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Updated: Jan 8, 2026

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings
Published on: March 30, 2014
[Analysis of genetic evolution and mutation patterns of HIV-1 drug-resistant strains in Changzhou City from 2019 to
J Y Jiang1, F M Wang1, J T Wang2
1Pathogen Inspection Center, Changzhou Centre for Disease Control and Prevention, Changzhou 213000, China.
Abstract:
Objective: To analyze the genetic evolution and mutation patterns of HIV-1 drug-resistant strains under drug pressure in Changzhou City from 2019 to 2023. Methods: Demographic data were collected from 380 HIV-1 patients with acquired drug resistance in Changzhou from January 2019 to December 2023. The virus pol gene region was amplified using nested polymerase chain reaction, and the drug resistance analysis was performed using the Stanford University HIV drug resistance database. In addition, phylogenetic trees were constructed using the maximum likelihood model. The mutation rates and the time to the most recent common ancestor were estimated using the Markov Chain Monte Carlo method. The molecular transmission networks were constructed using the Tamura-Nei 93 pairwise gene distance model. A multivariate logistic regression model was simultaneously used to analyze the influencing factors of infection with different HIV genotypes. Results: The predominant drug-resistant subtypes were CRF01_AE (41.50%, 144/347) and CRF07_BC (32.85%, 114/347). The primary resistance mutation sites for Non-nucleoside Reverse Transcriptase Inhibitors (NNRTIs), Nucleoside Reverse Transcriptase Inhibitors (NRTIs), and Protease Inhibitors (PIs), were K103N (21.20%), M184V/I (33.64%), and M46I/L (26.67%), respectively. The multivariate logistic regression analysis results showed that, compared with other transmission routes, individuals infected through homosexual transmission had a higher risk of infection with the CRF01_AE subtype than with the CRF07_BC subtype (aOR=7.27, 95%CI:1.74-49.81). Compared with non-drug users, drug users had a lower risk of infection with the CRF01_AE subtype than with the CRF07_BC subtype (aOR=0.15, 95%CI:0.02-0.61). Evolutionary analysis revealed a yearly growth in the genetic diversity of both subtypes, culminating in the observation of greater genetic distances during 2022-2023. The evolutionary rates of the pol gene for CRF01_AE and CRF07_BC subtypes were 2.14×10-2 substitutions per site per year (95%CI: 1.90×10-2 substitutions per site per year to 2.40×10-2 substitutions per site per year) and 1.97×10-2 substitutions per site per year (95%CI: 1.90×10-2 substitutions per site per year to 2.30×10-2 substitutions per site per year), respectively. The construction of molecular transmission networks revealed that individuals who were non-drug users, male, middle-aged, involved in heterosexual transmission, and with the CRF01_AE subtype were more likely to form transmission networks. Conclusions: From 2019 to 2023, HIV-1 drug-resistant strains in Changzhou City are characterized by subtype diversity, complex drug resistance, and significant influences from sociodemographic factors, with novel evolutionary clusters emerging in the past two years.
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