Bioinformatics analysis of rheumatoid arthritis tissues identifies genes and potential drugs that are expressed specifically

Discovery and optimization of 3-(indolin-5-yloxy)pyridin-2-amine derivatives as potent necroptosis inhibitors

Necroptosis is a form of regulated necrotic cell death and has been confirmed to play pivotal roles in the pathogenesis of multiple autoimmune diseases such as rheumatoid arthritis (RA) and psoriasis. The development of necroptosis inhibitors may offer a promising therapeutic strategy for the treatment of these autoimmune diseases. Herein, starting from the in-house hit compound 1, we systematically performed structural optimization to discover potent necroptosis inhibitors with good pharmacokinetic profiles. The resulting compound 33 was a potent necroptosis inhibitor for both human I2.1 cells (IC50 < 0.2 nM) and murine Hepa1-6 cells (IC50 < 5 nM). Further target identification revealed that compound 33 was an inhibitor of receptor interacting protein kinase 1 (RIPK1) with favorable selectivity. In addition, compound 33 also exhibited favorable pharmacokinetic profiles (T1/2 = 1.32 h, AUC = 1157 ng·h/mL) in Sprague-Dawley rats. Molecular docking and molecular dynamics simulations confirmed that compound 33 could bind to RIPK1 with high affinity. In silico ADMET analysis demonstrated that compound 33 possesses good drug-likeness profiles. Collectively, compound 33 is a promising candidate for antinecroptotic drug discovery.

Comprehensive analysis revealed the immunoinflammatory targets of rheumatoid arthritis based on intestinal flora, miRNA, transcription factors, and RNA-binding proteins databases, GSEA and GSVA pathway observations, and immunoinfiltration typing

OBJECTIVE

Rheumatoid arthritis (RA) is a chronic inflammatory arthritis. This study aimed to identify potential biomarkers and possible pathogenesis of RA using various bioinformatics analysis tools.

METHODS

The GMrepo database provided a visual representation of the analysis of intestinal flora. We selected the GSE55235 and GSE55457 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) separately. With the intersection of these DEGs with the target genes associated with RA found in the GeneCards database, we obtained the DEGs targeted by RA (DERATGs). Subsequently, Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to analyze DERATGs functionally. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on the data from the gene expression matrix. Additionally, the protein-protein interaction network, transcription factor (TF)-targets, target-drug, microRNA (miRNA)-mRNA networks, and RNA-binding proteins (RBPs)-DERATGs correlation analyses were built. The CIBERSORT was used to evaluate the inflammatory immune state. The single-sample GSEA (ssGSEA) algorithm and differential analysis of DERATGs were used among the infiltration degree subtypes.

RESULTS

There were some correlations between the abundance of gut flora and the prevalence of RA. A total of 54 DERATGs were identified, mainly related to immune and inflammatory responses and immunodeficiency diseases. Through GSEA and GSVA analysis, we found pathway alterations related to metabolic regulations, autoimmune diseases, and immunodeficiency-related disorders. We obtained 20 hub genes and 2 subnetworks. Additionally, we found that 39 TFs, 174 drugs, 2310 miRNAs, and several RBPs were related to DERATGs. Mast, plasma, and naive B cells differed during immune infiltration. We discovered DERATGs' differences among subtypes using the ssGSEA algorithm and subtype grouping.

CONCLUSIONS

The findings of this study could help with RA diagnosis, prognosis, and targeted molecular treatment.

RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images

Rheumatoid arthritis is an autoimmune disease which affects the small joints. Early prediction of RA is necessary for the treatment and management of the disease. The current work presents a deep learning and quantum computing-based automated diagnostic approach for RA in hand thermal imaging. The study's goals are (i) to develop a custom RANet model and compare its performance with the pretrained models and quanvolutional neural network (QNN) to distinguish between the healthy subjects and RA patients, (ii) To validate the performance of the custom model using feature selection method and classification using machine learning (ML) classifiers. The present study developed a custom RANet model and employed pre-trained models such as ResNet101V2, InceptionResNetV2, and DenseNet201 to classify the RA patients and normal subjects. The deep features extracted from the RA Net model are fed into the ML classifiers after the feature selection process. The RANet model, RA Net+ SVM, and QNN model produced an accuracy of 95%, 97% and 93.33% respectively in the classification of healthy groups and RA patients. The developed RANet and QNN models based on thermal imaging could be employed as an accurate automated diagnostic tool to differentiate between the RA and control groups.