High-throughput Treg cell receptor sequencing reveals the relationship between disease activity and immunosenescence in patients with rheumatoid arthritis

High-throughput Treg cell receptor sequencing reveals the relationship between disease activity and immunosenescence in patients with rheumatoid arthritis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article High-throughput Treg cell receptor sequencing reveals the relationship between disease activity and immunosenescence in patients with rheumatoid arthritis Congqi Hu, Wei Jiao, Lu Zhang, Yanqing Duan, Jiduo Liu, Jia Xu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7953908/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Treg cells play an important role in the induction of autoimmune tolerance, and their ability of antigen recognition and regulation is determined by the receptor (TCR). TCR diversity determines the autoimmune responses in RA and is closely associated with autoimmune diseases prognosis and prevention. Previous studies have found that there is immunosenescence in RA, that is, the decrease of the number of naïve CD4 + T cells and the reduction of TCR repertoire diversity. High disease activity is an independent risk factor for RA. We speculate that high disease activity is related to immunosenescence—the decline of adaptive immune function and a chronic inflammation, but its biological mechanism is still unclear and needs to be further elucidated. In this study, high-throughput Immuno-Seq sequencing and flow cytometry were used to compare normal subjects and RA patients, as well as RA patients whose disease activity changed to clinical remission after standard treatment. It was found that high disease activity not only led to a decrease in the number of naïve Treg cells, but also led to a decrease in the diversity of Treg cell receptor repertoire, weakening their ability to recognize self-antigens and induce immune tolerance, leading to the occurrence and development of RA. Besides, these characteristics of the TCR repertoire, particularly the disease activity related clones, can potentially serve as biomarkers and provide novel insights for disease status and therapeutical targets in autoimmune diseases. Rheumatoid arthritis Treg TCR immunosenescence Immuno-Seq sequencing Disease activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Rheumatoid arthritis (RA) is an aging-related disease characterized by autoimmune disorders, inflammatory cell infiltration of joints, and progressive bone destruction[ 1 ]. Autoreactive T cells have been observed in the synovium and peripheral blood (PB) of patients with RA, where they are activated to secret inflammatory cytokines, so it is considered to be a T cell-mediated disease[ 2 – 4 ]. The imbalance between autoreactive T cells and regulatory T cells (Treg) is the basis of RA immune imbalance. As Treg cells play an important role in the peripheral induction of autoimmune tolerance, the functional defects of Treg in patients with RA and the treatment strategies for Treg have attracted more and more attention in recent years. The adaptive immune system is prone to age-related degeneration in response to high reproductive demands, a process that leads to a decline in protective immune function accompanied by excessive inflammatory activity[ 5 ]. With the increase of age, the number of naïve T cells and the diversity of TCR repertoire gradually decreased, resulting in immunosenescence which means the ability to recognize self-antigen and regulate autoimmune response decreased, inflammatory factors increased, and the body was in a state of chronic inflammation. The antigen recognition and response ability of T cells is determined by T cell receptor (TCR). In TCR, the high diversity is generated by genomic rearrangement of the variable (V), diversity (D) and joining (J) regions, along with palindromic and random nucleotide additions, which is crucial for understanding of adaptive immunity in health and disease[ 6 – 8 ]. The specificity and diversity of TCRs predominantly depend on the complementarity determining region 3 (CDR3), which is encoded by V(D)J recombination and interacts with the peptide presented by the major histocompatibility complex (MHC)[ 9 ]. In the process of adaptive immunity, the TCR gene of peripheral blood T cells will undergo rearrangement to form a variety of immune repertoires patterns to meet the needs of external environment and immune regulation of the body[ 10 ]. The decrease of the diversity of TCR repertoire will affect the immunomodulatory function of T cells and cause autoimmune diseases. RA has both an immune disorder and chronic inflammation and is thought to have immunosenescence as well[ 11 , 12 ]. Previous RA studies have only focused on the CD4 + T cell TCR repertoires, but no studies have sequenced the TCR repertoires of Treg cells, which are more difficult to obtain and play an important role in autoimmune tolerance dysfunction in RA[ 13 ]. Moreover, High disease activity is an independent risk factor for RA. Long-term high disease activity will lead to synovial hyperplasia and bone destruction in patients with RA. However, as far as we know, the correlation between disease activity and immunosenescence has not been found. More importantly, TCR diversity is closely related to treatment effectiveness and patient prognosis in different diseases[ 14 – 16 ]. In summary, we speculate that there may be a correlation between disease activity and immunosenescence in RA patients during acquired adaptive immunity. High disease activity may be closely associated with a decreased diversity of Treg cell receptor repertoire and ultimately a diminished ability of Treg cells to recognize autoantigens and induce autoimmune tolerance. Therefore, in this study, high-throughput repertoire sequencing and flow cytometry were used to study the relationship between disease activity and immunosenescence, and to further explore the pathogenesis of RA. Materials and Methods Participants Four RA patients with high disease activity in this study for TCR sequencing were recruited from the Department of Rheumatology in The First Affiliated Hospital of Guangzhou University of Chinese Medicine (Table 1 ). Disease activity of every subject was assessed using DAS28 of The European League Against Rheumatism (EULAR)[ 17 ]. Low disease activity was 3.2 > DAS28 > 2.6, moderate active disease was 5.1 > DAS28 > 3.2 and high active disease was DAS28 > 5.1, while DAS28 < 2.6 means RA in remission. We treated RA patients according to the American College of Rheumatology (ACR) Guideline for treatment[ 18 ]. To compare the changes in the immunome pool of Treg cells in PBMC of patients before and after treatment, peripheral blood from additional 34 RA patients and 10 healthy volunteers enrolled in this study were used for flow cytometry (Table 2 ). Only 22 of 34 RA patients finished the treatment lasting 3 months. These patients with DAS28 > 2.6 before treatment were defined as the active disease group(AD group), while with DAS28 < 2.6 after treatment were defined as the non-active group(Non-AD group). Informed consent for peripheral blood sampling was obtained from these study subjects. This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (ZYYECK[2018]141) and filed in Chinese Clinical Trial Registry (ChiCTR1900028762). Table 1 Baseline characteristics of RA patients before and after treatment recruited for TCR sequencing. No. SEX AGE Pre-treatment(AD group) Post-treatment(Non-AD group) ESR (mm/h) CRP (mg/L) DAS28 (CRP) DAS28 (ESR) Rheumatoid factor ACPA ESR (mm/h) CRP (mg/L) DAS28 (CRP) DAS28 (ESR) Rheumatoid factor ACPA 1 FEMALE 47 30 6.02 4.24 4.96 870.00 173.70 23.00 3.57 1.65 2.33 936.00 > 200 2 FEMALE 39 30 6.93 4.00 4.68 < 20 < 0.5 12.00 1.37 1.83 2.30 < 20 200 12.00 1.66 1.73 2.16 22.10 > 200 4 FEMALE 46 42 2.39 4.17 5.39 456.00 133.40 22.00 2.36 1.54 2.30 176.00 127.40 ESR, Erythrocyte sedimentation rate; CRP, C reactive protein; DAS28, Disease Activity Score with 28-joint counts; ACPA, anti-cyclic citrullinated peptide. Table 2 Baseline characteristics of healthy participants and RA patients before and after treatment. Characteristics Healthy participants (n = 10) RA participants (n = 34) Pre-treatment (AD group, n = 22) Post-treatment (Non-AD group, n = 22) Female, n(%) 8(80.00) 27(79.41) 19(86.36) 19(86.36) Age(years) 53(6.22) 52.82(7.38) 52.09(7.95) 52.09(7.95) ESR(mm/h) N/A 50.90(21.64) 50.22(19.80) 36.05(25.72) * CRP(mg/L) N/A 31.69(27.52) 32.06(28.97) 13.77(18.59) * DAS28-ESR N/A 6.18(0.88) 6.18(0.78) 4.40(1.54) * DAS28-CRP N/A 5.59(0.87) 5.58(0.86) 3.94(1.11) * Rheumatoid factor, n(%) N/A 28(82.35) 17(77.27) 15(68.18) ACPA, n(%) N/A 32(94.11) 20(90.90) 20(90.90) N/A, not applicable; * , significance with pre-treatment(AD group) vs. post-treatment(non-AD group). Process of flow analysis and High-throughput Treg cell receptor sequencing This study mainly analyzed Treg cells (CD3 + CD4 + CD25 + CD127 − ), naïve Treg cells (CD3 + CD4 + CD45RA + CD25 + CD127 − ), effect Treg cells (CD3 + CD4 + CD45RA-CD25 + CD127 − ) and activated CD4 + T cells (CD3 + CD4 + HLA-DR + ) (Fig. 1 a ) . Firstly, we analyzed the percentage of activated CD4 + T cells, Treg cells and their subsets in the peripheral blood of 10 healthy people and 34 RA patients by flow cytometry, and then further analyzed the changes in the percentage of these cells in 22 RA patients after treatment. Lastly, we recruited another 4 RA patients who changed from high disease activity to clinical remission after standard treatment, and Treg cells were selected from peripheral blood before and after treatment for high-throughput Treg cell receptor sequencing. The specific steps include: cell sorting, template extraction, amplification and construction of database, sequencing, bioinformatics analysis(Fig. 1 b). PBMC isolation and cell sorting by FACS Briefly, PBMCs were isolated using standard Ficoll-Paque (GE Healthcare, USA) by density gradient centrifugation. For detection of Treg cells, PBMCs were labeled with surface markers anti-CD4-FITC (A07750), anti-CD25-PC5 (IM2646), CD127-PE (IM1980U) antibody (BECKMAN COULTER, INC. USA) antibodies. Fluorescence activated cell sorting (FACS) was performed on the MoFlo Astrios (BECKMAN COULTER, INC. USA). Tregs with high purity (> 98%) were obtained for the following experiments. RNA extraction and cDNA synthesis Total RNA was extracted according to the TRIZOL kit (Invitrogen, California, USA) instructions. Qualified RNA was synthesized by reverse transcription using SMARTScribe™ reverse transcriptase (Clontech, California, USA) for the preparation of 5'RACE cDNA for high-throughput sequencing and RT-qPCR experiments. TCR repertoire preparation and sequencing Two rounds of nested PCR was performed for TCR library preparation. For the first round of PCR amplification, 45µl of cDNA from the synthesis reaction was mixed with primers (Supplement Table 1 ) and Q5® High-Fidelity 2X Master Mix(NEB, USA). The PCR cycling conditions were as follows: an initial denaturation at 92℃for 2 mins, followed by 18 cycles of denaturation at 94°C for 30s, annealing of primer to DNA at 60°C for 30s, and extension at 68°C for 2 mins. PCR products were purified using MinElute PCR Purifcation Kit (Qiagen, Hilden, Germany). Products from the previous step was used in second round of PCR reaction under the same condition with 16 cycles. PCR products were purified using MinElute PCR Purifcation Kit (Qiage, Hilden, Germany). Libraries were built with NEB next Ultra DNA Library Prep kit (New England BioLabs, Massachusetts, USA) according to the manufacturer’s protocol. The validated libraries were used for sequencing on Illumina MiSeq platform (Illumina, San Diego, USA) following the standard pipelines of Illumina by Guangzhou Huayin Health Medical Group Co.,Ltd (GuangZhou, China), generating 2×150 bp paired-end reads[ 19 ]. Data processing and Bioinformatics Analysis The primary data obtained from high-throughput sequencing were transformed into raw sequence reads by base calling and recorded in FASTQ format. Reads without primers were discarded. PCR and sequencing errors were rectified by unique molecular identifiers (UMIs). A clonotype was identified by the CDR3 amino acid sequence for further analysis, and only sequences with reads ≥ 2 were enrolled in the analysis. TCRβ V, D, J genes and clonotypes were defined according to IMGT59 and Igblast; the TCRβ VDJ combination was defined by MIXCR. The V (D) J frequency and frequency statistics of each group were drawn with matplotlib (version 2.2.4) of python (version 2.7.15) and ggplot2 (version 3.3.5) of R (version 3.5.1) respectively. Differential heat map: use R (version 3.5.1) for chi-square test to calculate p value, ComplexHeatmap package (version 2.1.0) to draw heat map. Heatmap barplot uses R (version 3.5.1) pheatmap package (version 1.0.12) to draw heat maps with no difference calculation. Heat map uses R (version 3.5.1) ggplot2 (version 3.3.5) to draw heat map, no difference calculation. Collinearity analysis: scipy (version 1.2.2) of python (version 2.7.15) was used for linear regression calculation, and matplotlib (version 2.2.4) was used to draw scatter plot. Richness and diversity statistics: using the alakazam package of R (version 3.4.1) (version 0.2.8) to calculate the richness and diversity (Hill index) and draw the graph. Statistical chart of diversity index difference: using R (version 3.5.1) to compare the diversity index of the group for t -test, ggplot2 (version 3.3.5) to draw a dot map. Flow cytometric analysis Each fresh 100µl peripheral blood anticoagulated by EDTA from RA patient recruited were taken and removed to the bottom of the flow tube, labeled antibodies were then added to the flow tube mixing with the peripheral blood for incubating 15min at room temperature away from light. Anti-CD3-ECD (A07748), anti-CD4-FITC (A07750), anti-CD25-PC5 (IM2646), CD127-PE (IM1980U) antibody (BECKMAN COULTER, INC. USA), anti-human HLA-DR antibody (HPDR-025, 4A BIOTECH, Beijing) and anti-human CD45RA antibody (FNH0453-025, 4A BIOTECH, Beijing) were chosen to screen and label different T cell subsets. Mix 300µl of RBC lysing buffer per 100µl of antibodies-labeled peripheral blood, then incubate for 3 min at room temperature in the dark after shaking well. Wash twice with PBS wash buffer and then centrifuge at 1500 rpm for 5 minutes, discard the supernatant. Resuspended cells with 500µl PBS wash buffer were immediately detect using flow cytometry (BECKMAN COULTER, INC. USA). The flow analysis was repeated twice for each sample to ensure objective and reliable data. Quantitative reverse transcription PCR(RT-qPCR) Subsequently, we performed RT-qPCR validation on TRBV28|TRBJ2-1 and TRBV18|TRBJ2-3, which showed significant differences in the high-throughput sequencing results and had significantly higher gene frequencies compared to the Non-AD group. The cDNA samples obtained above were analyzed using the SYBR Green Premix Pro Taq HS qPCR Kit (#AG11701) and run on the Applied Biosystems QuantStudio 5 Real-Time PCR System (Thermo Fisher Scientific). RNA levels were normalized to GAPDH levels. The primer sequences were as follows:TRBV18: GCCAGCTCAACTCAGGGGGCGGAGACCCAGTAC;TRBJ2-3:AGTGCTATTCCCGGGACGGGCACAGATACGCAGTAT;TRBV28: GCCAGCAGTCTCGGAGGTCCCTGGCGAGATACGCAGTAT;TRBJ2-1: GCCAGCAGCCAAGTTTTTCTAGCGGGAGAGGATGAGCAGTTC. Statistical analysis Matched samples were compared using a paired t -test. Samples from different group were compared using the Wilcoxon test, independent-sample t -test and Chi-square test. Statistical analysis were performed using SPSS software version 22.0 (IBM Corp., Armonk, New York). P value < 0.05 were considered statistically significant. Results Flow analysis of activated CD4 + T cells, Treg cells and their subsets In our study, 10 healthy persons and 34 RA patients were analyzed. The percentage of total Treg cells and naïve Treg cells in RA patients were all significantly lower than that in healthy people ( P = 0.041, P = 0.048)(Fig. 2 a), but there was no statistical difference in the percentage of effector Treg cells ( P >0.05). On the contrary, when compared with the health group, the percentage of activated CD4 + T cells was significantly increased ( P = 0.023). Next, we selected the patients (n = 22) who completed the treatment for 3 months from the 34 RA patients, and analyzed these cells ratio before and after treatment. Interestingly, we found that compared with before treatment, there was no significant change in the percentage of total Treg cells ( P >0.05) (Fig. 2 b), but the percentage of naïve Treg cells after treatment were significantly higher than that before treatment ( P = 0.021). In addition to, the percentage of activated CD4 + T was decreased ( P <0.001). VDJ gene combination frequency of Treg cells Flow analysis showed that there was no significant difference in the percentage of total Treg cells in peripheral blood between the two groups. Therefore, we further analyzed the function of Treg cells. The Treg cells were selected from peripheral blood for high-throughput cell receptors. In this study, 10000 original reads were randomly selected to do the sequencing mass distribution map. The median value is greater than 35, and the density distribution is concentrated above the median value, indicating that the data quality is high(Fig. 3 a). The diversity of V(D)J constitutes the diversity of repertoire, and there are differences in the use frequency of V(D)J among different individuals. The study on the difference of use frequency is of great significance to reveal the difference of sample repertoire. Here, the use frequency of V(D)J of the sample is statistically analyzed, and the pie chart is drawn to display the first six most frequently used genes in the legend region in Fig. 3 b, there are some differences in the highest frequency of V gene between Non-AD group and AD group (Fig. 3 b). TRBV20-1 gene is obviously dominant in AD group, while there is no significant difference in frequency between TRBV5-1, TRBV20-1 and TRBV13, which are most frequently used in Non-AD group. The combination of different V(D)J genes greatly enriches the diversity of repertoire, in which the combination of VJ and V(D)J is particularly important. Here, statistics are made on the combination mode and frequency of samples, 2D map of V(D)J combination (showing the first 20 combinations with the highest frequency) and 3D map of VJ combination (showing the first 30 highest frequency V gene). There is a great difference in the dominant clone type between the AD group and the Non-AD group (Fig. 3 c). In the AD group, 3 dominant clones accounted for an unusually high percentage, and other clones were evenly distributed, while in the Non-AD group, TRBV13 | TRBD1 | TRBJ2-3 clones accounted for a higher percentage. In addition, the TRBV13|J2-3 combination is obviously dominant in the AD group, while the frequency of several combinations in the Non-AD group is higher, and the dominant type is not obvious (Fig. 3 d). Importantly, all results suggested that the frequency of V(D)J gene combination in Non-AD group was significantly higher than that in AD group (Supplement Table 2 –3). Analysis of characteristics, abundance and diversity of TCR CDR3 region of Treg cells Statistics of nucleic acid and amino acid length distribution information of CDR3 region contained in Clone in each sample, and draw a bar chart to fully show the characteristics of the sample and the differences between samples. There were more amino acids distributions between 12 and 14 in length. In addition, The distribution length of nucleic acid mostly concentrated between 12 and 14 (Fig. 4 a). However, CDR3 length did not discriminate AD group and Non-AD group. The length distributions of TCR CDR3 were nearly identical in both groups. The Rank-Abundance curve is a way to analyze diversity. The Rank-Abundance curve reflects the richness of CDR3 region and the uniformity of different sequence types of CDR3 region in the sample. In the horizontal direction, the higher the richness of the CDR3 region, the larger the range of the curve on the horizontal axis; the shape of the curve (smoothness) reflects the uniformity of the sequence type of the CDR3 region in the sample, and the steeper the curve is, the more uneven the distribution of the sequence type of the CDR3 region in the sample is. We found that the richness of Non-AD group is better than that of AD group (Fig. 4 b-c), but there was no statistical difference ( P = 0.2094). In addition, the distribution of sequence types in Non-AD group was relatively more uniform. The D50 index can directly reflect sample diversity indices, the minimum value of D50 is 0, which means that there is only one CDR3 sequence and the diversity is poor. The maximum value of D50 is 0.5, which means that the proportion of all CDR3 sequences is consistent and the diversity is good[ 20 , 21 ]. Therefore, we used the D50 index to compare TCR CDR3 diversity in AD and Non-AD groups. All results indicated that the Non-AD group exhibited significantly higher TCR CDR3 diversity compared with AD group (Fig. 4 b-c, P = 0.002). Collinear analysis and experimental validation of TCR CDR3 VDJ differential genes and their combinations in Treg cells Next, we used Chi-square test and Wilcoxon test methods to analyze the difference of V(D)J gene between the two groups of samples and found that there were significant differences in genes between AD-04 and Non-AD-04 (Fig. 5 a-b), especially TRBV18, TRBV28, TRBV7.9 and TRBJ2.7. Then we draw the correlation map based on the frequency difference of V gene, VJ gene combination and VDJ gene combination(Fig. 5 c). The analysis results show that the R2 of V gene and V family is greater than 0.8 and it shows that there is little difference between the two groups, but the R2 of VJ and VDJ gene is 0.627 and 0.542 respectively, indicating that there is a great difference between the two groups. In particular, TRBV28 | TRBJ2-1, TRBV18 | TRBJ2-3 (high frequency in AD group), TRBV5-1 | TRBJ2-5, TRBV5-1 | TRBJ2-7, TRBV18 | TRBJ2-7 (high frequency in Non-AD group) VJ gene combinations are worthy of further study. It is of great value to find unknown autoantigens and explore RA immune mechanism in looking for autoimmune Treg cell clones. To validate our sequencing findings, we measured the expression of TRBV28|TRBJ2-1 and TRBV18|TRBJ2-3. Consistent with the sequencing data, both pairs showed significantly increased mRNA levels in the AD group compared to the Non-AD controls (Fig. 7 a-b). Discussion RA is a chronic inflammatory disease characterized by autoimmune disorders, infiltration of activated inflammatory cells in the synovium, tumor-like hyperplasia of the synovium, and progressive destruction of cartilage and bone. CD4 + T (Th) cells are one of the main inflammatory cells in RA synovium. Due to the stimulation of autoantigens and related cytokines, naïve CD4 + T is activated and differentiated into various Th cell subsets, such as Th1, Th17, Treg and so on. These synovial T cells regulate the function of other cells in the synovium and, together with other cells, form ectopic lymphoid structures with chronic destructive effects in the synovium. It provides sufficient conditions for chronic and persistent immune response[ 22 – 24 ]. In the process of adaptive immunity, the thymus clears the naïve T cell clones that recognize self-antigen (central immune tolerance) through positive and negative selection. However, in some cases, some self-reactive T cells escape to peripheral blood, and peripheral autoimmune tolerance plays an important role[ 25 ]. Among them, Tregs are one of the regulatory T cell subsets, which have important functions of inhibiting autoreactive T cells, inducing immune anergy and maintaining immune stability. In recent years, it has attracted wide attention in the study of autoimmune diseases[ 26 ]. Treg cells can not only regulate T cell mediated immune response, but also inhibit the activation of autoreactive T cells, so as to ensure that the body will not react to autoantigens and effectively reduce the harmful autoimmune response[ 27 ]. Immunotherapy against Treg cells has become an important strategy for the treatment of autoimmune rheumatism[ 28 ]. Studies have shown that deletion of CD4 + CD25 + Treg cells can aggravate the condition of collagen-induced arthritis (CIA), while infusion of CD4 + CD25 + Treg can alleviate the progression of CIA arthritis[ 29 ]. Therefore, in this study, flow cytometry was used to study the proportion of Treg cells and their subsets in peripheral blood of normal subjects (n = 10), RA patients (n = 34) and RA patients (n = 22) after standard treatment. We found the number of naïve Treg cells decreased in RA, compared with healthy people. In contrast, the proportion of activated CD4 + T cells that reflect disease activity is significantly increased of RA patients than in healthy subjects, which has a similar outcome to previous studies[ 30 ]. Next, we found that after treatment, with the decrease of disease activity, the ratio of naïve Treg cells increased significantly, and the proportion of activated CD4 + T cells decreased significantly, but here's what we don't understand is that there was no significant difference in the proportion of total Treg cells. Previous studies have shown that the number of Treg cells in the peripheral blood of RA patients is lower than that of normal people, on the contrary, another part of the study believes that Treg cells have not decreased, which is controversial. The more consistent view is the increase of the number of CD4 + CD25 + Foxp3 + T cells in RA joint effusion[ 31 ], but why are the joint inflammation and autoreactive lymphocytes of RA not inhibited? Further study found that, as shown in Fig. 6 , the number of Foxp3 + Treg cells increased, but their function was seriously weakened, partly because Treg cells from RA patients had a deficiency in CTLA-4 inhibition function and could not effectively down-regulate T cell receptor signals[ 32 ]. In addition, TNF-α can induce dephosphorylation of Foxp3 through protein kinase 1 (protein phosphatase 1), which inactivates Treg cells[ 33 , 34 ]. However, the latest research shows that the abundance of CD4 + CD25 + Foxp3 + TCR repertoire decreases, its ability to recognize self-antigen is weakened, and autoimmune reaction can occur[ 9 ]. Therefore, we selected 4 RA patients with better therapeutic effect. After 3 months of standard treatment, DAS28 changed from high disease activity (AD group) to clinical remission (Non-AD group). We isolated Treg cells from the peripheral blood of patients before and after treatment, and compared the changes of Treg cell abundance and diversity before and after treatment by high-throughput repertoire sequencing technique. High-throughput sequencing can simultaneously sequence millions of molecules and has been proven to be helpful to analyze the true TCR or BCR repertoires in patients[ 35 ]. Previous studies have shown that the antigen recognition and regulation ability of Treg cells is determined by T cell receptors. More than 90% of the peripheral TCR is composed of α and β chain, most of the TCR sequences are concentrated in the TCR β chain, and CDR3 is the key region of antigen specific binding with, which determines the specificity and function of Treg cells[ 36 , 37 ]. A previous study reported an abnormally short length of TCRB CDR3 in type 1 diabetes patients, which was related to abnormal preselection process and induced an enrichment of auto-reactive TCR repertoire in peripheral blood of patients[ 38 ]. Thus, it is of profound interest to examine whether similar phenomenon exists in RA patients. However, we found that there was no significant difference in the distribution of amino acids in CDR3 region between AD group and Non-AD group. In addition, there was no significant difference in nucleic acid distribution between the two groups, and there were more nucleic acid distributions between 36 and 42 in length, which may be due to the small sample size (Fig. 4 a). In recent years, it has been found that there is a "rearrangement" of peripheral TCR, that is, reopening gene recombination activating enzyme (RAG) to rearrange the V (D) J gene, modify the original receptor or produce a new receptor, so as to form a peripheral T lymphocyte receptor bank, clear self-reactive cells, or change the responsiveness to self-antigen. This mechanism plays an important role in immune response mechanisms such as expansion of peripheral T cell bank, peripheral T cell tolerance and autoimmune diseases[ 37 ]. Our study found that the highest frequency of V gene was different between the Non-AD group and the AD group and the frequency and frequency of V(D)J gene combination in Non-AD group was significantly higher than that in AD group (Fig. 3 c-d). This suggests that the diversity of Non-AD group is due to AD group. In addition, we further compared the abundance and diversity of Treg cells between the two groups by analyzing Rank-Abundance curve and Hill index, which also suggested that high disease activity could indeed reduce the abundance and diversity of Treg cells to activate the autoreactive T cells(Fig. 4 b). Previous studies have also shown that high abundance of TCR repertoire helps Foxp3 + CD4 + Treg cells recognize and inhibit self-reactive T cells[ 39 ]. More and more studies have shown that aging is related to the decline of adaptive immune function. The diversity of TCR β chain CDR3 group decreased almost linearly with the increase of age[ 40 – 42 ]. This phenomenon of immunosenescence has three characteristics: a. the decline of thymic function leads to the gradual decrease of naïve T cells; b. continuous antigen stimulation leads to the increase of functionally deficient, monoclonal and senescent T cells and the decrease of T cell bank diversity; c. serum TNF-α, IL-6 and other inflammatory proteins are increased, and the body is in a state of chronic inflammation. The pro-inflammatory environment created by these factors may induce or accelerate the occurrence and development of RA, so RA is considered to be related to immunosenescence[ 5 , 11 ]. Our study found that: a. Compared with healthy people, the number of naïve Treg cells decreased, but after standard treatment, the number of naïve Treg cells increased with the decrease of disease activity, suggesting that there was a negative correlation between disease activity and the percentage of naïve Treg cells; b. The TCR abundance and diversity of Treg cells in Non-AD group were significantly higher than those in AD group, which suggested that the diversity of Treg cells was restored with the decrease of disease activity. To sum up, our research shows that there may be a certain correlation between disease activity and immunosenescence in patients with RA. High disease activity may lead to the decrease of the diversity of Treg cell receptor repertoire, weaken its ability to recognize self-antigen and induce immune tolerance, and lead to the accelerate the deterioration of RA. Under the condition that the antigen is still unclear, searching for autoimmune T cell clones by Treg cell receptor β chain CDR3 repertoire type and sequencing is a new idea to study the mechanism of autoimmune intolerance such as RA. We found differences between AD-04 and non-AD-04 genes, particularly TRBV18, TRBV28, TRBV7.9 and TRBJ2.7. For the frequencies of gene combination it was found that TRBV28|TRBJ2-1, TRBV18|TRBJ2-3VJ gene frequencies were significantly increased in the AD group compared to the Non -AD group. This observation was further validated by our subsequent RT-qPCR experiments. Previous studies have found that the beta chain of TCR can effectively transfer TCR to Jurkat cells by using TRBV18, TRBD1 and TRBJ2-7 by combining TCR V beta 18-specific mAb and DRB1*0101 Art V 1 tetramer, thus activating the immune response[ 43 ]. TRBV18 is highly expressed in breast cancer patients, and T cell clones that express TRBV18 are preferentially activated when stimulated by antigens[ 44 ]. In addition, in the comparative analysis of the characteristics of chronic hepatitis B before and after treatment, it was found that TRBV28 may be an important site for T cells to be regulated and induced to eliminate the invading virus[ 45 ]. In children with sepsis, compared with the health group, the case group had significantly higher frequencies of TRBJ2-3, TRBJ2-5, and TRBJ2-7 ( P < 0.05)[ 46 ]. Based on the above studies, we believe that these gene combinations can be used as biomarker for further study, which is of great value for finding autoimmune T cell clones, finding unknown autoantigens and exploring the immune mechanism of RA. As with all researches, potential limitations should to be noted. On the one hand, It is well known that there is great heterogeneity in clinical samples. However, we collected 10 healthy people and 34 RA patients, and repeated measurements were used in flow cytometry analysis to ensure the authenticity of the data as much as possible. On the other hand, in the aspect of repertoire sequencing, we only examined 4 RA patients before and after treatment. Although the sample size of this study is small, but most of the key indicators have statistical differences, more importantly, the main significance of our research is to put forward a novel point of view and research direction. Due to these limitations, our findings need to be interpreted cautiously. Nevertheless, our study also had advantages. First, as far as we know, this study was the first study investigated the relationship between disease activity and immunosenescence in patients with RA. Next in importance, in order to explore the correlation between disease activity and immunosenescence, we used repertoire sequencing and flow cytometry to study the number and function of Treg cells, which is a new idea to study the mechanism of autoimmune intolerance such as RA, and has important clinical significance for guiding the immunotherapy of diseases and evaluating immune remodeling. Conclusion Briefly, we confirmed that there is a correlation between disease activity and immunosenescence in RA patients by high-throughput repertoire sequencing and flow cytometry. High disease activity can not only reduce the number of immature Treg cells, but also lead to the decrease of their abundance and diversity, weaken their ability to recognize their own antigens and induce immune tolerance, leading to the occurrence and development of RA. In addition, we also found many VJ gene combinations worthy of further study, which are of great value for finding autoimmune T cell clones, discovering unknown autoantigens and exploring the immune mechanism of RA. Statement of Ethics This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (ZYYECK[2018]141) and filed in Chinese Clinical Trial Registry (ChiCTR1900028762). Declarations Statement of Ethics This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (ZYYECK[2018]141) and filed in Chinese Clinical Trial Registry (ChiCTR1900028762). Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding Sources This work has been supported by the Construction Project of Guangzhou Clinical Core Technology of Chinese Medicine [Guangzhou Health Commission] and Guangzhou Municipal Science and Technology Bureau [grant number 202201020555]; the National Natural Science Foundation of China [grant numbers 82305164]; the Science and Technology Program of Guangzhou [grant number 2023A04J1170]; the Guangdong Basic and Applied Basic Research Foundation [2022A1515220064]; the National Traditional Chinese Medicine Inheritance and Innovation Center Research Special Project [grant numbers 2023ZJ04]; the Guangzhou Health and Medical Science and Technology Projects [grant numbers 20252A011031]; the Guangdong Provincial Administration of Traditional Chinese Medicine[grant numbers 20251294]; and the Traditional Chinese Medicine Bureau Of Guangdong Province [grant number 20221134]; State Key Laboratory of Traditional Chinese Medicine Syndrome. 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Cancer Biomark. 2018;22(4):733–45. Huang Y, Ma H, Wei S, Luo G, Sun R, Fan Z, et al. Analysis of the complementarity determining regions beta-chain genomic rearrangement using high-throughput sequencing in periphery cytotoxic T lymphocytes of patients with chronic hepatitis B. Mol Med Rep. 2016;14(1):762–8. Huang XB, Ye SZ, Wu JW, Fu QS, Liu BH, Qiu HX, et al. Diversity of the T cell receptor beta chain complementarity-determining region 3 in peripheral blood of neonates with sepsis: an analysis based on immune repertoire sequencing. Zhongguo Dang Dai Er Ke Za Zhi. 2021;23(11):1154–60. Additional Declarations No competing interests reported. Supplementary Files SupplementTable1.docx SupplementTable2.docx SupplementTable3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":23022143,"visible":true,"origin":"","legend":"\u003cp\u003eFlow analysis of Treg cells and sequencing of high-throughput repertoire. \u003cstrong\u003ea\u003c/strong\u003e This study mainly analyzed the Treg cells (CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eCD127\u003csup\u003e-\u003c/sup\u003e), the naïve Treg cells\u0026nbsp;\u0026nbsp; (CD4\u003csup\u003e+\u003c/sup\u003eCD45RA\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eCD127\u003csup\u003e-\u003c/sup\u003e), the effect Treg cells (CD4\u003csup\u003e+\u003c/sup\u003eCD45RA-CD25\u003csup\u003e+\u003c/sup\u003eCD127\u003csup\u003e-\u003c/sup\u003e) and the activated CD4\u003csup\u003e+\u003c/sup\u003eT cells (CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003e). \u003cstrong\u003eb\u003c/strong\u003e The sequencing steps of high-throughput repertoire include: cell separation, template extraction, amplification and construction of database, sequencing, bioinformatics analysis.\u003c/p\u003e","description":"","filename":"Fig1LZW.png","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/f6dd6eb012fc7766b12f0c9d.png"},{"id":95846931,"identity":"c957d0c3-eef9-4dad-9f4b-611f3bd61c31","added_by":"auto","created_at":"2025-11-13 14:53:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":945950,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of Treg cells and their subsets in peripheral blood. \u003cstrong\u003ea\u003c/strong\u003e Compared with the healthy controls (n=10), total Treg cells and naïve Treg cells decreased significantly (\u003cem\u003eP\u003c/em\u003e＜0.05, \u003cem\u003eP\u003c/em\u003e＜0.05), and activated CD4+T cells increased significantly in patients with RA (n=34, \u003cem\u003eP\u003c/em\u003e＜0.05). \u003cstrong\u003eb\u003c/strong\u003e Selected RA patients who completed 3-month standard treatment (n=22), compared with those before treatment, there was no significant difference in total Treg cells after treatment, but naïve Treg cells and effector Treg cells increased significantly (\u003cem\u003eP\u003c/em\u003e＜0.05, \u003cem\u003eP\u003c/em\u003e＜0.05), while activated CD4+T cells decreased significantly (\u003cem\u003eP\u003c/em\u003e＜0.05).\u003c/p\u003e","description":"","filename":"Fig2LZW.png","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/06d54f5c218ffdacff179ef1.png"},{"id":95846990,"identity":"a4f4c9ae-8f28-4bff-9bfe-756448ba4a52","added_by":"auto","created_at":"2025-11-13 14:54:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53994377,"visible":true,"origin":"","legend":"\u003cp\u003eTCR repertoire sequencing analysis of peripheral Treg cells in RA patients. Sorted Treg cells from peripheral of four RA patients（AD group）were gathered together and analyzed by TCR sequencing. The control sample(Non-AD group)was collected from the peripheral blood of the same four patients after treatment. \u003cstrong\u003ea\u003c/strong\u003e 10000 original reads were randomly selected to make the sequencing quality distribution map. The results showed that the median value of all data was more than 35, and the density distribution was concentrated above the median value, indicating that the data quality was high. \u003cstrong\u003eb\u003c/strong\u003e Statistical analysis of the frequency of V (D) J usage and pie chart showing the first six most frequently used genes in the legend region. \u003cstrong\u003ec\u003c/strong\u003e VDJ combination 2D map (showing the top 20 combinations of the highest frequency). \u003cstrong\u003ed\u003c/strong\u003e 3D map of VDJ combination (showing the first 30 highest frequency V gene).\u003c/p\u003e","description":"","filename":"Fig3LZW.png","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/2e6fa2c09bd6efa6c2cc89e8.png"},{"id":96240790,"identity":"8fda6c65-3a65-470b-a380-ed73cef32f56","added_by":"auto","created_at":"2025-11-19 07:09:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3872547,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of characteristics, abundance and diversity of TCR CDR3 region of Treg cells. \u003cstrong\u003ea\u003c/strong\u003eLength Distribution of CDR3 Amino acids and Nucleic acids. \u003cstrong\u003eb\u003c/strong\u003e The Rank-Abundance curve is a way to analyze diversity. The abundance is reflected by the width of the curve, the higher the abundance of the CDR3 region, the larger the range of the curve on the horizontal axis. Compared with the AD group, the richness of the Non-AD group is higher. Hill index is a function of continuous variable Q, which can directly reflect the diversity index of three samples, that is, Richness index, Shannon index and Simpson index. Abscissa represents different Q values, and ordinates indicate the diversity of samples under that Q value. When Q approaches infinity, the Hill index can reflect the largest component frequency in the sample. The clones of Treg cells of AD group were less diverse than those of Non-AD group. \u003cstrong\u003ec\u003c/strong\u003e Distribution of D50 index and richness.\u003c/p\u003e","description":"","filename":"Fig4LZW.png","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/9c1576a4aba1bbc255f4aedc.png"},{"id":96240102,"identity":"0d95871f-3063-476c-b7c9-32c445a433d1","added_by":"auto","created_at":"2025-11-19 07:08:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4613670,"visible":true,"origin":"","legend":"\u003cp\u003eCollinear analysis of TCR CDR3 VDJ differential genes and their combinations in Treg cells. \u003cstrong\u003ea\u003c/strong\u003e Chi-square test was used to analyze the difference of VDJ usage gene. \u003cstrong\u003eb\u003c/strong\u003e Rank sum test (Wilcoxon test) was used to analyze the differences of V and J genes between the two groups. \u003cstrong\u003ec\u003c/strong\u003e The collinearity graph of V gene, V family gene, VD gene and VDJ gene between AD and Non-AD group.\u003c/p\u003e","description":"","filename":"Fig5LZW.png","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/697e874b95247e55fba1c711.png"},{"id":95846954,"identity":"87c4c64d-5241-41b6-9ce2-ea1b42f047a0","added_by":"auto","created_at":"2025-11-13 14:53:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":95555,"visible":true,"origin":"","legend":"\u003cp\u003eThe abnormal activation of effector T cells is induced by three aspects, one of which may be related to the decrease of Treg cell abundance and diversity caused by high disease activity.\u003c/p\u003e","description":"","filename":"Fig6LZW.png","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/137f46ba17c0ec4c55cd73e2.png"},{"id":95846967,"identity":"d934d6f7-c5ea-40f4-b761-a1ce1aff43e2","added_by":"auto","created_at":"2025-11-13 14:53:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":69018641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eThe mRNA levels expression of TRBV28|TRBJ2-1 \u0026nbsp;in the AD group compared to the Non-AD controls. \u003cstrong\u003eb \u003c/strong\u003eThe mRNA levels expression of TRBV18|TRBJ2-3 in the AD group compared to the Non-AD controls.\u003c/p\u003e","description":"","filename":"Fig7LZW.png","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/67b42305a4d9dc8f9b8bcc3a.png"},{"id":95846959,"identity":"9e71aa32-84ee-42b2-9dd7-06a5c8c369d4","added_by":"auto","created_at":"2025-11-13 14:53:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15236,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/f69ac335b9102eafdec8d388.docx"},{"id":95846921,"identity":"dd3e8d2d-4083-4a14-af90-49dadbb3226e","added_by":"auto","created_at":"2025-11-13 14:53:53","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13251,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/87ef620122682b0cfe5a5f51.docx"},{"id":95846932,"identity":"77cd3b5c-8df5-4a28-8efe-9f810954947f","added_by":"auto","created_at":"2025-11-13 14:53:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16069,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7953908/v1/1c4b64d97b82e7abf220e3ec.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"High-throughput Treg cell receptor sequencing reveals the relationship between disease activity and immunosenescence in patients with rheumatoid arthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is an aging-related disease characterized by autoimmune disorders, inflammatory cell infiltration of joints, and progressive bone destruction[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Autoreactive T cells have been observed in the synovium and peripheral blood (PB) of patients with RA, where they are activated to secret inflammatory cytokines, so it is considered to be a T cell-mediated disease[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The imbalance between autoreactive T cells and regulatory T cells (Treg) is the basis of RA immune imbalance. As Treg cells play an important role in the peripheral induction of autoimmune tolerance, the functional defects of Treg in patients with RA and the treatment strategies for Treg have attracted more and more attention in recent years.\u003c/p\u003e\u003cp\u003eThe adaptive immune system is prone to age-related degeneration in response to high reproductive demands, a process that leads to a decline in protective immune function accompanied by excessive inflammatory activity[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. With the increase of age, the number of na\u0026iuml;ve T cells and the diversity of TCR repertoire gradually decreased, resulting in immunosenescence which means the ability to recognize self-antigen and regulate autoimmune response decreased, inflammatory factors increased, and the body was in a state of chronic inflammation. The antigen recognition and response ability of T cells is determined by T cell receptor (TCR). In TCR, the high diversity is generated by genomic rearrangement of the variable (V), diversity (D) and joining (J) regions, along with palindromic and random nucleotide additions, which is crucial for understanding of adaptive immunity in health and disease[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The specificity and diversity of TCRs predominantly depend on the complementarity determining region 3 (CDR3), which is encoded by V(D)J recombination and interacts with the peptide presented by the major histocompatibility complex (MHC)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the process of adaptive immunity, the TCR gene of peripheral blood T cells will undergo rearrangement to form a variety of immune repertoires patterns to meet the needs of external environment and immune regulation of the body[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The decrease of the diversity of TCR repertoire will affect the immunomodulatory function of T cells and cause autoimmune diseases. RA has both an immune disorder and chronic inflammation and is thought to have immunosenescence as well[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious RA studies have only focused on the CD4\u003csup\u003e+\u003c/sup\u003e T cell TCR repertoires, but no studies have sequenced the TCR repertoires of Treg cells, which are more difficult to obtain and play an important role in autoimmune tolerance dysfunction in RA[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, High disease activity is an independent risk factor for RA. Long-term high disease activity will lead to synovial hyperplasia and bone destruction in patients with RA. However, as far as we know, the correlation between disease activity and immunosenescence has not been found. More importantly, TCR diversity is closely related to treatment effectiveness and patient prognosis in different diseases[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In summary, we speculate that there may be a correlation between disease activity and immunosenescence in RA patients during acquired adaptive immunity. High disease activity may be closely associated with a decreased diversity of Treg cell receptor repertoire and ultimately a diminished ability of Treg cells to recognize autoantigens and induce autoimmune tolerance. Therefore, in this study, high-throughput repertoire sequencing and flow cytometry were used to study the relationship between disease activity and immunosenescence, and to further explore the pathogenesis of RA.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eFour RA patients with high disease activity in this study for TCR sequencing were recruited from the Department of Rheumatology in The First Affiliated Hospital of Guangzhou University of Chinese Medicine (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Disease activity of every subject was assessed using DAS28 of The European League Against Rheumatism (EULAR)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Low disease activity was 3.2\u0026thinsp;\u0026gt;\u0026thinsp;DAS28\u0026thinsp;\u0026gt;\u0026thinsp;2.6, moderate active disease was 5.1\u0026thinsp;\u0026gt;\u0026thinsp;DAS28\u0026thinsp;\u0026gt;\u0026thinsp;3.2 and high active disease was DAS28\u0026thinsp;\u0026gt;\u0026thinsp;5.1, while DAS28\u0026thinsp;\u0026lt;\u0026thinsp;2.6 means RA in remission. We treated RA patients according to the American College of Rheumatology (ACR) Guideline for treatment[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To compare the changes in the immunome pool of Treg cells in PBMC of patients before and after treatment, peripheral blood from additional 34 RA patients and 10 healthy volunteers enrolled in this study were used for flow cytometry (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Only 22 of 34 RA patients finished the treatment lasting 3 months. These patients with DAS28\u0026thinsp;\u0026gt;\u0026thinsp;2.6 before treatment were defined as the active disease group(AD group), while with DAS28\u0026thinsp;\u0026lt;\u0026thinsp;2.6 after treatment were defined as the non-active group(Non-AD group). Informed consent for peripheral blood sampling was obtained from these study subjects. This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (ZYYECK[2018]141) and filed in Chinese Clinical Trial Registry (ChiCTR1900028762).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of RA patients before and after treatment recruited for TCR sequencing.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSEX\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAGE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u003cp\u003ePre-treatment(AD group)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c15\" namest=\"c10\"\u003e\u003cp\u003ePost-treatment(Non-AD group)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eESR (mm/h)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDAS28 (CRP)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDAS28 (ESR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRheumatoid\u003c/p\u003e\u003cp\u003efactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eACPA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eESR (mm/h)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eDAS28 (CRP)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eDAS28 (ESR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eRheumatoid\u003c/p\u003e\u003cp\u003efactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" 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colname=\"c10\"\u003e\u003cp\u003e23.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e936.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFEMALE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFEMALE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e202.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e22.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFEMALE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e456.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e133.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e22.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e176.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e127.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e\u003cp\u003eESR, Erythrocyte sedimentation rate; CRP, C reactive protein; DAS28, Disease Activity Score with 28-joint counts; ACPA, anti-cyclic citrullinated peptide.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of healthy participants and RA patients before and after treatment.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealthy participants\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRA participants\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePre-treatment\u003c/p\u003e\u003cp\u003e(AD group, n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePost-treatment\u003c/p\u003e\u003cp\u003e(Non-AD group, n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(80.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27(79.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19(86.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19(86.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53(6.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.82(7.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.09(7.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.09(7.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESR(mm/h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.90(21.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.22(19.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.05(25.72)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.69(27.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.06(28.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.77(18.59)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDAS28-ESR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.18(0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.18(0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.40(1.54)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDAS28-CRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.59(0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.58(0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.94(1.11)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRheumatoid factor, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28(82.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17(77.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15(68.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACPA, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32(94.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20(90.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20(90.90)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eN/A, not applicable; \u003csup\u003e*\u003c/sup\u003e, significance with pre-treatment(AD group) vs. post-treatment(non-AD group).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcess of flow analysis and High-throughput Treg cell receptor sequencing\u003c/h3\u003e\n\u003cp\u003eThis study mainly analyzed Treg cells (CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eCD127\u003csup\u003e\u0026minus;\u003c/sup\u003e), na\u0026iuml;ve Treg cells (CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD45RA\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eCD127\u003csup\u003e\u0026minus;\u003c/sup\u003e), effect Treg cells (CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD45RA-CD25\u003csup\u003e+\u003c/sup\u003eCD127\u003csup\u003e\u0026minus;\u003c/sup\u003e) and activated CD4\u003csup\u003e+\u003c/sup\u003eT cells (CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Firstly, we analyzed the percentage of activated CD4\u003csup\u003e+\u003c/sup\u003eT cells, Treg cells and their subsets in the peripheral blood of 10 healthy people and 34 RA patients by flow cytometry, and then further analyzed the changes in the percentage of these cells in 22 RA patients after treatment. Lastly, we recruited another 4 RA patients who changed from high disease activity to clinical remission after standard treatment, and Treg cells were selected from peripheral blood before and after treatment for high-throughput Treg cell receptor sequencing. The specific steps include: cell sorting, template extraction, amplification and construction of database, sequencing, bioinformatics analysis(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\n\u003ch3\u003ePBMC isolation and cell sorting by FACS\u003c/h3\u003e\n\u003cp\u003eBriefly, PBMCs were isolated using standard Ficoll-Paque (GE Healthcare, USA) by density gradient centrifugation. For detection of Treg cells, PBMCs were labeled with surface markers anti-CD4-FITC (A07750), anti-CD25-PC5 (IM2646), CD127-PE (IM1980U) antibody (BECKMAN COULTER, INC. USA) antibodies. Fluorescence activated cell sorting (FACS) was performed on the MoFlo Astrios (BECKMAN COULTER, INC. USA). Tregs with high purity (\u0026gt;\u0026thinsp;98%) were obtained for the following experiments.\u003c/p\u003e\n\u003ch3\u003eRNA extraction and cDNA synthesis\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted according to the TRIZOL kit (Invitrogen, California, USA) instructions. Qualified RNA was synthesized by reverse transcription using SMARTScribe\u0026trade; reverse transcriptase (Clontech, California, USA) for the preparation of 5'RACE cDNA for high-throughput sequencing and RT-qPCR experiments.\u003c/p\u003e\n\u003ch3\u003eTCR repertoire preparation and sequencing\u003c/h3\u003e\n\u003cp\u003eTwo rounds of nested PCR was performed for TCR library preparation. For the first round of PCR amplification, 45\u0026micro;l of cDNA from the synthesis reaction was mixed with primers (Supplement Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and Q5\u0026reg; High-Fidelity 2X Master Mix(NEB, USA). The PCR cycling conditions were as follows: an initial denaturation at 92℃for 2 mins, followed by 18 cycles of denaturation at 94\u0026deg;C for 30s, annealing of primer to DNA at 60\u0026deg;C for 30s, and extension at 68\u0026deg;C for 2 mins. PCR products were purified using MinElute PCR Purifcation Kit (Qiagen, Hilden, Germany). Products from the previous step was used in second round of PCR reaction under the same condition with 16 cycles. PCR products were purified using MinElute PCR Purifcation Kit (Qiage, Hilden, Germany). Libraries were built with NEB next Ultra DNA Library Prep kit (New England BioLabs, Massachusetts, USA) according to the manufacturer\u0026rsquo;s protocol. The validated libraries were used for sequencing on Illumina MiSeq platform (Illumina, San Diego, USA) following the standard pipelines of Illumina by Guangzhou Huayin Health Medical Group Co.,Ltd (GuangZhou, China), generating 2\u0026times;150 bp paired-end reads[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData processing and Bioinformatics Analysis\u003c/h2\u003e\u003cp\u003eThe primary data obtained from high-throughput sequencing were transformed into raw sequence reads by base calling and recorded in FASTQ format. Reads without primers were discarded. PCR and sequencing errors were rectified by unique molecular identifiers (UMIs). A clonotype was identified by the CDR3 amino acid sequence for further analysis, and only sequences with reads\u0026thinsp;\u0026ge;\u0026thinsp;2 were enrolled in the analysis. TCRβ V, D, J genes and clonotypes were defined according to IMGT59 and Igblast; the TCRβ VDJ combination was defined by MIXCR. The V (D) J frequency and frequency statistics of each group were drawn with matplotlib (version 2.2.4) of python (version 2.7.15) and ggplot2 (version 3.3.5) of R (version 3.5.1) respectively. Differential heat map: use R (version 3.5.1) for chi-square test to calculate p value, ComplexHeatmap package (version 2.1.0) to draw heat map. Heatmap barplot uses R (version 3.5.1) pheatmap package (version 1.0.12) to draw heat maps with no difference calculation. Heat map uses R (version 3.5.1) ggplot2 (version 3.3.5) to draw heat map, no difference calculation. Collinearity analysis: scipy (version 1.2.2) of python (version 2.7.15) was used for linear regression calculation, and matplotlib (version 2.2.4) was used to draw scatter plot. Richness and diversity statistics: using the alakazam package of R (version 3.4.1) (version 0.2.8) to calculate the richness and diversity (Hill index) and draw the graph. Statistical chart of diversity index difference: using R (version 3.5.1) to compare the diversity index of the group for \u003cem\u003et\u003c/em\u003e-test, ggplot2 (version 3.3.5) to draw a dot map.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFlow cytometric analysis\u003c/h3\u003e\n\u003cp\u003eEach fresh 100\u0026micro;l peripheral blood anticoagulated by EDTA from RA patient recruited were taken and removed to the bottom of the flow tube, labeled antibodies were then added to the flow tube mixing with the peripheral blood for incubating 15min at room temperature away from light. Anti-CD3-ECD (A07748), anti-CD4-FITC (A07750), anti-CD25-PC5 (IM2646), CD127-PE (IM1980U) antibody (BECKMAN COULTER, INC. USA), anti-human HLA-DR antibody (HPDR-025, 4A BIOTECH, Beijing) and anti-human CD45RA antibody (FNH0453-025, 4A BIOTECH, Beijing) were chosen to screen and label different T cell subsets. Mix 300\u0026micro;l of RBC lysing buffer per 100\u0026micro;l of antibodies-labeled peripheral blood, then incubate for 3 min at room temperature in the dark after shaking well. Wash twice with PBS wash buffer and then centrifuge at 1500 rpm for 5 minutes, discard the supernatant. Resuspended cells with 500\u0026micro;l PBS wash buffer were immediately detect using flow cytometry (BECKMAN COULTER, INC. USA). The flow analysis was repeated twice for each sample to ensure objective and reliable data.\u003c/p\u003e\n\u003ch3\u003eQuantitative reverse transcription PCR(RT-qPCR)\u003c/h3\u003e\n\u003cp\u003eSubsequently, we performed RT-qPCR validation on TRBV28|TRBJ2-1 and TRBV18|TRBJ2-3, which showed significant differences in the high-throughput sequencing results and had significantly higher gene frequencies compared to the Non-AD group. The cDNA samples obtained above were analyzed using the SYBR Green Premix Pro Taq HS qPCR Kit (#AG11701) and run on the Applied Biosystems QuantStudio 5 Real-Time PCR System (Thermo Fisher Scientific). RNA levels were normalized to GAPDH levels. The primer sequences were as follows:TRBV18: GCCAGCTCAACTCAGGGGGCGGAGACCCAGTAC;TRBJ2-3:AGTGCTATTCCCGGGACGGGCACAGATACGCAGTAT;TRBV28: GCCAGCAGTCTCGGAGGTCCCTGGCGAGATACGCAGTAT;TRBJ2-1: GCCAGCAGCCAAGTTTTTCTAGCGGGAGAGGATGAGCAGTTC.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eMatched samples were compared using a paired \u003cem\u003et\u003c/em\u003e-test. Samples from different group were compared using the Wilcoxon test, independent-sample \u003cem\u003et\u003c/em\u003e-test and Chi-square test. Statistical analysis were performed using SPSS software version 22.0 (IBM Corp., Armonk, New York). \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFlow analysis of activated CD4\u003csup\u003e+\u003c/sup\u003eT cells, Treg cells and their subsets\u003c/h2\u003e\u003cp\u003eIn our study, 10 healthy persons and 34 RA patients were analyzed. The percentage of total Treg cells and na\u0026iuml;ve Treg cells in RA patients were all significantly lower than that in healthy people (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), but there was no statistical difference in the percentage of effector Treg cells (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). On the contrary, when compared with the health group, the percentage of activated CD4\u003csup\u003e+\u003c/sup\u003eT cells was significantly increased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). Next, we selected the patients (n\u0026thinsp;=\u0026thinsp;22) who completed the treatment for 3 months from the 34 RA patients, and analyzed these cells ratio before and after treatment. Interestingly, we found that compared with before treatment, there was no significant change in the percentage of total Treg cells (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), but the percentage of na\u0026iuml;ve Treg cells after treatment were significantly higher than that before treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). In addition to, the percentage of activated CD4\u003csup\u003e+\u003c/sup\u003eT was decreased (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eVDJ gene combination frequency of Treg cells\u003c/h2\u003e\u003cp\u003eFlow analysis showed that there was no significant difference in the percentage of total Treg cells in peripheral blood between the two groups. Therefore, we further analyzed the function of Treg cells. The Treg cells were selected from peripheral blood for high-throughput cell receptors. In this study, 10000 original reads were randomly selected to do the sequencing mass distribution map. The median value is greater than 35, and the density distribution is concentrated above the median value, indicating that the data quality is high(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The diversity of V(D)J constitutes the diversity of repertoire, and there are differences in the use frequency of V(D)J among different individuals. The study on the difference of use frequency is of great significance to reveal the difference of sample repertoire. Here, the use frequency of V(D)J of the sample is statistically analyzed, and the pie chart is drawn to display the first six most frequently used genes in the legend region in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, there are some differences in the highest frequency of V gene between Non-AD group and AD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). TRBV20-1 gene is obviously dominant in AD group, while there is no significant difference in frequency between TRBV5-1, TRBV20-1 and TRBV13, which are most frequently used in Non-AD group. The combination of different V(D)J genes greatly enriches the diversity of repertoire, in which the combination of VJ and V(D)J is particularly important. Here, statistics are made on the combination mode and frequency of samples, 2D map of V(D)J combination (showing the first 20 combinations with the highest frequency) and 3D map of VJ combination (showing the first 30 highest frequency V gene). There is a great difference in the dominant clone type between the AD group and the Non-AD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In the AD group, 3 dominant clones accounted for an unusually high percentage, and other clones were evenly distributed, while in the Non-AD group, TRBV13 | TRBD1 | TRBJ2-3 clones accounted for a higher percentage. In addition, the TRBV13|J2-3 combination is obviously dominant in the AD group, while the frequency of several combinations in the Non-AD group is higher, and the dominant type is not obvious (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Importantly, all results suggested that the frequency of V(D)J gene combination in Non-AD group was significantly higher than that in AD group (Supplement Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of characteristics, abundance and diversity of TCR CDR3 region of Treg cells\u003c/h2\u003e\u003cp\u003eStatistics of nucleic acid and amino acid length distribution information of CDR3 region contained in Clone in each sample, and draw a bar chart to fully show the characteristics of the sample and the differences between samples. There were more amino acids distributions between 12 and 14 in length. In addition, The distribution length of nucleic acid mostly concentrated between 12 and 14 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). However, CDR3 length did not discriminate AD group and Non-AD group. The length distributions of TCR CDR3 were nearly identical in both groups. The Rank-Abundance curve is a way to analyze diversity. The Rank-Abundance curve reflects the richness of CDR3 region and the uniformity of different sequence types of CDR3 region in the sample. In the horizontal direction, the higher the richness of the CDR3 region, the larger the range of the curve on the horizontal axis; the shape of the curve (smoothness) reflects the uniformity of the sequence type of the CDR3 region in the sample, and the steeper the curve is, the more uneven the distribution of the sequence type of the CDR3 region in the sample is. We found that the richness of Non-AD group is better than that of AD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c), but there was no statistical difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2094). In addition, the distribution of sequence types in Non-AD group was relatively more uniform. The D50 index can directly reflect sample diversity indices, the minimum value of D50 is 0, which means that there is only one CDR3 sequence and the diversity is poor. The maximum value of D50 is 0.5, which means that the proportion of all CDR3 sequences is consistent and the diversity is good[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, we used the D50 index to compare TCR CDR3 diversity in AD and Non-AD groups. All results indicated that the Non-AD group exhibited significantly higher TCR CDR3 diversity compared with AD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c, P\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCollinear analysis and experimental validation of TCR CDR3 VDJ differential genes and their combinations in Treg cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNext, we used Chi-square test and Wilcoxon test methods to analyze the difference of V(D)J gene between the two groups of samples and found that there were significant differences in genes between AD-04 and Non-AD-04 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b), especially TRBV18, TRBV28, TRBV7.9 and TRBJ2.7. Then we draw the correlation map based on the frequency difference of V gene, VJ gene combination and VDJ gene combination(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The analysis results show that the R2 of V gene and V family is greater than 0.8 and it shows that there is little difference between the two groups, but the R2 of VJ and VDJ gene is 0.627 and 0.542 respectively, indicating that there is a great difference between the two groups. In particular, TRBV28 | TRBJ2-1, TRBV18 | TRBJ2-3 (high frequency in AD group), TRBV5-1 | TRBJ2-5, TRBV5-1 | TRBJ2-7, TRBV18 | TRBJ2-7 (high frequency in Non-AD group) VJ gene combinations are worthy of further study. It is of great value to find unknown autoantigens and explore RA immune mechanism in looking for autoimmune Treg cell clones.\u003c/p\u003e\u003cp\u003eTo validate our sequencing findings, we measured the expression of TRBV28|TRBJ2-1 and TRBV18|TRBJ2-3. Consistent with the sequencing data, both pairs showed significantly increased mRNA levels in the AD group compared to the Non-AD controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-b).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRA is a chronic inflammatory disease characterized by autoimmune disorders, infiltration of activated inflammatory cells in the synovium, tumor-like hyperplasia of the synovium, and progressive destruction of cartilage and bone. CD4\u003csup\u003e+\u003c/sup\u003eT (Th) cells are one of the main inflammatory cells in RA synovium. Due to the stimulation of autoantigens and related cytokines, na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003eT is activated and differentiated into various Th cell subsets, such as Th1, Th17, Treg and so on. These synovial T cells regulate the function of other cells in the synovium and, together with other cells, form ectopic lymphoid structures with chronic destructive effects in the synovium. It provides sufficient conditions for chronic and persistent immune response[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the process of adaptive immunity, the thymus clears the na\u0026iuml;ve T cell clones that recognize self-antigen (central immune tolerance) through positive and negative selection. However, in some cases, some self-reactive T cells escape to peripheral blood, and peripheral autoimmune tolerance plays an important role[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Among them, Tregs are one of the regulatory T cell subsets, which have important functions of inhibiting autoreactive T cells, inducing immune anergy and maintaining immune stability. In recent years, it has attracted wide attention in the study of autoimmune diseases[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Treg cells can not only regulate T cell mediated immune response, but also inhibit the activation of autoreactive T cells, so as to ensure that the body will not react to autoantigens and effectively reduce the harmful autoimmune response[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Immunotherapy against Treg cells has become an important strategy for the treatment of autoimmune rheumatism[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Studies have shown that deletion of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eTreg cells can aggravate the condition of collagen-induced arthritis (CIA), while infusion of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eTreg can alleviate the progression of CIA arthritis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, in this study, flow cytometry was used to study the proportion of Treg cells and their subsets in peripheral blood of normal subjects (n\u0026thinsp;=\u0026thinsp;10), RA patients (n\u0026thinsp;=\u0026thinsp;34) and RA patients (n\u0026thinsp;=\u0026thinsp;22) after standard treatment. We found the number of na\u0026iuml;ve Treg cells decreased in RA, compared with healthy people. In contrast, the proportion of activated CD4\u003csup\u003e+\u003c/sup\u003eT cells that reflect disease activity is significantly increased of RA patients than in healthy subjects, which has a similar outcome to previous studies[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Next, we found that after treatment, with the decrease of disease activity, the ratio of na\u0026iuml;ve Treg cells increased significantly, and the proportion of activated CD4\u003csup\u003e+\u003c/sup\u003eT cells decreased significantly, but here's what we don't understand is that there was no significant difference in the proportion of total Treg cells. Previous studies have shown that the number of Treg cells in the peripheral blood of RA patients is lower than that of normal people, on the contrary, another part of the study believes that Treg cells have not decreased, which is controversial. The more consistent view is the increase of the number of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFoxp3\u003csup\u003e+\u003c/sup\u003eT cells in RA joint effusion[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], but why are the joint inflammation and autoreactive lymphocytes of RA not inhibited? Further study found that, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the number of Foxp3\u003csup\u003e+\u003c/sup\u003eTreg cells increased, but their function was seriously weakened, partly because Treg cells from RA patients had a deficiency in CTLA-4 inhibition function and could not effectively down-regulate T cell receptor signals[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, TNF-α can induce dephosphorylation of Foxp3 through protein kinase 1 (protein phosphatase 1), which inactivates Treg cells[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, the latest research shows that the abundance of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eFoxp3\u003csup\u003e+\u003c/sup\u003eTCR repertoire decreases, its ability to recognize self-antigen is weakened, and autoimmune reaction can occur[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, we selected 4 RA patients with better therapeutic effect. After 3 months of standard treatment, DAS28 changed from high disease activity (AD group) to clinical remission (Non-AD group). We isolated Treg cells from the peripheral blood of patients before and after treatment, and compared the changes of Treg cell abundance and diversity before and after treatment by high-throughput repertoire sequencing technique. High-throughput sequencing can simultaneously sequence millions of molecules and has been proven to be helpful to analyze the true TCR or BCR repertoires in patients[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous studies have shown that the antigen recognition and regulation ability of Treg cells is determined by T cell receptors. More than 90% of the peripheral TCR is composed of α and β chain, most of the TCR sequences are concentrated in the TCR β chain, and CDR3 is the key region of antigen specific binding with, which determines the specificity and function of Treg cells[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A previous study reported an abnormally short length of TCRB CDR3 in type 1 diabetes patients, which was related to abnormal preselection process and induced an enrichment of auto-reactive TCR repertoire in peripheral blood of patients[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Thus, it is of profound interest to examine whether similar phenomenon exists in RA patients. However, we found that there was no significant difference in the distribution of amino acids in CDR3 region between AD group and Non-AD group. In addition, there was no significant difference in nucleic acid distribution between the two groups, and there were more nucleic acid distributions between 36 and 42 in length, which may be due to the small sample size (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eIn recent years, it has been found that there is a \"rearrangement\" of peripheral TCR, that is, reopening gene recombination activating enzyme (RAG) to rearrange the V (D) J gene, modify the original receptor or produce a new receptor, so as to form a peripheral T lymphocyte receptor bank, clear self-reactive cells, or change the responsiveness to self-antigen. This mechanism plays an important role in immune response mechanisms such as expansion of peripheral T cell bank, peripheral T cell tolerance and autoimmune diseases[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our study found that the highest frequency of V gene was different between the Non-AD group and the AD group and the frequency and frequency of V(D)J gene combination in Non-AD group was significantly higher than that in AD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d). This suggests that the diversity of Non-AD group is due to AD group. In addition, we further compared the abundance and diversity of Treg cells between the two groups by analyzing Rank-Abundance curve and Hill index, which also suggested that high disease activity could indeed reduce the abundance and diversity of Treg cells to activate the autoreactive T cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Previous studies have also shown that high abundance of TCR repertoire helps Foxp3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003eTreg cells recognize and inhibit self-reactive T cells[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMore and more studies have shown that aging is related to the decline of adaptive immune function. The diversity of TCR β chain CDR3 group decreased almost linearly with the increase of age[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This phenomenon of immunosenescence has three characteristics: a. the decline of thymic function leads to the gradual decrease of na\u0026iuml;ve T cells; b. continuous antigen stimulation leads to the increase of functionally deficient, monoclonal and senescent T cells and the decrease of T cell bank diversity; c. serum TNF-α, IL-6 and other inflammatory proteins are increased, and the body is in a state of chronic inflammation. The pro-inflammatory environment created by these factors may induce or accelerate the occurrence and development of RA, so RA is considered to be related to immunosenescence[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our study found that: a. Compared with healthy people, the number of na\u0026iuml;ve Treg cells decreased, but after standard treatment, the number of na\u0026iuml;ve Treg cells increased with the decrease of disease activity, suggesting that there was a negative correlation between disease activity and the percentage of na\u0026iuml;ve Treg cells; b. The TCR abundance and diversity of Treg cells in Non-AD group were significantly higher than those in AD group, which suggested that the diversity of Treg cells was restored with the decrease of disease activity. To sum up, our research shows that there may be a certain correlation between disease activity and immunosenescence in patients with RA. High disease activity may lead to the decrease of the diversity of Treg cell receptor repertoire, weaken its ability to recognize self-antigen and induce immune tolerance, and lead to the accelerate the deterioration of RA.\u003c/p\u003e\u003cp\u003eUnder the condition that the antigen is still unclear, searching for autoimmune T cell clones by Treg cell receptor β chain CDR3 repertoire type and sequencing is a new idea to study the mechanism of autoimmune intolerance such as RA. We found differences between AD-04 and non-AD-04 genes, particularly TRBV18, TRBV28, TRBV7.9 and TRBJ2.7. For the frequencies of gene combination it was found that TRBV28|TRBJ2-1, TRBV18|TRBJ2-3VJ gene frequencies were significantly increased in the AD group compared to the Non -AD group. This observation was further validated by our subsequent RT-qPCR experiments. Previous studies have found that the beta chain of TCR can effectively transfer TCR to Jurkat cells by using TRBV18, TRBD1 and TRBJ2-7 by combining TCR V beta 18-specific mAb and DRB1*0101 Art V 1 tetramer, thus activating the immune response[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. TRBV18 is highly expressed in breast cancer patients, and T cell clones that express TRBV18 are preferentially activated when stimulated by antigens[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In addition, in the comparative analysis of the characteristics of chronic hepatitis B before and after treatment, it was found that TRBV28 may be an important site for T cells to be regulated and induced to eliminate the invading virus[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In children with sepsis, compared with the health group, the case group had significantly higher frequencies of TRBJ2-3, TRBJ2-5, and TRBJ2-7 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Based on the above studies, we believe that these gene combinations can be used as biomarker for further study, which is of great value for finding autoimmune T cell clones, finding unknown autoantigens and exploring the immune mechanism of RA.\u003c/p\u003e\u003cp\u003eAs with all researches, potential limitations should to be noted. On the one hand, It is well known that there is great heterogeneity in clinical samples. However, we collected 10 healthy people and 34 RA patients, and repeated measurements were used in flow cytometry analysis to ensure the authenticity of the data as much as possible. On the other hand, in the aspect of repertoire sequencing, we only examined 4 RA patients before and after treatment. Although the sample size of this study is small, but most of the key indicators have statistical differences, more importantly, the main significance of our research is to put forward a novel point of view and research direction. Due to these limitations, our findings need to be interpreted cautiously. Nevertheless, our study also had advantages. First, as far as we know, this study was the first study investigated the relationship between disease activity and immunosenescence in patients with RA. Next in importance, in order to explore the correlation between disease activity and immunosenescence, we used repertoire sequencing and flow cytometry to study the number and function of Treg cells, which is a new idea to study the mechanism of autoimmune intolerance such as RA, and has important clinical significance for guiding the immunotherapy of diseases and evaluating immune remodeling.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBriefly, we confirmed that there is a correlation between disease activity and immunosenescence in RA patients by high-throughput repertoire sequencing and flow cytometry. High disease activity can not only reduce the number of immature Treg cells, but also lead to the decrease of their abundance and diversity, weaken their ability to recognize their own antigens and induce immune tolerance, leading to the occurrence and development of RA. In addition, we also found many VJ gene combinations worthy of further study, which are of great value for finding autoimmune T cell clones, discovering unknown autoantigens and exploring the immune mechanism of RA.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStatement of Ethics\u003c/h2\u003e\u003cp\u003eThis study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (ZYYECK[2018]141) and filed in Chinese Clinical Trial Registry (ChiCTR1900028762).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (ZYYECK[2018]141) and filed in Chinese Clinical Trial Registry (ChiCTR1900028762).\u003c/p\u003e\n\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\n\u003cp id=\"_Toc472330566\"\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding Sources\u003c/h2\u003e\n\u003cp id=\"_Toc472330568\"\u003eThis work has been supported by the Construction Project of Guangzhou Clinical Core Technology of Chinese Medicine [Guangzhou Health Commission] and Guangzhou Municipal Science and Technology Bureau [grant number 202201020555]; the National Natural Science Foundation of China [grant numbers 82305164]; the Science and Technology Program of Guangzhou [grant number 2023A04J1170]; the Guangdong Basic and Applied Basic Research Foundation [2022A1515220064]; the National Traditional Chinese Medicine Inheritance and Innovation Center Research Special Project [grant numbers 2023ZJ04]; the Guangzhou Health and Medical Science and Technology Projects [grant numbers 20252A011031]; the Guangdong Provincial Administration of Traditional Chinese Medicine[grant numbers 20251294]; and the Traditional Chinese Medicine Bureau Of Guangdong Province [grant number 20221134]; State Key Laboratory of Traditional Chinese Medicine Syndrome.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eDesign of the entire study: Guangxing Chen. Experimental studies: Congqi Hu, Wei Jiao, Lu Zhang, Yanqing Duan, Jia Xu, Hui Deng, Jiahui Yu. Experimental data analysis and statistic: Jiduo Liu, Lijuan Liu, Mingying Zhang. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll the raw data in this manuscript have uploaded to FigShare, \u0026nbsp;which can access after publication via this DOI: 10.6084/m9.figshare.20347413. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSparks JA. Rheumatoid arthritis. Ann Intern Med. 2019;170(1):ITC1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakeuchi Y, Hirota K, Sakaguchi S. Impaired T cell receptor signalling and development of T cell-mediated autoimmune arthritis. 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Zhongguo Dang Dai Er Ke Za Zhi. 2021;23(11):1154\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid arthritis, Treg TCR, immunosenescence, Immuno-Seq sequencing, Disease activity","lastPublishedDoi":"10.21203/rs.3.rs-7953908/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7953908/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTreg cells play an important role in the induction of autoimmune tolerance, and their ability of antigen recognition and regulation is determined by the receptor (TCR). TCR diversity determines the autoimmune responses in RA and is closely associated with autoimmune diseases prognosis and prevention. Previous studies have found that there is immunosenescence in RA, that is, the decrease of the number of na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003eT cells and the reduction of TCR repertoire diversity. High disease activity is an independent risk factor for RA. We speculate that high disease activity is related to immunosenescence\u0026mdash;the decline of adaptive immune function and a chronic inflammation, but its biological mechanism is still unclear and needs to be further elucidated. In this study, high-throughput Immuno-Seq sequencing and flow cytometry were used to compare normal subjects and RA patients, as well as RA patients whose disease activity changed to clinical remission after standard treatment. It was found that high disease activity not only led to a decrease in the number of na\u0026iuml;ve Treg cells, but also led to a decrease in the diversity of Treg cell receptor repertoire, weakening their ability to recognize self-antigens and induce immune tolerance, leading to the occurrence and development of RA. Besides, these characteristics of the TCR repertoire, particularly the disease activity related clones, can potentially serve as biomarkers and provide novel insights for disease status and therapeutical targets in autoimmune diseases.\u003c/p\u003e","manuscriptTitle":"High-throughput Treg cell receptor sequencing reveals the relationship between disease activity and immunosenescence in patients with rheumatoid arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 14:53:17","doi":"10.21203/rs.3.rs-7953908/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"429ab746-fccf-461d-91de-3384dfda1937","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 14:53:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7953908","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7953908","identity":"rs-7953908","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}