Biology subjects

Waschina, S.

Publications and source records attributed to Waschina, S..

2 recordsLinked to original sources

bioRxiv2020

Metabolic dissimilarity determines the establishment of cross-feeding interactions in bacteria

The exchange of metabolites among different bacterial genotypes profoundly impacts the structure and function of microbial communities. However, the factors governing the establishment of these cross-feeding interactions remain poorly understood. While shared physiological features may facilitate interactions among more closely related individuals, a lower relatedness should reduce competition and thus increase the potential for synergistic interactions. Here we investigate how the relationship between a metabolite donor and recipient affects the propensity of strains to engage in unidirectional cross-feeding interactions. For this, we performed pairwise cocultivation experiments between four auxotrophic recipients and 25 species of potential amino acid donors. Auxotrophic recipients grew in the vast majority of pairs tested (78%), suggesting metabolic cross-feeding interactions are readily established. Strikingly, both the phylogenetic distance between donor and recipient and the dissimilarity of their metabolic networks were positively associated with the growth of auxotrophic recipients. Analysing the co-growth of species from a gut microbial community in-silico also revealed that recipient genotypes benefitted more from interacting with metabolically dissimilar partners, thus corroborating the empirical results. Together, our work identifies the metabolic dissimilarity between bacterial genotypes as key factor determining the establishment of metabolic cross-feeding interactions in microbial communities. HighlightsO_LIThe exchange of essential metabolites is common in microbial communities C_LIO_LIMetabolic cross-feeding interactions readily establish between auxotrophic and prototrophic bacterial strains C_LIO_LIBoth the phylogenetic and the metabolic dissimilarity between donors and recipients determines the successful establishment of metabolic cross-feeding interactions C_LI

ecology↗

bioRxiv2020

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

Microbial metabolic processes greatly impact ecosystem functioning and the physiology of multi-cellular host organisms. The inference of metabolic capabilities and phenotypes from genome sequences with the help of reference biomolecular knowledge stored in online databases remains a major challenge in systems biology. Here, we present gapseq: a novel tool for automated pathway prediction and metabolic network reconstruction from microbial genome sequences. gapseq combines databases of reference protein sequences (UniProt, TCDB), in tandem with pathway and reaction databases (MetaCyc, KEGG, ModelSEED). This enables the prediction of an organism’s metabolic capabilities from sequence homology and pathway topology criteria. By incorporating a novel LP-based gap-filling algorithm, gapseq facilitates the construction of genome-scale metabolic models that are suitable for metabolic phenotype predictions by using constraint-based flux analysis. We validated gapseq by comparing predictions to experimental data for more than 3, 000 bacterial organisms comprising 14, 895 phenotypic traits that include enzyme activity, energy sources, fermentation products, and gene essentiality. This large-scale phenotypic trait prediction test showed, that gapseq yields an overall accuracy of 81% and thereby outperforms other commonly used reconstruction tools. Furthermore, we illustrate the application of gapseq-reconstructed models to simulate biochemical interactions between microorganisms in multi-species communities. Altogether, gapseq is a new method that improves the predictive potential of automated metabolic network reconstructions and further increases their applicability in biotechnological, ecological, and medical research. gapseq is available at https://github.com/jotech/gapseq.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗