Molecular network analysis for detecting HIV transmission clusters: insights and implications

Area of Science:

• Virology
• Epidemiology
• Bioinformatics

Background:

• Understanding HIV transmission dynamics is crucial for effective prevention and control.
• Molecular epidemiology offers tools to analyze viral genetic diversity and transmission patterns.

Purpose of the Study:

• To apply molecular cluster analysis for objective detection of human immunodeficiency virus (HIV) genetic sequence similarity clusters.
• To enhance knowledge of HIV transmission dynamics and inform public health strategies.

Main Methods:

• Analysis of HIV-1 pol gene fragment sequences from 89 HIV-positive couples and Nanjing patients (2015-2019).
• Utilized HYPHY and Cytoscape for molecular network generation and visualization.
• Employed pairwise gene distance and phylogenetic tree approaches to identify clusters.

Main Results:

• The pairwise gene distance method accurately identified 82.02% of positive couples at a gene distance of 0.014.
• Phylogenetic tree and gene distance approaches achieved 86.25% accuracy with optimal parameters.
• Molecular networks constructed revealed distinct clustering patterns, with one method showing 565 sequences without misconnections.

Conclusions:

• Molecular cluster analysis provides novel insights into HIV research.
• Objective identification of genetic clusters aids in understanding HIV transmission and developing targeted interventions.
• Further advancements in this methodology will bolster global HIV/AIDS research and prevention efforts.