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. 2018 Oct 16;13:467–474. doi: 10.1515/med-2018-0062

The CCR5-Delta32 Genetic Polymorphism and HIV-1 Infection Susceptibility: a Meta-analysis

Jun Ni 2, Dan Wang 3, Sheng Wang 1,*
PMCID: PMC6227735  PMID: 30426084

Abstract

The CC chemokine receptor 5 (CCR5) is a chemokine receptor which is widely expressed in several immune cells involved in the inflammatory responses. Previous published studies revealed the relation of the CCR5 gene (CCR5-delta32) with the risk of HIV-1 infection, but the results are debatable and inconclusive. Here by meta-analysis, we have systematically evaluated the relation between the CCR5-delta32 polymorphism and the risk of HIV-1 infection. A comprehensive search in PubMed, EMBASE, CNKI, Cochrane Library, and WanFang database was performed up to April 15, 2018. The pooled odds ratio (ORs) along with its 95% credible interval (95%CI) was used to evaluate the relation between the CCR5-delta32 polymorphism and HIV-1 infection risk. The study included 24 case-control studies involving 4,786 HIV-1 infection patients and 6,283 controls. Compared with the wild-type homozygous genotypes, the results showed that the CCR5-delta32 heterozygotes (OR=1.16, 95%CI=1.02-1.32) had an increased susceptibility to HIV-1 and the delta32 homozygous (OR=0.25, 95%CI=0.09-0.68) had significantly reduced the susceptibility to HIV-1 for healthy controls. Moreover, we have found the delta32 allele carriers (OR=0.71, 95%CI=0.54-0.94) had significantly cut down the HIV-1 infection susceptibility when using exposed uninfected (EU) as controls. We also conducted the stratified analysis by ethnicity, and there significant association was detected in Caucasian in delta32 allele carrier genotype. To summarize, our meta-analysis suggests that the CCR5-delta32 homozygous genotype (delta32/delta32) confer possible protection against HIV-1, especially the exposed uninfected groups.

Keywords: CCR5-Delta32, Polymorphism, HIV-1, Susceptibility, Meta-analysis

1. Introduction

Human Immunodeficiency Virus-1 (HIV-1)/Acquired Immunodeficiency Syndrome (AIDS), the world major infectious killer remains one of the most important public health challenges in the world. It was estimated that about 36.7 (34.0-39.8) million people were living with HIV in 2016, and 1.8 million people have died from HIV/AIDS every year. Therefore, HIV prevalence can be considered as the greatest issue in contemporary society, including economic and health crisis [1]. However, many basic questions about the HIV-1 infection pathogenesis have not been answered. Several studies demonstrated that they have a significant difference in susceptibility and progression of HIV-1 infection [2, 3, 4]. Host genetic diversity has an important role in either disease susceptibility or resistance [4, 5]. However, the positive role of different genes in HIV/AIDS progression has still remained controversial [6, 7].

Its chemokines and their natural receptors act a key part in HIV-1 binding and entry [8]. Chemokine receptors act on CD4 as a relevant receptor for HIV-1 to regulate the first step in the entry of HIV-1 virus. CCR5, a chemokine receptor of gene product, is expressed on macrophages, monocytes, T and dendritic cells. This is a specific receptor for the CC ligand 3 (CCL3), CCL4, and CCL5 chemokine and a key part in the transferring of immune cells to inflammatory sites [9]. The CCR5Δ32 variant is characterized by a 32 base-pair (bp) deletion of the CCR5’s gene coding region; the deletion of CCR5-delta32 was initially discovered and gained the greatest interest in the relation to infection with the HIV-1. This lack of homozygosity is associated with preventing the risk of HIV-1 infection [10]. Liu et al performed a meta-analysis and demonstrated that there was no statistical correlation between the CCR5-delta32 polymorphism and the risk of HIV-1 infection. For adults, the CCR5-delta32 polymorphism was investigated for their association with the risk of HIV-1 infection, but the results from the previous published researches remains conflicting and inconclusive [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]. Thus, we conducted the present meta-analysis by pooling all available publications to evaluate the possible role of CCR5-delta32 polymorphism and HIV-1 infection susceptibility.

2. Methods

2.1. Literature search

From inception to April 2018, we conducted electronic searches using the terms “CCR5-delta32”, “polymorphism*” or “variant*” or “mutation”, “HIV” through PubMed, EMBASE, China National Knowledge Internet (CNKI), WanFang, and the Cochrane Library for relevant studies. No language restriction was applied.

2.2. Selection criteria

Articles must satisfy the following criteria: (a) evaluated the relation between CCR5-delta32 and the risk of HIV-1 infection; (b) case-control studies on human beings, no language restriction was applied; (c) sufficient data to evaluate the odds ratios (ORs) and 95% credible interval (CI), and P values; and (e) genotype distribution in controls must be in Hardy-Weinberg equilibrium (HWE) (P < 0.001). Review articles, conference abstracts, case reports and insufficient data to evaluate ORs and 95%CI were excluded.

2.3. Data extraction

Data extraction was done by two authors through a standardized form independently, such as first author, year of publication, country, ethnicity, source of the controls, genotype distribution of cases and controls, and P values for HWE in controls. Discrepancies were settled by discussion, with disagreements resolved by consensus.

2.4. Statistical analyses

The pooled odds ratio (ORs) along with 95% credible interval (95%CI) was utilized to access the strength of relation between the CCR5-delta32 and HIV-1 infection risk. We also conducted stratified analyses by ethnicity and sources of controls. The I2 and Cochran’s Q-test statistics were used to quantify the statistical heterogeneity, and the random-effect model was conducted if heterogeneity was significant (P < 0.05) [35]; otherwise, the fixed-effect model was conducted[36]; P < 0.05 was considered as a significant difference in the value between the two groups. Sensitivity analysis was performed by sequentially excluding studies to assess the stability of the pooled results. Begg’s funnel plot and the Egger’s tests was performed to evaluate the potential publication bias of the researches (P < 0.05 was considered significant) [37, 38]. The present meta-analysis was carried out by STATA 12.0 (Stata Corp LP, College Station, TX, USA).

3. Results

3.1. Characteristics of included studies

In this meta-analysis, the selection of eligible researches included is shown in Figure 1, 517 potentially relevant researches were initially obtained from the PubMed, EMBASE, China National Knowledge Internet (CNKI), WanFang, and the Cochrane Library. After the exclusion of irrelevant studies, a total of 24 published researches were identified to be eligible for the current study. The flow diagram describing selected studies inclusion or exclusion is in Figure 1. The baseline features of the selected researches are recorded in Table 1.

Figure 1.

Figure 1

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Flow diagram of the publication selection process.

Table 1.

Main characteristics of studies included in the meta-analysis

Author Year Country Ethnicity Genotyping methods Sample size Case Control HWE
Tan 2010 China Asians PCR 251 238 0.1899
Desgranges 2001 Santiago Mixed PCR 63 62 0.8452
Rathore 2008 India Asians PCR 190 370 0.8656
Xu 2009 China Asians PCR-RFLP 78 70 0.9035
Shrestha 2006 American Caucasian PCR 266 532 0.7247
Liu 2004 American Caucasian PCR 316 519 0.8926
Veloso 2010 Spanish Caucasian PCR-RFLP 184 236 0.3255
Munerato 2003 Brazilian Caucasian PCR 183 115 0.4544
Adojaan 2007 Estonia Caucasian PCR 3000 488 0.0026
Alvarez 1998 Spanish Caucasian PCR 150 250 0.6120
Zimmerman 1997 American Caucasian PCR 745 1096 0.8830
Mandl 1998 Austria Caucasian PCR 225 451 0.4286
Wang 2008 China Asians PCR-RFLP 104 155 0.9863
Tiensiwakul 2004 Thailand Asians PCR-RFLP 116 622 0.9610
Rigato 2008 Brazil Caucasian PCR-RFLP 200 82 0.5990
Roman 2014 Luxembourg Mixed PCR-RFLP 288 155 0.7356
Wang 2003 China Asians PCR-RFLP 330 474 0.9817
Deng 2004 China Asians PCR 88 119 0.9263
Balotta 1997 Italy Caucasian PCR 152 122 0.3977
Ellwanger 2018 Brazil Caucasian PCR 300 274 0.4210
Heydarifard 2017 Iran Asians PCR 140 300 0.7920
Zapata 2013 Colombia Mixed PCR 57 112 0.8091
Li 2003 China Asian PCR-RFLP 24 46 0.9406
Rugeles 2002 Colombia Mixed PCR 36 50 0.0381
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HWE, Hardy-Weinberg equilibrium; AA, CCR5 homozygotes; AB, CCR5-delta32 heterozygotes; BB, delta32 homozygotes.

3.2. Meta-analysis results

A total of 24 case-control studies were included in the present work to estimate the relation between the CCR5-delta32 polymorphism and the HIV-1 infection risk. To sum up, pooled risk evaluations shows a statistically significant relation between the CCR5-delta32 polymorphism and increased HIV-1 infection risk in the CCR5-delta32 heterozygotes genotype (OR=1.16, 95%CI=1.02-1.32, P=0.024) for healthy controls (Figure 2 and Table 3). Meanwhile, we found the risk of HIV-1 infection was significantly reduced in the delta32 homozygous genotype (OR=0.25, 95%CI=0.09-0.68, P=0.006) for healthy controls (Table 3). When we conducted stratified analysis by sources of control, we also detected a significantly risk decline of HIV-1 infection in the delta32 allele carriers (OR=0.71, 95%CI=0.54-0.94, P=0.015) among exposed uninfected populations (Figure 3 and Table 3). We also performed the stratified analysis by ethnicity, there was significant association in Caucasian with delta32 allele carrier genotype.

Figure 2.

Figure 2

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Forest plots demonstrating the association between CCR5-delta32 polymorphism and HIV-1 infection susceptibility in the CCR5-delta32 heterozygote model.

Table 3.

Meta-analysis of the association between CCR5-delta32 polymorphism and HIV-1 infection susceptibility

Comparison Subgroup Studies Heterogeneity test Association test Model Publication bias
P Value I2(%) OR(95%CI) P Value Egger
B vs. A Overall 24 0.037 39.4 1.08(0.96-1.22) 0.222 F 0.125
Mixed 3 0.000 0 1.26(0.83-1.92) 0.277 R
Caucasian 11 0.070 56.8 0.99(0.79-1.23) 0.918 R
Asian 6 0.267 24.2 0.90(0.39-2.06) 0.803 R
EUs 6 0.355 9.5 0.71(0.54-0.94) 0.015 F
AB vs. AA Overall 20 0.035 39.8 1.16(1.02-1.32) 0.024 F 0.078
Mixed 3 0.564 0 1.38(0.88-2.16) 0.157 R
Caucasian 11 0.010 57.0 1.05(0.83-1.33) 0.665 R
Asian 6 0.228 27.5 0.89(0.37-2.13) 0.791 R
EUs 6 0.549 0 0.91(0.66-1.25) 0.568 F
BB vs. AA Overall 8 0.965 0 0.25(0.09-0.68) 0.006 F 0.058
Caucasian 6 0.966 0 0.22(0.07-0.69) 0.009 F
EUs 3 0.274 22.8 0.06(0.01-0.32) 0.001 F
AB+BB vs. AA Overall 20 0.028 41.3 1.12(0.99-1.28) 0.071 F 0.096
Mixed 3 0.541 0 1.34(0.86-2.09) 0.196 R
Caucasian 11 0.007 58.5 1.02(0.81-1.29) 0.871 R
Asian 6 0.235 26.6 0.89(0.38-2.10) 0.791 R
EUs Overall 6 0.441 0 0.80(0.59-1.08) 0.141 F
BB vs. AA+AB Caucasian 8 0.963 0 0.25(0.09-0.67) 0.006 F 0.058
EUs 6 0.962 0 0.21(0.07-0.68) 0.009 F
3 0.292 18.8 0.06(0.01-0.32) 0.001 F
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OR, odds ratio; CI, confidence interval; F, fixed-effects model; R, random-effects model; EUs, exposed uninfected.

Figure 3.

Figure 3

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Forest plots demonstrating the association between CCR5-delta32 polymorphism and HIV-1 infection susceptibility in the delta32 homozygote model.

Table 2.

The distribution of CCR5-delta32 genotype of included studies.

Author Ethnicity HIV-1 infected Healthy Controls Exposed uninfected
AA AB BB AA AB BB AA AB BB
Tan Asians 226 24 1 222 15 1
Desgranges Mixed 60 3 0 59 3 0
Rathore Asians 190 0 0 314 6 0 50 0 0
Xu Asians 74 4 0 68 2 0
Shrestha Caucasian 258 8 0 516 16 0
Liu Caucasian 261 55 0 354 68 3 69 22 3
Veloso Caucasian 144 40 0 174 26 0 31 5 0
Munerato Caucasian 162 21 0 100 15 0
Adojaan Caucasian 230 70 0 371 117 0
Alvarez Caucasian 138 12 0 205 42 3
Zimmerman Caucasian 601 144 0 846 121 4 94 26 5
Mandl Caucasian 182 43 0 367 78 6
Wang Asians 104 0 0 104 0 0 51 0 0
Tiensiwakul Asians 116 0 0 432 0 0 190 0 0
Rigato Caucasian 185 15 0 73 9 0
Roman Mixed 226 62 0 127 27 1
Wang Asians 329 1 0 473 1 0
Deng Asians 88 0 0 117 2 0
Balotta Caucasian 136 15 1 108 13 1
Ellwanger Caucasian 265 35 0 240 32 2
Heydarifard Asians 139 1 0 291 9 0
Zapata Mixed 51 6 0 107 5 0 63 7 0
Li Asian 23 1 0 45 1 0
Rugeles Mixed 33 3 0 47 2 1
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AA, CCR5 homozygotes; AB, CCR5-delta32 heterozygotes; BB, delta32 homozygotes.

3.3. Sensitivity analysis and publication bias

Sensitivity analysis was conducted by sequentially excluding individual studies to assess the impact of each study on the summarized findings. This revealed that the findings were statistically robust and credible (data not shown) (Figure 4). Begg’s and Egger’s test (Table 3) was utilized to examine the potential bias of the publication [37, 38]. The shape of the funnel plot was symmetrical as shown in Figure 5 suggesting there was no obvious publication bias.

Figure 4.

Figure 4

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Sensitivity analysis for the influences of CCR5-delta32 polymorphism and HIV-1 infection susceptibility under the allele model.

Figure 5.

Figure 5

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Funnel plot of publication biases on the association between CCR5-delta32 polymorphism and HIV-1 infection susceptibility.

4. Discussion

AIDS remains one of the biggest public health challenges of the world, as we all know, it is a complex infectious disease, including HIV-1 infection, host immune response, and gene-environment interactions. Several studies have already found that both viral genetics and host genetic factors are important determinants of HIV-1 infection [4, 5, 10]. Chemokines and chemokine receptors are critical for immune response in HIV-1 infection. Although many researches demonstrated the association between chemokine and chemokine receptor gene polymorphisms, and host’s susceptibility to HIV-1 infection, the conclusions are still controversial [11, 12, 13].

Meta-analysis, a useful statistical tool through integrating and comparing the results of many related researches and taking into consideration of variations in characteristics that can affect overall estimate of the outcome of interest, which is used to evaluate the literature in both quantitative and qualitative ways. So it is especially worthy when previous researches could not provide significant differences among treatments because of sample sizes limitations, or when there is no consensus [39]. Despina et al. performed a meta-analysis and demonstrated that perinatal infection is not determined by heterozygosity for CCR5-delta32 in the children [40]. In addition, Liu et al. performed a meta-analysis suggested that no statistical relation was detected between the CCR5-delta32 polymorphism and HIV-1 infection risk in any genetic model [41].

Considering that many researches have produced conflicting results, we conducted the present meta-analysis involving 24 eligible researches with 4,786 cases and 6,283 controls. The present researches showed there is a significant relation between CCR5-delta32 polymorphism and the risk of HIV-1 infection in the total population. In CCR5-delta32 heterozygotes genetic model, we have detected a statistically significant increased susceptibility to HIV-1 infection (OR=1.16, 95%CI=1.02-1.32, P=0.024) for healthy control. Meanwhile, we have found significantly reduced the risk of HIV-1 infection in the delta32 homozygous genetic model (OR=0.25, 95%CI=0.09-0.68, P=0.006). In healthy subjects, CCR5-delta polymorphism may be protective effects against HIV-1 infection only in the delta32 homozygous individuals (OR=0.25, 95%CI=0.09-0.68, P=0.006). Stratified analysis by EU population, the results demonstrated that CCR5-delta32 polymorphism may be protect effects against HIV-1 infection susceptibility in the delta32 allele carriers (OR=0.71, 95%CI=0.54-0.94, P=0.015), which was in accordance with that in the healthy subjects. Meanwhile, we also conducted the stratified analyses by ethnicity, we have detected significant relation between the CCR5-delta32 polymorphism and HIV-1 infection susceptibility in Caucasian subjects.

There are several limitations in the current study. Firstly, suitable English or Chinese-language studies were only enrolled in current meta-analysis, which means related researches published in other languages may have been overlooked, which may also lead to selection bias. Secondly, the number as well as the sample size of some included studies was limited and the results should be interpreted with caution. Finally, the influences of other relevant components such as age, gender, life style as well as their interactions with CCR5-delta32 polymorphism on HIV-1 infection susceptibility were not analyzed due to the lack of original data.

5. Conclusion

In conclusion, our findings indicated that the CCR5-delta32 homozygous genotype (delta32/delta32) confer possible protection against HIV-1 infection, especially in exposed uninfected population. However, this conclusion should be confirmed by multi-center and large-scale studies based on multiple ethnic groups.

Footnotes

Conflict of interest

Interest conflict: The authors claim no conflict of interest.

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