Host Associated with HIV-1 Replication in Lymphatic Tissue Anthony J. Smith, Qingsheng Li, Stephen W. Wietgrefe, Timothy W. Schacker, Cavan S. Reilly and Ashley T. Haase This information is current as of September 25, 2021. J Immunol published online 8 October 2010 http://www.jimmunol.org/content/early/2010/10/08/jimmun ol.1002197 Downloaded from

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The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. Published October 8, 2010, doi:10.4049/jimmunol.1002197 The Journal of Immunology

Host Genes Associated with HIV-1 Replication in Lymphatic Tissue

Anthony J. Smith,* Qingsheng Li,*,1 Stephen W. Wietgrefe,* Timothy W. Schacker,† Cavan S. Reilly,‡ and Ashley T. Haase*

Much effort has been spent recently in identifying host factors required for HIV-1 to effectively replicate in cultured human cells. However, much less is known about the genetic factors in vivo that impact viral replication in lymphatic tissue, the primary an- atomical site of virus–host interactions where the bulk of viral replication and pathogenesis occurs. To identify genetic determi- nants in lymphatic tissue that critically affect HIV-1 replication, we used microarrays to transcriptionally profile and identify host genes expressed in inguinal lymph nodes that were associated determinants of viral load. Strikingly, ∼95% of the transcripts (558) in this data set (592 transcripts total) were negatively associated with HIV-1 replication. Genes in this subset 1) inhibit cellular

activation/proliferation (e.g., TCFL5, SOCS5 and SCOS7, KLF10), 2) promote heterochromatin formation (e.g., HIC2, CREBZF, Downloaded from ZNF148/ZBP-89), 3) increase collagen synthesis (e.g., PLOD2, POSTN, CRTAP), and 4) reduce cellular transcription and trans- lation. Potential anti–HIV-1 restriction factors were also identified (e.g., NR3C1, HNRNPU, PACT). Only ∼5% of the transcripts (34) were positively associated with HIV-1 replication. Paradoxically, nearly all of these genes function in innate and adaptive immunity, particularly highlighting heightened expression in the IFN system. We conclude that this conventional host response cannot contain HIV-1 replication and, in fact, could well contribute to increased replication through immune activation.

More importantly, genes that have a negative association with virus replication point to target cell availability and potentially new http://www.jimmunol.org/ viral restriction factors as principal determinants of viral load. The Journal of Immunology, 2010, 185: 000–000.

ver the last decade, systems biology has taken on an licates in the complex environment of lymphatic tissue (LT) in increasingly important role in investigating microbial dis- the context of a host responding to infection. In previous micro- O eases, delineating salient features of the host-pathogen array studies of HIV-1 infection in LT, we have shown that infection relationship, and identifying potential host genes that are critical massively perturbs host gene expression and that this transcrip- determinants of microbial replication and pathogenesis. In the case tional profile is highly dependent on stage of disease (9). In this of HIV-1, which like any obligate intracellular pathogen relies on work, we report studies that go beyond this initial identification of the transcriptional and translational machinery of the host cell to stage-specific features of the host response in LT to now identify by guest on September 25, 2021 complete its life cycle (1–3), these studies have revealed com- genes that play important roles in viral replication in vivo. We now ponents of host gene expression that establish a favorable intra- show the following: 1) there is little overlap between genes in vivo cellular environment for efficient virus replication. For example, compared with genes in vitro that correlate with viral replication; genomics-based approaches have, to date, documented changes in 2) paradoxically, host immune responses correlate with high viral gene expression in cultured cells during HIV-1 infection (4), and loads; and 3) ∼95% of the correlations are inverse correlations that more recently, small interfering RNA technology has identified point to the importance of target cell availability, cellular activa- hundreds of host genes seemingly indispensable for HIV-1 repli- tion, transcriptional factors, and new inhibitors as determinants of cation in vitro (5–8). viral load in vivo. In contrast, much less is known about host genes that play important roles in viral replication in vivo in which HIV-1 rep- Materials and Methods Ethics statement This study was conducted according to the principles expressed in the *Department of Microbiology, †Department of Medicine, Medical School, and ‡Division of Biostatistics, School of Public Health, University of Minnesota, Minne- Declaration of Helsinki. This study was approved by the Institutional apolis, MN 55455 Review Board of the University of Minnesota. All patients provided written informed consent for the collection of samples and subsequent analysis. 1Current address: Nebraska Center for Virology, School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE. Lymph node biopsy specimens Received for publication July 2, 2010. Accepted for publication August 25, 2010. Inguinal lymph node biopsies from 22 untreated HIV-1–infected individuals This work was supported by National Institutes of Health Grant R01 AI056997 (to at different clinical stages were obtained for this University of Minnesota A.T.H.). Institutional Review Board-approved microarray study. Viral load meas- The sequences presented in this article have been submitted to the Gene Expression urements were obtained the same day as biopsies. Each lymph node biopsy Omnibus Web site under accession number GSE21589. was placed into a Falcon tube and snap frozen by dropping it into liquid Address correspondence and reprint requests to Dr. Ashley T. Haase, Department of nitrogen. Microbiology, Medical School, University of Minnesota, MMC 196, 420 Delaware RNA extraction, synthesis of biotin-labeled cRNA probes, and Street S.E., Minneapolis, MN 55455. E-mail address: [email protected] microarray hybridization The online version of this article contains supplemental material. Abbreviations used in this paper: CIA, chronic immune activation; LT, lymphatic Frozen lymph nodes were homogenized with a power homogenizer (Heat tissue; PKR, dsRNA-dependent kinase; TReg, regulatory T. Systems Ultrasonic, Farmingdale, NY) in TRIzol (Invitrogen, Carlsbad, CA) without thawing. Total RNA was isolated, according to the manu- Copyright Ó 2010 by The American Association of Immunologists, Inc. 0022-1767/10/$16.00 facturer’s protocol, and further purified with an RNeasy mini kit (Qiagen,

www.jimmunol.org/cgi/doi/10.4049/jimmunol.1002197 2 HOST GENES IN LYMPH NODE ASSOCIATED WITH HIV-1

Valencia, CA). Double-stranded cDNA and biotin-labeled cRNA probes reagent (Biocare Medical) for 15 min at room temperature. A primary Ab were synthesized from 5 mg total RNA with a MessageAmp II aRNA kit specific for collagen type 1 (Sigma-Aldrich; clone Col-1, catalog C2456) (Ambion, Austin, TX). The cRNA probes were column purified and was diluted 1:100 in Tris-NaCl-blocking buffer (0.1 M Tris-HCl [pH 7.5]; fragmented with a fragmentation kit (Ambion). 0.15 M NaCl; 0.05% Tween 20 with DuPont [Wilmington, DE] blocking Fifteen micrograms of fragmented cRNA was hybridized to an Affy- buffer) and incubated overnight at 4˚C. After the primary Ab incubation, metrix Human Genome U133 Plus 2.0 array (Santa Clara, CA). After sections were washed with PBS and then incubated with fluorophore- hybridization, chips were washed, stained with streptavidin-PE, and conjugated secondary Abs (Alexa Fluor dyes; Invitrogen) in 5% nonfat scanned with GeneChip Operating Software at the Biomedical Genomics milk for 2 h at room temperature. These sections were washed and Center at the University of Minnesota. The experiments from each RNA mounted using Aqua Poly/Mount (Polysciences, Warrington, PA). Immu- sample were duplicated in the preparation of each cRNA probe and nofluorescent micrographs were taken using an Olympus BX61 Fluoview microarray hybridization. confocal microscope with the following objectives: 320 (0.75 NA), 340 (0.75 NA), and 360 (1.42 NA); images were acquired using Olympus Microarray data analysis Fluoview software (Melville, NY; version 1.7a). Isotype-matched IgG- or IgM-negative control Abs in all instances yielded negative staining results. The .cel files produced by the Affymetrix data analysis platform were uploaded into the Expressionist program (Genedata, Pro version 4.5, Basel, Microarray data accession number Switzerland), and the expression level for each of the ∼56,000 probe sets All microarray results have been deposited in the Gene Expression Omnibus on the arrays was quantified using the robust multi-chip average algorithm. database (http://www.ncbi.nlm.nih.gov/geo/; accession number GSE21589). The mean expression level from duplicate chips from the same individual’s RNA was computed and used in the subsequent analysis. The robust multi- chip average algorithm produces a summary of gene expression that is of log scale; we used this log scale for all analyses. Results

To examine the relationship between gene expression and viral load, Predominance of negative correlations with viral replication Downloaded from a linear regression model was fit with gene expression as the response To identify genes within LT related to viral replication, we ex- variable and disease stage and the log of viral load as predictor variables. We controlled for disease stage when examining the relationship between viral amined host gene expression and its association with viral load load and gene expression because we previously found an association in inguinal lymph nodes from 22 HIV-1–infected individuals (Table between gene expression and disease stage for HIV-1 infection (9). The I). We identified 592 transcripts significantly associated with viral p values for the null hypothesis (testing no association between viral load load (20.6 . partial correlation coefficient . 0.6; 7% false dis- and gene expression) were then transformed to q values using the q value ∼ http://www.jimmunol.org/ package in the statistical software program R. By varying the threshold for covery rate) (Fig. 1A). Strikingly, 95% of the transcripts (558) in the q values, one can obtain a set of genes that are found to be associated this data set are negatively associated with HIV-1 replication, with viral load at a given false discovery rate. Because the number of genes whereas only ∼5% (34 transcripts) have a positive association (Fig. found to be associated with viral load increases dramatically as this 1B). Based on /annotations from the NetAffx Analy- ∼ threshold rises above 7%, we used the conservative cutoff value of 7% sis Center, Ingenuity Pathways Analysis, and extensive examination for the false discovery rate. To summarize the association between log of ∼ viral load and gene expression, we computed the partial correlation be- of published literature, we classified 60% of all altered tran- tween these two quantities for all genes while controlling for the effect of scripts into functional categories, resulting in a list of 345 genes (the disease stage. remaining transcripts have, as of yet, no identification or functional information available) (Figs. 2, 3, Supplemental Table 1).

Immunofluorescence by guest on September 25, 2021 Antiviral host response positively correlates with virus Immunofluorescence was performed using a biotin-free detection system on 5-mm tissue sections mounted on glass slides. Tissues were deparaffinized replication and rehydrated in deionized water. Heat-induced epitope retrieval was Surprisingly, only a small subset of genes was positively associated performed using a water bath (95–98˚C for 10–20 min) in DiVA Decloaker (Biocare Medical, Concord, CA) retrieval buffer, followed by cooling to with viral replication (32 genes + 2 transcripts of unknown function), room temperature. Tissue sections were then treated with Proteinase K with most of these genes (∼70%) paradoxically coding for (4 mg/ml) for 20 min at 37˚C, followed by blocking with SNIPER blocking involved in innate and adaptive defenses, which might have been

Table I. Clinical characteristics of study subjects

Patient Peripheral Blood CD4+ Plasma HIV-1 RNA Identification Disease Stage Gender Age Race T Cell Count (cells/ml) Levels (copies/ml) MH58 Acute Male 51 Caucasian 400 439,000 JS30 Acute Male 26 Caucasian 683 3,610 DP24 Acute Male 41 Caucasian 494 14,696 WB55 Acute Male 23 African American 209 19,400 PH29 Acute Male 59 Caucasian 370 484,694 WB91 Acute Male 37 African American 234 24,718 PR89 Acute Male 32 Caucasian 824 32,173 TH49 Acute Male 30 Caucasian 333 100,000 TS35 Acute Male 42 Caucasian 663 100,000 JR31 Asymptomatic Male 34 Caucasian 333 71,200 WU59 Asymptomatic Male 36 Caucasian 286 100,000 KS64 Asymptomatic Male 34 Caucasian 202 122,000 RJ56 Asymptomatic Male 49 Caucasian 478 174,000 DS35 Asymptomatic Male 32 Caucasian 400 15,284 RM08 Asymptomatic Male 39 Caucasian 685 20,014 CF63 Asymptomatic Male 23 African American 259 27,200 TK07 Asymptomatic Male 35 Caucasian 372 31,922 SU36 Asymptomatic Male 63 Caucasian 248 46,400 GL38 AIDS Male 49 Caucasian 147 4,960 TK62 AIDS Male 43 Caucasian 81 35,000 VM27 AIDS Female 40 African American 112 12,046 OB06 AIDS Male 45 African American 188 10,684 The Journal of Immunology 3 Downloaded from http://www.jimmunol.org/

FIGURE 1. The majority of transcripts in lymphatic tissue are negatively associated with viral load. A, Histogram of the partial correlation coefficients for each transcript in the Affymetrix Human Genome U133 Plus 2.0 array. The partial correlation coefficient represents the strength of association between . viral load and gene expression while controlling for stage of disease. Transcripts with an absolute value 0.6 were deemed differentially expressed while by guest on September 25, 2021 controlling for a false discovery rate of 0.7. B, Pie chart depicting the number of transcripts remaining after applying the selection criteria (20.6 . partial correlation coefficient . 0.6; 7% false discovery rate). The green and red sectors identify transcripts with a negative and positive association, respectively, between gene expression and viral load.

expected to decrease viral load (Fig. 2). IFN-stimulated genes were rescence intensity of specified genes for each sample and calcu- a prominent component of this response (16 of the 22 genes as- lated as follows: cribed to immune defenses), but there were also genes encoding n + Mean fluorescence intensity proteins involved in cell-mediated cytotoxicity (granzyme H, per- ¼ ; CIA n forin, CD8, and NKG7), antiviral chemokines (RANTES and MIP- b 1 ), and ligands for the chemokine CXCR3 (CXCL9, where n equals the number of selected genes. In this work, we CXCL10, and CXCL11). Finally, STAT1, a master regulator of explored the relationship between CIA and viral replication in our transcription of numerous immune defense genes, was also pos- study cohort. We used 18 genes for the CIA index, genes that had itively associated with HIV-1 replication. In sum, this analysis re- the highest increase in expression in LT during HIV-1 infection flects a robust antiviral host response that parallels virus replica- (9) (Fig. 4A). The CIA values and viral load measurements were tion, but is apparently associated with higher viral loads rather than plotted for each individual, revealing a significant association containment of HIV-1 replication. between CIA and viral load (r = 0.6288; p , 0.0001) (Fig. 4B). In a previous analysis of LT comparing uninfected with HIV-1– Additionally, these LT genes with the highest upregulation upon infected individuals (9), we found signatures of gene expression infection also have the strongest positive correlation to viral load dependent on stage of disease, with the most striking signature in (Fig. 4A). Overall, this analysis provides a glimpse into the in- early infection in which expression of genes that control immune tricate relationship between immune defenses, immune activation, activation and innate and adaptive immune defenses was highly and viral replication, whereby individuals with a higher CIA index upregulated. Because immune activation is now widely believed to are more likely to have higher viral loads. be a factor in overall immune dysfunction and negative prog- nosticator of disease progression (10, 11), we used a chronic im- Negative correlations: immune activation and transcriptional mune activation (CIA) index based on gene expression, developed accessibility important for HIV-1 replication by Rempel et al. (12), to characterize cellular immune dysfunction Strikingly, the vast majority of host genes expressed in LT during during HIV-1 infection. This index is comprised of genes related HIV-1 infection were negatively associated with virus replication to immune activation that are elevated during infection and can be (313 genes + 245 transcripts of unknown function). A large pro- computed for each infected individual by taking the mean fluo- portion of genes in this data set encodes for proteins involved in 4 HOST GENES IN LYMPH NODE ASSOCIATED WITH HIV-1

overall collagen deposition and fibrosis in LT and negatively affect virus replication by potentially decreasing the viability/availability of target immune cells for HIV-1. To examine this in more detail, we explored the relationship between mRNA expression of a collagen-synthesizing gene and the resulting end product of this process, protein levels of collagen deposited in LT. One gene we looked at was PLOD2, a telopeptide lysyl hydroxylase primarily responsible for increasing pyridino- line cross-links within collagen molecules during fibrillogenesis, a process that leads to the irreversible accumulation of collagen in fibrotic tissues (14). We used immunofluorescence to visualize collagen type I deposition in inguinal lymph nodes from HIV-1– infected individuals in our study cohort. We initially compared subject WU59 with GL38 because of their divergent PLOD2 mRNA expression (Fig. 5A) and found a relationship between PLOD2 mRNA expression and collagen type I deposition, whereby greater levels of fibrosis were detected in the lymph node of subject GL38 coincident with higher levels of PLOD2 mRNA FIGURE 2. Differentially expressed genes in HIV-1–infected lymphatic (compare Fig. 5B,5C). Previously reported to be increased during Downloaded from tissue can be classified into functional categories. Genes positively asso- HIV-1 infection (15), much less collagen deposition was seen in ciated with viral load reveal a heightened IFN-system response. The size of each sector in a pie diagram is proportional to the number of genes in its the lymph node of an uninfected individual (Fig. 5D). There was category (in parentheses). All genes and their names derived from abbre- also a negative correlation between collagen deposition in LT and + viations can be found in Supplemental Table 1. numbers of peripheral blood CD4 T cells in a group of our study subjects (Fig. 5E), in agreement with previous reports (15, 16), immune activation, target cell availability, and transcriptional/ suggesting that increased fibrosis during HIV-1 infection may http://www.jimmunol.org/ + translational metabolism (Fig. 3), highlighting the importance of negatively impact viral replication by adversely affecting CD4 activation status and cellular transcriptional/translational machin- T cell viability and reducing access to and availability of target ery in virus replication. In this list of genes negatively correlated cells for viral replication. with viral load (Supplemental Table 2), there are genes involved in Candidates for new host restriction factors inhibiting cellular activation and proliferation, such as the sup- pressors of cytokine signaling, SOCS5 and SCOS7; TCFL5/Cha, In our list of genes negatively associated with viral load, we a implicated in the maintenance of a resting identified genes encoding potential anti–HIV-1 restriction factors state in T cells; CCNG2, an unconventional cyclin that blocks cell (Supplemental Table 5). One such gene is an intracellular gluco- cycle entry; KLF10, a transcription factor involved in inhibiting corticoid receptor known as subfamily 3, group by guest on September 25, 2021 cellular proliferation through the TGF-b signaling pathway; and C, member 1(NR3C1). This receptor has been shown to impede GPR83, a G protein-coupled receptor involved in the generation of proviral integration in PBMCs in the absence of steroidal ligands + (17); this block is alleviated in the presence of NR3C1’s ligand Foxp3 regulatory T (TReg) cells. We also identified genes that regulate the intracellular envi- and requires the HIV-1 protein, Vpr. Another candidate restriction ronment for virus replication, such as genes involved in suppress- factor is CUG triplet repeat, RNA-binding protein 1 (CUGBP1), b ing cellular transcription, particularly through the modification of a previously unrecognized downstream effector of IFN- signal- DNA histones (Supplemental Table 3). Examples include genes ing in primary macrophages that induces a transcriptional in- whose products recruit histone deacetylases, such as CREBZF and hibitory protein that works to suppress HIV/SIV replication (18). ZNF148/ZBP-89, and genes whose products interact/recruit his- Heterogeneous nuclear ribonucleoprotein U is another candidate tone methyltransferases, such as HP1BP3, HIC2, and DPF3. This identified in our analysis, a ribonucleoprotein that has been shown 9 analysis suggests that these gene products adversely affect virus to specifically target the 3 long terminal repeat in the viral mRNA replication by modifying DNA histones, promoting heterochro- and block the cytoplasmic accumulation of HIV-1 mRNAs (19). matin formation, and suppressing cellular and viral transcription. Discussion Negative correlations: collagen deposition and virus The major and unexpected finding of this study is the unique replication transcriptome profile in LT during HIV-1 infection related to viral One pathological hallmark of HIV-1 infection is the aberrant deposi- replication, whereby the vast majority of changes in mRNA ex- tion of collagen and consequent fibrotic damage in both gut and LT pression are negatively associated with HIV-1 replication, whereas (13), a process that may have adverse consequences for the mainte- only a small subset of genes (∼5%) is positively associated with nance and preservation of CD4+ T cells and overall immune function virus levels. Surprisingly, most of the genes in this small subset in these anatomical niches. In this study, we show that genes related mediate innate and adaptive defenses mounted by the host to to collagen synthesis/deposition are negatively associated with virus contain HIV-1, a host response that would have been expected to replication (Supplemental Table 4). Examples include PLOD2, an negatively correlate with viral load. Although counterintuitive, enzyme responsible for the irreversible cross-linking of collagen this LT analysis is in agreement with a transcriptome analysis of fibers; POSTN, a regulator of collagen fibrillogenesis; CRTAP, a primary, peripheral blood CD4+ T cells from HIV-1–infected in- scaffolding protein linking collagen-synthesizing enzymes to their dividuals (20), in which expression of a large proportion of IFN- precursor substrates; CTHRC1, a regulator of TGF-b respon- related genes also correlated with increasing levels of virus (Sup- siveness and their target genes such as collagens; and COL4 and plemental Table 6). COL13, collagen members important for the structure of the base- In addition to a heightened IFN-system response, key genes ment membrane. The products of these genes most likely increase important for cell-mediated immunity (e.g., perforin, granzyme H, The Journal of Immunology 5 Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021

FIGURE 3. Differentially expressed genes in HIV-1–infected lymphatic tissue can be classified into functional categories. Genes negatively associated with viral load point to immune activation, target cell availability, and transcriptional/translational factors as important determinants for HIV-1 replication. The size of each sector in a pie diagram is proportional to the number of genes in its category (in parentheses). All genes and their names derived from abbreviations can be found in Supplemental Table 1.

CD8, NKG7) (Fig. 2) and recruitment of immune cells to sites of tracellular environment for permissiveness in target cells. The infection (e.g., CXCL9, CXCL10, CXCL11) (21) were also posi- genes we did detect with a positive correlation to viral load may tively associated with HIV-1 replication. Interestingly, granzyme actually be more representative of cellular activation and per- H, in addition to its proapoptotic function as a serine protease missiveness in individual target cells. The significant association highly expressed in NK cells and closely related to granzyme B between the CIA index and viral load suggests that this may indeed (22, 23), has recently been shown to mediate antiviral activity be the case (Fig. 4). through direct cleavage of intracellular viral substrates (24, 25). How then to explain the paradoxical, positive association be- Genes related to chemotaxis may provide some clues regarding the tween host defenses and viral load, and even more strikingly, the surprising association between host defense genes and high viral negative correlations that dominate the data set? One explanation loads—the chemotactic response may actually be part of a double- could be that HIV-1 itself is driving host gene expression; for edged sword, on the one hand, serving to recruit HIV-1–specific example, as virus replication increases, the host responds com- cytotoxic CD8+ T cells to eliminate virus-infected cells (26) but parably by increasing expression of genes controlling immune also serving to fuel the infection further by recruiting susceptible, defenses. However, due to the complex relationship between im- activated CD4+ T cells to enhance viral dissemination (27). mune defenses, immune activation, and virus replication, another Surprisingly, we did not identify classical activation markers explanation could be a model in which the level of viral replica- such as Ki-67, HLA-DR, CD69, and CD38 in our data set, genes tion is determined by a balance between both factors that provide predicted to be positively correlated with virus replication due, in virus access to the largest number of susceptible host cells and part, to their role in cellular activation as a means to provide the increase replication and factors that decrease target cell avail- virus with additional target cells for infection. One interpretation ability/permissiveness and inhibit viral replication (Fig. 6). This would be that these gene products actually represent general ac- schematic is consistent with a previous model proposed from tivation of the immune system as a whole and are not specifically transcriptional profiling of the response to highly active anti- indicative of activation in the subsets of cells that are specific retroviral therapy (28), aimed to explain the slow progress of targets of the virus. In other words, these classical activation HIV-1 infection. On one side of the balance are the supportive markers may not be the primary determinants of a suitable in- determinants of viral replication, such as target cell availability, 6 HOST GENES IN LYMPH NODE ASSOCIATED WITH HIV-1

Beyond the principal hypothesis of target cell availability and permissiveness as the key determinant of viral load, there may be new, host restriction factors that also play an important role. By identifying genes that are both negatively associated with virus replication and code for proteins that display antiviral properties, we found several candidate genes that fit into this category (Supplemental Table 5). One gene in this list, PACT, warrants additional comment. PACT encodes a protein kinase that acts upstream of the important antiviral, sentinel-like molecule, dsRNA-dependent protein kinase (PKR) (33). PACT has been shown to serve as a cellular activator of PKR in the absence of viral RNA (34), but has also recently been demonstrated to pos- sess a role in type I IFN production during viral infection, spe- cifically bypassing PKR activation during amplification of the IFN response (35). Thus, we have a gene that acts upstream of the IFN- response pathway and is negatively associated with viral replica- tion in a data set in which all other IFN-responsive genes are positively associated with HIV-1 replication. This may indicate

that PACT is acting outside the IFN-response pathway and ad- Downloaded from versely affecting HIV-1 replication through its other functions, such as inhibiting cellular translation and inducing apoptosis (36) or amplifying levels of micro-RNAs (37) that may serve to inhibit HIV-1 replication (38, 39). We are currently investigating PACT and other candidate genes for their potential role as novel anti–

HIV-1 restriction factors. http://www.jimmunol.org/ We have previously documented changes in LT gene expression FIGURE 4. CIA correlates with HIV-1 viral load. A, The top 18 genes in during HIV-1 infection and found that infection substantially alters lymphatic tissue during HIV-1 infection with the highest increase in ex- the transcriptional profile compared with uninfected individuals (9) pression (9) were chosen for the chronic immune activation index com- and that these transcriptional changes are dependent on stage of putation. The partial correlation coefficient and stage of disease at which the gene’s expression is increased is also listed. B, The chronic immune disease. A comparison between the present analysis and our pre- activation index for HIV-1–infected individuals is significantly correlated vious microarray findings is challenging because the previous with peripheral blood viral load. study stratified changes in gene expression based on disease stage, whereas our current analysis stratifies gene expression based strictly on virus levels, variables that do not share high concor- by guest on September 25, 2021 the activation state of CD4+ T cells and other susceptible cells, dance (virus levels versus disease stage). Nevertheless, when com- and intrinsic intracellular factors that support replication. On the paring the present analysis with our previous microarray findings, other side of the balance are innate and adaptive defenses and host we find that the majority of genes positively associated with virus restriction factors that suppress HIV-1 replication. In this model, levels are significantly upregulated early in infection (acute stage host defenses most likely inhibit viral replication to some extent, of disease), overlapping data illustrating a shared relationship yet, because they are inseparable from immune activation, related between immune defense genes and viral levels in early disease. proinflammatory cytokines, and recruitment of activated target In contrast, most of the genes negatively associated with virus cells, on balance host defenses actually may contribute to in- levels do not overlap with our previous data set, illustrating the creased viral load. overall discordance between disease stage and virus levels. One In this model, many of the negatively associated genes that would expect to observe greater concordance between data sets dominate the data set reflect the importance of target cell avail- if viral levels were indeed correlated with disease stage, but ability and the ability of host intracellular machinery to support even in our limited study cohort, viral levels vary appreciably viral replication. Collagen deposition in this model negatively across stage. Thus, this comparative analysis highlights two sets of impacts viral load through fibrotic damage of the lymph node niche genes—one set of genes may be directly influencing virus repli- and its documented effects of decreasing CD4+ T cells (Fig. 5E) cation (current study) and another set of genes that are stage de- (15, 16), essentially decreasing access and opportunities for virus pendent and, on a global scale, may impact virus replication to interact with target cells. through direct or indirect means (9). There may also be a complex relationship between collagen In this microarray study, we used whole lymph nodes for pro- deposition, TReg cells, and HIV-1 replication, suggested by find- cessing and generation of the template RNA for microarray chip ings in the rhesus macaque model of SIV infection that TGF-b1– hybridization as a means to capture the sum of all interactions in producing TReg cells are primarily responsible for inducing col- an important microenvironment (lymph node) where the bulk of lagen synthesis/deposition in LT (29). In HIV-1 infection, TReg HIV-1 replication and pathogenesis occurs. As such, cells were not cells might dampen cellular activation in bystander immune cells, first separated into individual subpopulations for RNA preparation + a classical feature of TReg cells (30), thus decreasing overall viral (i.e., CD4 T cells), which likely explains why there was very output in HIV-infected cells. This would explain why GPR83, a little overlap (,3%) with recent small-interfering RNA-knock- G protein-coupled receptor reported to be involved in the pe- down screens (5–8) designed to identify host cell factors required ripheral generation of TReg cells in vivo (31, 32), along with me- for HIV-1 infection in vitro (Supplemental Table 7). The advan- diators of the TGF-b signaling pathway (e.g., ITGB8, SMAD5, tage of this type of tissue-population microarray study is that RNA PEG10, GDF10, KLF10), are all negatively associated with HIV-1 from all the essential cell populations residing within the lymph replication. node microenvironment are present during chip hybridization and The Journal of Immunology 7 Downloaded from http://www.jimmunol.org/

FIGURE 5. Increases in PLOD2 mRNA levels in lymphatic tissue correspond to increased collagen type I synthesis/deposition and decreased CD4+ T cells. A, PLOD2 mRNA expression in inguinal lymph nodes is negatively associated with viral load. Yellow, blue, and green points represent subject number’s WU59, GL38, and remaining cohort individuals, respectively. B–D, Representative immunofluorescent micrographs reveal collagen type I de- position (red) in the paracortical T cell zone of inguinal lymph nodes from subject number’s WU59 (B), GL38 (C), and an uninfected individual (D). E, Collagen type I deposition in inguinal lymph nodes is inversely correlated with numbers of peripheral blood CD4+ T cells (Pearson’s correlation co- efficient). Original magnification 3400; scale bars, 50 mm. by guest on September 25, 2021 subsequent analysis. Genes identified in this study are a good of a nuclear transcriptional complex known as the promyelocytic starting point for further investigations of their roles in HIV-1 leukemia nuclear body (40) and has been reported to play an replication and pathogenesis using in situ technologies to iden- important role in innate antiviral defense against a number of tify the types of cells in which gene expression has been altered different DNA and RNA viruses (41, 42). We found the size and and immunohistochemistry/immunofluorescence to identify po- number of SP100-containing nuclear bodies increased in LT CD3+ tential changes in protein expression. For example, we used im- T cells and CD163+ macrophages during HIV-1 infection com- munofluorescence to identify the cellular expression of SP100, an pared with uninfected individuals (Supplemental Fig. 1). In sum, IFN-responsive gene that codes for one of the major components we believe this global, tissue microarray methodology is an im-

FIGURE 6. HIV-1 infection in lymphatic tissue is determined by a balance of factors that increase or decrease virus replication. In this hypothetical model, HIV-1 replication within lymphatic tissue is largely determined by a balance of factors that support or inhibit virus replication. Supportive determinants in- clude target cell availability, cell activation, and in- tracellular intrinsic factors that promote a favorable transcriptional/translational environment. These sup- portive determinants are counterbalanced by inhibitory determinants such as innate and adaptive immune responses as well as intracellular host restriction fac- tors that suppress virus replication. 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