Identification of lncRNAs associated with the pathogenesis of ankylosing spondylitis.

BACKGROUND: Ankylosing spondylitis (AS) is a chronic autoimmune disease affecting the sacroiliac joint. To date, few studies have examined the association between long non-coding RNAs (lncRNAs) and AS pathogenesis. As such, we herein sought to characterize patterns of AS-related lncRNA expression and to evaluate the potential role played by these lncRNAs in this complex autoimmune context. METHODS: We conducted a RNA-seq analysis of peripheral blood mononuclear cell (PBMC) samples isolated from five AS patients and corresponding controls. These data were then leveraged to characterize AS-related lncRNA expression patterns. We further conducted GO and KEGG enrichment analyses of the parental genes encoding these lncRNAs, and we confirmed the validity of our RNA-seq data by assessing the expression of six lncRNAs via qRT-PCR in 15 AS and control patient samples. Pearson correlation analyses were additionally employed to examine the associations between the expression levels of these six lncRNAs and patient clinical index values. RESULTS: We detected 56,575 total lncRNAs in AS and control patient samples during our initial RNA-seq analysis, of which 200 and 70 were found to be up- and down-regulated (FC > 2 or < 0.05; P < 0.05), respectively, in AS samples relative to controls. In qRT-PCR validation assays, we confirmed the significant upregulation of NONHSAT118801.2, ENST00000444046, and NONHSAT183847.1 and the significant downregulation of NONHSAT205110.1, NONHSAT105444.2, and NONHSAT051856.2 in AS patient samples. We further found the expression of NONHSAT118801.2 and NONHSAT183847.1 to be positively correlated with disease severity. CONCLUSION: Overall, our findings highlight several lncRNAs that are specifically expressed in PBMCs of AS patients, indicating that they may play key functions in the pathogenesis of this autoimmune disease. Specifically, we determined that NONHSAT118801.2 and NONHSAT183847.1 may influence the occurrence and development of AS.

Network analysis and transcriptome profiling in peripheral blood mononuclear cells of patients with rheumatoid arthritis.

The present study aimed to investigate the differential expression of long non-coding RNAs (lncRNAs) in rheumatoid arthritis (RA). High-throughput gene sequencing technology was used to detect the expression of lncRNA and mRNA in three patients with RA (RA group) and normal controls (NC group). A Bioinformatics analysis was used to assess the effects of differentially expressed mRNAs on signaling pathways and biological functions. The selected dysregulated lncRNAs were verified by reverse transcription-quantitative (RT-q)PCR in the peripheral blood mononuclear cells (PBMCs) of patients with RA and age- and sex-matched controls. A correlation analysis was used to analyze the relationship between lncRNAs and clinical indexes. From the lncRNA sequencing data, significantly differentially expressed lncRNAs between the RA and NC groups were identified by a fold change ≥2 and P<0.05. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis suggested that the differentially expressed mRNAs were mainly involved in organelle composition, intracellular regulation, signaling pathways, cancer, virus and inflammation. A total of four of these lncRNAs were confirmed by RT-qPCR to be significantly differentially expressed (LINC00304, MIR503HG, LINC01504 and FAM95B1). Through the correlation analysis, it was confirmed that there was a strong correlation between these lncRNAs and clinical laboratory indicators and indexes such as course of disease, arthrocele and joint tenderness. Overall, the present results suggested that the expression levels of LINC00304, MIR503HG, LINC01504 and FAM95B1 in PBMCs from patients with RA may serve as potential biomarkers for RA diagnosis, influencing the occurrence and progress of RA.

[Correlations of echocardiographic parameters in Gout patients: a retrospective analysis].

OBJECTIVE: To explore the correlations of echocardiographic parameters in patients with gout. METHODS: The hospitalization data and medical records of patients with gout between January, 2012 and June, 2019 were retrieved from the database of Anhui Provincial Hospital of Traditional Chinese Medicine, and the echocardiographic parameters and clinical laboratory test results of the inflammatory, immunological and metabolic indicators were analyzed. SPSS 22.0, SPSS Clementine 11.1 Aprior and other statistical software were used to determine the association rules and carry out correlation analysis, heat map analysis and multi-factor logistic regression analysis of the indicators. RESULTS: Heat map analysis showed that the expressions of EF and SV were the most significant, followed by AODd, LADs, LVDd and FS. Cluster analysis showed that AODd, EF, FS, LADs, LVDd, and SV were all in cluster 1, and IVSTd, LVPWTd, MPAD, Pmax, and RVDd were in cluster 2. Correlation analysis showed that in the 383 patients, EF was negatively correlated with LVDd (P < 0.05) and positively correlated with FS and SV (P < 0.05); AODd was positively correlated with IVSTd, LADs, LVDd, LVPWTd, RVDd, SV, and ESR (P < 0.05); FS was positively correlated with EF and SV (P < 0.05) and negatively correlated with LVDd (P < 0.05);IVSTd was positively correlated with AODd, LADs, LVPWTd, and complement C4 (P < 0.05); LADs were positively correlated with AODd, IVSTd, MPAD, RVDd, and SV (P < 0.05); LVDd was positively correlated with AODd, IVSTd (P < 0.05), and negatively correlated with LVDd and complement C3 (P < 0.05); MPAD and LADs, HDLC and TC were positively correlated (P < 0.05)and negatively correlated with Pmax (P < 0.05); Pmax was positively correlated with LVDd, RVDd and SV (P < 0.05)and negatively correlated with FS and MPAD (P < 0.05); RVDd was positively correlated with AODd, LADs, LVDd, Pmax, SV (P < 0.05); SV was positively correlated with AODd, EF, LADs, LVDd, Pmax, and RVDd (P < 0.05); complement C3 was positively correlated with complement C4 and CRP (P < 0.05), and negatively correlated with LVPWTd (P < 0.05); complement C4 was positively correlated with IVSTd, complement C3, CRP, and ESR (P < 0.05); CRP was positively correlated with complement C3, complement C4, IgA, IgG (P < 0.05), and negatively correlated with TC, HDLC, and TG (P < 0.05); TG was positively correlated with HDLC, IgM, and TC (P < 0.05), and negatively correlated with CRP (P < 0.05); HDLC was positively correlated with MPAD, HDLC and TC (P < 0.05) and negatively correlated with CRP (P < 0.05); IgA was positively correlated with CRP, IgG and IgM (P < 0.05); IgG was positively correlated with CRP, IgA and IgM (P < 0.05); IgM is positively correlated with TG, IgA, IgG, UA (P < 0.05) and negatively correlated with CRP (P < 0.05); UA was positively correlated with IgM (P < 0.05); ESR was positively correlated with AODd and complement C4 (P < 0.05); HCY was negatively correlated with RVDd (P < 0.05); TC was positively correlated with MPAD and TG (P < 0.05), and negatively correlated with CRP (P < 0.05). The increase of Pmax was significantly associated with the increase of LDL-C, UA, complement C4, TG, HCY, HDL-C, IgG, ESR, CRP, and complement C3; the increase of SV was associated with the elevations of UA, LDL-C, complement C4, HDL-C, CRP, IgG, HCY, TC, ESR, TG, and complement C3. Multivariate logistic regression analysis indicated that FS was positively correlated with LDL-C (P < 0.05), Pmax was negatively correlated with IgM (P < 0.05), and SV was negatively correlated with ESR (P < 0.05). CONCLUSIONS: The changes of echocardiographic parameters in patients with gout are correlated with the increase in inflammation, immunity, and metabolic indexes. Patients with a history of smoking and drinking do not show obvious changes in cardiac function. The changes in metabolic indexes are risk factors for changes in echocardiographic parameters.

RNA-seq reveals the circular RNA and miRNA expression profile of peripheral blood mononuclear cells in patients with rheumatoid arthritis.

OBJECTIVE: Circular RNAs (circRNAs) are a significant class of molecules involved in a wide range of diverse biological functions that are abnormally expressed in many types of diseases. The present study aimed to determine the circRNAs specifically expressed in peripheral blood mononuclear cells (PBMCs) from rheumatoid arthritis (RA) patients to identify their possible molecular mechanisms. METHODS: To identify the circRNAs specifically expressed in RA, we started by sequencing the of PBMCs circRNA and microRNAs (miRNAs) from a RA group (n = 3) and a control group (n = 3). We constructed a network of differentially expressed circRNAs and miRNAs. Then, we selected differentially expressed circRNAs in PBMCs from 10 RA patients relative to 10 age- and sex-matched controls using real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR). Spearman's correlation test was used to evaluate the correlation of circRNAs with biochemical measurements. RESULTS: A total of 165 circRNAs and 63 miRNAs were differently expressed between RA patients and healthy people according to RNA-seq, including 109 circRNAs that were significantly up-regulated and 56 circRNAs that were down-regulated among the RA patients. RT-qPCR validation demonstrated that the expression levels of hsa_circ_0001200, hsa_circ_0001566, hsa_circ_0003972, and hsa_circ_0008360 were consistent with the results from the sequencing analysis. Then, we found that there were significant correlations between the circRNAs and disease severity. CONCLUSION: Generally, these results suggest that expression of hsa_circ_0001200, hsa_circ_0001566, hsa_circ_0003972, and hsa_circ_0008360 in PBMCs from RA patients may serve as potential biomarkers for the diagnosis of RA, and these circRNAs may influence the occurrence and development of RA.

[Differentially expressed inflammatory proteins in acute gouty arthritis based on protein chip].

OBJECTIVE: To detect the differentially expressed inflammatory proteins in acute gouty arthritis (AGA) with protein chip. METHODS: The Raybiotech cytokine antibody chip was used to screen the proteomic expression in serum samples of 10 AGA patients and 10 healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were applied to determine the biological function annotation of differentially expressed proteins and the enrichment of signal pathways. ELISA method was used to verify the differential protein expression in 60 AGA patients and 60 healthy subjects. The ROC curve was employed to evaluate the diagnostic value of differential proteins in AGA patients. RESULTS: According to|log2FC|>log2 1.2 and corrected P<0.01, 4 most differentially expressed proteins in AGA patients were identified, including tumor necrosis factor receptor super family members Ⅱ (TNF RⅡ), macrophage inflammatory protein 1ß (MIP-1ß), interleukin-8 (IL-8), and granulocyte-macrophage colony stimulating factor (GM-CSF). GO and KEGG enrichment analysis showed that the differentially expressed proteins were related to inflammation, metabolism and cytokine pathways. The ELISA results showed that serum levels of differentially expressed proteins were significantly different between AGA patients and healthy subjects(all P<0.01). ROC curve analysis showed that the areas under the curve (AUCs) of GM-CSF, IL-8, MIP-1ß and TNF RⅡ for predicting AGA were 0.657 (95% CI: 0.560-0.760, sensitivity: 68.33%, specificity: 50.00%), 0.994 (95% CI: 0.980-1.000, sensitivity: 100.00%, specificity: 61.67%), 0.980 (95% CI: 0.712-0.985, sensitivity: 95.00%, specificity: 98.33%) and 0.965 (95% CI: 0.928-1.000, sensitivity: 100.00%, specificity: 10.00%), respectively. CONCLUSIONS: Proteomics can be applied to identify the biomarkers of AGA, which may be used for risk prediction and diagnosis of AGA patients.