Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

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

Denitrification‐Driven Transcription and Enzyme Production at the River‐Groundwater Interface: Insights From Reactive‐Transport Modeling

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.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.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.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659https://doi.org/10.5281/zenodo.6584591https://gitlab.com/astoeriko/nitrogenehttps://doi.org/10.5281/zenodo.6584641https://gitlab.com/astoeriko/adrpyhttps://doi.org/10.5281/zenodo.5213947https://github.com/aseyboldt/sunod

Spectral Induced Polarization (SIP) of Denitrification‐Driven Microbial Activity in Column Experiments Packed With Calcareous Aquifer Sediments

Spectral Induced Polarization (SIP) has been suggested as a non-invasive monitoring proxy for microbial processes. Under natural conditions, however, multiple and often coupled polarization processes co-occur, impeding the interpretation of SIP signals. In this study, we analyze the sensitivity of SIP to microbially-driven reactions under quasi-natural conditions. We conducted flow-through experiments in columns equipped with SIP electrodes and filled with natural calcareous, organic-carbon-rich aquifer sediment, in which heterotrophic denitrification was bio-stimulated. Our results show that, even in the presence of parallel polarization processes in a natural sediment under field-relevant geochemical conditions, SIP is sufficiently sensitive to microbially-driven changes in electrical charge storage. Denitrification yielded an increase in imaginary conductivity of up to 3.1 urn:x-wiley:21698953:media:jgrg22384:jgrg22384-math-0001 (+140%) and the formation of a distinct peak between 1 and 10 Hz, that matched the timing of expected microbial activity predicted by a reactive transport model fitted to solute concentrations. A Cole-Cole decomposition allowed separating the polarization contribution of microbial activity from that of cation exchange, thereby helping to locate microbial hotspots without the need for (bio)geochemical data to constrain the Cole-Cole parameters. Our approach opens new avenues for the application of SIP as a rapid method to monitor a system's reactivity in situ. While in preceding studies the SIP signals of microbial activity in natural sediments were influenced by mineral precipitation/dissolution reactions, the imaginary conductivity changes measured in the biostimulation experiments presented here were dominated by changes in the polarization of the bacterial cells rather than a reaction-induced alteration of the abiotic matrix