HIV genome-wide diversity in large-scale patient populations

Backgroud: HIV genome diversity is related to viral pathogenesis, disease progression and drug development. Therefore, a comprehensive study of genome-wide HIV diversity in large-scale patient populations is needed. Materials and Methods: From HIV Los Alamos database we retrieved 2995 full-length genome sequences sampled from 1698 HIV-1 and 21 HIV-2 patients across 75 countries between 1982 and 2012 (Table 1). From RCSB Protein Data Bank, we downloaded 18 HIV-1 PDB data with (partially) crystallized structures for all HIV-1 proteins. We extracted 916 HIV-host interactions reported by both NCBI and VirusMINT HIV-host interaction databases. Human CD8+ T cell, CD4+ T cell and antibody epitope positions were obtained from HIV Los Alamos immunology database. Information on 97 HIV-1 peptide inhibitors was collected from 90 drug design articles. We designed a HIV full genome alignment tool for multiple protein sequence alignments. Protein secondary structures were assessed using 2Struc and protein solvent accessible surface areas using Chimera V1.6.1. The inter- and intra-genome diversity was calculated by pairwise amino acid comparisons Results: Amino acid diversity of intra-subtype (for HIV-1 group M) and intra-group (for the other HIV-1 and HIV-2 groups) genome sequences was the lowest (11.96±1.73%) compared to inter-subtype (18.3±1.2%), inter-group (36.19±0.63%) and inter-type (HIV-1 vs HIV-2: 55.99±1.32%) diversity. Among 16 HIV proteins, protease (4.4±1.3%), capsid (4.7±1.7%), RT (5.6±1.2%) and integrase (5.8±1.6%) have consistently shown the lowest amino acid diversity across HIV subtypes, groups and types. Statistical correlation between protein genetic diversity and HIV-host interactions was modest (Pearson-coefficient=0.51, p-value=0.04). Current HIV inhibitors targeting protease and RT have a significant impact on the diversity of the drug target regions and the C-terminal gag (p-value=0.024), but not on the remaining regions (p-value=0.67). Of 737 positions in HIV regions from which peptide inhibitors were derived, 68.48% were conserved (genetic diversity<5%) and 73.08% were on protein solvent accessible surface. While no correlation was found between CD8 T cell epitopes and proteins solvent accessibility, a strong correlation was found between CD4+ T cell/antibody epitopes and protein solvent accessibility of structural proteins (GP120, GP41, matrix, capsid) (OR=1.66, p-value<0.01). Conclusion: Beside HIV error-prone reverse transcription, current HIV therapies, HIV-host protein interactions, CD4+ T cells and antibodies play a role in genome-wide diversity, indicating conserved regions of HIV genome as ideal drug/vaccine targets.status: publishe

Genetic Diversity and Selective Pressure in Hepatitis C Virus Genotypes 1–6: Significance for Direct-Acting Antiviral Treatment and Drug Resistance

Treatment with pan-genotypic direct-acting antivirals, targeting different viral proteins, is the best option for clearing hepatitis C virus (HCV) infection in chronically infected patients. However, the diversity of the HCV genome is a major obstacle for the development of antiviral drugs, vaccines, and genotyping assays. In this large-scale analysis, genome-wide diversity and selective pressure was mapped, focusing on positions important for treatment, drug resistance, and resistance testing. A dataset of 1415 full-genome sequences, including genotypes 1–6 from the Los Alamos database, was analyzed. In 44% of all full-genome positions, the consensus amino acid was different for at least one genotype. Focusing on positions sharing the same consensus amino acid in all genotypes revealed that only 15% was defined as pan-genotypic highly conserved (≥99% amino acid identity) and an additional 24% as pan-genotypic conserved (≥95%). Despite its large genetic diversity, across all genotypes, codon positions were rarely identified to be positively selected (0.23%–0.46%) and predominantly found to be under negative selective pressure, suggesting mainly neutral evolution. For NS3, NS5A, and NS5B, respectively, 40% (6/15), 33% (3/9), and 14% (2/14) of the resistance-related positions harbored as consensus the amino acid variant related to resistance, potentially impeding treatment. For example, the NS3 variant 80K, conferring resistance to simeprevir used for treatment of HCV1 infected patients, was present in 39.3% of the HCV1a strains and 0.25% of HCV1b strains. Both NS5A variants 28M and 30S, known to be associated with resistance to the pan-genotypic drug daclatasvir, were found in a significant proportion of HCV4 strains (10.7%). NS5B variant 556G, known to confer resistance to non-nucleoside inhibitor dasabuvir, was observed in 8.4% of the HCV1b strains. Given the large HCV genetic diversity, sequencing efforts for resistance testing purposes may need to be genotype-specific or geographically tailored

Genetic diversity and selective pressure in hepatitis C virus genotypes 1-6: significance for direct-acting antiviral treatment and drug resistance

Treatment with pan-genotypic direct-acting antivirals, targeting different viral proteins, is the best option for clearing hepatitis C virus (HCV) infection in chronically infected patients. However, the diversity of the HCV genome is a major obstacle for the development of antiviral drugs, vaccines, and genotyping assays. In this large-scale analysis, genome-wide diversity and selective pressure was mapped, focusing on positions important for treatment, drug resistance, and resistance testing. A dataset of 1415 full-genome sequences, including genotypes 1–6 from the Los Alamos database, was analyzed. In 44% of all full-genome positions, the consensus amino acid was different for at least one genotype. Focusing on positions sharing the same consensus amino acid in all genotypes revealed that only 15% was defined as pan-genotypic highly conserved (¥99% amino acid identity) and an additional 24% as pan-genotypic conserved (¥95%). Despite its large genetic diversity, across all genotypes, codon positions were rarely identified to be positively selected (0.23%–0.46%) and predominantly found to be under negative selective pressure, suggesting mainly neutral evolution. For NS3, NS5A, and NS5B, respectively, 40% (6/15), 33% (3/9), and 14% (2/14) of the resistance-related positions harbored as consensus the amino acid variant related to resistance, potentially impeding treatment. For example, the NS3 variant 80K, conferring resistance to simeprevir used for treatment of HCV1 infected patients, was present in 39.3% of the HCV1a strains and 0.25% of HCV1b strains. Both NS5A variants 28M and 30S, known to be associated with resistance to the pan-genotypic drug daclatasvir, were found in a significant proportion of HCV4 strains (10.7%). NS5B variant 556G, known to confer resistance to non-nucleoside inhibitor dasabuvir, was observed in 8.4% of the HCV1b strains. Given the large HCV genetic diversity, sequencing efforts for resistance testing purposes may need to be genotype-specific or geographically tailored.status: publishe

HIV-1 Gag C-terminal amino acid substitutions emerging under selective pressure of protease inhibitors in patient populations infected with different HIV-1 subtypes

HIV-1 Gag amino acid substitutions associated with protease inhibitor (PI) treatment have mainly been reported in subtype B, while information on other subtypes is scarce. Using sequences from 11613 patients infected with different HIV-1 subtypes, we evaluated the prevalence of 93 Gag amino acid substitutions and their association with genotypic PI resistance. A significant association was found for 13 Gag substitutions, including A431V in both subtype B and CRF01_AE. K415R in subtype C and S451G in subtype B were newly identified. Most PI-associated Gag substitutions are located in the flexible C-terminal domain, revealing the key role this region plays in PI resistance.status: publishe

Mapping the genomic diversity of HCV subtypes 1a and 1b: Implications of structural and immunological constraints for vaccine and drug development

Despite significant progress in hepatitis C (HCV) treatment, global viral eradication remains a challenge. An in-depth map of its genome diversity within the context of structural and immunological constraints could contribute to the design of pan-genotypic antivirals and preventive vaccines. For such analyses, extensive information is only available for the highly prevalent HCV genotypes (GT) 1a and 1b. Using 647 GT1a and 408 GT1b full-genome sequences obtained from the Los Alamos database, we found that respectively 3 per cent and 82 per cent of all codon positions are under positive and negative selective pressure, suggesting variation mainly accumulates due to random genetic drift. An association between conservation and both structured RNA and secondary protein structures confirmed the important role of structural elements at nucleotide and at amino acid level. Remarkably, CD8+ T-cell epitopes in HCV GT1a were significantly more conserved, while at the same time containing more sites under positive selection. Similarly, CD4+ T-cell epitopes were significantly more conserved in both HCV subtypes, but under less positive selective pressure in GT1b and more negative selective pressure in GT1a. In contrast, B-cell epitopes in both subtypes were less conserved and under less stringent negative selection. These findings argue against immune selective pressure as the main force of between-host diversifying evolution. Despite its high variability, HCV is under strict evolutionary constraints, most probably to keep its genes and proteins functional during the replication cycle. These are encouraging findings for vaccine and drug design, which could consider these newly established genetic diversity profiles.status: publishe

A new ensemble coevolution system for detecting HIV-1 protein coevolution

BackgroundA key challenge in the field of HIV-1 protein evolution is the identification of coevolving amino acids at the molecular level. In the past decades, many sequence-based methods have been designed to detect position-specific coevolution within and between different proteins. However, an ensemble coevolution system that integrates different methods to improve the detection of HIV-1 protein coevolution has not been developed.ResultsWe integrated 27 sequence-based prediction methods published between 2004 and 2013 into an ensemble coevolution system. This system allowed combinations of different sequence-based methods for coevolution predictions. Using HIV-1 protein structures and experimental data, we evaluated the performance of individual and combined sequence-based methods in the prediction of HIV-1 intra- and inter-protein coevolution. We showed that sequence-based methods clustered according to their methodology, and a combination of four methods outperformed any of the 27 individual methods. This four-method combination estimated that HIV-1 intra-protein coevolving positions were mainly located in functional domains and physically contacted with each other in the protein tertiary structures. In the analysis of HIV-1 inter-protein coevolving positions between Gag and protease, protease drug resistance positions near the active site mostly coevolved with Gag cleavage positions (V128, S373-T375, A431, F448-P453) and Gag C-terminal positions (S489-Q500) under selective pressure of protease inhibitors.ConclusionsThis study presents a new ensemble coevolution system which detects position-specific coevolution using combinations of 27 different sequence-based methods. Our findings highlight key coevolving residues within HIV-1 structural proteins and between Gag and protease, shedding light on HIV-1 intra- and inter-protein coevolution.ReviewersThis article was reviewed by Dr. Zoltán Gáspári.status: publishe

A new ensemble coevolution system for detecting HIV-1 protein coevolution

Background: A key challenge in the field of HIV-1 protein evolution is the identification of coevolving amino acids at the molecular level. In the past decades, many sequence-based methods have been designed to detect position-specific coevolution within and between different proteins. However, an ensemble coevolution system that integrates different methods to improve the detection of HIV-1 protein coevolution has not been developed. Results: We integrated 27 sequence-based prediction methods published between 2004 and 2013 into an ensemble coevolution system. This system allowed combinations of different sequence-based methods for coevolution predictions. Using HIV-1 protein structures and experimental data, we evaluated the performance of individual and combined sequence-based methods in the prediction of HIV-1 intra- and inter-protein coevolution. We showed that sequence-based methods clustered according to their methodology, and a combination of four methods outperformed any of the 27 individual methods. This four-method combination estimated that HIV-1 intra-protein coevolving positions were mainly located in functional domains and physically contacted with each other in the protein tertiary structures. In the analysis of HIV-1 inter-protein coevolving positions between Gag and protease, protease drug resistance positions near the active site mostly coevolved with Gag cleavage positions (V128, S373-T375, A431, F448-P453) and Gag C-terminal positions (S489-Q500) under selective pressure of protease inhibitors. Conclusions: This study presents a new ensemble coevolution system which detects position-specific coevolution using combinations of 27 different sequence-based methods. Our findings highlight key coevolving residues within HIV-1 structural proteins and between Gag and protease, shedding light on HIV-1 intra- and inter-protein coevolution. Reviewers: This article was reviewed by Dr. Zoltán Gáspári. © Li et al