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BACKGROUND: Diabetic patients have a greater incidence of adhesive capsulitis (AC) and a more protracted disease course than patients with idiopathic AC. The purpose of this study was to compare gene expression differences between AC with diabetes mellitus and AC without diabetes mellitus. METHODS: Shoulder capsule samples were prospectively obtained from diabetic or nondiabetic patients who presented with shoulder dysfunction and underwent arthroscopy (N = 16). Shoulder samples of AC with and without diabetes (n = 8) were compared with normal shoulder samples with and without diabetes as the control group (n = 8). Shoulder capsule samples were subjected to whole-transcriptome RNA sequencing, and differential expression was analyzed with EdgeR. Only genes with a false discovery rate < 5% were included for further functional enrichment analysis. RESULTS: The sample population had a mean age of 47 years (range, 24–62 years), and the mean hemoglobin A(1c) level for nondiabetic and diabetic patients was 5.18% and 8.71%, respectively. RNA-sequencing analysis revealed that 66 genes were differentially expressed between diabetic patients and nondiabetic patients with AC whereas only 3 genes were differentially expressed when control patients with and without diabetes were compared. Furthermore, 286 genes were differentially expressed in idiopathic AC patients, and 61 genes were differentially expressed in diabetic AC patients. On gene clustering analysis, idiopathic AC was enriched with multiple structural and muscle-related pathways, such as muscle filament sliding, whereas diabetic AC included a greater number of hormonal and inflammatory signaling pathways, such as cellular response to corticotropin-releasing factor. CONCLUSIONS: Whole-transcriptome expression profiles demonstrate a fundamentally different underlying pathophysiology when comparing diabetic AC with idiopathic AC, suggesting that these conditions are distinct clinical entities. The new genes expressed explain the differences in the disease course and suggest new therapeutic targets that may lead to different treatment paradigms in these 2 subsets. LEVEL OF EVIDENCE: Basic Science Study; Molecular Biology

Background Rheumatoid arthritis (RA) is a common chronic autoimmune disease characterized by inflammation of the synovial membrane. However, the etiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify the key genes involved in RA. Methods GSE77298 was downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen the differentially expressed genes (DEGs). Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway were analyzed by using the DAVID online tool. The STRING database was used to analyze the interaction of differentially encoded proteins. PPI interaction network was divided into subnetworks using MCODE algorithm and was analyzed using Cytoscape. Gene set enrichment analysis (GSEA) was performed to identify relevant biological functions. qRT-PCR analysis was also performed to verify the expression of identified hub DEGs. Results A total of 4062 differentially expressed genes were selected, including 1847 upregulated genes and 2215 downregulated genes. In the biological process, DEGs were mainly concentrated in the fields of muscle filament sliding, muscle contraction, intracellular signal transduction, cardiac muscle contraction, signal transduction, and skeletal muscle tissue development. In the cellular components, DEGs were mainly concentrated in the parts of cytosol, Z disk, membrane, extracellular exosome, mitochondrion, and M band. In molecular functions, DEGs were mainly concentrated in protein binding, structural constituent of muscle, actin binding, and actin filament binding. KEGG pathway analysis shows that DEGs mainly focuses on pathways about lysosome, Wnt/β-catenin signaling pathway, and NF-κB signaling pathway. CXCR3, GNB4, and CXCL16 were identified as the core genes that involved in the progression of RA. By qRT-PCR analysis, we found that CXCR3, GNB4, and CXCL16 were significantly upregulated in RA tissue as compared to healthy controls. Conclusion In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the progression of RA, and provide candidate targets for diagnosis and treatment of RA.

The present study was designed to detect possible biomarkers associated with diabetic foot ulcer (DFU) incidence in an effort to develop novel treatments for this condition. The GSE7014 and GSE29221 gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, after which differentially expressed genes (DEGs) were identified between DFU and healthy samples. These DEGs were then arranged into a protein-protein interaction (PPI) network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) term enrichment analyses were performed to explore the functional roles of these genes. In total, 1192 DEGs were identified in the GSE7014 dataset (900 upregulated, 292 downregulated), while 1177 were identified in the GSE29221 dataset (257 upregulated, 919 downregulated). GO analyses revealed these DEGs to be significantly enriched in biological processes including sarcomere organization, muscle filament sliding, and the regulation of cardiac conduction, molecular functions including structural constituent of muscle, protein binding, and calcium ion binding, and cellular components including Z disc, myosin filament, and M band. These DEGs were also enriched in the adrenergic signaling in cardiomyoctes, dilated cardiomyopathy, and tight junction KEGG pathways. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with DFU development.

Rhabdomyosarcoma (RMS) is a common malignant soft tissue sarcoma, which is the third most common soft tissue sarcoma after malignant fibrohistoma and liposarcoma. The discovery of potential postbiomarkers could lead to early and more effective treatment measures to reduce the mortality of RMS. The discovery of biomarker is expected to be the direction of targeted therapy, providing a new direction for the precise treatment of RMS. Gene Expression Omnibus database was used to download the tow gene profiles, GSE28511 and GSE135517. GEO2R was applied to identify differently expressed genes (DEGs) between RMS and normal group. Database for Annotation, Visualization and Integrated Discovery and Metascape can perform the enrichment analysis for the DEGs. Protein-protein interaction network was constructed, and the hub genes was identified by the Cytoscape. Expression and overall survival analysis of hub genes were performed. A total of 15 common DEGs were screened between RMS and normal tissues. The enrichment analysis here showed that the DEGs mainly enriched in the muscle filament sliding, myofibril, protein complex, sarcomere, myosin complex, nuclear chromosome, and tight junction. The 6 hub genes (DNA Topoisomerase II Alpha, Insulin Like Growth Factor 2, HIST1H4C, Cardiomyopathy Associated 5, Myosin Light Chain 2 [MYL2], Myosin Heavy Chain 2) were identified. Compared with the normal tissues, MYL2 were down-regulated in the RMS tissues. RMS patients with low expression level of MYL2 had poorer overall survival times than those with high expression levels (P

Hypertrophic cardiomyopathy (HCM), the most common heritable cardiomyopathy, is associated with a high risk of sudden cardiac death. The complexity and behavior of the circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) network in HCM have not been thoroughly elucidated. Plasma circRNA and messenger RNA (mRNA) expression profiles were acquired by using a microarray. Weighted correlation network analysis (WGCNA) and linear models for microarray data (Limma) were used to analyze microarray data. Gene modules, consisting of genes with high correlations, were detected and represented by a designated color. The ceRNA network, including circRNA, microRNA (miRNA), and mRNA, was constructed based on the “ceRNA hypothesis” using an integrated systems biology method. By WGCNA, two modules, namely magenta and red modules, were identified as being positively correlated with HCM. In the combined analysis of WGCNA and Limma, 36 hub circRNAs in the magenta module and 83 hub circRNAs in the red module were significantly upregulated compared with the controls. By coexpression analysis, 270 circRNA–mRNA pairs were identified with a coefficient ≥0.9 and p < 0.05. With Starbase and miRWalk tools, circRNA–miRNA pairs and miRNA–mRNA pairs were predicted. Once these pairs were combined, the ceRNA network with 6 circRNAs, 29 miRNAs, and 6 mRNAs was constructed. Functional analysis demonstrated that these circRNAs in the ceRNA network were associated with calcium-release channel activity and muscle filament sliding. Our study provided a global perspective and systematic analysis of the circRNA-associated ceRNA network in HCM. The identified circRNAs hsa_circ_0043762, hsa_circ_0036248, and hsa_circ_0071269 may be key regulators involved in HCM pathogenesis.

To elucidate the time-resolved molecular events underlying the LV remodeling (LVR) process, we developed a large-scale network model that integrates the 24 molecular variables (plasma proteins and non-coding RNAs) collected in the REVE-2 study at four time points (baseline, 1month, 3months and 1year) after MI. The REVE-2 network model was built by extending the set of REVE-2 variables with their mechanistic context based on known molecular interactions (1310 nodes and 8639 edges). Changes in the molecular variables between the group of patients with high LVR (>20%) and low LVR (

Global change is impacting aquatic ecosystems, with high risks for food production. However, the molecular underpinnings of organismal tolerance to both ocean warming and acidification are largely unknown. Here we tested the effect of warming and acidification in a 42-day experiment on a commercial temperate fish, the gilt-head seabream Sparus aurata. Juvenile fish were exposed to control (C 18 °C pH 8), ocean warming (OW 22 °C pH 8), ocean acidification (OA 18 °C pH 7.5) and ocean warming and acidification (OWA 22 °C pH 7.5). Proxies of fitness (mortality; condition index) and muscle proteome changes were assessed; bioinformatics tools (Cytoscape, STRAP, STRING) were used for functional analyses. While there was no mortality in fish under OW, fish exposed to OA and both OWA showed 17% and 50% mortality, respectively. Condition index remained constant in all treatments. OW alone induced small proteome adjustments (up-regulation of 2 proteins) related to epigenetic gene regulation and cytoskeletal remodeling. OA and both OWA induced greater proteome changes (12 and 8 regulated proteins, respectively) when compared to OW alone, suggesting that pH is central to proteome modulation. OA exposure led to increased glycogen degradation, glycolysis, lipid metabolism, anion homeostasis, cytoskeletal remodeling, immune processes and redox based signaling while decreasing ADP metabolic process. OWA led to increased lipid metabolism, glycogen degradation, glycolysis, cytoskeleton remodeling and decreased muscle filament sliding and intermediate filament organization. Moreover, as rates of change in temperature and acidification depend on region we tested as proof of concept an (i) acidification effect in a hot ocean (22 °C pH 8 vs 22 °C pH 7.5) which led to the regulation of 7 proteins, the novelty being in a boost of anaerobic metabolism and impairment of proteasomal degradation; and (ii) warming effect in an acidified ocean (18 °C pH 7.5 vs 22 °C pH 7.5) which led to the regulation of 5 proteins, with an emphasis on anaerobic metabolism and transcriptional regulation. The negative synergistic effects of ocean warming and acidification on fish survival coupled to the mobilization of storage compounds, enhancement in anaerobic pathways and impaired proteasomal degradation could pose a serious threat to the viability of sea bream populations.

There are two main types of hair follicle in Inner Mongolia Cashmere goats, the primary hair follicle (PHF) producing hair fibers and the secondary hair follicle (SHF) producing cashmere fibers. Of both fibers from cashmere-bearing goats in Aerbasi, Inner Mongolia, the timing of cyclical phases for the cashmere have been well clarified but hair fibers have been less noticeable. Herein, we evaluated transcriptome of PHF and SHF from the same three goats in Aerbasi at the catagen- and telogen phase of cashmere growth. We totally found 1977 DEGs between PHFs at the telogen and catagen phases of SHF, 1199 DEGs between telogen- and catagen SHF, 2629 DEGs between PHF at the catagen phase of SHF and catagen SHF, and 755 DEGs between PHF at the telogen phase of SHF and telogen SHF. By analyzing gene functions based on GO and KEGG database, we found that the DEGs have functions in muscle contraction and muscle filament sliding between catagen- and telogen SHF, indicating that arrector pilli muscles might play a role on the transition from catagen to telogen. Moreover, considering that the enriched GO and KEGG categories of the DEGs between PHF and SHF, we suggested that part of PHF might rest in their own anagen phase when SHF are at catagen, but PHF might enter into the telogen phase at SHF's telogen. Finally, we highly recommended the several potential genes acting as the regulators of the transition between growth phases including IL17RB and eight members of ZNF. These results provide insight into molecular mechanisms on the transition of SHF from catagen to telogen together with PHF's growth situation at SHF's catagen and telogen in Inner Mongolia Cashmere goats.