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

Abstract

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.

Inference of epistatic effects leading to entrenchment and drug resistance in HIV-1 protease

“…doi:10.1093/molbev/msx095 MBE although very important, the information conveyed by pairs of primary and accessory mutations only tells a small part of the story; the context of the full sequence background is really necessary to understand how primary resistance mutations become stabilized. The results presented here advance recent work in the field, using Potts models to study HIV-1 evolution (Barton et al 2016b;Butler et al 2016), by providing systematic prospective predictions quantifying the influence of specific multi-residue patterns on the tolerance of drug resistance mutations. Recent publications have reported that mutations near or distal to Gag cleavage sites play a role in promoting cleavage by drug-resistant and enzymatically deficient proteases, by selecting for mutations that increase substrate contacts with the protease active site, altering the flexibility of the cleavage site vicinity, or by as of yet unknown mechanisms Kolli et al 2009;Breuer et al 2011;Parry et al 2011;Fun et al 2012;Flynn et al 2015).…”

“…The primary mutations are destabilizing in the context of the wildtype background, but become stabilizing on average as other resistance mutations accumulate in the background, similar to the concept of entrenchment in systems biology (Pollock et al 2012;Gong et al 2013;Shah et al 2015). Furthermore, we find that entrenchment is modulated by the collective effect of the entire sequence, including mutations at polymorphic residues, and the variance of the statistical energy cost of introducing a primary mutation increases as resistance mutations accumulate; this heterogeneity is another manifestation of epistasis (McCandlish et al , 2016Barton et al 2016b). These findings provide a framework for exploring mutational resistance mechanisms using probabilistic models.…”

“…The correlations induce epistatic effects, a primary or accessory resistance mutation can be either stabilizing or destabilizing depending on the genetic background. Recently epistasis has become a focus for analysis in structural biology and genomics as researchers have begun to successfully link the coevolutionary information in collections of protein sequences with the structural and functional fitness of those proteins (Hinkley et al 2011;Ferguson et al 2013;Mann et al 2014;Figliuzzi et al 2015;Hopf et al 2017;Barton et al 2016b;Butler et al 2016). In the current study, we have used the correlated mutations encoded in a MSA of drug-experienced HIV-1 protease sequences to parametrize a Potts model of sequence statistical energies that can be used as an estimator of stability and relative replicative capacity of individual protease sequences containing drug resistance mutations.…”

“…Recently, probabilistic models, called Potts models, have been used to assign scores to individual protein sequences which correlate with experimental measures of fitness (Haq et al 2012;Ferguson et al 2013;Mann et al 2014;Figliuzzi et al 2015;Hopf et al 2017). These advances build upon previous and ongoing work in which Potts models have been used to extract information from sequence data regarding tertiary and quaternary structure of protein families (Weigt et al 2009;Morcos et al 2011Morcos et al , 2014Marks et al 2012;Sulkowska et al 2012;Sutto et al 2015;Barton et al 2016a;Haldane et al 2016;Jacquin et al 2016) and sequencespecific quantitative predictions of viral protein stability and fitness (Haq et al 2012;Shekhar et al 2013;Barton et al 2016b;Butler et al 2016).…”

“…Further, phylogenetic corrections based on pairwise sequence similarity cut-offs of 40% of sequence length or more which are common in studies utilizing direct coupling analysis (DCA) (Weigt et al 2009;Morcos et al 2011Morcos et al , 2014 of protein families would drastically reduce the number of effective sequences in our MSA and would lead to mischaracterization of the true underlying mutational landscape. We note that Potts models of other HIV-1 protein sequences under immune pressure have been parameterized with no phylogenetic corrections Mann et al 2014;Barton et al 2016b). …”