Primary CXCR4 Co-receptor Use in Acute HIV Infection Leads to Rapid Disease Progression in the AE Subtype

Abstract

This is a comparative study of HIV co-receptor usage in the early stages of HIV infection between two distinct patient groups, one with a low CD4 count (group 1), and the other with a high CD4 count (group 2). Group 1 progressed to a CD4 count below 200 cells/μL within 2 y, while group 2 had a CD4 count above 500 cells/μL within 2 y. Viral RNA was extracted from the plasma of these patients, and the C2-V5 region of the HIV-1 env genes were cloned and sequenced. The co-receptor usage was predicated based on V3 loop amino acid sequences using Geno2pheno and PSSM programs. Our results indicate that in acute HIV infection of rapid progressors (low CD4 count; group 1), the primary co-receptor usage is CXCR4, while in the high CD4 count group (group 2), the co-receptor usage is predominantly CCR5. One-year follow-up data from these patients showed no obvious change in HIV co-receptor usage in either group. Sequence analysis of patients from both study groups showed prevalence of the AE subtype, and therefore we can speculate that the CXCR4 co-receptor may be the primary HIV-1 co-receptor used in the HIV-1 AE subtype, and may be responsible for rapid HIV-1 disease progression in the MSM cohort.

Introduction

HIV-1 enters the host cell via the attachment of GP120 to CD4, followed by interaction with a chemokine receptor, also known as an HIV-1 co-receptor (11,27,39). The chemokine receptors CCR5 and CXCR4 were identified as the major co-receptors for HIV-1, and HIV-1 tropism was defined as R5, X4, or dual/mixed R5/X4, based on the ability of the virus to use either of these chemokine receptors (4,7,8,18,21,37,45).

Identifying the biological factors of new infections is essential both to understanding the mechanisms of transmission and to developing approaches, including vaccines and microbicides, that might interrupt transmission. Controversies exist regarding co-receptor usage during early HIV infection (4,5,7,21,41,45). Evidence suggests that during the early stages of disease, HIV-1 isolates are preferentially monocyte tropic with a non-syncytium-inducing (NSI) phenotype, and use CCR5 as a co-receptor (21). Primary infection with CXCR4-using HIV-1 strains is believed to be a rare event, and mixed R5/X4 primary infections have been documented in a few patients studied longitudinally from an early stage post-infection (6,16,26). It is reported that the first CXCR4-using variants evolved from pre-existing R5 variants within the same individual, rather than remaining dormant for many years after an initial independent transmission (21).

The HIV-1 envelope is composed of relatively conserved (C1 to C5) and variable regions (V1 to V5) (11,24,29,32). Specifically, the presence of a positively-charged amino acid at either one or both of two specific positions in the V3 loop (positions 11 and 25) is strongly associated with a CXCR4-using phenotype (9,14), suggesting that these amino acids play a crucial role in the interaction of gp120 with the co-receptors. The V3 loop also seems to be the principal genetic determinant of the co-receptor choice (10). Replacements of charged amino acids within the V3 region are known to alter the co-receptor usage (29,20).

Co-receptor tropism of individual viral strains can be delineated using reporter cells expressing different co-receptors (43), and additional bioinformatics strategies have been developed to predict viral co-receptor usage from envelope (env) gene sequence information (22,31). It is reported that co-receptor usage can be predicted by analyzing the putative V3 loop amino acid sequences using different bioinformatics tools, such as support vector machine, PART, 11/25 charge rule, Geno2pheno (http://coreceptor.bioinf.mpi-inf.mpg.de/), and position-specific scoring matrix (PSSM) (3,42). Based on several special residues (especially amino acid profiles at sites 11 and 25 in the V3 loop), or the net charges of the V3 loop, these approaches employ a multiple linear regression (1), a neural network strategy (35), a machine-learning method (30), or a PSSM (22,23), to predict HIV-1 co-receptor usage and phenotype. The PSSM approach appears to have better predictive power over other methods (22).

In this study, we chose two distinct groups of patients to compare the HIV co-receptor use in early HIV infection (i.e., within a time period of 2 y post-infection). Group 1 consists of patients with low CD4 counts, and group 2 consists of patients with high CD4 counts. Group 1 consists of 7 patients who were rapid progressors with a CD4 count of <200 cells/μL, and group 2 consists of 5 patients who maintained a CD4 count of >500 cells/μL within 2 y of infection.

Ten monoclonal sequences of HIV-1 env C2–V5 region were sequenced for each patient at two time points during the study. Sequencing was first done when the patients were first detected with a positive HIV-1 infection, and sequence analysis was repeated 1 y post-detection. Putative V3 loop amino acids were compared using the Geno2pheno and PSSM methods to predict co-receptor (R5/X4) usage. Our results showed that in patients with low CD4 counts, HIV-1 primarily used CXCR4 as co-receptor; however, in patients with high CD4 counts, the co-receptor used was CCR5. No significant change in co-receptor usage was observed at 1-year follow-up in either group of patients.

Materials and Methods

Patients

Twelve patients recently infected with HIV-1 were recruited from an HIV-1-negative high-risk MSM (men who have sex with men) cohort who were screened every 2 mo for HIV-1 infection in Beijing You'an Hospital. Seven of the 12 patients showed rapid progression of HIV-1 disease, with CD4 counts <200 cells/μL within 2 y post-infection, while 5 of the 12 cases enrolled in the study maintained a CD4 count higher than 500 cells/μL. The progression of early HIV-1 infection can be depicted as six discrete stages, as proposed by Fiebig et al. (13,38). These stages of HIV-1 disease progression were marked by time points of appearance of HIV-1 markers and certain viral load concentrations during primary HIV infection. Samples were collected at the first HIV-1-positive time point, and then at 1, 2, 4, 8, 12, 24, 36, 48, 60, 72, 84, 96, and 108 weeks after the first positive time point. The project was reviewed and approved by the Beijing You'an Hospital Research Ethics Committee, and patients participated in the study following informed consent. Demographic and immunologic characteristics of the patients are reported in Table 1.

Table 1.

Demographic and Immunologic Characteristics of the Study Patients

			CD4 cell count		Plasma viral load		Infection time
Patient	Age (years)	Gender	First (cells/μL)	Last (cells/μL)	First (copies/mL)	Last (copies/mL)	First Fiebig stage	Last Days from the first
Pt1	29	M	374	118	3,390,000	393,000	III–IV	272
Pt2	32	M	624	159	9240	19,300	III–IV	387
Pt3	27	M	299	153	43,300	84,200	V	346
Pt4	38	M	224	196	72,400	982,000	V	313
Pt5	26	M	438	117	228,000	169,000	III–IV	283
Pt6	30	M	752	72	12,100	409,000	III–IV	381
Pt7	28	M	439	162	197,000	415,000	III-IV	321
Pt8	27	M	653	530	14,100	79,200	V	392
Pt9	24	M	805	827	258,000	59,400	III–IV	320
Pt10	27	M	640	619	120,000	87,000	III–IV	384
Pt11	26	M	678	622	1,400	20,000	III–IV	332
Pt12	24	M	603	689	16,400	4290	III–IV	396

M, male; First, the first positive time point; Last, the last visiting time point.

Cloning and sequencing of the C2–V5 region of HIV-1 env

The C2–V5 region of the HIV-1 env gene of the patients was analyzed. The genomic RNA was extracted from plasma using the QIAamp viral RNA Mini Kit (QIAGEN, Hilden, Germany), and HIV-1 cDNA were amplified by a nested PCR (two rounds of 30 cycles, first round: 95°C for 3 min, 94°C for 30 sec, annealing at 50°C for 30 sec, extension at 72°C for 80 sec, and final elongation at 72°C for 5 min; second round: 95°C for 3 min, 94°C for 30 sec, annealing at 52°C for 30 sec, extension at 72°C for 50 sec, and final elongation at 72°C for 5 min). The outer primers used were TF1 (forward): 5′-ATG GGA TCA AAG CCT AAA GCC ATG TGT-3′ (HXB2 nt 5933–5959), and TR1 (reverse): 5′-GCG CCC ATA GTG CTT CCT GCT GCT GC-3′ (HXB2 nt 7203–7181), and the inner primers were TF2 (forward): 5′-CTG TTA AAT GGC AGT CTA GC-3′ (HXB2 nt 6405–6424), and TR2 (reverse): 5′-ACT TCT CCA ATT GTC CCT CAT-3′ (HXB2 nt 7046–7026). A PCR product of 642 bp was obtained, including the C2–V5 region of the HIV-1 env gene. Multiple HIV-1-negative controls were included with each PCR run to detect any possible contamination.

Purified products from PCR were separately ligated to the pMD-18T vectors (Takara, Dalian, China) according to the manufacturer's instructions. For each sample, about 10 single colonies of transformed Escherichia coli JM109 were picked and cultured. The plasmid DNA was purified using Rapid Extraction of a small amount of high purity plasmid kit (BioMed, Beijing, China), and digested with EcoRI restriction enzyme to confirm the presence of the insert. The plasmids containing the inserts were then sequenced using M13-47 primers synthesized by the BioMed Technology Development Company. The sequences were assembled and checked for errors using Vector NTI software (version 9.0; Invitrogen, Carlsbad, CA).

Coreceptor usage prediction and V3 loop characteristics analysis

The sequences of the C2–V5 region were aligned with a reference sequence data set (49) of all major subtypes and circulating recombinant forms (CRF) (downloaded from Los Alamos Sequence Database) by the ClustalW multiple sequence alignment program from MEGA 4.

The sequences of the V3 region were translated into amino acid sequences using BioEdit Sequence Alignment Editor 7.0 software, and co-receptor usage predictions based on V3 amino acid sequences were performed by Geno2phen (http://coreceptor.bioinf.mpisb.mpg.de/cgi-bin/coreceptor.pl), with a false-positive rate of 0.1, and the PSSM approach (http://ubik.microbiol.washington.edu/computing/pssm/), for which the higher the score, the more closely the sequence resembles those of known X4 viruses (3). For the purpose of this study, we considered a sequence as X4-tropic if one of the two approaches predicted it to be X4-tropic. The net amino acid charges of the V3 region were calculated with the Innovagen peptide property calculator (www.innovagen.se) (3). The V3 loop central motifs of each sequence were analyzed with respect to each subtype.

Statistical analysis

Comparisons were performed using the independent or paired sample t-test, and all reported p values were two-sided, and were considered to be significant at p<0.05. All data were analyzed using SPSS statistical software (version 13.0; SPSS, Chicago, IL).

Results

Predicted coreceptor usage

All HIV-1 env gene V3 region sequences for each patient of the first HIV-1-positive time point and the last sample time point were analyzed and translated into amino acid sequences. Viral tropism was interpreted using geno2pheno (false-positive rate=10%), and an optimized version of PSSM with greater sensitivity to detect X4 variants. Co-receptor (CCR5/CXCR4) usage data are shown in Supplementary Table 1 (See Supplementary Table S1 at www.liebertpub.com/vim) and statistical analyses are reported in Table 2. Our data show that the predominant virus in the acutely-infected patients of rapid progressors or group 1 patients with a low CD4 count was the T-tropic X4 virus; however, the predominant virus in group 2 patients with a high CD4 count was the M-tropic R5 virus.

Table 2.

Predicted Co-receptor Usage Based on Clones of the V3 Loop Sequence

	First		Last
Patient	X4 ^a	R5 ^b	X4 ^a	R5 ^b
Pt1	13	0	15	0
Pt2	15	0	14	0
Pt3	14	0	14	0
Pt4	9	0	12	1
Pt5	13	0	16	0
Pt6	1	12	7	8
Pt7	15	0	15	0
Pt8	2	12	2	11
Pt9	1	11	0	11
Pt10	0	12	0	12
Pt11	0	12	1	11
Pt12	0	12	0	10

Number of V3 sequences predicted as CXCR4-tropic.

Number of V3 sequences predicted as CCR5-tropic.

First, the first positive time point; Last, the last visiting time point.

V3 loop charge

After the sequence analysis of the HIV-1 env gene, V3 regions were translated into amino acid sequences. The number of positively-charged amino acids (K, H, and R) and net charges of the V3 loop were analyzed (See Supplementary Table S2 at www.liebertpub.com/vim). We found that both the positive charges and the net charges of the V3 loop increased significantly (p<0.05) after 1 y in patients with high CD4 counts (group 2), but not in rapid progressors or patients with low CD4 counts (group 1) (Table 3).

Table 3.

Predicted V3 Loop Charges of HIV-1 Sequences

	Total			High CD4 group			Low CD4 group
	First	Last	p Value	First	Last	p Value	First	Last	p Value
Positive charges	5.62	5.81	0.009	5.42	5.69	0.053	5.75	5.88	0.122
Net charges	3.9	4.04	0.16	3.44	3.7	0.045	4.22	4.24	0.888

First, the first positive time point; Last, the last visiting time point.

Subtypes and central motifs of theV3 loop

When using the Geno2pheno approach to predict co-receptor usage, the subtype of the V3 loop can be predicted at the same time (See Supplementary Table S1 at www.liebertpub.com/vim).<http://www.liebertpub.com/vim).>. Our results show that of the 194 sequences obtained in the rapid progessors or group 1 patients, all patients were of the CRF01_AE subtype, and of the 118 sequences obtained in patients with high CD4 counts or group 2, 76.67% were subtype CRF01_AE and 18.33% were subtype C (Table 4). Thus, group 1 patients were all CRF01_AE subtype, while although group 2 members were predominantly the CRF01_AE subtype, they also included patients with subtype C. The V3 loop could induce the host to produce tropism-restricted neutralizing antibodies and cytotoxic lymphocytes, and the central motifs of the V3 loop are some of the most important neutralizing antibody determinants. The distribution of the types of V3 loop central motifs in the 7 rapid progressor or group 1 patients were GPGQ (96.91%), and GPGR (3.09%). The distribution of the types of V3 loop central motifs in the 5 group 2 patients with high CD4 counts were GPGQ (85.59%), GPGR (13.56%), and GSGQ (0.85%) (Table 4).

Table 4.

Subtypes and Central Motifs of the V3 Loop

	Total								High CD4 group								Low CD4 group
V3 subtype	AE		B		C		A/AG		AE		B		C		A/AG		AE
V3 loop central	GPGQ	267^a	GPGR	4	GPGQ	22	GAGQ	1	GPGQ	79	GPGR	4	GPGQ	22	GAGQ	1	GPGQ	188
motifs	GPGR	18					AVGQ	1	GPGR	12					AVGQ	1	GPGR	6
	GSGQ	1							GSGQ	1
Total		286		4		22		2		92		4		22		2		194
Percentage		91.08%		1.27%		7.01%		0.64%		76.67%		3.33%		18.33%		1.67%		100%

Number of the certain central motifs of each subtype.

High CD4 group, CD4 counts above 500 cells/μL during the 2 y after HIV infection; Low CD4 group, CD4 counts below 200 cells/μL at 2 y after HIV infection.

Discussion

A majority of new HIV-1 infections are initiated by only a single genetic species, and although primary HIV infection is typically caused by the R5 viruses, there is an association between the emergence of CXCR4-utilizing strains and faster disease progression, and sometimes several closely-related variants are also transmitted (19,25,28,36,44,46). It remains controversial whether HIV could use CXCR4 as a co-receptor in primary HIV infection. It is well established, mainly from studies with subtype B, that new infections are characterized almost exclusively by macrophage-tropic/NSI variants, whereas chronic infections often have mixtures of macrophage-tropic/NSI, T-cell-line-tropic/SI, and dual-tropic variants (21,25,44). Primary infection with CXCR4-using HIV-1 strains is believed to be a rare event (39), mixed R5/X4 primary infections have been clearly documented in a few patients studied longitudinally from an early stage post-infection (6,15,16,26). The very limited number of individuals characterized thus far makes it difficult to evaluate whether the clinical and virological course of an X4 HIV-1 infection differs from that of conventional R5 HIV-1 infections in individuals with wild-type CCR5 genes.

In the current study, we compared co-receptor use in patient groups who are in the acute stage of HIV infection, and who can be subdivided into patients with low CD4 counts, called rapid progressors, and patients with high CD4 counts. Our results indicate that in rapid progressors (low CD4 counts) the primary co-receptor usage is CXCR4, while in the high CD4 count group the co-receptor usage is predominantly CCR5. One-year follow-up data from these patients showed no obvious change in HIV co-receptor usage in either group. Further, sequence analysis of patients from both study groups showed a prevalence of the AE subtype. Evidence suggests that the most important determinants of the V3 loop are the central motifs, which play an important role in influencing the induction of neutralizing antibodies (47,48). Sequence variation in this motif may have an impact on virus infectivity and disease progression (2). It is reported that strains of different subtypes have different central V3 motifs, with GPGR mainly found in subtype B isolates, while C and CRF01-AE subtypes mainly contain the GPGQ motif. In this study we found that the main central V3 motifs of subtype CRF01-AE and C were mainly GPGQ, while those of subtype B were GPGR. The motifs found in this study were consistent with previously published motifs in these subtypes, and indicates that R5 co-receptor usage is the most predominant genotype, and is associated with the V3 GPGQ crown motif.

Several studies indicate that the V3 genotype, combined with bioinformatic algorithms, accurately predicts the phenotype of HIV-1 co-receptor usage for subtype B viruses (12,17,33,34,40). It was also reported that co-receptor usage prediction by genotypic tools was in good agreement with GHOST cell culture-based phenotype determination (11), showing very high levels of sensitivity and specificity for the detection of X4 strains in subtype B and non-subtype B primary isolates, except for subtype F viruses. These results support the observations of the current study. Based on our results, we can speculate that an increase in genetic diversity among viral isolates may be associated with low CD4 counts and high viral loads, and further that V3 loop motif changes may modulate HIV-1 disease progression by facilitating a switch from R5 to X4 phenotype.

In conclusion, the clinical implications of the genetic diversity seen in the V3 loop motif, and the difference in the prevalence of CXCR4-using variants among different HIV-1 genetic subtypes, remain to be understood. Additional studies with larger numbers of patients infected with different HIV-1 subtype variants, and who are at varying stages of disease progression, are warranted.

Footnotes

Acknowledgments

This study was supported in part by the National 12th Five-Year Major Projects of China (2012ZX10001-003 and 2012ZX10001-006), the Beijing Municipal of Science and Technology Major Project (D09050703590901), and the National Natural Science Foundation of China (81101250).

Author Disclosure Statement

No competing financial interests exist.

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