Christina Boucher, Ph.D., associate professor in the UF Department of Computer & Information Science & Engineering (CISE), has received a $1.2 million grant from the National Science Foundation (NSF). The grant will give Dr. Boucher and her team the opportunity to develop a set of algorithms and an electronic interface that will allow public health investigators to test and analyze biological samples for antibiotic resistance in rural areas.
Antimicrobial resistance (AMR) refers to the ability of an organism to stop an antimicrobial (e.g., antibiotic) from working against it. AMR has become a serious threat to public health, as it causes antibiotics to be ineffective, resulting in microbial 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.
Moreover, according to a 2016 report by the National Academies of Medicine, antimicrobials for livestock account for 80% of the antimicrobials purchased in the U.S. Feeding sub-therapeutic concentrations of antibiotics to livestock causes them to grow bigger, faster, and less expensively. The fear is that this practice leads to bacteria in the guts and on the skin of livestock that become resistant to antibiotics, which are then passed on to humans.
One method of controlling AMR outbreaks is real-time identification of AMR. High-throughput sequencing technology has been proven to be effective in identification of AMR, but in the past both the technology and analysis were not portable. Now, advancements in sequencing technology have shrunken the size of the devices used so that they can fit into one hand, making the sequencing technology portable; but the analysis of the resulting data requires comparing millions or billions of DNA sequences. This analysis has been limited to high performance computers that have significant memory and disk space, limiting AMR identification in low-resource settings, such as rural areas.
Dr. Boucher’s research project will overcome the challenge of detection of AMR in rural areas by developing novel algorithms and interfaces for on-site, real-time detection of AMR using consumer portable computing devices such as smartphones and tablets. This will, in turn, lead to a completely portable system for AMR identification, which can be used in areas remote from large data analysis centers.
A multi-disciplinary team of researchers across UF will be joining Boucher in her efforts. Mattia Prosperi, Ph.D., associate professor, Department of Epidemiology, College of Public Health and Health Professions and UF College of Medicine, will be collaborating with Dr. Boucher on developing the algorithms and software.
Jaime Ruiz, Ph.D., a CISE associate professor, will be developing the interface between Dr. Boucher’s software and the program that will appear on the handheld devices in the field.
Kwang Cheol Jeong, Ph.D., associate professor of microbiology with a joint appointment in the Department of Animal Sciences at the UF Institute of Food and Agricultural Sciences (UF/IFAS) and the Emerging Pathogens Institute, researches the process by which bacteria infect and cause disease in a host. He will contribute to the algorithms to increase the efficiency with which they can analyze the data. Dr. Jeong will also facilitate interactions between the team and UF research farms during the trial phase of the software and interface on hand-held devices.
The outcome of this project will be a real-time portable identification of AMR, which can be used to dramatically increase the efficiency with which healthcare officials can control and monitor outbreaks. In addition, these techniques will help public health officials with identification of viral species, such as COVID-19, which will assist in rapid diagnosis in areas that typically have limited computing and sequencing resources.
Another outcome of the work will be research opportunities for underserved students through the Machen Florida Opportunity Scholars program, an organization that aims to foster the success of first-generation university scholars. During each year of this project, Dr. Boucher and her team will recruit a student from the program to be a research assistant to do hands-on work with the investigators. This effort will help develop a workforce trained to advance rapid resolution of AMR outbreaks.
Parts of the analysis solution are underway now. The full development of these novel algorithms and the interface to smartphones and tablets is expected to take about three years.
Engineering efficient answers
Public health officials typically comb through huge amounts of bacterial genomic data in an effort to hone in on the origin of a potentially drug-resistant outbreak. The size of genome sequence databases nowadays is staggering (approaching the range of a petabyte, equivalent to one quadrillion bytes of data), and it is always a race against time before the outbreak reaches a scale that becomes challenging to contain.
With a 2018 National Institutes of Health (NIH) grant, Dr. Boucher built a novel bioinformatics framework, developing computer algorithms that provide rapid and space-efficient means for analyzing very large data sets to determine how antimicrobial-resistant (AMR) genes evolve, grow, and persist in a system that has been affected by antibiotic use. Dr. Prosperi further developed machine-learning models to predict AMR features and outbreak location history from newly processed gene sequences.
To produce algorithms that can be used on ever-larger sets of data, they developed a novel means to create, compress, reconstruct and update very large graphs that display all possible outcomes. Dr. Prosperi established a process for querying the graphs of data, which helps locate the sources of pathogenic outbreaks and determine answers to other questions. This in turn facilitates the development of effective intervention methods that reduce resistant pathogens in agricultural and clinical settings.
Dr. Boucher explained, “With this NIH grant, we are applying our methods to samples collected from both agricultural and clinical settings in Florida. Analysis of preliminary and new data will allow us to draw conclusions about: (1) the public risk associated with antimicrobial use in agriculture; (2) the effectiveness of interventions used to reduce resistant bacteria, and (3) the factors that allow resistant bacteria to grow, thrive and evolve.”