The microbial activities on which humanity and ecosystems depend are determined by both microbial community membership and the resources and conditions in the microbes' local environments. In order to effectively explore "omics" (metatranscriptomic, metagenomic, proteomic, metabolomic, and amplicon-based membership) datasets for insight into controls over microbial community structure and function, a pervasive disconnect must be addressed: The length of a typical bacterium is approximately one micrometer, yet environmental sampling meant to provide contextual insight into environmental conditions surrounding, and resources available to, microbial communities is often (by necessity) carried out at much larger scales. "Metadata" (pH, nutrient concentrations, salinity, etc.) are measured from liters of well-mixed water samples, or from homogenized, coffee-mug-size cores of sediment or soil. This scale discrepancy makes it impossible to infer the localized, heterogeneous, micron-scale environmental conditions the microbes are actually reacting to, and influencing, at the time of sampling.
This project aims to develop a computational framework that embeds diverse microbial community function within high resolution 3D environmental structure at pore scales. The goal is to characterize the potential fingerprints of that physical structuring within "omics" datasets, ultimately to more effectively interpret our own and the freely-available omics information appearing from a wide range of environments and sampling conditions. Our work emerges from fundamental questions in basic and applied microbial ecology: (1) How does 3D microenvironmental structure affect, and how is it affected by, microbial community structural diversity and expression of microbial function? (2) How does environmental microheterogeneity affect resulting process rates measured at larger scales, and our ability to predict them, e.g. in bioremediation, ecosystem function, food, or fuel production?
Our approach recognizes biotic information in omics datasets as a signal resulting from structured interaction between the microbiotic and the abiotic at microscales. Recognizing this interaction will support greater understanding of how microbial functional and community diversity in natural and man-made ecosystems persist. The proposed research will also help resolve apparent contradictions in "omics" datasets from highly structured soils and sediments (e.g. simultaneous aerobic and anaerobic processes; operation of multiple nitrate reduction pathways though only one appears thermodynamically favored based on large-scale metadata). These contradictions are hypothesized to be caused by the inability of current environmental sampling methods to resolve small-scale variations in environmental conditions.
The 3D, pore-scale modeling framework developed at PNNL provides a one-of-a-kind simulation tool, and EMSL/PNNL houses the computational power required to use it. EMSL and PNNL also provide essential collaborative scientific and SPH code development expertise. This research directly addresses goal three of BER's Climate and Environmental Sciences Division: "advance fundamental understanding of coupled biogeochemical processes in complex subsurface environments to enable systems-level prediction and control." It also furthers the Biological Systems Science Division's objective to "integrate genome science with advanced computational and experimental approaches...to gain a predictive understanding of living systems". Project results will have relevance for carbon cycling in soils (Terrestrial Ecosystems Science program) and contaminant remediation (Subsurface Biogeochemical Research program).