Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often ...fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.
Recent advances focusing on the metabolic interactions within and between cellular populations have emphasized the importance of microbial communities for human health. Constraint-based modeling, ...with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena) to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena.
The reconstruction and application of genome-scale metabolic network models is a central topic in the field of systems biology with numerous applications in biotechnology, ecology, and medicine. ...However, there is no agreed upon standard for the definition of the nutritional environment for these models. The objective of this article is to provide a guideline and a clear paradigm on how to translate nutritional information into an in-silico representation of the chemical environment. Step-by-step procedures explain how to characterise and categorise the nutritional input and to successfully apply it to constraint-based metabolic models. In parallel, we illustrate the proposed procedure with a case study of the growth of Escherichia coli in a complex nutritional medium and show that an accurate representation of the medium is crucial for physiological predictions. The proposed framework will assist researchers to expand their existing metabolic models of their microbial systems of interest with detailed representations of the nutritional environment, which allows more accurate and reproducible predictions of microbial metabolic processes.
Bacteria that have adapted to nutrient-rich, stable environments are typically characterized by reduced genomes. The loss of biosynthetic genes frequently renders these lineages auxotroph, hinging ...their survival on an environmental uptake of certain metabolites. The evolutionary forces that drive this genome degradation, however, remain elusive. Our analysis of 949 metabolic networks revealed auxotrophies are likely highly prevalent in both symbiotic and free-living bacteria. To unravel whether selective advantages can account for the rampant loss of anabolic genes, we systematically determined the fitness consequences that result from deleting conditionally essential biosynthetic genes from the genomes of Escherichia coli and Acinetobacter baylyi in the presence of the focal nutrient. Pairwise competition experiments with each of 20 mutants auxotrophic for different amino acids, vitamins, and nucleobases against the prototrophic wild type unveiled a pronounced, concentration-dependent growth advantage of around 13% for virtually all mutants tested. Individually deleting different genes from the same biosynthesis pathway entailed gene-specific fitness consequences and loss of the same biosynthetic genes from the genomes of E. coli and A. baylyi differentially affected the fitness of the resulting auxotrophic mutants. Taken together, our findings suggest adaptive benefits could drive the loss of conditionally essential biosynthetic genes.
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 (63%), 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. Analyzing 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 a key factor determining the establishment of metabolic cross-feeding interactions in microbial communities.
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•Auxotrophic and prototrophic bacteria readily engaged in metabolic cross-feeding•Recipient growth correlated with phylogenetic and metabolic distance to donors•Cross-feeding is more likely to establish between metabolically dissimilar strains
Giri et al. study the factors that determine the establishment of obligate unidirectional cross-feeding between two bacterial populations. Combining experiments with in silico simulations of a gut bacterial community, the authors show that metabolic interactions are more likely to establish between unrelated and metabolically dissimilar genotypes.
Caenorhabditis elegans is associated in nature with a species-rich, distinct microbiota, which was characterized only recently 1. Thus, our understanding of the relevance of the microbiota for ...nematode fitness is still at its infancy. One major benefit that the intestinal microbiota can provide to its host is protection against pathogen infection 2. However, the specific strains conferring the protection and the underlying mechanisms of microbiota-mediated protection are often unclear 3. Here, we identify natural C. elegans microbiota isolates that increase C. elegans resistance to pathogen infection. We show that isolates of the Pseudomonas fluorescens subgroup provide paramount protection from infection with the natural pathogen Bacillus thuringiensis through distinct mechanisms. We found that the P. lurida isolates MYb11 and MYb12 (members of the P. fluorescens subgroup) protect C. elegans against B. thuringiensis infection by directly inhibiting growth of the pathogen both in vitro and in vivo. Using genomic and biochemical analyses, we further demonstrate that MYb11 and MYb12 produce massetolide E, a cyclic lipopeptide biosurfactant of the viscosin group 4, 5, which is active against pathogenic B. thuringiensis. In contrast to MYb11 and MYb12, P. fluorescens MYb115-mediated protection involves increased resistance without inhibition of pathogen growth and most likely depends on indirect, host-mediated mechanisms. This work provides new insight into the functional significance of the C. elegans natural microbiota and expands our knowledge of bacteria-derived compounds that can influence pathogen colonization in the intestine of an animal.
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•Natural C. elegans microbiota confer protection against pathogen infection•Different Pseudomonas isolates protect C. elegans through distinct mechanisms•P. lurida isolates produce massetolide E and directly inhibit pathogen growth•P. fluorescens-mediated protection may depend on indirect, host-mediated mechanisms
Kissoyan et al. provide novel insights into the function of the native microbiota of the model nematode C. elegans. Their work highlights the role of microbes in supporting C. elegans defense responses and the diversity of immune-protective mechanisms, including the involvement of microbiota-derived metabolites.
Bacterial communities are taxonomically highly diverse, yet the mechanisms that maintain this diversity remain poorly understood. We hypothesized that an obligate and mutual exchange of metabolites, ...as is very common among bacterial cells, could stabilize different genotypes within microbial communities. To test this, we developed a cellular automaton to model interactions among six empirically characterized genotypes that differ in their ability and propensity to produce amino acids. By systematically varying intrinsic (i.e. benefit-to-cost ratio) and extrinsic parameters (i.e. metabolite diffusion level, environmental amino acid availability), we show that obligate cross-feeding of essential metabolites is selected for under a broad range of conditions. In spatially structured environments, positive assortment among cross-feeders resulted in the formation of cooperative clusters, which limited exploitation by non-producing auxotrophs, yet allowed them to persist at the clusters' periphery. Strikingly, cross-feeding helped to maintain genotypic diversity within populations, while amino acid supplementation to the environment decoupled obligate interactions and favored auxotrophic cells that saved amino acid production costs over metabolically autonomous prototrophs. Together, our results suggest that spatially structured environments and limited nutrient availabilities should facilitate the evolution of metabolic interactions, which can help to maintain genotypic diversity within natural microbial populations.
A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined ...by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM
) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM
interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, Enterococcus faecalis KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM
interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.
The study of microbiomes by sequencing has revealed a plethora of correlations between microbial community composition and various life-history characteristics of the corresponding host species. ...However, inferring causation from correlation is often hampered by the sheer compositional complexity of microbiomes, even in simple organisms. Synthetic communities offer an effective approach to infer cause-effect relationships in host-microbiome systems. Yet the available communities suffer from several drawbacks, such as artificial (thus non-natural) choice of microbes, microbe-host mismatch (
, human microbes in gnotobiotic mice), or hosts lacking genetic tractability. Here we introduce CeMbio, a simplified natural
microbiota derived from our previous meta-analysis of the natural microbiome of this nematode. The CeMbio resource is amenable to all strengths of the
model system, strains included are readily culturable, they all colonize the worm gut individually, and comprise a robust community that distinctly affects nematode life-history. Several tools have additionally been developed for the CeMbio strains, including diagnostic PCR primers, completely sequenced genomes, and metabolic network models. With CeMbio, we provide a versatile resource and toolbox for the in-depth dissection of naturally relevant host-microbiome interactions in
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For producing ATP, tumour cells rely on glycolysis leading to lactate to about the same extent as on respiration. Thus, the ATP synthesis flux from glycolysis is considerably higher than in the ...corresponding healthy cells. This is known as the Warburg effect (named after German biochemist Otto H. Warburg) and also applies to striated muscle cells, activated lymphocytes, microglia, endothelial cells and several other cell types. For similar phenomena in several yeasts and many bacteria, the terms Crabtree effect and overflow metabolism respectively, are used. The Warburg effect is paradoxical at first sight because the molar ATP yield of glycolysis is much lower than that of respiration. Although a straightforward explanation is that glycolysis allows a higher ATP production rate, the question arises why cells do not re-allocate protein to the high-yield pathway of respiration. Mathematical modelling can help explain this phenomenon. Here, we review several models at various scales proposed in the literature for explaining the Warburg effect. These models support the hypothesis that glycolysis allows for a higher proliferation rate due to increased ATP production and precursor supply rates.
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