HIV-1 drug resistance and genetic transmission networks among patients with sexually transmitted HIV in Ningxia, China.

BACKGROUND

Over the past decade, sexual transmission has become a dominant source of new HIV-1 infection in China. However, very few studies have been conducted to characterize the two sexual transmissions, homosexual and heterosexual transmission. This study was conducted to better understand the relationship between genotypes, drug resistance, and molecular transmission networks in two groups of sexually transmitted HIV-1 in Ningxia, China.

METHODS

Plasma samples were collected from sexually transmitted HIV/AIDS patients in Ningxia between 2020 and 2021 for RNA extraction followed by HIV-1 genome sequencing, genotype and drug resistance analyses. The TN93 model in HyPhy2.2.4 with 1.25% as the threshold, was used to calculate the gene distance, and Cytoscape3.7.0 was used to generate a visual molecular transmission network.

RESULTS

A total of 269 samples were successfully sequenced, and 10 HIV-1 subtypes were detected. The two most common subtypes were CRF07_BC and CRF01_AE. All 10 subtypes were detected in heterosexually transmitted patients, and 7 subtypes were found in homosexually transmitted patients who were exclusively men sex with men (MSM). The drug resistance rates of heterosexual individuals and MSMs were 45.34 and 33.33%, respectively. Sequences from 120 patients entered the molecular transmission network, forming 35 clusters. The clustering rate for MSM (52.78%) was higher than that of heterosexual individuals (39.13%). Some MSM and HSTs were involved in the same cluster and might act as bridges for transmission between the two populations.

CONCLUSION

Our data showed that heterosexually transmitted HIV-1 was more likely to be a drug-resistant virus, whereas MSM was more likely to contract viruses through network connection. It is strongly recommended that resistance testing be conducted before ART to improve effective treatment and reduce the spread of resistant viruses. Molecular networks can help to identify transmission clusters and provide more precise interventions.