Coordinate linkage of HIV evolution reveals regions of immunological vulnerability

By V. Dahirel, Karthik Shekhar, F. Pereyra, T. Miura, M. Artyomov, Shiv Talsania, Todd M. Allen, M. Altfeld, M. Carrington, D. Irvine and 2 more. 2011

201 citations recorded by the source corpus.

Cellular immune control of HIV is mediated, in part, by induction of single amino acid mutations that reduce viral fitness, but compensatory mutations limit this effect. Here, we sought to determine if higher order constraints on viral evolution exist, because some coordinately linked combinations of mutations may hurt viability. Immune targeting of multiple sites in such a multidimensionally conserved region might render the virus particularly vulnerable, because viable escape pathways would be greatly restricted. We analyzed available HIV sequences using a method from physics to reveal distinct groups of amino acids whose mutations are collectively coordinated (“HIV sectors”). From the standpoint of mutations at individual sites, one such group in Gag is as conserved as other collectively coevolving groups of sites in Gag. However, it exhibits higher order conservation indicating constraints on the viability of viral strains with multiple mutations. Mapping amino acids from this group onto protein structures shows that combined mutations likely destabilize multiprotein structural interactions critical for viral function. Persons who durably control HIV without medications preferentially target the sector in Gag predicted to be most vulnerable. By sequencing circulating viruses from these individuals, we find that individual mutations occur with similar frequency in this sector as in other targeted Gag sectors. However, multiple mutations within this sector are very rare, indicating previously unrecognized multidimensional constraints on HIV evolution. Targeting such regions with higher order evolutionary constraints provides a novel approach to immunogen design for a vaccine against HIV and other rapidly mutating viruses.

Subfields: Genetics, Hepatology, Virology

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Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, background): “Binary representations are also appropriate to analyze sets of sufficiently close sequences, notably HIV proteins, allowing identification of their sectors [5] and predictions of their fitness landscapes [33].”
The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing (2014, methodology): “Ref [12]), (ii) HLA-associated Gag p24 pairs with high E values, (iii) Gag p24 pairs/triple with low E values, and (iv) Gag p17 pairs (Table 1).”
Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information (2014, methodology): “Other recent statistical methods have also been applied to sequence coevolution to explore a diverse range of topics ranging from protein structure and function (29 – 35) to the evolutionary fitness of HIV (36).”
Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes (2013, unknown): “The excellent agreement between experimental measurements of replicative fitness and the inferred prevalence landscape described by Ferguson et al. [9] may reflect the fact that different strains were phylogenetically proximal.”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “In a series of papers, Chakraborty and co-workers proposed a fitness model the so-called Gag protein family (group-specific antigen) of the HIV virus [59, 74, 135, 202].”
Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies (2020, result): “Similar to previous studies on HIV and HCV (Ahmed, Quadeer, Morales-Jimenez, & McKay, 2019; Dahirel et al., 2011; Quadeer et al., 2014), we estimated population coverages for various combinations of these 107 epitopes.”
A new ensemble coevolution system for detecting HIV-1 protein coevolution (2015, background): “For instance, five groups of Gag positions were coevolving under multidimensional constraints and one of these groups contains positions in the capsid N-terminal helices [7].”
Power law tails in phylogenetic systems (2018, methodology): “While this will differ for different phenotypes, recent work has focused on using covariance analysis to predict contacts in tertiary protein structure (11, 13, 14, 16–18).”