Identification and suppression of epidermal growth factor receptor variant III signaling in fibroblast-like synoviocytes from aggressive rheumatoid arthritis by the mimotope

Single-cell computational machine learning approaches to immune-mediated inflammatory disease: New tools uncover novel fibroblast and macrophage interactions driving pathogenesis

Recent advances in single-cell sequencing technologies call for greater computational scalability and sensitivity to analytically decompose diseased tissues and expose meaningful biological relevance in individual cells with high resolution. And while fibroblasts, one of the most abundant cell types in tissues, were long thought to display relative homogeneity, recent analytical and technical advances in single-cell sequencing have exposed wide variation and sub-phenotypes of fibroblasts of potential and apparent clinical significance to inflammatory diseases. Alongside anticipated improvements in single cell spatial sequencing resolution, new computational biology techniques have formed the technical backbone when exploring fibroblast heterogeneity. More robust models are required, however. This review will summarize the key advancements in computational techniques that are being deployed to categorize fibroblast heterogeneity and their interaction with the myeloid compartments in specific biological and clinical contexts. First, typical machine-learning-aided methods such as dimensionality reduction, clustering, and trajectory inference, have exposed the role of fibroblast subpopulations in inflammatory disease pathologies. Second, these techniques, coupled with single-cell predicted computational methods have raised novel interactomes between fibroblasts and macrophages of potential clinical significance to many immune-mediated inflammatory diseases such as rheumatoid arthritis, ulcerative colitis, lupus, systemic sclerosis, and others. Third, recently developed scalable integrative methods have the potential to map cross-cell-type spatial interactions at the single-cell level while cross-tissue analysis with these models reveals shared biological mechanisms between disease contexts. Finally, these advanced computational omics approaches have the potential to be leveraged toward therapeutic strategies that target fibroblast-macrophage interactions in a wide variety of inflammatory diseases.

Integrated strategy of LC-MS and network pharmacology for predicting active constituents and pharmacological mechanisms of Ranunculus japonicus Thunb. for treating rheumatoid arthritis

ETHNOPHARMACOLOGICAL RELEVANCE

Ranunculus japonicus Thunb. (short for R. japonicus) is a topically applied herb with the activities of removing jaundice, nebula and edema, preventing malaria, stopping asthma, promoting diuresis and relieving pain. It was firstly recorded in Zhouhou Beiji Fang and has been used for the treatment of malaria, ulcers, carbuncle, jaundice, migraine, stomachache, toothache and arthritis for over 1800 years.

AIM OF THE STUDY

This study aimed to uncover the potentially effective components of R. japonicus and the pharmacological mechanisms against rheumatoid arthritis (RA) by combing LC-MS and network pharmacology.

MATERIALS AND METHODS

Firstly, the chemical constituents of R. japonicus were qualitatively identified by UPLC-ESI-LTQ-Orbitrap MS. Then we performed target prediction by PharmMapper, protein-protein interaction (PPI) analysis via String, GO and KEGG pathway enrichment analysis by DAVID and constructed the compound-target-pathway network using Cytoscape. Thirdly, crucial compounds in the network were quantitatively analyzed to achieve quality control of R. japonicus. Finally, the pharmacological activities of R. japonicus and two potentially bioactive ingredients were validated in RA-FLSs (Rheumatoid Arthritis Fibroblast-like Synoviocytes) in vitro.

RESULTS

Overall fifty-four ingredients of R. japonicus were identified and forty-five components were firstly discovered in R. japonicus. Among them, twenty-seven validated compounds were predicted to act on twenty-five RA-related targets and they might exhibit therapeutic effects against RA via positive regulation of cell migration, etc. Nine potentially bioactive components of R. japonicus which played important roles in the compound-target-pathway network were simultaneously quantified by an optimized UPLC-ESI-Triple Quad method. In vitro, compared to control group, R. japonicus extract, berberine and yangonin significantly inhibited the migration capacity of RA-FLSs after 24 h treatment.

CONCLUSION

This study clarified that R. japonicus and the bioactive ingredients berberine and yangonin might exert therapeutic actions for RA via suppressing the aggressive phenotypes of RA-FLSs through combined LC-MS technology and network pharmacology tools for the first time. The present research provided deeper understanding into the chemical profiling, pharmacological activities and quality control of R. japonicus and offered reference for further scientific research and clinical use of R. japonicus in treating RA.

Intra-Articular Formulation of GE11-PLGA Conjugate-Based NPs for Dexamethasone Selective Targeting-In Vitro Evaluation

Selectively targeted nanoscale drug delivery systems have recently emerged as promising intravenously therapeutic option for most chronic joint diseases. Here, a newly synthetized dodecapeptide (GE11)-polylactide-co-glycolide (PLGA)-based conjugate was used to prepare smart nanoparticles (NPs) intended for intra-articular administration and for selectively targeting Epidermal Growth Factor Receptor (EGFR). GE11-PLGA conjugate-based NPs are specifically uptaken by EGFR-overexpressed fibroblast; such as synoviocytes; which are the primarily cellular component involved in the development of destructive joint inflammation. The selective uptake could help to tune drug effectiveness in joints and to decrease local and systemic side effects. Dexamethasone (DXM) is a glucorticoid drug commonly used in joint disease treatment for both systemic and local administration route. In the present research; DXM was efficiently loaded into GE11-PLGA conjugate-based NPs through an eco-friendly nanoprecipitation method set up for this purpose. DXM loaded GE11-PLGA conjugate-based NPs revealed satisfactory ex vivo cytocompatibility; with proper size (≤150 nm) and good dimensional stability in synovial fluid. Intra-articular formulation was developed embedding DXM loaded GE11-PLGA conjugate-based NPs into thermosetting chitosan-based hydrogel; forming a biocompatible composite hydrogel able to quickly turn from liquid state into gel state at physiological temperature; within 15 min. Moreover; the use of thermosetting chitosan-based hydrogel extends the local release of active agent; DXM.