新兴证据的基础上，这些证据表明肠道微生物群的组成（即物种/功能多样性）对宿主动物的生长和发育有很大的影响，这些影响在生物组织的几个水平上实现（即基因网络表达，细胞增殖和组织分化，生物体大小和成熟）。包括哺乳动物在内的许多动物都拥有物种丰富且功能复杂的肠道微生物组，在这些对动物生长和发育至关重要的复杂群落中识别细菌谱系提出了可以通过跨学科方法解决的挑战。具体而言，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种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相关的细菌物种子网络。研究小组将通过构建由纯青少年宿主中的预测物种组成的合成微生物组来测试这些网络是宿主支持的假设，并跟踪它们的发展。这种跨学科的方法利用了分子和微生物学方法，（即随机森林）和切割边缘（即具有三重损失的卷积神经网络）机器学习工具，以及无脊椎动物模型，其通常具有复杂的肠道微生物组，并且可以容易地在不使用抗生素的情况下无菌饲养（即美洲大蠊）揭示肠道微生物群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知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

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.