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An in-silico Approach for Enhancing the Lipid Productivity in Microalgae by Manipulating the Fatty Acid Biosynthesis

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 816))

Abstract

To fulfill the impetus of demands on alternative energy, microalgal biofuels have attracted significant attention due to the ease of cultivation, higher photosynthetic rate, as well as, the presence of significant quantity of lipids. However, from an energy perspective, the polyunsaturated fatty acids (PUFA) (substrate for transesterification to biodiesel) constitute only 10–20% of the total lipids. Approaches for increasing lipids include coercing the algal cells under nutrient depletion which also declines their growth rate. Improving the lipid accumulation without compromising growth requires strain modification via genomic or metabolic engineering which necessitates the core understanding of the critical regulators of denovo lipid biogenesis. Increase in activity of the enzyme acetyl-CoA carboxylase (ACCase) has been postulated to improve the lipid synthesis. Thus, the current study utilized the Chlamydomonas reinhardtii as the model organism for understanding the lipid metabolism. In-silico computational approach was used to design the 3D structure of ACCase, the key enzyme that catalyzes the rate-limiting step of lipid synthesis. The accuracy of the predicted structure was validated by the presence of 94% of amino acid residues in the favorable region of Ramachandran plot. The docking studies with four selected ligands (ACP, AMP, Biotin, and Glycine) showed biotin as the suitable ligand with a lowest binding affinity (−5.5 kcal/mol). The ligand–protein complex is expected to increase the enzyme activity driving lipid accumulation in vivo. Such in-silico studies are essential to design and decipher the role of different regulatory enzymes in improving the quantity and quality of microalgal biodiesel.

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References

  1. Rangabhashiyam, S., Behera, B., Aly, N., Balasubramanian, P.: Biodiesel from microalgae as a promising strategy for renewable bioenergy production—a review. J Environ. Biotechnol. Res. 6(4), 260–269 (2017)

    Google Scholar 

  2. Maity, S.K.: Opportunities, recent trends and challenges of integrated biorefinery: Part I. Renew. Sust. Energ. Rev. 43, 1427–1445 (2015)

    Article  Google Scholar 

  3. Chisti, Y.: Biodiesel from microalgae. Biotechnol. Adv. 25(3), 294–306 (2007)

    Article  Google Scholar 

  4. Yu, W.L., Ansari, W., Schoepp, N.G., Hannon, M.J., Mayfield, S.P., Burkart, M.D.: Modifications of the metabolic pathways of lipid and triacylglycerol production in microalgae. Microb. Cell Fact. 10(1), 1–11, 91 (2011)

    Article  Google Scholar 

  5. Bellou, S., Baeshen, M.N., Elazzazy, A.M., Aggeli, D., Sayegh, F., Aggelis, G.: Microalgal lipids biochemistry and biotechnological perspectives. Biotechnol. Adv. 32(8), 1476–1493 (2014)

    Article  Google Scholar 

  6. Banerjee, C., Dubey, K.K., Shukla, P.: Metabolic engineering of microalgal based biofuel production: prospects and challenges. Front Microbiol. 7, 1–8 (2016)

    Google Scholar 

  7. Tan, K.W.M., Lee, Y.K.: The dilemma for lipid productivity in green microalgae: importance of substrate provision in improving oil yield without sacrificing growth. Biotechnol. Biofuels 9(1), 255 (2016)

    Article  Google Scholar 

  8. Reijnders, M.J., van Heck, R.G., Lam, C.M., Scaife, M.A., dos Santos, V.A.M., Smith, A.G., Schaap, P.J.: Green genes: bioinformatics and systems-biology innovations drive algal biotechnology. Trends Biotechnol. 32(12), 617–626 (2014)

    Article  Google Scholar 

  9. Misra, N., Panda, P.K., Parida, B.K.: Agrigenomics for microalgal biofuel production: an overview of various bioinformatics resources and recent studies to link OMICS to bioenergy and bioeconomy. OMICS 17(11), 537–549 (2013)

    Article  Google Scholar 

  10. Ramakrishnan, G.S., Kamath, M.M., Niranjan, V.: Increasing Microbial Biofuel Production by In-silico Comparative Genomic Studies. Int. J. Biosci. Biochem. Bioinform. 4(5), 386–390 (2014)

    Google Scholar 

  11. Kumar, R., Biswas, K., Singh, P.K., Singh, P.K., Elumalai, S., Shukla, P., Pabbi, S.: Lipid production and molecular dynamics simulation for regulation of acc D gene in cyanobacteria under different N and P regimes. Biotechnol. Biofuels 10(1), 1–14, 94 (2017)

    Google Scholar 

  12. Bao, X., Ohlrogge, J.: Supply of fatty acid is one limiting factor in the accumulation of triacylglycerol in developing embryos. Plant Physiol. 120(4), 1057–1062 (1999)

    Article  Google Scholar 

  13. Roesler, K., Shintani, D., Savage, L., Boddupalli, S., Ohlrogge, J.: Targeting of the Arabidopsis homomeric acetyl-coenzyme a carboxylase to plastids of rapeseeds. Plant Physiol. 113(1), 75–81 (1997)

    Article  Google Scholar 

  14. Merchant, S.S., Prochnik, S.E., Vallon, O., Harris, E.H., Karpowicz, S.J., Witman, G.B., Terry, A., Salamov, A., Fritz-Laylin, L.K., Maréchal-Drouard, L., Marshall, W.F.: The Chlamydomonas genome reveals the evolution of key animal and plant functions. Science 318(5848), 245–250 (2007)

    Article  Google Scholar 

  15. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)

    Article  Google Scholar 

  16. Shi, J., Blundell, T.L., Mizuguchi, K.: FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J. Mol. Biol. 310(1), 243–257 (2001)

    Article  Google Scholar 

  17. Holm, L., Rosenstrom, P.: Dali server: conservation mapping in 3D. Nucleic Acids Res. 38, W545–W549 (2010)

    Article  Google Scholar 

  18. Andreeva, A., Howorth, D., Chothia, C., Kulesha, E., Murzin, A.G.: Investigating protein structure and evolution with SCOP2. Curr. Protoc. Bioinform. 49, 1–21 (2015)

    Google Scholar 

  19. Carbon, S., Ireland, A., Mungall, C.J., Shu, S., Marshall, B., Lewis, S.: AmiGO hub & web presence working group. AmiGO: online access to ontology and annotation data. Bioinformatics 25(2), 288–289 (2008)

    Article  Google Scholar 

  20. Hollingsworth, S.A., Karplus, P.A.: A fresh look at the Ramachandran plot and the occurrence of standard structures in proteins. Biomol. Concepts. 1(3–4), 271–283 (2010)

    Google Scholar 

  21. Beld, J., Lee, D.J., Burkart, M.D.: Fatty acid biosynthesis revisited: structure elucidation and metabolic engineering. Mol. BioSyst. 11(1), 38–59 (2015)

    Article  Google Scholar 

  22. Blatti, J.L., Beld, J., Behnke, C.A., Mendez, M., Mayfield, S.P., Burkart, M.D.: Manipulating fatty acid biosynthesis in microalgae for biofuel through protein-protein interactions. PLoS ONE 7(9), 1–12 (2012)

    Article  Google Scholar 

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Acknowledgements

The authors are thankful to the DBT sponsored Bioinformatics Infrastructure Facility (BIF) at Department of Biotechnology and Medical Engineering of NIT Rourkela for their support during the research work. The authors are grateful to Ministry of Human Resources and Development of Government of India (MHRD, GoI) for sponsoring the first author’s Ph.D. program.

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Correspondence to P. Balasubramanian .

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Behera, B., Selvanayaki, S., Jayabalan, R., Balasubramanian, P. (2019). An in-silico Approach for Enhancing the Lipid Productivity in Microalgae by Manipulating the Fatty Acid Biosynthesis. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_70

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