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.

scANANSE gene regulatory network and motif analysis of single-cell clusters.

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.

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.

Myeloid-derived suppressor cells and tolerogenic dendritic cells are distinctively induced by PI3K and Wnt signaling pathways.

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 ß-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.

genomepy: genes and genomes at your fingertips.

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.

Computational approaches to understand transcription regulation in development.

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.

Retinoic acid signaling drives differentiation toward the absorptive lineage in colorectal cancer.

Retinoic acid (RA) signaling is an important and conserved pathway that regulates cellular proliferation and differentiation. Furthermore, perturbed RA signaling is implicated in cancer initiation and progression. However, the mechanisms by which RA signaling contributes to homeostasis, malignant transformation, and disease progression in the intestine remain incompletely understood. Here, we report, in agreement with previous findings, that activation of the Retinoic Acid Receptor and the Retinoid X Receptor results in enhanced transcription of enterocyte-specific genes in mouse small intestinal organoids. Conversely, inhibition of this pathway results in reduced expression of genes associated with the absorptive lineage. Strikingly, this latter effect is conserved in a human organoid model for colorectal cancer (CRC) progression. We further show that RXR motif accessibility depends on progression state of CRC organoids. Finally, we show that reduced RXR target gene expression correlates with worse CRC prognosis, implying RA signaling as a putative therapeutic target in CRC.

ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination.

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

Combinatorial transcription factor activities on open chromatin induce embryonic heterogeneity in vertebrates.

During vertebrate gastrulation, mesoderm is induced in pluripotent cells, concomitant with dorsal-ventral patterning and establishing of the dorsal axis. We applied single-cell chromatin accessibility and transcriptome analyses to explore the emergence of cellular heterogeneity during gastrulation in Xenopus tropicalis. Transcriptionally inactive lineage-restricted genes exhibit relatively open chromatin in animal caps, whereas chromatin accessibility in dorsal marginal zone cells more closely reflects transcriptional activity. We characterized single-cell trajectories and identified head and trunk organizer cell clusters in early gastrulae. By integrating chromatin accessibility and transcriptome data, we inferred the activity of transcription factors in single-cell clusters and tested the activity of organizer-expressed transcription factors in animal caps, alone or in combination. The expression profile induced by a combination of Foxb1 and Eomes most closely resembles that observed in the head organizer. Genes induced by Eomes, Otx2, or the Irx3-Otx2 combination are enriched for maternally regulated H3K4me3 modifications, whereas Lhx8-induced genes are marked more frequently by zygotically controlled H3K4me3. Taken together, our results show that transcription factors cooperate in a combinatorial fashion in generally open chromatin to orchestrate zygotic gene expression.

Modulation of spectral properties and pump activity of proteorhodopsins by retinal analogues.

Proteorhodopsins are heptahelical membrane proteins which function as light-driven proton pumps. They use all-trans-retinal A1 as a ligand and chromophore and absorb visible light (520-540 nm). In the present paper, we describe modulation of the absorbance band of the proteorhodopsin from Monterey Bay SAR 86 gammaproteobacteria (PR), its red-shifted double mutant PR-D212N/F234S (PR-DNFS) and Gloeobacter rhodopsin (GR). This was approached using three analogues of all-trans-retinal A1, which differ in their electronic and conformational properties: all-trans-6,7-s-trans-locked retinal A1, all-trans-phenyl-retinal A1 and all-trans-retinal A2. We further probed the effect of these retinal analogues on the proton pump activity of the proteorhodopsins. Our results indicate that, whereas the constraints of the retinal-binding pocket differ for the proteorhodopsins, at least two of the retinal analogues are capable of shifting the absorbance bands of the pigments either bathochromically or hypsochromically, while maintaining their proton pump activity. Furthermore, the shifts implemented by the analogues add up to the shift induced by the double mutation in PR-DNFS. This type of chromophore substitution may present attractive applications in the field of optogenetics, towards increasing the flexibility of optogenetic tools or for membrane potential probes.