People living with HIV (PLHIV) that have access (and adhere) to combination antiretroviral therapy (cART) can anticipate a longer life expectancy and decreased morbidity and mortality relative to untreated patients. However, the increased risk to developing non-AIDS comorbidities remains due to persistently ongoing inflammation in chronic HIV infection.
Aging is associated with an impaired immune response and shares signature pathways and phenotypes which are also observed in chronic HIV infection. We therefore hypothesize that PLHIV exhibit a premature aging phenotype, reflected by age-related changes in the epigenome, transcriptome, proteome and immune cellular phenotype. Using machine learning techniques, we aim to build chronological age predictors (aging clocks) as precise estimators of biological age that will allow us to identify extreme aging phenotypes and possible related biomarkers in our cohort.
The aging of an organism is caused by accumulation of senescent cells secreting SASP-mediators (senescence-associated secretory phenotype) and the progressive loss of proliferation capacity. The cellular senescence research field is a growing area driven by -omics and bioinformatic methods that may help to provide new molecular insights. Taking advantage of the different -omics data layers available in this project, we are interested in to identify the cellular senescence mechanisms underlying premature aging in PLHIV and their relationship with the risk of developing non-AIDS comorbidities. The final goal is the identification of potential therapeutic targets that could either ameliorate or reduce the burden of age-related comorbidities, especially in young PLHIV.