The prevalence of HIV-associated neurocognitive disorders (HAND) remains high despite widespread use of intensive antiretroviral medicines. Up to 50% of HIV-infected individuals will develop some degree of HIV-related neurocognitive impairment during their lifetimes, ranging from mild deficits that do not significantly impact day-to-day functioning to debilitating dementia. HAND is likely the result of chronic neuroinflammation, mediated in large part by the infiltration of monocytes into the brain. This neuroinflammation, and subsequently the severity of HAND, are enhanced by use of stimulant drugs, including cocaine and methamphetamine. The cellular cascade of events associated with HIV-associated dementia has been well-described, including alterations in the phenotype of blood monocytes, and changes in gene expression and protein expression within brain tissue. However, the vast majority of HAND involves insidious, often chronic mild neurocognitive deficits that do not evolve into dementia. Despite this, the cellular changes associated with mild HAND have not yet been well-delineated. In the proposed study, we will apply innovative systems biology approaches to the investigation of mild HAND pathogenesis. We focus our investigation on circulating blood monocytes, as these cells are an early and key component of this pathogenesis. We will use integrated weighted gene co-expression network analysis (IWGCNA), developed by the co-PI/PD, to develop meaningful biological pathways derived from monocyte-specific gene expression microarrays, HAND-associated genetic markers, and clinical diagnosis of HAND. Using structural equation modeling, we will then determine how stimulant use and virologic biomarkers (e.g. viral load) modify these pathways. This analytic strategy will be first be implemented on a large cohort of HIV-infected individuals from the Multicenter AIDS Cohort Study (MACS). We will then validate our findings on a separate cohort from the National Neurological AIDS Bank (NNAB). To accomplish this ambitious study, we have assembled a collaborative team that includes leaders in immunology, systems biology, neuroAIDS, neuropsychology, and genetics. Further, because we will be relying largely on existing resources and data, the monetary cost and patient burden necessary for the study are extraordinarily low. The results of the study will lead to greater understanding of the mechanisms involved in HAND, provide potential biomarkers of HAND, and identify targets for pharmaceutical prophylactics and therapeutics.
to Public Health The impact of HIV-associated neurocognitive disorders (HAND) is substantial, as it is estimated to affect upwards of 50% of HIV-infected individuals at some point during their lives. Understanding the etiology of HAND, especially now that it has shifted to include primarily mild and chronic impairments, is paramount. This study seeks to identify biological pathways involved in mild HAND, and could lead to novel biomarkers and therapeutic targets.
|Fogel, Gary B; Lamers, Susanna L; Levine, Andrew J et al. (2015) Factors related to HIV-associated neurocognitive impairment differ with age. J Neurovirol 21:56-65|
|Levine, Andrew J; Panos, Stella E; Horvath, Steve (2014) Genetic, transcriptomic, and epigenetic studies of HIV-associated neurocognitive disorder. J Acquir Immune Defic Syndr 65:481-503|
|Langfelder, Peter; Mischel, Paul S; Horvath, Steve (2013) When is hub gene selection better than standard meta-analysis? PLoS One 8:e61505|
|Levine, Andrew J; Horvath, Steve; Miller, Eric N et al. (2013) Transcriptome analysis of HIV-infected peripheral blood monocytes: gene transcripts and networks associated with neurocognitive functioning. J Neuroimmunol 265:96-105|
|Levine, Andrew J; Miller, Jeremy A; Shapshak, Paul et al. (2013) Systems analysis of human brain gene expression: mechanisms for HIV-associated neurocognitive impairment and common pathways with Alzheimer's disease. BMC Med Genomics 6:4|
|Song, Lin; Langfelder, Peter; Horvath, Steve (2013) Random generalized linear model: a highly accurate and interpretable ensemble predictor. BMC Bioinformatics 14:5|