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Background: Osteoarthritis (OA) is a degenerative arthritis with high levels of clinical heterogeneity. Aberrant metabolism such as shifting from oxidative phosphorylation to glycolysis is a response to changes in the inflammatory microenvironment of OA. Therefore, there is a pressing need to identify novel glycolysis regulators during OA progression., Methods: We systematically studied glycolysis patterns mediated by 141 glycolysis regulators in 74 human synovial samples and discussed the characteristics of the immune microenvironment modified by glycolysis. The random forest (RF) method was applied to screen candidate hub glycolysis regulators in OA. RT-qPCR was performed to validate these key regulators. Then distinct glycolysis patterns were identified, and systematic correlation between these glycolysis patterns and immune cell infiltration was analyzed. The glycolysis score was constructed to quantify glycolysis patterns together with immune infiltration of individual OA patient., Results: 56 glycolysis-related differentially expressed genes (DEGs) were identified between OA and non-OA samples. STC1, VEGFA, KDELR3, DDIT4 and PGAM1 were selected as candidate genes to predict the probability of OA. Two glycolysis patterns in OA were identified. Glycolysis cluster A with higher glycolysis score was related to an inflamed phenotype., Conclusions: Taken together, our results established a glycolysis-based genetic signature for OA, guided in-depth studies on the metabolic mechanism of OA, and facilitated to explore new clinical treatment strategies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

A growing of evidence has showed that patients with osteoarthritis (OA) had a higher coronavirus 2019 (COVID-19) infection rate and a poorer prognosis after infected it. Additionally, scientists have also discovered that COVID-19 infection might cause pathological changes in the musculoskeletal system. However, its mechanism is still not fully elucidated. This study aims to further explore the sharing pathogenesis of patients with both OA and COVID-19 infection and find candidate drugs. Gene expression profiles of OA (GSE51588) and COVID-19 (GSE147507) were obtained from the Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) for both OA and COVID-19 were identified and several hub genes were extracted from them. Then gene and pathway enrichment analysis of the DEGs were performed; protein–protein interaction (PPI) network, transcription factor (TF)-gene regulatory network, TF-miRNA regulatory network and gene-disease association network were constructed based on the DEGs and hub genes. Finally, we predicted several candidate molecular drugs related to hub genes using DSigDB database. The receiver operating characteristic curve (ROC) was applied to evaluate the accuracy of hub genes in the diagnosis of both OA and COVID-19. In total, 83 overlapping DEGs were identified and selected for subsequent analyses. CXCR4, EGR2, ENO1, FASN, GATA6, HIST1H3H, HIST1H4H, HIST1H4I, HIST1H4K, MTHFD2, PDK1, TUBA4A, TUBB1 and TUBB3 were screened out as hub genes, and some showed preferable values as diagnostic markers for both OA and COVID-19. Several candidate molecular drugs, which are related to the hug genes, were identified. These sharing pathways and hub genes may provide new ideas for further mechanistic studies and guide more individual-based effective treatments for OA patients with COVID-19 infection. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA), the most common type of arthritis, is a complex biological response caused by cartilage wear and synovial inflammation that links biomechanics and inflammation. The progression of OA correlates with a rise in the number of senescent cells in multiple joint tissues. However, the mechanisms by which senescent cells and their involvement with immune infiltration promote OA progression are not fully understood. The gene expression profiles and clinical information of OA and healthy control synovial tissue samples were retrieved from the Gene Expression Omnibus database, and then differential analysis of senescence regulators between OA and normal samples was performed. The random forest (RF) was used to screen candidate senescence regulators to predict the occurrence of OA. The reverse transcription quantitative real-time PCR experiments at tissue's level was performed to confirm these biomarkers. Moreover, two distinct senescence patterns were identified and systematic correlation between these senescence patterns and immune cell infiltration was analyzed. The senescence score and senescence gene clusters were constructed to quantify senescence patterns together with immune infiltration of individual OA patient. 73 senescence differentially expressed genes were identified between OA patients and normal controls. The RF method was utilized to build an OA risk model based on two senescence related genes: BCL6 and VEGFA. Next, two distinct aging patterns were determined in OA synovial samples. Most patients from senescence cluster A were further classified into gene cluster B and high senescence score group correlated with a non-inflamed phenotype, whereas senescence cluster B were classified into gene cluster A and low senescence score group correlated with an inflamed phenotype. Our study revealed that senescence played an important role in in OA synovial inflammation. Evaluating the senescence patterns of individuals with OA will contribute to enhancing our cognition of immune infiltration characterization, providing novel diagnostic and prognostic biomarkers, and guiding more effective immunotherapy strategies. [ABSTRACT FROM AUTHOR]

Fatty liver is one of the most pervasive liver diseases worldwide. Probiotics play an important role in the progression of liver disease, but their effects on host regulation are poorly understood. This study investigated the protective effects of lactobacillus gasseri (L. gasseri) against high-cholesterol diet (HCD)-induced fatty liver injury using a zebrafish larvae model. Liver pathology, lipid accumulation, oxidative stress and hepatic inflammation were evaluated to demonstrate the changes in a spectrum of hepatic injury. Moreover, multiple indexes on host gene expression profiles were comprehensively characterized by RNA screening. The results showed that treatment with L. gasseri ameliorated HCD-induced morphological and histological alterations, lipid regulations, oxidative stress and macrophage aggregation in the liver of zebrafish larvae. Furthermore, the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway revealed that the core pathways of L. gasseri regulation were interleukin-17 (IL-17) signaling, phosphoinositide 3-kinase (PI3K)-AKT signaling pathway, the regulation of lipolysis and adipocytes and fatty acid elongation and estrogen signaling. The genes at key junction nodes, hsp90aa1.1, kyat3, hsd17b7, irs2a, myl9b, ptgs2b, cdk21 and papss2a were significantly regulated by L. gasseri administration. To conclude, the current research extends our understanding of the protective effects of L. gasseri against fatty liver and provides potential therapeutic options for fatty liver treatment. [ABSTRACT FROM AUTHOR]

• An OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1) was constructed. • Two m7G methylation patterns in OA were found to show distinct biological characteristics, and an m7G score were generated. • M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype. Accumulating evidence has shown that aberrant N7-methylguanosine (m7G) RNA methylation played an important role in the occurrence and development of cancer. However, knowledge of m7G modifications in inflammatory diseases is limited. Osteoarthritis (OA) is the most common arthritic disease with poor prognosis. Our research aimed to identify m7G-related hub biomarkers and investigate m7G regulator expression pattern in immune landscape of OA patients. Gene expression profiles and their clinical information were obtained from the Gene Expression Omnibus (GEO) database, and differential analysis of 14 m7G-related regulators between elective OA and normal samples was performed. M7G-related hub genes for OA were mined based on single-sample gene set enrichment analysis (ssGSEA) and the random forest (RF) algorithm, and qRT-PCR was performed to confirm the abnormal expression of hub genes. Enrichment, protein–protein interaction (PPI), transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory analysis based on m7G hub genes were performed. Then we predicted several candidate drugs related to m7G hub genes using DSigDB database. Moreover, we comprehensively evaluated m7G methylation patterns in OA samples and systematically correlated these modification patterns with the characteristics of immune cell infiltration. The m7G score was generated to quantify m7G methylation patterns for individual OA patients by the application of principal component analysis (PCA) algorithm. We constructed an OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1). Two m7G methylation patterns in OA were discovered to show distinct biological characteristics, and an m7G score were generated. M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype. Our study was the first to comprehensively investigate the m7G methylation dysregulations in immune landscape during the progression of OA. These 4 m7G gene-related signatures can be used as novel OA biomarkers to predict the occurrence of OA. Evaluating the m7G methylation patterns of OA individuals will contribute to enhancing our cognition of immune infiltration characterization and guiding more effective immunotherapy strategies. [ABSTRACT FROM AUTHOR]

The overall study design is illustrated. [Display omitted] • A four-gene signature (ZBTB16, TNFSF11, SCRG1 and KDELR3) was identified to be involved OA progression based on WGCNA, LASSO, SVM-RFE and RF algorithms combined with bioinformatic analyses. • The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell, natural killer cell and CD8 + T cell. Although osteoarthritis (OA) is one of the most prevalent joint disorders, effective biomarkers to diagnose OA are still unavailable. This study aimed to acquire some key synovial biomarkers (hub genes) and analyze their correlation with immune infiltration in OA. Gene expression profiles and clinical characteristics of OA and healthy synovial samples were retrieved from the Gene Expression Omnibus (GEO) database. Hub genes for OA were mined based on a combination of weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms. A diagnostic nomogram model for OA prediction was developed based on the hub genes. Receiver operating characteristic curves (ROC) were performed to confirm the abnormal expression of hub genes in the experimemtal and validation datasets. qRT-PCR using patients' samples were conducted as well. In addition, the infiltration level of 28 immune cells in the expression profile and their relationship with hub genes were analyzed using single-sample GSEA (ssGSEA). 4 hub genes (ZBTB16, TNFSF11, SCRG1 and KDELR3) were obtained by WGCNA, lasso, SVM-RFE, RF algorithms as potential biomarkers for OA. The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell and natural killer cell. A machine learning model to diagnose OA based on ZBTB16, TNFSF11, SCRG1 and KDELR3 using synovial tissue was constructed, providing theoretical foundation and guideline for diagnostic and treatment targets in OA. [ABSTRACT FROM AUTHOR]