Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable

• Published in: Nature communications Vol. 7; no. 1; p. 11660
• Main Authors: Barton, John P., Goonetilleke, Nilu, Butler, Thomas C., Walker, Bruce D., McMichael, Andrew J., Chakraborty, Arup K.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.05.2016; Nature Publishing Group; Nature Portfolio
• Subjects: 631/250/1933; 631/250/2152/1566; 631/250/255/1901; 631/326/596/2554; Entropy; Evolution; Evolution, Molecular; Female; Genetic Fitness; HIV; HIV - genetics; HIV - immunology; HIV Infections - immunology; HIV Infections - virology; Human immunodeficiency virus; Human Immunodeficiency Virus Proteins - genetics; Humanities and Social Sciences; Humans; Immune system; Immunity, Cellular; Infections; Male; Models, Genetic; multidisciplinary; Mutation; Pathogens; Polyproteins - genetics; Science; Science (multidisciplinary); Statistical physics; Vaccines; Viruses; United States > US; Massachusetts
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms11660

Human immunodeficiency virus (HIV) evolves within infected persons to escape being destroyed by the host immune system, thereby preventing effective immune control of infection. Here, we combine methods from evolutionary dynamics and statistical physics to simulate in vivo HIV sequence evolution, predicting the relative rate of escape and the location of escape mutations in response to T-cell-mediated immune pressure in a cohort of 17 persons with acute HIV infection. Predicted and clinically observed times to escape immune responses agree well, and we show that the mutational pathways to escape depend on the viral sequence background due to epistatic interactions. The ability to predict escape pathways and the duration over which control is maintained by specific immune responses open the door to rational design of immunotherapeutic strategies that might enable long-term control of HIV infection. Our approach enables intra-host evolution of a human pathogen to be predicted in a probabilistic framework. HIV evolves within infected persons to escape being destroyed by the immune system. Here, Barton et al . combine evolutionary dynamics and statistical physics to simulate this process, successfully predicting the relative rate and location of escape mutations in viral sequences for a cohort of HIV-infected persons.