MIM: Discovering in reverse – using isotopic translation of omics to reveal ecological interactions in microbiomes.

Microbiomes in freshwater rivers comprise hundreds of species of algae, bacteria, fungi and microscopic animals. How do these groups of micro-organisms interact? Are the interactions largely competitive, as groups of organisms vie for scarce resources?

How many are helpful, where one organism synthesizes a resource that another needs? How important are these interactions for food webs, for the growth of algae in freshwater, and for the cycling of nutrients in the environment? This research focuses on Cladophora glomerata, a green freshwater alga that occurs in temperate lakes and rivers worldwide, and specifically on its microbiome, the hundreds of micro-organisms that live right on the surface of Cladophora.

Researchers will use chemical tracers that reveal how elements like carbon and nitrogen move through the microbiome, because these elements are important for supporting productivity. Researchers will also test how the microbiome changes in response to nutrient and light, factors that drive the ecology of freshwaters. This research will improve understanding of how microbiomes support healthy ecosystems and the factors that can shift systems to harmful algal blooms.

Novel machine learning tools will be used to develop a predictive framework for understanding how microbial species interact with one another and affect macroscopic food webs (insects, fish, birds). In addition, over ten students will be trained and educational materials including videos, written stories and artwork geared towards general audiences will be created.

The training program emphasizes teaching researchers, at all career stages, and how to communicate their science through visual, written and oral storytelling.

This project will develop the green macro-alga, Cladophora, as a model system for understanding processes governing microbiomes. This research will advance microbiome science by combining hypothesis-driven ecological theory with the data-driven power of machine learning. Researchers will integrate ‘omics’ data with state-of-the-art stable isotope techniques, such as quantitative stable isotope probing, qSIP, which measures growth and mortality rates of individual microbial species using stable isotopes of 18O labeled water; qSIP and Chip-SIP using 13C and 15N, which quantifies taxon-specific C and N assimilation by measuring the isotopic composition of nucleic acids; and NanoSIMS, which analyzes spatial and temporal patterns of C and N movement through the microbiome and its host.

Experiments will be performed to study (1) shifts in the microbiome over a well-documented successional sequence, (2) changes in productivity in response to nutrient addition, and (3) responses to light. Interdisciplinary approaches will develop new science in biology and machine learning through data matrices that span beyond the commonly used species co-occurrence matrices.

Additional features that will be added include taxon-specific growth and mortality rates, carbon and nitrogen uptake, metabolism, and phylogeny. Adding these will improve our ability to understand microbial interactions in complex microbiomes. Using machine learning researchers will build and train models assimilating these multiple, simultaneous matrices of data (together, a tensor).

This approach will advance efforts to generate interaction terms that reflect the importance of the key interactions (competition, mutualism, facilitation, parasitism, or predation) that structure ecological communities.

This project is co-funded by the Division of Chemistry (CHE) in the Mathematics and Physical Sciences (MPS) Directorate and by the Division of Environmental Biology (DEB) in the Biological Sciences (BIO) Directorate.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.