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.@en

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates.

@en