Divergent distribution of HIV-1 drug-resistant variants on and off antiretroviral therapy.

OBJECTIVE: To investigate the distribution of drug-resistant HIV-1 variants in plasma RNA and peripheral blood monuclear cell (PBMC) DNA at treatment failure while on therapy and after stopping therapy. DESIGN: Fifty-eight patients failing their first highly active antiretroviral treatment while on therapy and 50 patients after a median of 18.6 weeks after treatment interruption following multiple treatment failures were analysed. METHODS: Paired plasma HIV-1 RNA and PBMC HIV-1 DNA were used for genotypic antiretroviral resistance testing. Drug resistance was computed by using the Stanford on-line genotype interpretation system. RESULTS: The extent of drug resistance was larger in plasma RNA than in PBMC DNA in the on-therapy group (P=0.004) and in PBMC DNA than in plasma RNA in the off-therapy group (P=0.04). Interpretation of genotype based on PBMC DNA and plasma RNA in the on-therapy and in the off-therapy group would have missed detection of resistance to one or more drugs in 21 and 22% of the patients, respectively, compared to interpretation based on the other blood compartment. In a subset of 27 patients for whom a sample before stopping therapy was available, there was a significant decrease in the number of RNA (mean 8.1 to 5.3, P=0.004), but not DNA (mean 6.8 to 5.7, P=0.143), resistance mutations following treatment interruption. CONCLUSION: While plasma RNA is the material of choice for early detection of drug resistance while on therapy, analysis of PBMC DNA may additionally support and possibly improve sensitivity of resistance testing in the absence of therapy.

Search for Chlamydia pneumoniae genes and their expression in atherosclerotic plaques of carotid arteries.

Samples of atherosclerotic tissue from 58 patients undergoing carotid surgery were analysed by tissue culture and PCR for Chlamydia pneumoniae; PCR was performed to detect Omp1, 16S rRNA and HSP-70 genes. To understand the active pathogenic role of C. pneumoniae, a reverse transcriptase-PCR (RT-PCR) assay was applied to detect the specific RNAs expressed either in the replicative form, or in the cryptic form found in chronic infection. The C. pneumoniae omp1 gene, encoding the major outer-membrane protein (MOMP), was detected in 13 of 58 samples. Among these, the result was confirmed in 11 samples after amplification of a further target, the 16S rRNA, and the presence of the HSP-70 gene, encoding heat-shock protein 70, was revealed in only five cases. All the samples were negative for evidence of specific RNAs by RT-PCR. The presence of genomic DNA and absence of specific RNAs in atherosclerotic tissue samples suggests a lack of an active metabolic or persistent infective role for C. pneumoniae. Thus, traces of C. pneumoniae DNA in these samples could be due to a degradative pathway of the host defensive cellular and biochemical mechanisms.

Development and significance of resistance to protease inhibitors in HIV-1-infected adults under triple-drug therapy in clinical practice.

Development of drug resistance is considered a major cause for failure of antiretroviral therapy in human immunodeficiency virus type 1 (HIV-1)-infected patients adherent to treatment. However, the rate of emergence and the significance of HIV-1 drug resistance in clinical practice have been not investigated thoroughly. Selection of HIV-1 variants that are genotypically resistant to protease inhibitors was studied in all the patients (n = 169) who completed at least 18 months of treatment with a protease inhibitor plus two nucleoside reverse transcriptase inhibitors at two urban Italian hospitals. HIV-1 carrying primary protease inhibitor resistance mutations was detected in 70 (41.4%) patients. The estimated proportion of patients developing genotypic resistance to protease inhibitors at 12 and 24 months was 18.3% (95% CI, 12.5-24.2%) and 33.9% (95% CI, 26.4-41.5%), respectively. Independent predictors of development of resistance to protease inhibitors were higher HIV-1 RNA levels at the nadir (P < 0.0001) and inclusion of ritonavir or saquinavir versus indinavir in the starting regimen (P = 0.0313). Resistance to protease inhibitors was strongly associated with a lower response to treatment, as shown by HIV-1 RNA load (P = 0.0001) and CD4 cell counts (P = 0.005). However, a linear increase in CD4 cell counts was maintained up to the end of follow-up even in the protease inhibitor-resistant population. Resistance to protease inhibitors develops in a relevant proportion of patients under long-term triple-drug therapy in clinical practice and is associated with virological treatment failure and limitation of CD4 cell increase.

Comparative evaluation of three computerized algorithms for prediction of antiretroviral susceptibility from HIV type 1 genotype.

OBJECTIVES: To compare three methods for using HIV-1 genotype to predict antiretroviral drug susceptibility. METHODS: We applied three genotypic interpretation algorithms to 478 reverse transcriptase (RT) and 410 protease sequences for which phenotypic data were available. Sequences were obtained from clinical practice and from published sequences in the Stanford HIV-1 RT and Protease Sequence Database. The genotypic interpretation algorithms included: Stanford HIVdb program (HIVdb), the Visible Genetics/Bayer Diagnostics Guidelines 6.0 (VGI) and a genotypic interpretation program (AntiRetroScan, ARS) developed at the University of Siena, Italy. Genotypic interpretations were normalized to a three-level output: susceptible, intermediate and resistant. Discordances were defined as differences between genotype and phenotype for the same virus isolate. Discordances for which an isolate was considered susceptible by one test but resistant by another test were considered major discordances. RESULTS: The frequency of major discordances between genotype and phenotype was 10.6, 13.7 and 15.7% for ARS, VGI and HIVdb, respectively (P < 0.0001 for ARS versus HIVdb and for ARS versus VGI; P = 0.002 for VGI versus HIVdb). The correlation between genotype and phenotype was highest for non-nucleoside RT inhibitors and lowest for nucleoside RT inhibitors. Half of the major discordances involved stavudine, didanosine and zalcitabine. The concordance among the three genotypic algorithms was high, with weighted Kappa values ranging between 0.76 and 0.84 for the pairwise comparisons between each of the algorithms. CONCLUSIONS: Genotype interpretation algorithms correctly predict phenotype in 85-90% of cases, but the rate of concordance is not uniformly distributed among different drugs. These data provide insight into the potential additional benefit derived from phenotyping.