Polymorphisms in Toll-like receptor 9 influence the... : AIDS (original) (raw)
Introduction
While most HIV-positive patients develop AIDS within 10 years, some progress to profound immunodeficiency in 1–5 years (rapid progressors) and others remain immunocompetent for up to 20 years [1]. It has been clearly demonstrated that genetic variations in the host can influence disease progression [2–5].
Toll-like receptor (TLR) genes encode a family of transmembrane proteins, that are essential for the innate immune recognition of pathogens. Through their extracellular domain, TLRs sense conserved molecular motifs from a variety of organisms, including bacteria, fungi, parasites and viruses [6–8]. TLR3, TLR7, TLR8 and TLR9 are located exclusively in the endosomal compartment and detect nucleic acids [9]. TLR9 recognizes cytidine–phosphate–guanosine (CpG) DNA motifs that are present in bacteria and viruses [10]. Other TLRs initially implicated in the recognition of bacterial products, such as TLR2 and TLR4, can also detect viral compounds [11]. Through the intracellular domains, TLRs interact with several adaptor proteins to activate transcription factors, leading to the production of inflammatory cytokines and the activation of the adaptive immunity.
Common polymorphisms in TLR genes have been associated with increased susceptibility to or protection against several infectious diseases, such as tuberculosis (TLR2), bacterial sepsis (TLR4), meningococcaemia (TLR4), Legionnaire's disease (TLR4, TLR5) and infection with respiratory syncytial virus (TLR4) [12–19]. Rare inherited immunodeficiencies are associated with mutations affecting molecules involved in TLR signalling pathways (IRAK4, IKKγ and IκBα) [20]. These data clearly demonstrate that mutations in TLRs and their associated signal transduction molecules can have a dramatic influence on individual susceptibility to human diseases [21,22] and it can be hypothesized that polymorphisms in TLR might influence the course of HIV-1 infection. To test these hypotheses we analysed single-nucleotide polymorphism (SNP) frequencies in six TLR (TLR2, TLR3, TLR4, TLR7, TLR8 and TLR9) in participants of the Swiss HIV Cohort Study (SHCS).
Methods
Patients
The SHCS is an ongoing multicentre cohort study that includes > 12 000 HIV-infected adults, mostly prevalent cases, enrolled since 1988. Participants in the genetic study were recruited as previously described [6]. Patients gave written informed consent for clinical data collection and genetic testing. The study was approved by the Ethics Committee from each SHCS centre (Switzerland) and by the Western Institutional Review Board (Olympia, Washington State, USA).
Procedures
The SNPs examined were selected from dbSNP [23] and the Innate Immunity Program in Genomic Applications database (http://innateimmunity.net/) if they met one of the following criteria: (a) non-synonymous SNPs; (b) SNPs located in or close to the coding region (±150 base pairs from each exon boundary) at an allele frequency of > 10%; or (c) SNPs located elsewhere (outside the coding region) at an allele frequency of > 30%. This strategy identified 28 SNPs in TLR2, TLR3, TLR4, TLR7, TLR8 and TLR9 that were detected using the Sequenom technology [18]. In TLR9, two SNPs were haplotype-tagging SNPs that were sufficient to determine the three most frequent Caucasian haplotypes, accounting for 97% of haplotypes in this population [24]. Among 1092 SNPs from 39 individuals that were examined in duplicate, 14 were not interpretable (missing, 1.28%) and 1078 were concordant. The SNPs were numbered using their position relative to the ‘A’ of the translational start site (‘ATG’) (base pair 1) on the mRNA (coding region) or gDNA sequences. The signs + or − were used to indicate the direction of the gDNA sequence.
Statistical analysis
Test for Hardy–Weinberg equilibrium was performed using the genhw program in Stata 9.0 (StataCorp, College Station, Texas, USA). Haplotypes were inferred using an expectation–maximization algorithm implemented in the DECIPHER program [25]. The pwld.ado program developed by David Clayton was used to calculate pairwise linkage disequilibrium [26]. A value for _r_2 (or _D_2) > 0.75 was considered to represent strong linkage disequilibrium.
For each participant, the slope of CD4 cell decline was estimated using linear regression on the square root of CD4 T cell counts, as this scale has been found to be variance stabilizing [27]. The analysis was limited to Caucasian patients with a sufficient number of documented CD4 cell measurements before the initiation of HAART over a period of time > 18 months (428 patients). The following were acceptable a-priori criteria: (a) four or more measurements over a period of 18–24 months; (b) three or more measurements over 2–3 years; or (c) two or more measurements over more than 3 years. For most individuals (317), the slope was calculated in the absence of any antiretroviral therapy (ART). Because ART containing zidovudine and/or didanosine can transiently increase CD4 cell counts, measurements made while the patient was taking such therapy were used only if the slope of CD4 decline at this period was not flatter than the slope before the initiation of ART, or when ART was administered intermittently. The median follow-up time before the initiation of HAART was 6.0 years (interquartile range, 4.0). Consecutive CD4 cell counts below the threshold of 50 cells/mm3 were excluded to avoid a plateau effect that would underestimate the true slope. The log-transformed median HIV-1 viral RNA during the 18 months before the start of any ART was used to estimate HIV-1 viral load.
Based on the observation of a bimodal distribution of the slope in CD4 cell count in the 428 individuals selected for this analysis, rapid progressors were defined as individuals with a steep slope of CD4 cell decline, below the 15th percentile. It can be estimated that this threshold would select patients who will developed AIDS within 5 years of seroconversion [28]. Analyses were also conducted using other thresholds (i.e., 10th, 20th and 75th percentiles). Model with and without each SNP were compared using the likelihood ratio test after adjusting for age, sex and risk groups (heterosexual, homosexual, intravenous drug use). The association of SNPs with viral load was assessed in linear regression models.
The analysis excluded four invariant SNPs (TLR3 851A/T, TLR7 3087A/G, TLR8 2144G/A and TLR9 1010G/A) and five low-frequency (< 1%) SNPs (TLR3 919T/G, TLR3 2209T/A, TLR7 1343C/T, TLR8 28A/G and TLR9 1149G/A). All invariant or low-frequency SNPs were also found to have very low frequencies in Caucasians individuals recorded in available public databases. Among the remaining 19 SNPs, several were in strong linkage disequilibrium. Since the analyses for these SNPs are redundant, 15 independent tests were considered for multiple testing correction, according to the Bonferroni method. For haplotypes and diplotypes, six independent tests were considered (i.e., one for each gene) for multiple testing correction.
Results
The demographic and baseline characteristics of the 428 patients included in the genetic cohort study were similar to those included in the entire Swiss HIV cohort study (12 285). In the genetic cohort compared with the whole Swiss HIV cohort, the median age was 31 years (range, 11) versus 33 years (range, 12), 65% versus 76% were male Caucasians, 35% versus 37% were homosexuals, 27% versus 26% were heterosexuals, and 23% versus 25% were using intravenous drugs. The frequencies of the last Centers for Disease Control and Prevention stages recorded in these patients were also similar (stage A, 37% in the genetic cohort and 40% in the general cohort; stage B, 28% and 25%; and stage C 34% in both cohorts).
The Hardy–Weinberg equilibrium test, linkage disequilibrium test and minor allele frequencies for rapid progressors and other HIV-positive patients are given in Table 1. Two TLR9 SNP in linkage disequilibrium (+1174G/A and 1635A/G) were more frequent among rapid progressors than among the other patients. When using alleles (dominant model), the odds ratio (OR) was 4.1 [95% confidence interval (CI),1.8–9.5] for the presence versus the absence of the 1635G allele (P = 0.0008; Table 1). The association remained significant after correction for multiple testing (P = 0.012). When using genotypes (codominant model), OR was 3.9 (95% CI,1.7–9.2) for 1635GA versus 1635AA and 4.7 (95% CI,1.9–12.0) for 1635GG versus 1635AA (P = 0.0005, corrected P = 0.007; Table 2). Sensitivity analyses using the 10th or 20th percentile as the threshold to separate rapid progressors from others gave similar results. Similar results were obtained when slopes were calculated using CD4 cell counts before the initiation of ART, though the associations were weaker because fewer slopes could be estimated and fewer available CD4 cell measurements may have impaired the accuracy of estimating the slope. There was no significant association when slow progressors (slope ≥ 75th percentile) were compared with the other patients. The associations between TLR4 896G and rapid progression, and TLR2 597C and protection against rapid progression, disappeared after correction for multiple testing. The association was not modified when the CCR5d32 polymorphism was included in the model. No additional associations were found between TLR polymorphisms and viral load or CD4 cell count progression.
Single-nucleotide polymorphisms and minor allele frequencies in rapid progressors and other HIV-positive patients.
Frequencies of TLR9 genotypes and haplotypes in rapid progressors and other HIV-positive patients.
Discussion
There is increasing evidence for a role for TLRs in HIV-1 pathogenesis [29,30]. In the present study, we found that the minor alleles of two TLR9 SNPs that are in linkage disequilibrium were more frequent among rapid progressors than among other HIV-positive patients.
While our observations further support the hypothesis of a role for TLRs in HIV-1 infection, the precise mechanisms of the interaction between HIV-1 and innate immune cells remain poorly understood. During reverse transcription, HIV-1 produces double-stranded DNA containing CpG motifs; these are transferred from the cytoplasm to the nucleus and integrated in the host cell genome [31]. Since TLR9 is exclusively located in the endosomal compartment, debris phagocytosed from HIV-1-infected cells containing sufficient amounts of proviral DNA may activate macrophages through TLR9. In addition, TLR9 as well as other TLRs, may be activated by pathogens that are frequently associated with HIV-1 infection (e.g., mycobacteria, cytomegalovirus, herpes simplex virus and hepatitis C virus), thereby inducing the production of inflammatory cytokines and modulating viral replication [29,32]. TLRs activation results in the production of inflammatory cytokines that can enhance viral replication [29,33,34]. HIV-1 replication is directly dependent on NF-κB, a critical TLR-induced transcription factor that promotes activation of the HIV-1 long terminal repeat [29]. This might be of importance in the mechanism underlying the increased HIV-1 replication and disease progression associated with concomitant and opportunistic infections [29].
This study suggests an important role for TLR9 and serves to generate several hypotheses for the pathogenesis of HIV-1 infection. However, it was limited by several factors. First, one cannot formally exclude the possibility that the SNPs in TLR9 associated with rapid CD4 cell decline are in linkage disequilibrium with functional SNPs in another gene located nearby (i.e., responsible for the alterations in the phenotype). In addition, the SNP selected in each TLR were not halotype tagging SNPs (apart from those in TLR9) and might have been insufficient to detect haplotypes associated with one of the study's endpoints. Second, the SHCS is a prevalent cohort and can be limited by potential biases such as onset confounding and differential length-bias sampling. Finally, genetic associations can be biased by population stratification. In order to minimize this risk, we have limited the analyses to Caucasians. It has been argued that population stratification may not play a major role in case–control studies conducted among individuals of European descent [35]. The fact that all other SNPs tested, some of which have high variability among different ethnic populations, were found at similar frequencies in cases and controls suggests that confounding by population stratification is unlikely to have a major influence on the present association. Because of its importance for possible intervention strategies or vaccine development, the association of TLR9 SNPs with rapid progression of CD4 cell decline needs to be confirmed in other settings, for example in incident patients with known times to AIDS.
Acknowledgements
We thank Marta Janer for expertise in SNP genotyping Irina Podolsky, Sarah Li for expert technical assistance and Mary Brunkow and Véronique Erard for editing the paper.
The members of the Swiss HIV Cohort Study are M. Battegay, E. Bernasconi, J. Böni, H. Bucher, Ph. Bürgisser, S. Cattacin, M. Cavassini, R. Dubs, M. Egger, L. Elzi, P. Erb, M. Fischer, M. Flepp, A. Fontana, P. Francioli (President of the SHCS, Centre Hospitalier Universitaire Vaudois), H. Furrer (Chairman of the Clinical and Laboratory Committee), M. Gorgievski, H. Günthard, B. Hirschel, I. Hösli, Ch. Kahlert, L. Kaiser, U. Karrer, O. Keiser, C. Kind, Th. Klimkait, B. Ledergerber, B. Martinez, N. Müller, D. Nadal, M. Opravil, F. Paccaud, G. Pantaleo, L. Perrin, J.-C. Piffaretti, M. Rickenbach (Head of Data Centre), C. Rudin (Chairman of the Mother & Child Substudy), P. Schmid, D. Schultze, J. Schüpbach, R. Speck, P. Taffé, P. Tarr, A. Telenti, A. Trkola, P. Vernazza (Chairman of the Scientific Board), R. Weber, S. Yerly.
Sponsorship: This study has been financed in the framework of the Swiss HIV Cohort Study, supported by the Swiss National Science Foundation [grants from the Swiss Foundation for Medical and Biological Grants to P.Y.B. (1121) and from the Swiss National Science Foundation to P.Y.B. (81LA-65462], the US National Center for Research Resources Multidisciplinary Clinical Research Career Development Programs (grant 8K12RR023264 to C.S.) and the Bill of Melinda Gates Foundation. Some of the results of this paper were obtained by using the program package SAGE, which is supported by a US Public Health Service Resource Grant (RR03655) from the National Center for Research Resources.
References
1. O'Brien SJ, Nelson GW. Human genes that limit AIDS. Nat Genet 2004; 36:565–574.
2. Liu R, Paxton WA, Choe S, Ceradini D, Martin SR, Horuk R, et al. Homozygous defect in HIV-1 coreceptor accounts for resistance of some multiply-exposed individuals to HIV-1 infection. Cell 1996; 86:367–377.
3. Samson M, Libert F, Doranz BJ, Rucker J, Liesnard C, Farber CM, et al. Resistance to HIV-1 infection in caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature 1996; 382:722–725.
4. Dean M, Carrington M, Winkler C, Huttley GA, Smith MW, Allikmets R, et al. Genetic restriction of HIV-1 infection and progression to AIDS by a deletion allele of the CKR5 structural gene. Science 1996; 273:1856–1862.
5. Bleiber G, May M, Martinez R, Meylan P, Ott J, Beckmann JS, et al. Use of a combined ex vivo/in vivo population approach for screening of human genes involved in the human immunodeficiency virus type 1 life cycle for variants influencing disease progression. J Virol 2005; 79:12674–12680.
6. Aderem A, Ulevitch RJ. Toll-like receptors in the induction of the innate immune response. Nature 2000; 406:782–787.
7. Beutler B. Inferences, questions and possibilities in Toll-like receptor signalling. Nature 2004; 430:257–263.
8. Janeway CA Jr, Medzhitov R. Innate immune recognition. Annu Rev Immunol 2002; 20:197–216.
9. Kawai T, Akira S. Innate immune recognition of viral infection. Nat Immunol 2006; 7:131–137.
10. Hemmi H, Takeuchi O, Kawai T, Kaisho T, Sato S, Sanjo H, et al. A Toll-like receptor recognizes bacterial DNA. Nature 2000; 408:740–745.
11. Bowie AG, Haga IR. The role of Toll-like receptors in the host response to viruses. Mol Immunol 2005; 42:859–867.
12. Tabeta K, Georgel P, Janssen E, Du X, Hoebe K, Crozat K, et al. Toll-like receptors 9 and 3 as essential components of innate immune defense against mouse cytomegalovirus infection. Proc Natl Acad Sci USA 2004; 101:3516–3521.
13. Ogus AC, Yoldas B, Ozdemir T, Uguz A, Olcen S, Keser I, et al. The Arg753GLn polymorphism of the human toll-like receptor 2 gene in tuberculosis disease. Eur Respir J 2004; 23:219–223.
14. Lorenz E, Mira JP, Cornish KL, Arbour NC, Schwartz DA. A novel polymorphism in the toll-like receptor 2 gene and its potential association with staphylococcal infection. Infect Immun 2000; 68:6398–6401.
15. Lorenz E, Mira JP, Frees KL, Schwartz DA. Relevance of mutations in the TLR4 receptor in patients with Gram-negative septic shock. Arch Intern Med 2002; 162:1028–1032.
16. Smirnova I, Mann N, Dols A, Derkx HH, Hibberd ML, Levin M, et al. Assay of locus-specific genetic load implicates rare Toll-like receptor 4 mutations in meningococcal susceptibility. Proc Natl Acad Sci USA 2003; 100:6075–6080.
17. Hawn TR, Verbon A, Lettinga KD, Zhao LP, Li SS, Laws RJ, et al. A common dominant TLR5 stop codon polymorphism abolishes flagellin signaling and is associated with susceptibility to legionnaires' disease. J Exp Med 2003; 198:1563–1572.
18. Hawn TR, Verbon A, Janer M, Zhao LP, Beutler B, Aderem A. Toll-like receptor 4 polymorphisms are associated with resistance to Legionnaires' disease. Proc Natl Acad Sci USA 2005; 102:2487–2489.
19. Tal G, Mandelberg A, Dalal I, Cesar K, Somekh E, Tal A, et al. Association between common Toll-like receptor 4 mutations and severe respiratory syncytial virus disease. J Infect Dis 2004; 189:2057–2063.
20. Puel A, Picard C, Ku CL, Smahi A, Casanova JL. Inherited disorders of NF-kappaB-mediated immunity in man. Curr Opin Immunol 2004; 16:34–41.
21. Cook DN, Pisetsky DS, Schwartz DA. Toll-like receptors in the pathogenesis of human disease. Nat Immunol 2004; 5:975–979.
22. Kiechl S, Lorenz E, Reindl M, Wiedermann CJ, Oberhollenzer F, Bonora E, et al. Toll-like receptor 4 polymorphisms and atherogenesis. N Engl J Med 2002; 347:185–192.
23. Sherry ST, Ward M, Sirotkin K. dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res 1999; 9:677–679.
24. Lazarus R, Klimecki WT, Raby BA, Vercelli D, Palmer LJ, Kwiatkowski DJ, et al. Single-nucleotide polymorphisms in the Toll-like receptor 9 gene (TLR9): frequencies, pairwise linkage disequilibrium, and haplotypes in three US ethnic groups and exploratory case-control disease association studies. Genomics 2003; 81:85–91.
25. Case Western Reserve University. SAGE 5.0. Statistical Analysis for Genetic Epidemiology. Cleveland, OH: Case Western Reserve University; 2005.
26. Devlin B, Risch N. A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 1995; 29:311–322.
27. Boscardin WJ, Taylor JM, Law N. Longitudinal models for AIDS marker data. Stat Meth Med Res 1998; 7:13–27.
28. Collaborative Group on AIDS Incubation and HIV Survival. Time from HIV-1 seroconversion to AIDS and death before widespread use of highly-active antiretroviral therapy: a collaborative re-analysis. Lancet 2000; 355:1131–1137.
29. Bafica A, Scanga CA, Schito M, Chaussabel D, Sher A. Influence of coinfecting pathogens on HIV expression: evidence for a role of Toll-like receptors. J Immunol 2004; 172:7229–7234.
30. Beignon AS, McKenna K, Skoberne M, Manches O, Dasilva I, Kavanagh DG, et al. Endocytosis of HIV-1 activates plasmacytoid dendritic cells via Toll-like receptor- viral RNA interactions. J Clin Invest 2005; 115:3265–3275.
31. Bednarik DP, Cook JA, Pitha PM. Inactivation of the HIV LTR by DNA CpG methylation: evidence for a role in latency. EMBO J 1990; 9:1157–1164.
32. Kawai T, Akira S. Innate immune recognition of viral infection. Nat Immunol 2006; 7:131–137.
33. Schlaepfer E, Audige A, von Beust B, Manolova V, Weber M, Joller H, et al. CpG oligodeoxynucleotides block human immunodeficiency virus type 1 replication in human lymphoid tissue infected ex vivo. J Virol 2004; 78:12344–12354.
34. Schlaepfer E, Audige A, Joller H, Speck RF. TLR7/8 triggering exerts opposing effects in acute versus latent HIV infection. J Immunol 2006; 176:2888–2895.
35. Wacholder S, Rothman N, Caporaso N. Counterpoint: bias from population stratification is not a major threat to the validity of conclusions from epidemiological studies of common polymorphisms and cancer. Cancer Epidemiol Biomarkers Prev 2002; 11:513–520.
Keywords:
AIDS; innate immunity; Toll-like receptors; genetics; genetic epidemiology; pathogenesis
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