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

Abstract

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

Investigating Global Lipidome Alterations with the Lipid Network Explorer

“…This makes the workflow for lipid-metabolic networks fundamentally different to working with PPI or GR networks. Dedicated algorithms such as KeyPathwayMiner [ 3 , 4 ], DOMINO [ 5 ] or HotNet2 [ 6 ] perform an enrichment of deregulated genes on the whole network of possible interactions. However, with lipid species networks, the networks themselves carry information about the composition of the lipidome and its associations.…”

“…In the case of lipid metabolic networks, these characterize transformations of lipids catalyzed by enzymes. Dedicated bioinformatics tools such as KeyPathwayMiner [ 3 , 4 ], DOMINO [ 5 ] or HotNet2 [ 6 ] have been developed, which extract functionally associated network modules enriched with deregulated genes/proteins from PPI networks in a case/control setting. Such network modules can hint towards biochemical mechanisms, which connect a phenotype to its underlying molecular machinery.…”

Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil

“…Simulations were based on an existing model of P . aurescens TC1 ( 34 ) and were carried using flux balance analysis (FBA), following protocols described by Dhakar et al ( 33 ) and Ofaim et al ( 36 ). As described by Dhakar et al ( 33 ), the objective function was defined as growth through the maximization of the biomass reaction under different conditions.…”

“…In a recent series of research works, we provided pioneering evidence that genomic-based algorithms can serve in the development of biostimulation approaches of several herbicides, including atrazine ( 32 – 35 ). Atrazine is a widely used herbicide for maize, sorghum, and sugarcane as a control against broadleaf weeds ( 32 , 36 ).…”

“…Although it is an effective agrochemical in increasing crop productivity, its persistence results in pollution of soil, surface water, and groundwater ( 38 ). To enhance the rate of degradation of atrazine in contaminated soils, the combination of sequencing technologies and metabolic modeling was shown efficient for designing strategies that will optimize its degradation by indigenous soil bacteria under in vitro conditions ( 32 , 33 , 34 ). Here, we pursued metabolic modeling for promoting realistic in terra solutions by simulation-based prioritization of alternative supplements as potential biostimulants, considering a collection of indigenous bacteria.…”