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Spectral similarity networks, also known as molecular networks, are crucial in non-targeted metabolomics to aid identification of unknowns aiming to establish a potential structural relation between different metabolite features. However, too extensive differences in compound structures can lead to separate clusters, complicating annotation. To address this challenge, we developed an automated Annotation Propagation through multiple EXperimental Networks (APEX) workflow, which integrates spectral similarity networks with mass difference networks and homologous series. The incorporation of multiple network tools improved annotation quality, as evidenced by high matching rates of the molecular formula derived by SIRIUS. The selection of manual annotations as the Seed Nodes Set (SNS) significantly influenced APEX annotations, with a higher number of seed nodes enhancing the annotation process. We applied APEX to different Caenorhabditis elegansmetabolomics data sets as a proof-of-principle for the effective and comprehensive annotation of glycerophospho N-acyl ethanolamides (GPNAEs) and their glyco-variants. Furthermore, we demonstrated the workflow’s applicability to two other, well-described metabolite classes in C. elegans, specifically ascarosides and modular glycosides (MOGLs), using an additional publicly available data set. In summary, the APEX workflow presents a powerful approach for metabolite annotation and identification by leveraging multiple experimental networks. By refining the SNS selection and integrating diverse networks, APEX holds promise for comprehensive annotation in metabolomics research, enabling a deeper understanding of the metabolome.

The microbiota is generally assumed to have a substantial influence on the biology of multicellular organisms. The exact functional contributions of the microbes are often unclear and cannot be inferred easily from 16S rRNA genotyping, which is commonly used for taxonomic characterization of bacterial associates. In order to bridge this knowledge gap, we here analyzed the metabolic competences of the native microbiota of the model nematode Caenorhabditis elegans. We integrated whole-genome sequences of 77 bacterial microbiota members with metabolic modeling and experimental characterization of bacterial physiology. We found that, as a community, the microbiota can synthesize all essential nutrients for C. elegans. Both metabolic models and experimental analyses revealed that nutrient context can influence how bacteria interact within the microbiota. We identified key bacterial traits that are likely to influence the microbe’s ability to colonize C. elegans(i.e., the ability of bacteria for pyruvate fermentation to acetoin) and affect nematode fitness (i.e., bacterial competence for hydroxyproline degradation). Considering that the microbiota is usually neglected in C. elegansresearch, the resource presented here will help our understanding of this nematode’s biology in a more natural context. Our integrative approach moreover provides a novel, general framework to characterize microbiota-mediated functions.

The microbiota is generally assumed to have a substantial influence on the biology of multicellular organisms. The exact functional contributions of the microbes are often unclear and cannot be inferred easily from 16S rRNA genotyping, which is commonly used for taxonomic characterization of bacterial associates. In order to bridge this knowledge gap, we here analyzed the metabolic competences of the native microbiota of the model nematode Caenorhabditis elegans. We integrated whole-genome sequences of 77 bacterial microbiota members with metabolic modeling and experimental characterization of bacterial physiology. We found that, as a community, the microbiota can synthesize all essential nutrients for C. elegans. Both metabolic models and experimental analyses revealed that nutrient context can influence how bacteria interact within the microbiota. We identified key bacterial traits that are likely to influence the microbe’s ability to colonize C. elegans(i.e., the ability of bacteria for pyruvate fermentation to acetoin) and affect nematode fitness (i.e., bacterial competence for hydroxyproline degradation). Considering that the microbiota is usually neglected in C. elegansresearch, the resource presented here will help our understanding of this nematode’s biology in a more natural context. Our integrative approach moreover provides a novel, general framework to characterize microbiota-mediated functions.

A key question in current immunity research is how the innate immune system can generate high levels of specificity. Evidence is accumulating that invertebrates, which exclusively rely on innate defense mechanisms, can differentiate between pathogens on the species and even strain level. In this review, we identify and discuss the particular potential of C-type lectin-like domain (CTLD) proteins to generate high immune specificity. Whilst several CTLD proteins are known to act as pattern recognition receptors in the vertebrate innate immune system, the exact role of CTLD proteins in invertebrate immunity is much less understood. We show that CTLD genes are highly abundant in most metazoan genomes and summarize the current state of knowledge on CTLD protein function in insect, crustacean and nematode immune systems. We then demonstrate extreme CTLD gene diversification in the genomes of Caenorhabditis nematodes and provide an update of data from CTLD gene function studies in C. elegans, which indicate that the diversity of CTLD genes could contribute to immune specificity. In spite of recent achievements, the exact functions of the diversified invertebrate CTLD genes are still largely unknown. Our review therefore specifically discusses promising research approaches to rectify this knowledge gap.