Biogeographic patterns of biosynthetic potential and specialized metabolites in marine sediments

The ISME Journal, Journal Year: 2023, Volume and Issue: 17(7), P. 976 - 983

Published: April 15, 2023

Abstract While the field of microbial biogeography has largely focused on contributions abiotic factors to community patterns, potential influence biotic interactions in structuring communities, such as those mediated by production specialized metabolites, remains unknown. Here, we examined relationship between structure and metabolism at local spatial scales marine sediment samples collected from Long-Term Ecological Research (LTER) site Moorea, French Polynesia. By employing a multi-omic approach characterize taxonomic, functional, metabolite composition within find that biogeographic patterns were driven scale processes (e.g., interactions) independent dispersal limitation. Specifically, observed high variation biosynthetic (based Bray-Curtis dissimilarity) samples, even 1 m2 plots, reflected uncharacterized chemical space associated with site-specific metabolomes. Ultimately, connecting metabolomes facilitated situ detection natural products revealed new insights into complex metabolic dynamics communities. Our study demonstrates integrate genes assessments dynamics.

Language: Английский

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Integrated microbiome and metabolome analyses reveal the effects of low pH on intestinal health and homeostasis of crayfish (Procambarus clarkii)

Aquatic Toxicology, Journal Year: 2024, Volume and Issue: 270, P. 106903 - 106903

Published: March 16, 2024

Language: Английский

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Inter-kingdom interactions and stability of methanogens revealed by machine-learning guided multi-omics analysis of industrial-scale biogas plants

The ISME Journal, Journal Year: 2023, Volume and Issue: 17(8), P. 1326 - 1339

Published: June 7, 2023

Multi-omics analysis is a powerful tool for the detection and study of inter-kingdom interactions, such as those between bacterial archaeal members complex biogas-producing microbial communities. In present study, microbiomes three industrial-scale biogas digesters, each fed with different substrates, were analysed using machine-learning guided genome-centric metagenomics framework complemented metatranscriptome data. This data permitted us to elucidate relationship abundant core methanogenic communities their syntrophic partners. total, we detected 297 high-quality, non-redundant metagenome-assembled genomes (nrMAGs). Moreover, assembled 16 S rRNA gene profiles these nrMAGs showed that phylum Firmicutes possessed highest copy number, while representatives domain had lowest. Further investigation anaerobic characteristic alterations over time but remained specific plant. The relative abundance various microorganisms revealed by metagenome was independent from corresponding activity Archaea considerably higher than expected abundance. We 51 in all plant abundances. microbiome correlated main chemical fermentation parameters, no individual parameter emerged predominant shaper community composition. Various interspecies H2/electron transfer mechanisms assigned hydrogenotrophic methanogens plants ran on agricultural biomass wastewater. Analysis methanogenesis pathways most active metabolic pathways.

Language: Английский

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Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Analytical and Bioanalytical Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 14, 2024

Abstract Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate are commonly retrieved based on the tandem spectral information either from or databases; however, vast majority detected remain unannotated, constituting what we refer as a part unknown chemical space. Recently, exploration this space has become accessible through generative models. Furthermore, evaluation benefits complementary empirical analytical such retention time, collision cross section values, ionization type. In critical review, provide an overview current approaches retrieving prioritizing structures. These come own set advantages limitations, showcase example ten known features. We emphasize that these limitations stem both experimental computational considerations. Finally, highlight three key considerations future development silico methods. Graphical

Language: Английский

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Multi-omics and advance technologies in biodegradation of emerging contaminants and eco-estrogens in environmental waste

Total Environment Advances, Journal Year: 2024, Volume and Issue: 11, P. 200113 - 200113

Published: Aug. 16, 2024

Language: Английский

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