Computational protocol: High HPgV replication is associated with improved surrogate markers of HIV progression

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Protocol publication

[…] Genotyping was performed in 445 HPgV-positive samples. We extracted nucleic acids by standard procedures and performed nested PCR. Briefly, the first pair of primers amplified the HPgV 5’ untranslated (5’UTR) region fragment (139-400nt) according to GenBank accession number U44402; a second PCR was done to differentiate genotypes 1–4 by molecular weight as described by Naito et al. []. Capillary sequencing of the 5’UTR region fragment was performed to test the samples that did not amplify or those with inconclusive results due to the presence of more than one genotype. HPgV genotype was established according to alignment with the reference sequences corresponding to genotypes 1 to 7 [], GenBank accession numbers: U59547 and AF131116, genotype 1; AB003289, U45966, U44402, U59520, U59521, U59519, AF131118, AY196904, AF104403, and U59518, genotype 2a; U59529, U59532, U59530, U59531, and U59533, genotype 2b; D87711, AB003288, D87251, D87710, AB008335, D87714, D87708, D87713, D87712, AB003290, D87709, and D90601, genotype 3; AB021287, HQ331172, and AB018667, genotype 4; AF131111, and AF131112, genotype 5; AB003292, genotype 6; HQ331234, HQ331233, and HQ331235, genotype 7. Phylogenetic and molecular evolutionary analyses were done using MEGA version 6 []. [...] Due to the wide HPgV viral load heterogeneity among blood donors and the HIV-positive ART-naïve group, we verified the existence of clusters dependant of HPgV viral load. For this purpose, we performed a Finite Mixture Model (FMM) in R software (Version 3.1.3 released 2015-03-09), fitting a Gaussian distribution in both HPgV-positive populations (blood donors and HIV-positive ART-naïve) separately by using NormalmixEM and plot.mixEM functions contained in mixtools package (Version 1.0.3 released 2015-04-18) []. The optimal cut-off point is the value where the probability density curves of both clusters cross.The NormalmixEM uses the Expectation-Maximization (EM) algorithm to determine, by repeating iterations, the joining likelihood between each sample’s viral loads until the model converges. To improve the model and ensure that the analyses of HPgV viral load distribution of both populations (blood donors and HIV-positive ART-naïve) where comparable, we decided to specify the initial values of lambda and sigma instead of leaving them unsupervised [,].The initial sigma value in the model was 1.4 for both populations, calculated as the standard deviation (SD) of HPgV viral load distributions within the entire healthy blood donors HPgV-positive population (SD = 1.4). By selecting only this population to determine the initial sigma value (standard deviation, determined as the initial factor for the FMM model for both groups), we avoided the influence of HIV infection on the SD of the whole population analysed. The initial lambda value (proportion of the subjects in each formed subgroup) used was 0.5, defined as an initial proportion for the formed clusters. This approach helped us reduce the number of iterations until the model converged, and increase the loglik at the estimate number []. Co-infected HAART patients were excluded to avoid treatment as a confounding factor. We then compared by Student’s t-Test the means of HPgV viral load of HPgV -low and HPgV -high, controlling with HPgV genotypes 2 and 3. [...] We compared the means of the following parameters: HIV viral load, CD4+ cell counts [,], and CD4+/CD8+ ratio []. These comparisons were performed according to HPgV major prevalent genotypes (2 and 3) and HPgV condition (HPgV-negative and HPgV-positive) by Student’s t-Test, one-way ANOVA and Bonferroni multiple-comparison test in overall ART-naïve patient population and in the subsequent division into recent (< 6 months) and late (≥ 6 months) HIV infection time groups. Analyses and graphs were made in STATA and SIGMAPLOT software. […]

Pipeline specifications

Software tools MEGA, mixtools, SigmaPlot
Applications Miscellaneous, Phylogenetics
Organisms Homo sapiens
Diseases HIV Infections