The functional repertoire contained within the native microbiota of the model nematodeCaenorhabditis elegans

Abstract

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 wholegenome 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. elegans research, 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.

CeMbio - TheC. elegansmicrobiome resource

“…As an illustration of the potential for interactions between the strains, we subsequently used metabolic modeling to predict the range of carbon sources that each strain can utilize and compared it with experimental results obtained from Biolog microarrays (File S2). We found that 66% of the experimental data on carbon utilization were consistent with the predictions, which was similar to our previous study (Zimmermann et al 2020a) . In addition, the experimental data were subsequently used to further optimize the metabolic models (File S4).…”

“…All strains were able to colonize the intestines of C. elegans , in overall agreement with previous studies, where some of these strains had been examined (Montalvo-Katz et al 2013;Berg et al 2016aBerg et al , 2019Dirksen et al 2016;Zimmermann et al 2020a) . However, the extent and persistence of colonization over time varied among strains.…”

Community assembly of the native C. elegans microbiome is influenced by time, substrate and individual bacterial taxa

“…A similar pattern was recently inferred by metabolic network modelling for a representative set of 77 fully sequenced bacterial genomes from the C . elegans microbiome (Zimmermann et al ., ). Both findings support the hypothesis that closely related species share the ability to compete for a particular resource and as a result outcompete less closely related species, leading to phylogenetically clustered patterns (Mayfield and Levine, ).…”

“…Taxonomies for Miseq and isolate sequences were assigned using the SILVA trainset v132. We used ASVs for further analyses, because these were shown to provide more reliable information on taxon composition and generate fewer spurious sequences than alternative measures (Callahan et al ., ) and as different strains can already have different gene repertoires (Zimmermann et al ., .). Sequence tables were finally analysed using Phyloseq version 1.24.0 (McMurdie and Holmes, ).…”

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

“…Metabolic models not only have demonstrated their ability to predict phenotypes on the level of cellular growth and gene knockouts, but also provide potential molecular mechanisms in form of gene and reaction activities, which can be validated experimentally [87]. Due to this predictive potential, genome-scale metabolic models have been applied to identify metabolic interactions between different organisms [1,32,44,80,96], to study host-microbiome interactions [33,64,95], to predict novel drug targets to fight microbial pathogens [55,85], and for the rational design of microbial genotypes and growth-media conditions for the industrial production or degradation of biochemicals [59,66]. Furthermore, recent advances in DNA-sequencing technologies have led to a vast increase in available genomic-and metagenomic sequences in databases [48], which further expands the applicability of genome-scale metabolic network reconstructions.…”