Characterization of Novel HIV Drug Resistance Mutations Using Clustering, Multidimensional Scaling and SVM-Based Feature Ranking

Detection of Gag C-terminal mutations among HIV-1 non-B subtypes in a subset of Cameroonian patients

Response to ritonavir-boosted-protease inhibitors (PI/r)-based regimen is associated with some Gag mutations among HIV-1 B-clade. There is limited data on Gag mutations and their covariation with mutations in protease among HIV-1 non-B-clades at PI/r-based treatment failure. Thus, we characterized Gag mutations present in isolates from HIV-1 infected individuals treated with a PI/r-regimen (n = 143) and compared them with those obtained from individuals not treated with PI/r (ART-naïve [n = 101] or reverse transcriptase inhibitors (RTI) treated [n = 118]). The most frequent HIV-1 subtypes were CRF02_AG (54.69%), A (13.53%), D (6.35%) and G (4.69%). Eighteen Gag mutations showed a significantly higher prevalence in PI/r-treated isolates compared to ART-naïve (p < 0.05): Group 1 (prevalence < 1% in drug-naïve): L449F, D480N, L483Q, Y484P, T487V; group 2 (prevalence 1–5% in drug-naïve): S462L, I479G, I479K, D480E; group 3 (prevalence ≥ 5% in drug-naïve): P453L, E460A, R464G, S465F, V467E, Q474P, I479R, E482G, T487A. Five Gag mutations (L449F, P453L, D480E, S465F, Y484P) positively correlated (Phi ≥ 0.2, p < 0.05) with protease-resistance mutations. At PI/r-failure, no significant difference was observed between patients with and without these associated Gag mutations in term of viremia or CD4 count. This analysis suggests that some Gag mutations show an increased frequency in patients failing PIs among HIV-1 non-B clades.

Characterization of two HIV-1 infectors during initial antiretroviral treatment, and the emergence of phenotypic resistance in reverse transcriptase-associated mutation patterns

BACKGROUND

Highly active antiretroviral therapy (HAART) is recommended to control the infection of HIV-1. HIV-1 drug resistance becomes an obstacle to HAART due to the accumulation of specific mutations in the RT coding region. The development of resistance mutations may be more complex than previously thought.

METHODS

We followed two HIV-1 infectors from a HIV-1 drug resistance surveillance cohort in Henan province and evaluated CD4+ T-cell number and viral load thereafter at ten time-periods and characterized their reverse transcriptase-associated mutation patterns at each time point. Then we constructed the recombinant virus strains with these mutation patterns to mimick the viruses and test the phenotypic resistance caused by the mutation patterns on TZM-b1 cells.

RESULTS

CD4+ T-cell number initially increased and then decreased rapidly, while viral load decreased and then dropped sharply during initial antiretroviral treatment. The number of mutations and the combination patterns of mutations increased over time. According to the phenotypic resistance performed by recombinant virus strains, VirusT215Y/V179E/Y181C/H221Y exhibited high levels of resistance to EFV (5.57-fold), and T215Y/V179E-containing virus increased 20.20-fold in AZT resistance (p < 0.01). VirusT215Y/V179E/Y181C increased markedly in EFV resistance (p < 0.01). The IC50 for VirusT215Y/V179E/H221Y was similar to that for VirusT215Y/V179E/Y181C. VirusT215Y/K103N/Y181C/H221Y induced a dramatic IC50 increase of all the four agents (Efavirenz EFV, Zidovudine AZT, Lamivudine 3TC, and Stavudine d4T) (p < 0.01). As for VirusT215Y/K103N/Y181C, only the IC50 of EFV was significantly increased. T215Y/K103N resulted in a 26.36-fold increase in EFV (p < 0.01). T215Y/K103N/H221Y significantly increased the resistance to AZT and 3TC. The IC50 of EFV with T215Y/V179E was lower than with T215Y/K103N (F = 93.10, P < 0.0001). With T215Y/V179E, Y181C significantly increase in EFV resistance, while the interaction between 181 and 221 in EFV was not statistically significant (F = 1.20, P = 0.3052). With T215Y/K103N, neither H221Y nor Y181C showed a significant increase in EFV resistance, but the interaction between 181 and 221 was statistically significant (F = 38.12, P = 0.0003).

CONCLUSIONS

Data in this study suggests that pathways of viral evolution toward drug resistance appear to proceed through distinct steps and at different rates. Phenotypic resistance using recombinant virus strains with different combination of mutation patterns reveals that interactions among mutations may provide information on the impact of these mutations on drug resistance. All the result provides reference to optimize clinical treatment schedule.

Impact of Y181C and/or H221Y mutation patterns of HIV-1 reverse transcriptase on phenotypic resistance to available non-nucleoside and nucleoside inhibitors in China

BACKGROUND

The aim of this study was to investigate the role of K101Q, Y181C and H221Y emerging in HIV-1 reverse transcriptase with different mutations patterns in phenotypic susceptibility to currently available NNRTIs (nevirapine NVP, efavirenz EFV) and NRTIs (zidovudine AZT, lamivudine 3TC, stavudine d4T) in China.

METHODS

Phenotype testing of currently available NNRTIs (NVP, EFV) and NRTIs (AZT, 3TC, d4T) was performed on TZM-b1 cells using recombined virus strains. P ≤ 0.05 was defined significant considering the change of 50% inhibitory drug concentration (IC50) compared with the reference, while P ≤ 0.01 was considered to be statistically significant considering multiple comparisons.

RESULTS

Triple-mutation K101Q/Y181C/H221Y and double-mutation K101Q/Y181C resulted in significant increase in NVP resistance (1253.9-fold and 986.4-fold), while only K101Q/Y181C/H221Y brought a 5.00-fold significant increase in EFV resistance. Remarkably, K101Q/H221Y was hypersusceptible to EFV (FC = 0.04), but was significantly resistant to the three NRTIs. Then, the interaction analysis suggested the interaction was not significant to NVP (F = 0.77, P = 0.4061) but significant to EFV and other three NRTIs.

CONCLUSION

Copresence of mutations reported to be associated with NNRTIs confers significant increase to NVP resistance. Interestingly, some may increase the susceptibility to EFV. Certainly, the double mutation (K101Q/H221Y) also changes the susceptibility of viruses to NRTIs. Interaction between two different sites makes resistance more complex.

Using iterative ridge regression to explore associations between conditioned variables

We address a specific case of joint probability mapping, where the information presented is the probabilistic associations of random variables under a certain condition variable (conditioned associations). Bayesian and dependency networks graphically map the joint probabilities of random variables, though both networks may identify associations that are independent of the condition (background associations). Since the background associations have the same topological features as conditioned associations, it is difficult to discriminate between conditioned and non-conditioned associations, which results in a major increase in the search space. We introduce a modification of the dependency network method, which produces a directed graph, containing only condition-related associations. The graph nodes represent the random variables and the graph edges represent the associations that arise under the condition variable. This method is based on ridge-regression, where one can utilize a numerically robust and computationally efficient algorithm implementation. We illustrate the method's efficiency in the context of a medically relevant process, the emergence of drug-resistant variants of human immunodeficiency virus (HIV) in drug-treated, HIV-infected people. Our mapping was used to discover associations between variants that are conditioned by the initiation of a particular drug treatment regimen. We have demonstrated that our method can recover known associations of such treatment with selected resistance mutations as well as documented associations between different mutations. Moreover, our method revealed novel associations that are statistically significant and biologically plausible.

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.

Docking analysis and resistance evaluation of clinically relevant mutations associated with the HIV-1 non-nucleoside reverse transcriptase inhibitors nevirapine, efavirenz and etravirine

An integrated computational and statistical approach was used to determine the association of non-nucleoside reverse transcriptase inhibitors (NNRTIs) nevirapine, efavirenz and etravirine with resistance mutations that cause therapeutic failure and their impact on NNRTI resistance. Mutations detected for nevirapine virological failure with a prevalence greater than 10% in the used patient set were: K103N, Y181C, G190A, and K101E. A support vector regression model, based on matched genotypic/phenotypic data (n=850), showed that among 6365 analyzed mutations, K103N, Y181C and G190A have the first, third, and sixth greatest significance for nevirapine resistance, respectively. The most common indicator of treatment failure for efavirenz was K103N mutation present in 56.7% of the patients where the drug failed, followed by V108I, L100I, and G190A. For efavirenz resistance, K103N, G190, and L100I have the first, fourth, and eighth greatest significance, respectively, as determined in support vector regression model. No positive interactions were observed among nevirapine resistance mutations, while a more complex situation was observed with treatment failure of efavirenz and etravirine, characterized by the accumulation of multiple mutations. Docking simulations and free energy analysis based on docking scores of mutated human immunodeficiency virus (HIV) RT complexes were used to evaluate the influence of selected mutations on drug recognition. Results from support vector regression were confirmed by docking analysis. In particular, for nevirapine and efavirenz, a single mutation K103N was associated with the most unfavorable energetic profile compared to the wild-type sequence. This is in line with recent clinical data reporting that diarylpyrimidine etravirine, a very potent third generation drug effective against a wide range of drug-resistant HIV-1 variants, shows increased affinity towards K103N/S mutants due to its high conformational flexibility.

Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance

Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway.