Monday, October 23, 2017

CDC funds Emory project to automate analysis of mixed strains of antibiotic-resistant bacteria

An electron micrograph shows human immune system cells attacking methicillin-resistant Staphylococcus aureus (MRSA). MRSA is an example of antibiotic-resistant bacteria that can occur in multiple strains in an infection, further complicating diagnosis, treatment and interventions.

By Carol Clark

The Centers for Disease Control and Prevention (CDC) awarded $380,000 to three Emory University faculty to develop and refine a promising technique to detect and respond to threats from drug-resistant pathogens.

The grant investigators include Lars Ruthotto and Ymir Vigfusson — both assistant professors in the Department of Mathematics and Computer Science — and Rebecca Mitchell, a visiting professor with a joint appointment in the Department of Mathematics and Computer Science and the Nell Hodgson Woodruff School of Nursing. 

The trio is developing a method to quickly and cost-effectively diagnose multiple strains of antibiotic-resistant bacteria within a single biological sample.

“This project harmonizes our different scientific specialties,” Vigfusson says. He is a computer scientist who develops software and programming algorithms that work at scale, while Ruthotto is a mathematician who focuses on solving inverse problems. Mitchell is a veterinarian and epidemiologist experienced in gathering biological samples and testing them for pathogens. 

Antibiotic-resistant infections are a growing national and global problem, causing at least two million illnesses and 23,000 deaths in the United States annually, according to the CDC.

The Emory grant is part of a $9 million package of CDC funding announced today, including awards to projects at 25 leading research institutions around the country that are exploring gaps in knowledge about antibiotic resistance and piloting innovative solutions in the healthcare, veterinary and agriculture industries. The work complements broader CDC efforts to support known strategies for protecting people and slowing antibiotic resistance, collectively known as the CDC Antibiotic Resistance Solutions Initiative.

The Emory project seeks to tame the complexity of analyzing multiple infections within a biological specimen, from a drop of blood to a fecal sample. In the case of a widespread outbreak of antibiotic-resistant E. coli for instance, it would be useful to quickly determine whether fecal samples contained multiple strains of the bacteria and what those strains were, in order to more rapidly trace the sources of the outbreak and design effective interventions.

It is costly and labor-intensive, however, to culture biological samples at the local level, and then send them to the CDC for testing. And if multiple strains of a pathogen are within a single sample, only some strains that are present may grow in the culture while other strains may be missed.

“It’s a challenge to deal with samples containing mixed strains of a pathogen in a lab setting,” Mitchell says. “You have to do a large amount of work to get the finer gradations of what species of pathogens are present, and in what proportions.”

The Emory researchers are striving to balance accuracy with the need to simplify and streamline the process. Their method eliminates labor-intensive, technical steps, such as culturing the sample. “We want to automate the process so that you need less expertise at the local level, and so that data coming from individual states can be easily integrated into a central system,” Mitchell explains.

They use multiple short polymorphic regions in the genome to look for genetic variations among the DNA templates present within a biological sample. In the case of antibiotic-resistant bacteria, the number of reference sites ranges between the hundreds to the thousands, depending on the specific bacteria targeted.

“We’ve developed an algorithm and software and mathematical models to rapidly run these comparisons and estimate the number of strains in a single sample, and the percentage of each,” Ruthotto says. “Now we are trying to quantify the accuracy of this estimate, which is a mathematical challenge. The grant gives us the resources to refine our method for real-world applications.”

The ultimate goal is to develop a system that will work not just on antibiotic-resistant bacteria, but for mixed-strains of any pathogen within a biological sample. In a separate project, for example, Mitchell and Vigfusson are applying the method to test for multiple strains of the malaria parasite within a blood sample.

“Quickly teasing apart mixed-strain samples is a big challenge in public health, and it’s essential in order to plan effective interventions,” Mitchell says.

“We’re using math and computer science to draw more information from a single biological sample than was previously practical,” Vigfusson says. “We hope that our method could turn into a work engine that helps to understand multiple-strain infections and makes an impact on public health.”

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