Cellular Physiology and Biochemistry

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Original Paper

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Analysis of the Repertoire Features of TCR Beta Chain CDR3 in Human by High-Throughput Sequencing

Hou X.a · Wang M.b · Lu C.a · Xie Q.a · Cui G.a · Chen J.a · Du Y.c · Dai Y.d · Diao H.a

Author affiliations

aState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, bShenzhen Imuno Biotech Co., Ltd., Shenzhen, cCollege of pharmaceutical science, Zhejiang university of technology, Hangzhou, dClinical medical research center, the Second Clinical Medical College of Jinan University (Shenzhen People's Hospital), Shenzhen, China

Corresponding Author

Hongyan Diao and Yong Dai

State Key Lab Diagnosis and Treatment of Infect Dise, Collaborative Innovation center

for Diagnosis and Treatment of Infectious Diseases, 1st Affiliated Hospital, College of

Medicine, Zhejiang University, 310003 Hangzhou, (China); Clin Med Res center, 2nd

Clinical Medical College of Jinan University (Shenzhen People's Hospital), Shenzhen,

Guangdong, 518020, (China); E-Mail diao.hy@163.com / xm183647168@126.com

Keywords: T-cell receptorPublic sequenceRepertoire featureHigh-throughput sequencing

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Cell Physiol Biochem 2016;39:651-667
https://doi.org/10.1159/000445656

Abstract

Background/Aims: To ward off a wide variety of pathogens, the human adaptive immune system harbors a vast array of T-cell receptors, collectively referred to as the TCR repertoire. Assessment of the repertoire features of TCR is vital for us to deeper understand of immune behaviour and immune response. Methods: In this study, we used a combination of multiplex-PCR, Illumina sequencing and IMGT (ImMunoGeneTics)/HighV-QUEST for a standardized analysis of the repertoire features of TCR beta chain in the blood of healthy individuals, including the repertoire features of public TCR complementarity-determining regions (CDR3) sequences, highly expanded clones, long TCR CDR3 sequences. Results: We found that public CDR3 sequences and high-frequency sequences had the same characteristics, both of them had fewer nucleotide additions and shorter CDR3 length, which were closer to the germline sequence. Moreover, our studies provided evidence that public amino acid sequences are produced by multiple nucleotide sequences. Notably, there was skewed VDJ segment usage in long CDR3 sequences, the expression levels of 10 TRβV segments, 7 TRβJ segments and 2 TRβD segments were significantly different in the long CDR3 sequences compared to the short CDR3 sequences. Moreover, we identified that extensive N additions and increase of D gene usage contributing to TCR CDR3 length, and observed there was distinct usage frequency of amino acids in long CDR3 sequences compared to the short CDR3 sequences. Conclusions: Some repertoire features could be observed in the public sequences, highly abundance clones, and long TCR CDR3 sequences, which might be helpful for further study of immune behavior and immune response.

© 2016 The Author(s) Published by S. Karger AG, Basel

Introduction

The ability of the adaptive immune system to respond to any of the vast number of potential foreign antigens to which a person might be exposed relies on the highly polymorphic receptors expressed by B cells (immunoglobulins) and T cells (TCRs). According to the type of TCR, T cells are classified into αβ T cells and γδ T cells, which both have been identified in the deciduas [1,2]. Mature T cells use the α/β heterodimeric TCR, which consists of 2 polypeptide chains (α and β), each containing one variable and one constant domain, to specific recognize the antigenic peptides in context with major histocompatibility complex (MHC) molecules. Three hyper variable CDRs (CDR1, CDR2, CDR3) have been found in the variable regions of β-chain and α-chain. The antigen specificity of each TCR is largely determined by the CDR3 of the receptor beta chain, as it is generated by rearrangement of multiple V, D, and J gene segments, random trimming and addition of non-template nucleotides at the junction sites (N-diversity mechanisms) greatly increases its diversity further [3,4]. Theoretically, a repertoire of approximately 1018 different TCRs could be generated in humans. As CDR3 interacts most closely with the antigenic peptide, the diversity of CDR3 amino acid sequences provides a measure of T cell diversity in an antigen-selected T cell repertoire. The analysis of TCR usage, especially regarding CDR3 and the TRBV family, is currently an essential tool for deciphering mechanisms of autoimmunity, transplantation, cancer therapy, and infectious diseases [5,6,7].

However, before the emerging of immune repertoire sequencing technology, the complexity and dynamics of the T-cell repertoire remain unknown because the potential repertoire size has made conventional sequence analysis intractable. Nowadays, high-throughput TCR sequencing allows in-depth molecular analysis of T cell clones to get an unprecedented level of detail when examining the T cell repertoire of individuals. In this regard, to comprehend the significance of the vast array of T cell clones identified and their relative quantitative relationship in the normal and disease states, a better understanding of the normal TCR repertoire features is needed. In this study, we used high-throughput TCR sequencing to examining the T cell repertoire of ten normal blood donors, in order to investigate the repertoire features of public TCR sequences, highly abundance clones, long TCR CDR3 sequences, for deeper understanding of immune behavior and immune response, thus formed the basis for possible new clinical implications in immune control of pathogens, personalized medicine, and vaccine design.

Materials and Methods

Clinical samples

Peripheral blood samples were collected from 10 healthy donors who tested negative for anti-HBsAg (Hepatitis B surface antigen) antibodies and anti-HIV antibodies and exhibited no clinical or laboratory signs of other infectious diseases or immunological disorders. Among these 10 healthy donors, five were males and five were females. The patient cohort had a mean age of 32.16 ± 13.56 years, ranging from 19 to 45 years. PBMCs were prepared from whole blood treated with 5 ml of fresh EDTA-K2 anticoagulate by a Ficoll-Hypaque centrifugation (Pharmacia Biotec, Roosendaal, The Netherlands) [8]. This study was conducted in accord with the tenets ofthe Declaration of Helsinki and was approved by the Ethics Committee ofthe First Affiliated Hospital, College of Medicine, Zhejiang University (Ref No 2015-313).

T cell isolation and DNA extraction

Informed consent was obtained from blood donors. Peripheral blood T cells were isolated with anti-human CD3 magnetic beads according to manufacturer protocol (Miltenyi Biotec, Bergisch, Gladbach, Germany) [9]. T-cell purity was shown to be greater than 90% (date not shown), as determined with flow cytometry using mouse anti-human antibodies CD3-PE (BD Biosciences, San Jose, CA, USA). DNA was prepared from 0.5 - 2 × 106 T cells from each sample, which was sufficient for analyzing the diversity of TCR β-chain. DNA was extracted from T cells using GenFIND DNA (Agencourt/Beckman Coulter, Brea, CA, USA) extraction kits following the manufacturer's instructions.

Multiplex-PCR amplification of the TCR-β CDR3 region

The TCR-β CDR3 region was defined according to the criteria of the International Immunogenetics collaboration, which started with the second conserved cysteine encoded by the 3' position of the Vβ gene segment and ended with the conserved phenylalanine encoded by the 5' position of the Jβ gene segment [10]. To generate a template library for analysis using Genome Analyzer, multiplex-PCR was designed to amplify rearranged TCR-β CDR3 regions from genomic DNA using a set of 32 forward primers that were each specific to a functional TCR-Vβ segment and 13 reverse primers that were each specific to a TCR-Jβ segment (Table 1). The forward and reverse primers contained universal forward and reverse primer sequences, respectively, at their 5'-ends, which were compatible with GA2 cluster station solid-phase PCR. PCRs (50 µL) were set up at 1 µM VF pool (22 nM for each unique TCR Vβ F primer), 1 µM JR pool (77 nM for each unique TCR Jβ R primer), l×QIAGEN Multiplex PCR master mix, 10% Qsolution (QIAGEN), and 16 ng/µL gDNA. The following thermal cycling conditions were used in a PCR Express thermal cycler (Hybaid): 1 cycle at 95°C for 15 minutes, 30 cycles at 94°C for 30 seconds, 59°C for 30 seconds, and 72°C for 1 minute, followed by 1 cycle at 72°C for 10 minutes. To sample millions of rearranged TCR CDR3 loci, 12 to 20 wells of PCR were performed for each library [10]. After amplification and separation by agarose gel electrophoresis, products were purified using a QIAquick PCR Purification Kit. The final library was quantified in two ways: by determining the average molecule length using an Agilent 2100 Bioanalyzer (Agilent DNA 1000 Reagents) and by real-time quantitative PCR (qPCR; TaqMan Probe). Libraries were amplified using cBot to generate clusters on the flow cell, and an amplified flow cell was pair-end sequenced using a HiSeq2000 instrument, generally using a read length of 100 bp.

Table 1

TRB V/J primers

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High-throughput sequencing and data analysis

PCR products were sequenced using an Illumina Genome Analyzer. In the sequencing process, we used a well-defined human DNA fragment (internal control) in the control lane to monitor the sequencing quality. The DNA fragment is a small fragment (170 - 800 bp), which enables quick alignment and estimation of error rates. In addition, illumina cluster generation algorithms were optimized around a balanced representation of A, T, G, and C nucleotides. The quality of HiSeq sequencing ranged from scores of 0 to 40. This quality will be used in the criteria for filtering out low quality reads. The relationship between sequencing error rate (E) and sequencing quality (sQ) is shown in the below formula:

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In addition, the following table (Table 2) shows some common situations of sequencing error rate and sequencing quality correspondence.

Table 2

Correspondence between solexa sequencing quality and error rate

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First, we filtered the raw data, including adapter contamination. Reads with an average quality score lower than 15 (based on the Illumina 0-41 quality system, the sequencing error rate is 3% when the quality score is 15) were removed, and a threshold for the proportion of N bases was set as less than 5% (sequences with higher values were removed). Next, a few bases with low quality (lower than 10) were trimmed; the quality score was expected to be greater than 15 after trimming and the remaining sequence length was expected to be greater than 60 nt. After filtering, pair-end (PE) read pairs were merged into a single contig (from contiguous) sequence by two steps: 1) by aligning tail regions of two sequences and assessing the identity (using software developed by BGI, COPE vl.1.3) or sequences with at least 10 bp overlap and the overlapping section showing at least 90% base match; and 2) as different primers might result in sequences of different lengths, some sequences might be very short (less than 100 bp), so all bases in these sequences would be analyzed and such reads would be merged by aligning the head part of the sequence (using software developed by BGI, FqMerger). In this manner, merged contig sequences and a length distribution plot were obtained.

For alignment, miTCR (developed by MiLaboratory; http://mitcr.milaboratory.com/downloads/) was used. This program has an automated adjustment mechanism for errors that are introduced by sequencing and PCR and can provide statistical data for alignments, such as CDR3 expression and indel (insertion and deletion). After alignment, the following method was used for sequence structural analysis: (1) the number of each nucleotide and the proportion at each position was analyzed; (2) according to the final position of the V gene, the start site of the D gene, the end site of the D gene, and the start site of the J gene after alignment were determined and the indel that were introduced during V(D)J rearrangement were identified; and (3) nucleotides were translated into amino acids. According to the identity of each sequence after alignment, the frequency of expression for each clone could be calculated. The expression of each distinct DNA sequence, amino acid sequence, and V-J combination was also identified [11].

Statistical analysis

Statistical significance was calculated using the independent sample T test using SPSS19. P values < 0.05 were considered significant.

Results

To investigate TCR repertoire features at sequence-level resolution, we generated high-resolution maps of repertoires of T-cell receptor β-chains (TCR β) in ten healthy individuals, based on massive parallel sequencing (TCR-seq) of T-cell DNA (Table 3) [12]. We collected an average of 17.9 million reads, which met our quality requirements. After data integration of ten samples, about 8.50 × 105 unique (nonredundant) nucleotide sequences were obtained, which corresponded to about 7.77 × 105 unique (nonredundant) TCR amino acid sequences. Our analysis here was focused mainly on the nucleotide and amino acid sequences of the CDR3, which was the most diverse region of the TCR molecule and was associated with antigen epitope recognition.

Table 3

TCRβ CDR3 sequence statistics

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The repertoire features of public CDR3 sequences

We found that on average, any two individuals in our data set shared 4.85% ± 2.50% of their DNA sequences and 12.17% ± 0.81% of their expressed CDR3 amino acid sequences (nonredundant sequences for each subject). Hence, we combined all ten healthy individuals for further analysis. We binned unique CDR3 nucleotide sequences and amino acid sequences according to the number of subjects in which they occurred. Most of the CDR3 nucleotide sequences (∼ 97.75% of all nucleotide sequences) and amino acid sequences (∼ 94.57% of all amino acid sequences) were found in only one subject. However, hundreds of sequences were highly shared among individual; 36 DNA sequences and 178 aa sequences were shared by >80% (n>8) of the healthy subjects. Notably, we found 15 CDR3 DNA (∼1.77 × 10-3% of all nucleotide sequences) and 30 amino acid sequences (∼3.86 × 10-3% of all amino acid sequences) that were shared by all 10 healthy individuals [12]. Thus, CDR3 amino acid sequences had a wide range of sharing levels, from private to highly public. A summary of the number of CDR3 sequences according to the degree of sharing can be seen in Table 4. To make our "public" TCR sequence argument much stronger, we analyzed whether the 15 CDR3 DNA and 30 amino acid sequences were shared by a new set of 8 individuals. The result of data showed that these DNA and amino acid sequences were also highly shared among the new set of individuals (Table 5). It was worth noting among of these 15 nucleotide sequences, TGC GCC AGG GTC CCC AGG GCT GTG CGA ACA CCG GGG AGC TGT TTT TT, and TGT GCT AAC TAT GGC TAC ACC TTC, were shared by all new set of 8 individuals. Among of these 30 nucleotide sequences, CANYGYTF, CARVPRAV∼NTGELFF, and CAS∼YF were shared by all new set of 8 individuals. Therefore, the "public" TCR sequence we were discussing here was reliable.

Table 4

A summary of the number of CDR3 sequences according to the degree of sharing

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Table 5

The sharing extent of the 15 CDR3 DNA and 30 amino acid sequences in a new set of 8 individuals

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We defined a sequence as "private" if it appeared in only one subject in our data set, as "relatively private" if it was shared by two to five subjects, as "relatively public" if shared by 6-9 subjects, and as "public" if shared by all 10 subjects.

Public ammo acid sequences are produced by multiple nucleotide sequences

Codon degeneracy enables multiple nucleotide sequences to encode for the same amino acid sequence. Accordingly, a major indicator of TCR sequence production efficiency is the number of nucleotide sequences that can encode for that particular amino acid sequence [13,14,15,16,17]. In this study, our analysis of ten healthy individuals revealed public amino acid sequences are produced by multiple nucleotide sequences (Fig. 1). In all ten healthy individuals, significant positive correlations were found between the sharing level and the number of encoding nucleotide sequences. Private CDR3 amino acid sequences were encoded on average by one nucleotide sequence, the "relatively public" shared by 9 subjects were encoded by 11.2 nucleotide sequences on average, and the public sequences were encoded by 9.6 nucleotide sequences on average.

Fig. 1

Relationship between the number of nucleotide sequences observed to encode each unique amino acid sequence and the number of subjects in which a TCRβ amino acid sequence was detected. Sharing levels is plotted along the x-axis and the mean number of unique nucleotide sequences coding for a amino acid sequence is plotted along the y-axis.

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Table 6 showed an example of multiple nucleotide sequences (n=88) encode for a CDR3 amino acid sequences. This amino acid sequences (CASSISQDTEAFF) was presented in 7 subjects, showed high sharing level. All of these 88 unique nucleotide sequences could be found in the sample S2. In the sample S2, three nucleotide sequences (TGT GCC AGT AGT ATC TCC CAA GAC ACT GAA GCT TTC TTT, TGT GCC AGT AGT ATA TCT CAG GAC ACT GAA GCT TTC TTT, and TGT GCC AGT AGT ATA AGT CAA GAC ACT GAA GCT TTC TTT) were present at high frequencies. It was interesting to note thatthe three nucleotide sequences were highly shared among individual. Notably, the rest of sequences (85 nucleotide sequences) were present at very low frequencies, and can be only found in the sample S2. In order to explore whether there were sequencing errors, or evolutionary TCR repertoire in the process of recognizing target antigen, we further analyzed these 85 nucleotide sequences. Compared to either one of the three high abundant clones, base differences in the 85 nucleotide sequences were found mainly in the 15 to 21 loci (Fig. 2). This region contains Vβ-Dβ junction, D segment and Dβ-Jβ junction, which is a highly variable region. As we know, sequencing errors generally exist in both ends of the sequence, however, this region is located in the middle of these sequences. In addition, although some sequences were only 1 and 2 reads, but some of the sequences were fairly high, such as two sequences belonged to clones having > 100 reads. Therefore, these observed CDR3 sequences were likely to have undergone evolution processes greater than process errors.

Table 6

Multiple nucleotide sequences encoding an amino acid sequence. The 88 unique nucleotide sequences that code for the amino acid sequence CASSISQDTEAFF are shown, along with the number of T cells that bear each of the unique nucleotide sequences in the sample NC-2. The subject in which the nucleotide sequences were found, the VDJ segment combination of each of the sequences and the number of nucleotide additions in them are also shown

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Fig. 2

Sequence variation analysis. Take sequence 1 (Left), sequence 2 (Center), and sequence 3 (Right) as the reference sequence respectively, the remaining 87 sequences are compared with it. The representation of nucleotide identical site by means of a point, variable nucleotide position are displayed.

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Public sequences differ from private sequences in CDR3 length

Further analysis of the public CDR3 sequences revealed other distinct characteristics. Figure 3A showed the mean CDR3 lengths in each sharing category. The more public CDR3 amino acid sequences tended to be shorter on average by about one nucleotide and, in addition, showed a significantly lower number of nucleotide insertions in the VD and DJ junctions (Fig. 3B); these suggested that public sequences in humans were closer to germ-line configurations. These observations were in accordance with previous report on mice [18].

Fig. 3

The mean CDR3 lengths (A) and the mean number of nt insertions summed over the V-D and D-J junctions (B) in each sharing category.

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The repertoire features of T cell with different clonotype abundance

Clonotype abundance distributions are another feature that provides an overall view of repertoire composition. As we know, the high abundance clones may be the result of physiological responses to environmental antigens or pathogens. The expression abundance of each individual clone was based on its unique CDR3 sequence frequency within a sample. Through statistical analysis, we found that clonotype abundance varied from one to a maximum of 2836517. In this study, we defined clones with a frequency of more than 0.1% of the analyzed TCR to be high abundance clones. Only 4.18 × 10-2% (range 1.16 × 10-2%-7.32×10-2%) of the clones was expanded beyond this value. In addition, we found that higher abundance clones with fewer insertions had receptor sequences that were closer to the germline sequence (Fig. 4A). This was also confirmed by the findings that the length of high abundance clones was relatively short than that of low-frequency clones (Fig. 4B). Despite this significant inverse correlation, the high abundance clones with more insertions than that of the CDR3 sequences with clonotype frequency among 0.01 - 0.1%. Thus, TCR sequences with no or few nucleotide additions should be easily made and commonly generated; conversely, TCR sequences should become progressively more difficult to make and relatively less frequent as the required number of nucleotide additions increases. Moreover, the sharing of TCRβ sequences between individual could be predicted by the frequency of these sequences within subject. The mean frequency of private CDR3 sequences was 8.30 × 10-6, whereas the mean frequency of the sequences shared by six subjects was 2.72 × 10-5.

Fig. 4

Relative abundance of unique TCRβ CDR3 sequences is inversely correlated with total junctional insertions (A) and the CDR3 sequence length (B).

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The repertoire features of long CDR3 sequences

The length of the TCR CDR3 loop is an important determinant of diversity in the T cell repertoire, as longer loops not only have greater potential for sequence variation but also can potentially reach into narrow antigenic pockets [19,20]. In this study, we investigated the characteristics of different CDR3 nucleotide length in human peripheral blood lymphocytes of ten normal volunteers by the high-throughput sequencing technique.

Extensive N additions contribute to longer CDR3 length

For the unique CDR3 sequence (regardless of sequence abundance), the average length of the CDR3 region was 43.9 nt, and the average length of total insertions in the Vβ-Dβ and Dβ-Jβ junctions was 8.4 nt. First, we divided the variable CDR3 lengths into 5 grades (16-34, 35-52, 53-70, 71-88 and 89-106 nt). We found that the number of insertions at the Vβ-Dβ and Dβ-Jβ junctions were the features that were most closely correlated with CDR3 length, and demonstrated a positive correlation (Fig. 5A). The shortest CDR3 sequences (16-34nt) contained on average 3.41 N additions compared with 56.05 N additions in the longest CDR3 sequences (89-106 nt). Although the vast majority of TCRβ and IgH rearrangements have been shown to have a canonical V-D-J structure, some studies have reported the potential use of tandem D gene segments giving rise to functional V-D-D-J rearrangements in mice and humans [21,22,23,24], whereas others concluded there is no evidence of tandem D gene usage in humans [25,26]. In this study, we found that there were tandem D gene segments in some of the TCRβ CDR3 sequences, and also observed an increase in tandem D gene usage in long CDR3 loops compared with short CDR3 sequences (Fig. 5B), except the length category 35-52 nt. In addition, the most prevalent CDR3 lengths was 35-52 nt, which accounted for 88.99 ± 1.06% of the unique CDR3 sequence present (Fig. 6A). Whereas the longest CDR3 sequences (89-106 nt) accounted for only 5.95 × 10-2 ± 3.53 × 10-2% of the unique CDR3 sequence present. Moreover, it was noteworthy that CDR3 length was correlated with the sequence abundance, and demonstrated an inverse correlation (Fig. 6B). On average, the number of T cells bearing the shortest CDR3 sequences (16-34 nt) was 20 times higher than that of longest CDR3 sequences.

Fig. 5

Extensive N additions (A) and increase of D gene usage (B) contribute to longer CDR3 length. CDR3 length is plotted along the x-axis and the number of total nucleotides insertions or the relative frequencies of D gene usage is plotted along the y-axis.

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Fig. 6

Length distribution of the total unique CDR3 sequence presented in ten healthy individuals (A) and the mean expression abundance of T cell with different CDR3 length (B).

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There is skewed VDJ segment usage in long CDR3 sequences

Then, to identify whether there was a skewed and restricted VDJ segment usage in longer CDR3 sequences, we compared the TRβV, TRβJ and TRβD repertoires between the short CDR3 sequences and long CDR3 sequences. For this analysis, we defined "long CDR3 loops" as being > 52 nt in length, as this was well above the mean CDR3 length (43.9 nt). The results showed that the expression levels of 10 TRβV segments, 7 TRβJ segments and 2 TRβD segments were significantly different in the long CDR3 sequences compared to the short CDR3 sequences by the T-test and Bonferroni correction (Fig. 7). For the 50 Vβ gene segments, TRBV4-1 (P = 1.52 × 10-4), TRBV4-2 (P = 2.10 × 10-4), TRBV6-7 (P = 2.52 × 10-5), TRBV14 (P = 1.96 × 10-4), TRBV12-5 (P = 2.10 × 10-4) showed higher usage in long CDR3 sequences, while TRBV6-1 (P = 3.02 × 10-5), TRBV18 (P = 7.84 × 10-7), TRBV20-1 (P = 6.28 × 10-6), TRBV29-1 (P = l.00×10-4), TRBV30 (P = 1.92 × 10-7) showed significantly lower usage when compared with the short CDR3 sequences. For the 13 Jβ gene segments, long CDR3 sequences demonstrated higher frequency of TRBJ2-2 (P = 3.30 × 10-6), TRBJ2-3 (P = 2.47×10-5), TRBJ2-4 (P = 5.34 × 10-8), TRBJ2-6 (P = 1.82 × 10-13) and lower frequencies of TRBJ1-1 (P = 9.82 × 10-7), TRBJ1-2 (P = 0.004), TRBJ2-7 (P = 7.34 × 10-6) in comparison with the short CDR3 sequences. The levels of other Vβ and Jβ segments appeared to be consistent between the long CDR3 sequences and the short CDR3 sequences. In addition, the long CDR3 sequences preferential use of TRBD2 segment compared to the short CDR3 sequences (59.74% Vs 51.32%, P = 2.48 × 10-6). The reason may be that TRBD2 gene length is longer than TRBD1 gene. TRBD2 gene is composed of 16 nucleotides (GGGACTAGCGGGGGGG), while TRBD1 gene is composed of 12 nucleotides (GGGACAGGGGGC).

Fig. 7

Comparison of the relative frequencies of each TRBV gene (A), TRBJ gene (B) and TRBD gene (C) family in short CDR3 sequences and long CDR3 sequences. Bars and error bars indicate the respective mean frequencies and standard deviations of the results from 10 individuals. Significant differences were statistically analyzed by the T-test and Bonferroni correction (p < 0.05).

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There is distinct usage frequency of amino acids in long CDR3 sequences

Using this data, we also examined the patterns of nucleotide composition, amino acid usage in CDR3 intervals by the T-test and Bonferroni correction. We found that the usage frequency of individual nucleotides was both remarkably consistent within individuals (long CDR3 sequences and short CDR3 sequences) and between individuals (Fig. 8A). However, for the 20 amino acids, Glycine (G, P = 9.72 × 10-8), Valine (V, P = 1.61 × 10-4), Leucine (L, P = 4.92 × 10-9), Isoleucine (I, P = 4.92 × 10-9), Proline (P, P = 2.51 × 10-7), Threonine (T, P = 1.68 × 10-4), Methionine (M, P = 2.74 × 10-4), Asparagine (N, P = 7.56 × 10-7), Aspartic acid (D, P = 1.84 × 10-5), Lysine (K, P = 6.96 × 10-5), Arginine (R, P = 3.37 × 10-9) showed higher usage in long CDR3 sequences, while Alanine (A, P = 4.16 × 10-10), Phenylalanine (F, P = 3.44 × 10-12), Tyrosine (Y, P = 3.77 × 10-6), Cysteine (C, P = 6.15 × 10-15), Glutamine (Q, P = 1.60 × 10-8), Glutamic acid (E, P = 3.20 × 10-8), showed significantly lower usage when compared with the short CDR3 sequences (Fig. 8B).

Fig. 8

Comparison of the patterns of nucleotide composition (A), amino acid usage (B) between short CDR3 sequences and long CDR3 sequences by the T-test and Bonferroni correction. S1-L is the abbreviation of "The long CDR3 sequences in the Sample 1." S1-S is the abbreviation of "The short CDR3 sequences in the Sample 1." Similarly, the abbreviation applied to other sample.

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Discussion

An essential characteristic of T lymphocytes is their ability, as a population, to recognize an enormous number of peptide antigens. This capability is essential to the function of the adaptive immune system and is attributable to the diversity of the TCR they express. In this study, we used a combination of multiplex-PCR, Illumina sequencing and IMGT (ImMunoGeneTics) /HighV-QUEST for a standardized analysis of TCR beta chain repertoire features, including the repertoire features of public TCR sequences, high abundance clones, long TCR CDR3 sequences. Public TCR DNA sequences and amino acid sequences, which are shared between different individuals, have often been regarded as an unusual phenomenon due to the apparent low probability of the same TCR being observed in multiple individuals and responding to the same antigenic epitope. Here, we found hundreds of sequences were highly shared among individual. DNA sequences and 178 amino acid sequences were shared by > 80% (n > 8) of the healthy subjects. Notably, 15 CDR3 DNA and 30 amino acid sequences were shared by all 10 healthy individuals. The sharing level of these "public" TCR sequences was verified in another group of healthy individuals. In the present study, we also showed that sharing was much more extensive at the amino acid than at the nucleotide level. This is consistent with selective pressure on TCRs, whereby a TCR amino acid sequence with suitable antigen binding characteristics may be encoded, in different cells, by different nucleotide sequences and selected independently [27]. As reported in previous studies of public TCR repertoires [13,18,27], we also observed that for each donor, shared amino acid sequences were encoded by a much larger diversity of nucleotide sequences than were unshared amino acid sequences. The 88 nucleotide sequences encoding the highly shared amino acid sequence (CASSISQDTEAFF) could be used to illustrate this point. This probably represented the peripheral selection of this sequence during the immune history of these individuals to a previously undefined pathogen/stimulus. In addition, shared TCRβ nucleotide sequences had fewer nucleotide additions and shorter mean CDR3 length, which were closer to the germline sequence. Moreover, we found high-frequency TCRβ CDR3 sequences had fewer insertions and shorter mean CDR3 length as well. Thus, even with unbiased random rearrangement events, the probability of generating some nucleotide and amino acid sequences is higher than others [14]. A rearrangement event that involves few random nucleotide additions is likely to occur more often than an event requiring many nucleotide additions. Taken together, these findings explained the results of our and previous studies that higher-frequency clonotypes were more commonly shared between individuals. The number of different nucleotide sequences encoding a TCRβ amino acid sequence, combined with the estimated minimal number of nucleotide additions, and its clonotype abundance appear to predict the extent of sharing of this sequence.

In a previous study, the relationship between TCR sharing and convergent rearrangement was addressed by developing a computer simulation of unbiased V(D)J rearrangement to estimate the relative frequency with which different TCRβ amino acid or nucleotide sequences would be produced. They found that although some near-germ-line-encoded nucleotide sequences were produced repeatedly by the same V(D)J rearrangement mechanisms, many sequences were frequently produced with numerous nucleotide additions by multiple random rearrangement events because there were many independent ways to make them. Similarly, some amino acid sequences were frequently produced because they were encoded by highly recurrent nucleotide sequences, and/or rich in amino acids that are germ-line-encoded and/or have high codon degeneracy [13]. Although convergent rearrangement provides a mechanistic explanation for TCR sharing, there are other factors, such as TCR affinity for the pMHCI complex and stochastic events, might be plausible explanations for "public" T-cell clones [13]. Previous studies have observed a highly statistically significant association between numbers of shared sequences and shared HLA class I alleles [25]. Moreover, there are data suggested that public TCR usage can also reflect presentation of sterically demanding, bulged ligand structures and does not solely arise from the necessity to recognize featureless pMHC-I surfaces [28,29]. Understanding the basis of public T cell responses not only is important for our understanding of immune repertoire and diversity and hierarchy, but it also has implications for immune control of pathogens and vaccine design [12,30,31]. Further studies are warranted to investigate the relationship between TCR α/β repertoire diversity and pathogen resistance, and identify whether there is a minimal level of repertoire diversity required for protection against the spectrum of commonly encountered pathogens.

The distribution of CDR3 sequence lengths is another feature that provides an overall view of repertoire composition. Different rearrangements may lead to variable CDR3 lengths, and the characteristics of TCR clonality among different subfamilies can be determined by measuring the lengths of CDR3 subfamilies. Biases in CDR3 length are often observed in epitope-specific T cell repertoires. In this study, we investigated the characteristics of different CDR3 nucleotide length in human T cell of ten normal volunteers by the high-throughput sequencing technique. We found that extensive N additions and increase of D gene usage contribute to TCR CDR3 length. This confirmed the results from Larimore et al. [24]. They also found Tandem D gene segments, long D gene segments, and extensive N additions contribute to IgH CDR3 length. Moreover, it was noteworthy that the usage frequency of 10 TRβV segments, 7 TRβJ segments and 2 TRβD segments were significantly different in the longer CDR3 sequences compared to the short CDR3 sequences, which suggested that there was skewed VDJ segment usage in long CDR3 sequences. In addition, we found that the usage frequency of individual nucleotides was both remarkably consistent between long CDR3 sequences and short CDR3 sequences. However, there was distinct usage frequency of amino acids in long CDR3 sequences compared to short CDR3 sequences. It was also interesting to note that the most prevalent CDR3 lengths were 35-52 nt, and the CDR3 length displayed an inverse correlation with the expansion degree. Relevant to this investigation, many lines of evidence suggested that long IgH CDR3 loops were associated with self-reactive or polyreactive Abs [21,32,33,34], and this might be a major reason for their removal from the repertoire during B cell development. In addition, some studies reported shorter CDR3 lengths in relation to CD4 single-positive cells in the thymus [35]. The details regarding differences and functions of the longer and shorter CDR3 groups require further research.

In conclusion, we demonstrated a successful approach for determining the repertoire features of TCR beta chain at sequence-level resolution. Future investigation should be aimed at better understanding the TCR repertoire characteristic in health and disease, including infections, cancer, autoimmunity thus unlocking the potential to optimize T cell clonotype selection from the available repertoire for therapeutic benefit.

Abbreviations

NGS (next generation sequencing); TCR (T cell receptors); CDR (complementarity-determining regions); MHC (major histocompatibility complex); V (variable); D (diversity); J (joining); PBMC (peripheral blood mononuclear cell); Ds (Simpson index of diversity); H (Shannon-Wiener index); HEC (highly expanded clone).

Acknowledgements

This work was supported by funds received from the National Natural Science Foundation of China (No. 81271810, 81571953), 12-5 state S&T Projects for infectious diseases (2012ZX10002-007), Doctoral Fund of Ministry of Education of China (20120101110009), and Zhejiang medical science and technology project (2015118507).

Disclosure Statement

The authors have stated explicitly that there are no conflicts of interest in connection with this article.


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References

  1. Psarra K, Kapsimali V, Tarassi K, Dendrinos S, Athanasiadis T, Botsis D: TCR gammadelta + T lymphocytes in unexplained recurrent spontaneous abortions. Am J Reprod Immunol 2001;45:6-11.
  2. Lachapelle MH, Miron P, Hemmings R, Roy DC: Endometrial T, B, and NK cells in patients with recurrent spontaneous abortion. Altered profile and pregnancy outcome. J Immunol 1996;156:4027-4034.
  3. Pannetier C, Cochet M, Darche S, Casrouge A, Zoller M, Kourilsky P: The sizes of the CDR3 hypervariable regions of the murine T-cell receptor beta chains vary as a function of the recombined germ-line segments. Proc Natl Acad Sci USA 1993;90:4319-4123.
  4. Cabaniols JP, Fazilleau N, Casrouge A, Kourilsky P, Kanellopoulos JM: Most alpha/beta T cell receptor diversity is due to terminal deoxynucleotidyl transferase. J Exp Med 2001;194:1385-1390.
  5. Gillespie GM, Stewart-Jones G, Rengasamy J, Beattie T, Bwayo JJ, Plummer FA, Kaul R, McMichael AJ, Easterbrook P, Dong T, Jones EY, Rowland-Jones SL: Strong TCR conservation and altered T cell cross-reactivity characterize a B*57-restricted immune response in HIV-1 infection. J Immunol 2006;177:3893-3902.
  6. Trautmann L, Rimbert M, Echasserieau K, Saulquin X, Neveu B, Dechanet J, Cerundolo V, Bonneville M. Selection of T cell clones expressing high-affinity public TCRs within Human cytomegalovirus-specific CD8 T cell responses. J Immunol 2005;175:6123-6132.
  7. Du JW, Gu JY, Liu J, Cen XN, Zhang Y, Ou Y, Chu B, Zhu P: TCR spectratyping revealed T lymphocytes associated with graft-versus-host disease after allogeneic hematopoietic stem cell transplantation. Leuk Lymphoma 2007;48:1618-1627.
  8. VanderBorght A, Geusens P, Vandevyver C, Raus J, Stinissen P: Skewed T-cell receptor variable gene usage in the synovium of early and chronic rheumatoid arthritis patients and persistence of clonally expanded T cells in a chronic patient. Rheumatology (Oxford) 2000;39:1189-1201.
  9. Dziubianau M, Hecht J, Kuchenbecker L, Sattler A, Stervbo U, Rödelsperger C, Nickel P, Neumann AU, Robinson PN, Mundlos S, Volk HD, Thiel A, Reinke P, Babel N: TCR repertoire analysis by next generation sequencing allows complex differential diagnosis of T cell-related pathology. Am J Transplant 2013;13:2842-2854.
  10. Robins HS, Campregher PV, Srivastava SK, Wacher A, Turtle CJ, Kahsai O, Riddell SR, Warren EH, Carlson CS: Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. Blood 2009;114:4099-4107.
  11. Hou X, Lu C, Chen S, Xie Q, Cui G, Chen J, Chen Z, Wu Z, Ding Y, Ye P, Dai Y, Diao H. High throughput sequencing of T cell antigen receptors reveals a conserved TCR repertoire. Medicine (Baltimore) DOI:10.1097/MD.0000000000002839.
  12. Sui W, Hou X, Zou G, Che W, Yang M, Zheng C, Liu F, Chen P, Wei X, Lai L, Dai Y: Composition and variation analysis of the TCR β-chain CDR3 repertoire in systemic lupus erythematosus using high-throughput sequencing. Mol Immunol 2015;67:455-464.
  13. Venturi V, Kedzierska K, Price DA, Doherty PC, Douek DC, Turner SJ, Davenport MP: Sharing of T cell receptors in antigen-specific responses is driven by convergent recombination. Proc Natl Acad Sci 2006;103:18691-18696.
  14. Venturi V, Price DA, Douek DC, Davenport MP: The molecular basis for public T-cell responses? Nat Rev Immunol 2008;8:231-238.
  15. Venturi V, Quigley MF, Greenaway HY, Ng PC, Ende ZS, Mclntosh T, Asher TE, Almeida JR, Levy S, Price DA, Davenport MP, Douek DC: A mechanism for TCR sharing between T cell subsets and individuals revealed by pyrosequencing. J Immunol 2011;186:4285-4294.
  16. Quigley MF, Greenaway HY, Venturi V, Lindsay R, Quinn KM, Seder RA, Douek DC, Davenport MP, Price DA: Convergent recombination shapes the clonotypic landscape of the naive T-cell repertoire. Proc Natl Acad Sci USA 2010;107:19414-19419.
  17. Li H, Ye C, Ji G, Wu X, Xiang Z, Li Y, Cao Y, Liu X, Douek DC, Price DA, Han J: Recombinatorial biases and convergent recombination determine interindividual β sharing in murine thymocytes. J Immunol 2012;189:2404-2413.
  18. Madi A, Shifrut E, Reich-Zeliger S, Gal H, Best K, Ndifon W, Chain B, Cohen IR, Friedman N: T-cell receptor repertoires share a restricted set of public and abundant CDR3 sequences that are associated with self-related immunity. Genome Res 2014;24:1603-1612.
  19. Wesolowski J, Alzogaray V, Reyelt J, Unger M, Juarez K, Urrutia M, Cauerhff A, Danquah W, Rissiek B, Scheuplein F, Schwarz N, Adriouch S, Boyer O, Seman M, Licea A, Serreze DV, Goldbaum FA, Haag F, Koch-Nolte F: Single domain antibodies: promising experimental and therapeutic tools in infection and immunity. Med Microbiol Immunol (Berl) 2009;198:157-174.
  20. Xiang SH, Farzan M, Si Z, Madani N, Wang L, Rosenberg E, Robinson J, Sodroski J: Functional mimicry of a human immunodeficiency virus type 1 coreceptorbya neutralizing monoclonal antibody. J Virol 2005;79:6068-6077.
  21. Klonowski KD, Primiano LL, Monestier M: Atypical VH-D-JH rearrangements in newborn autoimmune MRL mice. J Immunol 1999;162:1566-1572.
  22. Souto-Carneiro MM, Longo NS, Russ DE, Sun HW, Lipsky PE: Characterization of the human Ig heavy chain antigen binding complementarity determining region 3 using a newly developed software algorithm, JOINSOLVER. J Immunol 2004;172:6790-6802.
  23. Briney BS, Willis JR, Hicar MD, Thomas JW, Crowe JE: Frequency and genetic characterization of V(DD)J recombinants in the human peripheral blood antibody repertoire. Immunology 2012;137:56-64.
  24. Larimore K, McCormick MW, Robins HS, Greenberg PD: Shaping of human germline IgH repertoires revealed by deep sequencing. J Immunol 2012;189:3221-3230.
  25. Corbett SJ, Tomlinson IM, Sonnhammer EL, Buck D, Winter G: Sequence of the human immunoglobulin diversity (D) segment locus: a systematic analysis provides no evidence for the use of DIR segments, inverted D segments, "minor" D segments or D-D recombination. J Mol Biol 1997;270:587-597.
  26. Ohm-Laursen L, Nielsen M, Larsen SR, Barington T: No evidence for the use of DIR, D-D fusions, chromosome 15 open reading frames orVH replacement in the peripheral repertoire was found on application of an improved algorithm, JointML, to 6329 human immunoglobulin H rearrangements. Immunology 2006;119:265-277.
  27. Warren RL, Freeman JD, Zeng T, Choe G, Munro S, Moore R, Webb JR, Holt RA: Exhaustive T-cell repertoire sequencing of human peripheral blood samples reveals signatures of antigen selection and a directly measured repertoire size of at least 1 million clonotypes. Genome Res 2011;21:790-797.
  28. Kjer-Nielsen L, Clements CS, Purcell AW, Brooks AG, Whisstock JC, Burrows SR, McCluskey J, Rossjohn J: A structural basis for the selection of dominant alphabeta T cell receptors in antiviral immunity. Immunity 2003;18:53-64.
  29. Stewart-Jones GB, McMichael AJ, Bell JI, Stuart DI, Jones EY: A structural basis for immunodominant human T cell receptor recognition. Nat Immunol 2003;4:657-663.
  30. Halmer R, Davies L, Liu Y, Fassbender K, Walter S: The Innate Immune Receptor CD14 Mediates Lymphocyte Migration in EAE. Cell Physiol Biochem 2015;37:269-275.
  31. Meng K, Zhang W, Zhong Y, Mao X, Lin Y, Huang Y, Lang M, Peng Y, Zhu Z, Liu Y, Zhao X, Yu K, Wu B, Ji Q, Zeng Q: Impairment of Circulating CD4+CD25+GARP+ regulatory T cells in patients with acute coronary syndrome. Cell Physiol Biochem 2014;33:621-632.
  32. Wardemann H, Yurasov S, Schaefer A, Young JW, Meffre E, Nussenzweig MC: Predominant autoantibody production by early human B cell precursors. Science 2003;301:1374-1377.
  33. Aguilera I, Melero J, Nuñez-Roldan A, Sanchez B: Molecular structure of eight human autoreactive monoclonal antibodies. Immunology 2001;102:273-280.
  34. IchiyoshiY, Casali P: Analysis of the structural correlates for antibody polyreactivity by multiple reassortments of chimeric human immuno-globulin heavy and light chain V segments. J Exp Med 1994;180:885-895.
  35. Yassai M, Ammon K, Goverman J, Marrack P, Naumov Y, Gorski J: A molecular marker for thymocyte-positive selection: selection of CD4 single-positive thymocytes with shorter TCRB CDR3 during T cell development. J Immunol 2002;168:3801-3807.

Author Contacts

Hongyan Diao and Yong Dai

State Key Lab Diagnosis and Treatment of Infect Dise, Collaborative Innovation center

for Diagnosis and Treatment of Infectious Diseases, 1st Affiliated Hospital, College of

Medicine, Zhejiang University, 310003 Hangzhou, (China); Clin Med Res center, 2nd

Clinical Medical College of Jinan University (Shenzhen People's Hospital), Shenzhen,

Guangdong, 518020, (China); E-Mail diao.hy@163.com / xm183647168@126.com

Article / Publication Details

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Accepted: June 09, 2016
Published online: July 21, 2016
Issue release date: July 2016

Number of Print Pages: 17
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ISSN: 1015-8987 (Print)
eISSN: 1421-9778 (Online)

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References

  1. Psarra K, Kapsimali V, Tarassi K, Dendrinos S, Athanasiadis T, Botsis D: TCR gammadelta + T lymphocytes in unexplained recurrent spontaneous abortions. Am J Reprod Immunol 2001;45:6-11.
  2. Lachapelle MH, Miron P, Hemmings R, Roy DC: Endometrial T, B, and NK cells in patients with recurrent spontaneous abortion. Altered profile and pregnancy outcome. J Immunol 1996;156:4027-4034.
  3. Pannetier C, Cochet M, Darche S, Casrouge A, Zoller M, Kourilsky P: The sizes of the CDR3 hypervariable regions of the murine T-cell receptor beta chains vary as a function of the recombined germ-line segments. Proc Natl Acad Sci USA 1993;90:4319-4123.
  4. Cabaniols JP, Fazilleau N, Casrouge A, Kourilsky P, Kanellopoulos JM: Most alpha/beta T cell receptor diversity is due to terminal deoxynucleotidyl transferase. J Exp Med 2001;194:1385-1390.
  5. Gillespie GM, Stewart-Jones G, Rengasamy J, Beattie T, Bwayo JJ, Plummer FA, Kaul R, McMichael AJ, Easterbrook P, Dong T, Jones EY, Rowland-Jones SL: Strong TCR conservation and altered T cell cross-reactivity characterize a B*57-restricted immune response in HIV-1 infection. J Immunol 2006;177:3893-3902.
  6. Trautmann L, Rimbert M, Echasserieau K, Saulquin X, Neveu B, Dechanet J, Cerundolo V, Bonneville M. Selection of T cell clones expressing high-affinity public TCRs within Human cytomegalovirus-specific CD8 T cell responses. J Immunol 2005;175:6123-6132.
  7. Du JW, Gu JY, Liu J, Cen XN, Zhang Y, Ou Y, Chu B, Zhu P: TCR spectratyping revealed T lymphocytes associated with graft-versus-host disease after allogeneic hematopoietic stem cell transplantation. Leuk Lymphoma 2007;48:1618-1627.
  8. VanderBorght A, Geusens P, Vandevyver C, Raus J, Stinissen P: Skewed T-cell receptor variable gene usage in the synovium of early and chronic rheumatoid arthritis patients and persistence of clonally expanded T cells in a chronic patient. Rheumatology (Oxford) 2000;39:1189-1201.
  9. Dziubianau M, Hecht J, Kuchenbecker L, Sattler A, Stervbo U, Rödelsperger C, Nickel P, Neumann AU, Robinson PN, Mundlos S, Volk HD, Thiel A, Reinke P, Babel N: TCR repertoire analysis by next generation sequencing allows complex differential diagnosis of T cell-related pathology. Am J Transplant 2013;13:2842-2854.
  10. Robins HS, Campregher PV, Srivastava SK, Wacher A, Turtle CJ, Kahsai O, Riddell SR, Warren EH, Carlson CS: Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. Blood 2009;114:4099-4107.
  11. Hou X, Lu C, Chen S, Xie Q, Cui G, Chen J, Chen Z, Wu Z, Ding Y, Ye P, Dai Y, Diao H. High throughput sequencing of T cell antigen receptors reveals a conserved TCR repertoire. Medicine (Baltimore) DOI:10.1097/MD.0000000000002839.
  12. Sui W, Hou X, Zou G, Che W, Yang M, Zheng C, Liu F, Chen P, Wei X, Lai L, Dai Y: Composition and variation analysis of the TCR β-chain CDR3 repertoire in systemic lupus erythematosus using high-throughput sequencing. Mol Immunol 2015;67:455-464.
  13. Venturi V, Kedzierska K, Price DA, Doherty PC, Douek DC, Turner SJ, Davenport MP: Sharing of T cell receptors in antigen-specific responses is driven by convergent recombination. Proc Natl Acad Sci 2006;103:18691-18696.
  14. Venturi V, Price DA, Douek DC, Davenport MP: The molecular basis for public T-cell responses? Nat Rev Immunol 2008;8:231-238.
  15. Venturi V, Quigley MF, Greenaway HY, Ng PC, Ende ZS, Mclntosh T, Asher TE, Almeida JR, Levy S, Price DA, Davenport MP, Douek DC: A mechanism for TCR sharing between T cell subsets and individuals revealed by pyrosequencing. J Immunol 2011;186:4285-4294.
  16. Quigley MF, Greenaway HY, Venturi V, Lindsay R, Quinn KM, Seder RA, Douek DC, Davenport MP, Price DA: Convergent recombination shapes the clonotypic landscape of the naive T-cell repertoire. Proc Natl Acad Sci USA 2010;107:19414-19419.
  17. Li H, Ye C, Ji G, Wu X, Xiang Z, Li Y, Cao Y, Liu X, Douek DC, Price DA, Han J: Recombinatorial biases and convergent recombination determine interindividual β sharing in murine thymocytes. J Immunol 2012;189:2404-2413.
  18. Madi A, Shifrut E, Reich-Zeliger S, Gal H, Best K, Ndifon W, Chain B, Cohen IR, Friedman N: T-cell receptor repertoires share a restricted set of public and abundant CDR3 sequences that are associated with self-related immunity. Genome Res 2014;24:1603-1612.
  19. Wesolowski J, Alzogaray V, Reyelt J, Unger M, Juarez K, Urrutia M, Cauerhff A, Danquah W, Rissiek B, Scheuplein F, Schwarz N, Adriouch S, Boyer O, Seman M, Licea A, Serreze DV, Goldbaum FA, Haag F, Koch-Nolte F: Single domain antibodies: promising experimental and therapeutic tools in infection and immunity. Med Microbiol Immunol (Berl) 2009;198:157-174.
  20. Xiang SH, Farzan M, Si Z, Madani N, Wang L, Rosenberg E, Robinson J, Sodroski J: Functional mimicry of a human immunodeficiency virus type 1 coreceptorbya neutralizing monoclonal antibody. J Virol 2005;79:6068-6077.
  21. Klonowski KD, Primiano LL, Monestier M: Atypical VH-D-JH rearrangements in newborn autoimmune MRL mice. J Immunol 1999;162:1566-1572.
  22. Souto-Carneiro MM, Longo NS, Russ DE, Sun HW, Lipsky PE: Characterization of the human Ig heavy chain antigen binding complementarity determining region 3 using a newly developed software algorithm, JOINSOLVER. J Immunol 2004;172:6790-6802.
  23. Briney BS, Willis JR, Hicar MD, Thomas JW, Crowe JE: Frequency and genetic characterization of V(DD)J recombinants in the human peripheral blood antibody repertoire. Immunology 2012;137:56-64.
  24. Larimore K, McCormick MW, Robins HS, Greenberg PD: Shaping of human germline IgH repertoires revealed by deep sequencing. J Immunol 2012;189:3221-3230.
  25. Corbett SJ, Tomlinson IM, Sonnhammer EL, Buck D, Winter G: Sequence of the human immunoglobulin diversity (D) segment locus: a systematic analysis provides no evidence for the use of DIR segments, inverted D segments, "minor" D segments or D-D recombination. J Mol Biol 1997;270:587-597.
  26. Ohm-Laursen L, Nielsen M, Larsen SR, Barington T: No evidence for the use of DIR, D-D fusions, chromosome 15 open reading frames orVH replacement in the peripheral repertoire was found on application of an improved algorithm, JointML, to 6329 human immunoglobulin H rearrangements. Immunology 2006;119:265-277.
  27. Warren RL, Freeman JD, Zeng T, Choe G, Munro S, Moore R, Webb JR, Holt RA: Exhaustive T-cell repertoire sequencing of human peripheral blood samples reveals signatures of antigen selection and a directly measured repertoire size of at least 1 million clonotypes. Genome Res 2011;21:790-797.
  28. Kjer-Nielsen L, Clements CS, Purcell AW, Brooks AG, Whisstock JC, Burrows SR, McCluskey J, Rossjohn J: A structural basis for the selection of dominant alphabeta T cell receptors in antiviral immunity. Immunity 2003;18:53-64.
  29. Stewart-Jones GB, McMichael AJ, Bell JI, Stuart DI, Jones EY: A structural basis for immunodominant human T cell receptor recognition. Nat Immunol 2003;4:657-663.
  30. Halmer R, Davies L, Liu Y, Fassbender K, Walter S: The Innate Immune Receptor CD14 Mediates Lymphocyte Migration in EAE. Cell Physiol Biochem 2015;37:269-275.
  31. Meng K, Zhang W, Zhong Y, Mao X, Lin Y, Huang Y, Lang M, Peng Y, Zhu Z, Liu Y, Zhao X, Yu K, Wu B, Ji Q, Zeng Q: Impairment of Circulating CD4+CD25+GARP+ regulatory T cells in patients with acute coronary syndrome. Cell Physiol Biochem 2014;33:621-632.
  32. Wardemann H, Yurasov S, Schaefer A, Young JW, Meffre E, Nussenzweig MC: Predominant autoantibody production by early human B cell precursors. Science 2003;301:1374-1377.
  33. Aguilera I, Melero J, Nuñez-Roldan A, Sanchez B: Molecular structure of eight human autoreactive monoclonal antibodies. Immunology 2001;102:273-280.
  34. IchiyoshiY, Casali P: Analysis of the structural correlates for antibody polyreactivity by multiple reassortments of chimeric human immuno-globulin heavy and light chain V segments. J Exp Med 1994;180:885-895.
  35. Yassai M, Ammon K, Goverman J, Marrack P, Naumov Y, Gorski J: A molecular marker for thymocyte-positive selection: selection of CD4 single-positive thymocytes with shorter TCRB CDR3 during T cell development. J Immunol 2002;168:3801-3807.
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