Ecology and evolution of methanogens in the human gut
Previous work in our department demonstrated that M. smithii and C. minuta grow synoptically in coaggregations (Ruaud et al., 2020, mSystems), which may explain their co-occurrence across human populations and observed associations with host health. These findings, along with comparative genomics, indicate that an array of adhesin-like proteins (ALPs) modulate cell-cell interactions between M. smithii and specific bacterial fermenters. We are investigating this possibility via multiple approaches, including transcriptomics, proteomics, and phage display technology. We are also investigating the evolution of Methanobrevibacter via comparative genomics and phenotypic characterization.
The influence of host evolution and ecology on gut microbiome diversity
This project expands the evolutionary scope of previous work in our department, which showed that human genetics modulates gut microbial diversity. In conjunction with collaborators at TU Wien and the Wildlife Conservation Society, we have compiled one of the largest and most diverse vertebrate gut microbiome datasets, comprising >300 samples from mostly wild individuals (~80% wild) spanning 5 taxonomic classes: Mammalia, Aves, Reptilia, Amphibia, and Actinopterygii. With this dataset, we have shown how host phylogeny and ecology influence microbiome diversity and also revealed a great wealth of novel microbial taxonomic and genomic diversity (Youngblut et al., Nature Communications 2019; Youngblut et al., mSystems 2020; Youngblut et al., Nature Microbiology in revision). We are currently investigating new biological questions with this existing dataset and planning a new sampling campaign.
Bioinformatics methods development
We have developed a number of novel bioinformatics methods in order to better investigate our biological questions. Such methods include i) tools for metagenomic reference database construction (Cuesta-Zuluaga et al., Bioinformatics 2020; Youngblut et al., PeerJ in revision), ii) deep learning approaches for improving metagenome assemblies (Mineeva et al., Bioinformatics 2020), iii) and improved prediction of host phenotypes via utilizing novel measures of metagenomic diversity (Youngblut et al., Environmental Microbiology in review).
We are collaborating with deep learning researchers to explore how state-of-the-art machine learning approaches can be applied to microbiome science. More generally, we are always pursing ways to innovate on how microbiome science is conducted.
Youngblut, N., J, Reischer, G., Dauser, S., Maisch, S., Walzer, C., Stalder, G., Farnleitner, A., Ley, R. (2020) Vertebrate host phylogeny influences gut archaeal diversity. Nature Microbiology, in press.
Youngblut, N., Cuesta-Zuluaga, J, Reischer, G., Dauser, S., Schuster, N., Walzer, C., Stalder, G., Farnleitner, A., Ley, R. (2020) Large-Scale Metagenome Assembly Reveals Novel Animal-Associated Microbial Genomes, Biosynthetic Gene Clusters, and Other Genetic Diversity. mSystems 5 (6).
Youngblut, N., Reischer, G., Walters, W., Schuster, N., Walzer, C., Stalder, G., Ley, R., Farnleitner A. (2019) Host Diet and Evolutionary History Explain Different Aspects of Gut Microbiome Diversity among Vertebrate Clades. Nature Communications 10 (1): 2200.
Mineeva, O., Rojas-Carulla, M., Ley R., Schölkopf, B., Youngblut, N. (2020). DeepMAsED: Evaluating the Quality of Metagenomic Assemblies. Bioinformatics 36 (10): 3011–17.
Cuesta-Zuluaga, J, Spector, T, Youngblut, N., and Ley, R. (2021) Genomic Insights into Adaptations of Trimethylamine-Utilizing Methanogens to Diverse Habitats, Including the Human Gut. mSystems 6 (1).
Ruaud, A., Esquivel-Elizondo, S., Cuesta-Zuluaga, J., Waters, J., Angenent, L., Youngblut, N., and Ley, R. (2020) Syntrophy via Interspecies H2 Transfer between Christensenella and Methanobrevibacter Underlies Their Global Cooccurrence in the Human Gut. mBio 11 (1).