Influences of organic carbon speciation on hyporheic corridor biogeochemistry and microbial ecology

Multi 'omics comparison reveals metabolome biochemistry, not microbiome composition or gene expression, corresponds to elevated biogeochemical function in the hyporheic zone

Biogeochemical hotspots are pervasive at terrestrial-aquatic interfaces, particularly within groundwater-surface water mixing zones (hyporheic zones), and they are critical to understanding spatiotemporal variation in biogeochemical cycling. Here, we use multi 'omic comparisons of hotspots to low-activity sediments to gain mechanistic insight into hyporheic zone organic matter processing. We hypothesized that microbiome structure and function, as described by metagenomics and metaproteomics, would distinguish hotspots from low-activity sediments by shifting metabolism towards carbohydrate-utilizing pathways and elucidate discrete mechanisms governing organic matter processing in each location. We also expected these differences to be reflected in the metabolome, whereby hotspot carbon (C) pools and metabolite transformations therein would be enriched in sugar-associated compounds. In contrast to expectations, we found pronounced phenotypic plasticity in the hyporheic zone microbiome that was denoted by similar microbiome structure, functional potential, and expression across sediments with dissimilar metabolic rates. Instead, diverse nitrogenous metabolites and biochemical transformations characterized hotspots. Metabolomes also corresponded more strongly to aerobic metabolism than bulk C or N content only (explaining 67% vs. 42% and 37% of variation respectively), and bulk C and N did not improve statistical models based on metabolome composition alone. These results point to organic nitrogen as a significant regulatory factor influencing hyporheic zone organic matter processing. Based on our findings, we propose incorporating knowledge of metabolic pathways associated with different chemical fractions of C pools into ecosystem models will enhance prediction accuracy.

A unified conceptual framework for prediction and control of microbiomes

Microbiomes impact nearly all systems on Earth, and despite vast differences among systems, we contend that it is possible and highly beneficial to develop a unified conceptual framework for understanding microbiome dynamics that is applicable across systems. The ability to robustly predict and control environmental and human microbiomes would provide impactful opportunities to sustain and improve the health of ecosystems and humans alike. Doing so requires understanding the processes governing microbiome temporal dynamics, which currently presents an enormous challenge. We contend, however, that new opportunities can emerge by placing studies of both environmental and human microbiome temporal dynamics in the context of a unified conceptual framework. Our conceptual framework poses that factors influencing the temporal dynamics of microbiomes can be grouped into three broad categories: biotic and abiotic history, internal dynamics, and external forcing factors. Both environmental and human microbiome science study these factors, but not in a coordinated or consistent way. Here we discuss opportunities for greater crosstalk across these domains, such as leveraging specific ecological concepts from environmental microbiome science to guide optimization of strategies to manipulate human microbiomes towards improved health. To achieve unified understanding, it is necessary to have a common body of theory developed from explicit iteration between models and molecular-based characterization of microbiome dynamics across systems. Only through such model-experiment iteration will we eventually achieve prediction and control across microbiomes that impact ecosystem sustainability and human health.

At the Nexus of History, Ecology, and Hydrobiogeochemistry: Improved Predictions across Scales through Integration

To improve predictions of ecosystem function in future environments, we need to integrate the ecological and environmental histories experienced by microbial communities with hydrobiogeochemistry across scales. A key issue is whether we can derive generalizable scaling relationships that describe this multiscale integration.

To improve predictions of ecosystem function in future environments, we need to integrate the ecological and environmental histories experienced by microbial communities with hydrobiogeochemistry across scales. A key issue is whether we can derive generalizable scaling relationships that describe this multiscale integration. There is a strong foundation for addressing these challenges. We have the ability to infer ecological history with null models and reveal impacts of environmental history through laboratory and field experimentation. Recent developments also provide opportunities to inform ecosystem models with targeted omics data. A major next step is coupling knowledge derived from such studies with multiscale modeling frameworks that are predictive under non-steady-state conditions. This is particularly true for systems spanning dynamic interfaces, which are often hot spots of hydrobiogeochemical function. We can advance predictive capabilities through a holistic perspective focused on the nexus of history, ecology, and hydrobiogeochemistry.