Periodic Reporting for period 2 - EvoGutHealth (Evolution of gut-associated microbial communities and its functional relevance in health and disease)
Período documentado: 2022-03-01 hasta 2023-08-31
The mammalian gut is a complex ecosystem inhabited by diverse microbial communities, which are highly dynamic throughout life and can substantially vary between individuals. Nevertheless, individual taxa within bacterial communities form organized metabolic networks, which is considered essential for overall ecosystem functionality. Microbial metabolite networks are influenced by three mechanisms: (i.) Compositional changes can alter the representation of metabolic pathways. (ii.) Variability arises by transcriptional alterations, for example in response to diet or during disease. (iii.) Evolutionary changes such as mutational events and horizontal gene transfer modify the genomic content and metabolic capabilities of individual taxa. Currently, little is known on how within-host evolution of the microbiome can result in functional changes.
In EvoGutHealth, we will address this question using a synthetic bacterial model community, termed Oligo-Mouse-Microbiota (OMM). Our previous work demonstrates that the OMM recapitulates central physiologic and functional traits of a complex mouse microbiome and exhibits long-term stability in gnotobiotic mice. Therefore, the model is exceptionally suited to study microbial community evolution in its native host. Our preliminary work shows that several taxa accumulate multiple, non-synonymous mutations in metabolic and transport functions during long-term co-colonization of the murine gut. Transplanting germ-free mice with “evolved” and original communities allowed us to reveal functional differences related to nutrient breakdown, metabolite production and host phenotypes. Therefore, the underlying hypothesis of EvoGutHealth is that within-host evolution of microbial communities in the gut shapes the metabolic interactions between microbial populations and their host and with this impact on microbiome functions. The specific objectives are
• to discern environmental and host factors governing microbial community evolution in the gut
• to identify pathways and functions of different taxa under selection during within-host evolution
• to assess global metabolic and disease-relevant phenotypes of evolved microbial communities using systems biology approaches, metabolic phenotyping and mouse disease models
• to characterize phenotypes of evolved strains by metabolite profiling, metabolic network models and generation of targeted mutations
• to verify and extend results to the human gut microbiota
EvoGutHealth will lead to fundamental and ground-breaking insights into mechanisms and functional implications of within-host microbiome evolution in the mammalian gut.
In EvoGutHealth, we will address this question using a synthetic bacterial model community, termed Oligo-Mouse-Microbiota (OMM). Our previous work demonstrates that the OMM recapitulates central physiologic and functional traits of a complex mouse microbiome and exhibits long-term stability in gnotobiotic mice. Therefore, the model is exceptionally suited to study microbial community evolution in its native host. Our preliminary work shows that several taxa accumulate multiple, non-synonymous mutations in metabolic and transport functions during long-term co-colonization of the murine gut. Transplanting germ-free mice with “evolved” and original communities allowed us to reveal functional differences related to nutrient breakdown, metabolite production and host phenotypes. Therefore, the underlying hypothesis of EvoGutHealth is that within-host evolution of microbial communities in the gut shapes the metabolic interactions between microbial populations and their host and with this impact on microbiome functions. The specific objectives are
• to discern environmental and host factors governing microbial community evolution in the gut
• to identify pathways and functions of different taxa under selection during within-host evolution
• to assess global metabolic and disease-relevant phenotypes of evolved microbial communities using systems biology approaches, metabolic phenotyping and mouse disease models
• to characterize phenotypes of evolved strains by metabolite profiling, metabolic network models and generation of targeted mutations
• to verify and extend results to the human gut microbiota
EvoGutHealth will lead to fundamental and ground-breaking insights into mechanisms and functional implications of within-host microbiome evolution in the mammalian gut.
During the first half of this project we have made progress on all main objectives.
We established a wide range of tools to characterize evolved communities. One major achievement during this time-period has been the elucidation of species interaction networks in the model community using a bottom-up approach in mono- and pairwise co-culture experiments as well as in community batch culture. We performed metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants which provided information into the metabolic potential and activity of the individual community members. Overall, this revealed that the OMM interaction network is shaped by both, exploitative and interference competition in vitro in nutrient-rich culture media and that by changing the nutritional environment we can shift community composition (Weiss et al., ISME 2021).
Moreover, we have developed an automated pipeline for SNP mapping and functional annotation from metagenomics datasets. This allows us to generate a database of OMM mutation profiles mutated pathway patterns during in vivo experimental evolution over time and in response to different environmental challenges. As a first example, we have addressed the impact of antibiotics, which are known to impose strong selective pressures on the gut microbiome a may therefore speed up bacterial evolution of a subset of taxa (Münch et al., accepted). We have developed protocols for re-isolation of evolved bacteria from complex in vivo samples allowing us to functionally characterize evolved bacteria, e.g. with regard to antibiotic resistance and metabolic phenotypes shaping the interaction network.
We established a wide range of tools to characterize evolved communities. One major achievement during this time-period has been the elucidation of species interaction networks in the model community using a bottom-up approach in mono- and pairwise co-culture experiments as well as in community batch culture. We performed metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants which provided information into the metabolic potential and activity of the individual community members. Overall, this revealed that the OMM interaction network is shaped by both, exploitative and interference competition in vitro in nutrient-rich culture media and that by changing the nutritional environment we can shift community composition (Weiss et al., ISME 2021).
Moreover, we have developed an automated pipeline for SNP mapping and functional annotation from metagenomics datasets. This allows us to generate a database of OMM mutation profiles mutated pathway patterns during in vivo experimental evolution over time and in response to different environmental challenges. As a first example, we have addressed the impact of antibiotics, which are known to impose strong selective pressures on the gut microbiome a may therefore speed up bacterial evolution of a subset of taxa (Münch et al., accepted). We have developed protocols for re-isolation of evolved bacteria from complex in vivo samples allowing us to functionally characterize evolved bacteria, e.g. with regard to antibiotic resistance and metabolic phenotypes shaping the interaction network.
The ERC project led to the development of tools and expertise to establish a novel line of research in the group, investigating the mechanisms and functional potential underlying within-host evolution. By the end of the funding period, we will have identified key pathways and contributing species responsible for functional changes in evolved communities. We will validate these functional changes by generating bacterial mutants, drop-out and drop-in communities together with metabolomics and metaproteomics profiling. With that, EvoGutHealth will contribute to uncovering the microbiome “dark matter” and finally further our understanding how on how natural selection shapes within-host microbial community evolution.