A point mutation in HIV-1 integrase redirects proviral integration into centromeric repeats.
Shelby WinansHyun Jae YuKenia de Los SantosGary Z WangVineet N KewalRamaniStephen P GoffPublished in: Nature communications (2022)
Retroviruses utilize the viral integrase (IN) protein to integrate a DNA copy of their genome into host chromosomal DNA. HIV-1 integration sites are highly biased towards actively transcribed genes, likely mediated by binding of the IN protein to specific host factors, particularly LEDGF, located at these gene regions. We here report a substantial redirection of integration site distribution induced by a single point mutation in HIV-1 IN. Viruses carrying the K258R IN mutation exhibit a high frequency of integrations into centromeric alpha satellite repeat sequences, as assessed by deep sequencing, a more than 10-fold increase over wild-type. Quantitative PCR and in situ immunofluorescence assays confirm this bias of the K258R mutant virus for integration into centromeric DNA. Immunoprecipitation studies identify host factors binding to IN that may account for the observed bias for integration into centromeres. Centromeric integration events are known to be enriched in the latent reservoir of infected memory T cells, as well as in elite controllers who limit viral replication without intervention. The K258R point mutation in HIV-1 IN is also present in databases of latent proviruses found in patients, and may reflect an unappreciated aspect of the establishment of viral latency.
Keyphrases
- antiretroviral therapy
- hiv positive
- hiv infected
- hiv testing
- high frequency
- human immunodeficiency virus
- hepatitis c virus
- hiv aids
- men who have sex with men
- sars cov
- wild type
- genome wide
- randomized controlled trial
- south africa
- end stage renal disease
- single molecule
- transcranial magnetic stimulation
- copy number
- newly diagnosed
- circulating tumor
- cell free
- protein protein
- dna methylation
- high resolution
- high throughput
- body composition
- peritoneal dialysis
- machine learning
- mass spectrometry
- circulating tumor cells