The immune system’s attacks on HIV leave their marks—genetic mutations that show how the virus responded. By studying these marks systematically, pairing genome-wide information from hosts and pathogens, researchers have created the first map of human HIV resistance. Ultimately, this information may point to new therapeutic targets and enable individualized treatment strategies.
To draw a map of human HIV resistance, the researchers had to analyze an enormous amount of data. They studied various strains of HIV from 1,071 seropositive individuals and crossed more than 3,000 potential mutations in the viral genome with more than 6 million variations in the patients’ genomes. Using supercomputers, the researchers studied all these possible combinations, testing for associations between host DNA polymorphisms, HIV-1 sequence variation and plasma viral load, while considering human and viral population structure.
The researchers who carried out this work are centered at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and the Vaud university hospital center (UNIL-CHUV), and they published their results October 29 in eLife, in a paper entitled “A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control.”
Besides facing a daunting amount of data detailing HIV’s conflicts with human hosts, the researchers also had the challenge of establishing a baseline. “We had to study the virus before the patient had undergone treatment, which is far from easy,” said Jacques Fellay, Ph.D., co-author of the study and EPFL researcher. This meant the researchers had to search in data banks established in the 1980s, before effective therapies were made available.
After using their pairing approach to map host genetic pressure on the HIV-1 genome, the researchers turned their attention to association signals. The strongest signals, they found, occurred between human SNPs tagging HLA class I alleles and viral mutations in their corresponding cytotoxic T lymphocyte (CTL) epitopes. “Additional association signals,” the authors wrote, “were observed outside of optimally defined CTL epitopes, which could indicate novel epitopes, or represent secondary (compensatory) mutations.”
In evaluating their results, the researchers suggested that their approach not only allows a better understanding of how people defend themselves from HIV’s attacks, but also how the virus adapts to our defense mechanisms. “We now have a true database that tells us which human genetic variation will induce which kind of mutation in the virus,” explained Amalio Telenti, M.D., Ph.D., co-author and UNIL-CHUV researcher.
This genomics research has two major implications. New therapies could be developed based on studying humans’ natural defenses, particularly those that result in a reduced replication of the virus. In addition, the scientists hope that by profiling the genome of HIV-infected individuals, it will be possible to develop individually targeted treatments that take into account the patients’ genetic strengths and weaknesses.
The researchers also expressed optimism that their approach—gathering of paired host-pathogen data to identify sites of genomic conflict—will be useful in other contexts: “Comparable approaches are immediately applicable to explore other important infectious diseases, as long as polymorphic host factors exert sufficient selective pressure to trigger escape mutations in the pathogen.”
Also, specifically with respect to HIV/AIDS, the authors venture that pathogen sequence variation can be more powerful than clinical and laboratory outcomes to identify some host factors. By using viral variation for identifying host factors, the researchers found that association signals are much stronger for HIV-1 sequence variants than viral load, reflecting the “intermediate phenotype” nature of viral variation.