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

Abstract

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.

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

“…The lack of obvious correlations between transcripts of functional genes and reaction rates observed in our modeling results are likely representative for such dynamic environments. A non-linear, hysteretic relationship between transcript concentrations and reaction rates has also been found in a gene-centric model of pesticide degradation (Chavez Rodriguez et al, 2020). Interestingly, this is the case although pesticide degradation acts on much longer time scales than denitrification in this study and the observed relationship between rates and transcripts shows different patterns than the ones presented in Figure 4 (e.g., direction of the hysteresis).…”

“…Here, we apply our model to a dynamic reactive system, that is, one with shifts in the predominant electron accepting species, and inform it with a highly temporally resolved dataset of transcript abundances. Previous studies have highlighted a potentially hysteretic relationship between transcript concentrations and reaction rates (Baelum et al, 2008;Chavez Rodriguez et al, 2020). Our model allows us to further explore the relationship between transcripts, enzymes, and reaction rates and develop mechanistic interpretations of the observations.…”

“…The nitrogen cycle is thus an ideal test case for developing and testing new, enzyme-based models informed by measurements of functional genes and transcripts. Previous gene-centric and enzyme-based modeling studies focused on systems with slow dynamics (kinetics) (Li et al, 2017c;Song et al, 2017;Chavez Rodriguez et al, 2020) or at steady state (Reed et al, 2014;Louca et al, 2016). Here, we apply our model to a dynamic reactive system, that is, one with shifts in the predominant electron accepting species, and inform it with a highly temporally resolved dataset of transcript abundances.…”

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

“…Under extreme electron‐donor limitation, our model predicts very low absolute transcript concentrations even without explicitly accounting for DOC‐controlled downregulation of transcription because DOC‐limitation restricts microbial growth, leading to low biomass and, thereby, low transcript concentrations. However, if there is evidence for a large abundance of inactive denitrifiers, the model might need to distinguish between the active and an inactive microbial pool, in which transcription is shut off (e.g., Chavez Rodriguez et al., 2020).…”

Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

“…Pesticide utilization varied by province, however, the principal pesticides being used are constant across the area but were also connected with wheat as well as canola plants. Chavez Rodriguez et al [18] developed gene-centric frameworks of combined microbial dynamics as well as pesticide degradation relying on assessments of genetic abundance & function. Utilizing such an informative paradigm towards genetic data.…”