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      pH-sensing G protein-coupled orphan receptor GPR68 is expressed in human cartilage and correlates with degradation of extracellular matrix during OA progression

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          Abstract

          Background

          Osteoarthritis (OA) is a debilitating joints disease affecting millions of people worldwide. As OA progresses, chondrocytes experience heightened catabolic activity, often accompanied by alterations in the extracellular environment’s osmolarity and acidity. Nevertheless, the precise mechanism by which chondrocytes perceive and respond to acidic stress remains unknown. Recently, there has been growing interest in pH-sensing G protein-coupled receptors (GPCRs), such as GPR68, within musculoskeletal tissues. However, function of GPR68 in cartilage during OA progression remains unknown. This study aims to identify the role of GPR68 in regulation of catabolic gene expression utilizing an in vitro model that simulates catabolic processes in OA.

          Methods

          We examined the expression of GPCR by analyzing high throughput RNA-Seq data in human cartilage isolated from healthy donors and OA patients. De-identified and discarded OA cartilage was obtained from joint arthroplasty and chondrocytes were prepared by enzymatic digestion. Chondrocytes were treated with GPR68 agonist, Ogerin and then stimulated IL1β and RNA isolation was performed using Trizol method. Reverse transcription was done using the cDNA synthesis kit and the expression of GPR68 and OA related catabolic genes was quantified using SYBR® green assays.

          Results

          The transcriptome analysis revealed that pH sensing GPCR were expressed in human cartilage with a notable increase in the expression of GPR68 in OA cartilage which suggest a potential role for GPR68 in the pathogenesis of OA. Immunohistochemical (IHC) and qPCR analyses in human cartilage representing various stages of OA indicated a progressive increase in GPR68 expression in cartilage associated with higher OA grades, underscoring a correlation between GPR68 expression and the severity of OA. Furthermore, IHC analysis of Gpr68 in murine cartilage subjected to surgically induced OA demonstrated elevated levels of GPR68 in knee cartilage and meniscus. Using IL1β stimulated in vitro model of OA catabolism, our qPCR analysis unveiled a time-dependent increase in GPR68 expression in response to IL1β stimulation, which correlates with the expression of matrix degrading proteases suggesting the role of GPR68 in chondrocytes catabolism and matrix degeneration. Using pharmacological activator of GPR68, our results further showed that GPR68 activation repressed the expression of MMPs in human chondrocytes.

          Conclusions

          Our results demonstrated that GPR68 was robustly expressed in human cartilage and mice and its expression correlates with matrix degeneration and severity of OA progression in human and surgical model. GPR68 activation in human chondrocytes further repressed the expression of MMPs under OA pathological condition. These results identify GPR68 as a possible therapeutic target in the regulation of matrix degradation during OA.

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          Most cited references54

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap

              The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web interface that scientists with little programming skills could use to make plots of their own data. The same applies to creating heatmaps: it is possible to add conditional formatting for Excel cells to show colored heatmaps, but for more advanced features such as clustering and experimental annotations, more sophisticated analysis tools have to be used. We present a web tool called ClustVis that aims to have an intuitive user interface. Users can upload data from a simple delimited text file that can be created in a spreadsheet program. It is possible to modify data processing methods and the final appearance of the PCA and heatmap plots by using drop-down menus, text boxes, sliders etc. Appropriate defaults are given to reduce the time needed by the user to specify input parameters. As an output, users can download PCA plot and heatmap in one of the preferred file formats. This web server is freely available at http://biit.cs.ut.ee/clustvis/.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                5 December 2023
                2023
                : 11
                : e16553
                Affiliations
                Orthopaedics, Emory University , Atlanta, GA, United States
                Author information
                http://orcid.org/0000-0002-6023-8198
                Article
                16553
                10.7717/peerj.16553
                10704986
                38077417
                4d65d40f-c29f-4247-a8f5-d207c11dffd5
                © 2023 Khan et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 7 May 2023
                : 9 November 2023
                Funding
                Funded by: NIH-National Center for Advancing Translational Sciences (NCATS)
                Award ID: R03TR003669-01A1
                Funded by: Emory University to Hicham Drissi
                This work was supported by the NIH-National Center for Advancing Translational Sciences (NCATS) (R03TR003669-01A1) to Nazir M Khan and Funds from Emory University to Hicham Drissi. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Bioinformatics
                Cell Biology
                Computational Biology
                Molecular Biology

                orphan receptor,chondrocytes,osteoarthritis,gpr68,matrix degeneration

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