Isolation and characterization of α ‐amylase encoding gene in Bacillus amyloliquefaciens PAS

https://doi.org/10.22146/ijbiotech.61425

Achmad Rodiansyah(1), Sitoresmi Prabaningtyas(2*), Mastika Marisahani Ulfah(3), Ainul Fitria Mahmuda(4), Uun Rohmawati(5)

(1) Laboratory of Microbiology, Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Semarang No.5 Malang 65145, Indonesia
(2) Laboratory of Microbiology, Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Semarang No.5 Malang 65145, Indonesia
(3) Laboratory of Microbiology, Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Semarang No.5 Malang 65145, Indonesia
(4) Laboratory of Microbiology, Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Semarang No.5 Malang 65145, Indonesia
(5) Laboratory of Microbiology, Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Semarang No.5 Malang 65145, Indonesia
(*) Corresponding Author

Abstract


Amylolytic bacteria are a source of amylase, which is an essential enzyme to support microalgae growth in the bioreactor for microalgae culture. In a previous study, the highest bacterial isolate to hydrolyze amylum (namely PAS) was successfully isolated from Ranu Pani, Indonesia, and it was identified as Bacillus amyloliquefaciens. That bacterial isolate (B. amyloliquefaciens PAS) also has been proven to accelerate Chlorella vulgaris growth in the mini bioreactor. This study aims to detect, isolate, and characterize the PAS’s α‐amylase encoding gene. This study was conducted with DNA extraction, amplification of α‐amylase gene with polymerase chain reaction (PCR) method with the specific primers, DNA sequencing, phylogenetic tree construction, and protein modeling. The result showed that α‐amylase was successfully detected in PAS bacterial isolate. The α‐amylase DNA fragment was obtained 1,468 bp and that translated sequence has an identity of about 98.3% compared to the B. amylolyquefaciens α‐amylase 3BH4 in the Protein Data Bank (PDB). The predicted 3D protein model of the PAS’s α‐amylase encoding gene has amino acid variations that predicted affect the protein’s structure in the small region. This research will be useful for further research to produce recombinant α‐amylase.


Keywords


α-amylase; Bacillus amyloliquefaciens; Homology modeling; Ranu Pani

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References

Abd­Elhalem BT, El­Sawy M, Gamal RF, Abou­Taleb KA. 2015. Production of amylases from Bacillus amyloliquefaciens under submerged fermentation using some agro­industrial by­products. Ann Agric Sci. 60(2):193–202. doi:10.1016/j.aoas.2015.06.001.

Ali R, Shafiq MI. 2015. Sequence, structure, and binding analysis of cyclodextrinase (TK1770) from T. Kodakarensis (KOD1) using an in silico approach. Archaea 2015. doi:10.1155/2015/179196.

Alikhajeh J, Khajeh K, Ranjbar B, Naderi­Manesh H, Lin YH, Liu E, Guan HH, Hsieh YC, Chuankhayan P, Huang YC, Jeyaraman J, Liu MY, Chen CJ. 2010. Structure of Bacillus amyloliquefaciens α­amylase at high resolution: Implications for thermal stability. Acta Crystallogr., Sect F Struct Biol Cryst Commun. 66(2):121–129. doi:10.1107/S1744309109051938.

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. 1990. Basic local alignment search tool. J Mol Biol. 215(3):403–410. doi:10.1016/S0022­ 2836(05)80360­2.

Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. 1997. Gapped BLAST and PSIBLAST: A new generation of protein database search programs. doi:10.1093/nar/25.17.3389.

Bateman A. 2019. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 47(D1):D506–D515. doi:10.1093/nar/gky1049.

Benkert P, Tosatto SC, Schomburg D. 2008. QMEAN: A comprehensive scoring function for model quality assessment. Proteins Struct, Funct, Genet. 71(1):261– 277. doi:10.1002/prot.21715.

Benkert P, Tosatto SC, Schwede T. 2009. Global and local model quality estimation at CASP8 using the scoring functions QMEAN and QMEANclust. Proteins: Struct, Funct, Bioinf. 77(SUPPL. 9):173–180. doi:10.1002/prot.22532.

Bunz F. 2008. Principles of cancer genetics. Netherlands: Springer Netherlands. doi:10.1007/978­1­ 4020­6784­6. Dayhoff MO, Schwartz RM, Orcutt B. 1978. A model of evolutionary change in proteins. In: Atlas protein Seq. Struct. p. 345–352.

Deb P, Talukdar SA, Mohsina K, Sarker PK, Sayem SM. 2013. Production and partial characterization of extracellular amylase enzyme from Bacillus amyloliquefaciens P­001. SpringerPlus. 2(1):1–12. doi:10.1186/2193­1801­2­154.

Far BE, Ahmadi Y, Khosroushahi AY, Dilmaghani A. 2020. Microbial alpha­amylase production: Progress, challenges and perspectives. Adv Pharm Bull. 10(3):350–358. doi:10.34172/apb.2020.043.

Felsenstein J. 1985. Confidence Limits on Phylogenies: an Approach Using the Bootstrap. Evolution. 39(4):783– 791. doi:10.1111/j.1558­5646.1985.tb00420.x.

Fuentes JL, Garbayo I, Cuaresma M, Montero Z, González­Del­Valle M, Vílchez C. 2016. Impact of microalgae­bacteria interactions on the production of algal biomass and associated compounds. Mar Drugs. 14(5). doi:10.3390/md14050100.

Gazali A, Suheriyanto D, Romaidi R. 2015. Keanekaragaman Makrozoobentos sebagai Bioindikator Kualitas Perairan Ranu Pani­Ranu Regulo di Taman Nasional Bromo Tengger Semeru [Biodiversity of Macrozoobenthos as Bioindicator of Ranu Pani­Ranu Regulo Watering Quality Bromo Tengger Semeru National Park. Masterthesis, UNS.

Geospiza. 2004. Finch, T.V. Geospiza, Inc.

Gopinath SC, Anbu P, Arshad MK, Lakshmipriya T, Voon CH, Hashim U, Chinni SV. 2017. Biotechnological Processes in Microbial Amylase Production. BioMed Res Int. 2017. doi:10.1155/2017/1272193.

Gregory TR. 2008. Understanding Evolutionary Trees. Evol Educ Outreach. 1(2):121–137. doi:10.1007/s12052­008­0035­x.

Gupta G, Srivastava S, Khare S, Prakash V. 2014. Extremophiles: An Overview of Microorganism from Extreme Environment. Int J Agric Env. Biotechnol. 7(2):371. doi:10.5958/2230­732x.2014.00258.7.

Hall T. 1999. BIOEDIT: a user­friendly biological sequence alignment editor and analysis program for Windows 95/98/ NT. Nucleic Acids Symp Ser. 41:95–98.

Han J, Zhang L, Wang S, Yang G, Zhao L, Pan K. 2016. Co­culturing bacteria and microalgae in organic carbon containing medium. J Biol Res. 23(1). doi:10.1186/s40709­016­0047­6.

Hwang K, Song H, Chang C, Lee J, Lee S, Kim K, Choe S, Sweet R, Suh S. 1997. Crystal structure of thermostable alpha­amylase from Bacillus licheniformis refined at 1.7 A resolution. Mol Cells. 7(2):251–258.

Janeček Š, Kuchtová A. 2012. In silico identification of catalytic residues and domain fold of the family GH119 sharing the catalytic machinery with the α­ amylase family GH57. FEBS Lett. 586(19):3360– 3366. doi:10.1016/j.febslet.2012.07.020.

Janeček Š, Kuchtová A, Petrovičová S. 2015. A novel GH13 subfamily of α­amylases with a pair of tryp­tophans in the helix α3 of the catalytic TIM­barrel, the LPDlx signature in the conserved sequence region v and a conserved aromatic motif at the C­terminus. Biol. 70(10):1284–1294. doi:10.1515/biolog­2015­ 0165.

Janeček Š, Svensson B, MacGregor EA. 2003. Relation between domain evolution, specificity, and taxonomy of the α­amylase family members containing a C­terminal starch­binding domain. Eur J Biochem. 270(4):635–645. doi:10.1046/j.1432­ 1033.2003.03404.x.

Kang Y, Kim M, Shim C, Bae S, Jang S. 2021. Potential of Algae–Bacteria Synergistic Effects on Vegetable Production. Front Plant Sci. 12. doi:10.3389/fpls.2021.656662.

Kumar S, Stecher G, Li M, Knyaz C, Tamura K. 2018. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 35(6):1547–1549. doi:10.1093/molbev/msy096.

Larkin MA, Blackshields G, Brown NP, Chenna R, Mcgettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG. 2007. Clustal W and Clustal X version 2.0. Bioinformatics. 23(21):2947–2948. doi:10.1093/bioinformatics/btm404.

Lopes R, Tsui S, Gonçalves PJ, de Queiroz MV. 2018. A look into a multifunctional toolbox: endophytic Bacillus species provide broad and underexploited benefits for plants. World J Microbiol Biotechnol. 34(7). doi:10.1007/s11274­018­2479­7.

Mehta D, Satyanarayana T. 2016. Bacterial and archaeal α­amylases: Diversity and amelioration of the desirable characteristics for industrial applications. Front Microbiol. 7(JUL). doi:10.3389/fmicb.2016.01129.

Montor­Antonio JJ, Hernández­Heredia S, ÁvilaFernández Á, Olvera C, Sachman­Ruiz B, del Moral S. 2017. Effect of differential processing of the native and recombinant α­amylase from Bacillus amyloliquefaciens JJC33M on specificity and enzyme properties. 3 Biotech. 7(5). doi:10.1007/s13205­017­0954­8.

Nafi’ah I, Prabaningtyas S, Witjoro A, Basitoh YK, Rodiansyah A, Aridhowi D. 2021. Exploration of IAA Producing Bacteria And Amylolitic Bacteria From Several East Java Lakes, and Their Potency For Microbial Consortium To Accelerate Chlorella Vulgaris Growth. doi:10.21203/rs.3.rs­520439/v1. URL https: //www.researchsquare.com/article/rs­520439/v1.

Nisa IK, Prabaningtyas S, Lukiati B, Saptawati RT, Rodiansyah A. 2021. The potential of amylase enzyme activity against bacteria isolated from several lakes in east Java, Indonesia. Biodiversitas. 22(1):42–49. doi:10.13057/biodiv/d220106.

Niu D, Zuo Z, Shi GY, Wang ZX. 2009. High yield recombinant thermostable α­amylase production using an improved Bacillus licheniformis system. Microb Cell Factories. 8:58. doi:10.1186/1475­2859­8­58.

Owczarzy R, Tataurov AV, Wu Y, Manthey JA, McQuisten KA, Almabrazi HG, Pedersen KF, Lin Y, Garretson J, McEntaggart NO, Sailor CA, Dawson RB, Peek AS. 2008. IDT SciTools: a suite for analysis and design of nucleic acid oligomers. Nucleic Acids Res. 36. doi:10.1093/nar/gkn198.

Prabaningtyas S, Witjoro A. 2017. Eksplorasi bakteri sinergis dari beberapa danau di Jawa Timur untuk mempercepat pertumbuhan mikroalga renewable energy [Synergistic bacterial exploration of several lakes in East Java to accelerate the growth of microalgae renewable energy]. Biologi dan Bioteknologi Umum 113, Universitas Negeri Malang, Malang.

Prabaningtyas S, Witjoro A, Saptasari M. 2018. Eksplorasi bakteri sinergis dari beberapa danau di Jawa Timur untuk mempercepat pertumbuhan mikroalga renewable energy tahun ke 2 [Synergistic bacterial exploration of several lakes in East Java to accelerate the growth of microalgae renewable energy 2nd year]. Biologi dan Bioteknologi Umum 113, Universitas Negeri Malang, Malang.

Pramanik K, Ghosh PK, Ray S, Sarkar A, Mitra S, Maiti TK. 2017. An in silico structural, functional and phylogenetic analysis with three dimensional protein modeling of alkaline phosphatase enzyme of Pseudomonas aeruginosa. J Genet Eng Biotechnol. 15(2):527–537. doi:10.1016/j.jgeb.2017.05.003.

Rao VS, Srinivas K, Sujini GN, Kumar GNS. 2014. Protein­Protein Interaction Detection: Methods and Analysis. Int J Proteomics. 2014:1–12. doi:10.1155/2014/147648.

Rodiansyah A, Mahmudah AF, Ulfah MM, Rohmawati U, Listyorini D, Suyono EA, Prabaningtyas S. 2021. Identification of Potential Bacteria on Several Lakes in East Java, Indonesia Based on 16S rRNA Sequence Analysis. HAYATI J Biosci. 28(2):136–136. doi:10.4308/hjb.28.2.136.

Roy A, Kucukural A, Zhang Y. 2010. I­TASSER: A unified platform for automated protein structure and function prediction. Nat Protoc. 5(4):725–738. doi:10.1038/nprot.2010.5.

Roy A, Yang J, Zhang Y. 2012. COFACTOR: An accurate comparative algorithm for structure­based protein function annotation. Nucleic Acids Res. 40(W1). doi:10.1093/nar/gks372.

Schrodinger. 2010. LLC, The PyMOL Molecular Graphics System. LLC, PyMOL Mol. Graph. Syst. Version 1.3r1 .

Sneath PH, Sokal RR, et al. 1973. Numerical taxonomy. The principles and practice of numerical classification. Srivastava S, Bist V, Srivastava S, Singh PC, Trivedi PK, Asif MH, Chauhan PS, Nautiyal CS. 2016. Unraveling aspects of Bacillus amyloliquefaciens mediated enhanced production of rice under biotic stress of Rhizoctonia solani. Front Plant Sci. 7(MAY2016). doi:10.3389/fpls.2016.00587.

SRL HB. 2014. DNA Baser Sequence Assembler. Waterhouse A, Bertoni M, Bienert S, Studer G, TaurielloG, Gumienny R, Heer FT, De Beer TA, Rempfer C, Bordoli L, Lepore R, Schwede T. 2018. SWISSMODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res. 46(W1):W296– W303. doi:10.1093/nar/gky427.

Wheeler DL, Church DM, Federhen S, Lash AE, Madden TL, Pontius JU, Schuler GD, Schriml LM, Sequeira E, Tatusova TA, et al. 2003. Database resources of the National Center for Biotechnology. Nucleic acids research 31(1):28–33. doi:10.1093/nar/gkg033.

Xiang Z. 2006. Advances in Homology Protein Structure Modeling. Curr Protein Pept Sci. 7(3):217–227. doi:10.2174/138920306777452312.

Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL. 2012. Primer­BLAST: a tool to design target­specific primers for polymerase chain reaction. BMC bioinformatics. 13:134. doi:10.1186/1471­ 2105­13­134.



DOI: https://doi.org/10.22146/ijbiotech.61425

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