Effect of Circulating lncRNAs on Type 2 Diabetic Peripheral Neuropathy

Overview

This project intends to use high-throughput sequencing, molecular biology and bioinformatics technology to clarify the role of target lncrnas in the inflammatory response of DPN, to explore the correlation between target lncrnas and DPN pathogenesis, and to clarify the feasibility of target lncrnas as a marker of DPN cycle.

1. Screening lncrnas that regulate DPN inflammatory response and bind to miR-146a ① Experimental grouping The experiment was divided into T2DM group and DPN group. T2DM group was simple type 2 diabetes, without complications and other basic diseases; DPN group was simple T2DM patients with DPN. The diagnostic criteria for T2DM patients were in accordance with the Chinese guidelines for the prevention and treatment of type 2 diabetes (2017 Edition). The screening criteria for T2DM patients with DPN are as follows: 1. Clear history of type 2 diabetes mellitus. 2. Neuropathy at or after the diagnosis of diabetes. 3. The clinical symptoms and signs were consistent with those of DPN. Clinical symptoms include: numbness or sensation; tingling or tingling; pain; abnormal sensitivity or tenderness after touching. 4. Examination: A. abnormal temperature sense; B. 10 g nylon thread examination, foot sensation decreased or disappeared; C. abnormal vibration sense; D. ankle reflex disappeared; e. two or more items of nerve conduction velocity were slowed down (electromyography or sensory threshold measurement). 5. Nerve injury caused by other diseases or drugs was excluded. Two of the above five items were abnormal, or clinical symptoms + 1 item were abnormal. ② Under the condition of full informed consent, 3ml plasma samples were collected from T2DM group and DPN group, 4 copies in each group, and stored in refrigerator at - 80 ℃. ③ Screening of lncrnas and mRNAs differentially expressed in two groups of samples by gene sequencing. Total RNA was extracted from tissue samples. The concentration and purity of RNA were detected by Nanodrop2000. The integrity of RNA was detected by agarose gel electrophoresis, and RIN value was determined by Agilent2100. The total RNA of a single library was 2 UG, the concentration was more than 100 ng / μ L, od260 / 280 was between 1.8 \~ 2.2, and rRNA was removed. Using metal ions, the mRNA was randomly broken into small fragments of about 200 bp. Under the action of reverse transcriptase, a single strand of cDNA was synthesized by using random primers and using mRNA as template. The second strand of cDNA was synthesized by substituting dUTP for dTTP. The structure of the double stranded cDNA is a sticky end. The end repair mix is added to make up the flat end, and then an a base is added at the 3 'end to connect the Y-shaped junction. The second strand of cDNA was digested by ung enzyme so that only the first strand of cDNA was contained in the library. Then the Illumina sequencer platform was used for sequencing to obtain the original sequencing data. The differentially expressed lncrnas and mRNAs were obtained by quality control and sequence alignment. ④ The lncrnas with difference multiple ≥ 2 were obtained, and the lncrnas co expressed with inflammatory factors were determined by co expression analysis. ⑤ Different lncnras were retrieved from mircode and Starbase databases to obtain lncnras combined with miR-146a. 2. In vitro validation of candidate lncnras: ① high concentration glucose (200mmol / L) induced injury of Schwann cells; ② detection of the expression of candidate lncnras and miR-146a in high glucose induced Schwann cells to obtain lncnras with high expression; ③ double luciferase reporter gene experiment to verify the combination of candidate lncnras and miR-146a, and obtain candidate lncnras binding to miR-146a; ④ construction Methods: lncnras silencing vector was used to transfer candidate lncnras into high glucose induced Schwann cells; ⑤ Western blot and liquid chip were used to detect the levels of inflammatory factors (TNF - α, IL-1 β, IL-6 and IL-10) in Schwann cells; and ⑥ correlation analysis was used to determine the lncrnas positively correlated with inflammatory factors. 3. Large plasma samples were used to verify the correlation between candidate lncrnas and miR-146a and inflammatory factors. * the experimental grouping was the same as before; ② with full informed consent, 50 plasma samples were collected from T2DM patients and T2DM patients with DPN, each 3ml each. ③ candidate lncrnas and miR-146a primers were designed and QRT PCR was used to detect the candidate lncrnas and miR-146a The levels of inflammatory factors (TNF - α, IL-1 β, IL-6 and IL-10) in the plasma of the two groups were detected by liquid chip technology; ⑤ lncrnas negatively correlated with miR-146a and positively correlated with the expression of inflammatory factors were determined by correlation analysis. 4\. Statistical treatment: SPSS 23.0 software was used for statistical analysis. The measurement data are expressed as mean ± SD One way ANOVA or nonparametric test was used to analyze the distribution and grouping characteristics. Pearson correlation analysis was used to analyze the correlation of measurement data. With P \< 0.05, the difference was statistically significant. 5\. Technical route this study is mainly divided into three parts. Firstly, lncrnas that regulate DPN inflammatory response and bind with miR-146a are screened; on this basis, candidate lncnras are obtained by in vitro validation; finally, lncnras with molecular marker function are verified on large sample plasma samples. 6\. Technical difficulties In this study, we used gene sequencing technology to detect the differential expression of lncrnas and mRNAs in plasma of T2DM patients and DPN patients, combined with bioinformatics technology to screen lncrnas related to inflammation and bind to miR-146a. Objective to screen for miR-146a binding proteins associated with inflammation Lncrnas are the technical difficulties in this study.

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Outcomes

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