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

Advancing environmental biotechnology with microbial community modeling rooted in functional 'omics

Emerging biotechnologies that solve pressing environmental and climate emergencies will require harnessing the vast functional diversity of the underlying microbiomes driving such engineered processes. Modeling is a critical aspect of process engineering that informs system design as well as aids diagnostic optimization of performance. 'Conventional' bioprocess models assume homogenous biomass within functional guilds and thus fail to predict emergent properties of diverse microbial physiologies, such as product specificity and community interactions. Yet, recent advances in functional 'omics-based approaches can provide a 'lens' through which we can probe and measure in situ ecophysiologies of environmental microbiomes. Here, we overview microbial community modeling approaches that incorporate functional 'omics data, which we posit can advance our ability to design and control new environmental biotechnologies going forward.

Potential and limitations for monitoring of pesticide biodegradation at trace concentrations in water and soil

Pesticides application on agricultural fields results in pesticides being released into the environment, reaching soil, surface water and groundwater. Pesticides fate and transformation in the environment depend on environmental conditions as well as physical, chemical and biological degradation processes. Monitoring pesticides biodegradation in the environment is challenging, considering that traditional indicators, such as changes in pesticides concentration or identification of pesticide metabolites, are not suitable for many pesticides in anaerobic environments. Furthermore, those indicators cannot distinguish between biotic and abiotic pesticide degradation processes. For that reason, the use of molecular tools is important to monitor pesticide biodegradation-related genes or microorganisms in the environment. The development of targeted molecular (e.g., qPCR) tools, although laborious, allowed biodegradation monitoring by targeting the presence and expression of known catabolic genes of popular pesticides. Explorative molecular tools (i.e., metagenomics & metatranscriptomics), while requiring extensive data analysis, proved to have potential for screening the biodegradation potential and activity of more than one compound at the time. The application of molecular tools developed in laboratory and validated under controlled environments, face challenges when applied in the field due to the heterogeneity in pesticides distribution as well as natural environmental differences. However, for monitoring pesticides biodegradation in the field, the use of molecular tools combined with metadata is an important tool for understanding fate and transformation of the different pesticides present in the environment.

Spatial Control of Microbial Pesticide Degradation in Soil: A Model-Based Scenario Analysis

Microbial pesticide degraders are heterogeneously distributed in soil. Their spatial aggregation at the millimeter scale reduces the frequency of degrader-pesticide encounter and can introduce transport limitations to pesticide degradation. We simulated reactive pesticide transport in soil to investigate the fate of the widely used herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) in response to differently aggregated distributions of degrading microbes. Four scenarios were defined covering millimeter scale heterogeneity from homogeneous (pseudo-1D) to extremely heterogeneous degrader distributions and two precipitation scenarios with either continuous light rain or heavy rain events. Leaching from subsoils did not occur in any scenario. Within the topsoil, increasing spatial heterogeneity of microbial degraders reduced macroscopic degradation rates, increased MCPA leaching, and prolonged the persistence of residual MCPA. In heterogeneous scenarios, pesticide degradation was limited by the spatial separation of degrader and pesticide, which was quantified by the spatial covariance between MCPA and degraders. Heavy rain events temporarily lifted these transport constraints in heterogeneous scenarios and increased degradation rates. Our results indicate that the mild millimeter scale spatial heterogeneity of degraders typical for arable topsoil will have negligible consequences for the fate of MCPA, but strong clustering of degraders can delay pesticide degradation.

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

Most soil quality measurements have been limited to laboratory-based methods that suffer from time delay, high cost, intensive labor requirement, discrete data collection, and tedious sample pretreatment. Real-time continuous soil monitoring (RTCSM) possesses a great potential to revolutionize field measurements by providing first-hand information for continuously tracking variations of heterogeneous soil parameters and diverse pollutants in a timely manner and thus enable constant updates essential for system control and decision-making. Through a systematic literature search and comprehensive analysis of state-of-the-art RTCSM technologies, extensive discussion of their vital hurdles, and sharing of our future perspectives, this critical review bridges the knowledge gap of spatiotemporal uninterrupted soil monitoring and soil management execution. First, the barriers for reliable RTCSM data acquisition are elucidated by examining typical soil monitoring techniques (e.g., electrochemical and spectroscopic sensors). Next, the prevailing challenges of the RTCSM sensor network, data transmission, data processing, and personalized data management are comprehensively discussed. Furthermore, this review explores RTCSM data application for updating diverse strategies including high-fidelity soil process models, control methodologies, digital soil mapping, soil degradation, food security, and climate change mitigation. Finally, the significance of RTCSM implementation in agricultural and environmental fields is underscored through illuminating future directions and perspectives in this systematic review.

Integrating Genome-Resolved Metagenomics with Trait-Based Process Modeling to Determine Biokinetics of Distinct Nitrifying Communities within Activated Sludge

Conventional bioprocess models for wastewater treatment are based on aggregated bulk biomass concentrations and do not incorporate microbial physiological diversity. Such a broad aggregation of microbial functional groups can fail to predict ecosystem dynamics when high levels of physiological diversity exist within trophic guilds. For instance, functional diversity among nitrite-oxidizing bacteria (NOB) can obfuscate engineering strategies for their out-selection in activated sludge (AS), which is desirable to promote energy-efficient nitrogen removal. Here, we hypothesized that different NOB populations within AS can have different physiological traits that drive process performance, which we tested by estimating biokinetic growth parameters using a combination of highly replicated respirometry, genome-resolved metagenomics, and process modeling. A lab-scale AS reactor subjected to a selective pressure for over 90 days experienced resilience of NOB activity. We recovered three coexisting Nitrospira population genomes belonging to two sublineages, which exhibited distinct growth strategies and underwent a compositional shift following the selective pressure. A trait-based process model calibrated at the NOB genus level better predicted nitrite accumulation than a conventional process model calibrated at the NOB guild level. This work demonstrates that trait-based modeling can be leveraged to improve our prediction, control, and design of functionally diverse microbiomes driving key environmental biotechnologies.

Synthesizing Laboratory and Field Experiments to Quantify Dominant Transformation Mechanisms of 2,4-Dichlorophenoxyacetic Acid (2,4-D) in Aquatic Environments

Laboratory studies used to assess the environmental fate of organic chemicals such as pesticides fail to replicate environmental conditions, resulting in large errors in predicted transformation rates. We combine laboratory and field data to identify the dominant loss processes of the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) in lakes for the first time. Microbial and photochemical degradation are individually assessed using laboratory-based microcosms and irradiation studies, respectively. Field campaigns are conducted in six lakes to quantify 2,4-D loss following large-scale herbicide treatments. Irradiation studies show that 2,4-D undergoes direct photodegradation, but modeling efforts demonstrated that this process is negligible under environmental conditions. Microcosms constructed using field inocula show that sediment microbial communities are responsible for degradation of 2,4-D in lakes. Attempts to quantify transformation products are unsuccessful in both laboratory and field studies, suggesting that their persistence is not a major concern. The synthesis of laboratory and field experiments is used to demonstrate best practices in designing laboratory persistence studies and in using those results to mechanistically predict contaminant fate in complex aquatic environments.

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.