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SLU Team IDs Covariance Networks Predicting Hep C Treatment Response

NEW YORK (GenomeWeb News) – Amino acid covariance networks — maps of amino acids that vary together — can help predict which individuals will respond to existing hepatitis C virus treatments, new research suggests.
 
In a paper appearing online last night in the Journal of Clinical Investigation, researchers from Saint Louis University School of Medicine used covariance analysis to assess amino acid sequences of hepatitis C viruses isolated from nearly 100 patients. By integrating the covariance data into networks, they identified patterns coinciding with treatment response that may not only reveal new aspects of hepatitis C biology, but could potentially yield biomarkers for predicting patient response to existing therapies.
 
“[W]e found that genome-wide networks of co-varying amino acids existed, that the connections within the networks … were different in the responders and non-responders, and that the non-responder network had more hydrophobic amino acid pairs than did the responder networks,” senior author John Tavis, a molecular microbiology and immunology researcher at Saint Louis University’s Liver Center, and his colleagues wrote.
 
Nearly four million Americans and an estimated 130 million people around the world are chronically infected with hepatitis C. The virus not only causes liver inflammation (hepatitis) and damage, but also ups an individual’s risk of developing liver cancer. In the US, hepatitis C is linked to as many as ten thousand deaths annually.
 
The virus is generally treated with 24 to 48 weeks of pegylated interferon and ribavirin, an expensive and intense treatment with potential side effects including flu-like symptoms and autoimmune disorders. Roughly half of individuals carrying genotype 1 hepatitis C — the most common form of hepatitis C in the US — respond to the treatment.
 
In an effort to understand how hepatitis C viral variation affects treatment outcomes, the researchers isolated pre-treatment genotype 1a and 1b hepatitis C virus samples from 94 infected individuals enrolled in SLU’s Viral Resistance to Antiviral Therapy of Chronic Hepatitis C clinical study.
 
The patients were initially stratified based on their treatment response after 28 days of therapy. By the time the researchers got to the point of analyzing covariance data and its relationship to treatment response, though, all of the patients had completed their therapy, Tavis told GenomeWeb Daily News.
 
Tavis said the team first examined sequence alignments of the viral genomes but found that all of the positions in the alignment were not independent. Instead, they found that sequences at certain positions tended to vary together. By examining this covariance, they speculated that it might be possible to gain insights into treatment-specific selective pressures on hepatitis C.
 
The basic concept of covariance analysis is not new, Tavis emphasized. It has been widely used for a variety of other applications, including analysis of protein interactions. But, he said, as far as he knows, this particular application of covariance analysis is novel.
 
Because the overall number of co-varying positions did not correlate with treatment response, the team used clustering analysis to develop networks from the covariance they observed. Within these networks, the researchers were able to pick out hub amino acids, which were more likely to vary with other amino acids, as well as sub-networks that were characteristic of treatment-resistant hepatitis C.
 
The nature of the covariance was also informative. For instance, the team found that patients who did not respond to treatment carried viruses with roughly three times as many hydrophobic amino acid pairs. Since such hydrophobic interactions can hike up protein stability, the researchers speculated that much of the hepatitis C amino acid covariance is involved in stabilizing protein-protein interactions.
 
The researchers noted that their covariance network approach could also prove useful for investigating the drug sensitivity and diagnostic and therapeutic characteristics of other RNA viruses. “This is a very generalizeable approach,” Tavis said, noting that he and his colleagues are currently gearing up to study hepatitis B virus, HIV, and some flaviviruses.
 
In a commentary article appearing in the same issue of JCI, Rockefeller University researchers Thomas Oh and Charles Rice suggested covariance network analysis could “not only reveal biomarkers for therapeutic outcome, but also shed light on the mechanistic bases for resistance to treatment and even identify novel targets for antiviral drugs.”
 
And while they noted that the results of the latest paper still need to be validated in follow-up studies, Oh and Rice expressed enthusiasm about the study. Oh, a graduate student in Rice’s lab, told GenomeWeb Daily News that at this point, it’s not clear if this work will definitely yield useful biomarkers, but he called it “an interesting and promising technique.”
 
The duo predicts a surge in such covariation analyses for hepatitis C and other RNA viruses as computational and sequencing technologies improve. “We look forward to seeing further application of covariance networks to questions ranging from protein structure and protein-protein interactions to drug resistance, host selection pressures, and viral evolution,” Oh and Rice concluded.
 
Indeed, Tavis and his colleagues view the work as a step towards developing markers for treatment response that could help those with resistant strains avoid the cost and discomfort of unnecessary treatment.
 
For his part, Tavis envisions a chip-based assay for interrogating 20 to 30 key amino acid positions from the network via the nucleic acid sequences coding for those amino acids. Because there are so many covariance biomarkers, Tavis said it should be possible to design an algorithm weighting covariance markers based on how informative they are — something he and his colleagues are in the process of doing.
 
The group has not started developing and commercializing a prognostic test, but Tavis said the team has had interest from a few undisclosed companies and is exploring possibilities in that area.
 

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