Analysis of HIV-1 integrase genotypes and polymorphisms among integrase inhibitors-based antiretroviral treatment naïve patients in South Sudan.
Marta GiovanettiStefania FarcomeniLeonardo SernicolaSara VirtuosoLucia Fontanelli SulekovaMaria T MaggiorellaStefano ButtòGloria TalianiMassimo CiccozziAlessandra BorsettiPublished in: Journal of medical virology (2022)
HIV-1 genetic diversity and drug resistance mutations remain public health challenges especially in regions where treatment is limited. The aim of this study was to characterize the HIV-1 integrase (IN) subtype and the possible occurrence of drug-resistance mutations or polymorphisms in resource-poor settings in South Sudan. Dried blood spots from integrase inhibitor treatment (Integrase strand transfer inhibitor [INSTI]) naïve HIV-1 infected patients were subjected to DNA amplification and direct sequencing of integrase genes. The sequences were interpreted for drug resistance according to the Stanford algorithm and the International AIDS Society-USA guidelines. Phylogenetic analysis revealed that HIV-1 subtype D, C, G, A1, and recombinant forms accounted for 40%, 10%, 13.3%, 23.4%, and 13.3%, respectively. Furthermore, inter-subtype recombinants were interspersed within viral strains sampled in other African countries, highlighting complex transmission dynamics within a mobile host population. A total of 78 of 288 (27%) amino acid IN positions presented at least one polymorphism each. Major INSTI resistance mutations were absent, however, polymorphic accessory mutations at positions M50ILR (26.6%) and L74I (3.3%) were detected. Despite the limited size of the study population, our findings underscore the need for monitoring minor and natural polymorphisms that may influence the outcome of treatment regimens.
Keyphrases
- antiretroviral therapy
- hiv infected
- hiv positive
- human immunodeficiency virus
- hiv infected patients
- public health
- hiv aids
- hepatitis c virus
- hiv testing
- genetic diversity
- sars cov
- end stage renal disease
- machine learning
- chronic kidney disease
- men who have sex with men
- amino acid
- deep learning
- ejection fraction
- risk assessment
- genome wide
- clinical practice
- gene expression
- single molecule
- patient reported outcomes