Search

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 β-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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Importance: Bacterial pathogens have vastly distinct sites that they inhabit during infection. This requires adaptation due to changes in nutrient availability and antimicrobial stress. The bacterial surface is a primary barrier, and here, we show that the bacterial pathogen Shigella flexneri increases its surface decorations when it transitions to an intracellular lifestyle. We also observed changes in bacterial and host cell fatty acid homeostasis. Specifically, intracellular S. flexneri increased the expression of their fatty acid degradation pathway, while the host cell lipid pool was significantly depleted. Importantly, bacterial proliferation could be inhibited by fatty acid supplementation of host cells, thereby providing novel insights into the possible link between human malnutrition and susceptibility to S. flexneri ., Competing Interests: The authors declare no conflict of interest.

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., Competing Interests: The authors declare that they have no competing interests., (© 2023 van der Sande et al.)

Imbalanced immune responses are a prominent hallmark of cancer and autoimmunity. Myeloid cells can be overly suppressive, inhibiting protective immune responses or inactive not controlling autoreactive immune cells. Understanding the mechanisms that induce suppressive myeloid cells, such as myeloid-derived suppressor cells (MDSCs) and tolerogenic dendritic cells (TolDCs), can facilitate the development of immune-restoring therapeutic approaches. MDSCs are a major barrier for effective cancer immunotherapy by suppressing antitumor immune responses in cancer patients. TolDCs are administered to patients to promote immune tolerance with the intent to control autoimmune disease. Here, we investigated the development and suppressive/tolerogenic activity of human MDSCs and TolDCs to gain insight into signaling pathways that drive immunosuppression in these different myeloid subsets. Moreover, monocyte-derived MDSCs (M-MDSCs) generated in vitro were compared to M-MDSCs isolated from head-and-neck squamous cell carcinoma patients. PI3K-AKT signaling was identified as being crucial for the induction of human M-MDSCs. PI3K inhibition prevented the downregulation of HLA-DR and the upregulation of reactive oxygen species and MerTK. In addition, we show that the suppressive activity of dexamethasone-induced TolDCs is induced by β-catenin-dependent Wnt signaling. The identification of PI3K-AKT and Wnt signal transduction pathways as respective inducers of the immunomodulatory capacity of M-MDSCs and TolDCs provides opportunities to overcome suppressive myeloid cells in cancer patients and optimize therapeutic application of TolDCs. Lastly, the observed similarities between generated- and patient-derived M-MDSCs support the use of in vitro-generated M-MDSCs as powerful model to investigate the functionality of human MDSCs., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Smits JGA et al.)

Motivation: Analyzing a functional genomics experiment, such as ATAC-, ChIP-, or RNA-sequencing, requires genomic resources such as a reference genome assembly and gene annotation. These data can generally be retrieved from different organizations and in different versions. Most bioinformatic workflows require the user to supply this genomic data manually, which can be a tedious and error-prone process., Results: Here, we present genomepy, which can search, download, and preprocess the right genomic data for your analysis. Genomepy can search genomic data on NCBI, Ensembl, UCSC, and GENCODE, and inspect available gene annotations to enable an informed decision. The selected genome and gene annotation can be downloaded and preprocessed with sensible, yet controllable, defaults. Additional supporting data can be automatically generated or downloaded, such as aligner indexes, genome metadata, and blacklists., Availability and Implementation: Genomepy is freely available at https://github.com/vanheeringen-lab/genomepy under the MIT license and can be installed through pip or Bioconda., (© The Author(s) 2023. Published by Oxford University Press.)

Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation., (© 2023 The Author(s).)