| 1. |
- Kattge, Jens, et al.
(author)
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TRY plant trait database - enhanced coverage and open access
- 2020
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In: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Journal article (peer-reviewed)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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| 2. |
- Sikkema, Lisa, et al.
(author)
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An integrated cell atlas of the lung in health and disease
- 2023
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In: Nature Medicine. - : Springer Nature. - 1078-8956 .- 1546-170X. ; 29:6, s. 1563-1577
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Journal article (peer-reviewed)abstract
- Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.
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| 3. |
- Berthenet, Elvire, et al.
(author)
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A GWAS on Helicobacter pylori strains points to genetic variants associated with gastric cancer risk
- 2018
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In: BMC Biology. - : BioMed Central. - 1741-7007. ; 16:1
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Journal article (peer-reviewed)abstract
- BACKGROUND:Helicobacter pylori are stomach-dwelling bacteria that are present in about 50% of the global population. Infection is asymptomatic in most cases, but it has been associated with gastritis, gastric ulcers and gastric cancer. Epidemiological evidence shows that progression to cancer depends upon the host and pathogen factors, but questions remain about why cancer phenotypes develop in a minority of infected people. Here, we use comparative genomics approaches to understand how genetic variation amongst bacterial strains influences disease progression.RESULTS:We performed a genome-wide association study (GWAS) on 173 H. pylori isolates from the European population (hpEurope) with known disease aetiology, including 49 from individuals with gastric cancer. We identified SNPs and genes that differed in frequency between isolates from patients with gastric cancer and those with gastritis. The gastric cancer phenotype was associated with the presence of babA and genes in the cag pathogenicity island, one of the major virulence determinants of H. pylori, as well as non-synonymous variations in several less well-studied genes. We devised a simple risk score based on the risk level of associated elements present, which has the potential to identify strains that are likely to cause cancer but will require refinement and validation.CONCLUSION:There are a number of challenges to applying GWAS to bacterial infections, including the difficulty of obtaining matched controls, multiple strain colonization and the possibility that causative strains may not be present when disease is detected. Our results demonstrate that bacterial factors have a sufficiently strong influence on disease progression that even a small-scale GWAS can identify them. Therefore, H. pylori GWAS can elucidate mechanistic pathways to disease and guide clinical treatment options, including for asymptomatic carriers.
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| 4. |
- Dormann, Carsten F., et al.
(author)
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Biotic interactions in species distribution modelling : 10 questions to guide interpretation and avoid false conclusions
- 2018
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In: Global Ecology and Biogeography. - : Wiley. - 1466-822X .- 1466-8238. ; 27:9, s. 1004-1016
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Journal article (peer-reviewed)abstract
- Aim: Recent studies increasingly use statistical methods to infer biotic interactions from co‐occurrence information at a large spatial scale. However, disentangling biotic interactions from other factors that can affect co‐occurrence patterns at the macroscale is a major challenge.Approach: We present a set of questions that analysts and reviewers should ask to avoid erroneously attributing species association patterns to biotic interactions. Our questions relate to the appropriateness of data and models, the causality behind a correlative signal, and the problems associated with static data from dynamic systems. We summarize caveats reported by macroecological studies of biotic interactions and examine whether conclusions on the presence of biotic interactions are supported by the modelling approaches used.Findings: Irrespective of the method used, studies that set out to test for biotic interactions find statistical associations in species’ co‐occurrences. Yet, when compared with our list of questions, few purported interpretations of such associations as biotic interactions hold up to scrutiny. This does not dismiss the presence or importance of biotic interactions, but it highlights the risk of too lenient interpretation of the data. Combining model results with information from experiments and functional traits that are relevant for the biotic interaction of interest might strengthen conclusions.Main conclusions: Moving from species‐ to community‐level models, including biotic interactions among species, is of great importance for process‐based understanding and forecasting ecological responses. We hope that our questions will help to improve these models and facilitate the interpretation of their results. In essence, we conclude that ecologists have to recognize that a species association pattern in joint species distribution models will be driven not only by real biotic interactions, but also by shared habitat preferences, common migration history, phylogenetic history and shared response to missing environmental drivers, which specifically need to be discussed and, if possible, integrated into models.
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