bioRxivMicrobiologyPreprint
Integration of absolute multi-omics reveals translational and metabolic interplay in mixed-kingdom microbiomes.
F. Delogu, B. J. Kunath, M. O. Arntzen, T. R. Hvidsten, P. B. Pope
Abstract
Microbiology is founded on well-known model organisms. For example, the majority of our fundamental knowledge regarding the quantitative levels of DNA, RNA, and protein backdates to keystone pure culture-based studies. Nowadays, meta-omic approaches allow us to directly access the molecules that constitute microbes and microbial communities, however due to a lack of absolute measurements, many original culture-derived "microbiology statutes" have not been updated or adapted to more complex microbiome settings. Within a cellulose-degrading and methanogenic consortium, we temporally measured genome-centric absolute RNA and protein levels per gene, and obtained a protein-to-RNA ratio of 102-104 for bacterial populations, whereas Archaeal RNA/protein dynamics (103-105: Methanothermobacter thermoautotrophicus) were more comparable to Eukaryotic representatives humans and yeast. The linearity between transcriptome and proteome had a population-specific change over time, highlighting a minimal subset of four functional carriers (cellulose degrader, fermenter, syntrophic acetate-oxidizer and methanogen) that coordinated their respective metabolisms, cumulating in the overarching community phenotype of converting polysaccharides to methane. Our findings show that upgrading multi-omic toolkits with traditional absolute measurements unlocks the scaling of core biological questions to dynamic and complex microbiomes, creating a deeper insight into inter-organismal relationships that drive the greater community function.
- organism: bacteria (other)
- organism: virus
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: cell-cell communication
- research focus: metabolism & bioenergetics
- research focus: ecology & biodiversity
- approach: experimental assay
- approach: statistical model
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