Covariance Network Analysis Predicts Hepatitis Outcomes
Differences in network connections identify hepatitis C patients who respond to antiviral therapy
FRIDAY, Dec. 26 (HealthDay News) -- In patients with hepatitis C infection, analyzing genome-wide virus amino acid covariance networks can predict response to treatment with interferon-alpha and ribavirin, according to a report published online Dec. 22 in the Journal of Clinical Investigation.
Rajeev Aurora, Ph.D., of the Saint Louis University School of Medicine, and colleagues analyzed amino acid covariance within the full viral coding region of virus sequences obtained from 94 patients who subsequently underwent treatment.
The researchers found the existence of genome-wide networks of covarying amino acids, and found that the connections within the networks differed in responders and non-responders, with non-responders having three times as many hydrophobic amino acid pairs. After detecting patterns within the networks, they were able to predict treatment outcomes with greater than 95 percent coverage and 100 percent accuracy.
"Furthermore, the hub positions in the networks are attractive antiviral targets because of their genetic linkage with many other positions that we predict would suppress evolution of resistant variants," the authors conclude. "Finally, covariance network analysis could be applicable to any virus with sufficient genetic variation, including most human RNA viruses."