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.