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      Potential mechanisms of action of celastrol against rheumatoid arthritis: Transcriptomic and proteomic analysis

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          Abstract

          The clinical efficacy for treating of celastrol rheumatoid arthritis (RA) has been well-documented, but its mechanism of action remains unclear. Here we explored through what proteins and processes celastrol may act in activated fibroblast-like synoviocytes (FLS) from RA patients. Differential expression of genes and proteins after celastrol treatment of FLS was examined using RNA sequencing, label-free relatively quantitative proteomics and molecular docking. In this paper, expression of 26,565 genes and 3,372 proteins was analyzed. Celastrol was associated with significant changes in genes that respond to oxidative stress and oxygen levels, as well as genes that stabilize or synthesize components of the extracellular matrix. These results identify several potential mechanisms through which celastrol may inhibit inflammation in RA.

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          Fibroblast-like synoviocytes: key effector cells in rheumatoid arthritis.

          Rheumatoid arthritis (RA) remains a significant unmet medical need despite significant therapeutic advances. The pathogenesis of RA is complex and includes many cell types, including T cells, B cells, and macrophages. Fibroblast-like synoviocytes (FLS) in the synovial intimal lining also play a key role by producing cytokines that perpetuate inflammation and proteases that contribute to cartilage destruction. Rheumatoid FLS develop a unique aggressive phenotype that increases invasiveness into the extracellular matrix and further exacerbates joint damage. Recent advances in understanding the biology of FLS, including their regulation regulate innate immune responses and activation of intracellular signaling mechanisms that control their behavior, provide novel insights into disease mechanisms. New agents that target FLS could potentially complement the current therapies without major deleterious effect on adaptive immune responses.
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            Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package

            As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression.
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              Rheumatoid Arthritis

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                Author and article information

                Contributors
                Role: Writing – review & editing
                Role: Data curation
                Role: Methodology
                Role: Data curation
                Role: Data curation
                Role: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 July 2020
                2020
                : 15
                : 7
                : e0233814
                Affiliations
                [1 ] Department of Biological Sciences, Xinyang Normal University, Xinyang, China
                [2 ] Institute for Conservation and Utilization of Agro-Bioresources in Dabie Mountains, Xinyang, China
                Chang Gung University, TAIWAN
                Author notes

                Competing Interests: The authors have no conflicts of interest to declare.

                Article
                PONE-D-20-13479
                10.1371/journal.pone.0233814
                7390347
                32726313
                3bffd7e2-b908-4db0-b157-0a5e87bb8745
                © 2020 Xinqiang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 12 May 2020
                : 6 July 2020
                Page count
                Figures: 6, Tables: 0, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: U1804179
                Award Recipient : xinqiang song
                Funded by: Henan Science and Technology Innovation Team
                Award ID: Henan Science and Technology Innovation Team
                Award Recipient : xinqiang song
                Funded by: Henan key scientific and technological projects
                Award ID: 202102310190
                Award Recipient : xinqiang song
                Funded by: Nanhu Scholars Program for Young Scholars of Xinyang Normal University (CN)
                Award ID: 2018001
                Award Recipient : xinqiang song
                This study was supported by the National Natural Science Foundation of China (grant no. U1804179), the Henan Science and Technology Innovation Team, the Investigation on Plant Resources in Dabie Mountains and the Study and Utilization of Active Components of Special plants (grant no. 2017083), Henan key scientific and technological projects (202102310190) and the Nanhu Scholars Program for Young Scholars of Xinyang Normal University (grant no. 2018001).
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Proteins
                Extracellular Matrix Proteins
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Biochemistry
                Proteomics
                Biology and Life Sciences
                Genetics
                Gene Expression
                Medicine and Health Sciences
                Rheumatology
                Arthritis
                Rheumatoid Arthritis
                Medicine and Health Sciences
                Clinical Medicine
                Clinical Immunology
                Autoimmune Diseases
                Rheumatoid Arthritis
                Biology and Life Sciences
                Immunology
                Clinical Immunology
                Autoimmune Diseases
                Rheumatoid Arthritis
                Medicine and Health Sciences
                Immunology
                Clinical Immunology
                Autoimmune Diseases
                Rheumatoid Arthritis
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Extracellular Matrix
                Medicine and Health Sciences
                Medical Conditions
                Inflammatory Diseases
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Gene Expression and Vector Techniques
                Protein Expression
                Research and Analysis Methods
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Gene Expression and Vector Techniques
                Protein Expression
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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