- Research article
- Open Access
Genome-wide analysis of primary peripheral blood mononuclear cells from HIV + patients-pre-and post- HAART show immune activation and inflammation the main drivers of host gene expression
© da Conceicao et al.; licensee BioMed Central Ltd. 2014
- Received: 12 November 2013
- Accepted: 23 February 2014
- Published: 3 April 2014
Although the host gene expression in the context of HIV has been explored by several studies, it remains unclear how HIV is able to manipulate and subvert host gene machinery before and after highly active antiretroviral therapy (HAART) in the same individual. In order to define the underlying pharmaco-genomic basis of HIV control during HAART and genomic basis of immune deterioration prior to HAART initiation, we performed a genome-wide expression analysis using primary peripheral blood mononuclear cells (PBMC) derived from 14 HIV + subjects pre-highly active antiretroviral therapy (HAART) (time point-1 or TP1) with detectable plasma viremia and post-HAART (time point-2 or TP2) with effective control of plasma viremia (<40 HIV RNA copies/mL of plasma).
Genomic RNA extracted from the PBMCs was used in microarray analysis using HT-12V3 Illumina chips. Illumina®BeadStudio Software was used to obtain differentially expressed (DE) genes. Only the genes with p value <0.01 and FDR of <5% were considered for analysis. Pathway analysis was performed in MetaCore™ to derive functional annotations. Functionally significant genes were validated by qRT-PCR.
Between TP1 and TP2, 234 genes were differentially expressed (DE). During viremic phase (TP1), there was an orchestrated and coordinated up-regulation of immune, inflammation and antiviral genes, consistent with HIV infection and immune activation, which comprised of genes mainly involved in antiviral action of interferons and their signalling. In contrast, the therapy-mediated control phase (TP2) showed systematic down-regulation of these pathways, suggesting that the reduction in plasma viremia with HAART has a considerable influence on reducing the immune activation, thereby implying a definitive role of HIV in subverting the human gene machinery.
This is the first study to show the evidence for the differential regulation of gene expression between the untreated and treated time points, suggesting that gene expression is a consequence of cellular activation during plasma viremia. Affirmation to these observations comes from down-modulation of genes involved in cellular activation and inflammation upon initiation of HAART coinciding with below detectable levels of plasma viremia.
- Differentially Express
- Differentially Express Gene
- Plasma Viral Load
- Host Gene Expression
- Genomic Basis
The natural history of HIV is tightly governed by the plasma viral load and T cell modulation in the absence of HAART, which results in massive destruction of CD4+ T cells by multiple mechanisms, leading to T cell exhaustion. HAART leads to dramatic decrease in HIV RNA levels and aids in reducing incidence of opportunistic infections and co-morbidities, mortality rates and stopping secondary transmission [1, 2].
It is apparent that HIV not only targets CD4+ T cells, but also has the ability to infect a variety of blood leukocytes [1, 3–5]. This shows that HIV has the inherent ability to subvert and manipulate the host gene machinery at the transcriptomic level [6–8], thereby having considerable influence on the cell morphology, gene expression, and metabolism. Increased gene expression changes have also been associated with increased viral loads in viremic patients. Nonetheless the global effects of viral infection on host cell gene expression patterns pre- and post- antiretroviral therapy in HIV + patients, still remain poorly understood . Recently, the high-density genome-wide microarrays have greatly facilitated the understanding of genomic basis of host-pathogen and pharmaco-genomic interactions [9, 10].
Following the introduction of HAART, most HIV + patients who adhere to treatment show a good response, defined by a decrease of plasma viral load (pVL) to undetectable levels and an immunological reconstitution with a significant increase of CD4+ T cell levels from baseline values leading to prolonged survival . The immune reconstitution fails under the value of 200 CD4+ T cells/ml, which is considered as a critical threshold, and this occurs in 5–27% of patients receiving HAART [12, 13]. In fact, CD4 + T-cell counts persistently <250 cells/ml, or a percentage of CD4+ T cells <17%, has been considered a sign of poor immune reconstitution . In our study, all our patients before the start of therapy showed 300 CD4+ T cells/mL of blood. Therefore, we hypothesize that successful HAART treatment in responders can guide us to find pharmaco-genomic basis of immunological reconstitution and identify key immune pathways that cooperate during immune reconstitution upon effective therapy. To better characterize genomic events occurring in vivo before and after therapy in HIV + patients, we have used genome-wide analysis using cDNA microarrays on frozen primary ex-vivo-derived (uncultured) PBMCs from HIV + patients at two times points- before (being referred to as TP1 - with detectable plasma viremia) and after the initiation of HAART (being referred to as TP2 with below-detectable <20 copies HIV RNA/mL plasma). The TP2 samples were collected after 2-3 years apart from the initiation of HAART. All patients showed rise in CD4+ T cell counts upon suppressive HAART and reduction in plasma viremia to below detectable levels of HIV RNA and were therefore, termed responders.
In the past, studies using paired design on HIV infected patients (before and after HAART) have shown that several genes are differently expressed between the patients. They also show that the treatment has a limited effect on gene expression in vivo[15, 16]. In this study, we show for the first time a genome-wide snapshot of transcriptomic expression demonstrating that the up-regulation of genes involved in innate and adaptive immunity, inflammation, apoptosis and antiviral functions were unique to pre-therapy with detectable plasma viremia, while their down-regulation coincided with complete suppression of plasma virus to below detectable levels (<40 copies/ml plasma) post-HAART. This distinct regulation of host genes pre-and post-HAART underpins the significant role of genes involved in immune activation and inflammation in driving host gene expression during active HIV infection.
For the total RNA extraction, from the frozen PBMCs the cells were first lysed RNA was extracted as per manufacturer’s instructions (Qiagen RNeasy purification kit, Germany). An on-column digestion of DNA during RNA purification was performed to purify the samples. The DNase was efficiently removed in subsequent wash steps. Total RNA quality was assessed and its concentration was measured using the Agilent RNA 6000 series II Nano kit (Agilent Technologies, CA, USA) through Agilent 2100 Bioanalzer as per manufacturer’s protocol. All RNA integrity numbers considered suitable for microarray were 7 or higher for all samples analysed.
Total RNA was reverse transcribed to synthesize the first strand of cDNA, followed by a second-strand synthesis. Double-stranded cDNA was then transcribed and amplified in vitro to synthesize biotin labelled complementary mRNA (cRNA). cRNA amplification and labeling with biotin were performed using Illumina TotalPrep RNA amplification kit (Ambion, Inc., Austin, USA) with 250 ng total RNA as input material. cRNA yields were quantified with Agilent Bioanalyzer. Seven hundred and fifty nanogram of cRNA sample was hybridized on a HumanHT12 V3 Expression Bead Chip (Illumina, Inc., CA, USA). The chips were stained with streptavidin-Cye3 conjugate and scanned using an Illumina BeadArray Reader (Illumina, Inc).
Differential gene expression analysis
Preliminary gene expression analysis was first performed using Bead Studio software version 3, followed by a detailed analysis in BRB Array Tools. Data was normalized with cubic spline function in order to minimize variation due to non-biological factors. The average signal intensity for each gene was measured using Beadstudio v3. Around 24, 000 genes were selected for differential expression analysis with detection P-value less than 0.01 .
The data acquired from the Illumina Microarray BeadChip, was further used for obtaining the DE list in Illumina®BeadStudio Data Analysis Software. Further, this DE gene list was analysed using the BRB-ArrayTools software installed as an Excel package in order to perform a variety of pre-processing steps including computing probe-set expression summaries, normalization, filtering and calculating quality control indices [18–20]. Quantile normalization was performed to diminish the biological errors due to false positives, to see more clearly the systematic biological differences between the samples and to compensate for systematic technical differences between chips. On the BRB-Array Tools software, the Significance Analysis of Microarrays (SAM) was also performed to identify the genes with statistically significant changes in expression by assimilating a set of gene-specific t-tests. During this process, each gene is assigned a score on the basis of its change in gene expression relative to the standard deviation of repeated measurements for that gene. Genes with scores greater than a threshold are deemed potentially significant. The percentage of such genes identified by chance is the false discovery rate (FDR). To estimate the FDR, nonsense genes were identified by analysing permutations of the measurements. The thresholds were adjusted to identify smaller or larger sets of genes, and FDRs were calculated for each set . In this part of the study we used a FDR of <0.05 with p-value of <0.01 and -1.10 ≤ fold change ≥ 2.85, so the genes that received a fold-change value higher or equal to -1.10 were appointed down-regulated and the genes that received a fold-change minor or equal than 2.85 were up-regulated. The cut off for the fold change was just a consequence of the selection of FDR and p-value.
Further, the differentially expressed genes between TP1 and TP2 were identified using MeV (MultiExperiment Viewer) a desktop application for the analysis, visualization and data-mining of large-scale genomic data. We used false discovery rate, which defined the expected proportion of false positives among the declared significant results. FDR of 5% was used for the differential gene expression analysis of TP1 and TP2, while using significance analysis of microarray (SAM) in order to identify the significant genes for this group. The differential expressed genes were analysed as paired samples.
Pathways enrichment analysis
We used the commercial software MetaCore™ (GeneGo, MI, USA). The differentially expressed genes from the MeV analysis were further analysed to identify the biological networks using the GeneGo Maps modules and GeneGo Folders of the program. Metacore™ conducts functional analysis in the form of network pathways based on a manually curated database of human protein–protein, protein–DNA and protein–compound interactions, metabolic and signalling pathways and the effects of bioactive molecules on gene expression. From each group we selected the most significant pathways. A p-Value is calculated for the common and unique groups. The results are ranked by the –log(p-Value). By default the most significant results for the common part is displayed .
To further verify these findings, we further carried out gene enrichment analysis that consisted of mapping gene IDs of the dataset onto gene IDs in entities of built-in functional ontologies represented in MetaCore by pathway maps and networks. The DE list provided by MeV, was uploaded in the website of MetaCore™ from Thomson Reuters Systems Biology Solutions in order to compare our samples and learn what genes are overrepresented in functionally relevant pathways. This analysis assessed the overall functional character of the sample set, providing a ranked representation of ontologies that are most saturated or “enriched” with the input data. Each GeneGo Process Network represents a comprehensive biological process with a specific functional theme.
Quantitative real time PCR (q-RTPCR)
RT-qPCR was used to corroborate the relationship between expression trends between microarray and RT-qPCR. 14 genes were selected to be used in RT-qPCR due to their high observed-score and relevance to this study. 5ng of total RNA was reverse transcribed using oligo d(T) and Superscript III followed by RNase H treatment (Invitrogen Life Technologies), according manufacture’s protocol. PCR primers were designed for the genes selected on the basis of the microarray data as well as for the control genes (GAPDH: Glyceraldehyde 3-phosphate dehydrogenase) using Primer 3 (http://frodo.wi.mit.edu/primer3/). The cDNA was subjected to RT-PCR with defined primers and SYBR Green (Invitrogen Life Technologies) using MX3000p Stratagene real-time cycler (Stratagene, La Jolla, CA, USA). The data were analysed using the MxPro™ QPCR software version 4.0.1 (Stratagene, La Jolla, CA, USA). For all the experiments duplicates were used and relative mRNA expression was calculated by the comparative ΔΔCt method for all data. Values of fold changes represent averages from duplicates experiments. The data were further analysed by Wilcox test to check the statistical significance.
Clinical profiles of patients enrolled in the study
Clinical profiles showing age, CD4+, CD8+ T cell counts, plasma viral load and the HAART regimen of patients enrolled in the study
CD4 count (cell/μl)
CD8 count (cell/μl)
Viral load (COPIES/ML)
Drugs used during treatment
2 years 07/09/98
day 3 19/09/96
3 years 08/09/99
1.5 years 06/07/98
2 years 23/03/99
3 years 05/04/00
SC peak 18/04/97
on + off Rx 16/03/99
1 year 28/10/98
2 years 11/05/00
2 years 10/05/00
3 years 23/04/01
2 years 13/12/00
1 year 23/09/99
2 years 21/03/00
3 year 26/09/00
Hierarchical clustering analysis of the DE genes
We showed in Figure 1, the groups TP1 and TP2 separated into two clusters (TP1 = patients before treatment; TP2 = patients after treatment), clearly showing genomic distinction between TP1 and TP2 based on the differentially expressed gene list. This gave the confidence to the dataset to further derive specific DE list between TP1 and TP2 for downstream analysis.
Differential expression analysis of genes between TP1 and TP2
The final list of DE genes for the comparison between TP1 (pre-therapy) and TP2 (post-therapy) contained 234 genes of which 212 DE genes were down-regulated and 22 up-regulated (see Additional file 1 for the DE genes list), suggesting significant separable differential expression between these two time points. This difference segregating the two biological entities between pre-and post-therapy time points, based on 234 DE genes, was considered confident enough for downstream pathway, gene networks and gene enrichment analysis.
Pathway enrichment analysis
Pathway map prediction reveals dysregulation of intrinsic Immune response pathways during HIV-1 infection
Following a clear segregation between TP1 and TP2, next we analysed the functional importance of the DE gene list at the pathway level in order to derive functionally annotated genes relevant to TP1 and TP2. Genetic pathway map folders were evaluated using the MetaCore Analytical Suite (GeneGo Inc) (see Additional file 2 for legend of the pathways). The aim of this analysis was to determine what effect virus has on the immune system (seen in TP1) and how antiretroviral drugs ease the heightened gene expression, when virus is suppressed to below detectable levels with therapy in TP2 (see Additional file 1 for the genes involved in the pathways).
MetaCore Map Folders and Pathways identified in the comparison between TP1 and TP2 (p < 0.01, FDR < 0.05)
Immune system response
Immune response_IFN alpha/beta signaling pathway
Cell cycle and its regulation
Immune response_HMGB1/TLR signaling pathway
Vascular development (Angiogenesis)
Tissue remodeling and wound repair
Cell adhesion_Cell-matrix glycoconjugates
Apoptosis and survival_Role of PKR in stress-induced apoptosis
Some pathways of EMT in cancer cells
Hypoxia response regulation
Development_EPO-induced PI3K/AKT pathway and Ca(2+) influx
1.000e + 0
Generation of biological networks by MetaCore™ for the defense response for the disease section of the enrichment analysis
Key network objects
AKT(PKB), Caspase-9, HMGB1, Bcl-XL, IKK-beta
Regulation of defense response (74.0%), regulation of response to stress (80.0%), regulation of immune system process (82.0%), regulation of response to stimulus (92.0%), toll-like receptor 1 signaling pathway (40.0%)
ATM, p21, MLL/MEN fusion protein, E1B-AP5, HEXIM1
Regulation of cell cycle (38.0%), cell cycle checkpoint (24.0%), chromatin organization (30.0%), negative regulation of cellular process (60.0%), chromatin modification (28.0%)
CD19, CD79 complex, MHC class II, CD79B, IL-8
Immune response-regulating cell surface receptor signaling pathway (53.1%), positive regulation of T cell activation (55.1%), T cell co-stimulation (44.9%), lymphocyte co-stimulation (44.9%), response to interferon-gamma (49.0%)
G3BP1 (hdhVIII), H-Ras, GPIAP1, ACSL3, IFNAR2
Leukocyte migration (46.0%), cell activation (54.0%), cell migration (52.0%), hemostasis (48.0%), localization of cell (52.0%)
Casein kinase II, alpha chains, CD4, HSP27, IRF4, HDAC6
Regulation of macromolecule metabolic process (87.8%), regulation of primary metabolic process (87.8%), developmental process (87.8%), regulation of cellular metabolic process (87.8%), regulation of cellular biosynthetic process (79.6%)
Scoring and prioritization of networks/pathways according to the relevance to input data
Further, the examination of disease by biomarker folder was also consistent with top 3 maps related to RNA virus infection, which was again consistent with the GO processes (Figure 5e) with the top 2 maps in the defence response and response to virus. Thus, the process networks, disease by biomarkers and GO processes were all in concordance with the DE genes relevant to immune response to viral infection and RNA virus, which are highly relevant to HIV infection.
Over representation of genes significant in TP1 versus TP2 comparison, based on the top 5 significant pathways
p < = 0.001198
Regulation of apoptosis
p < = 0.001647
Antiviral activity which is Mainly involved in innate immune response
p < = 0.008242
Critical for innate and adaptive immunity against Viral and intracellular bacterial infections and for tumor control
p < = 0.005439
An important mediator of the inflammatory response, and is involved in a variety of cellular activities, including cell proliferation, differentiation, and apoptosis
p < = 0.002616
Acts as both a pro-inflammatory and anti-inflammatory cytokine; stimulate immune response
p < = 0.0007325
Inhibits the activity of IL-1 by binding to its receptor
p < = 0.0007325
Antiviral activity during viral infections
p < = 0.0004398
Restrict virus infection through alteration of cellular protein synthesis
p < = 0.0003696
Involved in the acute inflammatory state in the recruitment and activation of polymorphonuclear leukocytes
p < = 0.002616
Involved in the innate Immune response to viral infection
p < = 0.004717
Involved in the innate Immune response to viral infection
p < = 2.622e-05
Involved in the innate Immune response to viral infection
p < = 0.01396
Inhibits further cellular mRNA translation, thereby preventing viral Protein synthesis; induce cellular apoptosis, to prevent further viral spread
p < = 0.002616
Signal transducer and activator of transcription that mediates signaling by interferons
p < = 0.01797
Signal transducer and activator of transcription that mediates signaling by type I IFNs
p < = 0.006259
Catalyzes the aminoacylation of tRNA(trp) with tryptophan and is induced by interferon; associated with angiogenesis
Thus, these analyses unambiguously confirm the involvement and up-regulation of genes involved in the immune response and inflammation being the most significant driving force during the viremic (TP1) phase, which could be largely attributed to immune activation during viremic state (TP1). Most notable was the regulation in TP2, characterized by systematic down-modulation of the very genes, which predominated TP1. This clearly demonstrates that HIV not only plays a significant role in subverting the host gene machinery, but the gene expression is possibly the main driver of immune activation, which became evident from the reduction in plasma viremia to below detectable levels (<40 copies/ml) upon the initiation of HAART treatment, leading to down-modulation of the very genes that underlie viremia and immune activation.
Quantitative real time PCR corroboration of 14 DE genes
Even though there have been many reports of gene expression changes at different stages of HIV infection  the information on how genomic modulation occurs in HIV-infected individuals assessing whole transcriptome before and after HAART therapy and the genomic basis of the effects of anti-HIV drugs leading to recovery of genes involved in immune reconstitution is still very limited [15, 16, 24].
In this study, we have carried out a genome-wide transcriptomic analysis of primary PBMCs derived from 14 HIV + patients before and after the initiation of HAART therapy to ascertain whether HIV leads to perturbations in immunity-related genes and if so, how these immune function-related genes recover from insults incurred by HIV following HAART when virus becomes below detectable to <20 copies of HIV RNA/ml. These study patients were classed as responders, which was consistent with concomitant rise in their CD4+ T cell counts coupled with reduction in plasma viremia following HAART. The immune reconstitution fails under the value of 200 CD4+ T cells/ml, which is considered as a critical threshold and this occurs in 5–27% of patients receiving HAART [14, 25]. In fact, CD4 + T-cell counts persistently < 250 cells/ml or a percentage of CD4 T cells < 17, have been considered a sign of poor immune reconstitution . In our study, all our patients showed CD4+ T cell counts of 300/uL blood and above. Therefore, we believe that successful HAART treatment in responders will better reflect the genomic basis of immunologic reconstitution and the genes involved in the immune system recovery.
We analysed the peripheral blood mononuclear cells in order to obtain a global snapshot of the differences in gene expressions emanating as a consequence of host-virus interaction. We believe that a single cell type will not be informative in the context of global gene expression. 234 differentially expressed genes were detected between pre-and post-therapy time points, which demonstrate the evidence for distinct transcriptional signatures that segregated the pre-therapy (viremic), and post-therapy (aviremic) time points of HIV + patients. The untreated stage (TP1) was characterized by up-regulation of genes that were related to immune system function, inflammation and activation with 8 of top 10 pathway maps functionally related to immune response genes encompassing antiviral interferons and their signalling, DC maturation and migration, IL-1 signalling, etc. Other 2 pathway maps were related to development and transcriptional regulation. In contrast, treated stage (TP2) when below detectable levels of plasma viremia (<20 copies/ml plasma) was achieved with successful HAART, systematic down-regulation of these functional categories was observed, suggesting that most of the alterations in gene expression observed in untreated stage resumed desired functional levels upon the initiation of HAART treatment. This finding is consistent with the work of Massanella et al.  and Li et al. , where they showed an abatement of innate immune response following treatment. It is important to iterate that post-therapy down-regulation of immune function related genes, specifically to interferon antiviral/signalling genes, inflammatory and cellular activation may be indicative of reduction in cellular activation which gradually occurs with potent antiretroviral therapy and more rapidly in responders- as evidenced here in this study. This argument becomes even more conceivable when the degree of T cell activation is compared between two groups. It has been shown that the degree of T cell activation is lower in controllers than in non-controllers, but higher than observed in HIV uninfected individuals and higher than observed in antiretroviral-treated patients . This corroborates with our observations showing considerably reduced cellular activation levels upon successful HAART signalled by systematic down regulation of the very genes involved in these functional pathways. Therefore, it is plausible to hypothesize that gene expression and its modulation pre-therapy is possibly guided, in part, by cellular activation as a consequence of HIV infection, whereas the down-regulation of these functional pathways post-HAART aid immune reconstitution, pointing at possible genomic basis of immune-reconstitution upon successful HAART. Overall, this analysis showed an aspect of host gene expression pre-and post HAART and is consistent with the findings on the role of HAART treatment in slowing the rate of HIV disease progression and AIDS with improved patient outcomes .
The interferon (IFN) system is a well-studied branch of the innate immune system active against viruses. The infection of cells by many viruses provokes synthesis and secretion of IFNs, which mediates induction of a cellular antiviral state that obstructs further viral spread. For HIV, previous studies have implicated IFNs in blocking both early and late stages of the HIV-1 life cycle [29, 30]. Our data not only confirmed these previous studies, but also are unique in showing molecular signatures, up-regulation and over-representation of immune response genes enriched in IFN and IFN-related pathways with over 25 statistically significant genes during untreated HIV infection as opposed to the treated stage. A closer examination of top 5 pathways immune response pathways showed that even though considerable numbers of genes were overlapping between them, they were all functionally related to IFN family within the immune response folder. The top-ranking pathway was antiviral action of Interferon, enrichment of over 12 DE genes (IFN-beta, IFN-gamma, STAT1, STAT2, IRF9, WARS, PKR, OAS1, OAS2, OAS3, MxA, ADAR1) during untreated stage. All these genes have in common is the activation of the IFN family. It is known that HIV-1 interacts through multiple signalling pathways to reprogram the transcriptome and the proteome of host cells by subverting the host immune defense and antiviral function .
The second most significant pathway involved during the untreated stage was related to signalling of IFN-beta and gamma, both produced by immune cells. These interferons were also related to antiviral responses and are implicated in blocking both early and late stages of the HIV-1 life cycle . Most of the genes overlapped with the top-ranked pathway and all of these genes were related to IFN signalling and they were all up-regulated during the untreated stages of HIV infection.
The third most significant pathway that we show here was related to HMGB1 and TLR signalling down-regulated in TP2 (Figure 7d). High mobility group box 1 (HMGB1) is a DNA-binding nuclear protein that can act as an alarmin, a danger signal to alert the innate immune system for the initiation of host defence . They recognize distinct pathogen-associated molecular patterns and play a critical role in innate immune responses [34–36]. The most significant genes in this particular pathway were Ik-β, MIP1-α, IL-6, IL1RN, IL-1β, IRF7, IFN-β. Previous studies have shown that Toll-like receptor (TLR) pathway contributes to the persistent immune activation observed in chronically HIV-1–infected individuals. Even though the link between immune activation and HIV-1 disease progression is well established, the underlying genomic basis through which HIV-1 induces immune activation remains unclear. In our study, the TLR7 was enriched in the top ranked immune response pathway (p < 0.001926). It has been demonstrated that HIV-1 encodes for multiple TLR7/8 ligands that can mediate direct activation of the immune system in vitro. Moreover, evidence for a role of chronic TLR stimulation in HIV-1 pathogenesis has been recently described in a murine model, which showed that chronic activation of TLR7 can directly lead to immune activation as well as an array of immune dysfunction similar to those observed in human HIV-1 chronic infection . Interestingly, recent data from the non-human primate model of HIV-1 infection provided additional support for a role of the TLR7 pathway in HIV-1 pathogenesis . Apart from this, other genes involved in immune activation (such as CD38, CD83 and TNFSR family) were also significantly expressed and were up-regulated during the untreated phase, suggesting immune activation as one key aspect of the viremic stage and acute infection, which is also consistent with the increased gene expression changes associated with increased plasma viral loads in viremic patients .
The most notable aspect of these studies is an unambiguous demonstration of up-regulation of the most of the pathways related to immune response during the untreated stage of HIV infection followed by down-regulation of the same pathways observed during the HAART-treated stage of HIV infection. By up-regulating most of the pathways related to the IFN system, the host cells could be more in control of virus replication which will determine the course of the infection. It becomes more conceivable, when we see that the increase of IFN production is negatively linked to viral loads. Therefore, it is obvious that the immune activation in patients without HAART treatment affected by the virus is trying to protect the disease spread, which is a beneficial aspect aiding the untreated stage. If we compare this scenario in chronically infected HIV patients, who control virus in the absence of therapy and have below detectable HIV, there is an emerging consensus that much of the inflammatory response related to cellular activation over time does more harm than good.
What we have shown here is that the virus causes more profound functional changes by subverting and in manipulating the host cell gene machinery by regulating the most important part of the host immune system, thereby leading to inflammation and immune system activation. In this context, not only the IFN system pathway and the immune response pathways were the most significant, but the high mobility group box 1 (HMB1) was also up-regulated in patients before treatment, which signals heightened innate immune responses to virus infection. This pathway has more profound importance and is related to the response to exogenous pathogens molecules, acting as a danger signal and triggering inflammation . That means that the HIV-1 virus regulates all the levels of the innate immune response including the signalling inflammation by up-regulating the genes related to this role such as STAT-1, MIP-1α, IL-6, IL1RN and IL1β, as seen in our study. The affirmation to this proof is reflected in the down-modulation of these very genes upon successful HAART and below detectable levels of plasma viremia (TP2) in our study.
Overall, our study is unique not only in demonstrating how virus subverts the host gene machinery by up-regulating key immune response and genes involved in inflammation and cellular activation, but also in showing that HAART therapy modulates gene expression and overcomes the insults that are neutralized upon the initiation of HAART therapy. In simple terms, the successful outcome of HAART therapy can now be visualized genomically through the down-regulation of particular immune and inflammation genes, as this study has shown. It is known that therapy leads to the reconstitution of the immune system and we show that the down-regulation of the most important genes related to immune system response and inflammation may provide respite from cellular activation/inflammation in HIV patients. HAART treatment can improve clinical, virologic, and immunologic characteristics, but very little is known of the host molecular mechanisms underlying untreated versus HAART treated stage of the same patient. Our work shows that although there is a common set of key genes associated with HIV with altered expression in both stages, each stage (pre-and-post therapy) was characterized by unique molecular signatures, which have immense clinical significance.
In summary, the results presented in this study offer new comparative insights related to disease status that can distinguish differentiated patterns of gene expression between HIV patients before and after HAART, which can reveal how genomic insults incurred by HIV recover following the initiation of HAART. Taken together, the innovative approach we have used can be applied to disease conditions other than HIV where correlating complex cellular and molecular signatures of immunity may provide new insights in the mechanisms of pathogenesis and success of therapy.
Our results demonstrate for the first time a clear genome-wide distinction between HIV patients pre-and post highly active antiretroviral therapy. They clearly show that immune activation and inflammation play a significant role in host gene expression in HIV + individuals with viremia. Affirmation to these observations came from successful HAART therapy down-regulating the very genes and pathways that guided viremia and immune activation. This is the first study to show key genomic differences pre-and post-HAART in the same patient, highlighting how virus subverts the host gene machinery and how antiretroviral drugs neutralize this effect at the level of host genome, thereby providing the first snapshot of genomic basis of immune deterioration during viremia and its reconstitution upon successful HAART. Many of the genes and pathways identified in this study will further facilitate the development of new generation of diagnostic markers and novel genome-based strategies for therapeutic interventions for HIV patients.
VC is thankful to the University of Sydney for the IPRS Fellowship for Ph.D studies and the WMIRF for the Top-Up grant. We thank all the patients who participated in this study. This work was approved by the Human Ethics Committee of the Sydney West Area Health Service, Westmead Hospital.
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