Possibilities of using information resources In bioremediation
- Authors: Babynin E.V.1,2, Degtyareva I.A.1,3
-
Affiliations:
- Kazan Federal University
- Tatar Research Institute of Agrochemistry and Soil Science
- Kazan National Research Technological University
- Issue: Vol 11, No 3 (2021)
- Pages: 372-383
- Section: Physico-chemical biology
- URL: https://ogarev-online.ru/2227-2925/article/view/301098
- DOI: https://doi.org/10.21285/2227-2925-2021-11-3-372-383
- ID: 301098
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Abstract
Abstract: Bioremediation using microorganisms has a number of advantages over physical and chemical methods of water, soil and atmosphere purification. Microorganisms have a wide range of metabolic capabilities that enable them to convert, modify and utilize toxic pollutants for energy and biomass production. This article shows their participation in the decomposition of various industrial wastes, such as dyes, hydrocarbons, chlorinated aromatic compounds and pesticides, among others. Although the use of microorganisms is an environmentally friendly and promising way of solving environmental threats, many factors affect the effectiveness of bioremediation, such as the chemical nature of pollutants, their accessibility to microorganisms, the physical and chemical characteristics of the environment, as well as the interaction of the destructive organisms with each other. The search for new effective strains or the creation of superdestructors using genetic and protein engineering methods proves to be crucial under current circumstances. This task can be solved using such “tools” as genomics, proteomics, transcriptomics and metabolomics. These technologies require the integration of a huge amount of data, which cannot be achieved without the use of bioinformatics. Bioinformatics is used in microbial bioremediation in different ways: analysis of genome sequencing data, identification of protein-coding genes, comparative analysis to identify the function of unknown genes, automatic reconstruction and comparison of metabolic pathways, and study of protein–protein and protein–DNA interactions to understand regulatory mechanisms. This review aims to highlight various resources that store information about possible pathways of microbial metabolism involved in the biodegradation of petroleum products. The use of such information resources can become a starting point for many studies in bioremediation.
About the authors
E. V. Babynin
Kazan Federal University; Tatar Research Institute of Agrochemistry and Soil Science
Email: edward.b67@mail.ru
I. A. Degtyareva
Kazan Federal University; Kazan National Research Technological University
Email: peace-1963@mail.ru
References
- Ellis L.B.M., Roe D., Wackett L.P. Biodegradation Database: the first decade // Nucleic Acids Research. 2006. Vol. 34. P. D517–D521. https://doi.org/10.1093/nar/gkj076
- Arora P.K., Shi W. Tools of bioinformatics in biodegradation // Reviews in Environmental Science and Biotechnology. 2010. Vol. 9. P. 211–213. https: //doi.org/10.1007/s11157-010-9211-x
- Gao J., Ellis L.B.M., Wackett L.P. The university of Minnesota biocatalysis/biodegradation database: improving public access // Nucleic Acids Research. 2010. Vol. 38. P. D488-D491. https://doi.org/10.1093/nar/gkp771
- Дегтярева И.А., Яппаров И.А., Яппаров А.Х., Ежкова А.М., Давлетшина А.Я., Шайдуллина И.А. Создание и применение биоудобрения на основе эффективного консорциума микроорганизмов-деструкторов углеводородов для рекультивации нефтезагрязненных почв Республики Татарстан // Нефтяное хозяйство. 2017. N 5. С.100–103. https://doi.org/10.24887/0028-2448-2017-5-100-103
- Costa A.S., Romão L.P.C., Araújo B.R., Lucas S.C.O., Maciel S.T.A., Wisniewski A. Jr., et al. Environmental strategies to remove volatile aromatic fractions (BTEX) from petroleum industry wastewater using biomass // Bioresource Technology. 2012. Vol. 105. P. 31–39. https://doi.org/10.1016/j.biortech.2011.11.096
- Chandra S., Sharma R., Singh K., Sharma A. Application of bioremediation technology in the environment contaminated with petroleum hydrocarbon // Annals of Microbiology. 2013. Vol. 63. Issue 2. P. 417–431. https://doi.org/10.1007/s13213-012-0543-3
- Souza E.C., Vessoni-Penna T.C., de Souza Oliveira R.P. Biosurfactant-enhanced hydrocarbon bioremediation: an overview // International Biodeterioration & Biodegradation. 2014. Vol. 89. P. 88–94. https://doi.org/10.1016/j.ibiod.2014.01.007
- Шайдуллина И.А., Яппаров А.Х., Дегтярева И.А., Латыпова В.З., Гадиева Э.Ш. Рекультивация нефтезагрязненных почв на примере выщелоченных черноземов Татарстана // Нефтяное хозяйство. 2015. N 3. С. 102–105.
- Дегтярева И.А., Бабынин Э.В., Мотина Т.Ю., Султанов М.И. Полногеномное секвенирование штамма Staphylococcus warneri, изолированного из загрязненной нефтью почвы // Известия вузов. Прикладная химия и биотехнология, 2020. Т. 10. N 1. С. 48–55.
- Abatenh E., Gizaw B., Tsegaye Z., Wassie M. The role of microorganisms in bioremediation // Open Journal of Environmental Biology. 2017. Vol. 1. Issue 1. P. 038–046. https://doi.org/10.17352/ojeb.000007
- Bhandari S., Poudel D.K., Marahatha R., Dawadi S., Khadayat K., Phuyal S., et al. Microbial enzymes used in bioremediation // Journal of Chemistry. 2021. Vol. 2021. Issue 4. Article ID 8849512. 17 p. https://doi.org/10.1155/2021/8849512
- Abou Seeda M.A., Yassen A.A., Abou El-Nour E.Z.A.A. Microorganism as a tool of bioremediation technology for cleaning waste and industrial water // Bioscience Research. 2017. Vol. 14. Issue 3. P. 633–644.
- Dave S., Das J. Role of microbial enzymes for biodegradation and bioremediation of environmental pollutants: challenges and future prospects. In: Bioremediation for Environmental Sustainability. Saxena G., Kumar V., Shah M.P. (eds.) Elsevier, 2021. P. 325–346. https://doi.org/10.1016/B978-0-12-820524-2.00013-4
- Singh P., Jain R., Srivastava N., Borthakur A., Pal D.B., Singh R., et al. Current and emerging trends in bioremediation of petrochemical waste: a review // Critical Reviews in Environmental Science and Technology. 2017. Vol. 47. Issue 3. P. 155–201. https://doi.org/10.1080/10643389.2017.1318616
- Ghaly A.E., Yusran A., Dave D. Effects of biostimulation and bioaugmentation on the degradation of pyrene in soil // Journal of Bioremediation & Biodegradation. 2013. S7:005. 13 p. https://doi.org/10.4172/2155-6199.S7-005
- Koshlaf E., Ball A.S. Soil bioremediation approaches for petroleum hydrocarbon polluted environments // AIMS Microbiology. 2017. Vol. 3. Issue 1. P. 25–49. https://doi.org/10.3934/microbiol.2017.1.25
- Dvořák P., Nikel P.I., Damborský J., de Lorenzo V. Bioremediation 3.0: engineering pollutant-removing bacteria in the times of systemic biology // Biotechnology Advances. 2017. Vol. 35. Issue 7. P. 845–866. https://doi.org/10.1016/j.biotechadv.2017.08.001
- Chandran H., Meena M., Sharma K. Microbial biodiversity and bioremediation assessment through omics approaches // Frontiers Environmental Chemistry. 2020. Vol. 1. P. 570326. https://doi.org/10.3389/fenvc.2020.570326
- Jesmok E.M., Hopkins J.M., Foran D.R. Next-generation sequencing of the bacterial 16S rRNA gene for forensic soil comparison: a feasibility study // Journal Forensic Sciences. 2016. Vol. 61. Issue 3. P. 607–617. https://doi.org/10.1111/1556-4029.13049
- Rahmeh R., Akbar A., Kumar V., Al-Mansour H., Kishk M., Ahmed N., et al. Insights into bacterial community involved in bioremediation of aged oilcontaminated soil in arid environment // Evolutionary Bioinformatics Online. 2021. Vol. 17. 13 p. https://doi.org/10.1177/11769343211016887
- Misra B.B., Langefeld C.D., Olivier M., Cox L.A. Integrated omics: tools, advances, and future approaches // Journal of Molecular Endocrinology. 2018. Vo. 62. Issue 1. P. R21–R45. https://doi.org/10.1530/JME-18-0055
- Pandey A., Tripathi P.H., Tripathi A.H., Pandey S.C., Gangola S. Omics technology to study bioremediation and respective enzymes. In: Smart bioremediation technologies. Microbial enzymes. Bhatt P. (ed.). New Delhi: Academic Press, 2019. P. 23–43. https://doi.org/10.1016/B978-0-12-818307-6.00002-0
- Singh A.K., Bilal M., Iqbal H.M.N., Raj A. Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: recent progress and future outlook // Science of The Total Environment. 2021. Vol. 770. P. 144561. https://doi.org/10.1016/j.scitotenv.2020.144561
- Goh H.-H. Integrative multi-omics through bioinformatics. // Advances in Experimental Medicine and Biology. 2018. Vol. 1102. P. 69–80. https:// doi.org/10.1007/978-3-319-98758-3_5
- Ejigu G.F., Jung J. Review on the computational genome annotation of sequences obtained by nextgeneration sequencing // Biology. 2020. Vol. 9. Issue 9. P. 295. https://doi.org/10.3390/biology9090295
- Zhang P., Berardini T.Z., Ebert D., Li Q., Mi H., Muruganujan A., et al. PhyloGenes: An online phylogenetics and functional genomics resource for plant gene function inference // Plant Direct. 2020. Vol. 4. Issue 12. P. e00293. https://doi.org/10.1002/pld3.293
- Tong H., Phan N.V.T., Nguyen T.T., Nguyen D.V., Vo N.S., Le L. Review on databases and bioinformatic approaches on pharmacogenomics of adverse drug reactions // Pharmacogenomics and Personalized Medicine. 2021. Vol. 14. P. 61–75. https://doi.org/10.2147/PGPM.S290781
- Caspi R., Altman T., Billington R., Dreher K., Foerster H., Fulcher C.A., et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases // Nucleic Acids Research. 2012. Vol. 42 (Database issue). P. D459–D471. https://doi.org/10.1093/nar/gkt1103
- Mohan C.G., Gandhi T., Garg D., Shinde R. Computer-assisted methods in chemical toxicity prediction // Mini-Reviews in Medicinal Chemistry. 2007. Vol. 7. Issue 5. P. 499–507. https://doi.org/10.2174/138955707780619554
- Chou C.H., Chang W.C., Chiu С.С., Huang С.С., Huang H.D. FMM: a web server for metabolic pathway reconstruction and comparative analysis // Nucleic Acids Research. 2009. Vol. 37. P. W129–W134. https://doi.org/10.1093/nar/gkp264
- Finley S.D., Broadbelt L.J., Hatzimanikatis V. Computational framework for predictive biodegradation // Biotechnology and Bioengineering. 2009. Vol. 104. Issue 6. P. 1086–1097. https://doi.org/10.1002/bit.22489
- Moriya Y., Shigemizu D., Hattori M., Tokimatsu T., Kotera M., Goto S., et al. PathPred: an enzyme-catalyzed metabolic pathway prediction server // Nucleic Acids Research. 2010. Vol. 38. P.W138–W143. https://doi.org/10.1093/nar/gkq318
- Gao J., Ellis L.B.M., Wackett L.P. The University of Minnesota pathway prediction system: multi-level prediction and visualization // Nucleic Acids Research. 2011. Vol. 39. (Web Server issue). P. W406–W411. https://doi.org/10.1093/nar/gkr200
- Kotera M., Goto S. Metabolic pathway reconstruction strategies for central metabolism and natural product biosynthesis // Biophysics & Physicobiology. 2016. Vol. 13. P. 195–205. https://doi.org/10.2142/biophysico.13.0_195
- Shah H.A., Liu J., Yang Z., Feng J. Review of machine learning methods for the prediction and reconstruction of metabolic pathways // Frontiers in Molecular Biosciences. 2021. Vol. 8. P. 634141. https://doi.org/10.3389/fmolb.2021.634141
- Wang L., Dash S., Ng C.Y., Maranas C.D. A review of computational tools for design and reconstruction of metabolic pathways // Synthetic and Systems Biotechnology. 2017. Vol. 2. Issue 4. P. 243–252. https://doi.org/10.1016/j.synbio.2017.11.002
- Wackett L.P. The Metabolic Pathways of Biodegradation. In: The prokaryotes. Applied Bacteriology and Biotechnology. 4th edition. Rosenberg E. (editor-in-chief); DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds.). Springer, Berlin, Heidelberg. 2013. P. 383–393. https://doi.org/10.1007/978-3-642-31331-8_76
- Dombrowski N., Donaho J.A., Gutierrez T., Seitz K.W., Teske A.P., Baker B.J. Reconstructing metabolic pathways of hydrocarbon-degrading bacteria from the Deepwater Horizon oil spill // Nature Microbiology. 2016. Vol. 1. Issue 7. Article number 16057. https://doi.org/10.1038/nmicrobiol.2016.57
- Jaiswal S., Shukla P. Alternative strategies for microbial remediation of pollutants via synthetic biology // Frontiers in Microbiology. 2020. Vol. 11. P. 808. https://doi.org/10.3389/fmicb.2020.00808
- Henry C.S., DeJongh M., Best A.A., Frybarger P.M., Linsay B., Steven R.L. Highthroughput generation, optimization and analysis of genome-scale metabolic models // Nature Biotechnology. 2010. Vol. 28. P. 977–982. https://doi.org/10.1038/nbt.1672
- Kanehisa M., Furumichi M., Tanabe M., Sato Y., Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs // Nucleic Acids Research. 2017. Vol. 45. Issue D1. P. D353–D361. https://doi.org/10.1093/nar/gkw1092
- Caspi R., Billington R., Ferrer L., Foerster H., Fulcher C.A., Keseler I.M., et al.The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases // Nucleic Acids Research. 2016. Vol. 44. Issue D1. P. D471–D480. https://doi.org/10.1093/nar/gkv1164
- Rentzsch R., Orengo C.A. Protein function prediction – the power of multiplicity // Trends in Biotechnology. 2009. Vol. 27. Issue 4. P. 210–219. https://doi.org/10.1016/j.tibtech.2009.01.002
- Calderón-González K.G., Hernández-Monge J., Herrera-Aguirre M.E., Luna-Arias J.P. Bioinformatics tools for proteomics data interpretation // Advances in Experimental Medicine and Biology. 2016. Vol. 919. P. 3281–341. https://doi.org/10.1007/978-3-319-41448-5_16
- Oliveira J.S., Araújo W., Lopes Sales A.I., de Brito Guerra A., da Silva Araújo S.C., de Vasconcelos A.T.R., et al. BioSurfDB: knowledge and algorithms to support biosurfactants and biodegradation studies. // Database. The Journal of Biology Databases and Curation. 2015. Vol. 2015. bav 033. https://doi.org/10.1093/database/bav033
- Medema M.H., van Raaphorst R., Takano E., Breitling R. Computational tools for the synthetic design of biochemical pathways R // Nature Reviews Microbiology. 2012. Vol. 10. Issue 3. P. 191–202. https://doi.org/10.1038/nrmicro2717
- Hadadi N., Hatzimanikatis V. Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways // Current Opinion in Chemical Biology. 2015. Vol. 28. P. 99–104. https://doi.org/10.1016/j.cbpa.2015.06.025
- Langowski J., Long A. Computer systems for the prediction of xenobiotic metabolism // Advanced Drug Delivery Reviews. 2002. Vol. 54. Issue 3. P. 407–415. https://doi.org/10.1016/s0169-409x(02)00011-x
- Wicker J., Lorsbach T., Gütlein M., Schmid E., Latino D., Kramer S., et al. EnviPath – the environmental contaminant biotransformation pathway resource // Nucleic Acids Research. 2016. Vol. 44. Issue D1. P. D502–D508. https://doi.org/10.1093/nar/gkv1229
- Pazos F., Guijas D., Valencia A., de Lorenzo V. MetaRouter: bioinformatics for bioremediation // Nucleic Acids Research. 2005. Vol. 33. P. D588–D592. https://doi.org/10.1093/nar/gki068
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