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Gene expression studies can explore various information about different diseases more specifically the working mechanism of diseases. Fortunately, HIV (Human Immunodeficiency Virus)-1 has a well-known pattern of progression. There are three stages through which HIV-1 progresses namely acute, chronic, and non-progressor. The detection of different stages is very important because late detection can lead to AIDS. Automated frameworks can be helpful in the precise detection of different progression stages. In this work, an automated framework has been proposed to detect several stages of HIV-1 progression. This work is based on the analysis of transcriptional profiles of CD4+ and CD8+ T cells. The microarray array data has been processed and reduced before classification. The detection process is based on the Artificial Neural Network which is trained with the help of meta-heuristic algorithms for better convergence. Three different metaheuristic algorithms namely GA, CPSO, and NSGA-II have been compared. The experimental results show that the artificial neural network trained with the Genetic Algorithm achieves 72.22% accuracy, 69.05% precision, 70.73% recall, and 69.88% F-Measure whereas the artificial neural network trained with the Constrained Particle Swarm Optimization achieves 86.67% accuracy, 78.79% precision, 83.87% recall, and 81.25% F-Measure. In contrast, the proposed approach i.e., the artificial neural network trained with the NSGA-II approach achieves 88.24% accuracy, 82.56% precision, 88.87% recall, and 85.6% F-Measure values and outperforms other approaches including decision tree, SVM, and KNN. The results have been verified using the cross-validation procedure that ensures and reflects the usefulness of the method. [ABSTRACT FROM AUTHOR]

Elite and viremic HIV controllers are able to control their HIV infection and maintain undetectable or low-level viremia in the absence of antiretroviral treatment. Despite extensive studies, the immune factors responsible for such exclusive control remain poorly defined. We identified a cohort of 14 HIV controllers that suffered an abrupt loss of HIV control (LoC) to investigate possible mechanisms and virological and immunological events related to the sudden loss of control. The indepth analysis of these subjects involved the study of cell tropism of circulating virus, evidence for HIV superinfection, cellular immune responses to HIV, as well as an examination of viral adaptation to host immunity by Gag sequencing. Our data demonstrate that a poor capacity of T cells to mediate in vitro viral suppression, even in the context of protective HLA alleles, predicts a loss of viral control. In addition, the data suggest that inefficient viral control may be explained by an increase of CD8 T-cell activation and exhaustion before LoC. Furthermore, we detected a switch from C5- to X4-tropic viruses in 4 individuals after loss of control, suggesting that tropism shift might also contribute to disease progression in HIV controllers. The significantly reduced inhibition of in vitro viral replication and increased expression of activation and exhaustion markers preceding the abrupt loss of viral control may help identify untreated HIV controllers that are at risk of losing control and may offer a useful tool for monitoring individuals during treatment interruption phases in therapeutic vaccine trials. [ABSTRACT FROM AUTHOR]

Background: Previous genetic association studies of human immunodeficiency virus-1 (HIV-1) progression have focused on common human genetic variation ascertained through genome-wide genotyping.Methods: We sought to systematically assess the full spectrum of functional variation in protein coding gene regions on HIV-1 progression through exome sequencing of 1327 individuals. Genetic variants were tested individually and in aggregate across genes and gene sets for an influence on HIV-1 viral load.Results: Multiple single variants within the major histocompatibility complex (MHC) region were observed to be strongly associated with HIV-1 outcome, consistent with the known impact of classical HLA alleles. However, no single variant or gene located outside of the MHC region was significantly associated with HIV progression. Set-based association testing focusing on genes identified as being essential for HIV replication in genome-wide small interfering RNA (siRNA) and clustered regularly interspaced short palindromic repeats (CRISPR) studies did not reveal any novel associations.Conclusions: These results suggest that exonic variants with large effect sizes are unlikely to have a major contribution to host control of HIV infection. [ABSTRACT FROM AUTHOR]

Human immunodeficiency virus (HIV) infection causes acquired immunodeficiency syndrome (AIDS), one of the most devastating diseases affecting humankind. Here, we have proposed a framework to examine the differences among microarray gene expression data of uninfected and three different HIV-1 infection stages using module preservation statistics. We leverage the advantage of gene co-expression networks (GCN) constructed for each infection stages to detect the topological and structural changes of a group of differentially expressed genes. We examine the relationship among a set of co-expression modules by constructing a module eigengene network considering the overall similarity/dissimilarity among the genes within the modules. We have utilized different module preservation statistics with two composite statistics: " Z summary " and " MedianRank " to examine the changes in co-expression patterns between modules. We have found several interesting results on the preservation characteristics of gene modules across different stages. Some genes are identified to be preserved in a pair of stages while altering their characteristics across other stages. We further validated the obtained results using permutation test and classification techniques. The biological significances of the obtained modules have also been examined using gene ontology and pathway-based analysis. Additionally, we have identified a set of key immune regulatory hub genes in the associated protein-protein interaction networks (PPINs) of the differentially expressed (DE) genes, which interacts with HIV-1 proteins and are likely to act as potential biomarkers in HIV-1 progression. [ABSTRACT FROM AUTHOR]