PubMed · 11362045
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1998. Persistent proviral DNA.. https://doi.org/10.1089/apc.1998.12.871
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BACKGROUND: HIV proviral sequencing overcomes the limit of plasma viral load requirement by detecting all the 'archived mutations', but the clinical relevance remains to be evaluated. METHODS: We included 25 participants with available proviral sequences (both intact and defective sequences available) and utilized the genotypic sensitivity score (GSS) to evaluate the level of resistance in their provirus and plasma virus. Defective sequences were further categorized as sequences with and without hypermutations. Personalized GSS score and total GSS score were calculated to evaluate the level of resistance to a whole panel of antiretroviral therapies and to certain antiretroviral therapy that a participant was using. The rate of sequences with drug resistance mutations (DRMs) within each sequence compartment (intact, defective and plasma viral sequences) was calculated for each participant. RESULTS: Defective proviral sequences harbored more DRMs than other sequence compartments, with a median DRM rate of 0.25 compared with intact sequences (0.0, P = 0.014) and plasma sequences (0.095, P = 0.30). Defective sequences with hypermutations were the major source of DRMs, with a median DRM rate of 1.0 compared with defective sequences without hypermutations (0.042, P < 0.001). Certain Apolipoprotein B Editing Complex 3-related DRMs including reverse transcriptase gene mutations M184I, E138K, M230I, G190E and protease gene mutations M46I, D30N were enriched in hypermutated sequences but not in intact sequences or plasma sequences. All the hypermutated sequences had premature stop codons due to Apolipoprotein B Editing Complex 3. CONCLUSION: Proviral sequencing may overestimate DRMs as a result of hypermutations. Removing hypermutated sequences is essential in the interpretation of proviral drug resistance testing.
Human immunodeficiency virus type 1 (HIV-1) and type 2 (HIV-2) are the causative agents of AIDS. HIV-2 is prevalent at moderate to high rates in West African countries, such as Senegal, Guinea, Gambia, and Cape Verde. Diagnosis of HIV-2 is made with a positive HIV-1/HIV-2 ELISA or simple/rapid assay, followed by one or two confirmatory tests specific for HIV-2. Following CD(4)(+)T cell counts, HIV-2 viral burden and clinical signs and symptoms of immunodeficiency are beneficial in monitoring HIV-2 disease progression. Although non-nucleoside reverse transcriptase inhibitors are ineffective in treating HIV-2, nucleoside reverse transcriptase inhibitors and protease inhibitors can be effective in dual and triple antiretroviral regimens. Their use can decrease HIV-2 viral load, increase CD(4)(+)T cell counts and improve AIDS-related symptoms. HIV-2 resistance to various nucleoside reverse transcriptase inhibitors and protease inhibitors, including zidovudine, lamivudine, ritonivir and indinavir, has been identified in some HIV-2 infected patients on antiretroviral therapy. The knowledge of HIV-2 peculiarities, when compared to HIV-1, is crucial to helping diagnose and guide the clinician in the choice of the initial antiretroviral regimen and for monitoring therapy success.
Medical management of HIV infection requires an understanding of the relationship between viral genetic sequences and viral susceptibility to antiretroviral drugs. Because of the high dimensionality of the data on viral genotype, traditional statistical methods are not well suited for investigating this relationship. We develop non-parametric methods specifically for the setting where high-dimensional data provides a basis for predicting a low-dimensional response variable. Our non-recursive methods proceed in three stages: (i) build models, in a forward-stepwise manner, that predict phenotype response from genotype sequence; (ii) identify specific patterns of amino acid sequence that are most influential in predicting phenotype, and (iii) identify combinations of codons that have either a concordant or a discordant association in the occurrence of a mutation. The methods are applied to a data set provided by the Virco Group that contains protease genome sequences and IC50 measurements on a drug from the protease inhibitor class, amprenavir, for 2747 patient samples. From these methods, we were able to identify eight codons from the protease region of the HIV genome that predict resistance to amprenavir, and to determine pairs of codons that tend either to occur together or to preclude the occurrence of the other member of the pair.