Grant Awarded To Understand Tuberculosis Susceptibility

by Mary Martialay on March 1, 2019

The tuberculosis bacteria—Mycobacterium tuberculosis—infects more than 2 billion people each year. Some will develop full-blown tuberculosis, while others, although infected, never develop symptoms of the disease. The population is divided among people who are resistant, susceptible, and super susceptible, and no one is exactly sure why.

With a grant from the National Institutes of Health (NIH), a team including Rensselaer Polytechnic Institute researcher Bulent Yener will search for markers—genetic, phenotypic, and pathological—that distinguish the three groups. Yener, a professor of computer science and director of the Data Science Research Center, joins lead researchers at Tufts University, and researchers at Wake Forest University, in the five-year $3.3 million project, supported by an R01 award from the National Heart, Lung, and Blood Institute of the NIH.

Yener developed “cell graphs,” a computational method that combines image processing with graph theory to reveal the function of cells in tissue based on microstructural images of the tissue. Where a pathologist looking at a tissue sample may be able to detect five to 10 patterns, the computer finds more than 100 features invisible to the human eye, allowing it to more quickly and accurately classify the function of tissue using the cell graph method.

Yener explained this method in a Communication of the Association of Computing Machinery video that can be found here.

In the tuberculosis research, the team will study Diversity Outbred mice, a population with abundant genetic diversity and variety similar to the human population, as a model for the possible outcomes of infection with Mycobacterium tuberculosis. After grouping results into the three categories of resistant, susceptible, and super susceptible, the team will search for markers and produce predictive models based on the markers they identify.

Preliminary research, funded with an earlier NIH R 21 grant, identified some promising markers among super-susceptible mice, including a 10-protein lung biomarker signature, and a pattern of cell granulomas with neutrophils and necrosis. They also found that weight loss is correlated with severity of the reaction.

Yener will analyze tissue images using the cell graphs technique he developed. Yener will also use the images and other data gathered from experiments to establish which features are most relevant to classification, producing signatures that he will use to build and validate predictive models.