Transcriptomic profiling of osteoarthritis synovial macrophages reveals a tolerized phenotype compounded by a weak corticosteroid response.

OBJECTIVES: It is well-known that long-term osteoarthritis prognosis is not improved by corticosteroid treatments. Here we investigate what could underlie this phenomenon by measuring the short term corticosteroid response of OA-Mf. METHODS: We determined the genome-wide transcriptomic response to corticosteroids of end-stage osteoarthritic joint synovial macrophages (OA-Mf). This was compared with LPS-tolerized and ß-glucan-trained circulating blood monocyte-derived macrophage models. RESULTS: Upon corticosteroid stimulation, the trained and tolerized macrophages significantly alter the abundance of 201 and 257 RNA transcripts, respectively. By contrast, by the same criteria, OA-Mf have a very restricted corticosteroid response of only 12 RNA transcripts. Furthermore, while metalloproteinases 1, -2, -3 and -10 expression clearly distinguish OA-Mf from both the tolerized and trained macrophage models, OA-Mf Interleukin 1 (IL1), chemokine (CXCL) and cytokine (CCL) family member profiles resemble the tolerized macrophage model, with the exception that OA-Mf show high levels of CCL20. CONCLUSION: Terminal osteoarthritis joints therefore harbor macrophages with an inflammatory state that closely resembles the tolerized macrophage state and this is compounded by a weak corticosteroid response capacity that may explain the lack of positive long-term effects of corticosteroid treatment for osteoarthritis patients.

Seq2science: an end-to-end workflow for functional genomics analysis.

Sequencing databases contain enormous amounts of functional genomics data, making them an extensive resource for genome-scale analysis. Reanalyzing publicly available data, and integrating it with new, project-specific data sets, can be invaluable. With current technologies, genomic experiments have become feasible for virtually any species of interest. However, using and integrating this data comes with its challenges, such as standardized and reproducible analysis. Seq2science is a multi-purpose workflow that covers preprocessing, quality control, visualization, and analysis of functional genomics sequencing data. It facilitates the downloading of sequencing data from all major databases, including NCBI SRA, EBI ENA, DDBJ, GSA, and ENCODE. Furthermore, it automates the retrieval of any genome assembly available from Ensembl, NCBI, and UCSC. It has been tested on a variety of species, and includes diverse workflows such as ATAC-, RNA-, and ChIP-seq. It consists of both generic as well as advanced steps, such as differential gene expression or peak accessibility analysis and differential motif analysis. Seq2science is built on the Snakemake workflow language and thus can be run on a range of computing infrastructures. It is available at https://github.com/vanheeringen-lab/seq2science.