Big Data

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A team of researchers from the University of Maryland School of Medicine, University of Buffalo and the Yale University used data mining in studying how the bacteria Haemophilus influenzae can adapt quickly to its environment, according to Newswise.

The microbe is particularly dangerous to people suffering from chronic obstructive pulmonary disease, with as many as 12 million American adults diagnosed with the disease every year, and 120,000 individuals dying from it yearly. Identifying the genetic variations of this microorganism is important in treating COPD patients effectively.

Herve Tettelin, a researcher from UMSOM, said they wanted to find out why certain strains of H. influenzae is more virulent than others. The severity depends on which genes are activated and which are not. Certain genetic patterns allow H. influenzae to adapt more efficiently to human lungs and cause more damage. Tettelin said they were able to discover a genetic pattern that may explain the virulence, and may offer a hint at what it does to evolve in the lungs of people suffering from COPD. The newly-discovered genetic pattern also opens up avenues for developing treatments and vaccines for the future.

The use of data mining enabled the researchers to gain a better understanding of how certain strains of H. influenzae adapt to the lungs. They relied on data mining to analyze how genomic isolates look like when Haemophilus is contracted by a patient and how the isolates look like when they are expelled from the lungs.

The researchers discovered that much of the genetic variation is random, and a subset of the genes is constantly being turned off and on. Some of the mutations are useful to the bacteria and are conserved. On the other hand, mutations that are not used by the bacteria are eliminated.