Dexamethasone is a synthetic corticosteroid that has historically been used to treat inflammation, such as from osteoarthritis, spinal cord injury and, more recently, COVID-19. The mechanism of action of dexamethasone is generally known to include attenuation of pro-inflammatory responses as well as upregulation of anti-inflammatory elements. A major issue with the use of dexamethasone is its delivery, as it is normally administered in large quantities via methods like bolus injection to attempt to maintain sufficient concentrations days or weeks after administration. In this review, we examine the mechanism of action of dexamethasone and its effects on three major cell types in the context of specific diseases: macrophages in the context of COVID, chondrocytes in the context of osteoarthritis, and astrocytes in the context of neuro-inflammatory disease. From this, we identify the key proinflammatory cytokines interleukin-1 (IL-1) and Tumor Necrosis Factor alpha (TNF-a) as universal effectors of inflammation that should be targeted alongside dexamethasone administration. Additionally, we review current extended release dosing mechanisms for dexamethasone to act over periods of weeks and months. We suggest that dual treatment of dexamethasone with IL-1 and/or TNF-a monoclonal antibodies will be an effective immediate treatment for inflammation, while the addition of fully developed dexamethasone extended release mechanisms will allow for effective long-term control of inflammatory disease.
Many computational pipelines exist for the detection of differentially expressed genes. However, computational pipelines for functional gene detection rarely exist. We developed a new computational pipeline for functional gene identification from transcriptome profiling data. Key features of the pipeline include batch effect correction, clustering optimization by gap statistics, gene ontology analysis of clustered genes, and literature analysis for functional gene discovery. By leveraging this pipeline on RNA-seq datasets from two mouse retinal development studies, we identified 7 candidate genes involved in the formation of the photoreceptor outer segment. The expression of top three candidate genes (Pde8b, Laptm4b, and Nr1h4) in the outer segment of the developing mouse retina were experimentally validated by immunohistochemical analysis. This computational pipeline can accurately predict novel functional gene for a specific biological process, e.g., development of the outer segment and synapses of the photoreceptor cells in the mouse retina. This pipeline can also be useful to discover functional genes for other biological processes and in other organs and tissues.
In vitro tools, which can enable development of models that replicate the cell microenvironment associated with complex diseases such as osteoarthritis (OA), are critically needed. In OA, catabolic and inflammatory processes orchestrated by multiple cell types lead to the eventual destruction of articular cartilage. To address this need, our group developed a device that will enable investigation of complex cell systems. Our stackable tissue culture insert was fabricated and characterized with respect to biocompatibility, ease of use, and potential for tissue culture applications. The stackable tissue culture inserts can be easily modified, fabricated, and assembled into commercially available multi-well plates. In vitro studies conducted with three different cell types demonstrated high cell viability and functional secretion when cultured in the stackable inserts. Furthermore, synergistic effects when the three cell types were cultured together were observed. This demonstrates the need to more fully interrogate in vitro culture systems, and this stackable insert can provide a tool to fill the current technological void to do so.
Many computational pipelines exist for the detection of differentially expressed genes. However, computational pipelines for functional gene detection are rarely exist. We developed a new computational pipeline for functional gene identification from transcriptome profiling data. Key features of the pipeline include clustering optimization by gap statistics, gene ontology analysis for each cluster, and literature analysis for functional gene discovery. By leveraging this pipeline on RNA-seq datasets of mouse retinal development studies, we identified 14 candidate genes involved in the formation of the photoreceptor outer segment. The expression of top three candidate genes (Pde8b, Laptm4b, and Nr1h4) in the outer segment of the developing mouse retina were experimentally validated by immunohistochemical analysis. This computational pipeline can accurately predict novel functional gene for a specific biological process, e.g., the outer segment development of the photoreceptor cells in the mouse retina. This pipeline is also applicable to functional gene discovery for any other biological processes and in any other organs and tissues.
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