Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease

Abstract

Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

Limits to detecting epistasis in the fitness landscape of HIV

“…P ( S ) describes the “prevalence” landscape of a protein and the marginals of P ( S ) can be compared with observed frequencies in a multiple sequence alignment. Previous studies have indicated that the Potts model is an accurate predictor of “prevalence” in HIV proteins [ 20 , 21 , 23 , 32 – 36 ]; “prevalence” is often used as a proxy for “fitness” with covariation models serving as a natural extension for measures of “fitness” based on experiments and model predictions have been compared to different experimental measures of “fitness” with varying degrees of success [ 1 , 21 , 23 , 28 , 32 , 34 , 36 ]. Site-independent models, devoid of interactions between sites have also been reported to capture experimentally measured fitness well, in particular for viral proteins [ 1 , 31 ] with studies (on HIV Nef and protease) implying that the dominant contribution to the Potts model predicted sequence statistical energy comes from site-wise “field” parameters h i (see Methods ) in the model [ 28 , 36 ].…”

“…Previous studies have indicated that the Potts model is an accurate predictor of “prevalence” in HIV proteins [ 20 , 21 , 23 , 32 – 36 ]; “prevalence” is often used as a proxy for “fitness” with covariation models serving as a natural extension for measures of “fitness” based on experiments and model predictions have been compared to different experimental measures of “fitness” with varying degrees of success [ 1 , 21 , 23 , 28 , 32 , 34 , 36 ]. Site-independent models, devoid of interactions between sites have also been reported to capture experimentally measured fitness well, in particular for viral proteins [ 1 , 31 ] with studies (on HIV Nef and protease) implying that the dominant contribution to the Potts model predicted sequence statistical energy comes from site-wise “field” parameters h i (see Methods ) in the model [ 28 , 36 ]. In this study, we show that interaction between sites is necessary to capture the higher order (beyond pairwise) mutational landscape of HIV proteins for functionally relevant sites, such as those involved in engendering drug resistance, and cannot be predicted by a site-independent model.…”

HIV Protease: Historical Perspective and Current Research

“…Resistance to PR inhibitors arises primarily by mutations in PR, although other mutations also occur in its Gag and Gag-Pol substrates [ 69 ]. Major mutations associated with resistance are often deleterious for viral replication [ 70 ]; however, viral fitness can be restored by additional, compensatory mutations [ 71 , 72 ]. The molecular mechanisms observed for PRs bearing single major mutations were reviewed in [ 73 ].…”

Deep Mutational Scanning of Viral Glycoproteins and Their Host Receptors

“…Deep mutagenesis data sets have inherent noise from multiple sources: insufficient experimental sampling of variants in the library, errors with accurately replicating collection gates associated with FACS-based selections, low signal-to-noise, and/or selections that are not properly stringent to discriminate between mutants of differing activities. Furthermore, epistatic interactions are often only assessed between a small number of sites to keep library diversity manageable (Wu et al, 2017a(Wu et al, , 2020Zhang et al, 2020) or are missed entirely in mutational scans based on single amino acid substitutions. As an alternative, statistical and computational methods are increasingly capable of accurately predicting mutational effects.…”

“…The model can be improved if additional structure in sequence families, due to higher order epistasis not captured by pairwise constraints, is considered (Riesselman et al, 2018). The methods have accurately (and impressively) captured aspects of viral protein evolution in the clinic, especially for HIV-1, including drug resistance and escape by HIV-1 proteins from cellular and humoral immunity (Ferguson et al, 2013;Mann et al, 2014;Barton et al, 2016;Flynn et al, 2017;Louie et al, 2018;Biswas et al, 2019;Zhang et al, 2020). The pairwise couplings are critical to model accuracy, and the likelihood of an escape mutation occurring is heavily influenced by epistatic interactions with the background sequence (Barton et al, 2016).…”