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Understanding bacterial pathogens

Closeup of Catanese next to a network model.
Helen Catanese, a computer science graduate student, explains how a network model can improve understanding of bacterial pathogens.

Siddharth Vodnala, Voiland College of Engineering and Architecture

A concise network model developed by Washington State University researchers could help advance understanding of disease-causing bacteria and guide vaccine development.

Assefaw Gebremedhin, assistant professor in the School of Electrical Engineering and Computer Science, led a team of researchers in creating the network model to represent similarities between short DNA sequences.

The team, which included Kelly Brayton, professor of veterinary microbiology and pathology, and computer science graduate student Helen Catanese, used their network model to study the DNA of Anaplasma marginale, a widely distributed tick‑borne pathogen that can cause a fatal blood disorder in cattle.

A. marginale, like many other bacterial pathogens, has a huge variety of strains characterized by their surface protein variation. The team first used short repeating sequences of DNA, called repeats, from one of these varying surface proteins to characterize strains and build their network.

The researchers then used the network to understand the heredity and geographic distribution of the bacterial strains.

They created a visualization of their network, in addition to a graphical representation of the geographic distribution of those same repeats on a map. By comparing this to their network structure, they discovered a previously unknown relationship between how “central” a repeat is in the network and how geographically dispersed it is in the real world.

More “central” repeats – meaning repeats that lie in the pathways of most connections between other repeats in the network – were also found to be more widely distributed in A. marginalestrains around the world, ranging from North and South America all the way to Asia.

A paper detailing their work has appeared in the journal BMC Bioinformatics.

If the repeats that are structurally important in the network are also the most widely distributed geographically, they could play a role in how easily the pathogen can be transmitted from one animal to another.

“This intriguing finding suggests that these sequences may be functionally important for the organism’s ability to adapt” Gebremedhin said.

The work can spur future research on why certain repeat sequences became so geographically prevalent and help in targeting vaccine development efforts, he said.

The same modeling and analysis technique can also be extended to other pathogen species that have similar repeating DNA sequences.

Network models have been used to represent similarities among DNA sequence elements in the past, but the new model developed by the WSU team is sparser and more efficient to construct than existing network models, without losing key structural components. It maintains its properties even when a large percentage of the sequence data is missing or has unwanted disturbances.

Catanese also recently won the National Academies’ Board of Mathematical Sciences and Analytics’ Elevating Mathematics Video Competition for the video she developed to explain her work.

This research was funded by Gebremedhin’s National Science Foundation CAREER award.

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