BioMed Research International

BioMed Research International / 2018 / Article

Research Article | Open Access

Volume 2018 |Article ID 9065181 | 9 pages | https://doi.org/10.1155/2018/9065181

Adaptation and Constraint in the Atypical Chemokine Receptor Family in Mammals

Academic Editor: Stefano Pascarella
Received16 May 2018
Revised24 Aug 2018
Accepted04 Sep 2018
Published24 Sep 2018

Abstract

Atypical chemokine receptors (ACKRs) are a subclass of G protein-coupled receptors characterized by promiscuity of ligand binding and an obvious inability to signal after ligand binding. Although some discoveries regarding this family in Homo sapiens and other species have been reported in some studies, the evolution and function of multiple ACKR in mammals have not yet been clearly understood. We performed an evolutionary analysis of ACKR genes (ACKR1, ACKR2, ACKR3, and ACKR4) in mammals. Ninety-two full-length ACKR genes from 27 mammal species were retrieved from the Genbank and Ensemble databases. Phylogenetic analysis showed that there were four well-conserved subfamilies in mammals. Synteny analysis revealed that ACKR genes formed conserved linkage groups with their adjacent genes across mammalian species, facilitating the identification of ACKRs in as yet unannotated genome datasets. Analysis of the site-specific profiles established by posterior probability revealed the positive-selection sites to be distributed mainly in the ligand binding region of ACKR1. This study highlights the molecular evolution of the ACKR gene family in mammals and identifies the critical amino acid residues likely to be relevant to ligand binding. Further experimental verification of these findings may provide valuable information regarding the ACKR’s biochemical and physiological functions.

1. Introduction

The chemokine system plays an important role in mammalian immunity, which can guide immune effector cells to sites of infection or inflammation and coordinate interactions between immune cells. The chemokine family is classified into four main subfamilies (XC, CC, CXC, CX3C) based on the positioning of their initial cysteine residues (here C indicates cysteine and X/X3 indicates one or there non-cysteine amino acids) [1]. All of chemokines exert their functions by interacting with chemokine receptors that are selectively expressed on the surfaces of their target immune cells [2]. Chemokine receptors belong to the largest rhodopsin family of G protein-coupled receptors (GPCRs) and they consist of seven transmembrane domains and multiple extracellular and intracellular loops. These are involved in ligand binding and signaling [3]. Chemokine receptors are mainly divided into four subfamilies depending on the type of chemokine they bind, including CC chemokine receptors (CCRs), CXC chemokine receptors (CXCRs), XC chemokine receptors (XCRs), and CX3C chemokine receptors (CX3CRs) [4]. Apart from signaling receptors, chemokine receptors also include some atypical receptors. These are similar in structure to the conventional receptors, but lack an intracellular motif required for signaling [5]. The conventional chemokine receptors represent a larger subgroup (eighteen in humans) of G protein coupled leukocyte chemotactic receptors, and the atypical chemokine receptors represent a smaller subgroup (four in humans) of chemotactic receptors that do not transduce signals through G proteins and lack chemotactic activity [6]. Atypical chemokine receptors mainly include four types of receptors and named atypical chemokine receptor 1 (ACKR1), ACKR2, ACKR3, and ACKR4 on the basis of the new standardized nomenclature system [7]. Members of ACKR have been previously referred to by scientists in this filed as ‘chemokine-binding proteins’, ‘scavengers’, ‘interceptors’, and ‘decoys’, which has caused confusion and has delayed recognition of these molecules as a functionally related groups. For instance, ACKR1 is previously known as Duffy antigen, Fy antigen, and CD234. ACKR2 is previously known as D6, CCBP2, CCR9, and CMKBR9. ACKR3 is previously known as CXCR7 and RDC1. ACKR4 is previously known as CCRL1 and CCX-CKR.

Atypical chemokine receptors are characterized by promiscuity of ligand binding and an apparent inability to signal after ligand binding. This inability to signal is mainly because of alterations in the canonical DRY motif located in the second intracellular loop of the typical chemokine receptors [8]. This motif is responsible for G protein coupling to the receptors and its absence is an essential unifying theme of the atypical receptors [9, 10]. These nonsignaling receptors efficiently internalize their cognate chemokines and act as chemokine scavengers. This is achieved by continuous internalization and constant surface expression of the atopic receptors in a β-arrestin signaling dependent manner [1113].

ACKR1 is mainly expressed in red blood cells and endothelial cells and has very little sequence similarity to other atypical receptors or chemokine receptors [14, 15]. ACKR genes exist in a specific chromosomal location, unlike those of the other receptors [15]. The ligand binding profile of ACKR1 includes several inflammatory chemokines, including CXCL1, CXCL5-9, CXCL11, and CXCL13, suggesting that ACKR1 plays an important role in maintaining chemokine receptors in the blood [16, 17]. ACKR2 is expressed in many different tissues including those of the skin, gut, and lung [18]. Within these classical barrier tissues, expression is largely confined to lymphatic endothelial cells, with no expression detected in blood endothelial cells [18]. The ligand binding profile includes CCL2-5, CCL7-8, CCL11, CCL13-14, CCL17, and CCL22 [1924]. ACKR2, which serves as a constitutively internalizing and recycling receptor, is also capable of internalizing and degrading the chemokines of its binding profile [25]. ACKR3 is the best characterized receptor in the ACKR family and it is mainly expressed in hematopoietic cells, neurons, mesenchymal cells, endothelial cells, and cancer cells. The ligands of ACKR3 contain CXCL11 and CXCL12. Binding of ACKR3 to CXCL11, which is an IFN-γ-induced chemokine, is involved in the regulation of lymphocyte migration [26]. ACKR3 also forms a heterodimer with CXCR4 and acts as a scavenger for the ligand CXCL12 and plays key roles in organ development and tumor development and progression [27]. ACKR4 is an atypical receptor for homeostatic CC and CXC chemokines including CCL19, CCL21, CCL25, and CXCL13 [28]. Like ACKR3, ACKR4 is capable of internalizing its ligands and targeting them for intracellular degradation, and it does so in much the same way. ACKR4 is also able to antagonize CXCR3-induced chemokines through heterodimer formation with the CXCR3 [29].

Two other ACKRs, CCRL2 (ACKR5) and PITPNM3 (ACKR6), have been proposed, but functional confirmation is pending, so they have been provisionally assigned ACKR designations [30].

Compared with numerous studies on typical chemokine receptors, especially in fish and mammals, little is known about ACKRs in mammals [30]. The increasing wealth of sequence data available from sequenced genome databases has allowed researchers to perform evolutionary analyses of ACKRs in mammals. In the present study, we performed an evolutionary analysis of 92 full-length ACKR (ACKR1, ACKR2, ACKR3, and ACKR4) genes from 27 mammal species retrieved from the Genbank and Ensemble databases.

2. Materials and Methods

2.1. Acquisition of ACKR Family Sequences and Synteny Analysis

For some well-annotated genomes, the amino acid sequences of ACKRs (ACKR1, ACKR2, ACKR3, and ACKR4) were retrieved directly from the Genbank and Ensemble databases. PSI-BLAST was performed to search these databases using Homo sapiens ACKRs (NP_001136269, NP_001008540, NP_001707, and NP_006555) as query sequences. The predicted coding sequences of the best hits were collected when the hits presented more than 70% in length and 50% in identity were aligned with the query sequence (with E values < e−10). These settings distinguished the potential ACKR members from different species but avoid involving other chemokine receptors effectively. After removal of redundant and incomplete sequences, the initial data set (S1 Table) for ACKR contained 92 protein sequences from 27 mammals. Because the functional confirmation of the two provisionally assigned ACKRs (ACKR5 and ACKR6) is pending, they were excluded from analysis in the present study.

Synteny analysis was conducted using the GENOMICUS v80.01 browser, which allows integration of the data available on the Ensemble database to provide a better visualization of conserved synteny blocks and to facilitate reconstruction of the organization of ancient genomes [31, 32]. Genes not annotated on the GENOMICUS browser were searched within the respective species by BLASTP and TBLASTN over the Genbank and Ensemble databases.

2.2. Sequence Alignment and Phylogenetic Analysis

A codon-based coding sequence alignment was constructed using MUSCLE with default parameters and manually adjusted using MEGA 6 [33] and viewed and edited in Jalview 2.0 [34]. The alignment was subsequently processed using Gblocks v0.91b [35] for phylogenetic reconstruction with default parameters. To access the selective pressures acting on the four mammals ACKR subfamilies, seven different alignments were produced: one for each paralog and a seventh with all sequences excluding outgroups. The substitution model that best fit the dataset was selected using Akaike Information Criterion (AIC) implemented in ProTest 3.2 [36], starting with 14 substitution matrices and using the fixed BIONJ tree for likelihood calculations. The phylogeny was estimated using the Maximum Likelihood (ML) methods. The ML phylogenetic tree was constructed in PhyML 3.0 [37], with 1000 bootstrap replicates and the NNI branch search algorithm. Finally, the phylogenetic trees were displayed using TreeView [38]. Besides, the neighbor joining (NJ), minimal evolution (ME) and maximum parsimony (MP) methods were used individually to reconstruct another three phylogenetic trees with MEGA v3.1 from the Gblocks alignment.

2.3. Codon-Based Analyses of Positive Selection

The selective pressures acting on coding region were evaluated across the phylogeny using a phylogenetic-based ML analysis. Accurate nucleotide sequences and related amino acid sequence alignments were retrieved with PAL2NAL [39], a program that constructs multiple codon alignments form matching protein sequences. The codeml program in PAML4.5 [40] was used to estimate the rates of synonymous (dS) and nonsynonymous substitution (dN) and the dN/dS ratio (omega, ω). ω>1 indicates positive selection, ω<1 indicates negative selection, and ω=1 is neutrality. Accurate nucleotide sequence alignments were constructed from matching related protein sequence with MUSCLE (MEGA 6). Then the resulting codon alignments and ML tree were used in the codeml program. The site-specific models were tested: Models M0 (one ratio), M1a (nearly neutral), M2a (positive selection), M3 (discrete), M7 (beta), and M8 (beta+ω) were all used in this analysis [41, 42]. Model M0 assumes one ratio for all sites. M1a presupposes a proportion p0 of conserved sites with ω<1 and p1=1-p0 of neutral sites with ω=1. M2a adds an additional class of sites with the frequency p2=1-p0-p1, and ω2 is estimated form the data. In the M3, the probabilities (p0, p1, and p2) of each site being submitted to purifying, neutral, and positive selection, respectively, and their corresponding ω ratios (ω0, ω1, and ω2) are inferred from the data. M7 and M8 assume a β-distribution for ω between 0 and 1, and M8 adds one extra class with the same ratio ω1. Subsequent likelihood rate comparisons of M0 and M3, M1a with M2a, and M7 with M8 were performed to determine which model fit the data best. The LRT was used to test positive selection of the two pairs of site model [43, 44]. Finally, the BEB approach was used to calculate the posterior probability that each site would belong to the site class of positive selection under each model [45].

3. Results

3.1. Identification and Distribution of ACKR Genes across Mammals

The final data sets contained 92 ACKR gene sequences from 27 representative species of mammals, including two primates, two rodents, one monotreme, one hyracoidean, one edentate, one pilosa, two perissodactyls, three artiodactyls, two carnivorans, one cetacean, one proboscid, two erinaceidae, one insectivoran, two lagomorphs, one dasyuromorph, one scandentia, one diprotodont, and two chiropterans. The results of genomic database searches showed that the majority of mammals investigated in this study (from orders Primate, Rodentia, Edentata, Perissodactyla, Carnivora, Proboscidea, Erinaceidae, Insectivora, Lagomorpha, Dasyuromorphia, Chiroptera, Artiodactylas) possess 4 members of ACKR family. No ACKR genes were identified in the representative of Pilosa, Choloepus hoffmanni. The remaining mammals only possessed 2 ACKRs and all of these species lacked the ACKR1 gene (S1 Table).

3.2. Synteny Analysis of ACKR Genes in Mammal Genomes

As shown in the additional file 1, several ACKR genes could not be identified in some mammal genomes using the sequence collection method. Synteny analysis was performed to determine why some ACKR genes were missed. We observed that the ACKR1 gene formed a conserved linkage group with AIM2, CADM3, and FCER1A genes in the most mammal genomes (Figure 1(a)). In the genome databases of C. hoffmanni, only two genes of the conserved linkage group, the AIM2 and CADM3, were found. These were located in Scaffold_33705 and Scaffold_ 5395, respectively. CADM3 and FCER1A, which were in the conserved group, were identified in the genomes of Tupaia belangeri but AIM2 and ACKR1 were not. In the genome of Ornithorhynchus anatinus, no members of the conserved linkage group were found. ACKR2 formed a conserved linkage group with CCDC13, HIGD1A and CYP8B1 in mammalian genomes (Figure 1(b)). However, no ACKR2 genes were found in the genomes of C. hoffmanni or O. anatinus. Only CCDC13 and HIGD1A of the conserved gene group were identified. The conserved ACKR3-specific gene group consisted of ASB18, IQCA, ACKR3, COPS8, and COL6A3 in mammal genomes (Figure 1(c)). Nevertheless, neither ASB18 nor ACKR3 were found in the genome of C. hoffmanni or T. belangeri. In mammalian genomes, the ACKR4-specific conserved linkage group was composed of ACPP, DNAJC13, ACAD11, ACKR4, UBA5 and NPHP5 (Figure 1(d)). Among these genes, AKCR4, ACAD11, UBA5 and NAHP shared the same transcript: “ACAD11-NPHP5”. The ACAD11 and ACKR4 of this conserved gene group were absent from the genomes of C. hoffmanni and T. belangeri.

3.3. Phylogenetic Analysis of ACKR Genes in Mammals

After the exclusion of partial and unfinished sequences, 92 sequences were retrieved from 27 mammal species. To determine the phylogenetic relationship of mammal ACKR genes, a rooted ML phylogenetic tree was constructed based on amino acids alignment under the best-fit model JTT+I+G+F. Here, the best-fit model (JTT+I+G+F) for amino acid substitution was selected by ProTest3.2 with discrete gamma distribution in four categories. All parameters (gamma shape = 1.687; proportion of invariants = 0.042) were estimated from the dataset. Tree topology was assessed using MEGA 6 with neighbor joining (NJ), minimum evolution (ME) and maximum parsimony (MP) methods, and it was found to be substantially similar to the ML tree (data not shown). Using the relaxin receptor from Ciona intestinalis as the outgroup of mammal ACKR genes [30], the ML trees showed the ACKRs of mammals to be grouped into four lineages: ACKR1 subfamily, ACKR2 subfamily, ACKR3 subfamily and ACKR4 subfamily (Figure 2). Our data suggested that two major duplications had occurred in mammal lineages. The first duplication led to the emergence of two lineages that evolved into ACKR1 and the ancestor of ACKR2, ACKR3 and ACKR4. The second duplication led to divergence of ACKR2, ACKR3 and ACKR4.

3.4. Adaptive Evolution of ACKR Genes in Mammals

To detect signatures of adaptive evolution over the ACKR1, ACKR2, ACKR3, and ACKR4 codon sequences, four smaller phylogenetic trees were built for each group and the topology used for each site-specific model was implanted using the codeml program of PAML v4.0 package. Parameter estimates and log-likelihood values under model of variable ω ratios among sites were shown in Table 1. In all cases, the LRT did not differ significantly between M1a and M2a, but the LRTs did show significant differences between M0 and M3 and between M7 and M8 for all receptors except the ACKR4 lineage, indicating that M3 and M8 fit the data better. However, no selected sites were detectable in M3. In model M8, one site (154 G) from ACKR1 lineage was found to be a positively selected site, showing a P-value over 99%.


GeneModelParametersModels comparedLRT(2Δl)P-valuedfPositively selected sites (BEB)

ACKR1M0ω0=0.43574M0 vs M3534.020.00004
M3ω0=0.03476 ω1= 0.35759
ω2= 1.15972
p0= 0.23127 p1= 0.47276
p2= 0.29597
M1aω0=0.16327 ω1=1.00000M1a vs M2a01.00002
p0=0.55005 p1=0.44995
M2aω0=0.16327 ω1= 1.00000
ω2=1.00000
p0=0.55005 p1=0.28372
p2=0.16623
M7p =0.53074 q=0.60339M7 vs M821.260.00002154
M8p0=0.85617 p=0.68793
q=1.15451
p1=0.14383 ω=1.45019
ACKR2M0ω0=0.20556M0 vs M3652.480.00004
M3ω0=0.01987 ω1=0.21352
ω2= 0.77475
p0= 0.34607 p1=0.48100
p2= 0.17293
M1aω0=0.12737 ω1=1.00000M1a vs M2a01.00002
p0=0.79265 p1=0.20735
M2aω0=0.12737 ω1= 1.00000
ω2=1.00000
p0=0.79265 p1=0.18741
p2=0.01994
M7p =0.52179 q=1.61923M7 vs M88.880.01172
M8p0=0.97423 p=0.59529
q=2.13409
p1=0.02577 ω=1.39256
ACKR3M0ω0=0.04691M0 vs M3569.640.00004
M3ω0=0.00786 ω1=0.12501
ω2= 0.49733
p0= 0.71583 p1=0.24584
p2=0.03833
M1aω0=0.03806 ω1=1.00000M1a vs M2a01.00002
p0=0.95014 p1=0.04986
M2aω0=0.03806 ω1= 1.00000
ω2=28.52785
p0=0.95014 p1=0.04986
p2=0.00000
M7p =0.24148 q=3.44384M7 vs M87.780.02042
M8p0=0.98766 p=0.27944
q=5.15904
p1=0.01234 ω=1.00000
ACKR4M0ω0=0.12972M0 vs M3416.550.00004
M3ω0=0.03081 ω1=0.32186
ω2=1.16947
p0= 0.65355 p1=0.33146
p2=0.01499
M1aω0=0.08280 ω1=1.00000M1a vs M2a01.00002
p0=0.84629 p1=0.15371
M2aω0=0.08280 ω1= 1.00000
ω2=31.41467
p0=0.84629 p1=0.15371
p2=0.00000
M7p =0.41473 q=2.39665M7 vs M85.540.06262139
M8p0=0.99543 p=0.43468
q=2.65383
p1=0.00457 ω=1.77610

4. Discussion

Chemokines are important regulators of leukocyte migration and play key roles in diverse physiological and pathological immune and inflammatory contexts [28]. In addition to the typical signaling chemokine receptors, a recently discovered subclass of atypical chemokine receptors are characterized by promiscuity of ligand binding and an obvious inability to signal after ligand binding [46]. The inability to signal is largely a consequence of alterations in the canonical DRY motif in the second intracellular loop of the typical chemokine receptors [47]. The motif is responsible for G-protein coupling to the receptors and its absence is the key unifying theme of these atypical receptors [8]. The DRY motif of the ACKRs was identified using multiple sequence alignment. No DRY motif was found in the ACKR1 subgroup. The DKYLEIV motif, DRYLSVT motif, and DRYWAVT motif were identified in the mammal ACKR2, ACKR3, and ACKR4 subgroups, respectively. As in the DRY motif of typical chemokine receptors, the last three amino acids were essential to maintaining the function of signal transduction [8, 48] (Figure 3).

The number of the ACKR genome loci varied across several mammalian genomes. Synteny analysis was performed to determine the reason for the absence of some ACKR genes. ACKR genes formed conserved linkage groups with their adjacent genes across mammalian genomes. The genome sequence datasets of O. anatinus, P. capensis, and C. hoffmanni available in Genbank and Ensemble databases were limited and presented in scaffold form. These sequence data did not meet the requirements for assembly into chromosomes. Partial segments of the conserved ACKR-specific blocks of genes were here identified. In this way, the absence of ACKRs from some of the mammalian genomes investigated here may be attributed to the incomplete information available in genome databases rather than to gene loss during evolution.

The positively selective sites 154 G of ACKR1 is located within the extracellular domain between the fourth trans-membrane and the fifth trans-membrane (Figure 4). This region is responsible for the direct interaction between ACKR1 and the ligands [15]. ACKR1 is mainly expressed in red blood cell. ACKR1 serves as the chemokine buffer for the blood, and it can bind to many different chemokines. Increasing amounts of evidence have shown that ACKR1 possesses a larger ligand binding profile than the other ACKRs [49, 50]. The positively selective site within the binding region of ACKR1 may provide direct evidence for extended ligand binding profile.

Data Availability

The sequences of ACKR family analyzed in this study are deposited in the NCBI and Ensemble databases. The accession number is listed in Supplementary file 1.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Li Pan and Yongguang Zhang designed and wrote the manuscript; Jianliang Lv and Zhongwang Zhang conducted the bioinformatic analyses. All authors read and approved the final manuscript.

Acknowledgments

This work was supported by grants from the National Key Research and Development Program of China (grant numbers 2016YFD0501503 and 2017YFD0501100), the Key Technology R&D Program of Gansu Province of China (grant number 1604NKCA045-2), and the National Pig Industrial System (CARS-36-06B).

Supplementary Materials

Table S1. List of species used for the Multiple Sequences Alignment (MSA). (Supplementary Materials)

References

  1. A. Zlotnik, O. Yoshie, and H. Nomiyama, “The chemokine and chemokine receptor superfamilies and their molecular evolution,” Genome Biology, vol. 7, no. 12, article 243, 2006. View at: Publisher Site | Google Scholar
  2. S. Mélik-Parsadaniantz and W. Rostène, “Chemokines and neuromodulation,” Journal of Neuroimmunology, vol. 198, no. 1-2, pp. 62–68, 2008. View at: Publisher Site | Google Scholar
  3. I. F. Charo and R. M. Ransohoff, “The many roles of chemokines and chemokine receptors in inflammation,” The New England Journal of Medicine, vol. 354, no. 6, pp. 610–621, 2006. View at: Publisher Site | Google Scholar
  4. A. Zlotnik and O. Yoshie, “The chemokine superfamily revisited,” Immunity, vol. 36, no. 5, pp. 705–712, 2012. View at: Publisher Site | Google Scholar
  5. C. L. Sokol and A. D. Luster, “The chemokine system in innate immunity,” Cold Spring Harbor Perspectives in Biology, vol. 7, no. 5, 2015. View at: Publisher Site | Google Scholar
  6. F. Bachelerie, G. J. Graham, M. Locati et al., “New nomenclature for atypical chemokine receptors,” Nature Immunology, vol. 15, no. 3, pp. 207-208, 2014. View at: Publisher Site | Google Scholar
  7. G. J. Graham, M. Locati, A. Mantovani, A. Rot, and M. Thelen, “The biochemistry and biology of the atypical chemokine receptors,” Immunology Letters, vol. 145, no. 1-2, pp. 30–38, 2012. View at: Publisher Site | Google Scholar
  8. C. Cancellieri, A. Vacchini, M. Locati, R. Bonecchi, and E. M. Borroni, “Atypical chemokine receptors: From silence to sound,” Biochemical Society Transactions, vol. 41, no. 1, pp. 231–236, 2013. View at: Publisher Site | Google Scholar
  9. K. Kraft, H. Olbrich, I. Majoul, M. Mack, A. Proudfoot, and M. Oppermann, “Characterization of sequence determinants within the carboxyl-terminal domain of chemokine receptor CCR5 that regulate signaling and receptor internalization,” The Journal of Biological Chemistry, vol. 276, no. 37, pp. 34408–34418, 2001. View at: Publisher Site | Google Scholar
  10. B. B. Damaj, S. R. McColl, K. Neote et al., “Identification of G-protein binding sites of the human interleukin-8 receptors by functional mapping of the intracellular loops,” The FASEB Journal, vol. 10, no. 12, pp. 1426–1434, 1996. View at: Publisher Site | Google Scholar
  11. E. Galliera, V. R. Jala, J. O. Trent et al., “β-arrestin-dependent constitutive internalization of the human chemokine decoy receptor D6,” The Journal of Biological Chemistry, vol. 279, no. 24, pp. 25590–25597, 2004. View at: Publisher Site | Google Scholar
  12. F. Y. Wu, J. Fan, L. Tang, Y. M. Zhao, and C. C. Zhou, “Atypical chemokine receptor D6 inhibits human non-small cell lung cancer growth by sequestration of chemokines,” Oncology Letters, vol. 6, no. 1, pp. 91–95, 2013. View at: Publisher Site | Google Scholar
  13. E. M. Borroni, C. Cancellieri, A. Vacchini et al., “β-arrestin-dependent activation of the cofilin pathway is required for the scavenging activity of the atypical chemokine receptor D6,” Science Signaling, vol. 6, no. 273, pp. S31–S33, 2013. View at: Publisher Site | Google Scholar
  14. A. Rot, “Contribution of Duffy antigen to chemokine function,” Cytokine & Growth Factor Reviews, vol. 16, no. 6, pp. 687–694, 2005. View at: Publisher Site | Google Scholar
  15. G. J. Graham, “D6 and the atypical chemokine receptor family: Novel regulators of immune and inflammatory processes,” European Journal of Immunology, vol. 39, no. 2, pp. 342–351, 2009. View at: Publisher Site | Google Scholar
  16. C. A. H. Hansell, C. E. Hurson, and R. J. B. Nibbs, “DARC and D6: Silent partners in chemokine regulation?” Immunology & Cell Biology, vol. 89, no. 2, pp. 197–206, 2011. View at: Publisher Site | Google Scholar
  17. L. Gardner, A. M. Patterson, B. A. Ashton, M. A. Stone, and J. Middleton, “The human Duffy antigen binds selected inflammatory but not homeostatic chemokines,” Biochemical and Biophysical Research Communications, vol. 321, no. 2, pp. 306–312, 2004. View at: Publisher Site | Google Scholar
  18. R. J. B. Nibbs, E. Kriehuber, P. D. Ponath et al., “The β-chemokine receptor D6 is expressed by lymphatic endothelium and a subset of vascular tumors,” The American Journal of Pathology, vol. 158, no. 3, pp. 867–877, 2001. View at: Publisher Site | Google Scholar
  19. R. J. B. Nibbs, S. M. Wylie, I. B. Pragnell, and G. J. Graham, “Cloning and characterization of a novel murine beta chemokine receptor, D6. Comparison to three other related macrophage inflammatory protein-1alpha receptors, CCR-1, CCR-3, and CCR-5,” The Journal of Biological Chemistry, vol. 272, no. 19, pp. 12495–12504, 1997. View at: Publisher Site | Google Scholar
  20. R. J. B. Nibbs, S. M. Wylie, J. Yang, N. R. Landau, and G. J. Graham, “Cloning and characterization of a novel promiscuous human β-chemokine receptor D6,” The Journal of Biological Chemistry, vol. 272, no. 51, pp. 32078–32083, 1997. View at: Publisher Site | Google Scholar
  21. J. A. Bonini and D. F. Steiner, “Molecular cloning and expression of a novel rat CC-chemokine receptor (rCCR10rR) that binds MCP-1 and MIP-1β with high affinity,” DNA and Cell Biology, vol. 16, no. 9, pp. 1023–1030, 1997. View at: Publisher Site | Google Scholar
  22. R. Bonecchi, M. Locati, E. Galliera et al., “Differential recognition and scavenging of native and truncated macrophage-derived chemokine (macrophage-derived chemokine/CC Chemokine Ligand 22) by the D6 decoy receptor,” The Journal of Immunology, vol. 172, no. 8, pp. 4972–4976, 2004. View at: Publisher Site | Google Scholar
  23. B. Savino, E. M. Borroni, N. M. Torres et al., “Recognition versus adaptive up-regulation and degradation of CC chemokines by the chemokine decoy receptor D6 are determined by their N-terminal sequence,” The Journal of Biological Chemistry, vol. 284, no. 38, pp. 26207–26215, 2009. View at: Publisher Site | Google Scholar
  24. C. A. H. Hansell, C. Schiering, R. Kinstrie et al., “Universal expression and dual function of the atypical chemokine receptor D6 on innate-like B cells in mice,” Blood, vol. 117, no. 20, pp. 5413–5424, 2011. View at: Publisher Site | Google Scholar
  25. F. Bachelerie, A. Ben-Baruch, A. M. Burkhardt et al., “International union of pharmacology. LXXXIX. Update on the extended family of chemokine receptors and introducing a new nomenclature for atypical chemokine receptors,” Pharmacological Reviews, vol. 66, no. 1, pp. 1–79, 2014. View at: Publisher Site | Google Scholar
  26. U. Naumann, E. Cameroni, M. Pruenster et al., “CXCR7 functions as a scavenger for CXCL12 and CXCL11,” PLoS ONE, vol. 5, no. 2, 2010. View at: Publisher Site | Google Scholar
  27. A. Levoye, K. Balabanian, F. Baleux, F. Bachelerie, and B. Lagane, “CXCR7 heterodimerizes with CXCR4 and regulates CXCL12-mediated G protein signaling,” Blood, vol. 113, no. 24, pp. 6085–6093, 2009. View at: Publisher Site | Google Scholar
  28. R. J. B. Nibbs and G. J. Graham, “Immune regulation by atypical chemokine receptors,” Nature Reviews Immunology, vol. 13, no. 11, pp. 815–829, 2013. View at: Publisher Site | Google Scholar
  29. J. Vinet, M. Van Zwam, I. M. Dijkstra et al., “Inhibition of CXCR3-mediated chemotaxis by the human chemokine receptor-like protein CCX-CKR,” British Journal of Pharmacology, vol. 168, no. 6, pp. 1375–1387, 2013. View at: Publisher Site | Google Scholar
  30. J. Zou, A. K. Redmond, Z. Qi, H. Dooley, and C. J. Secombes, “The CXC chemokine receptors of fish: Insights into CXCR evolution in the vertebrates,” General and Comparative Endocrinology, vol. 215, pp. 117–131, 2015. View at: Publisher Site | Google Scholar
  31. A. Louis, N. T. T. Nguyen, M. Muffato, and H. R. Crollius, “Genomicus update 2015: KaryoView and MatrixView provide a genome-wide perspective to multispecies comparative genomics,” Nucleic Acids Research, vol. 43, no. 1, pp. D682–D689, 2015. View at: Publisher Site | Google Scholar
  32. A. Louis, M. Muffato, and H. R. Crollius, “Genomicus: Five genome browsers for comparative genomics in eukaryota,” Nucleic Acids Research, vol. 41, no. 1, pp. D700–D705, 2013. View at: Publisher Site | Google Scholar
  33. K. Tamura, G. Stecher, D. Peterson, A. Filipski, and S. Kumar, “MEGA6: Molecular Evolutionary Genetics Analysis version 6.0,” Molecular Biology and Evolution, vol. 30, no. 12, pp. 2725–2729, 2013. View at: Publisher Site | Google Scholar
  34. A. M. Waterhouse, J. B. Procter, D. M. A. Martin, M. Clamp, and G. J. Barton, “Jalview Version 2-A multiple sequence alignment editor and analysis workbench,” Bioinformatics, vol. 25, no. 9, pp. 1189–1191, 2009. View at: Publisher Site | Google Scholar
  35. G. Talavera and J. Castresana, “Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments,” Systematic Biology, vol. 56, no. 4, pp. 564–577, 2007. View at: Publisher Site | Google Scholar
  36. D. Darriba, G. L. Taboada, R. Doallo, and D. Posada, “ProtTest 3: fast selection of best-fit models of protein evolution,” Bioinformatics, vol. 27, no. 8, pp. 1164-1165, 2011. View at: Publisher Site | Google Scholar
  37. S. Guindon, J. Dufayard, V. Lefort, M. Anisimova, W. Hordijk, and O. Gascuel, “New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0,” Systematic Biology, vol. 59, no. 3, pp. 307–321, 2010. View at: Publisher Site | Google Scholar
  38. R. D. Page, “Visualizing phylogenetic trees using treeview,” Current Protocols in Bioinformatics, vol. 00, no. 1, pp. 6.2.1–6.2.15, 2003. View at: Publisher Site | Google Scholar
  39. M. Suyama, D. Torrents, and P. Bork, “PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments,” Nucleic Acids Research, vol. 34, pp. W609–W612, 2006. View at: Publisher Site | Google Scholar
  40. B. Xu and Z. Yang, “PAMLX: a graphical user interface for PAML,” Molecular Biology and Evolution, vol. 30, no. 12, pp. 2723-2724, 2013. View at: Publisher Site | Google Scholar
  41. M. Anisimova, J. P. Bielawski, and Z. Yang, “Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution,” Molecular Biology and Evolution, vol. 18, no. 8, pp. 1585–1592, 2001. View at: Publisher Site | Google Scholar
  42. W. S. W. Wong, Z. Yang, N. Goldman, and R. Nielsen, “Accuracy and power of statistical methods for detecting adaptive evolution in protein coding sequences and for identifying positively selected sites,” Genetics, vol. 168, no. 2, pp. 1041–1051, 2004. View at: Publisher Site | Google Scholar
  43. Z. Yang, R. Nielsen, N. Goldman, and A. K. Pedersen, “Codon-substitution models for heterogeneous selection pressure at amino acid sites,” Genetics, vol. 155, no. 1, pp. 431–449, 2000. View at: Google Scholar
  44. Z. Yang, “Likelihood ratio tests for detecting positive selection and application to primate lysozyme evolution,” Molecular Biology and Evolution, vol. 15, no. 5, pp. 568–573, 1998. View at: Publisher Site | Google Scholar
  45. M. L. Porter, T. W. Cronin, D. A. McClellan, and K. A. Crandall, “Molecular characterization of crustacean visual pigments and the evolution of pancrustacean opsins,” Molecular Biology and Evolution, vol. 24, no. 1, pp. 253–268, 2007. View at: Publisher Site | Google Scholar
  46. A. Maho, A. Bensimon, G. Vassart, and M. Parmentier, “Mapping of the CCXCR1, CX3CR1, CCBP2 and CCR9 genes to the CCR cluster within the 3p21.3 region of the human genome,” Cytogenetic and Genome Research, vol. 87, no. 3-4, pp. 265–268, 1999. View at: Publisher Site | Google Scholar
  47. R. Nibbs, G. Graham, and A. Rot, “Chemokines on the move: Control by the chemokine "interceptors" Duffy blood group antigen and D6,” Seminars in Immunology, vol. 15, no. 5, pp. 287–294, 2003. View at: Publisher Site | Google Scholar
  48. H. Nomiyama and O. Yoshie, “Functional roles of evolutionary conserved motifs and residues in vertebrate chemokine receptors,” Journal of Leukocyte Biology, vol. 97, no. 1, pp. 39–47, 2015. View at: Publisher Site | Google Scholar
  49. I. Novitzky-Basso and A. Rot, “Duffy antigen receptor for chemokines and its involvement in patterning and control of inflammatory chemokines,” Frontiers in Immunology, vol. 3, p. 266, 2012. View at: Google Scholar
  50. R. Bonecchi and G. J. Graham, “Atypical chemokine receptors and their roles in the resolution of the inflammatory response,” Frontiers in Immunology, vol. 7, p. 224, 2016. View at: Publisher Site | Google Scholar

Copyright © 2018 Li Pan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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