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- Flood, Peter, et al.
(author)
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DNA sensor-associated type I interferon signaling is increased in ulcerative colitis and induces JAK-dependent inflammatory cell death in colonic organoids
- 2022
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In: American Journal of Physiology - Gastrointestinal and Liver Physiology. - : American Physiological Society. - 0193-1857 .- 1522-1547. ; 323:5, s. G439-G460
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Journal article (peer-reviewed)abstract
- DNA sensor pathways can initiate inflammasome, cell death, and type I interferon (IFN) signaling in immune-mediated inflammatory diseases (IMIDs), including type I interferonopathies. We investigated the involvement of these pathways in the pathogenesis of ulcerative colitis (UC) by analyzing the expression of DNA sensor, inflammasome, and type I IFN biomarker genes in colonic mucosal biopsy tissue from control (n = 31), inactive UC (n = 31), active UC (n = 33), and a UC single-cell RNA-Seq dataset. The effects of type I IFN (IFN-β), IFN-γ, and TNF-α on gene expression, cytokine production, and cell death were investigated in human colonic organoids. In organoids treated with cytokines alone, or in combination with NLR family pyrin domain-containing 3 (NLRP3), caspase, or JAK inhibitors, cell death was measured, and supernatants were assayed for IL-1β/IL-18/CXCL10. The expression of DNA sensor pathway genes-PYHIN family members [absent in melanoma 2 (AIM2), IFI16, myeloid cell nuclear differentiation antigen (MNDA), and pyrin and HIN domain family member 1 (PYHIN1)- as well as Z-DNA-binding protein 1 (ZBP1), cyclic GMP-AMP synthase (cGAS), and DDX41 was increased in active UC and expressed in a cell type-restricted pattern. Inflammasome genes (CASP1, IL1B, and IL18), type I IFN inducers [stimulator of interferon response cGAMP interactor 1 (STING), TBK1, and IRF3), IFNB1, and type I IFN biomarker genes (OAS2, IFIT2, and MX2) were also increased in active UC. Cotreatment of organoids with IFN-β or IFN-γ in combination with TNFα increased expression of IFI16, ZBP1, CASP1, cGAS, and STING induced cell death and IL-1β/IL-18 secretion. This inflammatory cell death was blocked by the JAK inhibitor tofacitinib but not by inflammasome or caspase inhibitors. Increased type I IFN activity may drive elevated expression of DNA sensor genes and JAK-dependent but inflammasome-independent inflammatory cell death of colonic epithelial cells in UC.NEW & NOTEWORTHY This study found that patients with active UC have significantly increased colonic gene expression of cytosolic DNA sensor, inflammasome, STING, and type I IFN signaling pathways. The type I IFN, IFN-β, in combination with TNF-α induced JAK-dependent but NLRP3 and inflammasome-independent inflammatory cell death of colonic organoids. This novel inflammatory cell death phenotype is relevant to UC immunopathology and may partially explain the efficacy of the JAKinibs tofacitinib and upadacitinib in patients with UC.
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| 2. |
- Kissling, W. Daniel, et al.
(author)
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Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale
- 2018
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In: Biological Reviews. - : Wiley. - 1464-7931 .- 1469-185X. ; 93:1, s. 600-625
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Journal article (peer-reviewed)abstract
- © 2017 Cambridge Philosophical Society. Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.
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