Ecological Modeling Applied to Metabolomics Opens New Area of Scientific Inquiry
Researchers propose and test foundational concepts for the new subdiscipline of “meta-metabolome ecology.”
Multiple spatially distributed communities of biological species can be considered an ecological meta-community. Processes governing meta-community dynamics are often studied through an approach called null modeling. Led by scientists at the U.S. Department of Energy’s (DOE) Pacific Northwest National Laboratory (PNNL), a research team have now shown that null modeling can be extended to organic molecules fundamental to biogeochemical cycling across all ecosystems. The team developed this new conceptual paradigm and created the tools to turn the concepts into a quantitative framework. To demonstrate the approach, the team further applied the methods to organic metabolites collected along a well-studied section of the Columbia River. The synthesis between environmental metabolomics and meta-community ecology is the foundation for a new line of scientific inquiry they call “meta-metabolome ecology.”
This approach enables researchers to track processes governing organic metabolite profiles through space and time in natural ecosystems. This is key to develop predictive, mechanistic models that link molecular properties to emergent ecosystem function from local to global scales, such as reactive transport and Earth system models. River corridors, major biogeochemical engines of the Earth system, are an example of an ecosystem that can benefit from this type of analysis. The ability to characterize the influences of stochasticity and determinism in the metabolome assemblages from a river corridor enables scientists to understand which parts of the meta-metabolome are more likely to be important in influencing how the existing river functions and how it might change over time. Furthermore, the science opened by this study will allow microbial taxa to be more directly coupled to the organic molecules involved with metabolic reactions that underlie biogeochemical function.
To better understand processes that constrain or promote variation in metabolomes of a given system, researchers integrated metabolite data with tools and concepts from community ecology. They used metabolite data collected from ultrahigh-resolution mass spectrometric analysis of filtered river water and subsurface pore water collected from a well-studied stretch of the Columbia River. These data were generated at the Environmental Molecular Sciences Laboratory (EMSL), a DOE Office of Science user facility at PNNL.
With these data, the researchers developed several metabolite dendrograms to group molecules based on common traits, such as elemental composition, structural features, and biochemical transformations. Next, they performed ecological null modeling, a common approach used in meta-community ecology but never before applied to organic metabolite assemblages. The null models quantified processes that governed the assembly of molecules into metabolomes.
The researchers found metabolites that were potentially biochemically active were more deterministically assembled than less active metabolites. Organic metabolites that are biochemically active and deterministically organized are most important to represent in mechanistic models.
This approach provides a tool for modelers to winnow the enormously complex milieu of organic molecules down to a subset that is most important for enhancing predictive capacity. This tool is poised to be used with large-scale molecular analysis of environmental metabolomes, such as those provided by the Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS).
Pacific Northwest National Laboratory
This research was supported as part of the Subsurface Biogeochemistry Research Program of the Office of Biological and Environmental Research, within the U.S. Department of Energy Office of Science. This contribution originates from the River Corridor and Water Hydro-biogeochemistry Scientific Focus Area at the Pacific Northwest National Laboratory.
Danczak, R.E. et al. “Using metacommunity ecology to understand environmental metabolomes.” Nature Communications 11, 6369 (2020). DOI:10.1038/s41467-020-19989-y