HCV genotypes are differently prone to the development of resistance to linear and macrocyclic protease inhibitors.

BACKGROUND: Because of the extreme genetic variability of hepatitis C virus (HCV), we analyzed whether specific HCV-genotypes are differently prone to develop resistance to linear and macrocyclic protease-inhibitors (PIs). METHODS: The study includes 1568 NS3-protease sequences, isolated from PI-naive patients infected with HCV-genotypes 1a (N = 621), 1b (N = 474), 2 (N = 72), 3 (N = 268), 4 (N = 54) 5 (N = 6), and 6 (N = 73). Genetic-barrier was calculated as the sum of nucleotide-transitions (score = 1) and/or nucleotide-transversions (score = 2.5) required for drug-resistance-mutations emergence. Forty-three mutations associated with PIs-resistance were analyzed (36A/M/L/G-41R-43S/V-54A/S/V-55A-Q80K/R/L/H/G-109K-138T-155K/Q/T/I/M/S/G/L-156T/V/G/S-158I-168A/H/T/V/E/I/G/N/Y-170A/T-175L). Structural analyses on NS3-protease and on putative RNA-models have been also performed. RESULTS: Overall, NS3-protease was moderately conserved, with 85/181 (47.0%) amino-acids showing <1% variability. The catalytic-triad (H57-D81-S139) and 6/13 resistance-associated positions (Q41-F43-R109-R155-A156-V158) were fully conserved (variability <1%). Structural-analysis highlighted that most of the NS3-residues involved in drug-stabilization were highly conserved, while 7 PI-resistance residues, together with selected residues located in proximity of the PI-binding pocket, were highly variable among HCV-genotypes. Four resistance-mutations (80K/G-36L-175L) were found as natural polymorphisms in selected genotypes (80K present in 41.6% HCV-1a, 100% of HCV-5 and 20.6% HCV-6; 80G present in 94.4% HCV-2; 36L present in 100% HCV-3-5 and >94% HCV-2-4; 175L present in 100% HCV-1a-3-5 and >97% HCV-2-4). Furthermore, HCV-3 specifically showed non-conservative polymorphisms (R123T-D168Q) at two drug-interacting positions. Regardless of HCV-genotype, 13 PIs resistance-mutations were associated with low genetic-barrier, requiring only 1 nucleotide-substitution (41R-43S/V-54A-55A-80R-156V/T: score = 1; 54S-138T-156S/G-168E/H: score = 2.5). By contrast, by using HCV-1b as reference genotype, nucleotide-heterogeneity led to a lower genetic-barrier for the development of some drug-resistance-mutations in HCV-1a (36M-155G/I/K/M/S/T-170T), HCV-2 (36M-80K-155G/I/K/S/T-170T), HCV-3 (155G/I/K/M/S/T-170T), HCV-4-6 (155I/S/L), and HCV-5 (80G-155G/I/K/M/S/T). CONCLUSIONS: The high degree of HCV genetic variability makes HCV-genotypes, and even subtypes, differently prone to the development of PIs resistance-mutations. Overall, this can account for different responsiveness of HCV-genotypes to PIs, with important clinical implications in tailoring individualized and appropriate regimens.

Selected amino acid changes in HIV-1 subtype-C gp41 are associated with specific gp120(V3) signatures in the regulation of co-receptor usage.

The majority of studies have characterized the tropism of HIV-1 subtype-B isolates, but little is known about the determinants of tropism in other subtypes. So, the goal of the present study was to genetically characterize the envelope of viral proteins in terms of co-receptor usage by analyzing 356 full-length env sequences derived from HIV-1 subtype-C infected individuals. The co-receptor usage of V3 sequences was inferred by using the Geno2Pheno and PSSM algorithms, and also analyzed to the "11/25 rule". All reported env sequences were also analyzed with regard to N-linked glycosylation sites, net charge and hydrophilicity, as well as the binomial correlation phi coefficient to assess covariation among gp120(V3) and gp41 signatures and the average linkage hierarchical agglomerative clustering were also performed. Among env sequences present in Los Alamos Database, 255 and 101 sequences predicted as CCR5 and CXCR4 were selected, respectively. The classical V3 signatures at positions 11 and 25, and other specific V3 and gp41 amino acid changes were found statistically associated with different co-receptor usage. Furthermore, several statistically significant associations between V3 and gp41 signatures were also observed. The dendrogram topology showed a cluster associated with CCR5-usage composed by five gp41 mutated positions, A22V, R133M, E136G, N140L, and N166Q that clustered with T2V(V3) and G24T(V3) (bootstrap=1). Conversely, a heterogeneous cluster with CXCR4-usage, involving S11GR(V3), 13-14insIG/LG(V3), P16RQ(V3), Q18KR(V3), F20ILV(V3), D25KRQ(V3), Q32KR(V3) along with A30T(gp41), S107N(gp41), D148E(gp41), A189S(gp41) was identified (bootstrap=0.86). Our results show that as observed for HIV-1 subtype-B, also in subtype-C specific and different gp41 and gp120V3 amino acid changes are associated individually or together with CXCR4 and/or CCR5 usage. These findings strengthen previous observations that determinants of tropism may also reside in the gp41 protein.

Comparative analysis of drug resistance among B and the most prevalent non-B HIV type 1 subtypes (C, F, and CRF02_AG) in Italy.

In recent years, increasing numbers of patients infected with HIV-1 non-B subtypes have been treated with modern antiretroviral regimens. Therefore, a better knowledge of HIV drug resistance in non-B strains is crucial. Thus, we compared the mutational pathways involved in drug resistance among the most common non-B subtypes in Italy (F, C, and CRF02_AG) and the B subtype. In total, 2234 pol sequences from 1231 virologically failing patients from Central Italy were analyzed. The prevalence of resistance mutations in protease and reverse transcriptase between non-B and B subtypes has been evaluated. Among patients treated with nucleoside/nucleotide reverse transcriptase inhibitors (NRTI) and with thymidine analogues (TA) experience, TAMs1 M41L and L210W were less prevalent in CRF02_AG, while TAMs2 T215F and K219E were more prevalent in the F subtype. In NRTI-treated patients having experience with abacavir, didanosine, tenofovir, or stavudine the K65R mutation was mostly prevalent in the C subtype. In non-NRTI (NNRTI)-treated patients infected by the C subtype the prevalence of K103N was lower than in patients infected with other subtypes, while the prevalence of Y181C and Y188L was higher compared to subtype B. The prevalence of Y181C was higher also in subtype F as compared to subtype B. In patients treated with protease inhibitors, L89V was predominantly found in CRF02_AG, while the TPV resistance mutation T74P was predominantly found in the C subtype. Some differences in the genotypic drug resistance have been found among patients infected with B, C, F, and CRF02_AG subtypes in relationship to treatment. These results may be useful for the therapeutic management of individuals infected with HIV-1 non-B strains.

Genetic and structural analysis of HIV-1 Rev responsive element related to V38A and T18A enfuvirtide resistance mutations.

BACKGROUND: For the expression of late viral genes, HIV-1 efficiently exploits the nuclear export by using Rev viral protein, which specifically binds the RNA Rev Responsive Element (RRE). This region is contained within the gp120-gp41 encoding sequence. Enfuvirtide is the first approved HIV-1 fusion-inhibitor, and gp41 codons associated with primary enfuvirtide-resistance (amino-acids 36-45) are localized within the RRE structure. We previously found the co-presence of V38A+T18A resistance mutations in patients failing enfuvirtide. METHODS: Collecting 476 and 135 HIV-1 B-subtype gp41 sequences from enfuvirtide-naïve and enfuvirtide-treated patients, respectively, two mutations previously found associated with enfuvirtide treatment, T18A and V38A, were analyzed. Moreover, the RNA secondary structure was displayed by CONTRAfold-software and the gp41 evolutionary pathways by a mutagenetic tree. RESULTS: By modeling the RRE structure, we show that the T18 and V38 codons are base pairing within the RRE-stem-IIA, an important domain involved in Rev binding. While a structural RRE impairment in the presence of V38A alone was found, a restoration of the original RRE structure occurred in co-presence of V38A+T18A. By mutagenetic tree analysis, a compensatory evolution confirming our hypothesis on the structural modification mechanism was observed. CONCLUSION: We show that enfuvirtide pressure may also affect specific RRE domains involved in Rev binding, thus requiring a compensatory evolution able to preserve the secondary structure of the RRE.

Identification and structural characterization of novel genetic elements in the HIV-1 V3 loop regulating coreceptor usage.

BACKGROUND: The interaction between HIV-1 gp120 and CCR5 N terminus is critical for R5-virus entry and affects CCR5 antagonists' activity. Knowledge of how different genetic signatures of gp120 V3 domain effect the strength of this interaction is limited. METHODS: HIV-1 coreceptor usage was assessed in 251 patients using enhanced-sensitivity Trofile assay and V3 sequencing plus tropism prediction by Geno2pheno algorithm. Bayesian partitional model and recursive model selection have been used to define V3 genetic determinants correlated with different coreceptor usage. Gp120 interaction with CCR5 N terminus was evaluated by docking-analysis/molecular-dynamic simulations starting from the model described previously. RESULTS: Selected V3 genetic determinants (beyond known aminoacidic positions) significantly correlate with CCR5- or CXCR4-usage, and modulate gp120 affinity for CCR5 N terminus. This is the case for N5Y and N7K, absent in CCR5-using viruses and present in 4.5% and 6% of CXCR4-using viruses, respectively, and A19V, occurring in 2.6% of CCR5-using viruses and 22.0% of CXCR4-using viruses (P=10(-2) to 10(-7)). Their presence determines a decreased affinity for CCR5 N terminus even stronger than that observed in the presence of the well-known mutation S11R (N5Y: -6.60 Kcal/mol; N7K: -5.40 Kcal/mol; A19V: -5.60 Kcal/mol; S11R: -6.70 Kcal/mol; WT: -6.90 Kcal/mol). N7K significantly increases the distance between V3 position 7 and sulphotyrosine at CCR5 position 14 (crucial for binding to gp120; from 4.22 Å to 8.30 Å), thus abrogating the interaction between these two important residues. CONCLUSIONS: Key determinants for tropism within the V3 sequence, confirmed by structure- and by phenotypic-tropism, have been identified. This information can be used for a finer tuning of potential efficacy of CCR5-antagonists in clinical practice, and to provide molecular implications for design of new entry inhibitors.

Role of hepatitis B virus genetic barrier in drug-resistance and immune-escape development.

BACKGROUND: Impact of hepatitis B virus genetic barrier, defined as the number and type of nucleotide substitutions required to overcome drug/immune selective pressure, on drug-resistance/immune-escape development is unknown. METHODS: Genetic barrier was calculated according to Van de Vijver (2006) in 3482 hepatitis B virus-reverse transcriptase/HBV surface antigen sequences from 555 drug-naïve patients and 2927 antiviral-treated patients infected with hepatitis B virus genotypes A-G. RESULTS: Despite high natural variability, genetic barrier for drug-resistance development is identical amongst hepatitis B virus genotypes, but varies according to drug-resistance mutation type. Highest genetic barrier is found for secondary/compensatory mutations (e.g. rtL80I/V-rtL180M-rtV173L), whilst most primary mutations (including rtM204V-rtA181T/V-rtI169T-rtA194T) are associated with low genetic barrier. An exception is rtM204I, which can derive from a transition or a transversion. Genotypes A and G are more prone to develop immune/diagnostic-escape mutations sT114R and sG130N. Vaccine-escape associated sT131N-mutation is a natural polymorphism in both A and G genotypes. CONCLUSION: Genetic barrier and reverse transcriptase/HBV surface antigen overlapping can synergistically influence hepatitis B virus drug-resistance/immune-escape development. The different immune-escape potential of specific hepatitis B virus genotypes could have important clinical consequences in terms of disease progression, vaccine strategies and correct HBV surface antigen detection.

Selected amino acid mutations in HIV-1 B subtype gp41 are associated with specific gp120v3 signatures in the regulation of co-receptor usage.

BACKGROUND: The third variable loop (V3) of the HIV-1 gp120 surface protein is a major determinant of cellular co-receptor binding. However, HIV-1 can also modulate its tropism through other regions in gp120, such as V1, V2 and C4 regions, as well as in the gp41 protein. Moreover, specific changes in gp41 are likely to be responsible for of damage in gp120-CCR5 interactions, resulting in potential resistance to CCR5 inhibitors.In order to genetically characterize the two envelope viral proteins in terms of co-receptor usage, we have analyzed 526 full-length env sequences derived from HIV-1 subtype-B infected individuals, from our and public (Los Alamos) databases. The co-receptor usage was predicted by the analysis of V3 sequences using Geno2Pheno (G2P) algorithm. The binomial correlation phi coefficient was used to assess covariation among gp120V3 and gp41 mutations; subsequently the average linkage hierarchical agglomerative clustering was performed. RESULTS: According to G2P false positive rate (FPR) values, among 526 env-sequences analyzed, we further characterized 196 sequences: 105 with FPR <5% and 91 with FPR >70%, for X4-using and R5-using viruses, respectively.Beyond the classical signatures at 11/25 V3 positions (S11S and E25D, R5-tropic viruses; S11KR and E25KRQ, X4-tropic viruses), other specific V3 and gp41 mutations were found statistically associated with the co-receptor usage. Almost all of these specific gp41 positions are exposed on the surface of the glycoprotein. By the covariation analysis, we found several statistically significant associations between V3 and gp41 mutations, especially in the context of CXCR4 viruses. The topology of the dendrogram showed the existence of a cluster associated with R5-usage involving E25DV3, S11SV3, T22AV3, S129DQgp41 and A96Ngp41 signatures (bootstrap = 0.88). Conversely, a large cluster was found associated with X4-usage involving T8IV3, S11KRV3, F20IVYV3, G24EKRV3, E25KRV3, Q32KRV3, A30Tgp41, A189Sgp41, N195Kgp41 and L210Pgp41 mutations (bootstrap = 0.84). CONCLUSIONS: Our results show that gp120V3 and several specific amino acid changes in gp41 are associated together with CXCR4 and/or CCR5 usage. These findings implement previous observations that determinants of tropism may reside outside the V3-loop, even in the gp41. Further studies will be needed to confirm the degree to which these gp41 mutations contribute directly to co-receptor use.

Rapid prediction of sustained virological response in patients chronically infected with HCV by evaluation of RNA decay 48h after the start of treatment with pegylated interferon and ribavirin.

The combination of pegylated interferons (PEG-IFNs) and ribavirin represents the standard of care for the treatment of chronic HCV-infected patients, yet with a success rate around 50% in genotypes 1 and 4, high costs and side effects. Therefore, early prediction of sustained virological response (SVR) is a relevant issue for HCV-patients. We evaluated the association between SVR and decline of HCV-RNA at 48h in a prospective cohort of 145 HCV-patients treated with PEG-IFNs and ribavirin (males=69.1%; genotypes 1/4=51.0%; HIV-1 coinfected=6.7%). SVR was obtained in 65.5% of patients, while 16.6% experienced relapse and 17.9% no response. The first-phase of HCV-RNA decline clearly differentiated patients with SVR from relapsers and non-responders, independently of genotype (P<0.001). In univariate and multivariate analyses, different infralogaritmic thresholds of HCV-RNA decay at 48h were tested, observing the highest predictive potential at 0.5log: decays above this threshold showed a 76.2% negative predictive value for SVR, whereas decays >0.5log indicated a 6.8 odds ratio (95% C.I.: 2.0-23.2) for SVR after controlling for genotype, baseline viremia, adherence to therapy and HIV coinfection. Decays beyond the 0.5log threshold were also strongly associated with and highly predictive of early virological response (95.0% positive predictive value, P<0.001).