Targeted quantification of functional enzyme dynamics in environmental samples for microbially mediated biogeochemical processes

Enzyme promiscuity in natural environments: alkaline phosphatase in the ocean

Alkaline phosphatase (APase) is one of the marine enzymes used by oceanic microbes to obtain inorganic phosphorus (P_i) from dissolved organic phosphorus to overcome P-limitation. Marine APase is generally recognized to perform P-monoesterase activity. Here we integrated a biochemical characterization of a specific APase enzyme, examination of global ocean databases, and field measurements, to study the type and relevance of marine APase promiscuity. We performed an in silico mining of phoA homologs, followed by de novo synthesis and heterologous expression in E. coli of the full-length gene from Alteromonas mediterranea, resulting in a recombinant PhoA. A global analysis using the TARA Oceans, Malaspina and other metagenomic databases confirmed the predicted widespread distribution of the gene encoding the targeted PhoA in all oceanic basins throughout the water column. Kinetic assays with the purified PhoA enzyme revealed that this enzyme exhibits not only the predicted P-monoester activity, but also P-diesterase, P-triesterase and sulfatase activity as a result of a promiscuous behavior. Among all activities, P-monoester bond hydrolysis exhibited the highest catalytic activity of APase despite its lower affinity for phosphate monoesters. APase is highly efficient as a P-monoesterase at high substrate concentrations, whereas promiscuous activities of APase, like diesterase, triesterase, and sulfatase activities are more efficient at low substrate concentrations. Strong similarities were observed between the monoesterase:diesterase ratio of the purified PhoA protein in the laboratory and in natural seawater. Thus, our results reveal enzyme promiscuity of APase playing potentially an important role in the marine phosphorus cycle.

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.

Determination of polonium-210 in environmental samples using diglycolamide-based cloud point extraction coupled to alpha spectrometry analysis

This study presents a novel cloud point extraction (CPE) methodology for the separation and enrichment of polonium-210 prior to alpha-spectrometric quantification in water, urine and digested samples. The extractive behaviour of diglycolamide-based ligands towards Po by CPE was determined and optimised in various acidic conditions. The extraction efficiency and selectivity of the CPE systems depend greatly on the choice of the extracting agent and acidic conditions. The thorough optimisation of those specific parameters has led to the development of a suitable cloud point extraction system for the determination of polonium-210 at ultra-trace levels compatible with alpha-spectrometry. To facilitate this coupling, a back-extraction procedure was optimised and performed on the surfactant-rich phase to enable the spontaneous deposition of polonium-210 onto a silver disk; this also avoids making the matrix transfer step mandatory due to the presence of a nitric medium. Method detection and quantification limits of 3.5 and 12 mBq L^-1, respectively, were determined by alpha spectrometry. The robustness of the proposed methodology was demonstrated by the determination of polonium ions concentration in various environmental and biological samples and solid certified reference materials.

Compositional changes of dissolved organic carbon during its dynamic desorption from hyporheic zone sediments

Dissolved organic matter (DOM) is an important driver for biogeochemical reactions that affect microbial community function, and regulate changes in porewater chemical composition and redox properties in the environment. This study investigated the variation in DOM molecular composition during the detachment of organic matter (OM) from hyporheic zone (HZ) sediments using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS). Diffusive mass transfer and microbial degradation were the two primary processes controlling the rate of OM release and molecular composition changes during the detachment from sediments. The diffusive mass transfer process limited the rate of OM release from the sediments, but had negligible effect on the molecular signature of the released OM. Microbial degradation on the other hand preferentially consumed the protein- and lipid-like fractions of the DOM, characterized by lower nominal oxidation states of carbon (NOSC), lower molecular weight, and a higher saturation of chemical bonds. The results have strong implication to the organic carbon dynamics and related microbial activities and contaminant transformation in hyporheic zones, an important critical area in river systems.

Functional Enzyme-Based Approach for Linking Microbial Community Functions with Biogeochemical Process Kinetics

The kinetics of biogeochemical processes in natural and engineered environmental systems is typically described using Monod-type or modified Monod-type models. These models rely on biomass as surrogates for functional enzymes in microbial communities that catalyze biogeochemical reactions. A major challenge of applying such models is the difficulty of quantitatively measuring functional biomass for the constraining and validation of the models. However, omics-based approaches have been increasingly used to characterize microbial community structure, functions, and metabolites. Here, we propose an enzyme-based model that can incorporate omics data to link microbial community functions with biogeochemical process kinetics. The model treats enzymes as time-variable catalysts for biogeochemical reactions and applies a biogeochemical reaction network to incorporate intermediate metabolites. The sequences of genes and proteins from metagenomes, as well as those from the UniProt database, were used for targeted enzyme quantification and to provide insights into the dynamic linkage among functional genes, enzymes, and metabolites that are required in the model. The application of the model was demonstrated using denitrification, as an example, by comparing model simulations with measured functional enzymes, genes, denitrification substrates, and intermediates.

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community's traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators.