br Introduction Human immunodeficiency virus HIV is a retrov

Introduction Human immunodeficiency virus-1 (HIV-1) is a retrovirus that primarily infects components of the human immune system, such as CD4+ T cells, macrophages and dendritic dinaciclib australia (Clapham and McKnight, 2001). HIV directly and indirectly destroys CD4+ T cells, which leads to severe immunodeficiency and increased susceptibility to opportunistic infections in most infected patients (Veazey et al., 1998). In addition, HIV also induces chronic immune activation, including cells involved in the innate immunity and acquired immunity, not only during the early phases of the infection but also throughout the chronic phase (Guha and Ayyavoo, 2013). The state of chronic immune activation contributes to the loss of CD4+ T cells and changes in the immune responses, ultimately leading to disease progression (Sivro et al., 2014). Highly active antiretroviral therapy (HAART) can suppress viral replication, reduce the virus load in a patient's body and partially restore circulating CD4+ T cells to allow the immune system to combat HIV infections (Zhang et al., 1999). However, the side effects of this treatment may accumulate and problems including HIV-associated hypertension disorders (Mateen et al., 2013) and cardiovascular disease (Palella and Phair, 2011) may emerge in certain patients during antiretroviral therapy (ART). Because an increasing number of patients suffer from drug toxicity, the emergence of drug resistant viruses and immune reconstitution inflammatory syndrome (IRIS) following the initiation of HAART represent new challenges in the battle against AIDS (Muller et al., 2010, Chan et al., 2003). Genome-wide gene expression profiling is an informative method used to reveal global changes of the immune system in health and/or disease conditions. It has been particularly useful in identifying biomarkers, examining disease states and investigating immune responses (Chaussabel et al., 2010). Although a number of transcriptomic studies of HIV infection have been conducted, most were based on microarray technologies that focused on a limited number of genes (Ryndak et al., 2014, Wu et al., 2013a, Giri et al., 2006, da Conceicao et al., 2014, Rotger et al., 2010). Thus, these methods are limited in their capacity to detect novel gene products that interact with the virus infection. Recently, next-generation sequencing (NGS) technology has provided a new methodology to both identify and quantify the gene transcripts detected in transcriptome studies (Metzker, 2010). This method, termed RNA-Seq (RNA sequencing), provides highly accurate measurements of genome-wide gene expression via high-throughput NGS sequencing and generates high quality transcriptomic data. This approach yields a plethora of information, including transcript abundance, gene structure, alternative splicing, profiles of non-coding RNA species and genetic polymorphisms (Nagalakshmi et al., 2010, Wang et al., 2009, Yeung et al., 2009). RNA-Seq has been applied in HIV-1 studies. For example, Stewart T. et al. used this technology to examine mRNA and MicroRNA changes in the transcriptome of CD4+ T cells infected with HIV in culture (Pacheco et al., 2013, Ostrowski et al., 2005). Ming D. et al. sequenced RNA transcripts in the brain of HIV-1 transgenic rats to identify differentially expressed genes (DEGs) and enriched pathways affected by the HIV transgene in different areas of the brain (Yamamoto et al., 2011). However, few studies have examined the utility of comparative transcriptomic analysis based on RNA-Seq to investigate HIV-host interactions in samples from HIV patients, especially the transcriptional changes of host genes after HAART. Many genome-wide expression studies of HIV infection are based on an analysis of total peripheral blood mononuclear cells (PBMCs) (da Conceicao et al., 2014, Twine et al., 2003, Showe et al., 2009, Pecankova et al., 2015), which consist of over a dozen cell subsets, including T cells, B cells, NK cells and monocytes. Although the specific gene expression signals of particular cell subsets will be diluted by those from the other cells and thus reduce the specificity of this approach (Wu et al., 2013a, Rotger et al., 2010, Ostrowski et al., 2005), PBMC is a good starting material to obtain generic information against HIV infection.