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In Silico Approach to Analyze the Biochemical Pathways of Bacterial Metabolite Synthesis

  • Tania
  • Mehendi Goyal
  • Manoj BaranwalEmail author
Chapter

Abstract

Plant growth-promoting bacteria are well known to produce various bacterial secondary metabolites (BSM) which are much diversified in their origin and structure. These metabolites provide beneficial effect to crops which lead to sustainable agriculture. Each BSM is produced inside the bacterial cell by the regulation of specific biochemical pathways. To increase the yield of crops, there is a need to understand the metabolic pathway for the synthesis of secondary metabolites. Experimental approach to study the metabolic pathway was found to be limiting in terms of time and money. Development in the genome sequencing technologies and computational methods shifts the focus of researchers toward in silico approaches to reconstruct the biochemical pathways of metabolite production in genome-scale metabolic level. There are varieties of computational databases and tools available for the analysis of biosynthetic gene clusters including the prediction of non-ribosomal peptide synthetases (NRPS) and polyketide synthase (PKS) enzymes which encode for bacterial metabolites. These tools are developed to analyze the biochemical pathways for metabolite production. The present chapter gives a comprehensive overview on the in silico approach for the reconstruction of biochemical pathway and different databases and computational tools associated with it.

Keywords

Biochemical pathways Bacterial secondary metabolites Biosynthetic gene clusters Non-ribosomal peptide synthetases Polyketide synthases Genome mining 

References

  1. Bachmann BO, Van Lanen SG, Baltz RH (2014) Microbial genome mining for accelerated natural products discovery: is a renaissance in the making? J Ind Microbiol Biotechnol 41(2):175–184CrossRefPubMedGoogle Scholar
  2. Barea J, Navarro E, Montoya E (1976) Production of plant growth regulators by rhizosphere phosphate-solubilizing bacteria. J Appl Microbiol 40(2):129–134Google Scholar
  3. Bashan Y, De-Bashan L (2005) Plant growth-promoting. Encycl Soils Environ 1:103–115CrossRefGoogle Scholar
  4. Bibb MJ (2005) Regulation of secondary metabolism in streptomycetes. Curr Opin Microbiol 8(2):208–215CrossRefPubMedGoogle Scholar
  5. Bode HB (2009) Entomopathogenic bacteria as a source of secondary metabolites. Curr Opin Chem Biol 13(2):224–230CrossRefPubMedGoogle Scholar
  6. Bottini R, Cassán F, Piccoli P (2004) Gibberellin production by bacteria and its involvement in plant growth promotion and yield increase. Appl Microbiol Biotechnol 65(5):497–503CrossRefPubMedGoogle Scholar
  7. Brader G, Compant S, Mitter B, Trognitz F, Sessitsch A (2014) Metabolic potential of endophytic bacteria. Curr Opin Biotechnol 27:30–37CrossRefPubMedPubMedCentralGoogle Scholar
  8. Chaudhary AK, Dhakal D, Sohng JK (2013) An insight into the “-omics” based engineering of streptomycetes for secondary metabolite overproduction. BioMed Res Int 2013Google Scholar
  9. Chen G, Li X, Waters B, Davies J (2000) Enhanced production of microbial metabolites in the presence of dimethyl sulfoxide. J Antibiot 53(10):1145–1153CrossRefPubMedGoogle Scholar
  10. Cimermancic P, Medema MH, Claesen J, Kurita K, Brown LCW, Mavrommatis K, Pati A, Godfrey PA, Koehrsen M, Clardy J (2014) Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters. Cell 158(2):412–421CrossRefPubMedPubMedCentralGoogle Scholar
  11. Compant S, Duffy B, Nowak J, Clément C, Barka EA (2005) Use of plant growth-promoting bacteria for biocontrol of plant diseases: principles, mechanisms of action, and future prospects. Appl Environ Microbiol 71(9):4951–4959CrossRefPubMedPubMedCentralGoogle Scholar
  12. Conde M, do Rosario P, Sauter T, Pfau T (2016) Constraint based modeling going multicellular. Front Mol Biosci 3:3Google Scholar
  13. Conway KR, Boddy CN (2012) Clustermine360: a database of microbial pks/nrps biosynthesis. Nucleic Acids Res 41(D1):D402–D407CrossRefPubMedPubMedCentralGoogle Scholar
  14. Covert MW, Schilling CH, Famili I, Edwards JS, Goryanin II, Selkov E, Palsson BO (2001) Metabolic modeling of microbial strains in silico. Trends Biochem Sci 26(3):179–186CrossRefPubMedGoogle Scholar
  15. Diminic J, Zucko J, Ruzic IT, Gacesa R, Hranueli D, Long PF, Cullum J, Starcevic A (2013) Databases of the thiotemplate modular systems (csdb) and their in silico recombinants (r-csdb). J Ind Microbiol Biotechnol 40(6):653–659CrossRefPubMedGoogle Scholar
  16. Durot M, Bourguignon P-Y, Schachter V (2008) Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol Rev 33(1):164–190CrossRefPubMedPubMedCentralGoogle Scholar
  17. Edwards JS, Palsson BO (2000) Metabolic flux balance analysis and the in silico analysis of Escherichia coli k-12 gene deletions. BMC Bioinforma 1(1):1CrossRefGoogle Scholar
  18. Edwards JS, Covert M, Palsson B (2002) Metabolic modelling of microbes: the flux-balance approach. Environ Microbiol 4(3):133–140CrossRefPubMedGoogle Scholar
  19. Franco-Correa M, Chavarro-Anzola V (2016) Actinobacteria as plant growth-promoting rhizobacteria. In: Actinobacteria-basics and biotechnological applications. InTechGoogle Scholar
  20. Gamalero E, Glick BR (2015) Bacterial modulation of plant ethylene levels. Plant Physiol 169(1):13–22CrossRefPubMedPubMedCentralGoogle Scholar
  21. Gaspar T, Kevers C, Penel C, Greppin H, Reid DM, Thorpe TA (1996) Plant hormones and plant growth regulators in plant tissue culture. In Vitro Cell Dev Biol-Plant 32(4):272–289CrossRefGoogle Scholar
  22. Glick BR (2014) Bacteria with acc deaminase can promote plant growth and help to feed the world. Microbiol Res 169(1):30–39CrossRefGoogle Scholar
  23. Gupta G, Parihar SS, Ahirwar NK, Snehi SK, Singh V (2015) Plant growth promoting rhizobacteria (pgpr): current and future prospects for development of sustainable agriculture. J Microb Biochem Technol 7(2):096–102Google Scholar
  24. Haggart CR, Bartell JA, Saucerman JJ, Papin JA (2011) Whole-genome metabolic network reconstruction and constraint-based modeling⋆. In: Methods in enzymology. Elsevier, Amsterdam, pp 411–433Google Scholar
  25. Hao Y, Charles TC, Glick BR (2007) Acc deaminase from plant growth-promoting bacteria affects crown gall development. Can J Microbiol 53(12):1291–1299CrossRefGoogle Scholar
  26. van Heel AJ, de Jong A, Montalban-Lopez M, Kok J, Kuipers OP (2013) Bagel3: automated identification of genes encoding bacteriocins and (non-) bactericidal posttranslationally modified peptides. Nucleic Acids Res 41(W1):W448–W453CrossRefPubMedPubMedCentralGoogle Scholar
  27. Hwang K-S, Kim HU, Charusanti P, Palsson BØ, Lee SY (2014) Systems biology and biotechnology of streptomyces species for the production of secondary metabolites. Biotechnol Adv 32(2):255–268CrossRefPubMedGoogle Scholar
  28. Ichikawa N, Sasagawa M, Yamamoto M, Komaki H, Yoshida Y, Yamazaki S, Fujita N (2012) Dobiscuit: a database of secondary metabolite biosynthetic gene clusters. Nucleic Acids Res 41(D1):D408–D414CrossRefPubMedPubMedCentralGoogle Scholar
  29. Jasim B, Joseph AA, John CJ, Mathew J, Radhakrishnan E (2014) Isolation and characterization of plant growth promoting endophytic bacteria from the rhizome of Zingiber officinale. 3. Biotech 4(2):197–204Google Scholar
  30. de Jesus Sousa JA, Olivares FL (2016) Plant growth promotion by streptomycetes: ecophysiology, mechanisms and applications. Chem Biolo Technol Agric 3(1):24CrossRefGoogle Scholar
  31. Kim HU, Charusanti P, Lee SY, Weber T (2016) Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites. Nat Prod Rep 33(8):933–941CrossRefPubMedGoogle Scholar
  32. Kudoyarova G, Arkhipova T, Melent’ev A (2015) Role of bacterial phytohormones in plant growth regulation and their development. In: Bacterial metabolites in sustainable agroecosystem. Springer, Cham, pp 69–86CrossRefGoogle Scholar
  33. Kumar RR, Prasad S (2011) Metabolic engineering of bacteria. Indian J Microbiol 51(3):403–409CrossRefPubMedPubMedCentralGoogle Scholar
  34. Li MH, Ung PM, Zajkowski J, Garneau-Tsodikova S, Sherman DH (2009) Automated genome mining for natural products. BMC Bioinforma 10(1):185CrossRefGoogle Scholar
  35. Little PF, Subramaniam S, Jorde LB, Dunn MJ (2005) Encyclopedia of genetics, genomics, proteomics and bioinformatics. Wiley, HobokenGoogle Scholar
  36. Liu M, Bienfait B, Sacher O, Gasteiger J, Siezen RJ, Nauta A, Geurts JM (2014) Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach “reverse pathway engineering”. PLoS One 9(1):e84769CrossRefPubMedPubMedCentralGoogle Scholar
  37. Llaneras F, Picó J (2008) Stoichiometric modelling of cell metabolism. J Biosci Bioeng 105(1):1–11CrossRefPubMedGoogle Scholar
  38. Mahadevan R, Edwards JS, Doyle FJ III (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83(3):1331–1340CrossRefPubMedPubMedCentralGoogle Scholar
  39. Matiru VN, Dakora FD (2004) Potential use of rhizobial bacteria as promoters of plant growth for increased yield in landraces of African cereal crops. Afr J Biotechnol 3(1):1–7CrossRefGoogle Scholar
  40. Niu G (2017) Genomics-driven natural product discovery in actinomycetes. Trends Biotechnol 36(3):238–241CrossRefPubMedGoogle Scholar
  41. Odoh CK (2017) Plant growth promoting rhizobacteria (pgpr): a bioprotectant bioinoculant for sustainable agrobiology. Rev Int J Adv Res Biol Sci 4(5):123–142Google Scholar
  42. van Pée K-H (1996) Biosynthesis of halogenated metabolites by bacteria. Ann Revin Microbiol 50(1):375–399CrossRefGoogle Scholar
  43. Price ND, Papin JA, Schilling CH, Palsson BO (2003) Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol 21(4):162–169CrossRefPubMedGoogle Scholar
  44. Price ND, Reed JL, Palsson BØ (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2(11):886CrossRefPubMedGoogle Scholar
  45. Radhakrishnan R, Hashem A, Abd_Allah EF (2017) Bacillus: a biological tool for crop improvement through bio-molecular changes in adverse environments. Front Physiol 8:667CrossRefPubMedPubMedCentralGoogle Scholar
  46. Schippers B, Bakker AW, Bakker PA (1987) Interactions of deleterious and beneficial rhizosphere microorganisms and the effect of cropping practices. Annu Rev Phytopathol 25(1):339–358CrossRefGoogle Scholar
  47. Schuster S, Dandekar T, Fell DA (1999) Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol 17(2):53–60CrossRefPubMedGoogle Scholar
  48. Seaver S, Bradbury LM, Frelin O, Zarecki R, Ruppin E, Hanson AD, Henry CS (2015) Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm. Front Plant Sci 6:142CrossRefPubMedPubMedCentralGoogle Scholar
  49. Singh RP, Shelke GM, Kumar A, Jha PN (2015) Corrigendum: biochemistry and genetics of acc deaminase: a weapon to “stress ethylene” produced in plants. Front Microbiol 6:937PubMedPubMedCentralGoogle Scholar
  50. Souza Rd, Ambrosini A, Passaglia LM (2015) Plant growth-promoting bacteria as inoculants in agricultural soils. Genet Mol Biol 38(4):401–419CrossRefPubMedPubMedCentralGoogle Scholar
  51. Spaepen S, Vanderleyden J (2011) Auxin and plant-microbe interactions. Cold Spring Harb Perspect Biol 3(4):a001438CrossRefPubMedPubMedCentralGoogle Scholar
  52. Stephanopoulos G, Vallino JJ (1991) Network rigidity and metabolic engineering in metabolite overproduction. Science 252(5013):1675–1681CrossRefPubMedGoogle Scholar
  53. Starcevic A, Zucko J, Simunkovic J, Long PF, Cullum J, Hranueli D (2008) Clustscan: an integrated program package for the semi-automatic annotation of modular biosynthetic gene clusters and in silico prediction of novel chemical structures. Nucleic Acids Res 36(21):6882–6892CrossRefPubMedPubMedCentralGoogle Scholar
  54. Stulberg ER, Lozano GL, Morin JB, Park H, Baraban EG, Mlot C, Heffelfinger C, Phillips GM, Rush JS, Phillips AJ (2016) Genomic and secondary metabolite analyses of streptomyces sp. 2aw provide insight into the evolution of the cycloheximide pathway. Front Microbiol 7:573CrossRefPubMedPubMedCentralGoogle Scholar
  55. Taffs R, Aston JE, Brileya K, Jay Z, Klatt CG, McGlynn S, Mallette N, Montross S, Gerlach R, Inskeep WP (2009) In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study. BMC Syst Biol 3(1):114CrossRefPubMedPubMedCentralGoogle Scholar
  56. Tak HI, Ahmad F, Babalola OO (2013) Advances in the application of plant growth-promoting rhizobacteria in phytoremediation of heavy metals. In: Reviews of environmental contamination and toxicology, vol 223. Springer, New York, pp 33–52Google Scholar
  57. Tatsis EC, O’Connor SE (2016) New developments in engineering plant metabolic pathways. Curr Opin Biotechnol 42:126–132CrossRefPubMedGoogle Scholar
  58. Tounekti T, Hernández I, Munné-Bosch S (2013) Salicylic acid biosynthesis and role in modulating terpenoid and flavonoid metabolism in plant responses to abiotic stress. In: Salicylic acid. Springer, Dordrecht, pp 141–162CrossRefGoogle Scholar
  59. Vessey JK (2003) Plant growth promoting rhizobacteria as biofertilizers. Plant Soil 255(2):571–586CrossRefGoogle Scholar
  60. Wang Y, Eddy JA, Price ND (2012) Reconstruction of genome-scale metabolic models for 126 human tissues using mcadre. BMC Syst Biol 6(1):153CrossRefPubMedPubMedCentralGoogle Scholar
  61. Weaver DS, Keseler IM, Mackie A, Paulsen IT, Karp PD (2014) A genome-scale metabolic flux model of Escherichia coli k–12 derived from the ecocyc database. BMC Syst Biol 8(1):79CrossRefPubMedPubMedCentralGoogle Scholar
  62. Weber T (2014) In silico tools for the analysis of antibiotic biosynthetic pathways. Int J Med Microbiol 304(3–4):230–235CrossRefPubMedGoogle Scholar
  63. Weber T, Rausch C, Lopez P, Hoof I, Gaykova V, Huson D, Wohlleben W (2009) Clusean: a computer-based framework for the automated analysis of bacterial secondary metabolite biosynthetic gene clusters. J Biotechnol 140(1–2):13–17CrossRefPubMedGoogle Scholar
  64. Weber T, Blin K, Duddela S, Krug D, Kim HU, Bruccoleri R, Lee SY, Fischbach MA, Müller R, Wohlleben W (2015) Antismash 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters. Nucleic Acids Res 43(W1):W237–W243CrossRefPubMedPubMedCentralGoogle Scholar
  65. Yadav G, Gokhale RS, Mohanty D (2003) Searchpks: a program for detection and analysis of polyketide synthase domains. Nucleic Acids Res 31(13):3654–3658CrossRefPubMedPubMedCentralGoogle Scholar
  66. Ziemert N, Alanjary M, Weber T (2016) The evolution of genome mining in microbes–a review. Nat Prod Rep 33(8):988–1005CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of BiotechnologyThapar Institute of Engineering and TechnologyPatialaIndia

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