Integrating haplotype-enhanced HIV surveillance with national databases improves the resolution of transmission networks, drug resistance, and risk assessment.
Integrating haplotype sequences into HIV surveillance significantly improves the resolution of transmission networks and drug resistance detection.
Where it sits
this study against the rest of the navepegritide corpusSummary and findings
This study evaluated the integration of haplotype sequences into HIV surveillance to improve analyses of transmission networks and drug resistance. A total of 95 newly diagnosed individuals were analyzed, leading to the reconstruction of 1,594 haplotypes. The integration of haplotypes resulted in a 91.1% sequence-level network inclusion rate.
Abstract
Sanger and other consensus-based HIV surveillance methods provide limited resolution for characterizing transmission dynamics. This study evaluated the added value of integrating haplotype sequences into the division of research on viral immunology (DRVI) pol database to improve analyses of viral diversity, transmission networks, and haplotype-level drug resistance. We deep sequenced the pol gene from 95 newly diagnosed individuals in the 2023 national survey, reconstructed haplotypes using CliqueSNV, and incorporated all haplotypes into the DRVI database for network construction. Principal component analysis and k-means clustering were used to identify subgroup structures based on viral diversity, selective pressures, and host factors, and these were linked with molecular network data. A total of 1,594 haplotypes were reconstructed, achieving a 91.1%sequence-level network inclusion rate (1452/1594 haplotypes), which was 2.4-fold higher than that of consensus sequences (37.9%, 36/95). Haplotype integration increased network connections by 2.14-fold (75 vs 35). Two subgroups were identified (<i>p</i> < 0.001), with high-diversity/low-selection individuals forming denser clusters (64 vs 6; <i>p</i> < 0.001). Among detected drug-resistant mutations, 69.7% were observed only at the haplotype level, and resistance patterns varied within the same genetic similarity-based cluster. These findings demonstrate that haplotype integration markedly enhances HIV molecular surveillance by resolving previously unrecognized links, clarifying diversity-associated network structure (which may partly reflect infection stage), and detecting low-frequency drug resistance missed by consensus methods.
Background
This paper addresses the limitations of traditional HIV surveillance methods, which often lack the resolution needed to characterize transmission dynamics effectively. Prior research has shown that consensus-based methods can overlook important viral diversity and drug resistance patterns. By integrating haplotype sequences into existing databases, this study aims to enhance the understanding of HIV transmission networks and drug resistance.
Methods
The study involved deep sequencing of the pol gene from 95 newly diagnosed individuals as part of the 2023 national survey. Haplotype reconstruction was performed using CliqueSNV, and the haplotypes were incorporated into the DRVI database for network analysis. Principal component analysis and k-means clustering were utilized to identify subgroup structures based on viral diversity and host factors.
Results
The primary endpoint revealed a 91.1% sequence-level network inclusion rate (1452/1594 haplotypes). The integration of haplotypes resulted in a 2.4-fold increase in inclusion rate compared to consensus sequences (37.9%, 36/95). Additionally, network connections increased by 2.14-fold (75 vs 35), and two statistically significant subgroups were identified with <i>p</i> < 0.001.
Interpretation
The findings suggest that haplotype integration significantly enhances the resolution of HIV molecular surveillance compared to traditional methods. While the statistical significance of the results is clear, the clinical implications of these findings require further exploration, particularly in terms of how they may influence treatment strategies. Limitations such as the specific population studied and potential confounding factors must be considered when interpreting the results.
Key findings
- 1,594 haplotypes reconstructed from 95 individuals.
- 91.1% sequence-level network inclusion rate (1452/1594 haplotypes).
- 2.4-fold higher inclusion rate than consensus sequences (37.9%, 36/95).
- Network connections increased by 2.14-fold (75 vs 35).
- Two subgroups identified with <i>p</i> < 0.001.
- 69.7% of detected drug-resistant mutations observed only at the haplotype level.
Limitations
- small n=95 study population
- results may not generalize beyond the sample
- potential complexities in interpreting haplotype data