Genome-Scale reconstruction ofPaenarthrobacter aurescensTC1 metabolic model towards the study of atrazine bioremediation

Abstract

Atrazine is an herbicide and pollutant of great environmental concern that is naturally biodegraded by microbial communities. The efficiency of biodegradation can be improved through the stimulating addition of fertilizers, electron acceptors, etc. In recent years, metabolic modelling approaches have become widely used as an in silico tool for organism-level phenotyping and the subsequent development of metabolic engineering strategies including biodegradation improvement. Here, we constructed a genome scale metabolic model, iRZ960, for Paenarthrobacter aurescens TC1 – a widely studied atrazine degrader - aiming at simulating its degradation activity. A mathematical stoichiometric metabolic model was constructed based on a published genome sequence of P. aurescens TC1. An Initial draft model was automatically constructed using the RAST and KBase servers. The draft was developed into a predictive model through semi-automatic gap-filling procedures including manual curation. In addition to growth predictions under different conditions, model simulations were used to identify optimized media for enhancing the natural degradation of atrazine without a need in strain design via genetic modifications. Model predictions for growth and atrazine degradation efficiency were tested in myriad of media supplemented with different combinations of carbon and nitrogen sources that were verified in vitro. Experimental validations support the reliability of the model’s predictions for both bacterial growth (biomass accumulation) and atrazine degradation. Predictive tools, such as the presented model, can be applied for achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation in changing environments.

Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling

“…First, further adaptations are required for the current version of iRZ1425 to meet the requirements of gold standard models (Thiele and Palsson, 2010). Mainly, the biomass composition is an approximation that relies on automatic protocols and genomic information (Oh et al, 2007;Henry et al, 2010), similar to other recent works (Bordel et al, 2019;Naizabekov and Lee, 2020;Ofaim et al, 2020). The construction of a biomass reaction specific to the SRS 16 reaction requires layers of experimental data that are currently missing, including the precursors of general biomass constructions (for example, proteins, lipids, and species-specific components).…”

“…The simulations pointed at a contribution of non-degrader species to the in situ process achieved thorough trophic exchanges with the degrader (Faust, 2019). The effect of specific metabolites on the degradation rate of atrazine by Paenarthrobacter aurescens TC1 was predicted by a genome-scale model and was further validated in vitro (Ofaim et al, 2020). The predictive biology facilitated the identification of optimal conditions for degradation that can promote the development of bioaugmentation or biostimulation approaches for better controlling the degradation processes.…”

“…As a first step, a draft model of Variovorax sp. SRS 16 was reconstructed by following a bottom-up approach as elaborated by Ofaim et al (2020). Briefly, the whole-genome sequence of SRS 16 was retrieved from the genome database of NCBI and annotated using RAST (Overbeek et al, 2014).…”

“…To this end, we simulated the behavior of our metabolic model across time. The simulation process follows (Xu et al, 2019;Ofaim et al, 2020) and is illustrated in Supplementary File 5. Briefly, the model works under the following assumptions: (1) a finite start amount of media components is available; (2) a maximal amount of uptake a single cell can acquire from the media in a given time point is defined (the lower bound of the exchange reaction value); (3) new substrate concentrations in each time point are determined by the predicted substrate concentration for the previous step augmented with any additional substrates provided or consumed in the current iteration.…”

Alternative Strategies for Microbial Remediation of Pollutants via Synthetic Biology

“…They utilized an ordinary differential equations (ODE)-based model and agent-based simulation on a consortium (Hawley et al, 2014;Atashgahi et al, 2018) of interacting species population for increasing the efficacy of the proposed feedback control strategy. The application of bioinformatics (Arora and Bae, 2014) resources is a prerequisite dimension for obtaining the data to begin the microbial bioremediation studies of recalcitrant compounds (Gong et al, 2012;Ofaim et al, 2019). This involves the information related to the degradation of xenobiotics by microbes and their pathways for dissimilation (Dao et al, 2019;Salam and Ishaq, 2019;Thelusmond et al, 2019;Wei et al, 2019).…”