Transcriptome sequencing analysis of lncRNA expression in peripheral blood mononuclear cells from patients with COPD

Quan-Ni Li, Jie Zhao, Yi-Xiu Yang, Juan Sun, Xiao-Man Zhou, Jian-Fang Liu, Qiong Feng, Yi-Peng Ding?

1.Hainan Hospital Affiliated to Hainan Medical University, Haikou 570311, China

2.Department of General Practice, Hainan General Hospital, Haikou 570311, China

3.Hainan Hospital Affiliated to Nanhua University

Keywords:

ABSTRACT

1. Introduction

Chronic obstructive pulmonary disease (COPD) is a common and frequently occurring respiratory disease[1]. Chronic bronchitis or emphysema is characterized by irreversible airflow obstruction and is usually associated with abnormal inflammatory reactions caused by harmful gases or particles[2]. COPD causes changes in lung tissue and organ function and causes abnormalities in other parts of the body (Such as systemic inflammation, weight loss, skeletal muscle dysfunction) [3]. Long non-coding RNA (lncRNA) is a group of noncoding RNAs (ncRNA) transcribed by RNA polymerase II [4]with a length of more than 200 nucleotides[5].Transcriptome sequencing is currently used in a variety of diseases, including COPD[6]. However, there is currently no screening for differential lncRNAs in COPD peripheral blood mononuclear cells using transcriptome sequencing. This study used transcriptome sequencing to detect lncRNA expression differences in peripheral blood mononuclear cells from patients with COPD, to find key lncRNA expressions and clinical signs of COPD disease, and to find new lncRNA targets for the treatment and diagnosis of COPD.

2. Materials and Methods:

2.1 Peripheral blood collection

Peripheral blood collected from 3 COPD patients and 3 gender- and age-matched normal controls using EDTA anticoagulation blood collection tubes. COPD patients came from the People's Emergency Department of Hainan Province, and were judged to be COPD.according to the guidelines for the diagnosis and treatment of chronic obstructive pulmonary disease. The normal controls were from the physical examination center. There was no significant difference in gender group words, average age, BMI, and smoking rate in the two groups(P <0.05, see Table 1), the predicted value of FEV1% in the two groups was significantly lower than that in the normal control group (t = 8.137, p = 0.001 <0.05), which was comparable.

Table 1 Comparison of general information between two groups of patients

2.2 Mononuclear cell isolation

Collect the fresh whole blood separately and add equal volume of PBS to mix the liquid thoroughly; Add an lymphocyte separation solution (Solarbio), centrifuge at 20℃3000g for 30 minutes using a centrifuge; Use a pipette to separate the white blood cell layer that has been left to stand. Add 20 times the volume of white blood cell TRIzol reagent, and repeatedly beat the cells until no clumps of cells are visible. -80 ℃ refrigerator.

2.3 RNA extraction and sequencing

Total RNA extraction using the Trizol kit (Invitrogen company).RNA quality was evaluated on an Agilent 2100 Bioanalyzer (Agilent Technologies) and checked using RNase-free agarose gel electrophoresis. After total RNA is extracted after quality inspection, ribosomal RNA is removed to retain mRNA and ncRNA. Fragment the enriched mRNA and ncRNA into short fragments using fragment buffer and reverse transcription to cDNA using random primers. Synthesis of second-strand cDNA from DNA polymerase I, RNase H, dNTP (replace dTTP with dUTP) and buffer. next，Use QiaQuick PCR extraction kit (Qiagen) to purify cDNA fragments, perform end repair, add poly (A), and connect to an Illumina sequencing adapter. Then use UNG (uracil-N-glycosylase) to digest the second-strand cDNA. The size of the digested product was selected by agarose gel electrophoresis, amplified by PCR, and then sequenced using Illumina HiSeqTM 4000 from Guangzhou Base Ao Biological Technology Co., Ltd.

2.4 Bioinformatics Analysis

Sequencing results delinker, ribosomal RNA, and low-quality data. Reconstruction of transcripts was performed using the software Stringtie (version 1.3.4), which, together with HISAT2 (version 2.1.0), identified known lncRAN and new lncRNA. Read pairs via cuffdiff software (https://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html) and FPKM (fragments per kilobase segment of transcripts read per million mappings) Screened transcripts were analyzed quantitatively.Calculate the expression difference between the two groups of samples according to FDR <0.05 andlog2FC |> 1. The Lncpro database (http://bioinfo.bjmu.edu.cn/lncpro/#) is used to predict mRNAs regulated by differential lncRNAs, perform mRNA (Gene Ontology, Gene Ontology) functions on mRNAs and perform KEGG Cluster analysis of signal pathways.

3. Results

3.1 Total number and type of lncRNAs identified

A total of 24,599 lncRNAs were identified in the COPD and control samples. The identified lncRNAs can be divided into four categories based on their position on the genome relative to protein-coding genes: Intergenic long-chain non-coding lncRNAs (Intergenic LncRNAs), intron long-chain non-coding RNAs (Intronic LncRNAs), antisense long-chain non-coding RNAs (Antisense LncRNAs), and other types (other)-including: sense long-chain Non-coding RNA (Sense overlapping LncRNAs) and bidirectional long-chain non-coding lncRNAs (Bidirectional LncRNAs). Among them, the long-chain non-coding lncRNAs (Intergenic LncRNAs) accounted for the highest proportion, accounting for 66.31% of the total.

Figure 1: Percentage of lncRNA classifications identified by sequencing.

3.2 Total lncRNA results and differences

A total of 67 lncRNAs meeting the p <0.05 and | log2FC |> 1 difference between the two groups in the COPD group and the control group were included, of which 33 were up-regulated and 34 were down-regulated. A LncRNA difference volcano map is shown in Figure 2.The list of the top 5 lncRNAs with the most obvious up- or down-regulation changes is shown in Table 2.The heat map of the difference lncRNA is shown in Figure 3.

Figure 2: Volcano map of differential lncRNA

Table 2 LncRNA differently up or down (top 5 each)

Figure 3: Heat map of differential lncRNA

3.3 Analysis

Differential lncRNA target gene GO function and KEGG signal pathway enrichment and analysis of differential lncRNA, mRNA target genes were predicted from the Lncpro database, and the target genes obtained were clustered for GO function and KEGG signal pathway. The results show that the main enriched GO functions are ranked according to the number of genes. The first five functions are: the regulatory function of multicellular biological processes (GO: 0051239), the regulatory function of development processes (GO: 0050793), and the structure.Morphogenesis function (GO: 0009653), system development function (GO: 0048731), and development process function (GO: 0032502), see Figure 4. Differential mRNAs are mainly enriched in KEGG signaling pathways, sorted by q value. The first five signaling pathways are: multi-species apoptotic pathway, TGF-β signaling pathway, complement and coagulation cascade pathway, colorectal cancer pathway and cell death. Death path, see Figure 5.

Figure 4: GO function clustering of lncRNA target genes

Figure 5: Clustering of KEGG signaling pathway of lncRNA target genes

4. Discussion

Chronic obstructive pulmonary disease (COPD) causes a large number of morbidities and deaths worldwide and is expected to become the third leading cause of death in 2020 [7]. Chronic obstructive pulmonary disease is characterized by progressive and irreversible airflow limitation, which is related to the lungs ' abnormal inflammatory response to harmful particles and gases. Smoking or long-term exposure to smoking is a major risk factor for COPD[8], Exposure to fumes from biofuels and occupational exposure to chemicals or dust are also considered significant threats[9].Airway and lung parenchymal inflammation promotes the development of COPD and, worse, worsens acute symptoms[10].The Clinically urgent need for effective diagnosis and treatment of COPD[11]. Therefore, in our study, the goal was to find potential peripheral blood mononuclear cell lncRNA markers for the diagnosis and treatment of COPD.

LncRNA is involved in various biological processes. As far as its biological function is concerned, lncRNA plays an important role, such as playing an important role in the structure and function of chromatin.[12], And in gene transcription and regulatory RNA splicing[13],Participating in epigenetic regulation[14] and genomic rearrangement[15] have important. functions.

Recent studies have shown that lncRNA has important functions in COPD, and LncRNA can mediate the SIRT1 / FoxO3a and SIRT1 / p53 signaling pathways to regulate the aging of type II alveolar epithelial cells in patients with chronic obstructive pulmonary disease[16]. lncRNA TUG1 can inhibit TGF-β-induced proliferation in COPD cell models[17].In this project, we sequenced and found that there were 67 lncRNAs between the COPD group and the control group that met. The difference of p <0.05 and | log2FC |> 1, of which 33 were up-regulated and 34 were down-regulated. These lncRNAs may participate in the development of COPD. By predicting differential lncRNA target genes, and enriching GO functions and KEGG signaling pathways, we found that the GO functions that are mainly enriched in target genes are regulating functions in multicellular biological processes, regulated functions in development processes, structural morphogenesis functions, and systems. Development function and development process function. Target genes are mainly enriched in the KEGG signaling pathway in a variety of apoptotic pathways, TGF-β signaling pathways, complement and coagulation cascade pathways, colorectal cancer pathways and apoptotic pathways. It is suggested that the differential lncRNA may regulate COPD through these functional pathways, and further molecular biological experiments are needed to further prove this. Among them, the TGF-β signaling pathway has important functions in COPD [18], which will be the focus of our subsequent research.Bi, Hui et al. [19] screened differential lncRNAs in COPD lung tissues by gene chip and found that 87 lncRNAs were significantly up-regulated and 244 were down-regulated.

However, lncRNA in lung tissue is not convenient for clinical detection. Qu Xiaoyan et al.[20] detected differential lncRNAs in COPD peripheral blood mononuclear cells by gene chip, and identified 158 differentially expressed lncRNAs. However, transcriptome sequencing is not the same as gene chip detection, and transcriptome sequencing can find unknown lncRNA. At the same time, samples from different regions have different conclusions. We collect clinical samples from Hainan, which is more conducive to explaining the situation of COPD in Hainan. However, our research has shortcomings:1) For chip sequencing differential lncRNA, it is necessary to further expand clinical samples, go for qPCR verification, and analyze the clinical significance of lncRNA based on clinical physiology and pathology and lncRNA expression statistics.2) Differential lncRNA was over-expressed or transfected with a silent vector to verify its function and mechanism. In summary, our results provide general information and possible regulatory functions and pathways of lncRNA expression changes in peripheral blood mononuclear cells of COPD, which may help clarify the underlying mechanism of COPD.