Distinguishing functional amino acid covariation from background linkage disequilibrium in HIV protease and reverse transcriptase

Antagonism of PP2A is an independent and conserved function of HIV-1 Vif and causes cell cycle arrest

The seminal description of the cellular restriction factor APOBEC3G and its antagonism by HIV-1 Vif has underpinned two decades of research on the host-virus interaction. We recently reported that HIV-1 Vif is also able to degrade the PPP2R5 family of regulatory subunits of key cellular phosphatase PP2A (PPP2R5A-E; Greenwood et al., 2016; Naamati et al., 2019). We now identify amino acid polymorphisms at positions 31 and 128 of HIV-1 Vif which selectively regulate the degradation of PPP2R5 family proteins. These residues covary across HIV-1 viruses in vivo, favouring depletion of PPP2R5A-E. Through analysis of point mutants and naturally occurring Vif variants, we further show that degradation of PPP2R5 family subunits is both necessary and sufficient for Vif-dependent G2/M cell cycle arrest. Antagonism of PP2A by HIV-1 Vif is therefore independent of APOBEC3 family proteins, and regulates cell cycle progression in HIV-infected cells.

A New Strategy to Reduce Influenza Escape: Detecting Therapeutic Targets Constituted of Invariance Groups

The pathogenicity of the different flu species is a real public health problem worldwide. To combat this scourge, we established a method to detect drug targets, reducing the possibility of escape. Besides being able to attach a drug candidate, these targets should have the main characteristic of being part of an essential viral function. The invariance groups that are sets of residues bearing an essential function can be detected genetically. They consist of invariant and synthetic lethal residues (interdependent residues not varying or slightly varying when together). We analyzed an alignment of more than 10,000 hemagglutinin sequences of influenza to detect six invariance groups, close in space, and on the protein surface. In parallel we identified five potential pockets on the surface of hemagglutinin. By combining these results, three potential binding sites were determined that are composed of invariance groups located respectively in the vestigial esterase domain, in the bottom of the stem and in the fusion area. The latter target is constituted of residues involved in the spring-loaded mechanism, an essential step in the fusion process. We propose a model describing how this potential target could block the reorganization of the hemagglutinin HA2 secondary structure and prevent viral entry into the host cell.

Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein

Epistatic interactions between residues determine a protein’s adaptability and shape its evolutionary trajectory. When a protein experiences a changed environment, it is under strong selection to find a peak in the new fitness landscape. It has been shown that strong selection increases epistatic interactions as well as the ruggedness of the fitness landscape, but little is known about how the epistatic interactions change under selection in the long-term evolution of a protein. Here we analyze the evolution of epistasis in the protease of the human immunodeficiency virus type 1 (HIV-1) using protease sequences collected for almost a decade from both treated and untreated patients, to understand how epistasis changes and how those changes impact the long-term evolvability of a protein. We use an information-theoretic proxy for epistasis that quantifies the co-variation between sites, and show that positive information is a necessary (but not sufficient) condition that detects epistasis in most cases. We analyze the “fossils” of the evolutionary trajectories of the protein contained in the sequence data, and show that epistasis continues to enrich under strong selection, but not for proteins whose environment is unchanged. The increase in epistasis compensates for the information loss due to sequence variability brought about by treatment, and facilitates adaptation in the increasingly rugged fitness landscape of treatment. While epistasis is thought to enhance evolvability via valley-crossing early-on in adaptation, it can hinder adaptation later when the landscape has turned rugged. However, we find no evidence that the HIV-1 protease has reached its potential for evolution after 9 years of adapting to a drug environment that itself is constantly changing. We suggest that the mechanism of encoding new information into pairwise interactions is central to protein evolution not just in HIV-1 protease, but for any protein adapting to a changing environment.

Evolution is often viewed as a process that occurs “mutation by mutation”, suggesting that the effect of each mutation is independent of that of others. However, in reality the effect of a mutation often depends on the context of other mutations, a dependence known as “epistasis”. Even though epistasis can constrain protein evolution, it is actually very common. Such interactions are particularly pervasive in proteins that evolve resistance to a drug via mutations that create defects, and that must be repaired with compensatory mutations. We study how epistasis between protein residues evolves over time in a new and changing environment, and compare these findings to protein evolution in a constant environment. We analyze the sequences of the human immunodeficiency virus type 1 (HIV-1) protease enzyme collected over a period of 9 years from patients treated with anti-viral drugs (as well as from patients that went untreated), and find that epistasis between residues continues to increase as more potent anti-viral drugs enter the market, while epistasis is unchanging in the proteins exposed to a constant environment. Yet, the proteins adapting to the changing landscape do not appear to be constrained by the epistatic interactions and continue to manage to evade new drugs.

CoVaMa: Co-Variation Mapper for disequilibrium analysis of mutant loci in viral populations using next-generation sequence data

Next-Generation Sequencing (NGS) has transformed our understanding of the dynamics and diversity of virus populations for human pathogens and model systems alike. Due to the sensitivity and depth of coverage in NGS, it is possible to measure the frequency of mutations that may be present even at vanishingly low frequencies within the viral population. Here, we describe a simple bioinformatic pipeline called CoVaMa (Co-Variation Mapper) scripted in Python that detects correlated patterns of mutations in a viral sample. Our algorithm takes NGS alignment data and populates large matrices of contingency tables that correspond to every possible pairwise interaction of nucleotides in the viral genome or amino acids in the chosen open reading frame. These tables are then analysed using classical linkage disequilibrium to detect and report evidence of epistasis. We test our analysis with simulated data and then apply the approach to find epistatically linked loci in Flock House Virus genomic RNA grown under controlled cell culture conditions. We also reanalyze NGS data from a large cohort of HIV infected patients and find correlated amino acid substitution events in the protease gene that have arisen in response to anti-viral therapy. This both confirms previous findings and suggests new pairs of interactions within HIV protease. The script is publically available at http://sourceforge.net/projects/covama.

Deep sequencing of protease inhibitor resistant HIV patient isolates reveals patterns of correlated mutations in Gag and protease

While the role of drug resistance mutations in HIV protease has been studied comprehensively, mutations in its substrate, Gag, have not been extensively cataloged. Using deep sequencing, we analyzed a unique collection of longitudinal viral samples from 93 patients who have been treated with therapies containing protease inhibitors (PIs). Due to the high sequence coverage within each sample, the frequencies of mutations at individual positions were calculated with high precision. We used this information to characterize the variability in the Gag polyprotein and its effects on PI-therapy outcomes. To examine covariation of mutations between two different sites using deep sequencing data, we developed an approach to estimate the tight bounds on the two-site bivariate probabilities in each viral sample, and the mutual information between pairs of positions based on all the bounds. Utilizing the new methodology we found that mutations in the matrix and p6 proteins contribute to continued therapy failure and have a major role in the network of strongly correlated mutations in the Gag polyprotein, as well as between Gag and protease. Although covariation is not direct evidence of structural propensities, we found the strongest correlations between residues on capsid and matrix of the same Gag protein were often due to structural proximity. This suggests that some of the strongest inter-protein Gag correlations are the result of structural proximity. Moreover, the strong covariation between residues in matrix and capsid at the N-terminus with p1 and p6 at the C-terminus is consistent with residue-residue contacts between these proteins at some point in the viral life cycle.

Understanding the structure of HIV proteins and the function of drug-resistant mutations of these proteins is critical for the development of effective HIV treatments. Selected gag mutations have been shown to provide compensatory functions for protease resistance mutations and may directly contribute to the development of drug resistance. To determine associations between protease inhibitor mutations and gag, we utilized deep sequencing of HIV gag and protease from a collection of viral isolates from patients treated with highly active retroviral protease inhibitors. Deep sequencing allows for accurate measurement of mutation frequencies at each position, allowing estimation, using a novel method we developed, of the covariation between any two residues on gag. Using this information, we characterize the variation within gag and protease and identify the most strongly correlated pairs of inter- and intra-protein residues. Our results suggest that matrix and p1/p6 mutations form the core of a network of strongly correlated gag mutations and contribute to recurrent treatment failure. Extracting gag residue covariation information from the deep sequencing of patient viral samples may provide insight into structural aspects of the Gag polyprotein as well new areas for small molecule targeting to disrupt Gag function.

Synthetic lethals in HIV: ways to avoid drug resistance : Running title: Preventing HIV resistance

Background

RNA viruses rapidly accumulate genetic variation, which can give rise to synthetic lethal (SL) and deleterious (SD) mutations. Synthetic lethal mutations (non-lethal when alone but lethal when combined in one genome) have been studied to develop cancer therapies. This principle can also be used against fast-evolving RNA-viruses. Indeed, targeting protein sites involved in SD + SL interactions with a drug would render any mutation of such sites, lethal.

Results

Here, we set up a strategy to detect intragenic pairs of SL and SD at the surface of the protein to predict less escapable drug target sites. For this, we detected SD + SL, studying HIV protease (PR) and reverse transcriptase (RT) sequence alignments from two groups of VIH⁺ individuals: treated with drugs (T) or not (NT). Using a series of statistical approaches, we were able to propose bona fide SD + SL couples. When focusing on spatially close co-variant SD + SL couples at the surface of the protein, we found 5 SD + SL groups (2 in the protease and 3 in the reverse transcriptase), which could be good candidates to form pockets to accommodate potential drugs.

Conclusions

Thus, designing drugs targeting these specific SD + SL groups would not allow the virus to mutate any residue involved in such groups without losing an essential function. Moreover, we also show that the selection pressure induced by the treatment leads to the appearance of new mutations, which change the mutational landscape of the protein. This drives the existence of differential SD + SL couples between the drug-treated and non-treated groups. Thus, new anti-viral drugs should be designed differently to target such groups.

Reviewers

This article was reviewed by Neil Greenspan Csaba Pal and István Simon.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0044-y) contains supplementary material, which is available to authorized users.

The use of information theory in evolutionary biology

Information is a key concept in evolutionary biology. Information stored in a biological organism's genome is used to generate the organism and to maintain and control it. Information is also that which evolves. When a population adapts to a local environment, information about this environment is fixed in a representative genome. However, when an environment changes, information can be lost. At the same time, information is processed by animal brains to survive in complex environments, and the capacity for information processing also evolves. Here, I review applications of information theory to the evolution of proteins and to the evolution of information processing in simulated agents that adapt to perform a complex task.

A multifaceted analysis of HIV-1 protease multidrug resistance phenotypes

Background

Great strides have been made in the effective treatment of HIV-1 with the development of second-generation protease inhibitors (PIs) that are effective against historically multi-PI-resistant HIV-1 variants. Nevertheless, mutation patterns that confer decreasing susceptibility to available PIs continue to arise within the population. Understanding the phenotypic and genotypic patterns responsible for multi-PI resistance is necessary for developing PIs that are active against clinically-relevant PI-resistant HIV-1 variants.

Results

In this work, we use globally optimal integer programming-based clustering techniques to elucidate multi-PI phenotypic resistance patterns using a data set of 398 HIV-1 protease sequences that have each been phenotyped for susceptibility toward the nine clinically-approved HIV-1 PIs. We validate the information content of the clusters by evaluating their ability to predict the level of decreased susceptibility to each of the available PIs using a cross validation procedure. We demonstrate the finding that as a result of phenotypic cross resistance, the considered clinical HIV-1 protease isolates are confined to ~6% or less of the clinically-relevant phenotypic space. Clustering and feature selection methods are used to find representative sequences and mutations for major resistance phenotypes to elucidate their genotypic signatures. We show that phenotypic similarity does not imply genotypic similarity, that different PI-resistance mutation patterns can give rise to HIV-1 isolates with similar phenotypic profiles.

Conclusion

Rather than characterizing HIV-1 susceptibility toward each PI individually, our study offers a unique perspective on the phenomenon of PI class resistance by uncovering major multidrug-resistant phenotypic patterns and their often diverse genotypic determinants, providing a methodology that can be applied to understand clinically-relevant phenotypic patterns to aid in the design of novel inhibitors that target other rapidly evolving molecular targets as well.

Correlated evolution of nearby residues in Drosophilid proteins

Here we investigate the correlations between coding sequence substitutions as a function of their separation along the protein sequence. We consider both substitutions between the reference genomes of several Drosophilids as well as polymorphisms in a population sample of Zimbabwean Drosophila melanogaster. We find that amino acid substitutions are “clustered” along the protein sequence, that is, the frequency of additional substitutions is strongly enhanced within ≈10 residues of a first such substitution. No such clustering is observed for synonymous substitutions, supporting a “correlation length” associated with selection on proteins as the causative mechanism. Clustering is stronger between substitutions that arose in the same lineage than it is between substitutions that arose in different lineages. We consider several possible origins of clustering, concluding that epistasis (interactions between amino acids within a protein that affect function) and positional heterogeneity in the strength of purifying selection are primarily responsible. The role of epistasis is directly supported by the tendency of nearby substitutions that arose on the same lineage to preserve the total charge of the residues within the correlation length and by the preferential cosegregation of neighboring derived alleles in our population sample. We interpret the observed length scale of clustering as a statistical reflection of the functional locality (or modularity) of proteins: amino acids that are near each other on the protein backbone are more likely to contribute to, and collaborate toward, a common subfunction.

Genes are templates for proteins, yet evolutionary studies of genes and proteins often bear little resemblance. Analyses of gene evolution typically treat each codon independently, quantifying gene evolution by summing over the constituent codons. In contrast, studies of protein evolution generally incorporate protein structure and interactions between amino acids explicitly. We investigate correlations in the evolution of codons as a function of their distance from each other along the protein coding sequence. This approach is motivated by the expectation that codons near each other in sequence often encode amino acids belonging to the same functional unit. Consequently, these amino acids are more likely to interact and/or experience similar selective regimes, introducing correlation between the evolution of the underlying codons. We find codon evolution in Drosophilids to be correlated over a characteristic length scale of ≈10 codons. Specifically, the presence of a non-synonymous substitution substantially increases the probability of further such substitutions nearby, particularly within that lineage. Further analysis suggests both functional interactions between amino acids and correlation in the strength of selection contribute to this effect. These findings are relevant for understanding the relative importance of different modes of selection, and particularly the role of epistasis, in gene and protein evolution.

Prevalence of epistasis in the evolution of influenza A surface proteins

The surface proteins of human influenza A viruses experience positive selection to escape both human immunity and, more recently, antiviral drug treatments. In bacteria and viruses, immune-escape and drug-resistant phenotypes often appear through a combination of several mutations that have epistatic effects on pathogen fitness. However, the extent and structure of epistasis in influenza viral proteins have not been systematically investigated. Here, we develop a novel statistical method to detect positive epistasis between pairs of sites in a protein, based on the observed temporal patterns of sequence evolution. The method rests on the simple idea that a substitution at one site should rapidly follow a substitution at another site if the sites are positively epistatic. We apply this method to the surface proteins hemagglutinin and neuraminidase of influenza A virus subtypes H3N2 and H1N1. Compared to a non-epistatic null distribution, we detect substantial amounts of epistasis and determine the identities of putatively epistatic pairs of sites. In particular, using sequence data alone, our method identifies epistatic interactions between specific sites in neuraminidase that have recently been demonstrated, in vitro, to confer resistance to the drug oseltamivir; these epistatic interactions are responsible for widespread drug resistance among H1N1 viruses circulating today. This experimental validation demonstrates the predictive power of our method to identify epistatic sites of importance for viral adaptation and public health. We conclude that epistasis plays a large role in shaping the molecular evolution of influenza viruses. In particular, sites with , which would normally not be identified as positively selected, can facilitate viral adaptation through epistatic interactions with their partner sites. The knowledge of specific interactions among sites in influenza proteins may help us to predict the course of antigenic evolution and, consequently, to select more appropriate vaccines and drugs.

Epistasis describes non-additive interactions among genetic sites: the consequence of a mutation at one site may depend on the status of the genome at other sites. In an extreme case, a mutation may have no effect if it arises on one genetic background, but a strong effect on another background. Epistatic mutations in viruses and bacteria that live under severe conditions, such as antibiotic treatments or immune pressure, often allow pathogens to develop drug resistance or escape the immune system. In this paper we develop a new phylogenetic method for detecting epistasis, and we apply this method to the surface proteins of the influenza A virus, which are important targets of the immune system and drug treatments. The authors identify and characterize hundreds of epistatic mutations in these proteins. Among those identified, we find the specific epistatic mutations that were recently shown, experimentally, to confer resistance to the drug Tamiflu. The results of this study may help to predict the course of influenza's antigenic evolution and to select more appropriate vaccines and drugs.

Pairwise and higher-order correlations among drug-resistance mutations in HIV-1 subtype B protease

Background

The reaction of HIV protease to inhibitor therapy is characterized by the emergence of complex mutational patterns which confer drug resistance. The response of HIV protease to drugs often involves both primary mutations that directly inhibit the action of the drug, and a host of accessory resistance mutations that may occur far from the active site but may contribute to restoring the fitness or stability of the enzyme. Here we develop a probabilistic approach based on connected information that allows us to study residue, pair level and higher-order correlations within the same framework.

Results

We apply our methodology to a database of approximately 13,000 sequences which have been annotated by the treatment history of the patients from which the samples were obtained. We show that including pair interactions is essential for agreement with the mutational data, since neglect of these interactions results in order-of-magnitude errors in the probabilities of the simultaneous occurence of many mutations. The magnitude of these pair correlations changes dramatically between sequences obtained from patients that were or were not exposed to drugs. Higher-order effects make a contribution of as much as 10% for residues taken three at a time, but increase to more than twice that for 10 to 15-residue groups. The sequence data is insufficient to determine the higher-order effects for larger groups. We find that higher-order interactions have a significant effect on the predicted frequencies of sequences with large numbers of mutations. While relatively rare, such sequences are more prevalent after multi-drug therapy. The relative importance of these higher-order interactions increases with the number of drugs the patient had been exposed to.

Conclusion

Correlations are critical for the understanding of mutation patterns in HIV protease. Pair interactions have substantial qualitative effects, while higher-order interactions are individually smaller but may have a collective effect. Together they lead to correlations which could have an important impact on the dynamics of the evolution of cross-resistance, by allowing the virus to pass through otherwise unlikely mutational states. These findings also indicate that pairwise and possibly higher-order effects should be included in the models of protein evolution, instead of assuming that all residues mutate independently of one another.

Amino acid covariation in a functionally important human immunodeficiency virus type 1 protein region is associated with population subdivision

The frequently reported amino acid covariation of the highly polymorphic human immunodeficiency virus type 1 (HIV-1) exterior envelope glycoprotein V3 region has been assumed to reflect fitness epistasis between residues. However, nonrandom association of amino acids, or linkage disequilibrium, has many possible causes, including population subdivision. If the amino acids at a set of sequence sites differ in frequencies between subpopulations, then analysis of the whole population may reveal linkage disequilibrium even if it does not exist in any subpopulation. HIV-1 has a complex population structure, and the effects of this structure on linkage disequilibrium were investigated by estimating within- and among-subpopulation components of variance in linkage disequilibrium. The amino acid covariation previously reported is explained by differences in amino acid frequencies among virus subpopulations in different patients and by nonsystematic disequilibrium among patients. Disequilibrium within patients appears to be entirely due to differences in amino acid frequencies among sampling time points and among chemokine coreceptor usage phenotypes of virus particles, but not source tissues. Positive selection explains differences in allele frequencies among time points and phenotypes, indicating that these differences are adaptive rather than due to genetic drift. However, the absence of a correlation between linkage disequilibrium and phenotype suggests that fitness epistasis is an unlikely cause of disequilibrium. Indeed, when population structure is removed by analyzing sequences from a single time point and phenotype, no disequilibrium is detectable within patients. These results caution against interpreting amino acid covariation and coevolution as evidence for fitness epistasis.

Evidence of a novel RNA secondary structure in the coding region of HIV-1 pol gene

RNA secondary structures play several important roles in the human immunodeficiency virus (HIV) life cycle. To assess whether RNA secondary structure might affect the function of the HIV protease and reverse transcriptase genes, which are the main targets of anti-HIV drugs, we applied a series of different computational approaches to detect RNA secondary structures, including thermodynamic RNA folding predictions, synonymous variability analysis, and covariance analysis. Each method independently revealed strong evidence of a novel RNA secondary structure at the junction of the protease and reverse transcriptase genes, consisting of a 107-nucleotide region containing three stems, A, B, and C. First, RNA folding calculations by mfold and RNAfold both predicted the secondary structure with high confidence. Moreover, the same structure was predicted in a diverse set of reference sequences in HIV-1 group M, indicating that it is conserved across this group. Second, the predicted base-pairing regions displayed markedly reduced synonymous variation (approximately threefold lower than average) in a data set of 20,000 HIV-1 subtype B sequences from clinical samples. Third, independent analysis of covariation between synonymous mutations in this data set identified 10 covariant mutation pairs forming two diagonals that corresponded exactly to the sites predicted to base-pair in stems A and B. Finally, this structure was validated experimentally using selective 2'-hydroxyl acylation and primer extension (SHAPE). Discovery of this novel secondary structure suggests many directions for further functional investigation.