TY  - JOUR
A1  - Störiko, Anna
A1  - Pagel, Holger
A1  - Mellage, Adrian
A1  - Van Cappellen, Philippe
A1  - Cirpka, Olaf A.
T1  - Denitrification‐Driven Transcription and Enzyme Production at the River‐Groundwater Interface: Insights From Reactive‐Transport Modeling
Y1  - 2022-08-26
VL  - 58
IS  - 8
JF  - Water Resources Research
DO  - 10.1029/2021WR031584
PB  - 
N2  - Molecular‐biological data and omics tools have increasingly been used to characterize microorganisms responsible for the turnover of reactive compounds in the environment, such as reactive‐nitrogen species in groundwater. While transcripts of functional genes and enzymes are used as measures of microbial activity, it is not yet clear how they are quantitatively related to actual turnover rates under variable environmental conditions. As an example application, we consider the interface between rivers and groundwater which has been identified as a key driver for the turnover of reactive‐nitrogen compounds, that cause eutrophication of rivers and endanger drinking water production from groundwater. In the absence of measured data, we developed a reactive‐transport model for denitrification that simultaneously predicts the distributions of functional‐gene transcripts, enzymes, and reaction rates. Applying the model, we evaluate the response of transcripts and enzymes at the river‐groundwater interface to stable and dynamic hydrogeochemical regimes. While functional‐gene transcripts respond to short‐term (diurnal) fluctuations of substrate availability and oxygen concentrations, enzyme concentrations are stable over such time scales. The presence of functional‐gene transcripts and enzymes globally coincides with the zones of active denitrification. However, transcript and enzyme concentrations do not directly translate into denitrification rates in a quantitative way because of nonlinear effects and hysteresis caused by variable substrate availability and oxygen inhibition. Based on our simulations, we suggest that molecular‐biological data should be combined with aqueous geochemical data, which can typically be obtained at higher spatial and temporal resolution, to parameterize and calibrate reactive‐transport models.
N2  - Plain Language Summary: Molecular‐biological tools can detect how many enzymes, functional genes, and gene transcripts (i.e., precursors of enzyme production) associated with a microbial reaction exist in a sample from the environment. Although these measurements contain valuable information about the number of bacteria and how active they are, they do not directly say how quickly a contaminant like nitrate disappears. Nitrate, from agriculture and other sources, threatens groundwater quality and drinking water production. In the process of denitrification, bacteria can remove nitrate by converting it into harmless nitrogen gas using specialized enzymes. The interface between rivers and groundwater is known as a place where denitrification takes place. In this study, we use a computational model to simulate the coupled dynamics of denitrification, bacteria, transcripts, and enzymes when nitrate‐rich groundwater interacts with a nearby river. The simulations yield complex and nonunique relationships between the denitrification rates and the molecular‐biological variables. While functional‐gene transcripts respond to daily fluctuations of environmental conditions, enzyme concentrations and genes are stable over such time scales. High levels of functional‐gene transcripts therefore provide a good qualitative indicator of reactive zones. Quantitative predictions of nitrate turnover, however, will require high‐resolution measurements of the reacting compounds, genes, and transcripts.
N2  - Key Points: We simulate the distributions of functional‐gene transcripts and enzymes related to denitrification at the river‐groundwater interface. Functional‐gene transcripts respond quickly to diurnal fluctuations of substrate and oxygen concentrations. Substrate limitation and oxygen inhibition impede the direct prediction of denitrification rates from transcript or enzyme concentrations.
UR  - http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/10326
ER  -