A simple phylogenetic approach to analyze hypermutated HIV proviruses reveals insights into their dynamics and persistence during antiretroviral therapy.

Hypermutated proviruses, which arise in a single Human Immunodeficiency Virus (HIV) replication cycle when host antiviral APOBEC3 proteins introduce extensive guanine to adenine mutations throughout the viral genome, persist in all people living with HIV receiving antiretroviral therapy (ART). However, hypermutated sequences are routinely excluded from phylogenetic trees because their extensive mutations complicate phylogenetic inference, and as a result, we know relatively little about their within-host evolutionary origins and dynamics. Using >1400 longitudinal single-genome-amplified HIV sequences isolated from six women over a median of 18 years of follow-up-including plasma HIV RNA sequences collected over a median of 9 years between seroconversion and ART initiation, and >500 proviruses isolated over a median of 9 years on ART-we evaluated three approaches for masking hypermutation in nucleotide alignments.

Heterogeneous depressive symptom trajectories among women with type 2 diabetes: findings from the Women's Interagency HIV Study.

Background: Depression affects 33% of women with type 2 diabetes (T2D) and leads to increased risks of premature mortality. Fluctuation and variation of depressive presentations can hinder clinical identification.

Purpose: We aimed to identify and examine subgroups characterized by distinct depressive symptom trajectories among women with T2D.

Multi-omic network analysis identifies dysregulated neurobiological pathways in opioid addiction.

Background: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. Yet, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed.

HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data.

Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data.