CAREER: Scalable approaches for systems virology

职业：系统病毒学的可扩展方法

• 批准号: 2047598
• 负责人: Karthik Anantharaman
• 金额: $ 99.94万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047598&HistoricalAwards=false
• 关键词: CAREER; Scalable; approaches; systems; virology

Viruses are the most abundant biological entities on Earth and are keystone components of environments and microbiomes whose contributions have mostly been overlooked. Understanding viruses is critical to the study and applications of microbiomes in diverse fields such as agriculture, medicine, biotechnology, ecosystem science, oceanography and biogeochemistry. In spite of this recognized importance, computational tools for studying viruses are lacking compared to similar tools for other microbes like bacteria. To that end, the goals of this project are to develop new machine learning-based approaches for the study of viruses and their ecology. The project has the potential to transform the study of microbiomes and the field of viral ecology by maximizing information gained from viruses and elucidating their roles in nature. The current SARS-CoV-2 epidemic is projected to vastly increase student interest in STEM and specifically, virology in the coming years. In parallel, there is an increasing demand for a workforce adept in bioinformatics and data science approaches in biology. The project aims to advance the development of a talented, educated, and skilled workforce and increase participation of underrepresented minorities and first-generation college students in STEM. The project will also increase literacy of virology and data science across K-12 and undergraduate education through teacher-training workshops & course-based undergraduate research experiences (CUREs) at the interface of bioinformatics, data science, and virology.This project will develop algorithms and bioinformatic tools to enable the study of uncultivated viruses from mixed communities with little to no biases (bacterial/archaeal/eukaryotic, DNA/RNA, lytic/lysogenic). The goals of the project are to develop new genome and protein databases and machine learning approaches to identify viruses; network-based frameworks for reference-free prediction of viral hosts; and network and statistical approaches for determination of viral taxonomy and estimates of genome completion. These methods will be validated using simulated and real-world metagenomics and metatranscriptomics data and formalized through the development and release of open access databases and software. The approaches will be applied to study viral ecology of deep-sea hydrothermal ecosystems, and the role of viral infections in impacting nutrient cycling in the oceans. Additionally, the project will also enable investigation of fundamental questions in viral ecology governing the roles of viruses in diverse microbiomes and environments such as soils, human health, freshwater, and marine systems. Two CUREs will be developed in virus cultivation and bioinformatics, respectively, using novel interactive education approaches including blended learning, and are expected to reach in excess of 1000 students. A teacher-training workshop “Viruses in nature” will be conducted to train K-12 biology teachers (especially from rural and underrepresented communities). The workshop will develop lesson plans, and hands-on laboratory activities that integrate concepts of virology and bioinformatics into teaching units on biology. For more information visit: https://github.com/AnantharamanLab/NSF_CAREER.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.

病毒是地球上最丰富的生物实体，也是环境和微生物组的关键组成部分，其贡献大多被忽视。了解病毒对于微生物组在农业、医学、生物技术、生态系统科学、海洋学和生物地球化学等不同领域的研究和应用至关重要。尽管其重要性已得到公认，但与细菌等其他微生物的类似工具相比，仍然缺乏用于研究病毒的计算工具。为此，该项目的目标是开发新的基于机器学习的方法来研究病毒及其生态。该项目有潜力通过最大限度地利用从病毒中获得的信息并阐明它们在自然界中的作用来改变微生物组和病毒生态学领域的研究。当前的 SARS-CoV-2 流行预计将在未来几年大大提高学生对 STEM，特别是病毒学的兴趣。与此同时，对精通生物学生物信息学和数据科学方法的劳动力的需求不断增加。该项目旨在促进有才华、受过良好教育和技术熟练的劳动力队伍的发展，并增加代表性不足的少数族裔和第一代大学生对 STEM 的参与。该项目还将通过生物信息学、数据科学和病毒学接口的教师培训研讨会和基于课程的本科生研究经验 (CURE) 来提高 K-12 和本科生教育中的病毒学和数据科学素养。该项目将开发算法和生物信息学工具，以便能够研究来自混合群落的未培养病毒，几乎没有偏见（细菌/古菌/真核、 DNA/RNA，裂解/溶原）。该项目的目标是开发新的基因组和蛋白质数据库以及机器学习方法来识别病毒；基于网络的病毒宿主无参考预测框架；用于确定病毒分类和估计基因组完整性的网络和统计方法。这些方法将使用模拟和现实世界的宏基因组学和宏转录组学数据进行验证，并通过开放获取数据库和软件的开发和发布来形式化。这些方法将用于研究深海热液生态系统的病毒生态学，以及病毒感染在影响海洋营养物循环中的作用。此外，该项目还将研究病毒生态学的基本问题，这些问题涉及病毒在不同微生物组和环境（如土壤、人类健康、淡水和海洋系统）中的作用。两个 CURE 项目将分别在病毒培养和生物信息学领域开发，采用包括混合学习在内的新型互动教育方法，预计将覆盖超过 1000 名学生。将举办“自然界的病毒”教师培训研讨会，以培训 K-12 生物教师（特别是来自农村和代表性不足社区的教师）。该研讨会将制定课程计划和实验室实践活动，将病毒学和生物信息学的概念融入生物学教学单元。欲了解更多信息，请访问：https://github.com/AnantharamanLab/NSF_CAREER。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

The expanding Asgard archaea invoke novel insights into Tree of Life and eukaryogenesis

• DOI: 10.1002/mlf2.12048
• 发表时间: 2022-12
• 期刊: mLife
• 作者: Zhichao Zhou;Yang Liu;Karthik Anantharaman;Meng Li

Virus genomics: what is being overlooked?

• DOI: 10.1016/j.coviro.2022.101200
• 发表时间: 2022-04
• 期刊: Current opinion in virology
• 影响因子: 5.9