{"type": "FeatureCollection", "features": [{"id": "38343140", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:27:36Z", "type": "Journal Article", "created": "2024-02-12", "title": "Modelling optimal ligninolytic activity during plant litter decomposition", "description": "Summary<p>   <p>A large fraction of plant litter comprises recalcitrant aromatic compounds (lignin and other phenolics). Quantifying the fate of aromatic compounds is difficult, because oxidative degradation of aromatic carbon (C) is a costly but necessary endeavor for microorganisms, and we do not know when gains from the decomposition of aromatic C outweigh energetic costs.</p>  <p>To evaluate these tradeoffs, we developed a litter decomposition model in which the aromatic C decomposition rate is optimized dynamically to maximize microbial growth for the given costs of maintaining ligninolytic activity. We tested model performance against &gt;\uffe2\uff80\uff89200 litter decomposition datasets collected from published literature and assessed the effects of climate and litter chemistry on litter decomposition.</p>  <p>The model predicted a time\uffe2\uff80\uff90varying ligninolytic oxidation rate, which was used to calculate the lag time before the decomposition of aromatic C is initiated. Warmer conditions increased decomposition rates, shortened the lag time of aromatic C oxidation, and improved microbial C\uffe2\uff80\uff90use efficiency by decreasing the costs of oxidation. Moreover, a higher initial content of aromatic C promoted an earlier start of aromatic C decomposition under any climate.</p>  <p>With this contribution, we highlight the application of eco\uffe2\uff80\uff90evolutionary approaches based on optimized microbial life strategies as an alternative parametrization scheme for litter decomposition models.</p>  </p", "keywords": ["0106 biological sciences", "Naturgeografi", "aromatic", "Climate", "lignin", "metabolic tradeoff", "litter decomposition", "04 agricultural and veterinary sciences", "Plants", "15. Life on land", "eco-evolutionary dynamics", "Lignin", "Models", " Biological", "01 natural sciences", "Carbon", "Plant Leaves", "optimal control", "Biodegradation", " Environmental", "Physical Geography", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Oxidation-Reduction"]}, "links": [{"href": "https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.19572"}, {"href": "https://doi.org/38343140"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/New%20Phytologist", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "38343140", "name": "item", "description": "38343140", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/38343140"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-11T00:00:00Z"}}, {"id": "PMC11304562", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-24T16:29:41Z", "type": "Journal Article", "created": "2023-11-15", "title": "Advancements in Biotransformation Pathway Prediction: Enhancements, Datasets, and Novel Functionalities in enviPath", "description": "<title>Abstract</title>         <p>enviPath is a widely used database and prediction system for microbial biotransformation pathways of primarily xenobiotic compounds. Data and prediction system are freely available both via a web interface and a public REST API. Since its initial release in 2016, we extended the data available in enviPath and improved the performance of the prediction system and usability of the overall system. We now provide three diverse data sets, covering microbial biotransformation in different environments and under different experimental conditions. This also enabled developing a pathway prediction model that is applicable to a more diverse set of chemicals. In the prediction engine, we implemented a new evaluation tailored towards pathway prediction, which returns a more honest and holistic view on the performance. We also implemented a novel applicability domain algorithm, which allows the user to estimate how well the model will perform on their data. Finally, we improved the implementation to speed up the overall system and provide new functionality via a plugin system. Overall, enviPath has developed into a reliable database and prediction system with a unique use case in research in microbial biotransformations.</p>", "keywords": ["10120 Department of Chemistry", "0301 basic medicine", "0303 health sciences", "Biodegradation database", "Information technology", "T58.5-58.64", "1704 Computer Graphics and Computer-Aided Design", "3. Good health", "Database", "Chemistry", "03 medical and health sciences", "Metabolic pathways", "540 Chemistry", "Machine learning", "1706 Computer Science Applications", "Biodegradation pathway prediction", "3309 Library and Information Sciences", "1606 Physical and Theoretical Chemistry", "QD1-999"]}, "links": [{"href": "https://doi.org/PMC11304562"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cheminformatics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC11304562", "name": "item", "description": "PMC11304562", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC11304562"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-15T00:00:00Z"}}, {"id": "PMC7022856", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-24T16:29:45Z", "type": "Journal Article", "created": "2019-12-20", "title": "Genome Analyses and Genome-Centered Metatranscriptomics of Methanothermobacter wolfeii Strain SIV6, Isolated from a Thermophilic Production-Scale Biogas Fermenter", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>In the thermophilic biogas-producing microbial community, the genus Methanothermobacter was previously described to be frequently abundant. The aim of this study was to establish and analyze the genome sequence of the archaeal strain Methanothermobacter wolfeii SIV6 originating from a thermophilic industrial-scale biogas fermenter and compare it to related reference genomes. The circular chromosome has a size of 1,686,891 bases, featuring a GC content of 48.89%. Comparative analyses considering three completely sequenced Methanothermobacter strains revealed a core genome of 1494 coding sequences and 16 strain specific genes for M. wolfeii SIV6, which include glycosyltransferases and CRISPR/cas associated genes. Moreover, M. wolfeii SIV6 harbors all genes for the hydrogenotrophic methanogenesis pathway and genome-centered metatranscriptomics indicates the high metabolic activity of this strain, with 25.18% of all transcripts per million (TPM) belong to the hydrogenotrophic methanogenesis pathway and 18.02% of these TPM exclusively belonging to the mcr operon. This operon encodes the different subunits of the enzyme methyl-coenzyme M reductase (EC: 2.8.4.1), which catalyzes the final and rate-limiting step during methanogenesis. Finally, fragment recruitment of metagenomic reads from the thermophilic biogas fermenter on the SIV6 genome showed that the strain is abundant (1.2%) within the indigenous microbial community. Detailed analysis of the archaeal isolate M. wolfeii SIV6 indicates its role and function within the microbial community of the thermophilic biogas fermenter, towards a better understanding of the biogas production process and a microbial-based management of this complex process.</p></article>", "keywords": ["2. Zero hunger", "0301 basic medicine", "570", "Methanothermobacter wolfeii", "metagenomics", "0303 health sciences", "metatranscriptomics", "thermophilic biogas fermenter", "comparative analyses", "CRISPR/<i>cas</i>", "metabolic pathway reconstruction", "7. 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