Characterization of Host Genetic Expression Patterns in HIV-Infected Individuals with Divergent Disease Progression

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Virology 411 (2011) 103–112

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Virology

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Characterization of host genetic expression patterns in HIV-infected individuals with divergent disease progression

María Salgado a,c, Pedro López-Romero b,1, Sergio Callejas b, Mariola López a,c, Pablo Labarga a, Ana Dopazo b, Vincent Soriano a, Berta Rodés a,c,⁎

a Infectious Diseases Department, Hospital Carlos III, Madrid, Spain b Genomic Unit, CNIC, Madrid, Spain c Fundación Investigación Biomédica del Hospital Carlos III, Madrid, Spain

article info abstract

Article history: The course of HIV-1 infection shows a variety of clinical phenotypes with an important involvement of host Received 8 September 2010 factors. We compare host gene expression patterns in CD3+ T cells from two of these phenotypes: long-term Returned to author for revision non-progressor patients (LTNP) and matched control patients with standard HIV disease progression. Array 31 October 2010 analysis revealed over-expression of 322 genes in progressors and 136 in LTNP. Up-regulated genes in Accepted 19 December 2010 progressors were mainly implicated in the regulation of DNA replication, cell cycle and DNA damage stimulus Available online 15 January 2011 and mostly localized into cellular organelles. In contrast, most up-regulated genes in LTNP were located at the – Keywords: plasmatic membrane and involved in cytokine cytokine receptor interaction, negative control of apoptosis or LTNP regulation of actin cytoskeleton. Regarding gene interactions, a higher number of viral genes interacting with Microarrays cellular factors were seen in progressors. Our study offers new comparative insights related to disease status HIV and can distinguish differentiated patterns of gene expression among clinical phenotypes. Gene expression © 2010 Elsevier Inc. All rights reserved. Progression

Introduction progression should provide a valuable approach to understand HIV pathogenesis. The natural course of HIV-1 infection shows variability among New high-throughput techniques, like microarray analysis, allow infected individuals. The majority of patients develop AIDS within 7– the management of huge information on gene expression patterns 10 years after infection but a small fraction (5%), called long-term- comparing different groups of samples. Using these methods, various non-progressors (LTNP), stay clinically asymptomatic for years in the studies analysed gene expression profiles in different cell types absence of antiretroviral therapy (Pantaleo et al., 1995). The infected with HIV-1 and using in vitro or in vivo approaches (Giri et al., mechanisms contributing to these different phenotypes of viral 2006). However, very few have monitored gene expression in T cells infection are a primary focus of current research in HIV pathogenesis. from patients with different stages of AIDS disease (Hyrcza et al., Despite great advances in recent years identifying several genetic and 2007; Sankaran et al., 2005; Wu et al., 2008). Most of these later viral factors associated with non-progression (Lama and Planelles, studies have examined limited number of samples for each group of 2007; Saksena et al., 2007), the cause for this lack of progression in patients (5 or less) and in some cases results oblige to combine similar many cases remains unclear. There is consensus, however, that is phenotypes in order to obtain more differently regulated genes governed by multiple factors resulting from the interaction between (Hyrcza et al., 2007). Sample size is important to detect not only the HIV and host response to the viral infection. It has been described that largest changes in the levels of gene expression but also more subtle HIV-1 infection has dramatic effects on host T cells physiology (Chan differences with any reliability, and also well define phenotypes are et al., 2007) with significant changes in the pattern of cell gene essential to avoid bias in large scale studies. For this reason, we expression. The identification of genes responsible for this non- decided to focus our analysis in two well defined different groups of patients with a good sample size in both. In the current study, we used a well characterized cohort of LTNP established in 1997 at the Hospital Carlos III (Rodes et al., 2004)to ⁎ Corresponding author. Laboratorio Biología molecular, Hospital Carlos III, Calle compare their gene expression profiles in CD3+ T cells with those in Sinesio Delgado, 10, 28029-Madrid, Spain. Fax: +34 91 733 6614. typical progressor HIV-1+ patients. Cells were obtained ex vivo from E-mail address: [email protected] (B. Rodés). 1 Present address: Epidemiology, Atherothrombosis and Imaging Department, CNIC, HIV-1 infected patients presenting these two different clinical Madrid, Spain. phenotypes.

Results Regarding the main localization of differentially expressed genes between LTNP and progressors, we noticed that despite a wide Patients distribution of genes in both groups, up-regulated genes in LTNPs were localized in higher proportion within the plasmatic membrane. Patient's characteristics at the time of the analysis are described in These include GPR15, IL-17RA, BMPR2, among others. Otherwise, up- detail in Table 1. Sequencing analyses showed all patients carried HIV- regulated genes in progressors were found inside the cell and 1 subtype B. The majority of patients had a wild type CCR5 genotype principally in the nucleus, associated to chromosomes and with and only 4 LTNPs and 1 progressor had the 32-bp CCR5 deletion in intracellular organelles (Fig. 1a and Table 2 section a). The location heterozygosis. determines the cell part activated in each case. For biological processes, some differences emerged in gene Microarray and differential analysis expression between LTNP and progressors that may be relevant to the progression of the infection. Despite up-regulated cell cycle genes RNA from ex vivo CD3+ T cells was used to obtain gene expression present in both groups (Fig. 1b and Table 2 section b), there was a data from Agilent microarrays; witch contained 43,377 probe sets significant higher number of expressed genes in progressors, where corresponding to approximately 30,000 genes described for whole the virus has a fairly high replication. In fact, most of the up-regulated human genome. genes in progressors were related to cell cycle, specifically to the Differences in gene expression were measured with a moderated mitosis phase, nucleic acid metabolic processes and regulation of cell t-statistic. The t-statistic means have been moderated across genes. To cycle progression. Furthermore, the remaining up-regulated genes do that, the sample standard deviation is shrunk towards a pooled were related to endogenous stimulus response, more specifically DNA standard deviation value using empirical Bayes methods. Data are damage stimulus and DNA repair. On the other hand, up-regulated presented as progressors versus LTNP gene expression (t=P-LTNP). genes in LTNPs were mostly associated with other specific functions The analysis revealed a total of 458 differentially expressed genes different than cell cycle. These included cell surface receptor linked between both groups of patients (see Supplementary data). Whereas signal transduction, actin cytoskeleton organization and biogenesis, as in the progressors’ group 322 genes were up-regulated genes well as negative regulation of apoptosis. (t=positive value), only 136 were seen in the non-progressors When analysing for molecular function most up-regulated genes (t=negative value), indicating a higher cell activity in more effective were within LTNPs and very few in progressors (Fig. 1c and Table 2 infections and two different patterns of gene expression according to section c). Functions detected were related to ion binding, trans- phenotype. membrane receptor and G-protein coupled receptor activities. An overlap analysis of these differentially expressed genes found Progressors had only up-regulated the nucleic acid binding function. in our study with other reported genome-wide analysis and host These results are in agreement with observations above, supporting factor studies (Brass et al., 2008; König et al., 2008; Zhou et al., 2008; the idea that in LTNP there was an activation of membrane signalling Bushman et al., 2009) showed 23 coincident genes (gene symbols: functions, whereas in progressors prevailed the cellular cycle HMGB1, THOC4, SNRPD1, RAD51, RANBP1, SUMO1, FEN1, BAZ2B, activities, specifically replication. By the analysis approach we could TUBB, NUDT1, SNRPA1, GINS4, PLK1, KIAA1012, MND1, PSMC3, also say that in LTNP the cellular cycle activities were reduced. YWHAQ, MICB, PRKX, SSR1, BCRA1, UBE2C, ASB1). Finally, specific metabolic pathways were analysed using the KEGG database. In LTNPs activated genes were associated to the cytokine– Functional analysis cytokine receptor interaction, regulation of actin cytoskeleton and focal adhesion processes whereas genes from progressors were Genes operate with an intricate network of interactions within the mainly related to cell cycle pathway (Fig. 2). cell and in some cases in a redundant manner. Complex traits such In conclusion, we have seen differential characteristic functions HIV infections are starting to be considered from a system biology (focused in different parts of cellular metabolism) from each group. perspective. To facilitate the understanding of the biological implica- Mainly, higher signalling between cells seems to be a characteristic fi – tions of these 458 differential genes, functional analyses using the pattern for LTNP, more speci cally the functions of cytokine cytokine FATIGO tool from GEPAS (Al-Shahrour et al., 2005, 2006) were receptor interaction, negative control of apoptosis or regulation of performed. This tool used gene-ontology and KEGG databases to give actin cytoskeleton. Otherwise, in progressors the cellular cycle seems an association between each gene and its function. The distribution of to be more active, with an important role of regulation of DNA any combination of terms between two groups of genes can be replication, cell cycle and DNA damage stimulus. simultaneously tested by means of a Fisher's exact test. From this analysis relevant data on location of the up-regulated genes (known as cellular component), parts of different biological processes they are Interactions implicated with, molecular functions, or involved metabolic pathways, could be obtained. Although biological meanings derived from our analyses are relevant to HIV pathogenesis, in an attempt to further complement these findings we performed an HIV–host interaction analysis. Genes Table 1 with a significant representation in up-regulated functions within Patient's characteristics. Means±SD for age, time of infection, CD4 counts, CD4 both groups were used to find any reported interaction with HIV-1. In percentage and viral load count are provided. Table 3 the main viral-host interactions are shown. Among LTNPs, tat Characteristics Patients (n=30) P value and env were the main viral genes involved with up-regulated host genes, while in progressors there was a greater range of viral genes LTNP Progressors (n=15) (n=15) involved. In the course of infection, the virus uses the cellular machinery to its benefits across the interaction of viral and host genes. Sex (numb. Men) 11 (73%) 13 (86%) 0.379 Age (years) 42.6±5.4 35±8.6 0.007 Furthermore, accessory proteins appear to be dedicated to various Time since diagnosis (years) 17.6±4.45 2.4±0.83 b0.001 aspects of evasion from adaptive and innate immunity (Malim and CD4abs (cell/μL) 638.2±265 488±281 0.146 Emerman, 2008). For this reason, a higher number of viral genes CD4% 33.6±7.76 24.2±9.95 0.007 interacting with cellular factors may be a sign of more advance stage b Viral load (log RNA copies/mL) 2.99±0.82 4.40±0.81 0.001 of infection. M. Salgado et al. / Virology 411 (2011) 103–112 105

a) Cellular component 100 % LTNP

90 % Progressors

0 membrane intracellular plasma intrinsic to intracellular intracellular intrinsic to nucleus chromosome membrane membrane organelle non- plasma membrane- membrane bound organelle

Level 4 Level 5 Level 6 Level 7 Level 8

b) Biological Function 100 % LTNP 90 % Progressors 80

0 response to cell cycle response to mitotic cell nucleobase, cell surface actin filament- DNA repair actin negative regulation of endogenous DNA damage cycle nucleoside, receptor based cytoskeleton regulation of apoptosis stimulus stimulus nucleotide and linked signal process organization programmed nucleic acid transduction and cell death metabolic biogenesis process

level 3 level 4 level 5 level 6 level 7 level 9

c) Molecular function 100 % LTNP 90 % Progressors 80 70 60 50 40 30 20 10 0 ion binding nucleic acid receptor activity lipid transporter transmembrane cation binding metal ion transition metal G-protein rhodopsin-like binding activity receptor activity binding ion binding coupled receptor activity receptor activity

Level 3 Level 4 Level 5 Level 6

Fig. 1. Graphic representation of gene distribution in LTNP and progressors. Data are expressed in gene representation percentage annotated for each GO category at different levels: a) cellular component: differences in localization into the cell; b) biological function: differences in activated biological processes; and c) molecular function: differences in molecular components.

Discussion MAPK and cytotoxic pathways in LTNP (Wu et al., 2008). However, a clearer picture of the subversion of cell machinery by HIV in Previous reports on gene expression in LTNP patients have LTNP is still needed. The goal of our study was to compare the identiﬁed interferon responses as a signature for progression host-virus interactions between a large series of well deﬁned LTNP (Hyrcza et al., 2007) and an up-regulation of components of with more than 20 years of follow up in some cases and a group of 106 M. Salgado et al. / Virology 411 (2011) 103–112

Table 2 List of genes expressed in each group of patients and annotated for each GO category.

GO level Location Genes expressed in LTNP Nº % Genes expressed in progressors Nº % p- genes LTNP genes progressors value

a) Cellular component Level 4 membrane ATP11A, PDPK1, VEZT, SYPL1, 33 47.14 BAK1, ITGB1BP1, LAMA5, CBX5, ALS2CR4, 27 17.31 b0.001 DDEF1, TNFRSF10B, EVI2A, SSR1, CYP4V2, ZDHHC21, CALM3, PKMYT1, IL12RB1, RAB14,SEC23A, GPR15, PIP5K3, TMEFF2, MICB, ARF5, FAM14B, KIRREL2, XYLT1, CYSLTR1, AMICA1, ARL6IP5, ARL6IP1, STOML2, TIMM13, FDXR, COX7C, IL17RA, SPTLC1, FGFRL1, P2RY5, TAPBP, TMEM97, CD38, CLDN11, CD8B, IDH2, RDX, PAG1, MPZL1, HOOK3, DPYD, PTPRK IL1RAP, TMEM55A, BMPR2, AFTPH, TMTC2, PDGFB, C18orf1, EVI2B intracellular C14orf43, FBXO9, SSBP2, EVI5, PDPK1, 49 70 EXO1, SOLH, CIT, FBXO4, RABEP1, CENPM, MVK, 139 89.10 b0.001 VEZT, SYPL1, CHD9, CENTG3, NPEPPS, MMAB, SUV39H2, SSBP3, GTSE1, FHL2, TACC1, SSR1, DCK, PITPNC1, RAB14, HIST3H2BB, CBX5, EZH2, CYP4V2, CIDEC, SEC23A, BAZ2B, ARID1A, TLE4, ZNF175, CDCA7, ISG15, THOC4, RANBP1, KIAA1967, SHOC2, HSD17B4, ZFP1, ROCK1, PIP5K3, TPX2, GLRX5, GTF3C4, RRM2, PPIH, CHEK1, XYLT1, RCOR1, KIAA1012, ARL6IP5, CDC45L, FEN1, ROD1, FABP5, CHAF1B, YTHDC1, SPTLC1, ACO1, RFWD2, RDX, RAD51AP1, GPS1, FOXM1, MYBL2, ANP32E, MDFIC, KLHL5, HOOK3, DPYD, AFTPH, PSMC3, ATP5S, NFEKBIB, CASC5, MAD2L1, SH3GL1, TMTC2, KLHL7, GLB1, SGK3, KIAA0101, CALM3, HMGN2, CORT, GINS2, PRR5, PTEN, FBXL5, HIPK3, EGLN3, CUL1 CCNB2, DEPDC1, TIA1, DEPDC1B, ORC1L, GSTZ1, GINS4, KIFC1, EWSR1, PKMYT1, PPP1CA, E2F7, TIPIN, TPM4, C16orf33, BRCA1, GMNN, C6orf173, CXorf26, ORC6L, ATXN10, ARF5, NUF2, KIF15, TK1, CDC6, DLG7, BUD31, KIF2C, KHSRP, TROAP, NUCKS1, STMN1, H2AFZ, TFDP1, HMGB1, ARL6IPI, MIER1, RAD54L, RAD51, MYL6B, NRL, DAZAP1, ASB1, STOML2, TIMM13, GSG2, SNRPA1, HNRPA3, FDXR, RECQL4, KIF4A, MCM5, FRG1, NOLA1, CCNA2, CDCA2, CDCA8, BIRC5, NUDT21, COX7C, TAPBP, CDT1, TUBB, HIST1H4D, DNAJC9, NUSAP1, NCAPG, TIMELESS, SUMO1, ZWILCH, DYNLRB1, RRM1, HMG1L1, MLF1IP, UBE2N, NCAPH, CENPM, UHRF1, PTTG1, E2F1, NUDC, NT5C, PLK1, SERBP1, SET, SPC24, IDH2, ESPL1, NIP7, MCM4 Level 5 Plasma PDPK1, VEZT, SYPL1, RAB14, GPR15, 14 21.54 CALM3, IL12RB1, MICB, ARF5, CD38, CLDN11, 8 12.16 b0.001 membrane PIP5K3, CYSLTR1, IL17RA, FGFRL1, CD8B, PTPRK RDX, MPZL1, IL1RAP, BMPR2, EVI2B Level 6 intrinsic to ATP11A, VEZT, SYPL1, TNFRSF10B, 23 35.38 BAK1, LAMA5, ALS2CR4, CYP4V2, ZDHHC21, 18 12.16 b0.001 membrane EVI2A, SSR1, GPR15, XYLT1, CYSLTR1, IL12RB1, TMEFF2, MICB, FAM14B, KIRREL2, AMICA1, ARL6IP5, IL17RA, SPTLC1, ARL6IP1, COX7C, TAPBP, TMEM97, CD38, FGFRL1, P2PRY5, PAG1, MPZL1, IL1RAP. CLDN11, CD8B, PTPRK TMEM55A, BMPR2, TMTC2, C18orf1, EVI2B intracellular C14orf43, SSBP2, EVI5, PDPK1, VEZT, 40 61.54 EXO1, CIT, RABEP1, CENPM, MVK, MMAB, 122 82.43 b0.01 organelle SYPL1, CHD9, CENTG3, NPEPPS, TACC1, SUV39H2, SSBP3, GTSE1, FHL2, HIST3H2BB, SSR1, DCK, RAB14, SEC23A, BAZ2B, CBX5, EZH2, CYP4V2, CDCA7, THOC4, RANBP1, ARID1A, TLE4, ZNF175, HSD17B4, ZFP1, KIAA1967, TPX2, GLRX5, GTF3C4, PPIH, CHEK1, ROCK1,PIP5K3, XYLT1, RCOR1, KIAA1012, CDC45L, FEN1, ROD1, CHAF1B, RAD51AP1, GPS1, ARL6IP5, YTHDC1, SPTLC1, RFWD2, FOXM1, MYBL2, ANP32E, PSMC3, ATP5S, RDX, MDFIC, KLHL5, HOOK3, AFTPH, NFKBIB, CASC5, MAD2L1, KIAA0101, HMGN2, TMTC2, KLHL7, GLB1, SGK3, HIPK3, EGLN3 CORT, GINS2, CCNB, TIA1, ORC1L, GSTZ1, GINS4, KIFC1, EWSR1, PKMYT1, PPP1CA, E2F7, TIPIN, TPM4 C16orf33, BRCA1, GMNN, C6orf173, ORC6L, NUF2, KIF15, CDC6, DLG7, BUD31, KIF2C, KHSRP, NUCKS1, STMN1, H2AFZ, TFDP1, HMGB1, ARL6IP1, MIER1, RAD54L, RAD51, MYL6B, NRL, DAZAP1, STOML2, TIMM13, GSG2, SNRPA1, HNRPA3. FDXR, RECQL4, KIF4A, MCM5, FRG1, NOLA1, CCNA2, CDC8, BIRC5, NUDT21, COX7C, TAPBP, CDT1, TUBB, HIST1H4D, DNAJC9, NUSAP1, NCAPG, TIMELESS, SUMO1, ZWILCH, DYNLRB1, HMG1L1, MLF1IP, UBE2N, NCAPH, CENPM, UHRF1, PTTG1, E2F1, NUDC, NT5C, PLK1, SERBP1, SET, SPC24, IDH2, ESPL1, NIP7, MCM4 Level 7 Intracellular CHD9, ROCK1, RDX, MDFIC, 7 12.07 CIT, CENPN, SUV39H2, GTSE1, HIST3H2BB, 48 34.78 b0.001 non-membrane KLHL55, HOOK3, KLHL7 CBX5, RANBP1, TPX2, CHEK1, MYBL2, MAD2L1, -bound HMGN2, CORT, CCNB2, KIFC1, TPM4, BRCA1, organelle ORC6L, NUF2, KIF15, CDC6, DLG7, KIF2C, STMN1, H2AFZ, HMGB1, RAD51, MYL6B, STOML2, KIF4A, NOLA1, CDCA8, BIRC5, NUDT21, CDT1, TUBB, HIST1H4D, DNAJC9, NUSAP1, ZWILCH, DYNLRB1, HMG1L1, MLF1IP, CENPM, NUDC, PLK1, SPC24, ESPL1 intrinsic to SYPL1, GPR15, CYSLTR1, IL17RA, 8 13.79 IL12RB1, CD8B, PTPRK 3 2.17 b0.01 membrane MPZL1, IL1RAP, BMPR2, EVI2B M. Salgado et al. / Virology 411 (2011) 103–112 107

Table 2 (continued) GO level Location Genes expressed in LTNP Nº % Genes expressed in progressors Nº % p- genes LTNP genes progressors value

Level 8 nucleus C14orf43, SSBP2, EVI5, VEZT, CHD9, 21 42.86 EXO1, CENPN, SUV39H2, SSBP3, FHL2, 96 73.85 b0.001 CENTG3, NPEPPS, TACC1, DCK, HIST3H2BB, CBX5, EZH2, CDCA7, THOC4, BAZ2B, ARID1A, TLE4, ZNF175, RANBP1, KIAA1967, TPX2, GTF3C4, PPIH, CHEK1, ZFP1, RCOR1, YTHDC1, RFWD2, CDC45L, FEN1, ROD1, CHAF1B, RAD51AP1, GPS1, MDFIC, KLHL7, HIPK3, EGLN3 FOXM1, MYBL2, ANP32E, PSMC3, NFKBIB, CASC5, MAD2L1, KIAA0101, HMGN2, CORT, GINS2, CCNB, TIA1, ORC1L, GINS4, EWSR1, PPP1CA, E2F7, TIPIN, C16orf33, BRCA1, GMNN, C6orf173, ORC6L, NUF2, CDC6, DLG7, BUD31, KIF2C, KHSRP, NUCKS1, H2AFZ, TFDP1, HMGB1, MIER1, RAD54L, RAD51, NRL, DAZP1, BIRC5, NUDT21, CDT1, HIST1H4D, NUSAP1, NCAPG, TIMELESS, SUMO1, HMG1L1, MLF1IP, UBE2N, NCAPH, CENPM, UHRF1, PTTG1, E2F1, NUDC, NT5C, PLK1, SERBP1, SET, SPC24, ESPL1, NIP7, MCM4 chromosome CHD9 1 2.04 CENPM, SUV39H2, HIST3H2BB, CBX5, CHEK1, 25 19.23 b0.01 MYBL2, MAD2L1, HMGN2, CORT, BRCA1, ORC6L, NUF2, KIF2C, H2AFZ, HMGB1, RAD51, CDCA8, BIRC5, CDT1, HIST1H4D, ZWILCH, HMG1L1, MLF1IP, CENPM, SPC24

b) Biological processes Level 3 cell cycle EVI5, TACC1, BMPR2, 6 8.22 EXO1, MDK, CIT, CKS1B, SUV39H2, GTSE1, 53 33.12 b0.001 PTEN, PDGFB, CUL1 RANBP1, TPX2, CHEK1, CEP55, CDC45L, YWHAQ, CHAF1B, GPS1, MYBL2, MAD2L1, CCNB2, KIFC1, PKMYT1, PPP1CA, E2F7, BRCA1, GMNN, NUF2, KIF15, CDC6, DLG7, KIF2C, MAPK13, STMN1, TFDP1, UBE2C, RAD51, RAD54L, GSG2, MCM5, CCNA2, CDCA8, BIRC5, CDT1, PBK, TUBB, NUSAP1, NCAPG, NCAPH, UHRF1, CDKN3, PTTG1, E2F1, NUDC, PLK1, SPC24, ESPL1 Response to No genes 0 0 EXO1, GTSE1, CHEK1, FEN1, CHAF1B, RAD51AP1, 139 89.1 b0.001 endogenous stimulus BRCA1, HMGB1, RAD51, RAD54L, NUDT1, C16orf35, TYMS, RECQL4, CCNA2, UBE2N, UHRF1, PTTG1 Level 4 mitotic cell cycle CUL1 1 1.43 CIT, GTSE1, TPX2, CHEK1, CEP55, MAD2L1, 32 20.38 b0.001 CCNB2, KIFC1, PKMYT1, NUF2, KIF15, CDC6, DLG7, KIF2C, STMN1, UBE2C, CCNA2, CDCA8, BIRC5, CDT1, PBK, TUBB, NUSAP1, NCAPG, NCAPH, CDKN3, PTTG1, E2F1, NUDC, PLK1, SPC24, ESPL1 nucleoside, nucleotide C14orf43, SSBP2, CHD9, DCK, 13 18.57 EXO1, SUV39H2, SSBP3, FHL2, HIST3H2BB, 71 12.16 b0.001 and nucleic acid BAZ2B, ARID1A, TLE4, ZNF175, CBX5, EZH2, CDCA7, THOC4, GTF3C4, RRM2, metabolic process ZFP1, RCOR1, YTHDC1, DPYD, PPIH, CHEK1, CDC45L, FEN1, ROD1, CHAF1B, HIPK3 RAD51AP1, FOXM1, MYBL2, CARHSP1, NFKBIB, HMGN2, GINS2, ORC1L, EWSR1, E2F7, C16orf33, BRCA1, GMNN, ORC6L, TK1, CDC6, BUD31, KHSRP, WDR4, DHFR, H2AFZ, TFDP1, HMGB1, MIER1, RAD54L, RAD51, NRL, NUDT1, GSG2, C16orf35, SNRPA1, TYMS, HNRPA3, RECQL4, MCM5, FRG1, NOLA1, CCNA2, NUDT21, CDT1, HIST1H4D, TIMELESS SUMO1, RRM1, HMG1L1, MLF1IP, UBE2N, UHRF1, PTTG1, E2F1, NT5C, SERBP1, SET, MCM4 response to DNA No genes 0 0 EXO1, GTSE1, CHEK1, FEN1, CHAF1B, RAD51AP1, 18 11.46 b0.01 damage stimulus BRCA1, HMGB1, RAD54L, RAD51, NUDT1, C16orf35, TYMS, RECQL4, CCNA2, UBE2N, UHRF1, PTTG1 Level 5 cell surface receptor PDPK1, TNFRSF10B, TLE4, SHOC2, 12 18.18 CALM3, CORT, IL12RB1, CD8B 4 2.61 b0.001 linked signal GPR15, CYSLTR1, IL17RA, P2RY5, transduction MPZL1, IL1RAP, BMPR2, DKK3 Level 6 DNA repair No genes 0 0 EXO1, CHEK1, FEN1, CHAF1B, RAD51AP1, 16 10.74 b0.01 BRCA1, HMGB1, RAD54L, RAD51, NUDT1, C16orf35, TYMS, RECQL4, UBE2N, UHRF1, PTTG1 actin ﬁlament-based PDPK1, ROCK1, RDX, KLHL5, PDGFB 5 8.06 MYL6B 1 0.67 b0.01 process Level 7 actin cytoskeleton PDPK1, ROCK1, RDX, KLHL5, PDGFB 5 10.20 No genes 0 0 b0.01 organization and biogenesis Level 9 negative regulation ROCK1, SGK3, PTEN, HIPK3 4 23.53 MYBL2, HMGB1, BIRC5 3 4.29 b0.05 of programmed cell death

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Table 2 (continued) GO level Location Genes expressed in LTNP Nº % Genes expressed in progressors Nº % p- genes LTNP genes progressors value

regulation of TNFRSF10B, ROCK1, SGK3, PTEN, HIPK3, 6 35.29 BAK1, CIDEC, TRAIP, MYBL2, TIA1, BRCA1, 10 14.29 b0.10 apoptosis CUL1 HMGB1, BIRC5, TUBB, CD38

c) Molecular Function Level 3 ion binding ATP11A, PXDN, DDEF1, CENTG3, NPEPPS, 24 31.17 SOLH, CIT, SUV39H2, CHI3L2, FHL2, CYP4V2, 25 15.53 b0.01 SSR1, IRF2BP2, SEC23A, BAZ2B, ZNF175, ZDHHC21, KIAA1967, RRM2, FEN1, TRAIP, LTA4H, RNF24, ZFP1, ROCK1, PIP5K3, PJA2, CALM3, EWSR1, PPP1CA, DOHH, BRCA1, CENTB2, ACO1, RFWD2, DPYD, BMPR2, ZFAND2B, BUD31, MYL6B, TIMM13, GSG2, GLB1, EGLN3, SMAP2 RECQL4, BIRC5, UHRF1, NT5C nucleic acid binding C14orf43, SSBP2, CHD9, BAZ2B, ARID1A, 12 15.58 EXO1, SOLH, SSBP3, RDM1, HIST3H2BB, EXH2, 50 31.06 b0.05 TLE4, PUM2, ZNF175, ZFP1, RCOR1, ACO1, DDX49, THOC4, GTF3C4, FEN1, ROD1, WDR44 RAD51AP1, FOXM1, MYBL2, CARHSP1, HMGN2, TIA1, ORC1L, EWSR1, E2F7, BRCA1, ORC6L, KIF15, BUD31, KIF2C, KHSRP, H2AFZ, TFDP1, HMGB1, MIER1, RAD54L, RAD51, NRL, DAZAP1, SNRPA1, HNRPA3, RECQL4, KIF4A, MCM5, NOLA1, NUDT21, CDT1, HIST1H4D, HMG1L1, UHRF1, PTTG1, E2F1, SERBP1, NIP7, MCM4, receptor activity TNFRSF10B, EVI2A, GPR15, CYSLTR1, 9 11.69 IL12RB1 KIF15 CD38 CD8B PTPRK 5 3.11 b0.05 IL17RA, FGFRL1, P2RY5, ILRAP, BMPR2 lipid transporter ATP11A PITPNC1 HSD17B4 3 3.90 No genes 0 0 b0.05 activity Level 4 transmembrane EVI2A GPR15 CYSLTR1 IL17RA FGFRL1 8 12.9 IL12RB1 PTPRK 2 1.47 b0.01 receptor activity P2RY5 IL1RAP BMPR2 cation binding PXDN DDEF1 CENTG3 NPEPPS SSR1 23 37.10 SOLH CIT SUV39H2 CHI3L2 FHL2 CYP4V2 23 16.91 b0.01 IRF2BP2 SEC23A BAZ2B ZNF175 LTA4H ZDHHC21 KIAA1967 RRM2 FEN1 TRAIP CALM3 RNF24 ZFP1 ROCK1 PIP5K3 PJA2 CENTB2 EWSR1 PPP1CA DOHH BRCA1 ZFAND2B BUD31 ACO1 RFWD2 DPYD BMPR2 GLB1 EGLN3 MYL6B TIMM13 RECQL4 BIRC5 UHRF1 SMAP2 metal ion binding ATP11A PXDN DDEF1 CENTG3 NPEPPS 23 37.10 SOLH CIT SUV39H2 FHL2 CYP4V2 ZDHHC21 24 17.65 b0.01 SSR1 IRF2BP2 SEC23A BAZ2B ZNF175 KIAA1967 RRM2 FEN1 TRAIP CALM3 EWSR1 LTA4H RNF24 ZFP1 ROCK1 PIP5K3 PJA2 PPP1CA DOHH BRCA1 ZFAND2B BUD31 MYL6B CENTB2 ACO1 RFWD2 DPYD BMPR2 EGLN3 TIMM13 GSG2 RECQL4 BIRC5 UHRF1 NT5C SMAP2 Level 5 transition metal ion PXDN DDEF1 CENTG3 NPEPPS IRF2BP2 20 35.09 SOLH CIT SUV39H2 FHL2 CYP4V2 ZDHHC21 19 16.69 b0.05 binding SEC23A BAZ2B ZNF175 LTA4H RNF24 ZFP1 RRM2 FEN1 TRAIP EWSR1 PPP1CA DOHH BRCA1 ROCK1 PIP5K3 PJA2 CENTB2 ACO1 RFWD2 ZFAND2B BUD31 TIMM13 RECQL4 BIRC5 UHRF1 DPYD BMPR2 EGLN3 G-protein coupled GPR15 CYSLTR1 P2RY5 3 5.26 No genes 0 0 b0.05 receptor activity

progressors, in an attempt to understand and characterize the Differential dysregulation of cell cycle pathways between LTNP and factors associated with different stages of disease. To avoid progressors masking effects over gene expression, progressors were also treatment naïve. It is known that HIV induces modifications in cell cycle in order HIV infection leads to extensive defects in T cells which play an to use it for viral replication and proliferation. These changes are important role in its progression rate (Sodora and Silvestri, 2008), generated by virus–host–cell interaction, as reported in recent and although cell populations have been extensively characterized, studies that showed the virus needs around 200 host proteins to the genetic bases of interactions in relation to disease progression develop infection (Brass et al., 2008; König et al., 2008; Zhou et al., are still poorly understood. LTNP maintain a highly functional T-cell 2008; Bushman et al., 2009). The relative importance of those population compared with the majority of HIV-infected patients proteins taking part in the disease progression has not been defined (Betts et al., 2006). By using the total T-cell population (CD3+) in yet. In our study, we observed up-regulated sets of genes related peripheral blood we could obtain a global picture of the differences mainly to the cellular mitosis in patients with an evident progression in interactions between virus and host cells in these two of the disease. Similar results had been seen in other tissues like phenotypes. In fact, we detected distinct transcriptional features GALT, where dysregulation of the cell cycle was observed in mucosa and 458 highly differential genes between LTNP and progressors. cells of both LTNP and viremic patients (Sankaran et al., 2005). In With the GEPAS analysis we identified significantly expressed gene our study, however, this cell cycle dysregulation was not evident in sets to further understand their biological mechanism and LTNP. It seems that in CD3+ T cells the bulk of up-regulated cell relationship within phenotypes. Some of these gene differences cycle genes were skewed towards progressor patients which were related to characteristic functions involved in DNA replica- indicates a more rapid turnover of CD4+ and CD8+ T cells in tion, cell cycle regulation and apoptosis which have been progressive infection in agreement with the Hyrcza and colleagues previously reported in the context of HIV replication (Chun et al., study (Hyrcza et al., 2007). 2003) and progression (Hyrcza et al., 2007; Wu et al., 2008). According to these data, the most advanced stages of the However we have found other non-reported pathways over- infection were accompanied by a higher cell replication which expressed only in one of the studied phenotypes, such as would lead to disease progression in our control group. A certain cytokine–cytokine receptor interaction and regulation of actin replicative state in the cell is necessary for a productive HIV cytoskeleton in LTNPs and response to DNA damage stimulus in infection. However this persistent turnover would disrupt the progressors. immune system organization and loss of targets cells resulting in M. Salgado et al. / Virology 411 (2011) 103–112 109

a) Kegg pathways 100 % LTNP

90 % Progressor

0 Cell cycle Cytokine-cytokine receptor Regulation of actin Focal adhesion interaction cytoskeleton b) KEGG pathways Pathways LTNP Genes N° genes % LTNP Progressors Genes N° genes % Progressor pvalue

SKP1 CHEK1 CDC45L YWHAQ MAD2L1 CCNB2 Cell cycle CUL1 14ORC1L PKMYT1 ORC6L CDC6 TFDP1 MCM5 18 32,14 <0,01 CCNA2 PTTG1 E2F1 PLK1 ESPL1 MCM4

Cytokine-cytokine TNFRSF10B IL17RA IL1RAP BMPR2 520 IL12RB1 1 1,79 <0,01 receptor interaction PDGFB Regulation of actin ROCK1 PIP5K3 RDX PDGFB 4 16 PPP1CA 1 1,79 <0,05 cytoskeleton Focal adhesion PDPK1 ROCK1 PTEN PDGFB 4 16 LAMA5 PPP1CA 2 3,57 <0,10

Fig. 2. a) Graphic representation of gene distribution in LTNP and progressors according to KEEG PATHWAYS. b) List of expressed genes in LTNP or progressors related to each studied pathway.

the advancement of disease (Grossman et al., 2006). The low cell Higher number of genes related to response to DNA damage stimulus in replication observed in LTNP would benefit those patients with a progressors vs. LTNP low frequency of infection and a lesser effect over the immune system function. The virus has a target selectivity (CCR5+ CD4+ HIV-1, like other viruses with distinct replication strategies, memory T cells), and an inappropriate activation state may result activates DNA damage response pathways. It seems activation of in too little expression of CCR5 in cell surface to become infected cellular DNA repair and recombination enzymes is beneficial for viral (Grossman et al., 2006). Moreover, the proportion of LTNP patients replication (Sinclair et al., 2006). with Delta32 mutation in CCR5 is higher than progressors in our Here we found up-regulated genes in the progressors’ group population which may contribute to lower the source of viral involved in DNA repair and response to DNA damage stimulus, production. whereas no genes related to this function were up-regulated in LTNPs. Therefore, these signals may correlate with higher replication but the role of them in the viral pathogenesis is not well known. Recently, it has been shown that DNA repair proteins are necessary Table 3 to early steps in the virus life cycle (König et al., 2008); specifically, Host–viral gene interactions in LTNP and progressors. cellular DNA repair machinery has been implicated in playing critical

Group Up-regulated host Viral gene Main effects roles in viral DNA integration and the completion of viral DNA gene synthesis after integration (Goff, 2007). Otherwise, activation of DNA damage response pathways can also promote apoptosis (Sinclair et al., LTNP bmpr2 tat Tat downregulates Bmpr pten tat Pten enhances Tat 2006; Corbeil et al., 2001). In fact, two of the genes obtained from the cul1 tat Cul1 is in a complex pathway of analysis (GTSE1 and BRCA1) are associated with P53 protein, which is Tat activated in HIV-1-induced apoptosis as shown in a recent in vitro tnfrsf10B env Gp120 upregulate trail study (Imbeault et al., 2009). In our study, some genes related to the (gp120) TNFRS10B gpr15 env Gp120 activates Gpr15 activation of apoptosis are expressed in progressors but not very (gp120) strongly. For this reason, DNA repair could indicate here a stabilization Progressors rad51 tat Rad51 enhances Tat of proviral integration more than cellular death. tat Tat upregualates Rad51 vpr Vpr inhibits Rad51 Increased regulation of actin cytoskeleton in LTNP patients integrase Rad51 inhibits HIV integration kif4 gag Pr55(Gag) binds KIF4 cd8b nef Nef downregulates CD8b Host cell microfilament cytoskeleton plays a wide range of roles in brca1 vpr Vpr stimulates Brca1 HIV-1 infection, including viral entry, reverse transcription, transport cd38 env Gp120 interacts with CD38 to the nucleus, integration and finally a correct budding and release (gp120) from the cell (Fackler and Krausslich, 2006). However, the interaction 110 M. Salgado et al. / Virology 411 (2011) 103–112 between HIV and actin cytoskeleton is only beginning to unravel; (GALT) and circulating lymphocyte (Sankaran et al., 2005). In polymerization of actin is required for viral binding and entry but agreement with this observation, our LTNP patients have activated inhibits some subsequent steps by acting as a physical barrier for the adhesion molecules and cytokine receptors that could relate to an virus. To overcome this barrier and continue with the viral cycle, a active immunological synapsis and a more efficient T-cell response protein known as cofilin is activated to depolymerize actin (Liu et al., respectively. Otherwise, some of these molecules can play other 2009; Bukrinsky, 2008). functions related to viral pathogens. An example is Gpr15 (up- In our study, we observed a higher expression of genes related to regulated in LTNP) which has been shown as an active coreceptor for actin cytoskeleton organization and biogenesis in the group of LTNPs, viral entrance of HIV-2 (Blaak et al., 2005). Some of the patients in our many of them related to phosphorylation of cofilin which renders it study have 32-bp deletion in the CCR5. The up-regulation of Gpr15 inactive and prevents its association with actin. In LTNPs an excess of can also compensate for a defective CCR5 although with less efficacy; the G-actin form as a result of low depolymerization could block the and which may influence by reducing viral fitness. More studies are virus trafficking through the cell. Normally, the virus is capable to needed to confirm this hypothesis. modulate and use the microtubules net for its own ends (Fackler and Krausslich, 2006; Lu et al., 2008) by means of viral proteins such as Differences across patterns of expression for apoptosis Nef, Gag or Tat which interact with the cytoskeleton (Campbell et al., 2004; Matarrese and Malorni, 2005; Naghavi and Goff, 2007). In our It has been reported that LTNP patients present a low level of group of LTNP both gag-p24 and nef gene sequences were character- apoptosis and a consequent reduced rate of CD4+ loss likely ized (data no shown) and except for one patient (deleted Nef), the accompanied by low viral burden or by the presence of a defective rest carried complete genes with, in appearance, conserved functional virus (Franceschi et al., 1997; Kirchhoff et al., 1995). In our study, both domains. However, without functional experiments we cannot groups have activated genes related to apoptosis and differences discard differences in modulation of actin with respect to Nef or Gag between groups did not reach a strong statistical significance. from progressors as well as Tat proteins. However, different patterns of apoptosis gene expression can be On the other hand, the increase in expression of these genes glimpsed with a trend towards inhibition in LTNP and activation in subsequent to viral infection may be associated with transmembrane controls; the ratio negative/positive apoptosis regulatory genes were events of viral binding and entry and other structural events involved higher in LTNP. On the contrary, progressors presented up-regulated with cellular activation and signalling (Vahey et al., 2002). When we genes like CD38, described to have a strong relationship with studied the specific function of most up-regulated genes, we saw they activation and cell death. These results are in full accordance with were also associated with adhesion processes, so another explanation previous studies showing CD38 expression on CD8+ T cells as a for non-progression could be that in these patients there is a higher marker associated with HIV disease progression (Liu et al., 1997). In a and better immunological synapsis between antigen specific CD8+ T recent report (Jiao et al., 2008), the activation level of CD8+ T cells, cells and infected CD4+ T cells due to an adhesion processes and especially CD38 expression, correlated inversely with CD4+ T cell remodelling cytoskeleton take place in the synapsis (Shen et al., counts and positively with HIV-1 RNA loads in typical progressors. 2005). At this point, we cannot distinguish whether up-regulation of Activated genes for positive regulation of apoptosis in progressors are the cytoskeletal proteins in LTNP decants towards a better immune in agreement with other activated functions in this group such as cell response or maintenance of the physical intracellular barrier. cycle and DNA replication, which were implicated in several pathways linked with cellular death. T-cell apoptosis is thought to be one Up-regulation of genes related to cytokine–cytokine receptor interaction strategy by which HIV-1 evades host immune supervision. in LTNPs Genome-wide analyses are important to enrich the list of factors in the HIV–host interaction in order to identify new candidates for HIV A successful immune response depends upon the ability of T- therapeutics. Overall, the results in this study offer new comparative lymphocytes to respond to antigenic stimulation by cellular activa- insights related to disease status and can distinguish differentiated tion. This activation results in a cascade of cytokine secretion, receptor patterns of gene expression between HIV+LTNP and progressor up-regulation, cellular proliferation and development of effector patients. The most notable features are the preserved regulation of functions (Smith et al., 1980). cytoskeleton and cell signalling functions in LTNP and a profound In our study, activated cell surface receptors linked to signal dysregulation of cell cycle in progressors. Many of the genes identified transduction are observed in LTNPs. The identified genes included will further facilitate the understanding of genetic basis of progressive cytokine receptors such as IL17RA, IL1RAP or TNFRSF1 which may be and non-progressive disease. part of a stronger, more preserved and protective immune response in this group of patients. IL-17RA controls the activity of IL-17A which regulates the expansion of Th17 CD4+ cell subset (Wright et al., 2008; Materials and methods Smith et al., 2008). A recent study found that HIV-1-specific IL-17- producing CD4+ T cells were reduced or non-detected in LTNP (Yue Patients et al., 2008) which would support our observation. Also data from our lab show a negative correlation between IL-17RA expression and viral Fifteen LTNPs from the Hospital Carlos III cohort and 15 load (unpublished data). Others have seen that the imbalance Progressors were studied. LTNP were defined at the time of cohort between Th17 cells with other T cells seems to be predictive of inclusion as HIV-1+ individuals with more than 10 years of infection disease progression in SIV infection (Cecchinato et al., 2008; Favre (since diagnosis), CD4 counts above 500 cells/μL and naïve for ART. At et al., 2009) which points out to the equilibrium between pro- and the time of microarray analysis all of them had viral loads (VL) under anti-inflammatory responses as a critical factor to distinguish 10,000 copies/mL and three of them were considered “elite between pathogenic and non-pathogenic infections. IL-1RAP is controllers” (Deeks and Walker, 2007). Typical progressor HIV-1+ essential for IL-33 induced T-cell activation and involved in innate patients were, at the time of study, drug-naïve individuals with less cellular immune responses (Ali et al., 2007), while TNFRSF1 would than 5 years of HIV infection after diagnosis and a CD4 decline higher be involved in the regulation of apoptosis. than 80 cells/μl per year. Two years after sample collection, 13 out of Previously, it was shown that viral suppression in LTNPs may 15 progressors (86%) have initiated HAART therapy. A signed result from efficient maintenance of CD4+ T helper and HIV-1- informed consent was obtained from participants in accordance specific CD8+ T-cell responses in both gut-associated lymphoid tissue with the hospital's Ethic Committee. M. Salgado et al. / Virology 411 (2011) 103–112 111

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