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Originally published online as doi:10.1189/jlb.0306157 on August 29, 2006

Published online before print August 29, 2006
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(Journal of Leukocyte Biology. 2006;80:1031-1043.)
© 2006 by Society for Leukocyte Biology

Microarray data on gene modulation by HIV-1 in immune cells: 2000–2006

Malavika S. Giri*,{dagger}, Michael Nebozhyn*, Louise Showe* and Luis J. Montaner*,1

* HIV Immunopathogenesis Laboratory, Wistar Institute, Philadelphia, Pennsylvania, USA; and
{dagger} Department of Immunology, University of Pennsylvania, Philadelphia, USA

1 Correspondence: HIV Immunopathogenesis Laboratory, Wistar Institute, 3601 Spruce St., Room 480, Philadelphia, PA 19104. E-mail: montaner{at}wistar.org


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUMMARY OF ANALYTICAL APPROACHES...
 INSIGHTS INTO MODULATION OF...
 CONCLUSIONS
 REFERENCES
 
Here, we review 34 HIV microarray studies in human immune cells over the period of 2000–March 2006 with emphasis on analytical approaches used and conceptual advances on HIV modulation of target cells (CD4 T cell, macrophage) and nontargets such as NK cell, B cell, and dendritic cell subsets. Results to date address advances on gene modulation associated with immune dysregulation, susceptibility to apoptosis, virus replication, and viral persistence following in vitro or in vivo infection/exposure to HIV-1 virus or HIV-1 accessory proteins. In addition to gene modulation associated with known functional correlates of HIV infection and replication (e.g., T cell apoptosis), microarray data have yielded novel, potential mechanisms of HIV-mediated pathogenesis such as modulation of cholesterol biosynthetic genes in CD4 T cells (relevant to virus replication and infectivity) and modulation of proteasomes and histone deacetylases in chronically infected cell lines (relevant to virus latency). Intrinsic challenges in summarizing gene modulation studies remain in development of sound approaches for comparing data obtained using different platforms and analytical tools, deriving unifying concepts to distil the large volumes of data collected, and the necessity to impose a focus for validation on a small fraction of genes. Notwithstanding these challenges, the field overall continues to demonstrate progress in expanding the pool of target genes validated to date in in vitro and in vivo datasets and understanding the functional correlates of gene modulation to HIV-1 pathogenesis in vivo.

Key Words: apoptosis • natural killer cells • CD4+ primary T cells • monocyte/macrophages • peripheral blood mononuclear cells • cell lines • up-regulation • down-regulation • latency


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUMMARY OF ANALYTICAL APPROACHES...
 INSIGHTS INTO MODULATION OF...
 CONCLUSIONS
 REFERENCES
 
Since the discovery of HIV-1, important immune correlates of HIV-1 pathogenesis have been identified, including HIV-1 mediated impairment of cellular immune responses; preferential CD4 T cell depletion by HIV-1 infection; and latency and persistence of HIV-1 in macrophages and memory "resting" CD4+ T cells. The mechanisms contributing to these outcomes of HIV-1 infection continue to be a primary focus of current research in HIV immunopathogenesis. The advent of microarrays has expanded the focus of previous studies from characterization of isolated genes to global gene expression analysis. Since the first report of microarrays-based studies of HIV-induced alterations in host cell gene expression nearly six years ago [1 ], additional studies examining cell lines and primary cells infected with HIV-1, cells exposed to HIV envelope, or cells expressing specific HIV accessory proteins have appeared inclusive of recent in vivo studies in HIV-infected patients defining gene expression correlates within immune cells. The in vitro and in vivo microarray studies discussed in this review appeared between 2000 and 2006 and examine the effects of HIV-1 infection and/or gene products on host gene expression within total PBMCs, T lymphocytes, monocyte/macrophages, B cells, NK cells, cell lines of human origin, and tissue samples. The assessment of data reliability requires the application of basic criteria to assess statistical significance of the results. We have highlighted some of the most common approaches to analyzing these huge amounts of data. Table 1 lists the approaches and analytical methods used for the microarray studies evaluated by cell subset. From these studies, Table 2 lists the genes independently validated subsequent to microarray-based detection of significant differences.


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  		Table 1. Analytical Approaches Undertaken by Gene Expression Microarray Studies

 

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  		Table 2. Genes Validated by an Alternate Method Following Initial Microarray Detection for Significancea

 

   SUMMARY OF ANALYTICAL APPROACHES TAKEN
TOP
 ABSTRACT
 INTRODUCTION
 SUMMARY OF ANALYTICAL APPROACHES...
INSIGHTS INTO MODULATION OF...
 CONCLUSIONS
 REFERENCES
 
The analytical approaches applied to HIV-1 microarray experiments tend to reflect those used to analyze other microarray experiments appearing contemporaneously. These 34 reviewed HIV-1 microarray experiments were carried on a wide variety of different types and several generations of microarrays. Affymetrix oligonucleotide chips were the most common, and the rest were split approximately evenly between two main cDNA-based microarray platforms: two-color Cy5/Cy3 biotin-labeled, spotted on glass slides, and 33P-labeled nylon filters (Table 1) . In each microarray experiment, the normalization or preprocessing of the raw data into the quantitative measurements of gene expression was done in a platform-specific manner. This step alone has a significant influence on the analysis outcome; there are many different available methods evolving continuously, and new methods are being brought up in this open area of active investigation in the microarray community. The various normalization methods used in the papers reviewed were in line with the practices commonly accepted at the time of publication. Therefore, the main emphasis will be given to the subsequent analysis of preprocessed microarray data.

Although the methods of analysis may differ considerably, we have focused on the following criteria as being important for assessing the validity of conclusions: whether statistical significance based on P value was considered; whether any estimation was made of the FDR; whether there was validation of important array results by an alternative method; whether the observations were repeated on a validation set of new samples, and whether a resampling procedure was used. Table 1 summarizes these five criteria within the studies evaluated in this review. The majority of early papers reviewed had limited their analysis to identification of genetic biomarkers by simple ratios. Several studies to date have examined limited numbers of samples and thus could only detect the largest changes in the levels of gene expression with any reliability [1 2 3 , 5 , 6 , 10 , 11 , 15 , 17 18 19 20 , 26 ]. That is not to say that no important observations have been made based on these studies, as in a number of cases, validation studies have been carried out to verify microarray results by retesting same samples analyzed in the array studies. Furthermore, these datasets now provide a source of data to estimate the number of samples that would be necessary to obtain more robust results through methods of power analysis and sample size calculations adapted to microarray settings [36 , 37 ].

More recent studies approached the identification of differentially expressed genes through Student’s t-test and ANOVA or permutation-based significance criteria, as implemented in popular Excel packages such as Significance Analysis of Microarrays (SAM) [7 , 12 , 14 , 16 , 21 , 24 , 28 , 30 , 32 , 33 , 35 , 38 ]. For both types of analyses, where comparisons are being carried out among several (biologically defined) groups, the classical problem of multiple testing remains a limitation in the analysis and interpretation. For example, when screening 10,000 genes for differential expression between HIV-infected individuals and healthy controls, t-tests would be performed on 10,000 gene expression values. If the significance criteria for each individual test/gene is set at the commonly chosen value {alpha} = 0.05, it would be expected that ~500 genes (false discoveries) will be found to be significantly, differentially expressed just by virtue of performing statistical testing many times. This would still hold even in cases where no truly differentially expressed genes are present. The FDR is defined as the percentage of false discoveries among significant genes. It is an important piece of information required for critical assessment of conclusions. There are several well-established methods to handle this issue, including those described by Bonferroni, Newman, Kuels, Tukey, Benjamini, and Hochberg [38 39 40 41 42 ]. These methods have become an integral part of current microarray data analysis. The omission of a FDR estimate limits interpretation of results and is included in most recent studies.

After the identification of differentially expressed genes, a confirmation of differential expression of candidate genes of interest should be carried out on an independent set of samples (not used for the initial gene selection), using the same (microarray) or a different platform (quantitative RT-PCR). It should be noted that performing PCR validation on the same set of samples, which were already used for biomarker selection in the microarray experiment, only validates microarray measurements and does not confirm the statistical significance of selected genes.

It is also possible to select a more robust set of biomarkers along with an estimate of their statistical significance by using resampling procedures, such as the jackknife procedure, where the available sample set is partitioned randomly into a training set (usually 90% of samples) used for biomarker selection and a test set consisting of the remaining 10% of the samples. The latter set is used to verify the gene selection. If the randomized partitioning procedure is repeated multiple times, the results for each training-test set pair can be summarized to identify those genes that were identified consistently as significant and performed well on withheld samples, regardless of the changing makeup of the training and testing sets [44 ].

In a number of studies, the combination of resampling procedures with newer, more sophisticated methods of data analysis, based on multivariate statistics and machine-learning techniques, has provided important information with statistical rigor behind conclusions [7 , 12 , 14 , 16 , 21 , 27 , 33 ]. The machine-learning techniques, such as Support Vector Machines [40 ] and Penalized Discriminant Analysis [41 ] are better suited to deal with multivariate data, especially when the number of genes (variables) is orders of magnitude higher than the number of samples (observations), the so-called "curse of dimensionality" when many methods of classical statistics fail or lead to over-fitting of the data. These "supervised" analysis methods require training the algorithm with gene expression for known class members (e.g., controls vs. cases) and then determine whether the selected genes also can distinguish new samples. These approaches have identified biomarkers, which could be validated successfully on independent, blinded data and were reliable enough to work well across multiple platforms [34 , 47 , 48 ].

About one-third of the 34 reviewed microarray papers grouped differentially expressed genes according to the similarity of their expression profiles. The vast majority of those papers used hierarchical clustering [3 , 7 , 8 , 11 12 13 , 24 , 25 , 27 , 32 , 33 ], four papers relied on the k-means clustering algorithm [2 , 7 , 32 , 33 ], and one paper applied self-organized maps [5 ]. In contrast to machine-learning approaches, these methods identify the expression patterns that define a specific class of samples in an unsupervised approach. This can be useful for the identification of expression patterns that identify previously unknown subclasses but nonspecific biases that may result from many technical variables such as sample processing and chip batch can lead to the identification of false subclasses.

For the functional annotation of genes, a majority of the studies has used publicly available NIH databases such as Gene Ontology (GO) [11 , 12 ], DAVID/Expression Analysis Systematic Explorer (EASE) [49 ], Ingenuity Pathways Analysis [24 , 32 ], GenMAPP, as well as Cancer Genome Anatomy Project and Biocarta [13 , 25 ] to identify over-represented gene families and pathways. Three papers [7 , 13 , 33 ] provided estimates of the statistical significance to support the contentions that particular gene families were over-represented in the set of differentially expressed genes. Statistical support for these observations was also assessed through P values given by Fisher exact test in two papers [7 , 33 ] and in one paper, through observed/expected ratio [13 ], based on the representation of the gene family on the array platform being used.

Microarray studies provide a first-level global view of gene expression; however, validation by a second method to assess reliability of expression levels is necessary and was carried out for most of the studies. Quantitative PCR is most frequently used to confirm RNA expression levels, but assays to determine whether protein levels are similarly affected have also been reported [1 , 3 , 4 , 6 , 8 9 10 11 , 13 , 15 , 17 18 19 , 21 , 23 , 24 , 26 27 28 29 30 31 32 33 , 35 ] (Table 2) . Although in most cases, gene expression and protein expression levels were correlated, in a few cases, a reverse correlation was found for some HIV-modulated genes [8 , 19 ]. The latter substantiates the need for complementing statistically stringent analyses with further validation of observations at the protein level and where possible, follow-up functional studies.


   INSIGHTS INTO MODULATION OF HIV-TARGET CELLS: IMMUNE FUNCTION, APOPTOSIS, AND VIRUS REPLICATION/LATENCY
TOP
 ABSTRACT
 INTRODUCTION
 SUMMARY OF ANALYTICAL APPROACHES...
 INSIGHTS INTO MODULATION OF...
CONCLUSIONS
 REFERENCES
 
PBMC-based studies
Microarray reports for PBMCs following in vitro HIV infection or exposure and/or expression of HIV-1 accessory proteins have reconfirmed previous observations of cellular modulation to favor virus replication and dissemination with accompanying cellular immunosuppression (Table 2) . Of interest are studies investigating the "priming" effects of viral envelope (gp120 exposure of PBMCs from four donors) to support virus replication before or after actual infection by leading to an up-regulation of proviral cytokines, chemokines, and transcription factors supporting long-terminal repeat (LTR) expression [4 , 32 ]. The potential for favoring R5 virus replication has been indicated by a greater activation of p38 MAPK pathway genes associated with virus replication by R5 gp120 as compared with X4 gp120 [32 , 50 , 51 ]. Of interest, cell cycle and transcriptional modulator genes previously shown to be up-regulated in CD4 resting T cells in vivo [7 ] were shown to be modulated exclusively by R5 envelope interaction with PBMCs in vitro [32 ]. Taken together, these studies suggest an active conditioning by the viral envelope, independent of infection, and suggest a more efficient R5 gp120-mediated signaling to enable a preferential retention of R5 viruses within a resting, infected CD4 T cell population in vivo.

Among viral factors studied for their modulatory effects within PBMC, HIV-1 viral protein R (vpr) [52 53 54 55 56 ] has been studied extensively with regard to down-modulation of immune-response genes required for accessory cell function and cell cycle genes [18 , 19 , 57 ].

Gene expression studies, to assess the in vivo gene expression status in PBMCs, have been limited. However, these studies have shown gene correlates of viremia, such as the alteration of HIV-specific CD8 T cell differentiation status to reflect a more differentiated and antigen-experienced population, as defined by low IL-7 receptor {alpha} expression and high perforin expression in six viremic patient PBMCs and not in twelve aviremic, untreated patients [27 ]. Apart from the limitation of gene expression studies in PBMCs to define gene expression within subpopulations [20 ], modulation of immune response genes, including genes involved in immature T lymphocyte differentiation, apoptosis, HIV replication, and changing immune homeostasis such as TECK, CD1D, PDCD5, and eotaxin, have been shown in PBMC to be correlated with clinical status and disease prognosis in twelve HIV-1-infected individuals [22 ].

Infection and viral replication of CD4 T lymphocytes
CD4 T cells contribute to HIV pathogenesis and immunodeficiency by producing copious amounts of virus, providing a reservoir CD4 T memory population, and by a loss in numbers as a result of HIV-induced apoptosis [58 59 60 ]. As reviewed below, microarray studies of HIV modulation of CD4 T cell gene expression have identified the cellular machinery required for virus replication; gene regulation confirming previously reported, proapoptotic mechanisms of CD4 T cell-induced cell death; and a prominent role for nef in enhancing expression of virus replication in CD4 T cells, including the identification of nef-mediated cholesterol biosynthesis and uptake as a novel mechanism for enhancing infectivity. Validated genes modulated by HIV infection/exposure in CD4 T cells are listed in Figure 1 and Table 2 .


Figure 1
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  		Figure 1. Summary listing of genes validated in HIV-infected/exposed CD4 T lymphocytes and CD4 T cell lines. Up-regulated genes are in red, and down-regulated genes are in blue. Classification of genes into families follows that provided by DAVID (NIH program) [1 2 3 , 8 , 15 , 26 , 29 ].

 
Data obtained from in vivo circulating cells on resting CD4 T cells from five viremic individuals have been shown to differ in gene expression from resting CD4 T cells in five aviremic individuals on highly active antiretroviral therapy (HAART) as well as CD4 T cells of four healthy controls. Changes include an up-regulation of genes required to sustain virus production, assembly, and release, including genes associated with transcriptional modulation, RNA processing, and protein modification/trafficking [7 ]. Genes associated with virus replication in vitro, including TCR component and signaling genes and associated transcription factor genes such as TCRa, CD8a, CD3d, CD3g, LCK, PAK2, PIKs, MAPKs, CDC42, LYN, C-MYB, GATA-3, STAT4, RUNX1, TFCP2, NFIB2, ATF2, NF-{kappa}B, NFAT, JUN, EGR-1, AP1 [1 , 3 , 8 ], were also modulated in CD4 cells in vivo [7 ].

Modulation of T cell activation with regard to viral production was noted by nef and anti-CD3, leading to up-regulation of transcription factors shown to support HIV-1 LTR transcription. However, nef specifically up-regulates transcription factors such as IFN-regulatory factor 1 (IRF-1), Tat-SF1, and U1SnRNP and not repressors such as IL-16 and YY1, which are up-regulated by anti-CD3, suggesting a nef-mediated mechanism to tip the balance of T cell activation in favor of the virus [2 ]. A nef-induced enhancement of virus infectivity and replication in CD4 T cells was shown to require modulation of cholesterol biosynthetic pathway genes such as HMGCR and SREBF-2 [29 ]. Nef-enhanced cholesterol synthesis has been proposed to enhance viral infectivity by supporting efficient virion assembly in lipid rafts and budding from the membrane [61 , 62 ]. Modulation of the cholesterol biosynthetic pathway as a HIV-specific effect was shown when compared with cell lines exposed to influenza A, heat shock, or IFN [8 ]. Taken together, several microarray studies indicate a role for regulation of T cell activation by HIV-1 nef, which may open the way for exploring interventions to impede nef domains responsible for this activity.

Of equal impact to T cell activation, modulation of cell cycle and apoptosis by HIV-1 infection are important as a result of the loss of CD4 T cells (direct or indirect) associated with viral replication. Viral infection of HIV-infected CD4 T cells lines shows a direct impact on cell cycling as indicated by the down-regulation of pro-cell cycle genes such as PROT{alpha}, CHC, and APC 7 and the up-regulation of negative regulators such as CCNG2 and INSIG1 [8 ]. Among viral products associated with cell cycle regulation is HIV-1 vpr, which was shown to induce cell cycle arrest as a result of diversion of the cell cycle machinery toward LTR expression [63 ].

Regarding apoptosis, HIV infection or HIV accessory proteins have been shown to mediate apoptosis of infected and bystander, uninfected T cells by activating several apoptotic pathways such as the death receptor pathway, the mitochondrial pathway [64 65 66 67 ], and cell cycle inhibition via activation of shared effectors such as cyclin B1, CDK1, mTOR, and p53 [68 69 70 71 72 ]. Microarray studies in primary CD4 T cells and CD4 cell lines point toward a genotoxic stress response in cells following HIV-1 infection and a specific down-modulation of DNA repair genes such as DNA-PK and mitochondrial pathway genes such as CYT-C, ENOYL Co-A, VDAC, and PORIN and up-regulation of the p53 apoptotic pathway genes potentially enhancing the sensitivity of CD4 T cells to apoptosis [3 ]. Dysregulation of p53 and p53-dependent apoptotic genes such as PDCD5, PUMA [73 74 75 ], and BAK has been demonstrated by the HIV envelope in HeLa CD4 cell lines and confirmed subsequently in primary cells and highlights mechanisms of envelope (syncitia)-dependent apoptotic gene modulation. Whether this mechanism contributes to the spontaneous activation-induced apoptosis of T cells in vivo remains to be shown [15 ]. Other proapoptotic genes identified to be up-regulated in CD4 T cells and cell lines were HSP90-ß, DAXX, DAP, FAS, FASL, PIN, GADD45, BAX, and SH3GL3, and antiapoptotic genes such as BCL2, BCLx, and HSP105 were down-regulated [1 , 3 , 8 ]. Taken together, apoptotic gene modulation in CD4 T cells by microarray studies indicates the presence of cell cycle inhibition together with extrinsic viral envelope-dependent and independent-induction pathways supporting gene expression favoring apoptosis.

HIV-1-mediated latency
Several mechanisms have emerged as potential players of HIV-mediated latency, such as modulation of genes controlling transcription, histone deacetylation, and proteasome-mediated protein degradation [76 , 77 ]. Validated genes modulated by HIV infection in latent cell lines are listed in Figure 2 and Table 2 . Taken together, microarray studies have expanded this area by identifying several candidate mechanisms contributing to latency in vitro, which could potentially be operating in vivo, such as transcriptional quiescence driven by a combination of down-regulation of chromatin-remodeling machinery (and related transcription factors) and up-regulation of transcriptional repressors; inhibition of RNA metabolism and processing and cell cycle; escape from immune recognition by down-regulation of surface receptors and immune genes; and a specific up-regulation of virus entry receptors and translation machinery to facilitate virus mRNA translation. Although not stressed by reports, up-regulation of genes promoting survival and of genes inhibiting cellular energy metabolism are also suggested, which may represent dependent functions to maintain latency. Up-regulation of genes associated with modulation of cell cycle and transcription in viremic resting CD4 T cells [7 ] such as WEE1, CAMK2G, NCOA3, YY1, and ERCC5 were also associated with gene modulation following interactions between PBMCs and R5 gp120 envelope protein in vitro [32 ]. Genes associated with signaling, RNA processing, protein transport, assembly, and exocytosis were additionally up-regulated only in resting viremic CD4 T cells and not in aviremic CD4 T cells [7 ], supporting the idea that sufficiency in cellular metabolic processes necessary for completing the virus life cycle is a hallmark of a successful reservoir population of HIV-1. These observations have to be validated in resting CD4 T cells in vivo.


Figure 2
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  		Figure 2. Summary listing of genes validated in latent, chronically HIV-infected cell lines. Up-regulated genes are in red, and down-regulated genes are in blue. Classification of genes into families follows that provided by DAVID (NIH program) [6 , 13 , 17 , 30 , 35 ].

 
Microarray gene expression comparisons among chronic HIV-1-infected ACH-2, U1, and J1.1 cell lines (pre- and post-reactivation from latency) identified common, modulated genes among the three cell lines, suggesting shared pathways for maintaining latency [12 , 13 ]. First, up-regulation of antiapoptotic/antistress genes such as chaperonins and heat shock proteins in latently infected ACH-2 cell lines could contribute to survival of latently infected cells. Second, signaling mediators associated with viral replication such as CDC42, LYN, and CEBPA are down-regulated [78 , 79 ]. Third, up-regulated gene expression of cell cycle inhibitor Egr-1, histone deacetylases HDAC1 and HDAC2, and proteasome subunits are implicated in latency by repression of chromatin and cell cycle (Fig. 2 and Table 2 ). Fourth, DEAD box protein RNA helicases (including, among others, DDX3 and DDX1) are also implicated by their role in export of unspliced/spliced HIV-1 RNA from the nucleus to cytoplasm. Fifth, up-regulation of genes associated with RNA metabolism (RNA processing, spliceosome assembly capping, and translational initiation) in latent, infected cells is proposed to represent candidate host mechanisms involved in latency. Of interest and not stressed by reports, genes of the glycolytic pathway including ALDOA, PGK1, LDHA, and ENO2 were down-regulated in latently infected ACH-2 cell lines, supporting the notion that repression of metabolic pathways required for ATP generation could be yet another mechanism to limit virus replication in these cells. Changes associated with activation out of a latent state have also been identified as potential mediators of latency. For example, genes encoding the ATP-dependent ABC transporter proteins were expressed differentially following activation; this was interpreted as a virus-dependent repression of cellular functions requiring ATP hydrolysis.

Additional latency-promoting genes identified from microarray studies in chronically infected U1 and ACH-2 cell lines include transcription coactivator NCoA3 and transcription factor IRF-8 [30 ]. NCoA3 down-regulation is shown to impede tat-mediated LTR transcription, whereas IRF-8 up-regulation represses IRF-1-mediated activation through the HIV-1 promoter IFN-stimulated response element. Immune response genes such as IL-7, GZM, TLR2, and IFN genes and several other transcription factors such as MYB, MYC, and STAT5A were down-regulated in latently infected cell lines, suggesting transcriptional quiescence in cells. Cell cycle inhibitors such as CDKN1A and histone deacetylases (HDAC3) and survival-promoting genes such as STAT3 were up-regulated in both cell lines. Among viral products and/or cellular factors shown to favor latency, H9 cells expressing HIV-1 tat displayed a down-regulation of genes of the receptor tyrosine kinase pathway and of transcriptional coactivators such as p300/CBP and SRC-1 alongside up-regulation of viral entry receptor genes, cell cycle genes, and translation genes [6 ]. A tat-specific induction and maintenance of latency by immune evasion and cellular differentiation while promoting enhanced virus mRNA translation is proposed. Furthermore, a comparison of gene expression between a SUPT1-derived HIV-resistant clone, which secretes a HIV-1-resistance factor and impedes virus transcription and a susceptible SUPT1 clone, indicated an up-regulation of transcriptional (LTR) repressors such as NFI and CTCF and a down-regulation of transcription (LTR) factors such as IRF-2, EGR-B, STAT5, BMI-1, and NUP214 in the resistant clone [17 ].

Taken together, analysis of latently infected cells have yielded several candidate genes, which have been shown to affect latency in in vitro systems. The challenge remains to translate latency models (most studies were done within a transformed and actively dividing cell) to identify how these host genes are engaged following HIV-1 infection in vivo.

Infection and viral replication in macrophages
HIV and HIV accessory proteins have been shown through several independent reports to modulate macrophage immune response and mediate virus replication in macrophages [82 83 84 85 86 87 88 89 90 91 ]. Gene expression modulation by in vitro HIV infection of macrophages has reconfirmed the proinflammatory gene commitment induced by HIV-1 in macrophages, interpreted to enhance virus replication and result in the persistence of chronically activated monocytes in vivo. Validated genes modulated by HIV infection/exposure in monocyte/macrophages are listed in Figure 3 and Table 2 . HIV-infected or gp120-exposed macrophages up-regulate IFN/NF-{kappa}B-responsive chemokines and cytokines, including CCL2, CCL7, CCL20, IL-6, IL-15, TNF-{alpha}, IFN-{gamma}, IL-1{alpha}, IFN-inducible protein 10, IL-8, MIP-1{alpha}, MIP-1ß, and G-CSF [4 , 23 ], which are proposed to enhance virus dissemination by promoting recruitment of target CD4 T cells and macrophages to sites of infection, as has been observed with HIV-1-infected MDDCs [10 ]. Up-regulation of cytoskeletal reorganization genes such as syntaxins and flotillins have also been identified and reported to enhance virus fusion to host cell membranes [4 ]. Proviral transcription factors (including IFN-stimulated genes, STAT/JAKs, NF-{kappa}B family genes, ETS members, JUN, AP1, and NFAT [4 , 23 ]) and cytokines (such as TNF-{alpha}, IL-6, and IL-15 [92 ]) are up-regulated in macrophages stimulated with gp120, indicating the potential for envelope interactions to mediate an activation state independently of infection.


Figure 3
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  		Figure 3. Summary listing of genes validated in HIV-infected/exposed macrophages and monocytes. Up-regulated genes are in red, and down-regulated genes are in blue. Classification of genes into families follows that provided by DAVID (NIH program) [11 , 16 , 28 , 23 , 31 ].

 
Other microarray studies have centered on modulation of the macrophage cell cycle by HIV-1 as a mechanism to support virus replication in vitro (Fig. 3 and Table 2 ). Macrophages support high levels of viral replication despite being nondividing-differentiated cells. A novel mechanism of macrophage cell cycle gene modulation that supports viral replication has been uncovered recently. Genes promoting cell cycle transition from G1/S-G2/M and arresting cell cycle in the G2/M checkpoint (including BRCA1, PP2A{alpha}, GADD45, and YWHAE) have been identified and validated in in vitro HIV-1 infected macrophages [11 ]. Furthermore, the mechanism of vpr-induced cell cycle arrest by p21 up-regulation in T lymphoid and myeloid cells [47 ] has now been shown in in vitro HIV-infected macrophages as a mechanism to enhance virus production, as inhibition of p21 inhibited viral replication in MDMs [28 ]. Modulation of genes such as p21, HMG-1, MUTL, and DAD1 are interpreted to contribute to cell cycle arrest, facilitating chromatin modification, DNA repair, and protection of in vitro-infected MDMs from apoptosis while permitting viral replication. A common theme that emerges from these studies is the p53-associated regulation of DNA damage response genes and cell cycle inhibition genes [93 94 95 ], potentially conferring cell survival while enhancing viral production. Supporting a greater survival of macrophages in HIV infection, recent studies in circulating monocytes in HIV-infected subjects indicate a gene-to-function relationship between antiapoptosis genes and greater cell survival [34 ]. HIV-infected macrophages in vitro display an over-representation of up-regulated antiapoptotic and antistress genes in difference to CD4 T cells [3 ], oral keratinocytes [26 ], B cells [21 ], and NK cells [33 ], which have been shown to over-represent up-regulated genes mediating apoptosis. Of interest is the possibility to use this information to develop new targets for a macrophage-specific intervention (for example, peroxisome proliferator-activated receptor-{gamma} activation by 2-cyano-3,12-dioxooleana-1,9-dien-28-oic acid inhibits p21 expression and virus replication).

Among viral products contributing to apoptotic gene regulation, macrophage up-regulation of antiapoptotic BCL2 has been associated specifically with tat, and up-regulation of BCLxl and STAT3, together with down-regulation of BAD and ASK, has been associated with nef [96 97 98 99 100 ].

Neurological complications and macrophage HIV infection
Macrophages have been shown to play a central role in the pathogenic outcome of neuro AIDS {HIV-associated dementia (HAD) [101 102 ]}. Circulating monocytes bearing enhanced expression of CD16, CCR5, CCL2, and sialoadhesion genes were identified in ten chronically infected patients with high viral replication in vivo when compared with thirteen patients with low viral load or five seronegative individuals; this was interpreted to suggest a greater susceptibility of these individuals to HAD as a result of enhanced invasive ability by these cells [16 , 103 ]. Microarray studies have explored factors contributing to neuronal apoptosis and pathogenesis of HAD by identifying genes modulated in macrophages, astrocytes, and microglia in vitro and in vivo [14 , 104 105 106 ]. Several studies have been undertaken to identify mechanisms contributing to pathogenesis of neuro AIDS, which is outside the scope of this review. However, comparative in vitro studies of macrophage and mixed glial cultures support the modulation of macrophage as a dominant component of HAD pathogenesis by an up-regulation of antiviral IFN response genes and down-regulation of cell cycle/cell division genes [14 ]. Of interest, a similar "HIV-infected macrophage-like" gene expression pattern was noted in HIV-infected astrocytes [104 ], which contrasts with the proapoptotic outcome for brain microvascular endothelial cells and neurons expressing nef and vpr, respectively [105 , 106 ].

Tissue-based studies and response to therapy studies
Microarray studies using whole tissues from HIV-infected persons have been limited. As in PBMC, gene expression comparisons of mixed populations in other anatomical compartments such as the gut mucosa between three long-term nonprogressors, four high viral load (HVL)-bearing subjects, and four uninfected, naïve individuals allowed for the identification of genes associated with loss of gut CD4 T cells and HIV disease progression in HVL patients [24 ]. An up-regulation of genes mediating inflammation and chemotaxis (such as CD53, RANTES, MCP-2, MIP-4, and TLR-1, among others) was identified in HVL gut mucosal cells, supportive of a state of chronic inflammation, which is interpreted to be a predominant factor contributing to the CD4 T cell loss in HVL patients (Table 2) .

Microarrays have also been applied to obtain genetic correlates of immune restoration pre- and post-ART and/or to investigate gene modulation events associated with vaccination. Mucosal gene expression of jejunal biopsies in six uninfected versus sixteen chronically infected HVL patients pre- and post-HAART treatment indicated that an early initiation of HAART resulted in a more effective restoration of a CD4 T cell population in the gut [9 ]. Following therapy, the CD4 T cell repopulation of the gut was shown to be associated with an increase of chemokine gene expression in the GALT, interpreted to be a major mechanism enabling homing of CD4 T cells as opposed to an increase in cell expansion, as no derepression of the cell cycle inhibition in the CD4 T cells that would permit local proliferation was noted [9 ] (Table 2) .

HIV-1 modulation of B cells, NK cells, and MDDCs
Single studies to date have addressed B cell gene expression in vivo (ten viremic, ten aviremic, and ten seronegative donors) [21 ], in vitro modulation of NK cells exposed to HIV-1 R5 and X4 envelope components (eight samples) [33 ], and in vitro modulation by HIV infection and tat in MDDCs (nineteen samples) [10 ]. Briefly, these studies have identified a partially active B cell phenotype driven by a combination of viral, inflammatory, and terminal differentiation mediators proposed to result in the imbalanced antibody production and increased apoptosis of B cells in vivo; envelope-dependent modulation of NK proliferation, survival, cytokine/chemokine secretion, and cytotoxicity, proposing several mechanisms that may impact NK in vivo; and an up-regulation of inflammatory responses in HIV-infected MDDCs better able to recruit uninfected target CD4 T cells and macrophages proposed to be critical to viral dissemination in vivo (Table 2) .


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 SUMMARY OF ANALYTICAL APPROACHES...
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 CONCLUSIONS
REFERENCES
 
Most studies to date have focused largely on specific genes for validation and interpretation; however, supplementing the analyses with examination of associated composite gene signatures and their correlation to functional responses will help compare and leverage outcomes across studies. There is also an opportunity for studies about defined targets of infection (e.g., DC subsets in vivo; HIV-specific CD8 T cells; isolated subsets from cryopreserved, clinical samples) and for validating the multiple pathways identified in vitro in in vivo models.

In conclusion, the reviewed studies of gene modulation in cells exposed to or infected with HIV-1 reiterate many previous observations and have provided valuable, additional information about modulated gene groups/families as well as coregulated genes. For instance, studies have identified novel gene modulation pathways that could contribute to virus expression and persistence in nondividing macrophage cells such as a stress-related gene response and a differential modulation of cell cycle genes in macrophages as compared with CD4 T cells. As another example, in CD4 T cells, the identification of gene families, which increase viral infectivity such as cholesterol biosynthetic genes, or gene families affecting latency has all been made possible through microarray studies. In addition, archived microarray databases continue to be a valuable resource for hypothesis-directed research of genes of potential significance to HIV pathogenesis.


   ACKNOWLEDGEMENTS
 
This work was supported by grants from the Philadelphia Foundation (Robert L. Jacobs Fund), the Stengel-Miller family, The Wistar Institute’s HIV-1 Partnership Program for Basic Research, the Pennsylvania Department of Health (PA DOH Commonwealth Universal Research Enhancement Program), Tobacco Settlement grants ME01-740 and SAP 4100020718 to L.C.S.; M.N. is supported by NCI T32 CA09171.

Received March 4, 2006; revised June 20, 2006; accepted July 3, 2006.


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