Human immunodeficiency virus type 1 (HIV-1) requires host cell factors for productive infection . Lens epithelium-derived growth factor p75 (LEDGF/p75) also known as proprotein convertase 4 (PC4) or splicing factor, arginine/serine-rich 1 (SFRS1)-interacting protein 1 (PSIP1), is ubiquitously expressed in all tissues and cell types (by convention, the gene and its protein are referred to as PSIP1 and LEDGF/p75, respectively). LEDGF/p75 is a member of the hepatoma-derived growth factor (HDGF)-related protein family (HRP-family) whose members are involved in chromosomal replication, transcription and chromatin structure formation [2–7]. LEDGF/p75 interacts with HIV-1 integrase through specific binding of the integrase-binding domain (IBD) of LEDGF/p75 to the catalytic core domain of integrase to tether HIV-1 to the chromosome and target HIV-1 integration into active genes [8–15]. Disruption of the interaction between LEDGF/p75 and integrase, either by integrase mutations or LEDGF/p75 knockdown, inhibits HIV-1 replication [16–19], confirming the important role of host LEDGF/p75 as an HIV-1 replication cofactor.
Association studies of the influence of human genetic variation on HIV-1 replication may reveal the essential in-vivo host factors that interact with HIV-1 and their epidemiologic importance at the population level . This approach has been used mostly in studies conducted on populations from developed countries [20–23]. However, there are differences in allele frequencies among potential disease-influencing gene variants between ethnic groups and geographically separated populations . Therefore, host genetic studies of HIV-1 infection need to be extended to developing world populations heavily burdened with HIV/AIDS. We investigated the influence of genetic variation in PSIP1 on HIV-1 infection and disease progression in black South Africans.
The Center for the AIDS Program of Research in South Africa Acute Infection 002 (CAPRISA AI 002) [25,26] and the Sinikithemba [27,28] cohorts were used for this study. The CAPRISA AI 002 cohort is an ongoing observational natural history study of HIV-1 subtype C infection established in Durban, KwaZulu-Natal, South Africa in 2004. HIV-negative women (n = 245) at high risk for HIV infection were enrolled into phase I of the study. Participants in this cohort were screened monthly for recent HIV-1 infection by two rapid HIV-1 antibody tests (Abbott Laboratories, Tokyo, Japan) and Capillus (Trinity Biotech, Jamestown, New York, USA). HIV-1 antibody-negative samples were tested for HIV-1 RNA in batches of 10 plasma samples per pool using the Ampliscreen v1.5 assay (Roche Diagnostics, Rotkreuz, Switzerland), which has a detection limit of 10 copies/ml. Samples that tested positive in pooled plasma were individually tested by quantitative RNA (Amplicor v2.0, Roche Diagnostics) and HIV enzyme immunoassay (BEP 2000; Dade Behring, Marburg, Germany) to identify HIV-1 infection. CD4+ T-cell counts were determined by a four-parameter FACSCalibur flow cytometer (Becton Dickinson). Participants with acute HIV-1 infection were enrolled into phase II of the study on the basis of a reactive HIV antibody test within 3 months of previously negative results or positive HIV RNA PCR in the absence of antibodies. Date of infection was estimated by taking the midpoint between the last HIV antibody-negative result and the first HIV antibody-positive result or 14 days before the first positive HIV RNA PCR assay result for those identified as antibody-negative but HIV RNA-positive. An additional 34 acutely infected participants (who met the criteria for acute infection, as aforementioned) were recruited from other ongoing CAPRISA cohorts. Participants in phase II were monitored weekly for 3 weeks, fortnightly for 2 months then monthly for 9 months and quarterly thereafter.
The Sinikithemba cohort comprises 450 antiretroviral naïve, HIV-1 subtype C chronically infected adults enrolled from McCord Hospital (Durban, South Africa) from August 2003 to 2008 and followed longitudinally [27,28]. Sociodemographic characteristics, plasma viral load and CD4 cell count measurements were obtained at baseline. CD4 cell counts and viral loads were measured every 3 and 6 months, respectively, from enrollment. Viral loads were determined using the automated Cobas Amplicor HIV-1 Monitor test (version 1.5; Roche Diagnostics). CD4+ T cells were enumerated using the Multitest kit (CD4/CD3/CD8/CD45) on a FACSCalibur flow cytometer (Becton Dickinson).
Ethical approval for this study was obtained from Biomedical Research Ethics Committee of the University of KwaZulu-Natal. All participants provided written informed consent.
Identification of polymorphisms in the C terminal region of PSIP1
Single-nucleotide polymorphisms (SNPs) in the C terminal region of PSIP1 were screened in a panel of 83 seronegative and 43 seropositive black South African women from the CAPRISA AI 002 cohort by resequencing (Table 1).
Genotyping of single-nucleotide polymorphisms in PSIP1 gene from the CAPRISA AI 002 participants
Four haplotype tagging (tag) SNPs rs2277191, rs1033056, rs12339417 and rs10283923 in this manuscript designated SNP1, SNP2, SNP3 and SNP4, respectively, available from NCBI dbSNP (http://www.ncbi.nlm.nih.gov/SNP) and HapMap databases (http://www.hapmap.org) selected by considering location, spacing, and allele frequency of at least 10% and one exonic SNP rs61744944 (Q472L) designated, SNP5 also available from the aforementioned databases (Fig. 1a and Table 1) were genotyped in 247 patient samples (195 seronegative and 52 seropositive individuals) from the CAPRISA AI 002 cohort. The tag SNPs were chosen because they are tag SNPs for PSIP1 in the Yoruba from Nigeria (http://www.snp.cshl.org) and SNP5 based on the preliminary analysis of the sequencing data that suggested an association between SNP5 mutation and lower CD4 cell count and higher viral load (P < 0.01 for both, data not shown). Genotyping was performed by TaqMan SNP assay as per manufacturer's protocol (Applied Biosystems). TaqMan assays were obtained from the Assay-by-Demand service of Applied Biosystems (http://www.appliedbiosystems.com). Eight negative water controls were included in each run to rule out PCR contamination. Samples were genotyped in duplicate and genotypes were accepted after confirmation of no contamination or inconsistencies between duplicates.
Genotyping of single-nucleotide polymorphisms in PSIP1 gene from the Sinikithemba participants
PSIP1 SNPs associated with HIV-1 outcomes in the CAPRISA AI 002 cohort were further analyzed in the larger Sinikithemba cohort of 450 seropositive individuals, in order to confirm the results obtained from the CAPRISA AI 002. Genotyping assays were performed as described above.
LEDGF/p75 mRNA expression analysis
Peripheral blood mononuclear cells (PBMCs) were isolated from 57 seronegative and 38 seropositive individuals from the CAPRISA AI 002 cohort. RNA was extracted from 2 × 106 PBMCs immediately after thawing using Trizol LS reagent (Invitrogen, Carlsbad, California, USA) according to the manufacturer's protocol and the integrity of RNA was confirmed using 3-(N-morpholino) propanesulfonic acid (MOPS) gels. RNA was reverse transcribed using the iScript complementary DNA (cDNA) synthesis kit (Bio-Rad). LEDGF/p75 mRNA expression was quantified by real-time PCR using SYBR Green Chemistry on a Roche Lightcycler version 1.5 (Roche Diagnostics) (Table 1).
Expression and purification of recombinant proteins
His6-tagged HIV-1 integrase, 3xflag-tagged LEDGF/p75, MBP-JPO2 and MBP-pogZ were purified for AlphaScreen applications as described previously [29,30].
The AlphaScreen assay is a technique used to measure protein–protein affinity interactions. To measure the influence of SNP5T (Q472L) on the binding affinity of LEDGF/p75 we performed AlphaScreen assay according to the manufacturer's protocol (Perkin Elmer, Benelux). Reactions were performed in 25 μl volume in 384-well Optiwell microtiter plates (Perkin Elmer). The reaction buffer contained 25 mmol/l Tris–HCl (pH 7.4), 150 mmol/l NaCl, 1 mmol/l MgCl2, 0.01% (v/v) Tween-20 and 0.1% (w/v) bovine serum albumin. Three-hundred mmol/l His6-tagged integrase was incubated with 100 nmol/l flag-LEDGF/p75 for an hour at 4°C. Subsequently 5 μl of Ni-chelate-coated acceptor beads and 5 μl antiflag donor beads were added to a final concentration of 20 μg/ml of both beads. Proteins and beads were incubated for 1 h at 30°C. Exposure of the reaction to direct light was avoided and the emission of light from the acceptor beads was measured in the EnVision plate reader (Perkin Elmer, Benelux) and analyzed using the EnVision manager software. Assays with JPO2 or pogZ, respectively, were essentially performed as described previously [29,30].
Complemented cell lines
Complemented HeLaP4/CCR5 knockdown cells (A3 clone) were generated and grown as described earlier . Briefly, the Q472L mutation was introduced in pLNC_LEDGF BC-Ires-Bsd and MLV-based vectors were generated . Following transduction, cells were selected with 3 μg/ml blasticidin (Invitrogen, Merelbeke, Belgium). Protein expression was verified by Western blot analysis and immunocytochemistry (data not shown).
The molecular clone pNL4.3 was obtained through the NIH AIDS Research and Reference Reagent Program. Virus stock (HIVNL4.3) was produced as described earlier .
HIV-1 breakthrough assay
Cells were seeded at 30 000 cells per well in a six-well dish and infected as described earlier with minor modifications . Briefly, cells were infected with 56 000 pg p24 HIVNL4.3 in a total volume of 2 ml (MOI 0.01). Twenty-four hours later, cells were washed twice with 1× PBS prior to addition of 4 ml of fresh medium. HIV replication was monitored by sampling the culture medium for p24 ELISA (HIV-1 p24 ELISA kit, Perkin Elmer).
Integrated proviral copies were quantified by real-time quantitative PCR (Q-PCR) on genomic DNA using the iQ5 Multicolor RT PCR detection system (BioRad, Nazareth, Belgium). In order to allow quantification of integrated proviral copies in HIV-1NL4.3 infected cells, cells were subcultured at day 6 and grown under zidovudine/ritonavir treatment for 10 days, 0.5 and 1.5 μmol/l, respectively, that is 25-fold IC50 as determined in MT4/MTT assay  to eliminate all nonintegrated viral DNA. Genomic DNA was extracted using the GenElute mammalian genomic DNA miniprep kit (Sigma, Bornem, Belgium); for each reaction 100 ng was used for Q-PCR. Integrated copies for HIV-1NL4.3 were quantified using a Gag-derived primer-probe set. Each 25 μl reaction contained 12.5 μl 2× iQ Supermix (BioRad), 40 nmol/l primer and 40 nmol/l probe. RNaseP was used as house-keeping gene control (TaqMan RNaseP control reagent; Applied Biosystems, the Netherlands). All samples were run in quadruplet and subjected to 3 min at 95°C, 50 cycles of 95°C for 10 s and 55°C for 30 s. Data were analyzed with iQ5 Optical System software (BioRad, Nazareth, Belgium).
The difference in allele frequency distribution between the HIV-1-positive and HIV-1-negative group was determined by Fisher's exact test (FET) for each SNP to test the null hypothesis that allele frequencies were the same in the two groups.
The effect of each SNP on HIV-1 viral load and CD4+ T-cell count was determined using a Generalized Estimating Equation (GEE) model  taking into account longitudinal measures for each participant. Viral loads were log-transformed and the square root of CD4+ T-cell count was used to normalize their measurements.
Kaplan–Meier survival statistics and the Cox proportional hazards model (Cox model) were used to assess the effect of each SNP on time to HIV-1 infection after enrollment and on the rate of progression to AIDS defined as CD4+ T-cell decline to less than 350 cells/μl (CD4 <350 cells/μl). Decline in CD4 levels was determined and compared for the group with one or two copies of the minor allele to a reference group with two copies of the major frequency allele (dominant genetic model), for each SNP. The significance of genotypic associations and relative hazard was determined by unadjusted Cox regression analysis for the dominant genetic model.
LEDGF/p75 mRNA expression levels were compared between seronegative and seropositive individuals by performing dot plot graphical representation, nonparametric statistical analysis, and correlation (Pearson). Values were expressed as median values. Differences between the two groups were evaluated using Dunn's multiple comparison test, whereas the Mann–Whitney U-test was used for any two-group comparisons. The software used for the analysis was SAS version 9.1.3 (SAS Institute Inc., Cary, North Carolina, USA).
Study design and selection of patient samples
One hundred and ninety-five seronegative and 52 seropositive individuals from the CAPRISA AI 002 cohort and 403 seropositive individuals from the Sinikithemba cohort were genotyped. Thirty-four samples from the CAPRISA AI cohort and 48 samples from the Sinikithemba cohort were excluded from all analysis due to sample unavailability or poor quality genotype data.
Identification of Q472L as an exonic polymorphism in the C-terminus of LEDGF/p75
The PSIP1 gene is 46.9 kb long and consists of 15 exons (Fig. 1a). We sequenced part of the C-terminal region to screen for SNPs in 83 seronegative and 43 seropositive individuals from the CAPRISA AI 002 cohort. Fourteen previously described SNPs (dbSNP, www.ncbi.nlm.nih.gov) were discovered (data not shown). Only one SNP (rs61744944) was in the exon region and nonsynonymous (Q472L).
Effect of PSIP1 single-nucleotide polymorphisms on susceptibility to HIV-1 infection in the CAPRISA AI cohort
The CAPRISA AI 002 cohort comprises high-risk individuals who were initially identified as seronegative and then followed longitudinally. This study design allowed us to test whether genetic variation in PSIP1 was associated with susceptibility to HIV-1 infection. The minor allele frequencies (MAFs) of SNPs and haplotype frequencies studied are shown (Fig. 1a). Kaplan–Meier survival analysis of time to HIV-1 acquisition suggested a trend of association between SNP1A and HIV-1 acquisition (relative hazard 2.21, 95% CI 0.92–5.28; uncorrected P = 0.08, Cox model) (Table 2).
Association of PSIP1 single-nucleotide polymorphisms with CD4+ T-cell counts and viral load in the CAPRISA AI 002 cohort
The approximate time of infection was known for seropositive individuals in the CAPRISA AI 002 cohort . Since viral loads and CD4+ T-cell counts fluctuate significantly during acute HIV-1 infection, we analyzed the data in two intervals post infection, 0–3 months (acute) and 3–12 months (early chronic), to detect possible differences between genotypes during these phases of infection.
A dominant model analysis using GEE showed significant associations between SNP4G and lower CD4+ T-cell count (P<0.01, uncorrected) (Fig. 1b) and between SNP5T and lower viral load (P = 0.02, uncorrected) during acute phase of infection (Fig. 1c). SNP1A was associated with lower CD4+ T-cell count (P < 0.01, uncorrected) and SNP3C with higher CD4+ T-cell count (P < 0.01, uncorrected) during early chronic phase of infection (Fig. 1d). The minor allele of all SNPs, except for SNP1, were associated with lower viral loads (P < 0.05, uncorrected) during early chronic phase of infection (Fig. 1e).
Effect of PSIP1 single-nucleotide polymorphisms and haplotypes on CD4+ T-cell decline in the CAPRISA AI 002 cohort
We tested association of genetic variation with the rate of CD4 decline to CD4 less than 350 in the CAPRISA cohort. SNP1A was associated with rapid CD4+ T-cell decline (P = 0.04 uncorrected, Cox model) (Table 3). Haplotype 4 (HAP4) – the only haplotype carrying SNP1A – was also trending towards association with rapid CD4+ T-cell decline (relative hazard 2.46, 95% CI 0.87–6.93; P = 0.09) (Table 3). However, the effect of SNP3C on CD4 decline could not be assessed in the CAPRISA AI 002 cohort as there were only three SNP3C participants whose CD4 cell count had dropped to 350 cells/μl and below, at the time of this analysis.
Effect of PSIP1 single-nucleotide polymorphisms on CD4+ T-cell decline in the Sinikithemba cohort
Interpretation of the data from the CAPRISA AI 002 cohort was complicated by small sample size of the cohort and fluctuations in CD4+ T-cell counts and viral loads that characterize acute and early chronic phases of HIV-1 infection. To further elucidate the role of PSIP1 genetic variants in HIV-1 pathogenesis, we extended our analysis to the larger Sinikithemba cohort from the same geographical area. SNP2 and SNP4 were excluded from this analysis because they were not associated with CD4+ T-cell count during early chronic phase of infection in the CAPRISA AI 002 cohort. Since the Sinikithemba cohort is a seroprevalent cohort with unknown dates of infection, we assessed the influence of each of SNPs on disease progression by comparing the slopes of CD4+ T-cell decline levels using the dominant model. SNP3C was associated with delayed disease progression as reflected by a slower rate of CD4+ T cells decline to CD4 less than 350 (P = 0.02 uncorrected, Cox model) (Fig. 1g).
LEDGF/p75 mRNA expression levels in the CAPRISA AI 002 cohort
Quantitative RT-PCR on LEDGF/p75 mRNA levels was performed in 57 seronegative and 38 seropositive individuals. PBMCs obtained from seropositive individuals had reduced LEDGF/p75 expression compared to PBMCs obtained from seronegative individuals (P = 0.02) (Fig. 2a). For 13 of seropositive individuals, samples pre (baseline) and post-HIV-1 infection were available for analysis. Baseline PBMCs from seroconverters had higher LEDGF/p75 expression compared to PBMCs from nonseroconverters (P < 0.01) (Fig. 2b). SNP3C was significantly associated with reduced levels of LEDGF/p75 (P < 0.01) (Fig. 2c) in both the seronegative and seropositive groups and slower rate of CD4+ T-cell decline (Fig. 1g) in the seropositive group.
Functional analysis of Q472L LEDGF/p75
To determine the impact of the Q472L mutation on the interaction with integrase – since this exonic mutation was located adjacent to the IBD (amino acids 347–429) – we measured LEDGF/p75-IN affinity in an AlphaScreen assay and evaluated the effect of the Q472L mutation on the interaction with JPO2 and pogZ, two cellular binding partners of LEDGF/p75 [29,30]. Potent RNAi-mediated knockdown of LEDGF/p75 also severely hampers HIV replication affecting the integration step of the provirus [19,34], a phenotype that is readily rescued upon re-introduction of RNAi resistant LEDGF/p75 (LEDGF BC) . In a similar setting, we complemented potent LEDGF/p75 knockdown cells with the Q472L variant (LEDGF BC Q472L). Q472L did not alter the binding affinity of LEDGF/p75 for integrase, JPO2 and pogZ and complementation of LEDGF/p75-depleted cells with mutant LEDGF/p75 (Q472L) rescued HIV-1 replication to near wild-type levels (data not shown).
LEDGF/p75 promotes HIV-1 replication in vitro[9–11,13,15,34,35]. In this study, we investigated the association between genetic variation in PSIP1 and susceptibility to HIV-1 infection and disease progression in two South African cohorts. We found that SNP1A was associated with higher likelihood of HIV-1 acquisition, lower CD4+ T-cell counts during the early chronic phase of infection and rapid CD4+ T-cell depletion in CAPRISA 002 acute infection cohort. SNP3C was associated with higher CD4+ T-cell count and lower viral load during the early chronic phase of infection in the CAPRISA AI 002 cohort.
The Sinikithemba cohort comprises chronically HIV-1-infected individuals with unknown date of infection and therefore we could examine the effect of SNPs on trajectory of CD4+ T-cell decline over 6 years of follow-up. SNP3C was significantly associated with delayed disease progression as was reflected by a slower rate of CD4+ T-cell decline in the Sinikithemba cohort. The protective effect of SNP3C was consistent between the two cohorts suggesting that SNP3C may be associated with reduced HIV-1 replication.
Next, we investigated the association between PSIP1 genetic variation and expression levels of LEDGF/p75 and found that SNP3C was significantly associated with lower levels of LEDGF/p75 which corroborates the plausible role of SNP3C in reducing HIV-1 replication. These findings suggest that SNP3C reduces the mRNA levels of LEDGF/p75 thereby inhibiting HIV-1 replication, a finding that is consistent with in-vitro knockdown studies [19,36]. Preinfection PBMCs from seroconverters had significantly higher LEDGF/p75 mRNA levels compared to nonseroconverters. HIV-1 infection reduced LEDGF/p75 expression to lower levels compared to uninfected individuals. These findings suggested that high levels of LEDGF/p75 may increase the likelihood of HIV-1 acquisition and the rate of disease progression confirming that LEDGF/p75 is an important host factor for productive HIV-1 infection . On the contrary, SNP1 associated with differential susceptibility and early HIV-1 infection outcomes in the CAPRISA acute infection cohort but not in the Sinikithemba chronic infection cohort. This SNP was not associated with differential LEDGF/p75 mRNA expression levels and future studies will need to address other possible contributing mechanisms.
Lastly, we analyzed the impact of the Q472L mutation on LEDGF/p75–integrase interaction and plausible effects on cellular binding factors of LEDGF/p75, JPO2 and pogZ. Q472L did not alter the binding affinity of LEDGF/p75 for integrase, JPO2 and pogZ and complementation of LEDGF/p75-depleted cells with mutant LEDGF/p75 (Q472L) rescued HIV-1 replication to near wild-type levels. It is not inconceivable that this mutation may affect other LEDGF/p75 functions such as integration site targeting.
These findings demonstrate that genetic variation in PSIP1 may influence susceptibility to HIV-1 infection and disease progression, which provides in-vivo evidence that LEDGF/p75 is an important host cofactor for HIV-1 replication. However, due to small sample size and heterogeneous nature of our cohorts, our findings should be interpreted with caution and will need to be replicated in additional studies.
We thank Taryn Page, Yuchun Zhou, Beth Binns-Roemer, Sofie Vets and Nam Joo Van der Veken for excellent technical support. We acknowledge the participants and their clinicians who participated in the CAPRISA AI 002 and Sinikithemba studies. The CAPRISA 002 study was supported by the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), US Department of Health and Human Services (grant U19 AI 51794). The Sinikithemba cohort was supported by a grant from the NIH (grant R01-AI067073, contract N01-AI-15422). This study was funded by the seventh framework program (FP7) of the European commission (THINC, HEALTH-F3-2008-201032). Additional funding was provided by the South African Department of Science and Technology/National Research Foundation Research Chairs Initiative. This project has been funded in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does it mention of trade names, commercial products, or organizations imply endorsement by the US Government. This research was supported [in part] by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.
Author contributions: P.M., C.W., Z.D. and T.N. conceived the study and designed the experiments. P.M., R.G., A.H. and P.A. performed the experiments. P.M., R.G., F.C., A.H., L.W., C.W., Z.D. and T.N. analyzed and interpreted the data. S.S.A.K., K.M. and other members of the CAPRISA 002 study team designed and oversaw the CAPRISA 002 study cohort. T.N. and other HPP investigators oversaw the Sinikithemba study cohort. All authors critically reviewed the manuscript.
Conflicts of interest
There are no conflicts of interest.
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