SCH: INT: Enabling real time surveillance of antimicrobial resistance

SCH：INT：实现抗菌药物耐药性的实时监测

• 批准号: 2013998
• 负责人: Christina Boucher
• 金额: $ 118.78万
• 依托单位: University of Florida
• 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: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2013998&HistoricalAwards=false
• 关键词: SCH; INT; Enabling; real; time

Antimicrobial resistance (AMR) refers to the ability of an organism to stop an antimicrobial (e.g., antibiotic) from working against it and has become a serious threat to public health since it causes antibiotics to be ineffective, resulting in outbreaks becoming more frequent, widespread, and severe. It is estimated that 2.8 million people per year in the United States are infected with resistant bacteria, and more than 35,000 of these infections are lethal. One manner to control these outbreaks is with real-time identification of AMR. Currently, the most effective method for identification of AMR is to apply high-throughput sequencing to a biological sample (e.g., nose swab or blood sample). Advancements in sequencing technology have shrunken the size of the devices so that they can fit into one hand, however the bioinformatics analysis – requires comparing millions or billions of DNA sequences -- has been limited to high performance computers that have significant memory and disk space. This, in turn, makes AMR identification limited in low-resource settings, such as rural areas of the U.S. This project will overcome the challenge of detection of AMR in rural areas by developing bioinformatics analysis methods for on-site, real-time detection of AMR using portable computing devices (such as phones and tablets). To realize this, the project will conceptualize and implement novel algorithms and interfaces due to computing limitations created by using portable computing devices. The outcome of this project will be a real-time portable identification of AMR, which can be used to dramatically increase the efficiency in which society can control and monitor outbreaks. In addition, these techniques will also help realize identification of viral species (such as COVID-19), which will assist in rapid diagnosis in areas with limited computing and sequencing resources. Lastly, an immediate outcome of the work will be research opportunities to under-served students through the Machen Florida Opportunity Scholars program, an organization that aims to foster the success of first-generation university scholars. For each year of the program, the investigators will work with the coordinator of the Machen program to recruit a student to be a research assistant and work hands-on the project with the investigators and their trainees. The goal of this project is to create mobile bioinformatics methods for on-site, real-time detection of AMR using Nanopore technology. The expected methods will work on-device, meaning they will only use the hardware (RAM, cache, hard disk, processors) on the portable device. In particular, the project will aim to: (1) create on-device methods to identify the bacteria in a biological samples; (2) create on-device methods to identify the AMR genes in a biological sample; and lastly, (3) evaluate the usability of the methods and prepare for their wide-spread dissemination. This will be accomplished by combining the recent advancements in cache-oblivious algorithms with that of space-efficient data structures. Briefly, cache-oblivious algorithms divide the input of a problem into smaller subsets so that each can be solved in cache and combined into a solution to the original problem. This proposal further brings advancements that will have impact beyond the stated application. Since portable devices pose significant computational challenges, including smaller memory, cache, hard disk, this work will result in novel algorithm and tool development that combine succinct data structures with cache oblivious approaches. Next, this work will advance the knowledge of AMR mechanisms. The use of antibiotics needs to be understood and preserved in order to ensure it is judicious. This project will contribute to acquiring such an understanding by detecting the drivers of AMR evolution, persistence and dissemination in real-time. Lastly, it will further the use of third sequencing technology that have broad application. One specific application of this work is the real-time detection of COVID-19 in areas that lack sequencing and computing facilities. Thus, this project will be the first in creating a benchwork-to-bedside bioinformatic system for detection of AMR and viral strains such as COVID. This will deepen the study of the technology, highlight specific areas of improvement and expansion, and have significant impact on public health.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.

抗菌素耐药性（AMR）是指生物体停止抗菌素（例如，抗生素）对它起作用，并已成为对公共卫生的严重威胁，因为它导致抗生素无效，导致爆发变得更加频繁，广泛和严重。 据估计，美国每年有280万人感染耐药细菌，其中超过35，000例感染是致命的。 控制这些疫情的一种方式是实时识别AMR。 目前，鉴定AMR的最有效方法是对生物样品（例如，鼻拭子或血液样品）。 测序技术的进步已经缩小了设备的尺寸，使它们可以适合一只手，但是生物信息学分析-需要比较数百万或数十亿的DNA序列-一直局限于具有大量内存和磁盘空间的高性能计算机。该项目将通过开发生物信息学分析方法，使用便携式计算设备（如手机和平板电脑）现场实时检测AMR，克服农村地区检测AMR的挑战。为了实现这一点，该项目将概念化和实现新的算法和接口，由于使用便携式计算设备创建的计算限制。 该项目的成果将是AMR的实时便携式识别，可用于显着提高社会控制和监测疫情的效率。 此外，这些技术还将有助于实现病毒种类（如COVID-19）的识别，这将有助于在计算和测序资源有限的地区进行快速诊断。 最后，这项工作的直接成果将是通过梅琴佛罗里达机会学者计划为服务不足的学生提供研究机会，该计划旨在促进第一代大学学者的成功。 对于该计划的每一年，调查人员将与Machen计划的协调员合作，招募一名学生担任研究助理，并与调查人员及其受训人员一起动手完成该项目。该项目的目标是创建移动的生物信息学方法，用于使用纳米孔技术进行AMR的现场实时检测。预期的方法将在设备上工作，这意味着它们将只使用便携式设备上的硬件（RAM、缓存、硬盘、处理器）。具体而言，该项目的目标是：（1）创建设备上的方法来识别生物样品中的细菌;（2）创建设备上的方法来识别生物样品中的AMR基因;最后，（3）评估方法的可用性并为其广泛传播做准备。这将通过将高速缓存无关算法的最新进展与空间高效数据结构相结合来实现。简单地说，缓存无关算法将问题的输入划分为较小的子集，以便每个子集可以在缓存中求解并组合成原始问题的解决方案。该提案进一步带来了将产生超出所述应用范围的影响的进步。由于便携式设备提出了重大的计算挑战，包括更小的内存，高速缓存，硬盘，这项工作将导致新的算法和工具的开发，结合联合收割机简洁的数据结构与高速缓存不经意的方法。接下来，这项工作将推进AMR机制的知识。抗生素的使用需要被理解和保存，以确保它是明智的。该项目将通过实时检测抗生素耐药性演变、持续和传播的驱动因素，为获得这种理解做出贡献。最后，进一步推广应用前景广阔的第三代测序技术。 这项工作的一个具体应用是在缺乏测序和计算设施的地区实时检测COVID-19。因此，该项目将是第一个创建用于检测AMR和COVID等病毒株的工作台到床边生物信息学系统的项目。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标准进行评估，被认为值得支持。

项目成果

KARGA: Multi-platform Toolkit for k-mer-based Antibiotic Resistance Gene Analysis of High-throughput Sequencing Data.

• DOI: 10.1109/bhi50953.2021.9508479
• 发表时间: 2021-07
• 期刊: ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics
• 作者: Prosperi M;Marini S
• 通讯作者: Marini S

Target-enriched long-read sequencing (TELSeq) contextualizes antimicrobial resistance genes in metagenomes.

• DOI: 10.1186/s40168-022-01368-y
• 发表时间: 2022-11-02
• 期刊: MICROBIOME
• 影响因子: 15.5
• 作者: Slizovskiy, Ilya B.;Oliva, Marco;Settle, Jonathen K.;Zyskina, Lidiya, V;Prosperi, Mattia;Boucher, Christina;Noyes, Noelle R.
• 通讯作者: Noyes, Noelle R.

Fast and exact quantification of motif occurrences in biological sequences.

• DOI: 10.1186/s12859-021-04355-6
• 发表时间: 2021-09-18
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Prosperi M;Marini S;Boucher C
• 通讯作者: Boucher C

Finding Maximal Exact Matches Using the r-Index.

使用 r 索引查找最大精确匹配。

• DOI: 10.1089/cmb.2021.0445
• 发表时间: 2022
• 期刊: Journal of computational biology : a journal of computational molecular cell biology
• 作者: Rossi,Massimiliano;Oliva,Marco;Bonizzoni,Paola;Langmead,Ben;Gagie,Travis;Boucher,Christina
• 通讯作者: Boucher,Christina

KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing.

• DOI: 10.3389/fbioe.2022.1016408
• 发表时间: 2022
• 期刊: FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
• 影响因子: 5.7
• 作者: Barquero, Alexander;Marini, Simone;Boucher, Christina;Ruiz, Jaime;Prosperi, Mattia
• 通讯作者: Prosperi, Mattia