MIM: Elucidating the Rules of Cooperation and Resiliency in Microbial Communities through Stochastic Graph Grammars

MIM：通过随机图语法阐明微生物群落的合作和弹性规则

• 批准号: 2126387
• 负责人: Todd Treangen
• 金额: $ 198.99万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126387&HistoricalAwards=false
• 关键词: MIM; Elucidating; Rules; Cooperation; Resiliency

Microbial communities in aquatic ecosystems are central to maintaining the resiliency of these important environments. These dense microbial populations are rich in species diversity and their genomes are dynamic because of processes such as gene duplication and horizontal gene transfer. Despite extensive genomic flux due to these evolutionary processes, microbial communities cooperate and maintain stable interactions and are resilient to environmental perturbations. This project aims to develop a suite of computational tools for deciphering the rules that govern cooperation and resiliency in hot spring microbial mats, or biofilms, from Yellowstone Park. These well-studied environments are ideal for examining how critical environmental parameters such as temperature, light and nutrients influence the diversity, abundance, and evolution of microbial populations. Novel computational tools will be developed to identify rules that govern relevant biological processes. Understanding how microbiomes evolve and adapt, especially with respect to the environment and to perturbations is crucial to understand many aspects of life. Thus, the results obtained from this project will have a significant impact on biology, health, conservation efforts, and animal and plant management. Furthermore, this research will support the interdisciplinary development of a diverse cohort of PhD and undergraduate students at Rice University and Carnegie Institution for Science located on the Stanford University campus. This project will also support the development of a summer REU focused on bioinformatics, ecology, evolution, and microbiology, and will also include summer training opportunities for graduate students with collaborators at the Pasteur Institute in Paris, France. This collaborative, multidisciplinary project presents an innovative plan for combining education and outreach activities to achieve real and lasting impact. All software developed will be made available on open-source code repositories. The overall goal of the project is to understand how microbiomes evolve and adapt, especially with respect to the environment and to perturbations. To meet these goals, the team of researchers will introduce methodological advances at the intersection of graph theory, formal languages, and machine learning to identify rules governing gene duplication and horizontal gene transfer from metagenomic sequencing data. In Aim 1, the project will construct and correlate species abundance networks with genome exchange networks through genome assembly graphs. In Aim 2, the project will produce a stochastic graph grammar framework for modeling network evolution over time, as well as unveil new rules with deep graph generators. In Aim 3, genome-level simulations will be combined with the genomic events identified in Aims 1 and 2 to assess the evolutionary benefits of these biological processes and their associated rates. The final project aim will focus on characterizing cooperation and resiliency through the study of graph grammars and network analysis techniques. The open-source computational methods developed across all four project aims will be evaluated on four distinct metagenomic datasets, including both environmental and host-associated microbiomes. Finally, a wide variety of metagenomic datasets will be used to show generalizability of the computational approaches to host-associated microbiomes.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.

水生生态系统中的微生物群落对于维持这些重要环境的弹性至关重要。这些密集的微生物种群具有丰富的物种多样性，并且由于基因复制和水平基因转移等过程，它们的基因组是动态的。尽管这些进化过程导致了广泛的基因组流动，微生物群落仍能合作并保持稳定的相互作用，并且对环境扰动具有弹性。该项目旨在开发一套计算工具，用于破译黄石公园温泉微生物垫或生物膜的合作和弹性规则。这些经过充分研究的环境非常适合研究温度、光照和营养物质等关键环境参数如何影响微生物种群的多样性、丰度和进化。 将开发新的计算工具来识别控制相关生物过程的规则。了解微生物组如何进化和适应，特别是在环境和扰动方面，对于理解生命的许多方面至关重要。因此，该项目获得的结果将对生物学、健康、保护工作以及动植物管理产生重大影响。 此外，这项研究还将支持位于斯坦福大学校园的莱斯大学和卡内基科学研究所的多元化博士和本科生群体的跨学科发展。该项目还将支持重点关注生物信息学、生态学、进化和微生物学的夏季 REU 的开发，还将为研究生与法国巴黎巴斯德研究所的合作者提供夏季培训机会。这个协作的多学科项目提出了一个创新计划，将教育和外展活动结合起来，以实现真正和持久的影响。所有开发的软件都将在开源代码存储库中提供。该项目的总体目标是了解微生物组如何进化和适应，特别是在环境和扰动方面。为了实现这些目标，研究团队将引入图论、形式语言和机器学习交叉领域的方法论进展，以从宏基因组测序数据中识别控制基因复制和水平基因转移的规则。在目标 1 中，该项目将通过基因组组装图构建物种丰度网络与基因组交换网络并将其关联起来。在目标 2 中，该项目将生成一个随机图语法框架，用于对网络随时间演化进行建模，并通过深度图生成器揭示新规则。在目标 3 中，基因组水平模拟将与目标 1 和 2 中确定的基因组事件相结合，以评估这些生物过程的进化效益及其相关速率。最终的项目目标将侧重于通过图语法和网络分析技术的研究来表征合作和弹性。跨所有四个项目目标开发的开源计算方法将在四个不同的宏基因组数据集上进行评估，包括环境和宿主相关的微生物组。最后，将使用各种宏基因组数据集来展示宿主相关微生物组计算方法的普遍性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams

应用于 TB 级基因组序列流的一次性多样化采样

• 发表时间: 2022-07
• 期刊: Proceedings of Machine Learning Research
• 作者: Coleman, Benjamin;Geordie, Benito;Chou, Li;Elworth, RA Leo;Treangen, Todd;Shrivastava, Anshumali
• 通讯作者: Shrivastava, Anshumali

KOMB: K-core based de novo characterization of copy number variation in microbiomes

KOMB：基于 K 核心的微生物组拷贝数变异的从头表征

• DOI: 10.1016/j.csbj.2022.06.019
• 发表时间: 2022
• 期刊: Computational and Structural Biotechnology Journal
• 影响因子: 6
• 作者: Advait Balaji;Nicolae Sapoval;Charlie T. Seto;R. A. Leo Elworth;Yilei Fu;Michael G. Nute;T. Savidge;Santiago Segarra;T. Treangen
• 通讯作者: T. Treangen

Bakdrive: identifying a minimum set of bacterial species driving interactions across multiple microbial communities

Bakdrive：确定驱动多个微生物群落相互作用的最小细菌种类集

• DOI: 10.1093/bioinformatics/btad236
• 发表时间: 2023-06-30
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Qi Wang;Michael G. Nute;T. Treangen
• 通讯作者: T. Treangen

Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data

鸸鹋：全长 16S rRNA Oxford Nanopore 测序数据的物种水平微生物群落分析

• DOI: 10.1038/s41592-022-01520-4
• 发表时间: 2022-07
• 期刊: Nature Methods
• 影响因子: 48
• 作者: Curry, Kristen D.;Wang, Qi;Nute, Michael G.;Tyshaieva, Alona;Reeves, Elizabeth;Soriano, Sirena;Wu, Qinglong;Graeber, Enid;Finzer, Patrick;Mendling, Werner;Savidge, Tor;Villapol, Sonia;Dilthey, Alexander;Treangen, Todd J.
• 通讯作者: Treangen, Todd J.

KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome

KombOver：基于高效 k 核和 K 桁架的人类肠道微生物组扰动表征

• DOI: 10.1142/9789811286421_0039
• 发表时间: 2023-12
• 期刊: Pacific Symposium on Biocomputing 2024
• 作者: Sapoval, Nicolae;Tanevski, Marko;Treangen, Todd J.
• 通讯作者: Treangen, Todd J.