Predicting the potential ankylosing spondylitis-related genes utilizing bioinformatics approaches

miRNAs dysregulation in ankylosing spondylitis: A review of implications for disease mechanisms, and diagnostic markers

Epigenetic processes, including non-coding RNA, histone modifications, and DNA methylation, play a vital role in connecting the environment to the development of a disorder, especially when there is a favorable genetic background. Ankylosing Spondylitis (AS) is a chronic type of spinal arthritis that highlights the significance of epigenetics in diseases related to autoimmunity and inflammation. MicroRNAs (miRNAs) are small non-coding RNAs that are involved in both normal and aberrant pathological and physiological gene expression. This study focuses on the pathophysiological pathways to clarify the role of miRNAs in AS. We have conducted a thorough investigation of the involvement of miRNAs in several processes, including inflammation, the production of new bone, T-cell activity, and the regulation of pathways such as BMP, Wnt, and TGFβ signaling. Undoubtedly, miRNAs play a crucial role in enhancing our comprehension of the pathophysiology of AS, and their promise as a therapeutic strategy is quickly expanding.

Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model

Background: Osteoporosis (OP) is a chronic bone metabolic disease and a serious global public health problem. Several studies have shown that mitophagy plays an important role in bone metabolism disorders; however, its role in osteoporosis remains unclear. Methods: The Gene Expression Omnibus (GEO) database was used to download GSE56815, a dataset containing low and high BMD, and differentially expressed genes (DEGs) were analyzed. Mitochondrial autophagy-related genes (MRG) were downloaded from the existing literature, and highly correlated MRG were screened by bioinformatics methods. The results from both were taken as differentially expressed (DE)-MRG, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Protein-protein interaction network (PPI) analysis, support vector machine recursive feature elimination (SVM-RFE), and Boruta method were used to identify DE-MRG. A receiver operating characteristic curve (ROC) was drawn, a nomogram model was constructed to determine its diagnostic value, and a variety of bioinformatics methods were used to verify the relationship between these related genes and OP, including GO and KEGG analysis, IP pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, a hub gene-related network was constructed and potential drugs for the treatment of OP were predicted. Finally, the specific genes were verified by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identified in the GSE56815 dataset. The weighted gene co-expression network analysis(WGCNA) identified 2291 key module genes, and 91 DE-MRG were obtained by combining the two. The PPI network revealed that the target gene for AKT1 interacted with most proteins. Three MRG (NELFB, SFSWAP, and MAP3K3) were identified as hub genes, with areas under the curve (AUC) 0.75, 0.71, and 0.70, respectively. The nomogram model has high diagnostic value. GO and KEGG analysis showed that ribosome pathway and cellular ribosome pathway may be the pathways regulating the progression of OP. IPA showed that MAP3K3 was associated with six pathways, including GNRH Signaling. The ssGSEA indicated that NELFB was highly correlated with iDCs (cor = -0.390, p < 0.001). The regulatory network showed a complex relationship between miRNA, transcription factor(TF) and hub genes. In addition, 4 drugs such as vinclozolin were predicted to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consistent with the results of bioinformatics analysis. Conclusion: Mitophagy plays an important role in the development of osteoporosis. The identification of three mitophagy-related genes may contribute to the early diagnosis, mechanism research and treatment of OP.

Identification of necroptosis-related genes in Parkinson's disease by integrated bioinformatics analysis and experimental validation

Background

Parkinson's disease (PD) is the second most common neurodegeneration disease worldwide. Necroptosis, which is a new form of programmed cell death with high relationship with inflammation, plays a vital role in the progression of PD. However, the key necroptosis related genes in PD are not fully elucidated.

Purpose

Identification of key necroptosis-related genes in PD.

Method

The PD associated datasets and necroptosis related genes were downloaded from the GEO Database and GeneCards platform, respectively. The DEGs associated with necroptosis in PD were obtained by gap analysis, and followed by cluster analysis, enrichment analysis and WGCNA analysis. Moreover, the key necroptosis related genes were generated by PPI network analysis and their relationship by spearman correlation analysis. Immune infiltration analysis was used for explore the immune state of PD brain accompanied with the expression levels of these genes in various types of immune cells. Finally, the gene expression levels of these key necroptosis related genes were validated by an external dataset, blood samples from PD patients and toxin-induced PD cell model using real-time PCR analysis.

Result

Twelve key necroptosis-related genes including ASGR2, CCNA1, FGF10, FGF19, HJURP, NTF3, OIP5, RRM2, SLC22A1, SLC28A3, WNT1 and WNT10B were identified by integrated bioinformatics analysis of PD related dataset GSE7621. According to the correlation analysis of these genes, RRM2 and WNT1 were positively and negatively correlated with SLC22A1 respectively, while WNT10B was positively correlated with both OIF5 and FGF19. As the results from immune infiltration analysis, M2 macrophage was the highest population of immune cell in analyzed PD brain samples. Moreover, we found that 3 genes (CCNA1, OIP5 and WNT10B) and 9 genes (ASGR2, FGF10, FGF19, HJURP, NTF3, RRM2, SLC22A1, SLC28A3 and WNT1) were down- and up- regulated in an external dataset GSE20141, respectively. All the mRNA expression levels of these 12 genes were obviously upregulated in 6-OHDA-induced SH-SY5Y cell PD model while CCNA1 and OIP5 were up- and down- regulated, respectively, in peripheral blood lymphocytes of PD patients.

Conclusion

Necroptosis and its associated inflammation play fundamental roles in the progression of PD and these identified 12 key genes might be served as new diagnostic markers and therapeutic targets for PD.

Circulating microRNA sequencing revealed miRNome patterns in hematology and oncology patients aiding the prognosis of invasive aspergillosis

Invasive aspergillosis (IA) may occur as a serious complication of hematological malignancy. Delays in antifungal therapy can lead to an invasive disease resulting in high mortality. Currently, there are no well-established blood circulating microRNA biomarkers or laboratory tests which can be used to diagnose IA. Therefore, we aimed to define dysregulated miRNAs in hematology and oncology (HO) patients to identify biomarkers predisposing disease. We performed an in-depth analysis of high-throughput small transcriptome sequencing data obtained from the whole blood samples of our study cohort of 50 participants including 26 high-risk HO patients and 24 controls. By integrating in silico bioinformatic analyses of small noncoding RNA data, 57 miRNAs exhibiting significant expression differences (P < 0.05) were identified between IA-infected patients and non-IA HO patients. Among these, we found 36 differentially expressed miRNAs (DEMs) irrespective of HO malignancy. Of the top ranked DEMs, we found 14 significantly deregulated miRNAs, whose expression levels were successfully quantified by qRT-PCR. MiRNA target prediction revealed the involvement of IA related miRNAs in the biological pathways of tumorigenesis, the cell cycle, the immune response, cell differentiation and apoptosis.

Identification of immune related cells and crucial genes in the peripheral blood of ankylosing spondylitis by integrated bioinformatics analysis

Background

Ankylosing spondylitis (AS) is a progressive rheumatic disease and studies reveal that the immune system is critical for the pathogenesis of AS. In the present study, various bioinformatics analysis methods were comprehensively applied, designed to identify potential key genes and inflammation states of AS.

Methods

The transcriptome profiles of GSE25101 and GSE73754 obtained from the Gene Expression Omnibus (GEO) database were merged for subsequent analyses. The differentially expressed genes (DEGs) were identified using the Bioconductor package Limma and threshold values. Functional enrichment and pathway enrichment analyses were performed using the clusterProfiler package and Gene Set Enrichment Analysis (GSEA). Next, protein-protein interaction (PPI) network of the identified DEGs was constructed by the online database, the Search Tool for the Retrieval of Interacting Genes (STRING), visualization and analysis were performed through Cytoscape software. Subsequently, we applied CIBERSORT algorithm to identify subpopulation proportions of immune cells in peripheral blood samples. Finally, we validated the hub genes with the GSE18781 dataset. Samples were collected from patients to validate gene and protein expression using qRT-PCR and ELISA.

Results

A total of 334 DEGs were identified, including 182 upregulated and 152 downregulated DEGs, between AS patients and normal human controls, which were primarily involved in immune response, autophagy, and natural killer cell-mediated cytotoxicity. The most prominent module and candidate biomarkers were identified from the PPI network. Biomarkers were selected for validation and their expressions were significantly decreased in peripheral blood samples which was consistent with transcriptome sequencing results. Nine genes with AUC > 0.70 were considered to be AS hub genes for ROC curve analysis, including GZMA, GZMK, PRF1, GNLY, NKG7, KLRB1, KLRD1, IL2RB and CD247. Furthermore, CIBERSORT results suggest that AS contained a higher proportion of CD8+ T cells, naive CD4+ T cells, neutrophils, and lower levels of gamma delta T cells compared with the normal controls.

Conclusion

In this study, we identified DEGs combined with their closely related biological functions and propose that granule-associated proteins and immune infiltration maybe involved in the progression of ankylosing spondylitis. These validated hub genes may provide new perspectives for understanding the molecular mechanisms of ankylosing spondylitis.

Dysregulation of ribosome-related genes in ankylosing spondylitis: a systems biology approach and experimental method

Background

Ankylosing spondylitis (AS) is an autoimmune rheumatic disease. Few candidate gene associations have been reported for AS and the current understanding of its pathogenesis remains still poor. Thus, the exact mechanism of AS is needed to urgently be disclosed. The purpose of this study was to identify candidate genes involving in AS disease.

Methods and results

GSE25101 publicly available microarray and GSE117769 RNA-seq datasets of AS patients were obtained for bioinformatics analyses. Gene set enrichment analysis showed that in the microarray dataset, the ribosome pathway was significantly up-regulated in AS compared with controls. Furthermore, some ribosomal components demonstrated overexpression in patients in the RNA-seq dataset. To confirm the findings, 20 AS patients and 20 matching controls were selected from the Rheumatology Research Center clinic, Shariati Hospital. PBMCs were separated from whole blood and RNA contents were extracted. Following the results of datasets analysis, the expression level of rRNA5.8S pseudogene, rRNA18S pseudogene, RPL23, RPL7, and RPL17 genes were measured through real-time PCR. Our findings showed dysregulation of rRNA5.8S and rRNA18S pseudogenes, and also the RPL17 gene in patients.

Conclusion

Considering that genes involved in ribosome biogenesis contributed to some AS-associated biological processes as well as diseases that have comorbidities with AS, our results might advance our understanding of the pathological mechanisms of ankylosing spondylitis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-021-04662-2.

Aberrant Methylation of miR-34b and IL-12B mRNA Promoters Contributes to the Reduced Severity of Ankylosing Spondylitis

DNA methylation of Interleukin-12B (IL-12B) and miR-34b was proved to affect the expression of IL-12B and miR-34b, which were found to be involved in the pathogenesis of ankylosing spondylitis (AS). However, the molecular mechanisms underlying the role of IL-12B and miR-34b in AS remain to be explored. AS patients were divided into four groups according to their status of DNA methylation of miR-34b and IL-12B by bisulfite sequencing: HYPER-miR-34b + HYPO-IL-12B, HYPER-miR-34b + HYPER-IL-12B, HYPO-miR-34b + HYPER-IL-12B and HYPO-miR-34b + HYPO-IL-12B groups. Functional indicators were examined for patients with different status of DNA methylation in their miR-34b and IL-12B promoters. QPCR was performed to examine the expression of miR-34b and IL-12B mRNA under different conditions. ELISA was used to measure the expression of IL-12B p40 in the peripheral blood. Western blot was used to analyze the expression of IL-12B proteins. Luciferase assay was carried out to explore the suppressive role of miR-34b in IL-12B expression. The level of Ankylosing Spondylitis Disease Activity Score with C-reactive protein (ASDAS-CRP) was gradually increased in HYPER-miR-34b + HYPO-IL-12B,HYPER-miR-34b + HYPER-IL-12B,HYPO-miR-34b + HYPER-IL-12B and HYPO-miR-34b + HYPO-IL-12B groups, whereas the levels of Bath Ankylosing Spondylitis Functional Index (BASFI) and Bath Ankylosing Spondylitis Metrology Index (BASMI) were significantly elevated in the HYPO-miR-34b + HYPO-IL-12B group and diminished in the HYPER-miR-34b + HYPO-IL-12B group. The expression of miR-34b in the PBMCs and peripheral blood was remarkably higher in the HYPER-miR-34b + HYPO-IL-12B and HYPER-miR-34b + HYPER-IL-12B groups, whereas the expression of IL-12B was gradually decreased in the HYPER-miR-34b + HYPO-IL-12B, HYPER-miR-34b + HYPER-IL-12B, HYPO-miR-34b + HYPER-IL-12B and HYPO-miR-34b + HYPO-IL-12B groups. Luciferase assays with the transfection of miR-34b precursors suggested that miR-34b strongly suppressed the expression of IL-12B in THP-1 cells. In conclusion, our study demonstrated that hypermethylated miR-34b promoter led to evident upregulation of miR-34b, thus inhibiting the expression of IL-12B and alleviated the severity of ankylosing spondylitis by reducing the levels of factors including ASDAS-CRP, BASFI and BASMI.

Co-expression Network Analysis Reveals Key Genes Related to Ankylosing spondylitis Arthritis Disease: Computational and Experimental Validation

BACKGROUND

Ankylosing spondylitis (AS) is a type of arthritis which can cause inflammation in the vertebrae and joints between the spine and pelvis. However, our understanding of the exact genetic mechanisms of AS is still far from being clear.

OBJECTIVE

To study and find the mechanisms and possible biomarkers related to AS by surveying inter-gene correlations of networks.

MATERIALS AND METHODS

A weighted gene co-expression network was constructed among genes identified by microarray analysis, gene co-expression network analysis, and network clustering. Then receiver operating characteristic (ROC) curves were conducted to identify a significant module with the genes implicated in the AS pathogenesis. Real-time PCR was performed to validate the results of microarray analysis.

RESULTS

In the significant module obtained from the network analysis there were eight AS related genes (LSM3, MRPS11, NSMCE2, PSMA4, UBL5, RPL17, MRPL22 and RPS17) which have been reported in previous studies as hub genes. Further, in this module, eight significant enriched pathways were found with adjusted p-values < 0.001 consisting of oxidative phosphorylation, ribosome, nonalcoholic fatty liver disease, Alzheimer's, Huntington's, and Parkinson's diseases, spliceosome, and cardiac muscle contraction pathways which have been linked to AS. Furthermore, we identified nine AS related genes (UQCRB, UQCRH, UQCRHL, UQCRQ, COX7B, COX5B, COX6C, COX6A1 and COX7C) in these pathways which can play essential roles in controlling mitochondrial activity and pathogenesis of autoimmune diseases. Real-time PCR results showed that three genes including UQCRH, MRPS11, and NSMCE2 in AS patients were significantly differentially expressed compared with normal controls.

CONCLUSIONS

The results of the present study may contribute to understanding of AS molecular pathogenesis, thereby aiding the early prognosis, diagnosis, and effective therapies of the disease.

Development and Validation of an RNA-Binding Protein-Based Prognostic Model for Ovarian Serous Cystadenocarcinoma

Ribonucleic acid-binding proteins (RBPs) are reportedly involved in tumor progression and recurrence; however, the functions and mechanisms of action of RBPs in ovarian serous cystadenocarcinoma (OSC) are not known. To address these issues, gene expression profiles of OSC tissues from The Cancer Genome Atlas (TCGA) and normal tissues from the Genotype-Tissue Expression database were compared in order to identify RBPs that are differentially expressed in OSC. We also analyzed the biological functions of these RBPs and their relationship to clinical outcome. There were 190 RBPs that were differentially expressed between OSC and normal tissues, including 93 that were upregulated and 97 that were downregulated. Five of the RBPs were used to construct a prediction model that was evaluated by univariate and multivariate Cox regression analyses. TCGA data were randomly divided into training and test cohorts, and further categorized into high- and low-risk groups according to risk score in the model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group (training cohort P = 0.0007596; test cohort P = 0.002219). The area under the receiver operating characteristic curve of the training and test cohorts was 0.701 and 0.638, respectively, demonstrating that the model had good predictive power. A nomogram was established to quantitatively describe the relationship between the five prognostic RBPs and OS in OSC, which can be useful for developing individualized management strategies for patients.

MicroRNAs in ankylosing spondylitis: Function, potential and challenges

Epigenetic mechanisms such as DNA methylation, histone modifications and non-coding RNA, are considered the essential connection between a disorder's onset and the environment, on a permissive genetic background. Among autoimmune and inflammatory-mediated disorders, Ankylosing Spondylitis (AS), a chronic arthritis of the spine, is a very good example for the weight of epigenetics' contribution. MicroRNAs (miRNAs) are single-stranded nucleotides which regulate gene expression and are involved in pathological and physiological processes. In this manuscript we provide a clarification on the role of microRNAs in AS, with a focus on the mechanisms of pathogenesis. In specific, we have examined the contribution of miRNAs in the processes of inflammation, new bone formation and T-cell function, and the pathways (i.e. Wnt, BMP, TGFβ signalling etc.) they regulate. The utility of miRNAs in better understanding AS pathogenesis is undisputed and their utility as therapeutic opportunity is strongly increasing.

Identification of differential modules in ankylosing spondylitis using systemic module inference and the attract method

The objective of the present study was to identify differential modules in ankylosing spondylitis (AS) by integrating network analysis, module inference and the attract method. To achieve this objective, four steps were conducted. The first step was disease objective network (DON) for AS, and healthy objective network (HON) inference dependent on gene expression data, protein-protein interaction networks and Spearman's correlation coefficient. In the second step, module detection was performed by utilizing a clique-merging algorithm, which comprised of exploring maximal cliques by clique algorithm and refining or merging maximal cliques with a high overlap. The third part was seed module evaluation through module pair matches by Jaccard score and module correlation density (MCD) calculation. Finally, in the fourth step, differential modules between the AS and healthy groups were identified based on a gene set enrichment analysis-analysis of variance model in the attract method. There were 5,301 nodes and 28,176 interactions both in DON and HON. A total of 20 and 21 modules were detected for the AS and healthy group, respectively. Notably, six seed modules across two groups were identified with Jaccard score ≥0.5, and these were ranked in descending order of differential MCD (ΔC). Seed module 1 had the highest ΔC of 0.077 and Jaccard score of 1.000. By accessing the attract method, one differential module between the AS group and healthy group was identified. In conclusion, the present study successfully identified one differential module for AS that may be a potential marker for AS target therapy and provide insights for future research on this disease.

Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways

Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.

Interactions of the Immune System with Skin and Bone Tissue in Psoriatic Arthritis: A Comprehensive Review

Cutaneous psoriasis (e.g., psoriasis vulgaris (PsV)) and psoriatic arthritis (PsA) are complex heterogeneous diseases thought to have similar pathophysiology. The soluble and cellular mediators of these closely related diseases are being elucidated through genetic approaches such as genome-wide association studies (GWAS), as well as animal and molecular models. Novel therapeutics targeting these mediators (IL-12, IL-23, IL-17, IL-17 receptor, TNF) are effective in treating both the skin and joint manifestations of psoriasis, reaffirming the shared pathophysiology of PsV and PsA. However, the molecular and cellular interactions between skin and joint disease have not been well characterized. Clearly, PsV and PsA are highly variable in terms of their clinical manifestations, and this heterogeneity can partially be explained by differences in HLA-associations (HLA-Cw*0602 versus HLA-B*27, for example). In addition, there are numerous other genetic susceptibility loci (LCE3, CARD14, NOS2, NFKBIA, PSMA6, ERAP1, TRAF3IP2, IL12RB2, IL23R, IL12B, TNIP1, TNFAIP3, TYK2) and geoepidemiologic factors that contribute to the wide variability seen in psoriasis. Herein, we review the complex interplay between the genetic, cellular, ethnic, and geographic mediators of psoriasis, focusing on the shared mechanisms of PsV and PsA.

The Role of MicroRNAS in Ankylosing Spondylitis

Ankylosing spondylitis (AS) is a common and genetically heterozygous inflammatory rheumatic disease characterized by new bone formation, ankylosis and inflammation of hip, sacroiliac joints and spine. Until now, there is no method for early diagnosis of AS and the effective treatment available for AS patients remain largely undefined.We searched articles indexed in PubMed (MEDLINE) database using Medical Subject Heading (MeSH) or Title/Abstract words ("microRNA" and "ankylosing spondylitis") from inception up to November 2015.Genetic polymorphisms of miRNAs and their targets might alter the risk of AS development whereas certain miRNAs exhibit correlation with inflammatory index.Let-7i and miR-124 were upregulated whereas miR-130a was downregulated in circulating immune cells of AS patients. These deregulated miRNAs could modulate key immune cell functions, such as cytokine response and T-cell survival.miRNA deregulation is key to AS pathogenesis. However, clinical utilization of miRNAs for management of AS patients requires further support from future translational studies.