N-Enrichement experiment
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ABSTRACT:
REPOSITORIES: ENA
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Project description:Deadwood plays a crucial role in forest ecosystems, but we have limited information about the specific fungal taxa and extracellular lignocellulolytic enzymes that are actively involved in the decomposition process in situ. To investigate this, we studied the fungal metaproteome of twelve deadwood tree species in a replicated, eight-year experiment. Key fungi observed included genera of white-rot fungi (Basidiomycota, e.g. Armillaria, Hypholoma, Mycena, Ischnoderma, Resinicium), brown-rot fungi (Basidiomycota, e.g. Fomitopsis, Antrodia), diverse Ascomycota including xylariacous soft-rot fungi (e.g. Xylaria, Annulohypoxylon, Nemania) and various wood-associated endophytes and saprotrophs (Ascocoryne, Trichoderma, Talaromyces). These fungi used a whole range of extracellular lignocellulolytic enzymes, such as peroxidases, peroxide-producing enzymes, laccases, cellulases, glucosidases, hemicellulases (xylanases) and lytic polysaccharide monooxygenases (LPMOs). Both the fungi and enzymes were tree-specific, with specialists and generalists being distinguished by network analysis. The extracellular enzymatic system was highly redundant, with many enzyme classes of different origins present simultaneously in all decaying logs. Strong correlations were found between peroxide-producing enzymes (oxidases) and peroxidases as well as LPMOs, and between ligninolytic, cellulolytic and hemicellulolytic enzymes. The overall protein abundance of lignocellulolytic enzymes was reduced by up to -30% in gymnosperm logs compared to angiosperm logs, and gymnosperms lacked ascomycetous enzymes, which may have contributed to the lower decomposition of gymnosperm wood. In summary, we have obtained a comprehensive and detailed insight into the enzymatic machinery of wood-inhabiting fungi in several temperate forest tree species, which can help to improve our understanding of the complex ecological processes in forest ecosystems.
2024-04-22 | PXD041962 | Pride
Project description:Prokaryotic community structure under different flow regimes
| PRJEB48365 | ENA
Project description:Effect of N deposition in a highly N-limited habitat: deadwood
| PRJEB15650 | ENA
Project description:Ectomycorrhizal (ECM) fungi are crucial for tree nitrogen (N) nutrition, however, mechanisms governing N transfer from fungal tissues to the host plant are not well understood. ECM fungal isolates, even from the same species, vary considerably in their ability to support tree N nutrition resulting in a range of often unpredictable symbiotic outcomes. In this study, we used isotopic labelling to quantify the transfer of N to the plant host by isolates from the ECM genus Pisolithus known to have significant variability in colonisation and transfer of nutrients to a host. We considered the metabolic fate of N acquired by the fungi and found that the percentage of plant N acquired through symbiosis significantly correlated to the concentration of free amino acids present in the ECM extra-radical mycelium. Transcriptomic analyses complemented these findings with isolates having high amino acid content and N transfer showing increased expression of genes in amino acid transport and catabolic pathways. These results suggest that fungal N metabolism drives transfer to the host plant in this interaction and that relative N transfer may be possible to predict through basic biochemical analyses.
2023-10-27 | GSE235669 | GEO
Project description:Wood-decomposition in terrestrial ecosystems is a very important process with huge ecologic consequences. This decomposition process is a combination of biological respiration, leaching and fragmentation, mainly triggered by organismic activities. In order to gain a deeper insight into these microbial communities and their role in deadwood decay, we used metaproteomics. Metaproteomics is an important tool and offers the ability to characterize the protein complement of environmental microbiota at a given point in time. In this dataset, we provide data of an exemplary beech wood log and applied different extraction methods to provide the proteome profile of beech dead wood and their corresponding fungal-bacterial community.
2020-05-27 | PXD016801 | Pride
Project description:This experiment was designed to analyze transcriptional changes that occur when the fungal plant pathogen M. grisea is shifted from a complete media to a nitrogen-starved media. Comparisons were made in the following way between fungal mycelial grown in minimal media supplemented with a nitrogen source (MM+N) and minimal media without a nitrogen source (NS) Keywords: parallel sample
2006-05-26 | GSE2716 | GEO
Project description:we analyzed hybrid poplar wood microcores collected thirteen months post-inoculation at the site of the active necrosis borders and identified proteins involved in molecular mechanisms responsible for preventing the Phytophthora invasion. This analysis provided novel insights into the plant-pathogen interaction and putative targets for improving tree resistance against Phytophthora
2022-10-04 | PXD035956 | Pride
Project description:This is a use case to show that, given any automatic metagenomic classification model for the documents, we can convert those to ONNX (Open Neural Network Exchange) format; it also consists of the Dockerfile that can be used to prepare a docker image. This conversion ensures interoperability and open access. The ONNX format utility can perform the following essential tasks: model conversion, inference, inspection, and optimization. Reference: 1) https://github.com/elixir-europe/biohackathon-projects-2022/tree/main/9 2) https://www.ebi.ac.uk/biomodels/search?query=Maaly+Nassar&domain=biomodels 3) https://gitlab.com/maaly7/emerald_metagenomics_annotations 4) This model is built upon the model of the following publication: Maaly Nassar, Alexander B Rogers, Francesco Talo', Santiago Sanchez, Zunaira Shafique, Robert D Finn, Johanna McEntyre, A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications, GigaScience, Volume 11, 2022, giac077, https://doi.org/10.1093/gigascience/giac077
2023-05-25 | BIOMD0000001068 | BioModels