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

被引:24
作者
Ofaim, Shany [1 ,2 ]
Zarecki, Raphy [1 ,3 ]
Porob, Seema [3 ]
Gat, Daniella [3 ]
Lahav, Tamar [1 ]
Kashi, Yechezkel [2 ]
Aly, Radi [1 ]
Eizenberg, Hanan [1 ]
Ronen, Zeev [3 ]
Freilich, Shiri [1 ]
机构
[1] Agr Res Org, NeweYaar Res Ctr, Ramat Yishay, Israel
[2] Technion Israel Inst Technol, Fac Biotechnol & Food Engn, Haifa, Israel
[3] Ben Gurion Univ Negev, Zuckerberg Inst Water Res, Jacob Blaustein Inst Desert Res, Dept Environm Hydrol & Microbiol, IL-8499000 Midreshet Ben Gurion, Beersheba, Israel
基金
以色列科学基金会;
关键词
GEOBACTER-SULFURREDUCENS; MICROBIAL-METABOLISM; STRATEGIES; CONTAMINANTS; DEGRADATION; POLLUTANTS; SEQUENCE; REMOVAL; BIOLOGY; NETWORK;
D O I
10.1038/s41598-020-69509-7
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:11
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