Use of two circular RNAs

Abstract

This invention discloses the application of two circular RNAs, hsa_circRNA_405498 and hsa_circRNA_100033, as biomarkers of circular RNA (circRNA) in peripheral blood mononuclear cells (PBMCs) of patients with type 1 diabetes (T1D), for the development of biomarkers and kits for early clinical diagnosis of T1D. The levels of hsa_circRNA_405498 and hsa_circRNA_100033 in PBMCs of T1D patients are significantly reduced, showing significant differences compared to adults with latent autoimmune diabetes (LADA), type 2 diabetes (T2D), and normal glucose tolerance (NGT). Clinically, they can be used as diagnostic biomarkers for the diagnosis and differential diagnosis of T1D patients. Furthermore, overexpression of hsa_circRNA_405498 also has the potential to treat type 1 diabetes.

Description

Technical Field

[0001] This invention belongs to the field of diabetes diagnosis and treatment technology, specifically involving the application of two circular RNAs, hsa_circRNA_405498 and hsa_circRNA_100033, in the field of diabetes diagnosis and treatment. Background Technology

[0002] Type 1 diabetes mellitus (T1DM) is an autoimmune disease caused by the interaction of genetic and environmental factors, which induces an immune attack on pancreatic β cells, leading to an absolute deficiency of insulin. T1DM has a rapid onset and severe symptoms, posing a greater threat and being more difficult to prevent and treat than type 2 diabetes mellitus (T2DM). Given my country's large population and increasing incidence rate, the overall number of T1DM patients in my country is significant. However, the precise pathogenesis of immune destruction of pancreatic β cells in T1DM remains unclear. This situation severely hinders the early diagnosis and precise intervention of T1DM, posing a huge challenge to its management. Therefore, in-depth exploration of the immunopathogenesis and regulatory mechanisms of T1DM is of great clinical significance.

[0003] CircRNAs are novel endogenous non-coding RNAs, mostly located in the cytoplasm, structurally stable, and exhibiting spatiotemporal and tissue-cell specificity. CircRNAs possess a variety of biological functions, among which their role as competing endogenous RNA (ceRNA) molecules—acting as miRNA sponges and upregulating the expression of their target genes—has attracted considerable attention from researchers. Our previous study found significant abnormalities in the expression profile of circRNAs in the peripheral blood of patients with type 1 diabetes mellitus (T1DM), and identified five circRNAs with the potential to encode protein peptides, which are significantly correlated with the pathogenesis of T1DM. Therefore, the role and mechanism of circRNAs in T1DM are still in the early stages of international research and warrant further investigation.

[0004] In summary, there is significant room for exploration in the study of the role of circRNA in type 1 diabetes mellitus (T1DM). This invention aims to analyze the expression characteristics of circRNA in peripheral blood PBMCs of T1D patients, expand the sample size to validate differentially expressed circRNAs, and explore whether circRNA can serve as a biomarker for the early diagnosis, differential diagnosis, and prediction of T1D. This invention has significant scientific value and potential clinical translational application value, and also possesses considerable exploratory and innovative characteristics. Furthermore, overexpression of hsa_circRNA_405498 also shows potential for the treatment of type 1 diabetes. Summary of the Invention

[0005] The primary objective of this invention is to provide the application of two circular RNAs as biomarkers for type 1 diabetes, namely hsa_circRNA_405498 and hsa_circRNA_100033, with sequences shown in SEQ ID NO. 1 and 2. This invention provides a new approach for the diagnosis of type 1 diabetes.

[0006] The hsa_circRNA_405498 sequence is:

[0007] GTTAAATCATTTCTTATATGAGCCATCTTTATGCTGCCTCCCTAAACCTCATCACCCCAAATTTGTCTTCAGAGGCACAGATTTTTTTTTCTGCAAGACAGACCAGTAATATGCGTAAATTCACAGA GAAACACGAATGGGTAACAACAGAAAATGGCATTGGAACAGTGGGAATCAGCAATTTTGCACAGGAAGCGTTGGGAGATGTTGTTTATTGTAGTCTCCCTGAAGTTGGGACAAAATTGAACAAACAAG

[0008] The hsa_circRNA_100033 sequence is:

[0009] AGGGAAGTGTAACATCTCCCATTTTTCTGACATATTTGCTGCTAGAGAGTTTAAAGCCCGAGTGGATTCATTTTCTACATATTAGGATATAACCCTGAGACAAGGAGGCTGAACAGTACCCAGGGGGAGATTCGTGTCGGTCCTAGTCATCAGGCCAAACTTCCAGATCTGCAACCATTTCCTTCTCC AGATGGTGATACAGTGACCCAACATGAGGAACTGGTCTGGATGCCTGGAGTTAACGACTGTGACCTCCTTATGTACTTGAGGGCAGCAAGGAGCATGGCGGCATTTGCAGGAATGTGTGATGGAGGCTCTACAGAGGACGGCTGTGTCGCAGCCTCTCGGGATGACACCACTCTGAATGCACTGAACACA

[0010] A second objective of this invention is to provide the application of reagents for detecting two circular RNAs in the preparation of diagnostic agents for type 1 diabetes, wherein the two circular RNAs are hsa_circRNA_405498 and hsa_circRNA_100033, and their sequences are shown in SEQ ID NO. 1 and 2.

[0011] T1D patients showed significant differences compared to LADA patients, T2D patients, and NGT patients; the lower the expression levels of the two circular RNAs, the higher the risk of developing T1D.

[0012] Furthermore, the expression levels of both circular RNAs increased progressively from T1D patients, LADA patients, T2D patients, and NGT.

[0013] T1D is type 1 diabetes, LADA is latent immune diabetes in adults, T2D is type 2 diabetes, and NGT is a healthy control group.

[0014] Furthermore,

[0015] To differentiate between T1D and NGT, a binary logistic regression analysis was performed using hsa_circRNA_405498, hsa_circRNA_100033, and age. The result was calculated as Y = -2 × hsa_circRNA_405498 expression level - 0.481 × hsa_circRNA_100033 expression level - 0.086 × age + 4.406. Since the Y value was greater than 0.432, the diagnosis was T1D.

[0016] To differentiate between T1D and LADA, a binary logistic regression analysis was performed using hsa_circRNA_405498, hsa_circRNA_100033, and age. The formula was Y = -2.055 × hsa_circRNA_405498 expression level - 0.553 × hsa_circRNA_100033 expression level - 0.107 × age + 5.770. Since the Y value was greater than 0.666, the diagnosis was T1D.

[0017] To differentiate between T1D and T2D, a binary logistic regression analysis was performed using hsa_circRNA_405498, hsa_circRNA_100033, and age. The resulting formula was Y = -2.543 × hsa_circRNA_405498 expression level - 0.735 × hsa_circRNA_100033 expression level - 0.122 × age + 6.95. Since the Y value was greater than 0.549, the diagnosis was T1D.

[0018] In the above formula, the expression level of circular RNA is expressed using 2^ -ΔΔCTThe relative expression level of RT-qPCR was calculated using this method.

[0019] A third objective of this invention is to provide a diagnostic preparation for type 1 diabetes, containing reagents for detecting two circular RNAs, hsa_circRNA_405498 and hsa_circRNA_100033, the sequences of which are shown in SEQ ID NO.1 and 2.

[0020] The aforementioned type 1 diabetes preparation showed significant differences in T1D compared to LADA patients, T2D patients, and NGT patients. The lower the expression level of the two circular RNAs, the higher the risk of developing T1D. The expression levels of the two circular RNAs gradually increased from T1D patients to LADA patients, T2D patients, and NGT patients.

[0021] Furthermore,

[0022] To differentiate between T1D and NGT, a binary logistic regression analysis was performed using hsa_circRNA_405498, hsa_circRNA_100033, and age. The result was calculated as Y = -2 × hsa_circRNA_405498 expression level - 0.481 × hsa_circRNA_100033 expression level - 0.086 × age + 4.406. Since the Y value was greater than 0.432, the diagnosis was T1D.

[0023] To differentiate between T1D and LADA, a binary logistic regression analysis was performed using hsa_circRNA_405498, hsa_circRNA_100033, and age. The formula was Y = -2.055 × hsa_circRNA_405498 expression level - 0.553 × hsa_circRNA_100033 expression level - 0.107 × age + 5.770. Since the Y value was greater than 0.666, the diagnosis was T1D.

[0024] To differentiate between T1D and T2D, a binary logistic regression analysis was performed using hsa_circRNA_405498, hsa_circRNA_100033, and age. The resulting formula was Y = -2.543 × hsa_circRNA_405498 expression level - 0.735 × hsa_circRNA_100033 expression level - 0.122 × age + 6.95. Since the Y value was greater than 0.549, the diagnosis was T1D.

[0025] A fourth objective of this invention is to provide the use of reagents overexpressing hsa_circRNA_405498 and / or hsa_circRNA_100033 in the preparation of drugs for treating type 1 diabetes, the sequences of hsa_circRNA_405498 and hsa_circRNA_100033 being shown in SEQ ID NO.1 and 2.

[0026] A fifth objective of this invention is to provide a drug for treating type 1 diabetes, which is a reagent for overexpressing hsa_circRNA_405498 and / or hsa_circRNA_100033, the sequences of which are shown in SEQ ID NO. 1 and 2.

[0027] This invention collected peripheral blood microarrays (PBMCs) from 6 patients with type 1 diabetes mellitus (T1D) and 6 controls with normal glucose tolerance (NGT), extracted RNA, and used the Arraystar Human circRNA Arrays V2 microarray to screen for differentially expressed circRNAs. Cluster analysis was performed, and gene function annotation and signaling pathway enrichment analyses were conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), respectively, to predict the role of differentially expressed circRNAs in the occurrence and development of T1D. Preliminary validation: Real-time quantitative PCR (RT-qPCR) was used to preliminarily validate differentially expressed circRNAs in 20 T1D patients and 20 NGT patients. Expanded sample validation: Peripheral blood samples from 81 T1D patients, 87 patients with classic late-adverse disease (LADA), 106 T2D patients, and 143 NGT patients were used for expanded sample validation using RT-qPCR. Correlation statistical analysis was performed in conjunction with routine clinical indicators, and receiver operating characteristic (ROC) curves were used to evaluate the clinical value of circRNAs as diagnostic biomarkers for T1D and to explore their relationship with clinical characteristics.

[0028] The results showed that the circRNAs obtained from the T1D group were compared with those from the NGT group. Based on the fold change in expression Fc>1.5 and P<0.05 as the screening criteria, 110 differentially expressed circRNAs were selected (53 upregulated and 57 downregulated). According to the following conditions: (1) length 200-1000bp; (2) exon circRNA; (3) raw-intensity>200, 15 circRNAs were selected and verified by RT-qPCR in 40 samples. It was found that the upregulated hsa_circRNA_000372, hsa_circRNA_100279 and the downregulated hsa_circRNA_405498, hsa_circRNA_100033, hsa_circRNA_007630 were consistent with the microarray results and had statistical differences. Expanding the sample size revealed that hsa_circRNA_405498 and hsa_circRNA_100033 showed significant statistical differences in the T1D, LADA, T2D and NGT groups (all P = 0.000), and their expression levels showed an increasing trend in these four groups. ROC curve analysis was performed to assess the diagnostic and differential diagnostic value of hsa_circRNA_405498 and hsa_circRNA_100033 (i.e., circR1 and circR2) for T1D. Results showed that for T1D vs. NGT, the area under the curve (AUC) for circR1 and circR2 was 0.721 (95% CI: 0.661-0.781), and the AUC for circR1 and circR2+ age was 0.841 (95% CI: 0.794-0.887); for T1D vs. LADA, the area under the curve (AUC) for circR1 and circR2 was 0.654 (95% CI: 0.576-0.732), and the AUC for circR1 and circR2+ age was 0.851 (95% CI: 0.799-0.904); for T1D vs. The area under the curve (AUC) for T2D, circR1 and circR2 was 0.697 (95% CI: 0.626-0.769), and the AUC for circR1 and circR2+ age was 0.899 (95% CI: 0.858-0.940).

[0029] This invention is the first to discover that the levels of hsa_circRNA_405498 and hsa_circRNA_100033 in PBMCs of T1D patients are significantly reduced, showing significant differences compared to LADA, T2D patients, and NGT patients. Clinically, these can be used as diagnostic biomarkers for the diagnosis and differential diagnosis of T1D patients. Furthermore, overexpression of hsa_circRNA_405498 also has the potential to treat type 1 diabetes. Attached Figure Description

[0030] Figure 1 : Lentiviral vector structure.

[0031] Figure 2 Volcano plot of differential expression of circRNA in peripheral blood PBMCs of patients with T1D;

[0032] Note: The two black vertical green lines represent upregulation and downregulation of expression levels by 1.5 times, respectively. The horizontal green line represents a p-value of 0.05. The red dots in the figure represent statistically significant upregulation and downregulation of circRNAs with expression levels >1.5 times, while the gray dots represent circRNAs with no statistically significant difference.

[0033] Figure 3 Clustering diagram of differential expression of circRNA in peripheral blood PBMCs of patients with T1D;

[0034] Note: Each column represents the expression level of a circRNA, and each row represents the circRNA expression distribution of a sample; the color scale shows the relative expression levels of circRNAs in different samples; red indicates upregulation, and blue indicates downregulation.

[0035] Figure 4 Statistical graph of differential expression of 5 circRNAs validated by RT-qPCR;

[0036] Figure 4 A: hsa_circRNA_405498 Figure 4 B: hsa_circRNA_100033 Figure 4 C: hsa_circRNA_000372 Figure 4 D: hsa_circRNA_100279 Figure 4 E: hsa_circRNA_007630;

[0037] Note: * represents P<0.05.

[0038] Figure 5 Scatter plot of differential expression validated by RT-qPCR of two expanded circRNA samples;

[0039] Figure 5A: hsa_circRNA_405498 Figure 5 B: hsa_circRNA_100033;

[0040] Note: The horizontal line represents the median, *P < 0.05, **P < 0.01, ***P < 0.001.

[0041] Figure 6 The diagnostic and differential diagnostic value of hsa_circRNA_405498 and hsa_circRNA_100033 (circR1 and circR2) for T1D was evaluated by ROC analysis.

[0042] Figure 6 A: To differentiate between T1D and NGT, the area under the curve (AUC) for circR1 and circR2 was 0.721 (95% CI: 0.661-0.781), and the AUC for circR1 and circR2+ age was 0.841 (95% CI: 0.794-0.887).

[0043] Figure 6 B: To differentiate between T1D and LADA, the area under the curve (AUC) for circR1 and circR2 was 0.654 (95% CI: 0.576-0.732), and the AUC for circR1 and circR2+ age was 0.851 (95% CI: 0.799-0.904).

[0044] Figure 6 C: To differentiate between T1D and T2D, the area under the curve (AUC) for circR1 and circR2 was 0.697 (95% CI: 0.626–0.769), and the AUC for circR1 and circR2+ age was 0.899 (95% CI: 0.858–0.940).

[0045] Figure 7 The expression of hsa_circRNA_405498 on T cells, B cells, monocytes and NK cells in human PBMCs was detected by RT-qPCR.

[0046] Note: * indicates p < 0.05.

[0047] Figure 8 Proliferation curve of a cell model overexpressing hsa_circRNA_405498;

[0048] Note: * indicates p < 0.05.

[0049] Figure 9Detection of apoptosis in cells overexpressing hsa_circRNA_405498;

[0050] Note: * indicates p < 0.05.

[0051] Figure 10 Effects of hsa_circRNA_405498 overexpression on Th cell subsets;

[0052] Note: * indicates p < 0.05. Detailed Implementation

[0053] The following examples are intended to further illustrate the present invention, but not to limit it.

[0054] Materials and methods

[0055] 1. Research Subjects

[0056] All patients and healthy controls involved in this invention signed informed consent forms. This study was reviewed and approved by the Ethics Committee of the National Clinical Research Center for Metabolic Diseases, Xiangya Second Hospital, Central South University (LYF2021053).

[0057] 1.1 T1D Group

[0058] A total of 101 patients with type 1 diabetes mellitus (T1D) who visited the Department of Metabolic Endocrinology at the Second Xiangya Hospital of Central South University from January 2021 to December 2022 were included in this study. The average age of the patients was 27.70 (range 18.5-36) years. Among them, there were 52 females and 49 males.

[0059] Inclusion criteria for T1D: ① Meets the 1999 WHO diagnostic criteria for diabetes; ② Spontaneous onset of ketosis or ketoacidosis within six months of onset; ③ Positive for glutamate decarboxylase autoantibody (GADA); ④ Insulin-dependent; ⑤ Exclusion of secondary diabetes, concurrent tumors, etc.

[0060] 1.2 LADA Group

[0061] We collected data from 87 patients with LADA who visited the Department of Metabolic Endocrinology at the Second Xiangya Hospital of Central South University between January 2021 and December 2022. The average age was 43.53 (range 33-53) years. Among them, there were 36 females and 51 males.

[0062] LADA inclusion criteria: ① Meets the 1999 WHO diagnostic criteria for diabetes; ② Age of onset ≥ 18 years; ③ No spontaneous onset of ketosis or ketoacidosis within six months of onset; ④ Positive for glutamate decarboxylase autoantibody (GADA); ⑤ Not dependent on insulin therapy within six months; ⑥ Excludes patients with secondary diabetes, concurrent tumors, etc.

[0063] 1.3 T2D Group

[0064] A total of 106 patients with type 2 diabetes mellitus (T2D) who visited the Department of Metabolic Endocrinology at the Second Xiangya Hospital of Central South University from January 2021 to December 2022 were included in this study. The average age of the patients was 47.55 years (range 37.75-56.25 years). Among them, there were 44 females and 62 males.

[0065] Inclusion criteria for type 2 diabetes mellitus (T2D): ① Meets the 1999 WHO diagnostic criteria for diabetes mellitus; ② No spontaneous onset of ketosis or ketoacidosis within six months of onset; ③ Negative for pancreatic autoantibodies (GADA, IA-2A, ZnT8A); ④ Not dependent on insulin therapy; ⑤ Excludes secondary or autoimmune or gene mutation-related diabetes mellitus, gestational diabetes mellitus, and patients with concurrent tumors.

[0066] 1.4 NGT Group

[0067] A total of 163 healthy volunteers who visited the Health Examination Center of Xiangya No. 2 Hospital of Central South University from January 2021 to December 2022 were included. The average age was 37.83 (range 26-48) years. Among them, there were 82 females and 81 males.

[0068] Inclusion criteria for normal controls: ① Screened by 75g oral glucose tolerance test (OGTT), fasting blood glucose <6.1mmol / L, and blood glucose 2 hours after glucose load <7.8mmol / L; ② Excluded from other autoimmune diseases, tumors, etc.

[0069] 2. Sample Collection

[0070] All subjects had 4.5 ml of peripheral fasting blood drawn intravenously into EDTA anticoagulant tubes, and their mononuclear cells (PBMCs) were isolated on the same day, aliquoted into 1.5 ml centrifuge tubes, and 1 ml of Trizol (invitrogen, 15596026) was added and stored at -80°C for subsequent total RNA extraction.

[0071] 3 Experimental Methods

[0072] 3.1 Isolation of peripheral blood PBMCs

[0073] Peripheral blood mononuclear cells (PBMCs) were separated using Ficoll Plus (GE, 17-1440-03) via density gradient centrifugation. 4.5 ml of peripheral blood was diluted 1:1:1 with PBS (Gibco, C10010500BT) using Ficoll Plus. After density gradient centrifugation (20℃, 800G, 25 min, +0-0), the liquid separated into three layers. The PBMCs (white membrane layer) were transferred to a 15 ml centrifuge tube using a Pasteur pipette. The PBMCs were then eluted with PBS, centrifuged again (20℃, 300G, 8 min, +8, -9), followed by erythrocyte lysis and elution. Finally, the separated PBMCs were transferred to a 1.5 ml centrifuge tube, lysed with 1 ml of Trizol, and stored at -80℃.

[0074] 3.2 Extraction of total RNA

[0075] Total RNA was extracted from three groups of frozen PBMCs using the Trizol method (Invitrogen, 17-1440-03) according to the manufacturer's instructions. RNA concentration and purity were determined using a NanoDrop ND-1000 instrument (NanoDropThermo). The average A260 / A280 ratio was 1.8–2.0.

[0076] 3.3 circRNA microarray detection

[0077] Sample labeling and array hybridization were performed according to the manufacturer's instructions (Arraystar Human circRNA ArrayV2, 8x15K). First, total RNA was digested with RNase R (Epicentre, Inc.) to remove linear RNA and enrich circRNA. Then, the enriched circRNA was amplified and transcribed into fluorescent cRNA using random primers (Arraystar Super RNA Labeling Kit; Arraystar). The labeled cRNA was purified using the RNeasy Mini Kit (Qiagen), and the concentration and specific activity (pmol Cy3 / μg cRNA) of the cRNAs were measured using a NanoDrop 2000 spectrophotometer. Then, 5 μL of blocking agent 10 and 1 μL of lysis buffer 25 were added, and the mixture was heated at 60°C for 30 minutes to fragment the labeled 1 μg cRNA. 25 μL of 2× hybridization buffer was added to dilute the labeled cRNA. Finally, 50 μL of the hybridization solution was dispensed into a slide and assembled onto a circRNA expression microarray slide. After incubating the slides in an Agilent hybridization oven at 65°C for 17 hours, they were washed and fixed. The slides were then scanned using an Agilent G2505C scanner. The scanned images were imported into Agilent feature extraction software (version 11.0.1.1) for raw data extraction.

[0078] 3.4 Validation of Differentially Determined circRNAs

[0079] Based on the analysis of the raw data and bioinformatics predictions, 15 differentially expressed circRNAs were selected for RT-qPCR validation. Five circRNAs were then further validated in the NGT, T1D, and T1D groups. β-actin was used as an internal control. Total RNA reverse transcription and RT-qPCR were performed using kits (Promega, A6001, A5001). Specific procedures are as follows:

[0080] 3.4.1 Total RNA reverse transcription

[0081] (1) Add 1ug RNA, 1ul random primer, and Nuclease-Free Water to a 200uL EP tube to make a 10ul system; Heater: 70℃, 5min, then immediately place on ice for ≥5min.

[0082] (2) Prepare another 10 μL system reaction mixture (RT-Mix) (Table 1)

[0083]

[0084] (3) Add 10ul of RT-Mix mixture to (1), anneal: 25℃, 5min; extension: 42℃, 1h; inactivation: 70℃, 15min. If not used for a long time, store in a -20℃ refrigerator.

[0085] 3.4.2 RT-qPCR Validation

[0086] The RT-qPCR reaction was performed using a 384-well plate with 10 μl of reaction mixture per well. Each sample was tested in triplicate. The instrument used was a Quantstudio 6 real-time PCR instrument.

[0087] (1) Primer preparation

[0088]

[0089] The primer sequences in the table above are as shown in SEQ ID NO.3-22.

[0090] (2) Configure the RT-qPCR amplification system (Table 3):

[0091]

[0092] (3) The PCR reaction program was as follows: pre-denaturation: 95℃, 10min; thermal cycling: 95℃, 15s; 60℃, 1min (40 cycles); melting curve: 95℃, 15s; 60℃, 1min; 95℃, 15s.

[0093] 3.5 Sorting of major cellular components in PBMCs using magnetic beads

[0094] (1) Draw 100 mL of peripheral blood, separate PBMCs, resuspend the cells in 13 mL of Washing Buffer (autoMACS® Running Buffer – MACS® Separation Buffer, Miltenyi Biotec, 130-091-221), and divide them into 5 portions in a ratio of 1:2:2:4:4.

[0095] (2) After centrifugation, use 80 μL / 10 7 Resuspend cells in Washing Buffer at 20 μL / 10 cells for each proportion. 7Each cell was incubated with CD4 magnetic beads (MiltenyiBiotec, 130-045-101), CD8 magnetic beads (MiltenyiBiotec, 130-045-201), CD14 magnetic beads (MiltenyiBiotec, 130-050-021), CD19 magnetic beads (MiltenyiBiotec, 130-050-301), and CD56 magnetic beads (MiltenyiBiotec, 130-040-401) at 2-8°C for 15 min.

[0096] (3) Wash the cells with Washing Buffer, lubricate the magnetic column (Miltenyi Biotec, 130-042-401), wash the column 3 times after passing through the column, and use the plunger to eject the positively selected cells.

[0097] (4) Wash the cells with PBS, transfer them into 1.5 mL EP tubes, add 1 mL Trizol to lyse the cells, extract total RNA and reverse transcribe it, and use RT-qPCR to detect the expression level of the target gene in each cell component.

[0098] 3.6 Cell Culture and Viral Infection

[0099] (1) Jurkat cell line (human acute T-cell leukemia cells Clone E6-1, Guangzhou Saiku, CC1902) was cultured in complete medium: RPMI-1640 (Gibco, C11875500BT) + 10% fetal bovine serum (FBS, Gibco, 10099-141C) + 1% penicillin-streptomycin (Gibco, 15140-122) and placed in a 37℃, 5% CO2 incubator (Thermo).

[0100] (2) The hsa_circRNA_405498 lentiviral vector was constructed by Shanghai Jikai Gene Medical Technology Co., Ltd. The original lentiviral vector was GV689 ( Figure 1 Purchased from Shanghai Jikai Gene Medical Technology Co., Ltd.

[0101] (3) Calculate the required control (the control is an empty viral vector) and hsa_circRNA_405498 virus volume based on infection MOI=30, and add them to complete culture medium to prepare 50 μL of viral mixture.

[0102] (4) Seed 430 μL / well of cell suspension into 24-well plates, with 5 × 10⁶ cells per well. 5 Then add 20 μL of virus transfection enhancement solution A (Gikai Gene) and 50 μL of control or hsa_circRNA_405498 virus to each well.

[0103] (5) Centrifuge the 24-well plate at 20°C, 1000g, for 60 min, and then place it in a constant temperature incubator.

[0104] (6) After 24 hours, add 500 μL of complete culture medium to each well.

[0105] (7) After 96h~120h, observe under a fluorescence microscope. When the infection efficiency is >80%, wash the cells with PBS, transfer them into 1.5mL EP tubes, add 1mL Trizol to lyse the cells, extract total RNA and reverse transcribe it, and detect the expression level of the target gene in each cell component by RT-qPCR.

[0106] 3.7 CCK8 assay for cell proliferation

[0107] (1) Add 5×10 to each well of the 96-well plate 5 100 μL of cell suspension per mL, 5 replicates for each of the experimental and control groups, 100 μL / well of PBS around the sample well, and 5 plates were plated.

[0108] (2) Take out a plate, add 10 μL of CCK8 proliferation assay reagent (DOJINDO) to each sample well, mix well and place in an incubator. After 4 hours, use a microplate reader (Bio-Tek) to detect the absorbance at a wavelength of 450 nm and a reference wavelength of 650 nm.

[0109] (3) The remaining four boards were tested with CCK8 after 24, 48, 72 and 96 respectively.

[0110] 3.8 Annexin V-APC / 7-AAD detection of cell apoptosis

[0111] The apoptosis detection kit (Annexin V-APC / 7-AAD, AP105) was used according to the instructions. Cells were collected in flow cytometry tubes, washed twice with pre-cooled PBS, and the supernatant was completely discarded. 200 μL of Blinding Buffer was added to each tube, followed by 2 μL of Annexin V-APC and 4 μL of 7-AAD in the dark. After incubation at room temperature for 5 min, the cells were immediately detected by flow cytometry (BD).

[0112] 4. Statistical Methods

[0113] Normally distributed data are expressed as mean ± standard deviation, non-normally distributed data as interquartile range, and categorical variables as frequency. 2^ -ΔΔCTThe relative expression levels of selected circRNAs in RT-qPCR were calculated. The Shapiro-Wilk test was used to assess the normality of the data. For normally distributed data, independent samples t-tests or one-way ANOVA were used to analyze differences between groups; otherwise, the rank-sum test (Mann-Whitney U test or Kruskal-Wallis H test) was used. The chi-square test was used for differences between categorical data groups. Spearman correlation coefficients were used to assess the relationship between circRNAs and T1D clinical data. Receiver operating characteristic (ROC) curves were used to assess the diagnostic and differential diagnostic performance of circRNAs for T1D. Binary logistic regression analysis was used to assess the relationship between circRNAs and the risk of developing T1D. Statistical analysis was performed using SPSS 26.0 (IBM SPSS, Inc., Chicago, IL, USA) and GraphPad Prism 8.4.3 (Graphpad Software Inc., San Diego, CA, USA). A p-value < 0.05 was considered statistically significant.

[0114] result

[0115] 1. Differential expression profile of circRNA in peripheral blood PBMCs of patients with T1D

[0116] A total of 13,614 circRNAs were detected in peripheral blood PBMCs of patients with type 1 diabetes mellitus (T1D) using the Arraystar circRNA microarray. Compared with the NGT group, 110 differentially expressed circRNAs were found in T1D patients, of which 53 were upregulated and 57 were downregulated. Volcano plots were used to analyze the results. Figure 2 The study revealed statistically significant differences in circRNAs between the T1D and NGT groups (FC > 1.5, p < 0.05). Cluster analysis was used to generate a heatmap that visually demonstrated the distribution characteristics and expression levels of differentially expressed circRNAs between T1D and NGT patients. Figure 3 Studies have shown that the expression levels of certain circRNAs differ significantly between peripheral blood peripheral blood mononuclear cells (PBMCs) in patients with type 1 diabetes (T1D) and non-gastrointestinal tract infections (NGT).

[0117] 2. RT-qPCR verification of circRNA expression

[0118] To verify the accuracy of the microarray results, we selected 15 differentially expressed circRNAs (hsa_circRNA_100632, hsa_circRNA_405498, hsa_circRNA_101145, hsa_circRNA_003201, hsa_circRNA_104405, hsa_circRNA_072654, hsa_circRNA_100278, hsa_circRNA_100033, hsa_circRNA_000372, hsa_circRNA_001052, hsa_circRNA_100279, hsa_circRNA_058192, hsa_circRNA_008042, hsa_ In peripheral blood peripheral blood microarrays (PBMCs) of 20 patients with type 1 diabetes mellitus (T1D) and 20 patients with non-gastrointestinal tract diseases (NGT), the relative expression levels of the above 15 circRNAs were detected by RT-qPCR in T1D patients and NGT patients, respectively. Differentially expressed circRNAs were further identified. Five circRNAs—hsa_circRNA_000372 and hsa_circRNA_100279 (upregulated) and hsa_circRNA_405498, hsa_circRNA_100033, and hsa_circRNA_007630 (downregulated)—were found to be consistent with the microarray results and showed statistically significant differences. The results are as follows: Figure 4 Expanding the sample size (101 cases in the T1D group, 87 cases in the LADA group, 106 cases in the T2D group, and 163 cases in the NGT group) revealed significant statistical differences in hsa_circRNA_405498 and hsa_circRNA_100033 among the T1D, LADA, T2D, and NGT groups (all P = 0.000), and their expression levels showed an increasing trend across these four groups. The results are as follows... Figure 5 .

[0119]

[0120] Note: *P < 0.05 compared to NGT. **P < 0.01 compared to NGT. ***P < 0.001 compared to NGT. # P < 0.05 compared with T2D. ## P < 0.01 compared to T2D. ### P < 0.001 compared with T2D. †††P < 0.001 compared with T1D. BMI: Body Mass Index; TG: Triglycerides; TC: Total Cholesterol; LDLC: Low-Density Lipoprotein Cholesterol; HDLC: High-Density Lipoprotein Cholesterol; FBG: Fasting Blood Glucose; FCP: Fasting C-peptide; HbA1c: Glycated Hemoglobin.

[0121] 3. Relationship between hsa_circRNA_405498 and hsa_circRNA_100033 and clinical characteristics

[0122] Spearman correlation analysis of expanded sample data showed that the expression levels of hsa_circRNA_405498 and hsa_circRNA_100033 were negatively correlated with fasting blood glucose and glycated hemoglobin in the four groups, suggesting that the higher the expression of hsa_circRNA_405498 and hsa_circRNA_100033, the lower the blood glucose level (Table 5).

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[0124] Note: *P< 0.05, **P< 0.01, ***P< 0.001; BMI: Body Mass Index; TG: Triglycerides; TC: Total Cholesterol; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; FPG: Fasting blood glucose; HbA1c: Glycated hemoglobin.

[0125] 4. Diagnostic value of Hsa_circRNA_405498 and hsa_circRNA_100033 for T1D

[0126] To explore the diagnostic and differential diagnostic value of hsa_circRNA_405498 (circR1) and hsa_circRNA_100033 (circR2) for T1D, further receiver operating characteristic (ROC) curve analysis showed that circR1 and circR2 combined with age had high accuracy in distinguishing T1D from LADA, T1D from T2D, and T1D from NGT (Table 6). Figure 6 ).

[0127] To differentiate between T1D and NGT, the area under the curves (AUC) for circR1 and circR2 was 0.721 (95% CI: 0.661–0.781), and the AUC for circR1 and circR2 plus age was 0.841 (95% CI: 0.794–0.887). Binary logistic regression analysis combining circR1, circR2, and age yielded the formula Y = -2 × circR1 - 0.481 × circR2 - 0.086 × age + 4.406. A Y value greater than 0.432 suggests a possible diagnosis of T1D.

[0128] To differentiate T1D from LADA, the area under the curves (AUC) for circR1 and circR2 was 0.654 (95% CI: 0.576–0.732), and the AUC for circR1 and circR2 plus age was 0.851 (95% CI: 0.799–0.904). Binary logistic regression analysis combining circR1, circR2, and age yielded the formula Y = -2.055 × circR1 - 0.553 × circR2 - 0.107 × age + 5.770. A Y value greater than 0.666 suggests a possible diagnosis of T1D.

[0129] To differentiate between T1D and T2D, the area under the curves (AUC) for circR1 and circR2 was 0.697 (95% CI: 0.626–0.769), and the AUC for circR1 and circR2 plus age was 0.899 (95% CI: 0.858–0.940). Binary logistic regression analysis combining circR1, circR2, and age yielded the formula Y = -2.543 × circR1 - 0.735 × circR2 - 0.122 × age + 6.95. A Y value greater than 0.549 suggests a possible diagnosis of T1D.

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[0131] 5. The therapeutic potential of Hsa_circRNA_405498 for T1D

[0132] To explore whether hsa_circRNA_405498 and hsa_circRNA_100033 could serve as potential therapeutic targets for T1D, we randomly selected hsa_circRNA_405498 for functional experiments. First, we isolated the main cell type from PBMCs: CD4+. + T cells, CD8 + T cells, CD14 + Monocytes, CD19+ B cells, CD56 + NK cells were found to show high expression of hsa_circRNA_405498 on T cells (CD4+ T cells and CD8+ T cells). Figure 7 Therefore, we selected the Jurkat cell line as a cell model and infected Jurkat cells with hsa_circRNA_405498 overexpression and control lentiviruses to observe and detect the effects of hsa_circRNA_405498 overexpression on Jurkat cell proliferation, apoptosis, and the proportion of T cell subsets.

[0133] Figure 8 The proliferation curve results of the hsa_circRNA_405498 overexpression cell model showed that the cell viability of the hsa_circRNA_405498 group was enhanced compared with the NC group.

[0134] Figure 9 Apoptosis detection in hsa_circRNA_405498 overexpressing cells was performed using Annexin V-APC / 7-AAD apoptosis detection kit followed by flow cytometry. The flow cytometer voltage was adjusted using the Jurkat blank control group, and fluorescence channel compensation was adjusted using Jurkat, APC, and 7-AAD single-staining tubes with GFP fluorescence. Gating groups were established using FMO tubes. Results showed that the apoptosis rate was decreased in the hsa_circRNA_405498 group compared to the NC group.

[0135] Figure 10 This study investigated the effect of hsa_circRNA_405498 overexpression on Th cell subsets. Jurkat cells were infected with viral vectors carrying or not carrying hsa_circRNA_405498, respectively. RNA was then extracted from both groups, and RT-qPCR was used to detect the expression levels of Th cell-specific transcription factors T-bet (Th1), GATA3 (Th2), RORC (Th17), and FOXP3 (Treg). The results showed that compared to the NC group, the hsa_circRNA_405498 group exhibited decreased expression levels of Th1 and Th17 transcription factors, while increasing expression levels of Th2 and Treg transcription factors. (Note: * indicates p < 0.05)

[0136] In summary, overexpression of hsa_circRNA_405498 can promote Jurkat cell proliferation, inhibit Jurkat cell apoptosis, and increase Th2 and Treg mRNA levels while decreasing Th1 and Th17 mRNA levels. hsa_circRNA_405498 levels are significantly decreased in T1D patients, who exhibit an immune-activated state characterized by increased Th1 and Th17 levels and decreased Th2 and Treg levels. Overexpression of hsa_circRNA_405498 shifts the proportion of Th cell subsets towards an anti-inflammatory profile, suggesting the potential of hsa_circRNA_405498 as a therapeutic target for T1D.

[0137] 6. Conclusion

[0138] The levels of hsa_circRNA_405498 and hsa_circRNA_100033 in PBMCs of patients with T1D were significantly reduced, showing significant differences compared to LADA, T2D, and NGT patients. Clinically, these can serve as diagnostic biomarkers for the diagnosis and differential diagnosis of T1D. hsa_circRNA_405498 can affect T cell proliferation and apoptosis, altering the proportion of T cell subsets, and may serve as a potential therapeutic target.

Claims

1. Biomarker of type 1 diabetes characterized in that, Two circular RNAs are hsa_circRNA_405498 and hsa_circRNA_100033, and sequences are shown as SEQ ID NO. 1 and 2.

2. Use of the reagent for detecting two circular RNAs in the manufacture of a diagnostic preparation for type 1 diabetes, characterized in that, Two circular RNAs are hsa_circRNA_405498 and hsa_circRNA_100033, and sequences are shown as SEQ ID NO. 1 and 2.

3. Use according to claim 2, characterized in that, T1D patients have significant difference compared with LADA patients, T2D patients and NGT, and the lower the expression of two circular RNAs, the higher the risk of T1D.

4. Use according to claim 2, characterized in that, The expression of two circular RNAs gradually increases according to T1D patients, LADA patients, T2D patients and NGT.

5. The application of claim 2, wherein, In order to identify T1D and NGT, binary Logistic regression analysis is carried out by combining hsa_circRNA_405498, hsa_circRNA_100033 and age, and a formula Y=-2×hsa_circRNA_405498 expression-0.481×hsa_circRNA_100033 expression-0.086×age+4.406 is obtained, and if Y value is greater than 0.432, it is diagnosed as T1D; In order to identify T1D and LADA, binary Logistic regression analysis is carried out by combining hsa_circRNA_405498, hsa_circRNA_100033 and age, and a formula Y=-2.055×hsa_circRNA_405498 expression-0.553×hsa_circRNA_100033 expression-0.107×age+5.770 is obtained, and if Y value is greater than 0.666, it is diagnosed as T1D; In order to identify T1D and T2D, binary Logistic regression analysis is carried out by combining hsa_circRNA_405498, hsa_circRNA_100033 and age, and a formula Y=-2.543×hsa_circRNA_405498 expression-0.735×hsa_circRNA_100033 expression-0.122×age+6.95 is obtained, and if Y value is greater than 0.549, it is diagnosed as T1D.

6. A diagnostic preparation for type 1 diabetes, characterized by, The reagent for detecting two circular RNAs hsa_circRNA_405498 and hsa_circRNA_100033, and sequences of hsa_circRNA_405498 and hsa_circRNA_100033 are shown as SEQ ID NO. 1 and 2.

7. The diagnostic preparation for type 1 diabetes according to claim 6, characterized in that, T1D patients have significant difference compared with LADA patients, T2D patients and NGT, and the lower the expression of two circular RNAs, the higher the risk of T1D; and the expression of two circular RNAs gradually increases according to T1D patients, LADA patients, T2D patients and NGT.

8. The type 1 diabetes diagnosis preparation of claim 6, wherein, To identify T1D and NGT, binary Logistic regression analysis was performed with hsa_circRNA_405498, hsa_circRNA_100033 and age, and the formula Y = -2 x hsa_circRNA_405498 expression amount - 0.481 x hsa_circRNA_100033 expression amount - 0.086 x age + 4.406 was obtained, and when Y value was greater than 0.432, it was diagnosed as T1D; To identify T1D and LADA, binary Logistic regression analysis was performed with hsa_circRNA_405498, hsa_circRNA_100033 and age, and the formula Y = -2.055 x hsa_circRNA_405498 expression amount - 0.553 x hsa_circRNA_100033 expression amount - 0.107 x age + 5.770 was obtained, and when Y value was greater than 0.666, it was diagnosed as T1D; To identify T1D and T2D, binary Logistic regression analysis was performed with hsa_circRNA_405498, hsa_circRNA_100033 and age, and the formula Y = -2.543 x hsa_circRNA_405498 expression amount - 0.735 x hsa_circRNA_100033 expression amount - 0.122 x age + 6.95 was obtained, and when Y value was greater than 0.549, it was diagnosed as T1D.

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