Frioux, C., Ansorge, R., Özkurt, E., Nedjad, C. G., Fritscher, J., Quince, C., Waszak, S. M., and Hildebrand, F. (2023). Enterosignatures define common bacterial guilds in the human gut microbiome. Cell Host & Microbe. https://doi.org/10.1016/j.chom.2023.05.024
The human gut microbiome composition is generally in a stable dynamic equilibrium, but it can deteriorate into dysbiotic states detrimental to host health. To disentangle the inherent complexity and capture the ecological spectrum of microbiome variability, we used 5,230 gut metagenomes to characterize signatures of bacteria commonly co-occurring, termed enterosignatures (ESs). We find five generalizable ESs dominated by either Bacteroides, Firmicutes, Prevotella, Bifidobacterium, or Escherichia. This model confirms key ecological characteristics known from previous enterotype concepts, while enabling the detection of gradual shifts in community structures. Temporal analysis implies that the Bacteroides-associated ES is “core” in the resilience of westernized gut microbiomes, while combinations with other ESs often complement the functional spectrum. The model reliably detects atypical gut microbiomes correlated with adverse host health conditions and/or the presence of pathobionts. ESs provide an interpretable and generic model that enables an intuitive characterization of gut microbiome composition in health and disease.
Jiménez, N. E., Acuña, V., Cortés, M. P., Eveillard, D., and Maass, A. E. (2023). Unveiling abundance-dependent metabolic phenotypes of microbial communities. mSystems, e00492-23. https://doi.org/10.1128/msystems.00492-23
Constraint-based modeling has risen as an alternative for characterizing metabolism of communities. Adaptations of flux balance analysis have been proposed to model metabolic interactions, which in most cases consider the maximization of biomass production as their objective. In nature, novel essential functions are not directly related to cell growth force communities to display suboptimal growth rates. These suboptimal states allow a degree of plasticity in their metabolism, thus allowing quick shifts between alternative flux distributions as an initial response to environmental changes. In this work, we introduce the abundance-growth space as a representation of metabolic phenotypes of a community. This space is defined by the composition of a community, represented by its members’ relative abundances, and their growth rate. The analysis of this space allows us to pinpoint how critical reactions respond to shifts of the environment, showing where changes in community plasticity occur. Interestingly, it highlights the relevance of the relative abundance of its members in the lost or gain of plasticity. This method is applied to two simple communities that exchange metabolites. A synthetic community of two mutant Escherichia coli strains and an environmental bioleaching community composed by Acidithiobacillus ferrooxidans Wenelen and Sulfobacillus thermosulfidooxidans Cutipay, where only Cutipay consumes organic matter disposed of by the community. In nature, organisms live in communities and not as isolated species, and their interactions provide a source of resilience to environmental disturbances. Despite their importance in ecology, human health, and industry, understanding how organisms interact in different environments remains an open question. In this work, we provide a novel approach that, only using genomic information, studies the metabolic phenotype exhibited by communities, where the exploration of suboptimal growth flux distributions and the composition of a community allows to unveil its capacity to respond to environmental changes, shedding light of the degrees of metabolic plasticity inherent to the community. In nature, organisms live in communities and not as isolated species, and their interactions provide a source of resilience to environmental disturbances. Despite their importance in ecology, human health, and industry, understanding how organisms interact in different environments remains an open question. In this work, we provide a novel approach that, only using genomic information, studies the metabolic phenotype exhibited by communities, where the exploration of suboptimal growth flux distributions and the composition of a community allows to unveil its capacity to respond to environmental changes, shedding light of the degrees of metabolic plasticity inherent to the community.
Belcour, A., Got, J., Aite, M., Delage, L., Collén, J., Frioux, C., Leblanc, C., Dittami, S. M., Blanquart, S., Markov, G. V., and Siegel, A. (2023). Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe. Genome Research. https://doi.org/10.1101/gr.277056.122
Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
