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

Abstract

23Drug-resistant mutations often have deleterious impacts on replication fit-24 ness, posing a fitness cost that can only be overcome by compensatory muta-25 tions. However, the role of fitness cost in the evolution of drug resistance has 26 often been overlooked in clinical studies or in vitro selection experiments, as 27 these observations only capture the outcome of drug selection. In this study, 28 we systematically profile the fitness landscape of resistance-associated sites in 29 HIV-1 protease using deep mutational scanning. We construct a mutant li-30 brary covering combinations of mutations at 11 sites in HIV-1 protease, all of 31 which are associated with resistance to protease inhibitors in clinic. Using deep 32 sequencing, we quantify the fitness of thousands of HIV-1 protease mutants 33 1 after multiple cycles of replication in human T cells. Although the majority 34 of resistance-associated mutations have deleterious effects on viral replication, 35 we find that epistasis among resistance-associated mutations is predominantly 36 positive. Furthermore, our fitness data are consistent with genetic interactions 37 inferred directly from HIV sequence data of patients. Fitness valleys formed 38 by strong positive epistasis reduce the likelihood of reversal of drug resistance 39 mutations. Overall, our results support the view that strong compensatory 40 effects are involved in the emergence of clinically observed resistance muta-41 tions and provide insights to understanding fitness barriers in the evolution 42 and reversion of drug resistance. 43 1 Introduction 44 Antibiotics and antiviral drugs have achieved great success in recent history [1]. 45However, therapeutic failure may occur due to low adherence and the emergence of 46 drug resistance [2, 3]. The increasing amount of drug resistant pathogens is a global 47 threat to public health [4][5][6][7][8][9][10][11]. The genetic barrier to drug resistance, defined as 48 the number of mutations needed to acquire resistance, is a major determining factor 49 of treatment outcomes [12][13][14]. Another important but often overlooked aspect of 50 drug resistance is the fitness barrier [15][16][17]. Drug resistance associated mutations 51 (DRAMs) in pathogen proteins may decrease enzymatic activities, interfere with 52 molecular interactions, or destabilize the protein structure [18][19][20][21][22]. Because of the 53 impaired replication capacity without drug selection, drug-resistant mutants can-54 not normally outcompete wild-type or establish in the population [23][24][25]. However, 55 drug-resistant mutants can sometimes reach substantial frequency in the population. 56 Fluctuating drug concentrations may create time windows when drug-resistant mu-57 tants replicate better than wild-type virus [26]. Moreover, compensatory mutations 58 can rescue the impaired replication capacity of mutants and stabilize drug resis-59 tance [22, 27, 27-29]. Thus, comprehensive quantification of the fitness landscape is 60 needed to predict the evolution of drug ...

The role of epistasis in determining the fitness landscape of HIV proteins

“…FoldX mutates protein side chains using a probability-based rotamer library while exploring alternative conformations of the surrounding side chains, in order to model the energetic effects of a mutation. We observe good correspondence between Potts model predicted likelihoods and FoldX predicted changes in structural stabilities of mutations in Fig 7B, and Supplementary File 1 Fig 8B for a set of multiple inhibitor-associated mutations (from [28]) in PR. There also exists statistically significant correlation between experimentally measured replicative capacities of these mutations and their Potts model predicted likelihoods (Fig 7A, and Supplementary File 1 Fig 8A), but the FoldX predicted changes in structural stabilities do not correlate so well with experimentally measured replicative capacities (Fig 7C, Supplementary File 1 Fig 8C).…”

“…Previous studies have indicated that the Potts model is an accurate predictor of “prevalence” in HIV proteins [20, 21, 23, 31–35]; “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, 31, 33, 35]. Site-independent models, devoid of interactions between sites have also been reported to capture experimentally measured fitness well, in particular for viral proteins [1, 36] 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, 35]. 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.…”

“…Spearman correlation coefficients ρ between prevalence-based measures of fitness as predicted by the Potts (blue) and independent (gray) models and experimental measurements related to replicative capacity are shown across four different HIV proteins: PR, RT, IN, and p24 CA. Experimental data are obtained from [3, 5, 21, 28, 39–41]. Experiments reporting fitness measurements for random mutations are marked with an “R” and experiments reporting drug-resistance only mutations are marked with a “D”.…”

“…Figure shows that the Potts model predicted Δ E s can capture different features of the fitness landscape that may be orthogonal, and may not correlate well with each other. (A) Relative fitness (replicative capacity) measurements obtained from deep mutational scanning of HIV-1 variants [28] involving combinations (of three or lesser) of mutations in protease associated with resistance to (particularly second-generation) inhibitors in clinic, are compared to changes in Potts statistical energies, Δ E s with a Spearman rank-order correlation, ρ = 0.66 ( p ≪ 0.001). [28] also report statistically significant correlation (| ρ | = 0.46) with a Potts model inferred using the Adaptive Cluster Expansion (ACE) algorithm.…”

“…(A) Relative fitness (replicative capacity) measurements obtained from deep mutational scanning of HIV-1 variants [28] involving combinations (of three or lesser) of mutations in protease associated with resistance to (particularly second-generation) inhibitors in clinic, are compared to changes in Potts statistical energies, Δ E s with a Spearman rank-order correlation, ρ = 0.66 ( p ≪ 0.001). [28] also report statistically significant correlation (| ρ | = 0.46) with a Potts model inferred using the Adaptive Cluster Expansion (ACE) algorithm. (B) FoldX predicted changes in folding energies, ΔΔ G s (PDB: 3S85) of the mutations also correlate well with Potts predicted changes in statistical energies, Δ E s for the same (Spearman ρ = —0.57).…”