Genome-scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation.

Atrazine is an herbicide and a pollutant of great environmental concern that is naturally biodegraded by microbial communities. Paenarthrobacter aurescens TC1 is one of the most studied degraders of this herbicide. Here, we developed a genome scale metabolic model for P. aurescens TC1, iRZ1179, to study the atrazine degradation process at organism level. Constraint based flux balance analysis and time dependent simulations were used to explore the organism's phenotypic landscape. Simulations aimed at designing media optimized for supporting growth and enhancing degradation, by passing the need in strain design via genetic modifications. Growth and degradation simulations were carried with more than 100 compounds consumed by P. aurescens TC1. In vitro validation confirmed the predicted classification of different compounds as efficient, moderate or poor stimulators of growth. Simulations successfully captured previous reports on the use of glucose and phosphate as bio-stimulators of atrazine degradation, supported by in vitro validation. Model predictions can go beyond supplementing the medium with a single compound and can predict the growth outcomes for higher complexity combinations. Hence, the analysis demonstrates that the exhaustive power of the genome scale metabolic reconstruction allows capturing complexities that are beyond common biochemical expertise and knowledge and further support the importance of computational platforms for the educated design of complex media. The model presented here can potentially serve as a predictive tool towards achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation.

Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions.

Microbial communities play a vital role in biogeochemical cycles, allowing the biodegradation of a wide range of pollutants. The composition of the community and the interactions between its members affect degradation rate and determine the identity of the final products. Here, we demonstrate the application of sequencing technologies and metabolic modeling approaches towards enhancing biodegradation of atrazine-a herbicide causing environmental pollution. Treatment of agriculture soil with atrazine is shown to induce significant changes in community structure and functional performances. Genome-scale metabolic models were constructed for Arthrobacter, the atrazine degrader, and four other non-atrazine degrading species whose relative abundance in soil was changed following exposure to the herbicide. By modeling community function we show that consortia including the direct degrader and non-degrader differentially abundant species perform better than Arthrobacter alone. Simulations predict that growth/degradation enhancement is derived by metabolic exchanges between community members. Based on simulations we designed endogenous consortia optimized for enhanced degradation whose performances were validated in vitro and biostimulation strategies that were tested in pot experiments. Overall, our analysis demonstrates that understanding community function in its wider context, beyond the single direct degrader perspective, promotes the design of biostimulation strategies.

PCR detection of ansA from marine bacteria and its sequence characteristics from Bacillus tequilensis NIOS4.

As many as 71 marine bacterial DNA extracts were PCR screened for L-asparaginase (ansA), a key gene in anti-cancer molecular-searches. Over 62% (44) of them were positive for ansA gene. The positive cultures were from genera Bacillus and Staphylococcus. The ansA gene cloned from isolate NIOS4 belonging to recently described Bacillus tequilensis is 1099 bp in length with a 990 bp ORF coding for 329 amino acids. BLASTx analysis revealed this sequence to be 98% similar to earlier reported ansA sequence from B. subtilis (Accession no. NP390239.1). By comparing its deduced amino acid sequence with other bacterial asparaginase sequences six substitutions at positions 305(Thr), 313(Lys), 314(Leu), 315(Asp), 318(Arg), and 320(Gln) are observed. Key residues like Thr(12), Thr(85), Asp(86), Lys(156), and Phe(165) taking part in active-site formation and imparting catalytic properties are conserved. The phylogenetic tree based of the ansA amino acid sequences revealed close relatedness of the NIOS4 ansA sequence with B. subtilis (Accession no. NP 390239.1). It's very close genetic resemblance to B. subtilis and conservation of certain key amino acid residues suggest it as a prospective candidate for evaluation and, production of L-asparaginases.