The ISME JournalMicrobiologyPeer-reviewed journal article
Emergence and disruption of cooperativity in a denitrifying microbial community
Alex V. CarriD, Anne E. Otwell, Kristopher A. HuntiD, Yan CheniD, James Wilson, José P. FariaiD, Filipe LiuiD, Janaka N. EdirisingheiD, Jacob J. ValenzuelaiD, Serdar TurkarslaniD, Lauren Michelle LuiiD, Torben NielseniD, Adam P. ArkiniD, Christopher S. HenryiD, Christopher J. PetzoldiD, David A. StahliD, Nitin S. BaligaiD
Abstract
Anthropogenic perturbations to the nitrogen cycle, primarily through use of synthetic fertilizers, is driving an unprecedented increase in the emission of nitrous oxide (N2O), a potent greenhouse gas and an ozone depleting substance, causing urgency in identifying the sources and sinks of N2O. Microbial denitrification is a primary contributor to biotic production of N2O in anoxic regions of soil, marine systems, and wastewater treatment facilities. Here, through comprehensive genome analysis, we show that pathway partitioning is a ubiquitous mechanism of complete denitrification within microbial communities. We have investigated mechanisms and consequences of process partitioning of denitrification through detailed physiological characterization and kinetic modeling of a synthetic community of Rhodanobacter R12 and Acidovorax 3H11. We have discovered that these two bacterial isolates, from a heavily nitrate (NO3-) contaminated superfund site, complete denitrification through the exchange of nitrite (NO2-) and nitric oxide (NO). The process partitioning of denitrification and other processes, including amino acid metabolism, contribute to increased cooperativity within this denitrifying community. We demonstrate that certain contexts, such as high NO3-, cause unbalanced growth of community members, due to differences in their substrate utilization kinetics. The altered growth characteristics of community members drives accumulation of toxic NO2-, which disrupts denitrification causing N2O off gassing.
- organism: bacteria (other)
- assay: metagenomics
- research focus: cell type annotation
- approach: statistical model
- Citations
- 19
- FWCI
- 4.11
- Field percentile
- top 4%
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