HIV-1 Group O Genotypes and Phenotypes: Relationship to Fitness and Susceptibility to Antiretroviral Drugs

Abstract

Despite only 30,000 group O HIV-1 infections, a similar genetic diversity is observed among the O subgroups H (head) and T (tail) (previously described as subtypes A, B) as in the 9 group M subtypes (A-K). Group O isolates bearing a cysteine at reverse transcriptase (RT) position 181, predominantly the H strains are intrinsically resistant to non-nucleoside reverse transcriptase inhibitors (NNRTIs). However, their susceptibility to newer antiretroviral drugs such as etravirine, maraviroc, raltegravir (RAL), and elvitegravir (EVG) remains relatively unknown. We tested a large collection of HIV-1 group O strains for their susceptibility to four classes of antiretroviral drugs namely nucleoside RT, non-nucleoside RT, integrase, and entry inhibitors knowing in advance the intrinsic resistance to NNRTIs. Drug target regions were sequenced to determine various polymorphisms and were phylogenetically analyzed. Replication kinetics and fitness assays were performed in U87-CD4(+)CCR5 and CXCR4 cells and peripheral blood mononuclear cells. With all antiretroviral drugs, group O HIV-1 showed higher variability in IC50 values than group M HIV-1. The mean IC50 values for entry and nucleoside reverse transcriptase inhibitor (NRTI) were similar for group O and M HIV-1 isolates. Despite similar susceptibility to maraviroc, the various phenotypic algorithms failed to predict CXCR4 usage based on the V3 Env sequences of group O HIV-1 isolates. Decreased sensitivity of group O HIV-1 to integrase or NNRTIs had no relation to replicative fitness. Group O HIV-1 isolates were 10-fold less sensitive to EVG inhibition than group M HIV-1. These findings suggest that in regions where HIV-1 group O is endemic, first line treatment regimens combining two NRTIs with RAL may provide more sustained virologic responses than the standard regimens involving an NNRTI or protease inhibitors.

Spiro-β-lactam BSS-730A Displays Potent Activity against HIV and Plasmodium

“…10 Similarly, HIV-1 group O isolates are naturally resistant to NNRTIs and show decreased sensitivity to some protease and integrase inhibitors. 11 Hence, options available to treat patients infected with these viruses are currently very limited. Thus, a continuing need exists for development of novel drugs and regimens in order to address the tolerability and long-term safety concerns associated with current treatment options, and the emergence of drug resistance.…”

“…An additional challenge is that HIV-2 isolates are naturally resistant to NNRTIs and fusion inhibitors (FIs) and present a decreased sensitivity to most protease inhibitors (PIs) . Similarly, HIV-1 group O isolates are naturally resistant to NNRTIs and show decreased sensitivity to some protease and integrase inhibitors . Hence, options available to treat patients infected with these viruses are currently very limited.…”

HIV-1 Non-Group M Strains and ART

“…Concerning HIV-1 group O, the natural genetic polymorphism is now well determined by comparison with HIV-1 group M [ 76 ]. The main issue since 1997 was the natural resistance associated with non-nucleoside RT inhibitors (NNRTIs) [ 78 , 79 , 80 , 81 ] through the well-known Y181C. This mutation was present in 54 to 79% of the strains, depending on the studies [ 76 , 82 ], and is likely to be subgroup-dependent (H or T) [ 83 , 84 ].…”

“…This hypothesis has already been demonstrated for etravirine. Indeed, its phenotypic activity on group O strains was less affected (fold change (FC) of 10) than that of nevirapine (FC of 89) or efavirenz (FC of 42) [ 79 ]. It has also been demonstrated that additional mutations on the RT region could decrease drug susceptibility.…”

CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure

“…Chimeric contigs can closely resemble expressed transcripts, but patterns such as those between co-evolving sites [ 42 ], remapped read counts [ 43 , 44 ] and polymorphisms [ 45 , 46 ] become obscured, and chimera presence has a poorly quantified impact on data analysis [ 41 , 47 , 48 ]. In relation to transcriptomics, included within data analysis is at times the characterization of gene differential expression patterns, where the primary signal utilized are the differences in mapped read counts, across datasets allocated to differing conditions, relative to sequences within a reference [ 49 ].…”