Virology 380 (2008) 124–135

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Virology

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Transcriptional profiles in CD8+ T cells from HIV+ progressors on HAART are characterized by coordinated up-regulation of oxidative phosphorylation enzymes and interferon responses

Jing Qin Wu a,1, Dominic E. Dwyer b,2, Wayne B. Dyer c,2, Yee Hwa Yang d,3, Bin Wang a,4, Nitin K. Saksena a,⁎,5 a Retroviral Genetics Division, Center for Virus Research, Westmead Millennium Institute, University of Sydney, Darcy Road, Westmead, NSW 2145, Australia b Department of Virology, Centre for Infectious Diseases and Microbiology Laboratory Services, ICPMR, Westmead Hospital, Westmead, NSW 2145, Australia c Immunovirology Laboratory, Australian Red Cross Blood Service, Sydney, NSW 2000, Australia d School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia article info abstract

Article history: The functional impairment and numerical decline of CD8+ T cells during HIV infection has a profound effect on Received 21 May 2008 disease progression, but only limited microarray studies have used CD8+ Tcells. To understand the interactions Returned to author for revision 9 June 2008 of HIV and host CD8+ T cells at different disease status, we used the Illumina Human-6 BeadChips to evaluate Accepted 25 June 2008 the transcriptional profile (N48,000 transcripts) in primary CD8+ T cells from HIV+ therapy-naive non- Available online 9 August 2008 progressors and therapy-experienced progressors. 68 differentially expressed genes were identified, of which 6 have been reported in HIV context, while others are associated with biological functions relevant to HIV Keywords: Microarray pathogenesis. By GSEA, the coordinated up-regulation of oxidative phosphorylation enzymes and interferon HIV responses were detected as fingerprints in HIV progressors on HAART, whereas LTNP displayed a CD8+ transcriptional signature of coordinated up-regulation of components of MAPK and cytotoxicty pathways. HAART These results will provide biological insights into natural control of HIV versus HIV control under HAART. Gene expression Crown Copyright © 2008 Published by Elsevier Inc. All rights reserved. Interferon Mitochondria Cytotoxicity

Introduction question. Among different blood cell types, the paramount role of CD8+ HIV infects all major blood cell types and distinct HIV-1 strains T cells in HIV pathogenesis and their importance in secreting CD8 evolve independently in different blood leukocytes in vivo (Potter et al., antiviral factor are well known (Mackewicz et al., 1995; Tsubota et al., 2004, 2006). The interactions between HIV and different blood cell 1989; Walker et al., 1989). During acute infection, the expansion of fi types remain unclear and studying the subversion of human gene HIV-speci c CD8+ T cells is associated with a decline in viremia (Koup machinery by HIV in different cell types may help to answer the above et al., 1994; Pantaleo et al., 1994). Despite their antiviral activity, CD8+ T cells can also be productively infected by HIV (Imlach et al., 2001; McBreen et al., 2001; Saha et al., 2001). HIV-infected, therapy-naive, Abbreviations: HAART, highly active antiretroviral therapy; NEG, HIV sero-negative long-term non-progressors (LTNP) usually maintain highly functional individuals; VIR, viremic patients; LTNP, long-term non-progressor; GO, gene ontology; CD8+ T cells, whereas HIV progressors display impaired CD8+ T cell GSEA, gene set enrichment analysis; FDR, false discovery rate; OXPHOS, oxidative functions (Betts et al., 2006; Migueles et al., 2002). Although the phosphorylation; NRTI, nucleoside reverse transcriptase inhibitors; AZT, zidovudine; ES, enrichment score; NES, normalized enrichment score; ISG, interferon stimulated multiple roles of CD8+ T cells during HIV infection are well genes; CTL, cytotoxic T lymphocyte. characterized, the genetic basis of interactions between host CD8+ T ⁎ Corresponding author. Fax: +612 9845 9103. cells and HIV in relation to disease stages remain poorly understood. E-mail addresses: [email protected] (J.Q. Wu), Previous gene microarray studies in relation to HIV disease have [email protected] (D.E. Dwyer), [email protected] (W.B. Dyer), [email protected] (Y.H. Yang), [email protected] used whole PBMC or cell lines, monocytes, macrophages, CD4+ T cells, (B. Wang), [email protected] (N.K. Saksena). lymphoid and gut tissue, etc (Giri et al., 2006; Ryo et al., 1999), but 1 Fully performed the work, analyzed data and wrote the paper. only a few studies have used CD8+ T cells to understand gene 2 Contributed to vital patient samples along with clinical/immunological interpreta- regulation during HIV infection. These include studies using CD8+ T tion of findings. cell gene expression profiling to detect genes responsible for non- 3 Contributed to statistical evaluation and the writing. 4 Contributed to the writing. cytotoxic activity of CD8+ T cells (Diaz et al., 2003; Martinez-Marino 5 Designed the research project, supervised this work and contributed to the writing. et al., 2007); the study by Martinez-Marino et al. identified 52

0042-6822/$ – see front matter. Crown Copyright © 2008 Published by Elsevier Inc. All rights reserved. doi:10.1016/j.virol.2008.06.039 J.Q. Wu et al. / Virology 380 (2008) 124–135 125 differentially expressed genes between infected subjects with high CD8+ cell non-cytotoxic anti-HIV responses and uninfected controls that lack this activity. Recently, the transcriptional profiling of CD4+ and CD8+ T cells from early infection, chronic infection and LTNP patients have been studied and interferon responses were character- ized as a transcriptional signature of T cells from early and chronic HIV infected individuals (Hyrcza et al., 2007). It has also been shown that CD8+ T cells contain more differential genes than CD4+ T cells, which is consistent with our recent antibody microarray study illustrating that CD8+ T cells have more cell surface molecules differentiating disease status then CD4+ T cells (Wu et al., 2007). Despite the aforementioned microarray studies on CD8+ T cells, a clear and comprehensive comparison at the transcriptional level on human genome scale Fig. 1. Clustering analysis of global gene expression profiles from CD8+ T cells. between primary CD8+ T cells from therapy-naive LTNPs and HIV Similarities in the gene expression patterns among individuals were evaluated and progressors on HAART is lacking. visualized with BeadStudio v3 Cluster Analysis tool. The algorithm used is named Using Illumina Human-6 v2 Expression BeadChips, we have Correlation, which computes the Pearson correlation using a 1-r distance measure. The compared expression profiles of the whole human genome (N48,000 distance on the X axis represents the similarity relationships among samples. gene transcripts) in ex vivo-derived CD8+ T cells from HIV+ therapy- naive LTNP and viremic (VIR) patients under HAART to identify corresponding to significance level of pb0.01 (down-regulation or transcriptional features related to HIV disease stages and HAART up-regulation, respectively) and fold change N2.7. When compared to effect. The comprehensive approach of profiling whole human the LTNP group, 57 genes were up-regulated and 11 down-regulated genome in these two HIV+ patient groups has provided new insights in the VIR group. into virus-host interactions at the CD8+ T cell transcriptome level in For annotation of these 68 differentially expressed genes, the relation to disease stages and antiretroviral therapy. This will facilitate Database for Annotation, Visualization and Integrated Discovery the understanding of HIV pathogenesis and HAART effects. (DAVID) (http://david.abcc.ncifcrf.gov/)(Huangda et al., 2007)was used as well as exhaustive searches in the PubMed literature (http:// Results www.ncbi.nlm.nih.gov/Literature/). These 68 genes were assigned to eight categories according to biological functions relevant to HIV Cluster and differential gene analysis pathogenesis as follows: (1) genes previously reported to be involved in HIV infection and disease (6/68); (2) Interferon induced, apoptosis and CD8+ T cell-derived total cellular RNA from 9 HIV+ patients (4 LTNP actin-related genes (11/68); (3) cell cycle, proliferation and activation and 5 VIR, Table 1) and 5 HIV sero-negative (NEG) healthy individuals genes (5/68); (4) adhesion and cell surface molecules (6/68); (5) were hybridized to the Sentrix Human-6 v2 Expression BeadChip. The endosome, lysosome and cytotoxicity (6/68); (6) mitochondria, lipid data were subjected to gene expression analysis using software and carbohydrate metabolism associated genes (8/68); (7) genes lacking BeadStudio v3. To determine the similarity of global expression relevance to HIV pathogenesis (13/68) and (8) less characterized genes between samples, hierarchical clustering was performed based on the (13/68) (Table 2). Pearson correlation metric and Correlation agglomeration method To confirm the differential expression of genes from Illumina implemented in BeadStudio. Cluster analysis revealed that the VIR microarray, mRNA expression levels of 18% of the randomly selected group formed an independent cluster away from LTNP and NEG above genes were measured by quantitative PCR (Table 3). The mRNA groups (Fig. 1). Differential expression analysis between LTNP and NEG from the CD8+Tcells of the same patient at the same time point was used showed limited differences (7 differentially expressed genes, data not for qPCR analysis. The fold changes evaluated by qPCR were generally shown), which is consistent with a previous study showing lack of consistent with the results from microarray except for genes SLC25A20 differences of gene expression profiles between CD8+ T cells from HIV and PYCARD, which repeatedly failed to be quantified by qPCR. non-progressors and negative controls (Hyrcza et al., 2007). Therefore, our subsequent rationale was to analyze differential gene expression L2L analysis in CD8+ T cells derived from VIR and LTNP groups. Differential expression analysis between LTNP and VIR groups To facilitate our understanding of the biological implications of revealed a candidate list of 68 genes. Each differential gene had a these 68 differential genes, we compared our gene list containing detection p value b0.01 (indicating that the transcript was detected) in these 68 differential genes to 953 differential gene lists derived from at least one group, with a differential score of b−20 or N20 197 published microarray data as well as 3 additional gene ontology (GO) lists (biological process, cell component and molecule function) using L2L microarray analysis tool (Newman and Weiner, 2005). Table 1 fi Patient clinical details of viral load, CD4+ and CD8+ T cell countsa Detection of signi cant overlap between lists may indicate the inclusion of genes with one or more similar biological functions Patient Disease Age Viral load CD4 counts CD8 counts RNA integrity defined in previously published studies. group (copies/ml) (cells/μl) (cells/μl) number For genes up-regulated in the VIR group, several relevant themes V1 VIR 55 334 282 2281 7.0 emerged from the L2L analysis. These include (1) interferon and TGFβ V2 VIR 39 2130 296 905 8.8 V3 VIR 40 4940 387 1239 8.9 responses and apoptotic pathways involving JAK-STAT and caspase V4 VIR 44 668,000 86 335 9.0 activation, (2) genes encoding mitochondrion components and (3) V5 VIR 61 713,000 40 70 9.3 endosome and lysomsome transport (Table 4). Although the biological b L1 LTNP 59 50 630 579 9.1 meanings derived from these lists are relevant to HIV pathogenesis and b L2 LTNP 51 50 714 476 8.5 fi L3 LTNP 79 b50 920 900 9.3 the p values are highly signi cant, the number of the actual L4 LTNP 33 57 780 900 9.7 overlapping genes is only around 2–3 in the majority of list comparisons, which may limit the derivation of the biological pathway a Plasma viral load was measured using the Quantiplex HIV RNA3.0 (Chiron bDNA) assay with a lower limit of detection of 50 HIV-1 copies/ml (Chiron Diagnostics, or mechanism involved. Thus, we attempted a complementary Halstead, United Kingdom). approach using Gene Set Enrichment Analysis (GSEA) (Subramanian 126 J.Q. Wu et al. / Virology 380 (2008) 124–135

Table 2 Differential genes grouped by the biological functions relevant to HIV pathogenesis

TargetID Gene FC Molecular function Category I: Previously reported in HIV pathogenesis ILMN_9420 BAG3 3.0 BCL2-associated athanogene 3 (BAG3) ILMN_8830 ANXA2 −5.7 Annexin A2 (ANXA2), transcript variant 2, mRNA. ILMN_21129 CHMP5 −2.8 Chromatin modifying protein 5 (CHMP5), mRNA. ILMN_3056 PARP1 −3.0 Poly (ADP-ribose) polymerase family, member 1 (PARP1), mRNA. ILMN_30038 RAB7 −2.9 RAB7, member RAS oncogene family (RAB7), mRNA. ILMN_760 TRIM5 −9.6 Tripartite motif-containing 5 (TRIM5), transcript variant alpha, mRNA.

Category II: Interferon induced, cell apoptosis and actin-related genes ILMN_1146 PYCARD −3.5 PYD and CARD domain containing (PYCARD), transcript variant 3, mRNA. ILMN_5069 PRR5 −3.3 Proline rich protein 5 (PRR5), transcript variant 4, mRNA. ILMN_10490 SERINC3 −3.9 Serine incorporator 3 (SERINC3), transcript variant 1, mRNA. ILMN_6588 ACTA2 −3.8 Actin, alpha 2, smooth muscle, aorta (ACTA2), mRNA. ILMN_13489 ARPC5 −4.3 Actin related protein 2/3 complex, subunit 5, 16 kDa (ARPC5), mRNA. ILMN_137049 DNCL1 −4.6 Dynein, cytoplasmic, light polypeptide 1 (DNCL1), mRNA. ILMN_18673 LY6E −3.0 Lymphocyte antigen 6 complex, locus E (LY6E), mRNA. ILMN_35118 LOC400759 −4.7 PREDICTED: similar to Interferon-induced guanylate-binding protein 1 (GTP-binding protein 1) (Guanine nucleotide-binding protein 1) (HuGBP-1) (LOC400759), misc RNA. ILMN_28413 GBP1 −5.0 Guanylate binding protein 1, interferon-inducible, 67 kDa (GBP1), mRNA. ILMN_12566 JAK2 −3.4 Janus kinase 2 (a protein tyrosine kinase) (JAK2), mRNA. ILMN_11054 STAT1 −4.3 Signal transducer and activator of transcription 1, 91 kDa (STAT1), transcript variant beta, mRNA.

Category III: Cell cycle, proliferation and activation related genes ILMN_14812 JARID1B 2.8 Jumonji, AT rich interactive domain 1B (RBP2-like) (JARID1B), mRNA. ILMN_26592 SIRPB2 −16.7 Signal-regulatory protein beta 2 (SIRPB2), transcript variant 1, mRNA. ILMN_24322 FCRLM2 −6.0 Fc receptor-like and mucin-like 2 (FCRLM2), mRNA. ILMN_25554 LPXN −2.7 Leupaxin (LPXN), mRNA. ILMN_3307 BATF −3.5 Basic leucine zipper transcription factor, ATF-like (BATF), mRNA.

Category IV: Adhesion and cell surface molecules ILMN_4953 CD9 −5.5 CD9 antigen (p24) (CD9), mRNA. ILMN_20825 ITGA4 −4.2 Integrin, alpha 4 (antigen CD49D, alpha 4 subunit of VLA-4 receptor) (ITGA4), mRNA. ILMN_23858 GPR56 −3.0 G protein-coupled receptor 56 (GPR56), transcript variant 3, mRNA. ILMN_21540 NINJ1 3.4 Ninjurin 1 (NINJ1), mRNA. ILMN_18826 TMEM126B −2.8 Transmembrane protein 126B (TMEM126B), mRNA. ILMN_25105 TMEM99 −4.4 Transmembrane protein 99 (TMEM99), mRNA.

Category V: Endosome, lysosome and cytotoxicity related genes ILMN_1682 M6PR −2.7 Mannose-6-phosphate receptor (cation dependent) (M6PR), mRNA. ILMN_27156 HPS3 −5.5 Hermansky-Pudlak syndrome 3 (HPS3), mRNA. ILMN_28086 GBA −4.6 Glucosidase, beta; acid (includes glucosylceramidase) (GBA), transcript variant 2, mRNA. ILMN_3762 KLRD1 −3.1 Killer cell lectin-like receptor subfamily D, member 1 (KLRD1), transcript variant 1, mRNA. ILMN_26813 RAC2 −2.7 Ras-related C3 botulinum toxin substrate 2 (rho family, small GTP-binding protein Rac2) (RAC2), mRNA. ILMN_26737 ATP6V1D −5.3 ATPase, H+ transporting, lysosomal 34 kDa, V1 subunit D (ATP6V1D), mRNA.

Category VI: Mitochondria, lipid and carbonhydrate metabolim ILMN_16589 ACADM −6.7 Acyl-Coenzyme A dehydrogenase, C-4 to C-12 straight chain (ACADM), nuclear gene encoding mitochondrial protein, mRNA. ILMN_2603 PCK2 −3.0 Phosphoenolpyruvate carboxykinase 2 (mitochondrial) (PCK2), nuclear gene encoding mitochondrial protein, transcript variant 1, mRNA. ILMN_29792 SLC25A20 −14.3 Solute carrier family 25 (carnitine/acylcarnitine translocase), member 20 (SLC25A20), nuclear gene encoding mitochondrial protein, mRNA. ILMN_4843 C10orf70 −3.5 Chromosome 10 open reading frame 70 (C10orf70), mRNA. ILMN_13962 CAT −3.2 Catalase (CAT), mRNA. ILMN_10698 GALM −3.1 Galactose mutarotase (aldose 1-epimerase) (GALM), mRNA. ILMN_14503 IRS2 6.4 Insulin receptor substrate 2 (IRS2), mRNA. ILMN_27212 THEM2 −4.1 Thioesterase superfamily member 2 (THEM2), mRNA.

Category VII: Genes lack of relevance to HIV pathogenesis ILMN_14263 HDHD2 −3.4 Haloacid dehalogenase-like hydrolase domain containing 2 (HDHD2), mRNA. ILMN_29250 BPNT1 −4.6 3′(2′), 5′-bisphosphate nucleotidase 1 (BPNT1), mRNA. ILMN_19664 PREPL −5.0 Prolyl endopeptidase-like (PREPL), mRNA. ILMN_26580 PCMT1 −4.6 Protein-l-isoaspartate (D-aspartate) O-methyltransferase (PCMT1), mRNA. ILMN_11141 GRASP 5.8 GRP1 (general receptor for phosphoinositides 1)-associated scaffold protein (GRASP), mRNA. ILMN_26206 GTF2H5 −3.0 General transcription factor IIH, polypeptide 5 (GTF2H5), mRNA. ILMN_16811 PER1 4.2 Period homolog 1 (Drosophila) (PER1), mRNA. ILMN_12903 CRIPT −4.7 Postsynaptic protein CRIPT (CRIPT), mRNA. ILMN_11477 ARL6IP6 −3.4 ADP-ribosylation-like factor 6 interacting protein 6 (ARL6IP6), mRNA. ILMN_20212 OBFC2A −3.9 Oligonucleotide/oligosaccharide-binding fold containing 2A (OBFC2A), transcript variant 2, mRNA. ILMN_20072 SULT1A4 −6.8 Sulfotransferase family, cytosolic, 1A, phenol-preferring, member 4 (SULT1A4), transcript variant 1, mRNA. ILMN_16255 YIPF4 −5.2 Yip1 domain family, member 4 (YIPF4), mRNA. ILMN_17231 ZNF395 4.1 Zinc finger protein 395 (ZNF395), mRNA. J.Q. Wu et al. / Virology 380 (2008) 124–135 127

Table 2 (continued)

TargetID Gene FC Molecular function

Category VIII: Less characterized genes ILMN_37437 BEXL1 2.6 PREDICTED: brain expressed X-linked-like 1 (BEXL1), mRNA. ILMN_28666 C14orf142 −3.2 Chromosome 14 open reading frame 142 (C14orf142), mRNA. ILMN_25394 CCDC53 −2.9 Coiled-coil domain containing 53 (CCDC53), mRNA. ILMN_19806 FLJ11200 −3.4 Hypothetical protein FLJ11200 (FLJ11200), mRNA. ILMN_16993 LOC442535 −7.0 Similar to T cell receptor gamma chain V region PT-gamma-1/2 precursor (LOC442535), mRNA. ILMN_43020 LOC642083 −5.0 PREDICTED: hypothetical protein LOC642083 (LOC642083), mRNA. ILMN_46540 LOC652637 6.8 PREDICTED: similar to junction-mediating and regulatory protein (LOC652637), mRNA. ILMN_34703 LOC653743 −8.3 PREDICTED: similar to hypothetical protein DKFZp566J091 (LOC653743), mRNA. ILMN_19396 MGC2463 −5.0 Hypothetical protein LOC79037 (MGC2463), mRNA. ILMN_138894 SCML1 6.2 Sex comb on midleg-like 1 (Drosophila) (SCML1), mRNA. ILMN_14718 SPCS3 −7.5 Signal peptidase complex subunit 3 homolog (S. cerevisiae) (SPCS3), mRNA. ILMN_96342 NA −4.5 cDNA clone IMAGE:5277162 ILMN_86958 NA 5.3 cDNA FLJ41813 fis, clone NT2RI2011450

FC: Fold Change, calculated by expression values of LTNP vs VIR. “−” indicates up-regulation in VIR, whereas “+” indicates up-regulation in LTNP.

et al., 2005), which performs unbiased global search for genes that are For Igf1, Ngf and insulin pathways enriched in LTNP, we found 9 coordinately regulated in predefined gene sets instead of examining overlapping core enrichment genes (ELK1, FOS, GRB2, MAPK3, PIK3CA, single differential genes. PIK3R1, RAF1, SHC1, TYROBP) coordinately up-regulated in the LTNP group and the majority were up-regulated N1.3 fold. Of these, GRB2, Gene set enrichment analysis RAF1, MAPK3, ELK1 and FOS are spread along a branch of MAPK signaling pathway, whereas SHC, GRB2, PIK3CA, PIK3R1, RAF1 and We used the normalized data of the entire 48,000 gene transcripts MAPK3 are components of NK cell-mediated cytotoxicity pathway from 4 LTNP and 5 VIR samples for GSEA (Subramanian et al., 2005). (Fig. 5). Notable is that this coordinated up-regulation of the Overall, 70 gene sets were highly significantly enriched in the VIR component of MAPK and cytotoxicity pathways was found to be a group with FDRb0.01 and nominal pb0.01 (FDR cut off value, transcriptional signature unique to the LTNP group. stringentlyb0.05 and normallyb0.25). Three outstanding groups We also did overlapping analysis for the gene sets enriched in the emerged from the 70 gene sets assessed, based on the similarity of VIR group related to interferon responses and cell proliferation and biological meanings of the gene sets: (1) interferon responses; (2) differentiation. Although overlapping genes were detected, no mitochondrion and (3) cell proliferation and differentiation (Table 5). particular pathway was found to be enriched. Out of 9 gene sets Within the LTNP group, only 12 gene sets were highly enriched with associated with interferon responses, 15 genes were found to be FDRb0.05 and nominal pb0.01. Unlike those in the VIR group, the present in at least 4 gene sets. Six of these 15 genes fell into the GO biological meanings associated with these gene sets were sporadic term of virus response (STAT1, MX1, ISG15, OAS1, TRIM22 and IFI35), and their relevance to HIV disease was not obvious. However, among and the other 9 were GBP1, OAS2, BST2, UBE2L6, TNSF10, NMI, EPS15, the top 30 gene sets, we noticed a group of 3 gene sets (Igf1, Ngf and IFI44L and IFIT3. For gene sets associated with cell proliferation and insulin pathways) enriched at moderately significant level (FDR differentiation, 3 out of 12 overlapping genes derived from Lee, Zhan around 0.15 and nominal p≤0.015). and Idx gene sets (Table 5) are involved in G2 phase (CCNB1, CCNB2 Each enriched gene set has core enrichment genes (enrichment and CDC2; lists of genes in supplementary file 1 Table 1b). Two out of 8 plot and heat map of the top core enrichment genes illustrated in overlapping genes from P21 and serum lists (Table 5) are involved in S Fig. 2) and we looked for the overlap between core enrichment genes and G2 phase (MCM4 and BUB1; lists of genes in supplementary file 1 across the gene sets possessing similar biological meanings (over- Table 1c). The up-regulation of the S and G2 phase genes in the VIR lapping analysis illustrated in Fig. 3), as these overlapping genes may group may indicate more frequent cell divisions in this group, which associate with common biological processes/events. Examination of is consistent with the theory of continual viral replication and rapid the four enriched gene sets associated with mitochondria revealed 35 CD4+ T cell turnover (Ho et al., 1995). overlapping core enrichment genes with 33 encoding the oxidative phosphorylation (OXPHOS) enzymes. 31 out of these 33 OXPHOS Discussion enzyme genes were up-regulated uniquely in the VIR group and the majority were up-regulated N1.2 fold (Fig. 4 illustrates the location of In an attempt to understand the HIV and host CD8+ T cell these overlapping core enrichment genes in the OXPHOS pathway, interactions in relation to HIV disease stages, we have evaluated the see list of genes shown in the supplementary file 1 Table 1a). This gene expression profiling between CD8+ T cells from HIV+ non- coordinated up-regulation of the OXPHOS enzymes is a transcrip- progressors and therapy-experienced progressors using the Illumina tional signature in viremic patients on HAART. Human-6 v2 Expression BeadChip encompassing N48,000 gene transcripts. We detected 68 highly differential genes between the Table 3 VIR and LTNP groups. Of these, 6 have been previously reported in the qPCR confirmation of selected genes context of HIV disease (Kameoka et al., 2005; Rosati et al., 2007; Gene symbol qPCR Microarray Gene symbol qPCR Microarray Ryzhova et al., 2006; Stremlau et al., 2004; Vidricaire and Tremblay, TRIM5a −4.7 −9.6 GBP1 −5.4 −5 2005; Ward et al., 2005), while others are known to associate with a STAT1 −9.8 −4.3 IRS2 5.8 6.4 wide range of biological functions. Some of these functions are also − − KLRD1 2.4 3.1 NINJ1 3.3 3.4 relevant to HIV disease progression, especially those involved in cell CD9 −4.1 −5.5 BAG3 1.5 3 ATP6V1D −2.0 −5.3 ANXA2 −3.1 −5.7 apoptosis, proliferation, activation and cytotoxicity (Table 2). How- ever, from the list of single differentially expressed genes, the All values represent fold change between expression levels of LTNP vs expression levels underlying biological mechanism could hardly be derived. Therefore of VIR. Minus sign indicates up-regulation in VIR whereas positive sign indicates up- regulation in LTNP. Housekeeping gene GAPDH was used as an internal control and the we carried out a complementary approach, the GSEA analysis, which is normalizer in qPCR. able to identify significantly differentially expressed gene sets, even 128 J.Q. Wu et al. / Virology 380 (2008) 124–135

Table 4 L2L analysis of differential genes between VIR and LTNP groups

VIR vs Data List name List description Fold Binomial LTNP sourcea enrichb p value Interferon, TGFb, Interleukin and apoptosis Interferon Upc L2LMDB Ifn_all_up Up-regulated 2-fold in HT1080 cells 6 h following treatment with interferon alpha, beta and gamma N10 7.2E-04 Up L2LMDB Ifn_alpha_up Up-regulated 2-fold in HT1080 cells 6 h following treatment with interferon alpha N10 2.7E-03 Up L2LMDB Ifn_any_up Up-regulated 2-fold in HT1080 cells 6 h following treatment with any of interferons alpha, N10 4.0E-04 beta and gamma Up L2LMDB Ifn_beta_up Up-regulated 2-fold in HT1080 cells 6 h following treatment with interferon beta N10 1.8E-04 Up L2LMDB Ifn_gamma_up Up-regulated 2-fold in HT1080 cells 6 h following treatment with interferon gamma N10 5.3E-05 Up L2LMDB ifna_hcmv_6 hrs_up Up-regulated in fibroblasts at 6 h following treatment with interferon-alpha N10 4.0E-03 TGFb Up L2LMDB tgfb_hmvec_2 h_up Up-regulated 2 h following treatment of human microvessel endothelial cells (HMVEC) with TGFbeta 6.22 1.7E-04 Up L2LMDB tgfb_hmvec_4 h_up Up-regulated 4 h following treatment of human microvessel endothelial cells (HMVEC) with TGFbeta 6.22 1.7E-04 Apoptpsis Up GO:BP JAK-STAT cascade STAT proteins are activated by members of the JAK family of tyrosine kinases. Once activated, N10 1.3E-03 STATs dimerize and translocate to the nucleus and modulate the expression of target genes. Up GO:BP Apoptotic program The intracellular signaling cascade that results when a cell is triggered to undergo apoptosis N10 4.2E-03

Adipogenesis Up L2LMDB Adip_vs_fibro_dn Down-regulated following 7-day differentiation of murine 3T3-L1 fibroblasts into adipocytes N10 8.6E-04 Up L2LMDB Adip_vs_preadip_dn Down-regulated in mature murine adipocytes (7 day differentiation) vs. preadipocytes N10 1.5E-03 (6 h differentiation) Downd L2LMDB Adip_diff_cluster2 Strongly up-regulated at 2 h during differentiation of 3T3-L1 fibroblasts into adipocytes (cluster 2) N10 9.6E-03 Down L2LMDB Adipogenesis_hmsc_class2_up Up-regulated 1–14 days following the differentiation of human bone marrow mesenchymal stem cells N10 3.5E-03 (hMSC) into adipocytes, versus untreated hMSC cells (Class II)

Endosome and lysosome Up GO:BP Lysosome organization and The formation, arrangement of constituent parts, or disassembly of lysosomes N10 7.2E-06 biogenesis Up GO:BP Endosome to lysosome The directed movement of substances from endosomes to lysosomes N10 3.1E-05 transport

Mitochondrion Up GO:CC Mitochondrion A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the 4.70 4.68e-03 cytoplasm of virtually all eukaryotic cells.

a The database used for L2L analysis. L2LMDB represents the L2L Microarray DataBase. GO: BP, GO: CC represent lists derived from gene ontology biological process and cellular component, respectively. b Fold enrichment, calculated from actual matches versus expected matches in a given number of genes. c,d: “Up” and “Down” represent up-regulation and down-regulation of expression values when VIR compared against LTNP. where no single gene shows statistically significant differential gene events such as mitochondrial proliferation and a transient increase in expression between phenotypes (Mootha et al., 2003). mtDNA level. Similar compensatory effects have also been reported in Using GSEA, we detected the coordinated up-regulation of hepatocytes from patients receiving HAART (Kamal and French, 2004). OXPHOS enzymes genes as a distinct transcriptional feature in However, in later stages at passage 71, the significant reduction in the CD8+ T cells from the VIR group as opposed to the LTNP group. mitochondrial number, the loss of mtDNA and the abrogation of Since all of the viremic patients had been on HAART for a con- mitochondrial function were observed, which were not manifested in siderable length of time at the time of enrollment in this study, it is the early stages (passage 5–36) (Divi et al., 2007). This suggests that plausible to hypothesize that the coordinated up-regulation of the compensatory effects may prolong but cannot reverse the process OXPHOS enzymes could be a compensatory event to the mitochon- of mitochondrial dysfunction. This characteristic long-term adverse drial toxicity incurred by the antiretroviral drug regimen. It is well effect of HAART is further confirmed by a study (Aurpibul et al., 2007) documented that the backbone of HAART regimens, nucleoside showing that in more than half of the studied population, lipodystro- reverse transcriptase inhibitors (NRTI), particularly block DNA phy developed from later stages of the therapy (144 weeks), but not at polymerase gamma, which is required for mtDNA synthesis early time points (48 and 96 weeks). (Kakuda, 2000). In addition, impairment of mitochondrial enzymes Coincidently, following the submission of this manuscript, a including adenylate kinase and the ADP/ATP translocator has also proteomic study has been recently reported showing that HIV been observed (Kakuda, 2000). All these interferences eventually infection of the T cell line PM1 led to the up-regulation of 7 proteins lead to mitochondrial dysfunction and depletion (Brinkman et al., involved in OXPHOS (Ringrose et al., 2008). Interestingly, 3 out of 1999; Kakuda, 2000), which underpins adverse effects of HAART these 7 proteins (ATP5H, NDUFS1 and UQCRC1) were encoded by such as lactic acidosis and the lipodystrophy syndrome (Carr, 2003; OXPHOS genes that were found to be up-regulated in the VIR group in Nerurkar et al., 2003). our study, which implied the contribution of the viral replication to Our speculation that the coordinated up-regulation of OXPHOS the up-regulation of these 3 genes. Since only the minority of the 31 enzymes could be a compensatory effect of mitochondrial toxicity is OXPHOS genes in our study overlapped with this proteomic study, we fully supported by the study of Divi et al., who documented further postulate that the up-regulation of the 31 OXPHOS genes could mitochondrial pathology during the long-term culture of human be mostly attributed to HAART effects and only partially to the HeLa cells in the presence and absence of zidovudine (AZT, belonging persistent viral replication. to the drug class NRTI) for up to 77 passages (Divi et al., 2007). It Both L2L and GSEA analyses revealed the interferon responses in demonstrated that most OXPHOS genes were substantially up- CD8+ T cells from HIV+ progressors as a transcriptional feature distinct regulated in AZT-exposed cells at passage 5, passage 36 and passage from LTNP, implicating the chronic immune activation status in HIV 75 compared to unexposed cells along with other compensatory viremic patients under HAART. Lists from the above analyses included J.Q. Wu et al. / Virology 380 (2008) 124–135 129

Table 5 Lists of the top gene sets enriched in VIR and LTNP by gene set enrichment analysis

Gene set name Gene set description NES NomP FDR Enriched lists in VIR Interferon responses IFNA_HCMV_6HRS_UP Up-regulated in fibroblasts at 6 h following treatment with interferon-alpha 2.21 b0.001 b0.001 DER_IFNA_UP Genes up-regulated by interferon-alpha in HT1080 (fibrosarcoma) 2.12 b0.001 b0.001 SANA_IFNG_ENDOTHELIAL_UP Up-regulated by interferon-gamma in colon, derm, iliac, aortic, lung endothelial cells 2.06 b0.001 b0.001 RADAEVA_IFNA_UP Genes up-regulated by interferon-alpha in primary hepatocyte 2.04 b0.001 b0.001 DER_IFNB_UP Genes up-regulated by interferon beta in HT1080 (fibrosarcoma) 1.95 b0.001 7.84E-05 IFN_ALPHA_UP Up-regulated 2-fold in HT1080 cells 6 h following treatment with interferon alpha 1.92 b0.001 1.08E-04 IFNALPHA_NL_UP Up-regulated by interferon alpha in normal primary hepatocytes 1.91 b0.001 1.54E-04 DER_IFNG_UP Genes up-regulated by interferon-gamma in HT1080 (fibrosarcoma) 1.91 b0.001 1.96E-04 IFN_BETA_UP Up-regulated 2-fold in HT1080 cells 6 h following treatment with interferon beta 1.82 b0.001 2.15E-03 Mitochondria MOOTHA_VOXPHOS Oxidative phosphorylation 1.89 b0.001 5.14E-04 HUMAN_MITODB_6_2002 Mitochondrial genes 1.85 b0.001 1.22E-03 ELECTRON_TRANSPORT_CHAIN Genes involved in electron transport 1.87 b0.001 6.64E-04 MITOCHONDRIA Mitochondrial genes 1.80 b0.001 2.89E-03 Cell proliferation and differentiation LEE_TCELLS3_UP Transcripts enriched in both ITTP and DP more than 3 fold, with average signal value 1.95 b0.001 6.86E-05 differences of at least 100 between less mature (ITTP, DP) and more mature cells ZHAN_MM_CD138_PR_VS_REST 50 top ranked SAM-defined over-expressed genes in each subgroup_PR 1.82 b0.001 2.05E-03 IDX_TSA_UP_CLUSTER3 Strongly up-regulated at 16–24 h during differentiation of 3T3-L1fibroblasts into adipocytes 1.81 b0.001 2.44E-03 P21_P53_ANY_DN Down-regulated at any timepoint (4–24 h) following ectopic expression of p21 (CDKN1A) 1.79 b0.001 3.78E-03 in OvCa cells, p53-dependent P21_P53_MIDDLE_DN Down-regulated at intermediate timepoint (12–16 h) 1.75 0.002 7.15E-03 SERUM_FIBROBLAST_CELLCYCLE Cell-cycle dependent genes regulated following exposure to serum in a variety of human fibroblast cell lines 1.75 b0.001 7.16E-03

Enriched lists in LTNP ADIP_DIFF_CLUSTER2 Strongly up-regulated at 2 h during differentiation of 3T3-L1 fibroblasts into adipocytes (cluster 2) −1.78 b0.001 2.38E-02 IGF1PATHWAY Growth factor IGF-1 stimulates growth and inhibits apoptosis by activating the MAP kinase pathway in a −1.6 0.012 1.54E-01 variety of cell types NGFPATHWAY Nerve growth factor (NEF) stimulates neural survival and proliferation via the TrKA and p75 receptors, −1.6 0.015 1.52E-01 which induce DAG and IP3 production and activate Ras INSULINPATHWAY Insulin regulates glucose levels via Ras-mediated transcriptional activation −1.58 0.011 1.54E-01

NES: normalized enrichment score; NomP: nominal p value; FDR: false discovery rate. responses to both type I (α and β) and type II (γ) interferon, which is surfaces (Neil et al., 2008). More than half of the 15 genes are less supported by a previous study showing that type 1 and 2 interferon characterized such as TNSF10 associated with induction of CD4+ T systems are engaged as a whole to combat HIV infection from the early cell death (Herbeuval et al., 2005), NMI and IFI35 involved in to late stages (Li et al., 2004). In addition, other studies have reported cytokine signaling (Zhang et al., 2007), and EPS15 involved in type I interferon response in HIV infection in PBMC, B and T cells clathrin-dependent endocytosis (Daecke et al., 2005). As the common (Hyrcza et al., 2007; Moir et al., 2004). In one study, it was shown that presence of these genes in a number of interferon response lists may the interferon responses occurred early in HIV infection and may be indicate some common biological roles, further investigation of the sustained throughout the infection in CD8+ T cells (Hyrcza et al., less characterized genes and the biological synergy between them is 2007). Our study further extends the conclusion that interferon warranted. responses persist in viremic patients under HAART. In contrast, the GSEA also revealed the coordinated up-regulation of the compo- study by Li et al. using lymph node biopsy samples showed that a large nents of the MAPK and NK cell-mediated cytotoxicity pathways as a group of interferon stimulated genes (ISGs) were down-regulated distinct transcriptional feature unique to the LTNP group when after HAART treatment (Li et al., 2004). This discrepancy could be compared to the VIR group. This is possibly related to CD8+ T cell- explained by the following possibilities. First, only two time points, mediated cytotoxicity function. The multiple roles of the MAPK the baseline and 1 month after therapy, were evaluated in Li's study, signaling pathway, such as activating HIV-1 replication and involve- while the ISG expression levels could rise over time as HAART can only ment in endothelial cell proliferation, have been reported in HIV partially contain the chronic immune activation. Second, it is possible disease (Toschi et al., 2006; Yang et al., 1999). Despite these roles, this that the expression level of ISG was down-regulated by HAART; it is pathway has also been documented in relation to NK effector function, still higher than those detected in LTNP. Besides the interferon which is associated with the mobilization of granule components responses, the significantly up-regulated genes associated with cell (Berg et al., 1998; Wei et al., 1998). apoptosis, such as PYCARD and those overlapping with the TGF-β Interestingly, part of the up-regulated components of MAPK stimulated gene lists also illustrated the chronic immune activation pathway (GRB2, RAF1 and MAPK3), together with other three core status in the VIR group. enriched genes (SHC, PIK3CA and PIK3R1) are distributed along two The 15 overlapping core enrichment genes associated with branches of NK cell-mediated ctytoxicity pathway (Fig. 5); one is interferon responses not only included well characterized genes signaling through SHC, GRB2, and RAF, and the other through PI3K, such as STAT1, OAS1 and OAS2, MX and GBP1 involved in IFN with both branches converge at MEK1/2 signaling to ERK1/2 (MAPK). signaling, RNA degradation and virus replication inhibition (Samuel, The biological processes involved in this NK cytotoxicity pathway are 2001), but also encompassed the very recently characterized TRIM 22 also common to other cytotoxic cells including cytotoxic T lympho- and BST genes. TRIM22 overexpression has been reported to inhibit cyte (CTL), NK T cells and γδ T cells (Russell and Ley, 2002). As MAPK HIV replication, and in TRIM22 depleted cells HIV particle release was pathway also has implications in cytotoxicity function as discussed significantly increased (Barr et al., 2008). BST2, a membrane protein above, we thus hypothesize that the up-regulated components of of previously unknown function, was characterized as a tetherin, these two pathways in LTNP are possibly associated with the effective which can inhibit HIV release through retention of virions on cell cytoxicity function in this group, which is consistent with strong anti- 130 J.Q. Wu et al. / Virology 380 (2008) 124–135

HIV immune responses in the LTNPs over time and their long-term were studied. The infection time for L1, L2 and L3 are N20 years, drug-free survival. Interestingly, the differential analysis detected the and L4 N14 years. These treatment-naive LTNPs have maintained significant up-regulation of the inhibitory NK cell receptor CD94 in high CD4+ T cell counts (N500 cells/μl) and below detectable levels the VIR group. CD94, also expressed on activated CD8+ T cells, of plasma viremia (b50 copies of HIV RNA/ml plasma) except one functions together with NKG2 to inhibit cell-mediated cytotoxicity patient (L4) with very low plasma viremia (57 HIV RNA copies/ml (Moser et al., 2002; Zeddou et al., 2007). The up-regulation of this plasma) (Table 1). In contrast, patients in the VIR group were on molecule may suggest impaired CTL in the VIR group as opposed to HAART and had detectable plasma viremia and CD4+ T cell counts the LTNP group. b500 cells/μl. These patients received two NRTIs in association with L2L analysis also picked up a few genes associated with endosome one or two protease inhibitors except V3 who received combined and lysosome transport significantly up-regulated in the VIR group therapy of one NNRTI (etravirine), two protease inhibitors and one (M6PR, CHMP5 and GBA), which may be related to HIV entry via integrase inhibitor (raltegravir). The NRTIs the patients received clathrin-mediated endocytosis and budding at late endosomes included zidovudine, lamivudine, stavudine, emtricitabine and teno- (Daecke et al., 2005; Damme and Guatelli, 2008; Ward et al., 2005). fovir; the protease inhibitors included darunavir, ritonavir, indinavir, Both L2L and GSEA analysis have significantly overlapping gene lists saquinavir, and atazanavir. Five HIV sero-negative and otherwise associated with adipocyte differentiation, but the biological relevance healthy individuals were included as controls to compare gene of this gene regulation remains to be elucidated. expression profiles and validate our system. Six patients came from The most notable feature of this study is that it distinguishes the HIV clinic at Westmead Hospital and three patients plus the five genetic regulation of CD8+ T cell transcriptome between HIV+ viremic healthy controls came from the Australian Red Cross Blood Service in patients on HAART and therapy-naive LTNPs. In summary, coordinated Sydney. This study was approved by the Sydney West Area Health up-regulation of oxidative phosphorylation enzymes and interferon Services Research Ethics Committee (HREC2008/2/4.12(2732)), and all responses in the VIR group and coordinated up-regulation of blood samples were collected after individual informed consent. components of MAPK and cytotoxicity pathways in the LTNP group are the transcriptional signatures that characterize these two groups. Purification of CD8+ T cells and RNA isolation The up-regulation of 31 OXPHOS genes in the VIR group could be mostly attributed to HAART effects and only partially to persistent A single blood sample (10 ml in EDTA) was obtained from each viral replication. Though mitochondrial toxicity incurred by HAART patient. After separation of plasma, PBMC were isolated immediately has been recognized for some years, this study is first to show the after obtaining blood samples by Ficoll-gradient centrifugation and compensatory effect of mitochondrial toxicity at the genetic level in ex purified. This aspect was strictly followed because of previously vivo collected CD8+ T cells from HIV+ progressors, indicating the described lower RNA yields and possible changes in gene expression oxidative stress induced by HAART during HIV disease progression. profiles upon storage of blood (Lyons et al., 2007). CD8+ T cells were Further characterization of these genes may facilitate a clear under- then obtained by positive isolation with antibody-conjugated standing of HAART-induced oxidative stress toxicity and various magnetic beads according to the manufacturer's instructions metabolic disturbances down to the individual gene level. This may (Dynal Biotech, Oslo, Norway). Lyons et al., have also shown that also aid the development of new therapeutic modalities. The persisted it is preferable to use positive selection to avoid contaminating cells interferon responses in viremic patients on HAART suggested the in purified fractions (Lyons et al., 2007). Flow-cytometric analysis chronic immune activation status in these patients. The coordinated performed on separated CD8+ T cell populations demonstrated that up-regulation of components of MAPK and cytotoxicity pathways in 99.1%±0.128 (mean ±SD) of purified CD8+ cells were single positive the LTNP group leads to the hypothesis that this may be one of the for the CD8 marker (Potter et al., 2004). Total RNA was isolated from molecular mechanisms contributing to the effective and sustained CTL purified cells using RNeasy Mini kit (Qiagen Pty Ltd., Clifton Hill, activity in the LTNPs, which may also be one of the major protective Victoria, Australia) with an integrated step of on-column DNase correlates during asymptomatic HIV disease. Overall, these results treatment. offer new comparative insights into HIV and host cell interactions in relation to HAART treatment and disease status. This will further cRNA preparation, microarray hybridization and scanning facilitate the understanding of the genetic basis of progressive and non-progressive HIV disease as well as gene regulation during HAART RNA quality was checked by Agilent Bioanalyzer and RNA Integrity in primary CD8+ T cells. A deeper understanding of interactions Scores are shown in Table 1. cRNA amplification and labeling with between HIV and individual cell types is sorely needed, if new biotin were performed using Illumina TotalPrep RNA amplification kit therapeutic interventions have to be designed or information on (Ambion, Inc., Austin, USA) with 250 ng total RNA as input material. innate /adaptive genes governing the natural control of HIV in LTNPs cRNA yields were quantified with Agilent Bioanalyzer and 1.5 μg have to be identified and characterized. cRNAs were hybridized to the Sentrix Human-6 v2 Expression BeadChips (Illumina, Inc., San Diego, USA). Each chip contains six Materials and methods arrays and each array contains N48,000 gene transcripts, of which, 46,000 derived from human genes in the National Center for Patient profiles Biotechnology Information (NCBI) Reference Sequence (RefSeq) and UniGene databases. All reagents and equipment used for hybridiza- Four HIV infected long-term non-progressors (LTNP; n=4) and five tion were purchased from Illumina, Inc. According to the manufac- HIV+ patients on HAART with detectable plasma viremia (VIR; n=5) turer's protocol, cRNA was hybridized to arrays for 16 h at 58 °C before

Fig. 2. Enrichment plot and heat map for the gene sets of electron transport chain and interferon-gamma induced genes by GSEA. (A) Enrichment plot for the gene set of electron transport chain. Bottom, plot of the ranked list of all genes. Y axis, value of the ranking metric; X axis, the rank for all genes. Genes whose expression levels are most closely associated with the VIR or LTNP group get the highest metric scores with positive or negative sign, and are located at the left or right edge of the list. Middle, the location of genes from the gene set electron transport chain within the ranked list. Top, the running enrichment score for the gene set as the analysis walks along the ranked list. The score at the peak of the plot is the enrichment score (ES) for this gene set and those genes appear before or at the peak are defined as core enrichment genes for this gene set. (B) Heat map of the top 38 core enrichment genes from the gene set of electron transport chain. The genes that contribute most to the ES, i.e., genes that appear in the ranked list before or at the peak point of ES, were defined as core enrichment genes. Rows, gene; columns, samples. Range of colors (red to blue) shows the range of expression values (high to low). (C) Enrichment plot for the gene set of SANA_IFNG_Endothelial_Up. (D) Heat map of the top 32 core enrichment genes from the gene set of SANA_IFNG_Endothelial_Up. J.Q. Wu et al. / Virology 380 (2008) 124–135 131 132 J.Q. Wu et al. / Virology 380 (2008) 124–135

Fig. 3. Core enrichment genes overlap in the four gene sets associated with mitochondrion. Rows, gene set; columns, genes. The core enrichment gene in each gene set was represented by a colored cell. Range of colors (red to blue) shows the range of enrichment signal (high to low). Common core enrichment genes across gene sets formed vertical stripes.

Fig. 4. Coordinately up-regulated OXPHOS genes in the VIR group illustrated in oxidative phosphorylation pathway obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/). Top, 3D illustrations of the electron transport chain. Enzyme identifiers (e.g. 1.6.5.3, 1.6.99.3, etc; according to KEGG namespace) associated with up- regulated genes are highlighted in red. Bottom, the subunit components of the 5 enzyme complexes along the electron transport chain. The proteins encoded by the 31 coordinately up-regulated genes are highlighted in red and these include (1) Complex I NADH dehydrogenase subunits (NDUF) S1, S4, S6, A3, A7, A8, AB1, B2, B3, B5, B6, B7 and C1, (2) Complex II succinate dehydrogenase subunits (SDH) C and D, (3) Complex III cytochrome c reductase components COR1, QCR2, QCR6 and QCR7, which are encoded by (UQCR) C1, C2, H, and B, respectively, (4) Complex IV cytochrome c oxidase subunits (COX) 5A, 6A1, 7B, 11, and 15, and (5) Complex V ATP synthase units gamma, delta, epsilon, b, d, f6 and f encoded by ATP 5C1, 5D, 5E, 5F1, 5H, 5J and 5J2, respectively. J.Q. Wu et al. / Virology 380 (2008) 124–135 133

Fig. 5. Coordinately up-regulated components of MAPK and cytotoxicity pathways in the LTNP group illustrated in the pathways obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/). Top, the classical MAPK pathway adopted from MAPK pathway. The proteins GRB2, RAF1, ERK (corresponding to gene symbol MAPK3), ELK-1 and C-FOS (corresponding to gene symbol FOS) encoded by the coordinately up-regulated genes are highlighted in red and denoted by dots. Bottom, NK cell-mediated cytotoxicity pathway. The proteins SHC (corresponding to gene symbol SHC1), GRB2, RAF (corresponding to gene symbol RAF1), PI3K (corresponding to gene symbol PIK3CA and PIK3R1) and ERK1/2 (corresponding to gene symbol MAPK3) encoded by coordinately up-regulated genes are highlighted in red and denoted by dots. being washed and stained with streptavidin-Cy3. Then the beadchips appropriate for normalization. Signal levels were good and noise and were centrifuged to dry and scanned on the Illumina BeadArray background levels were low. Following normalization of all samples Reader confocal scanner. using a cubic spline function (www.illumina.com/downloads/ GXBeadStudioNormalization_TechNote.pdf) (see supplementary file Differential gene and L2L analysis 2 Table 2), cluster analysis for the whole genome expression of 48,000 human gene transcripts was performed based on the Pearson BeadStudio version 3 was used to analyze Illumina data. Hybridi- correlation metric and Correlation agglomeration method implemen- zation controls showed that inter-array variability was within a level ted in BeadStudio. 134 J.Q. Wu et al. / Virology 380 (2008) 124–135

To compare gene expression profiles between the LTNP and VIR Appendix A. Supplementary data groups, the differential analysis was carried out for 4 LTNP and 5 VIR samples using a cubic spline function for normalization and the Supplementary data associated with this article can be found, in Illumina custom error model with false discovery rate correction the online version, at doi:10.1016/j.virol.2008.06.039. implemented in BeadStudio (Benjamini and Hochberg, 1995). Genes with detection pb0.01 (indicating the transcript was detected) in at References least one group were selected. With at least one group has detected transcripts, differential genes were identified by DiffScore N20 or Aurpibul, L., Puthanakit, T., Lee, B., Mangklabruks, A., Sirisanthana, T., Sirisanthana, V., b−20 (corresponding to significance level of Pb0.01). DiffScore, the 2007. Lipodystrophy and metabolic changes in HIV-infected children on non- expression difference score, takes into account background noise and nucleoside reverse transcriptase inhibitor-based antiretroviral therapy. Antivir. Ther. 12 (8), 1247–1254. sample variability (Chudin et al., 2006). The list of differential genes Barr, S., Smiley, J., Bushman, F., 2008. The interferon response inhibits HIV particle was then submitted through L2L analysis tool (http://depts.washing- production by induction of TRIM22. PLoS Pathog. 4 (2), e1000007. ton.edu/l2l/) to compare with 953 differential gene lists from L2L Benjamini, Y., Hochberg, J., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B. 57, 289–300. microarray database. Berg, N., Puente, L., Dawicki, W., Ostergaard, H., 1998. 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We thank Amanda Croft for her help with Illumina beadchip J. Virol. 68 (7), 4650–4655. technology, Dr. Andrew N Harman for his help with qPCR, Dr. Choo Li, Q., Schacker, T., Carlis, J., Beilman, G., Nguyen, P., Haase, A., 2004. Functional genomic Beng Chew and Dr. Jenny Learmont for their help with patient analysis of the response of HIV-1-infected lymphatic tissue to antiretroviral therapy. J. Infect. Dis. 189 (4), 572–582. samples. Jing Qin Wu is the recipient of a University of Sydney PhD Lyons, P., Koukoulaki, M., Hatton, A., Doggett, K., Woffendin, H., Chaudhry, A., Smith, K., Australian Postgraduate Award, and a Westmead Millennium Founda- 2007. Microarray analysis of human leucocyte subsets: the advantages of positive tion top-up scholarship. Bin Wang receives Career Development selection and rapid purification. BMC Genomics 8, 64. Mackewicz, C., Blackbourn, D., Levy, J., 1995. CD8+ T cells suppress human immunode- Award by NHMRC. This work was supported by grants from the AIDS ficiency virus replication by inhibiting viral transcription. Proc. Natl. Acad. Sci. U. S. A. Foundation Budget. 92 (6), 2308–2312. J.Q. Wu et al. / Virology 380 (2008) 124–135 135

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