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

Abstract

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

“…Profiles of pore‐water nitrite in several studies indicate that the concentrations are usually below 30 μmol L −1 (Akbarzadeh et al., 2018; Harvey et al., 2013; Stief et al., 2002). The parameter set used here is based on laboratory batch experiments with a single strain where strong nitrite accumulation was observed (Störiko et al., 2021). Thus, the high model‐derived nitrite concentrations are likely a feature specific for the microbial strain used in the experiments.…”

“…We used an enzyme‐based model formulation of microbial denitrification (Störiko et al., 2021) that reflects the biological regulation of reaction rates by explicitly simulating concentrations of transcription factors, functional‐gene transcripts, and enzymes. The reaction model describes both aerobic respiration and reduction of nitrate to N 2 via $${{\text{NO}}_{2}}^{-}$$ as a reactive intermediate.…”

“…The concentrations of transcripts were assumed to be at quasi‐steady state with the transcription factor concentrations. The NAR and NIR enzymes are produced in response to narG and nirS levels and decay following a first‐order rate (Störiko et al., 2021).…”

“…Denitrification is coupled to the oxidation of organic carbon, formally expressed as succinate, serving as an electron donor and carbon source for the facultative anaerobe Paracoccus denitrificans. Herein, we applied the parameters of Störiko et al (2021) specific to P. denitrificans to simulate denitrification coupled to DOC oxidation (assuming that succinate acts as a generalized form of DOC) to the flow scenarios outlined in Figure 1. Despite the parameters being specific to a pure-culture batch experiment (Qu et al, 2015), which may not be fully representative for denitrification by the natural microbial community in riverbed sediments, they provide an opportunity with which to probe the thus far poorly characterized behavior of transcription and enzyme regulation in natural subsurface-transport settings, relevant for biogeochemical laboratory and field investigations.…”

“…Parameters related to transcript and enzyme concentrations, denitrification and aerobic respiration were obtained from our previous study (Störiko et al, 2021) in which we calibrated the enzyme-based model with the laboratory data of Qu et al (2015). In the simulations presented here, the median values of the parameter distributions in Störiko et al (2021) were imposed (Table 1). Values of new parameters, i.e., those that were not included in the previous model (transport and DOC-related parameters) were chosen based on literature values.…”