Clinical Determinants of HIV-1B Between-Host Evolution and their Association with Drug Resistance in Pediatric Patients

Abstract

Understanding the factors that modulate the evolution of virus populations is essential to design efficient control strategies. Mathematical models predict that factors affecting viral within-host evolution may also determine that at the between-host level. Although HIV-1 within-host evolution has been associated with clinical factors used to monitor AIDS progression, such as patient age, CD4 cells count, viral load, and antiretroviral experience, little is known about the role of these clinical factors in determining between-host HIV-1 evolution. Moreover, whether the relative importance of such factors in HIV-1 evolution vary in adult and children patients, in which the course of infection is different, has seldom been analysed. To address these questions, HIV-1 subtype B (HIV-1B) pol sequences of 163 infected children and 450 adults of Madrid, Spain, were used to estimate genetic diversity, rates of synonymous and non-synonymous mutations, selection pressures and frequency of drug-resistance mutations (DRMs). The role and relative importance of patient age, %CD4, CD4/mm3, viral load, and antiretroviral experience in HIV-1B evolution was analysed. In the pediatric HIV-1B population, three clinical factors were primary predictors of virus evolution: Higher HIV-1B genetic diversity was observed with increasing children age, decreasing CD4/mm3 and upon antiretroviral experience. This was mostly due to higher rates of non-synonymous mutations, which were associated with higher frequency of DRMs. Using this data, we have also constructed a simple multivariate model explaining between 55% and 66% of the variance in HIV-1B evolutionary parameters in pediatric populations. On the other hand, the analysed clinical factors had little effect in adult-infecting HIV-1B evolution. These findings highlight the different evolutionary dynamics of HIV-1B in children and adults, and contribute to understand the factors shaping HIV-1B evolution and the appearance of drug-resistance mutation in pediatric patients.

Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach

“…2014 ). We previously observed an increase of the mean between-host virus diversity during ART exposure across pediatric patients from the same cohort of the 24 children under study ( Pagán etal. 2016 ), indicating that plasma virus diversity is sustained during each phase of viral decay despite the large decreases in the replicating population size.…”

“…These parameters may be relevant for the course of the infection and for HIV-1 evolution. Previous results from our group indicate that some of these parameters (children age, decreasing CD4/mm 3 , and upon antiretroviral experience) are key determinants of HIV-1 between-host evolution ( Pagán etal. 2016 ).…”

“…Disease progression has been shown to differ between patients infected through mother-to-child transmission as compared to those infected by other routes ( Tobin and Aldrovandi 2013 ). In a previous study, our group demonstrates that the HIV-1 between-host evolutionary dynamics differs between children and adult populations of Madrid, and identified three clinical factors (age, CD4/mm 3 and antiretroviral experience) as major determinants of HIV-1 population genetic diversity ( Pagán etal. 2016) .…”

“…2016) . Higher HIV-1 subtype B genetic diversity was observed with increasing child age, decreasing CD4/mm 3 and upon antiretroviral experience ( Pagán etal. 2016) .…”

Effect of HIV/HCV Co-Infection on the Protease Evolution of HIV-1B: A Pilot Study in a Pediatric Population

“…Fifteen HIV-1B/HCV coinfected patients with available pol HIV-1 sequences collected between January 1993 and December 2015 were selected, as well as a control group of 56 HIV-1B monoinfected patients (Table S1 ). All 71 enrolled patients were Spanish and both groups had similar distributions of gender, age and time of HIV-1 infection, as the latter two factors have been previously associated with virus evolution 23 . With the aim of avoiding intrinsic subtype evolution features 24 only patients carrying HIV-1 subtype B pol sequences were enrolled in the study.…”