Identifying the differentially expressed microRNAs in autoimmunity: A systemic review and meta-analysis

The evidence indicates that microRNAs (miRNAs) can regulate gene expression and play an important role in the pathogenesis of autoimmune diseases, yet studies on expression profiles of miRNAs are still inconclusive. Our objective is to identify miRNAs that demonstrate enduring differential expression on autoimmune diseases. A systemic review and meta-analysis were performed by analysing the expression profile of miRNAs in several types of autoimmune disease, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and type-1 diabetes (T1D). Several most significant differentially expressed miRNAs were identified and showed significant deregulation in autoimmune diseases. The most compelling results for SLE were with miR-21, miR-148a, miR-223, miR-125b in blood and miR-26a in kidney samples, for RA, miR-21, miR-24, miR-26a, miR-155 and miR-223 in blood, and for T1D, miR-148a, miR-181a in blood and miR-21, miR-155 in urine samples. Interestingly, some of miRNAs were differentially expressed in more than one autoimmune disease, such as miR-21, miR-26a, miR-155, miR-148a, miR-223. These miRNAs are commonly associated with the immune response and increases in the activity of the immune system and inflammation in specific organs such as skin, joint, lung, and kidney. These miRNAs can potentially be not only good biomarkers for the prediction, diagnosis, but also therapeutic targets in autoimmune diseases.</p

Additional file 2: of Integrated analysis of mRNA, microRNA and protein in systemic lupus erythematosus-specific induced pluripotent stem cells from urine

Table S2. Differentially expressed microRNA. (XLSX 22 kb

Picea glehnii Masters

原著和名: アカエゾマツ科名: マツ科 = Pinaceae採集地: 北海道 根室市 落石 (北海道 根室 落石)採集日: 1972/7/12採集者: 萩庭丈壽整理番号: JH035993国立科学博物館整理番号: TNS-VS-98599

Image_1_Abnormalities in Gut Microbiota and Metabolism in Patients With Chronic Spontaneous Urticaria.tif

BackgroundIncreasing evidence suggests that the gut microbiome plays a role in the pathogenesis of allergy and autoimmunity. The association between abnormalities in the gut microbiota and chronic spontaneous urticaria (CSU) remains largely undefined.MethodsFecal samples were obtained from 39 patients with CSU and 40 healthy controls (HCs). 16S ribosomal RNA (rRNA) gene sequencing (39 patients with CSU and 40 HCs) and untargeted metabolomics (12 patients with CSU and 12 HCs) were performed to analyze the compositional and metabolic alterations of the gut microbiome in CSU patients and HCs.ResultsThe 16S rRNA gene sequencing results showed a significant difference in the β-diversity of the gut microbiota, presented as the Jaccard distance, between CSU patients and HCs. No significant differences were found in the α-diversity of the gut microbiota between patients and HCs. At the phylum level, the major bacteria in the gut microbiome of patients with CSU were Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. At the genus level, Lactobacillus, Turicibacter, and Lachnobacterium were significantly increased and Phascolarctobacterium was decreased in patients with CSU. PICRUSt and correlation analysis indicated that Lactobacillus, Turicibacter, and Phascolarctobacterium were positively related to G protein-coupled receptors. Metabolomic analysis showed that α-mangostin and glycyrrhizic acid were upregulated and that 3-indolepropionic acid, xanthine, and isobutyric acid were downregulated in patients with CSU. Correlation analysis between the intestinal microbiota and metabolites suggested that there was a positive correlation between Lachnobacterium and α-mangostin.ConclusionsThis study suggests that disturbances in the gut microbiome composition and metabolites and their crosstalk or interaction may participate in the pathogenesis of CSU.</p

Image_2_Abnormalities in Gut Microbiota and Metabolism in Patients With Chronic Spontaneous Urticaria.tif

BackgroundIncreasing evidence suggests that the gut microbiome plays a role in the pathogenesis of allergy and autoimmunity. The association between abnormalities in the gut microbiota and chronic spontaneous urticaria (CSU) remains largely undefined.MethodsFecal samples were obtained from 39 patients with CSU and 40 healthy controls (HCs). 16S ribosomal RNA (rRNA) gene sequencing (39 patients with CSU and 40 HCs) and untargeted metabolomics (12 patients with CSU and 12 HCs) were performed to analyze the compositional and metabolic alterations of the gut microbiome in CSU patients and HCs.ResultsThe 16S rRNA gene sequencing results showed a significant difference in the β-diversity of the gut microbiota, presented as the Jaccard distance, between CSU patients and HCs. No significant differences were found in the α-diversity of the gut microbiota between patients and HCs. At the phylum level, the major bacteria in the gut microbiome of patients with CSU were Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. At the genus level, Lactobacillus, Turicibacter, and Lachnobacterium were significantly increased and Phascolarctobacterium was decreased in patients with CSU. PICRUSt and correlation analysis indicated that Lactobacillus, Turicibacter, and Phascolarctobacterium were positively related to G protein-coupled receptors. Metabolomic analysis showed that α-mangostin and glycyrrhizic acid were upregulated and that 3-indolepropionic acid, xanthine, and isobutyric acid were downregulated in patients with CSU. Correlation analysis between the intestinal microbiota and metabolites suggested that there was a positive correlation between Lachnobacterium and α-mangostin.ConclusionsThis study suggests that disturbances in the gut microbiome composition and metabolites and their crosstalk or interaction may participate in the pathogenesis of CSU.</p

DataSheet_1_The Systemic Lupus Erythematosus Interventional Trials in Mainland China: A Continuous Challenge.pdf

ObjectivesMore than a quarter of single-country systemic lupus erythematosus (SLE) interventional randomized clinical trials (RCTs) were conducted in China. To help develop management guidelines and set benchmarks for future SLE research, a systematic review of current trials is needed.MethodsWe searched systematically three databases and four registries to summarize the interventional RCTs in mainland China and identify factors associated with participant loss. The internal validity of trials was assessed using the Cochrane risk-of-bias tool for assessing risk of bias. The odds ratio (OR) was defined as the ratio of the odds of less than 10% loss to follow-up in the presence or absence of different factors.ResultsA total of 188 trials met our inclusion criteria, and 15·5% of trials conducted in mainland China ranked low risk of bias. Participant loss was significantly higher among trials that had a defined primary outcome or were registered {primary outcome identification (0·02 [0·00-0·23]) and registration (0·14 [0·03-0·69])}. Trials examining traditional Chinese medicine (TCM) pharmacological treatments had an 8·16-fold (8·16 [1·28-51·98]) higher probability of having low participant loss than trials examining non-TCM pharmacological treatment trials, and trials that did not report masking status had a 15·95-fold (15·95 [2·45-103·88]) higher probability of having low participant loss than open-label trials. In addition, published articles in Chinese also had higher probability of having low participant loss (5·39 [1·10-26·37]).ConclusionSLE trials conducted in mainland China were of relatively poor quality. This situation, including nonrigorous design, lack of registration, and absence of compliance reporting, needs to be ameliorated. To maintain the fundamental repeatability and comparability of mainland China SLE RCTs, transparency of the clinical trial process and complete reporting of the trial data are crucial and urgently needed.</p

DataSheet_1_Abnormalities in Gut Microbiota and Metabolism in Patients With Chronic Spontaneous Urticaria.xlsx

BackgroundIncreasing evidence suggests that the gut microbiome plays a role in the pathogenesis of allergy and autoimmunity. The association between abnormalities in the gut microbiota and chronic spontaneous urticaria (CSU) remains largely undefined.MethodsFecal samples were obtained from 39 patients with CSU and 40 healthy controls (HCs). 16S ribosomal RNA (rRNA) gene sequencing (39 patients with CSU and 40 HCs) and untargeted metabolomics (12 patients with CSU and 12 HCs) were performed to analyze the compositional and metabolic alterations of the gut microbiome in CSU patients and HCs.ResultsThe 16S rRNA gene sequencing results showed a significant difference in the β-diversity of the gut microbiota, presented as the Jaccard distance, between CSU patients and HCs. No significant differences were found in the α-diversity of the gut microbiota between patients and HCs. At the phylum level, the major bacteria in the gut microbiome of patients with CSU were Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. At the genus level, Lactobacillus, Turicibacter, and Lachnobacterium were significantly increased and Phascolarctobacterium was decreased in patients with CSU. PICRUSt and correlation analysis indicated that Lactobacillus, Turicibacter, and Phascolarctobacterium were positively related to G protein-coupled receptors. Metabolomic analysis showed that α-mangostin and glycyrrhizic acid were upregulated and that 3-indolepropionic acid, xanthine, and isobutyric acid were downregulated in patients with CSU. Correlation analysis between the intestinal microbiota and metabolites suggested that there was a positive correlation between Lachnobacterium and α-mangostin.ConclusionsThis study suggests that disturbances in the gut microbiome composition and metabolites and their crosstalk or interaction may participate in the pathogenesis of CSU.</p

DataSheet_2_The Systemic Lupus Erythematosus Interventional Trials in Mainland China: A Continuous Challenge.xlsx

ObjectivesMore than a quarter of single-country systemic lupus erythematosus (SLE) interventional randomized clinical trials (RCTs) were conducted in China. To help develop management guidelines and set benchmarks for future SLE research, a systematic review of current trials is needed.MethodsWe searched systematically three databases and four registries to summarize the interventional RCTs in mainland China and identify factors associated with participant loss. The internal validity of trials was assessed using the Cochrane risk-of-bias tool for assessing risk of bias. The odds ratio (OR) was defined as the ratio of the odds of less than 10% loss to follow-up in the presence or absence of different factors.ResultsA total of 188 trials met our inclusion criteria, and 15·5% of trials conducted in mainland China ranked low risk of bias. Participant loss was significantly higher among trials that had a defined primary outcome or were registered {primary outcome identification (0·02 [0·00-0·23]) and registration (0·14 [0·03-0·69])}. Trials examining traditional Chinese medicine (TCM) pharmacological treatments had an 8·16-fold (8·16 [1·28-51·98]) higher probability of having low participant loss than trials examining non-TCM pharmacological treatment trials, and trials that did not report masking status had a 15·95-fold (15·95 [2·45-103·88]) higher probability of having low participant loss than open-label trials. In addition, published articles in Chinese also had higher probability of having low participant loss (5·39 [1·10-26·37]).ConclusionSLE trials conducted in mainland China were of relatively poor quality. This situation, including nonrigorous design, lack of registration, and absence of compliance reporting, needs to be ameliorated. To maintain the fundamental repeatability and comparability of mainland China SLE RCTs, transparency of the clinical trial process and complete reporting of the trial data are crucial and urgently needed.</p

Supplementary Tables – Comprehensive analysis of epigenetic modifications and immune-cell infiltration in tissues from SLE patients

Supplementary Table 1 Primers for RT-qPCR Supplementary Table 2 The number of patients in GEO datasets Supplementary Table 3 Marker genes of cells Supplementary Table 4 Analysis of key genes expressions in GSE185047 </p

Supplementary Figure – Comprehensive analysis of epigenetic modifications and immune-cell infiltration in tissues from SLE patients

Supplementary Figure 1 Identification of DEGs and enrichment of DEGs (A)Volcano plot of datasets in normal and SLE samples from GSE20864. Blue plots represent expressions of genes with P < 0.05 and log2FC < −0.8. Red plots represent expressions of genes mRNA with P  0.8. Grey plots represent genes expressed mRNA normally. The X-axis means the log2 of fold change in expressions of genes between primary and metastatic samples. The Y-axis means the −log10 of the P value of each gene. (B) Volcano plot of datasets in normal and SLE samples from GSE112087. (C) Volcano plot of datasets in normal and SLE samples from GSE122459. (D) Volcano plot of datasets in normal and SLE samples from GSE144390. (E) Enrichment of DEGs in 4 datasets. Supplementary Figure 2 Correlation analysis of immune cells and key genes The red circle represents positive correlation, and the purple circle represents negative correlation (P Supplementary Figure 3 Identification of different immune cells in single cell analysis (A) PBMC cells from 5 normal subjects and 7 SLE patients are divided into 19 clusters. Each point represents a single cell, colored according to clusters. (B) The amount of marker genes expressed by each cluster. (C) Relative frequency of expression of each immune cell in SLE and HC Supplementary Figure 4 Epigenetic analysis of SLE (A) Enrichment of hypomethylation gene. (B) Volcano plot of datasets in normal and SLE samples from GSE37426. (C) Heatmap of DEmiRNAs between normal and SLE samples from GSE37426. (D) Volcano plot of datasets in normal and SLE samples from GSE102547. (E) Heatmap of DElncRNAs between normal and SLE samples from GSE102547. (F) Volcano plot of datasets in normal and SLE samples from GSE84655. (G) Heatmap of DEcircRNAs between normal and SLE samples from GSE84655. Supplementary Figure 5 The mRNA expression of 5 key genes in HC and autoimmune diseases The relative mRNA expression of RSAD2, OAS2, IFIT1, IFIT3, PLSCR1 in the PBMCs of HC, SLE, RA, DM, SS. Supplementary Figure 6 The protein expression of IFIT1 and RSAD2 in SLE and HC skin lesions. (A) The statistical analysis percentage of IFIT1 positive cells between SLE and HC. (B) The statistical analysis percentage of RSAD2 positive cells between SLE and HC. </div