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

Emily B. Graham, Alex R. Crump, David W. Kennedy, Evan Arntzen, Sarah Fansler, Samuel O. Purvine, Carrie D. Nicora, William Nelson, Malak M. Tfaily, James C. Stegen

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)742-753
Number of pages12
JournalScience of the Total Environment
StatePublished - Nov 15 2018
Externally publishedYes


  • Carbon cycle
  • Coupled C-N cycling
  • Hydrobiogeochemistry
  • Respiration
  • Riparian

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

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