Molecular characteristics of HIV-1 subtype C and its impact on therapeutic outcome in Ethiopia

HIV-1 is characterized by a high genetic diversity which poses several challenges and implications with regard to disease progression, drug resistance and outcome of antiretroviral therapy (ART). HIV-1 subtype C (HIV-1C) is the most rapidly expanding subtype accounting for half of the global disease and nearly all infections in Ethiopia, Southern Africa and India which are the regions with the highest burden of HIV-1 infection. Molecular characteristics of the virus in such epidemic success need to be explored to better understand this subtype. In the thesis, we analysed plasma samples and patient data collected during 2009-2011 in a large country-wide cohort, Advanced Clinical Monitoring of ART (ACM) which was established to evaluate the longitudinal effectiveness of ART as practiced in real life in Ethiopia. The overall aim was to investigate the molecular characteristics of HIV-1C and its impact on first line ART outcome in Ethiopia. Both genotypic and phenotypic molecular techniques were employed to characterize different regions of the viral genome. In papers I and II, population sequencing (PBSS) of the V3 loop of the HIV-1 envelope from therapy naïve, patients failing therapy, as well as HIV-1C sequences from Ethiopia dated 1984-2003 was used to assess the molecular epidemiology of HIV-1C in different geographic regions and the trend of viral tropism over the last decades. We also investigated the utility of different genotypic tropism prediction tools and the impact of the predicted viral co-receptor tropism on the outcome of standard first line ART. Our results showed that the Ethiopian epidemic is still monophylogenetic, exclusively dominated by HIV-1C, CCR5 tropic viruses. Furthermore, baseline tropism had an impact on outcome of standard first line ART. While each tool predicted tropism with comparable frequency, there was yet a large discordance between the tools. We elucidated this discordance further in paper III by employing an in-house phenotypic tropism method compared with the prediction by bioinformatics tools used in paper II as well as in vitro sensitivity of HIV-1CEth strains for the co-receptor antagonist maraviroc. The results showed underestimation of R5 co-receptor usage by bioinformatics tools and effectiveness of maraviroc in HIV-1C. Expanding the exploration further to pol gene, we employed PBSS and next generation sequencing (NGS) to assess the prevalence of surveillance drug resistance mutations (sDRM) to reverse transcriptase- and protease-inhibitors as well as occurrence of DRM by NGS to the novel category of integrase strand inhibitors. The results in paper IV showed that NGS detected sDRM associated with RT- and PI- inhibitors more often than PBSS and major INSTI DRMs were found in minor viral variants. Furthermore, DRM identified before treatment was associated with a poorer treatment outcome. In conclusion, viral tropism and drug resistance mutations at baseline have an impact on subsequent treatment outcome. Currently available genotypic tropism prediction tools need further improvement for use in HIV-1C. The Ethiopian epidemic remains uniquely dominated by R5 tropic HIV-1C since its introduction. Further investigations should be done to delineate associated molecular and epidemiological factors contributing to its uniqueness

Prediction of coreceptor usage by five bioinformatics tools in a large Ethiopian HIV-1 subtype C cohort.

Genotypic tropism testing (GTT) has been developed largely on HIV-1 subtype B. Although a few reports have analysed the utility of GTT in other subtypes, more studies using HIV-1 subtype C (HIV-1C) are needed, considering the huge contribution of HIV-1C to the global epidemic.Plasma was obtained from 420 treatment-naïve HIV-1C infected Ethiopians recruited 2009-2011. The V3 region was sequenced and the coreceptor usage was predicted by five tools: Geno2Pheno clinical-and clonal-models, PhenoSeq-C, C-PSSM and Raymond's algorithm. The impact of baseline tropism on antiretroviral treatment (ART) outcome was evaluated.Of 352 patients with successful baseline V3 sequences, the proportion of predicted R5 virus varied between the methods by 12.5% (78.1%-90.6%). However, only 58.2% of the predictions were concordant and only 1.7% were predicted to be X4-tropic across the five methods. Compared pairwise, the highest concordance was between C-PSSM and Geno2Pheno clonal (86.4%). In bivariate intention to treat (ITT) analysis, R5 infected patients achieved treatment success more frequently than X4 infected at month six as predicted by Geno2Pheno clinical (77.8% vs 58.7%, P = 0.004) and at month 12 by C-PSSM (61.9% vs 46.6%, P = 0.038). However, in the multivariable analysis adjusted for age, gender, baseline CD4 and viral load, only tropism as predicted by C-PSSM showed an impact on month 12 (P = 0.04, OR 2.47, 95% CI 1.06-5.79).Each of the bioinformatics models predicted R5 tropism with comparable frequency but there was a large discordance between the methods. Baseline tropism had an impact on outcome of first line ART at month 12 in multivariable ITT analysis but only based on prediction by C-PSSM which thus possibly could be used for predicting outcome of ART in HIV-1C infected Ethiopians

Bivariate intention to treat analysis of treatment outcome of patients at month 6 and month 12 as baseline tropism predicted by five methods.

<p>Bivariate intention to treat analysis of treatment outcome of patients at month 6 and month 12 as baseline tropism predicted by five methods.</p

Concordance of tropism in HIV-1C infected patients as predicted by five bioinformatics tools, before antiretroviral treatment and at virological treatment failure.

<p>Concordance of tropism in HIV-1C infected patients as predicted by five bioinformatics tools, before antiretroviral treatment and at virological treatment failure.</p

Baseline characteristics of 352 HIV-1C infected Ethiopians in relation to predicted tropism by five bioinformatics tools.

<p>Baseline characteristics of 352 HIV-1C infected Ethiopians in relation to predicted tropism by five bioinformatics tools.</p

Multivariable analysis of the impact of baseline viral tropism on antiretroviral treatment response by five bioinformatic methods in an intention to treat analysis.

<p>Multivariable analysis of the impact of baseline viral tropism on antiretroviral treatment response by five bioinformatic methods in an intention to treat analysis.</p

Bivariate on-treatment analysis of treatment outcome of patients at month 6 and 12 as tropism predicted by five methods.

<p>Bivariate on-treatment analysis of treatment outcome of patients at month 6 and 12 as tropism predicted by five methods.</p

Antiretroviral treatment (ART) outcome at month six (a) and 12 (b) for 874 patients.

<p>The number of patients with treatment outcome data at month six and 12 were: Tikur Anbessa Specialized Hospital (TASH): 109, 105; Army: 101, 80; Gondar: 133, 113; Jimma: 109, 80; Mekelle: 119, 97; Harrar: 107, 70; Hawassa: 129, 98. Virologic failure = VL> 1000 copies/ml; LTFU = lost-to-follow-up due to death and other reasons; ART failure = virologic failure plus LTFU.</p

Multivariable associations between baseline characteristics and treatment failure at 6 and 12 months.

<p>Multivariable associations between baseline characteristics and treatment failure at 6 and 12 months.</p

Clinical and laboratory parameters of 874 HIV-infected patients at antiretroviral therapy initiation.

<p>Clinical and laboratory parameters of 874 HIV-infected patients at antiretroviral therapy initiation.</p