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

Arsenic redox disequilibrium in geogenic contaminated groundwater: Bioenergetic insights from organic molecular characterization and gene-informed modeling

Biotransformation of arsenic (As) influences its speciation and mobility, obscuring mechanistic comprehension on spatiotemporal variation of As concentration in geogenic contaminated groundwater. In particular, unresolved processes underlying As redox disequilibrium in comparison to major redox couples discourage practical efforts to rehabilitate the As-contaminated groundwater. Here, quantitative metagenomic sequencing and ultrahigh-resolution mass spectrometry (FT-ICR-MS) were jointly applied to reveal the links between vertical distribution of As metabolic gene assemblages and that of free energy density of dissolved organic matter (DOM) in As-contaminated groundwater of Datong Basin. Observed small excess of Gibbs free energy available by DOM relative to that required for As(V)-to-As(III) reduction exerts thermodynamic constraint on metabolism-mediated redox transformation of As. Accordingly, the vertical distribution of dissolved As(V)/As(III) ratio correlated significantly with that of ars+acr3 and arr encoding As(V) reduction and aio encoding As(III) oxidation in the moderately/strongly reducing groundwater. Further gene-informed biogeochemical modeling suggests that a net effect of these kinetics-restricted bidirectional metabolic pathways leads to co-preservation of As(V) and As(III) even at relatively high rates of ars+acr3 encoded As(V) reduction. This study therefore provides new insights into bioenergetic constraints on As hydrobiogeochemical behavior, with implications for other redox-sensitive contaminants in the groundwater systems.

Effect of florfenicol on nirS-type denitrifying communities structure of water in an aquatic microcosm model

Florfenicol is used worldwide for its low side effects and strong bactericidal effect. Florfenicol is physicochemically stable and can persist in natural water bodies and affect water denitrification. Indoor aquatic microcosm models were constructed and water samples were collected at different florfenicol concentrations (0.1, 1, 10, and 100 mg/L) on days 0, 7, 30, and 60 to extract the microbial genome DNA and determine the water properties. qPCR and amplicon sequencing were used to study the dynamic changes of nirS gene and nirS-type denitrifying communities structure, diversity and abundance, respectively. The results showed that higher florfenicol concentrations caused accumulation of nitrate and ammonium nitrogen in water. Florfenicol stress caused orders of magnitude changes in nirS gene abundance, showing a trend of increasing first and then decreasing. 100 mg/L florfenicol addition led to a sustained increase of nirS gene abundance in water bodies. The florfenicol addition altered denitrifying community structure and suppressed the richness and diversity index of denitrifying bacteria in water body. Over time, the richness and diversity index gradually recovered. Proteobacteria was always the dominant denitrifying phylum in water. The relative abundance of Pseudomonas and beta proteobacterium showed obvious positive correlation with nirS gene abundance and were the dominant genera under florfenicol stress. Our study provided a scientific basis for the rational use of florfenicol in aquaculture to maintain a healthy and stable microecological environment, and also provided a preliminary understanding of the response characteristics of water denitrifying microorganisms to florfenicol exposure.

Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience

Current environmental monitoring efforts often focus on known, regulated contaminants ignoring the potential effects of unmeasured compounds and/or environmental factors. These specific, targeted approaches lack broader environmental information and understanding, hindering effective environmental management and policy. Switching to comprehensive, untargeted monitoring of contaminants, organism health, and environmental factors, such as nutrients, temperature, and pH, would provide more effective monitoring with a likely concomitant increase in environmental health. However, even this method would not capture subtle biochemical changes in organisms induced by chronic toxicant exposure. Ecosurveillance is the systematic collection, analysis, and interpretation of ecosystem health-related data that can address this knowledge gap and provide much-needed additional lines of evidence to environmental monitoring programs. Its use would therefore be of great benefit to environmental management and assessment. Unfortunately, the science of 'ecosurveillance', especially omics-based ecosurveillance is not well known. Here, we give an overview of this emerging area and show how it has been beneficially applied in a range of systems. We anticipate this review to be a starting point for further efforts to improve environmental monitoring via the integration of comprehensive chemical assessments and molecular biology-based approaches. Bringing multiple levels of omics technology-based assessment together into a systems-wide ecosurveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.

Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling

Soil carbon (C) and nitrogen (N) cycles and their complex responses to environmental changes have received increasing attention. However, large uncertainties in model predictions remain, partially due to the lack of explicit representation and parameterization of microbial processes. One great challenge is to effectively integrate rich microbial functional traits into ecosystem modeling for better predictions. Here, using soil enzymes as indicators of soil function, we developed a competitive dynamic enzyme allocation scheme and detailed enzyme-mediated soil inorganic N processes in the Microbial-ENzyme Decomposition (MEND) model. We conducted a rigorous calibration and validation of MEND with diverse soil C-N fluxes, microbial C:N ratios, and functional gene abundances from a 12-year CO₂  × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO₂ fluxes and multiple N variables, the model correctly predicted microbial C:N ratios and their negative response to enriched N supply. Model validation further showed that, compared to the changes in simulated enzyme concentrations and decomposition rates, the changes in simulated activities of eight C-N-associated enzymes were better explained by the measured gene abundances in responses to elevated atmospheric CO₂ concentration. Our results demonstrated that using enzymes as indicators of soil function and validating model predictions with functional gene abundances in ecosystem modeling can provide a basis for testing hypotheses about microbially mediated biogeochemical processes in response to environmental changes. Further development and applications of the modeling framework presented here will enable microbial ecologists to address ecosystem-level questions beyond empirical observations, toward more predictive understanding, an ultimate goal of microbial ecology.

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.

Influence of Extraction Solvent on Nontargeted Metabolomics Analysis of Enrichment Reactor Cultures Performing Enhanced Biological Phosphorus Removal (EBPR)

Metabolome profiling is becoming more commonly used in the study of complex microbial communities and microbiomes; however, to date, little information is available concerning appropriate extraction procedures. We studied the influence of different extraction solvent mixtures on untargeted metabolomics analysis of two continuous culture enrichment communities performing enhanced biological phosphate removal (EBPR), with each enrichment targeting distinct populations of polyphosphate-accumulating organisms (PAOs). We employed one non-polar solvent and up to four polar solvents for extracting metabolites from biomass. In one of the reactor microbial communities, we surveyed both intracellular and extracellular metabolites using the same set of solvents. All samples were analysed using ultra-performance liquid chromatography mass spectrometry (UPLC-MS). UPLC-MS data obtained from polar and non-polar solvents were analysed separately and evaluated using extent of repeatability, overall extraction capacity and the extent of differential abundance between physiological states. Despite both reactors demonstrating the same bioprocess phenotype, the most appropriate extraction method was biomass specific, with methanol: water (50:50 v/v) and methanol: chloroform: water (40:40:20 v/v) being chosen as the most appropriate for each of the two different bioreactors, respectively. Our approach provides new data on the influence of solvent choice on the untargeted surveys of the metabolome of PAO enriched EBPR communities and suggests that metabolome extraction methods need to be carefully tailored to the specific complex microbial community under study.

Coupling Genomics and Hydraulic Information to Predict the Nitrogen Dynamics in a Channel Confluence

The simulation of nitrogen dynamics in urban channel confluences is essential for the evaluation and improvement of water quality. The omics-based modeling approaches that have been rapidly developed have been increasingly applied to characterize metabolisms of the microbial community and transformation of the associated materials. However, the transport of microorganisms and chemicals within and among different phases, which could be the rate-limiting step for the nitrogen dynamics, are always neglected or oversimplified in omics-based models. Therefore, this study proposes a novel simulation system coupling genomic and hydraulic information to simulate transport and transformation processes and provide predictions of nitrogen dynamics in a confluence. The proposed model was able to capture multiphase mass transport, microbial population dynamics, and nitrogen transformation and accurately predict gene abundances and nitrogen concentrations in both water and sediment; the mean relative errors were all lower than 40%. The model emphasized the importance of transport processes, which contributed more than 90% to gene abundances and chemical concentrations. Moreover, the simulation of reaction rates exhibited the specific nitrogen transformation processes in the confluence. The sulfide oxidation and the nitrate reduction and anaerobic ammonium oxidation, with the participation of the genes nap and hzo, respectively, were promoted as the main processes of nitrate and nitrite reduction.

Effects of norfloxacin on nitrate reduction and dynamic denitrifying enzymes activities in groundwater

The impact of antibiotics on denitrification has attracted widespread attention recently. Norfloxacin, as a representative of fluoroquinolone antibiotics, is extensively detected in groundwater. However, whether the release of norfloxacin into the groundwater poses potential risks to denitrification remains unclear. In this study, effect of norfloxacin on denitrification was investigated. The results showed that increasing norfloxacin from 0 to 100 μg/L decreased nitrate removal rate from 0.68 to 0.44 mg/L/h, but enhanced N2O emission by 177 folds. Additionally, 100 μg/L of norfloxacin decreased nitrite accumulation by 50.6%. Corresponding inhibition of norfloxacin on bacterial growth, carbon source utilization, electron transport system activity and genes expression was revealed. Furthermore, denitrifying enzyme dynamic monitoring results showed that norfloxacin inhibited nitrate reductase activity, and enhanced nitrite reductase activity to some extent in denitrification process, which was consistent with the variations of nitrate and nitrite. Meanwhile, sensitivity analysis demonstrated that nitrate reductase was more easily affected by norfloxacin than nitrite reductase. Overall, this study suggests that multiple regulation of denitrifying enzyme activity contributes to evaluating the comprehensive effects of antibiotics on groundwater denitrification.

Association between Aquatic Micropollutant Dissipation and River Sediment Bacterial Communities

Assessment of micropollutant biodegradation is essential to determine the persistence of potentially hazardous chemicals in aquatic ecosystems. We studied the dissipation half-lives of 10 micropollutants in sediment-water incubations (based on the OECD 308 standard) with sediment from two European rivers sampled upstream and downstream of wastewater treatment plant (WWTP) discharge. Dissipation half-lives (DT50s) were highly variable between the tested compounds, ranging from 1.5 to 772 days. Sediment from one river sampled downstream from the WWTP showed the fastest dissipation of all micropollutants after sediment RNA normalization. By characterizing sediment bacteria using 16S rRNA sequences, bacterial community composition of a sediment was associated with its capacity for dissipating micropollutants. Bacterial amplicon sequence variants of the genera Ralstonia, Pseudomonas, Hyphomicrobium, and Novosphingobium, which are known degraders of contaminants, were significantly more abundant in the sediment incubations where fast dissipation was observed. Our study illuminates the limitations of the OECD 308 standard to account for variation of dissipation rates of micropollutants due to differences in bacterial community composition. This limitation is problematic particularly for those compounds with DT50s close to regulatory persistence criteria. Thus, it is essential to consider bacterial community composition as a source of variability in regulatory biodegradation and persistence assessments.

Gene-Centric Model Approaches for Accurate Prediction of Pesticide Biodegradation in Soils

Pesticides are widely used in agriculture despite their negative impact on ecosystems and human health. Biogeochemical modeling facilitates the mechanistic understanding of microbial controls on pesticide turnover in soils. We propose to inform models of coupled microbial dynamics and pesticide turnover with measurements of the abundance and expression of functional genes. To assess the advantages of informing models with genetic data, we developed a novel "gene-centric" model and compared model variants of differing structural complexity against a standard biomass-based model. The models were calibrated and validated using data from two batch experiments in which the degradation of the pesticides dichlorophenoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were observed in soil. When calibrating against data on pesticide mineralization, the gene-centric and biomass-based models performed equally well. However, accounting for pesticide-triggered gene regulation allows improved performance in capturing microbial dynamics and in predicting pesticide mineralization. This novel modeling approach also reveals a hysteretic relationship between pesticide degradation rates and gene expression, implying that the biodegradation performance in soils cannot be directly assessed by measuring the expression of functional genes. Our gene-centric model provides an effective approach for exploiting molecular biology data to simulate pesticide degradation in soils.

Integrating Microbial Community Assembly and Fluid Kinetics to Decouple Nitrogen Dynamics in an Urban Channel Confluence

Understanding the characteristics of biogeochemical processes in urban channel confluences is essential for the evaluation and improvement of water environmental capacity. However, influences of biogeochemical processes in confluence were always overlooked or simply parametrized since the transformation processes controlled by microbial community assembly were hard to quantify. To address this knowledge gap, the present study proposed a novel mathematical modeling system, based on microbial community assembly theory and fluid kinetics, to decouple nitrogen dynamics into flow-induced transport and microorganism-induced transformation processes, and quantified their contributions to nitrogen concentrations. Results revealed that variable selection processes (including hydrodynamic conditions) contributed to significant difference in microbial communities among different hydraulic regions. Variation in microbial communities further shifted transformation processes. Rhodobacterales and Sphingomonadales, which were reported to be vital participants in denitrification process, were enriched in flow separation region, and promoted it as a hotspot for nitrogen removal. In the flow separation region, microorganism-induced transformation processes accounted for 56% of total nitrogen removal, which was significantly higher than that in other regions (12% on average; p < 0.01). Results and findings could provide useful information for the improvement of water environmental capacity.

Quantifying Microbially Mediated Kinetics of Ferrihydrite Transformation and Arsenic Reduction: Role of the Arsenate-Reducing Gene Expression Pattern

The behavior of arsenic (As) is usually coupled with iron (Fe) oxide transformation and mediated by both abiotic reactions and microbial processes in the environment. However, quantitative models for the coupled kinetic processes, which specifically consider the arsenate-reducing gene expression correspondent to different reaction conditions, are lacking. In this study, based on the pure cultured Shewanella putrefaciens incubation experiments, extended X-ray absorption fine structure spectroscopy, high resolution transmission electron microscopy, and a suite of microbial analyses, we developed a coupled kinetics model for microbially mediated As reduction and Fe oxide transformation and specifically quantified the As(V) reduction rate coefficients based on the expression patterns of arrA genes. The model reasonably described the temporal changes of As speciation and distribution. The microbial reduction rates of As(V) varied dramatically during the reactions, which were well represented by the varying transcript abundances of arrA genes at different As concentrations. The contributions of biotic and abiotic reactions to the overall reaction rates were assessed. The results improved our quantitative understanding on the key role of As(V)-reducing genes in regulating the speciation and distribution of As. The kinetic modeling approaches based on microbial gene expression patterns are promising for developing comprehensive biogeochemical models of As involving multiple coupled reactions.

Elucidating the negative effect of denitrification on aromatic humic substance formation during sludge aerobic fermentation

Humic substance (HS), as an aromatic compound, is the core product of aerobic fermentation. Denitrification-dependent degradation of aromatic compounds have been repeatedly observed in environment. However, few studies have elucidated the relationship between denitrification and aromatic HS during sludge aerobic fermentation. This study was conducted to investigate the effect of enhanced denitrification on aromatic HS formation. On the 24th day of sludge aerobic fermentation, five tests (CK, Run1, Run2, Run3 and Run4) were executed, and nitrate concentrations were adjusted to 480 ± 20, 500 ± 20, 1000 ± 20, 1500 ± 20 and 2000 ± 20 mg/kg with potassium nitrate, respectively. Analytical results demonstrated that nitrate addition increased denitrifying genes abundance and enhanced denitrification, which further reduced aromatic HS formation (p < 0.05). Especially in Run3, the concentrations of HS and humic acid on the 52nd day dramatically decreased by 12.9 % and 34.2 % in comparison with those on the 31st day. High-throughput sequencing revealed that enhanced denitrification effectively stimulated the metabolism of denitrifying microorganisms with aromatic-degrading capability. Co-occurring network analysis indicated that some keystone taxa of denitrification aromatic-degrading microorganisms involved in the conversion of nitrate to nitrite were the most crucial for enhancing denitrification and reducing aromatic HS formation.

Coupled Kinetics Model for Microbially Mediated Arsenic Reduction and Adsorption/Desorption on Iron Oxides: Role of Arsenic Desorption Induced by Microbes

The dynamic behavior of arsenic (As) species is closely associated with iron mineral dissolution/transformation in the environment. Bacterially induced As(V) desorption from iron oxides may be another important process that facilitates As(V) release from iron oxides without significant reductive dissolution of iron oxides. Under the impact of bacterially induced desorption, As kinetic behavior is controlled by both the microbial reduction of As(V) and the As(III)&As(V) reactions on iron oxide surfaces. However, there is still a lack of quantitative understanding on the coupled kinetics of these processes in complex systems. We developed a quantitative model that integrated the time-dependent microbial reduction of As(V) with nonlinear As(III)&As(V) adsorption/desorption kinetics on iron oxides under the impact of bacterially induced As(V) desorption. We collected and modeled literature data from 11 representative studies, in which microbial reduction reactions occurred with minimal iron oxide dissolution/transformation. Our model highlighted the significance of microbially induced As(V) desorption and time-dependent changes of microbial reduction rates. The model can quantitatively assess the roles and the coupling of individual reactions in controlling the overall reaction rates. It provided a basis for developing comprehensive models for As cycling in the environment by coupling with other chemical, physical, and microbial processes.

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.

Bulk soil bacterial community structure and function respond to long-term organic and conventional agricultural management

Understanding how soil microbiomes respond to management is essential to maximizing soil health. We contrasted microbiomes in bulk soil under long-term organic and conventional management in a grain production setting. Management category significantly impacted the relative abundances of 17% of the most abundant taxa. Both conventional and organic management favored particular taxa, but these effects were not reflected in summary richness and diversity indices. Management systems also lead to differences in soil edaphic properties, including pH and nutrient status; this may have been the mechanism by which change in the prokaryote community was enacted. Community change between years of sampling was less pronounced, with only 6 taxa differentially abundant among years. Management category also impacted the abundance of functional genes related to the production and consumption of greenhouse gases. Particulate methane monooxygenase genes were more frequent in soil under organic management, while soluble methane monooxygenase genes were more frequent in soil under conventional management in 1 of 2 years. Nitrous oxide reductase genes were significantly less abundant in soils under second-year alfalfa than in soils under corn. This work highlights the ability of agricultural management to enact broad rearrangements to the structure of bulk soil bacterial communities.