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.
