Modelling and in vitro testing of the HIV-1 Nef fitness landscape

Abstract

An effective vaccine is urgently required to curb the HIV-1 epidemic. We have previously described an approach to model the fitness landscape of several HIV-1 proteins, and have validated the results against experimental and clinical data. The fitness landscape may be used to identify mutation patterns harmful to virus viability, and consequently inform the design of immunogens that can target such regions for immunological control. Here we apply such an analysis and complementary experiments to HIV-1 Nef, a multifunctional protein which plays a key role in HIV-1 pathogenesis. We measured Nef-driven replication capacities as well as Nef-mediated CD4 and HLA-I down-modulation capacities of thirty-two different Nef mutants, and tested model predictions against these results. Furthermore, we evaluated the models using 448 patient-derived Nef sequences for which several Nef activities were previously measured. Model predictions correlated significantly with Nef-driven replication and CD4 down-modulation capacities, but not HLA-I down-modulation capacities, of the various Nef mutants. Similarly, in our analysis of patient-derived Nef sequences, CD4 down-modulation capacity correlated the most significantly with model predictions, suggesting that of the tested Nef functions, this is the most important in vivo. Overall, our results highlight how the fitness landscape inferred from patient-derived sequences captures, at least in part, the in vivo functional effects of mutations to Nef. However, the correlation between predictions of the fitness landscape and measured parameters of Nef function is not as accurate as the correlation observed in past studies for other proteins. This may be because of the additional complexity associated with inferring the cost of mutations on the diverse functions of Nef.

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

“…Previous studies have indicated that the Potts model is an accurate predictor of "prevalence" in HIV proteins [20,21,23,[31][32][33][34][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.…”

“…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, 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].…”

Simulating within host human immunodeficiency virus 1 genome evolution in the persistent reservoir

“…The active compartment begins the simulation with 10 genomes that are copies of full-length (9,719 bases) ancestral HIV-1 type B strain HX-B2 (GenBank accession: K03455 ) except 1, that nucleotide position 9,167 is a guanine (G) instead of an adenine (A) to change the premature stop codon in HX-B2’s nef into tryptophan (W) and 2, the nucleotide at position 6,063 is a thymine (T) instead of a cytosine (C) to change the threonine (T) into a start codon. Genomes in the active compartment are subject to selection due to CD4 and HLA-I down-modulation in nef based on ( Barton et al. 2019 ).…”

Inferring the intrinsic mutational fitness landscape of influenza-like evolving antigens from temporally ordered sequence data

“…The copyright holder for this preprint (which this version posted July 29, 2021. ; https://doi.org/10.1101/2021.07.28.454153 doi: bioRxiv preprint and a method called Adaptive Cluster Expansion (ACE) to computationally infer intrinsic mutational fitness landscapes for other highly mutable viruses, HIV as well as polio, from sequence prevalence data [37][38][39][40][41][42][43][44][45][46][47][48]. The result of such fitness inference was used to propose a novel cross-protective immunization method against HIV using multidimensionally conserved parts of the proteome, which has been shown to be immunogenic in rhesus macaques [49].…”