In silico analysis of promoter region and regulatory elements of mitogenome co-expressed trn gene clusters encoding for bio-pesticide in entomopathogenic fungus, Metarhizium anisopliae: strain ME1

Getachew Bantihun1, Mulugeta Kebede1
1Department of Applied Biology, School of Applied Natural Science, Adama Science and Technology University, Adama, Ethiopia

Tóm tắt

Pest control strategies almost entirely rely on chemical insecticides, which cause environmental problems such as biosphere deterioration and emergence of resistant pests. Bio-pesticide is an alternative approach, which uses organisms such as entomopathogenic fungi, Metarhizium anisopliae, to control pests. Screening such potential organism at a molecular level and understanding their gene regulation mechanism is an important approach to reduce emergence of pesticide resistance and worsening of the biosphere. Understanding promoter regions which play a pivotal role in gene regulation is crucial. In particular, identification of the promoter regions in M. anisopliae Strain ME1 remains poorly understood. To our knowledge, the mitogenome trn gene clusters of M. anisopliae Strain ME1 were not characterized. Here, we used machine learning approach to identify and characterize the promoter regions, regulatory elements, and CpG island densities of 15 protein coding genes of entomopathogenic fungi, M. anisolpliae Strain ME1. The current analysis revealed multiple transcription start sites (TSS) for all utilized sequences, except for promoter region genes of Pro-cob and Pro-nad5. With reference to the start codon (ATG), 85.3% of TSS was located above – 500 bp. Based on the standard predictive score at cut off value of 0.8a, the current study revealed 54.7% of predictive score greater than or equal from 0.9 promoter prediction score. Expectation maximization algorithm output identified five candidate motifs. Nonetheless, of all candidate motifs, MtrnI was revealed as the common promoter region motif with a value of 76.9% both in terms of size of binding sites and with an E value of 9.1E−054. Accordingly, we perceived that MtrnI serve as the binding site for tryptophan cluster with P value 0.0044 and C4 type zinc fingers functions as the binding site to regulate gene expression of M. anisopliae Strain ME1. The analysis revealed that mitogenome trn gene clusters of M. anisopliae Strain ME1 showed homologues evolutionary ancestor supported with a bootstrap value of 100%. Identified common candidate motifs and binding transcription factors through in silico approach are likely expected to contribute for better understanding of gene expression and strain improvement of M. anisopliae Strain ME1 for its bio-pesticides role.

Tài liệu tham khảo

Aktar W, Sengupta D, Chowdhury A (2009) Impact of pesticides use in agriculture : their benefits and hazards. Interdiscip Toxicol 2(1):1–12. https://doi.org/10.2478/v10102-009-0001-7 Jepson PC, Murray K, Bach O, Bonilla MA, Neumeister L (2020) Selection of pesticides to reduce human and environmental health risks : a global guideline and minimum pesticides list. Lancet Planet Heal 4(2):56–63. https://doi.org/10.1016/S2542-5196(19)30266-9 Dubovskiy MI et al (2013) Can insects develop resistance to insect pathogenic fungi ? PLoS One 8(4):1–9. https://doi.org/10.1371/journal.pone.0060248 Kuddus M, Ahmad IZ (2013) Isolation of novel chitinolytic bacteria and production optimization of extracellular chitinase. J Genet Eng Biotechnol 11(1):39–46. https://doi.org/10.1016/j.jgeb.2013.03.001 Wang C, Wang S (2017) Insect pathogenic fungi : genomics, molecular interactions , and genetic improvements. Annu Rev Entomol 62(1):73–90. https://doi.org/10.1146/annurev-ento-031616-035509 Gao Q, Jin K, Ying SH, Zhang Y, Xiao G, Shang Y, Duan Z, Hu X, Xie XQ, Zhou G, Peng G, Luo Z, Huang W, Wang B, Fang W, Wang S, Zhong Y, Ma LJ, St. Leger RJ, Zhao GP, Pei Y, Feng MG, Xia Y, Wang C (2011) Genome sequencing and comparative transcriptomics of the model entomopathogenic fungi Metarhizium anisopliae and M. acridum. PLoS Genet 7(1):1–18. https://doi.org/10.1371/journal.pgen.1001264 Golo PS, Santos HA, Perinotto WMS, S. Quinelato S (2015) The influence of conidial Pr1 protease on pathogenicity potential of Metarhizium anisopliae senso latu to ticks. Parasitol Res 114(6):2309–2315. https://doi.org/10.1007/s00436-015-4426-y Hong M, Peng G, Keyhani NO, Xia Y (2017) Application of the entomogenous fungus , Metarhizium anisopliae , for leafroller (Cnaphalocrocis medinalis) control and its effect on rice phyllosphere microbial diversity. Appl Microbial Biotechnol 10(17):6793–6807. https://doi.org/10.1007/s00253-017-8390-6 Jiang W, Peng Y, Ye J, Wen Y, Liu G, J. Xie J (2020) Effects of the entomopathogenic fungus Metarhizium anisopliae on the mortality and immune response of Locusta migratoria. Insects 11(1):1–12. https://doi.org/10.3390/insects11010036 Isaka M, Kittakoop P, Kirtikara K, Hywel-jones NL, Thebtaranonth Y (2005) Bioactive substances from insect pathogenic fungi. Acc Chem Res 38(10):813–823. https://doi.org/10.1021/ar040247r Ortiz-urquiza A, Keyhani NO (2013) Action on the surface: entomopathogenic fungi versus the insect cuticle. Insects 4(3):357–374. https://doi.org/10.3390/insects4030357 Shahbaz U, Yu X (2020) Cloning, isolation, and characterization of novel chitinase-producing bacterial strain UM01 (Myxococcus fulvus). J Genet Eng Biotechnol 18(1):1–11. https://doi.org/10.1186/s43141-020-00059-1 Li HLQ (2011) Eukaryotic and prokaryotic promoter prediction using hybrid approach. Theory Biosci 130(2):91–100. https://doi.org/10.1007/s12064-010-0114-8 Oubounyt M, Louadi Z, Tayara H, Chong KT (2019) DeePromoter : robust promoter predictor using deep learning. Front Genet 10:1–9. https://doi.org/10.3389/fgene.2019.00286 Won H, Kim M, Kim S, Kim J (2007) EnsemPro : an ensemble approach to predicting transcription start sites in human genomic DNA sequences. Genomics 91(3):259–266. https://doi.org/10.1016/j.ygeno.2007.11.001 Carvalho AM, Freitas AT, Oliveira AL, Sagot M (2006) An efficient algorithm for the identification of structured motifs in DNA promoter sequences IEEE/ACM trans. Comput Biol Bioinform 3(2):126–140. https://doi.org/10.1109/TCBB.2006.16 Santhoshkumar R, Yusuf A (2020) In silico structural modeling and analysis of physicochemical properties of curcumin proteins of Curcuma longa. J Genet Eng Biotechnol 18(1):24. https://doi.org/10.1186/s43141-020-00041-x Khan A, Ali A, Junaid M, Liu C, Kaushik AC, Cho WCS (2018) Identification of novel drug targets for diamond-blackfan anemia based on RPS19 gene mutation using protein-protein interaction network. BMC Syst Biol 12(S4):1–16. https://doi.org/10.1186/s12918-018-0563-0 Ayer DK, Modha K, Parekh V, Patel R, Vadodariya G, Ramtekey V, Bhuriya A (2020) Associating gene expressions with curcuminoid biosynthesis in turmeric. J Genet Eng Biotechnol 18(1):1–14. https://doi.org/10.1186/s43141-020-00101-2 Reese MG (2001) Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome. Comput Chem 26(1):51–56. https://doi.org/10.1016/s0097-8485(01)00099-7 Bailey TL, Johnson J, Grant CE, Noble WS (2015) The MEME suite. Nucleic Acids Res 43(W1):39–49. https://doi.org/10.1093/nar/gkv416 Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS (2009) MEME SUITE : tools for motif discovery and searching. Nucleic Acids Res 37(Web Server):1–8. https://doi.org/10.1093/nar/gkp335 Meena M, Gupta SK, Swapnil P, Zehra A, Dubey MK (2017) Alternaria toxins : potential virulence factors and genes related to pathogenesis. Front Microbiol 8:1–14. https://doi.org/10.3389/fmicb.2017.01451 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL (2005) Gene set enrichment analysis : a knowledge-based approach for interpreting genome-wide. PNAS 102(43):15545–15550. https://doi.org/10.1073/pnas.0506580102 Takai D, Jones PA (2002) Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc Natl Acad Sci U S A 99(6):3740–3745. https://doi.org/10.1073/pnas.052410099 Newman L, Duffus ALJ, Lee C (2016) Using the free program MEGA to build phylogenetic trees from molecular data. Am Biol Teach 78(7):608–612. https://doi.org/10.1525/abt.2016.78.7.608 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. https://doi.org/10.1093/molbev/msy096 Yamashita R, Wakaguri H, Sugano S, Suzuki Y, Nakai K (2010) DBTSS provides a tissue specific dynamic view of transcription start sites. Nucleic Acids Res 38(suppl_1):98–104. https://doi.org/10.1093/nar/gkp1017 Dai Z, Xiong Y, Dai X (2016) DNA signals at isoform promoters. Sci Rep 6(1):1–8. https://doi.org/10.1038/srep28977 Hudson ME, Quail PH (2003) Identification of promoter motifs involved in the network of phytochrome a-regulated gene expression by combined analysis of genomic sequence and microarray data. Plant Physiol 133(4):605–1616. https://doi.org/10.1104/pp.103.030437 Bilu Y, Barkai N (2005) The design of transcription-factor binding sites is affected by combinatorial regulation. Genome Biol 6(12):1–10. https://doi.org/10.1186/gb-2005-6-12-r103 Pan G, Tang J, Guo F (2017) Analysis of co-associated transcription factors via ordered adjacency differences on motif distribution. Sci Rep 7(1):1–9. https://doi.org/10.1038/srep43597 Boeva V (2016) Analysis of genomic sequence motifs for deciphering transcription factor binding and transcriptional regulation in eukaryotic cells. Front Genet 7:1–15. https://doi.org/10.3389/fgene.2016.00024 Li Y, Liu T (2020) Zinc finger proteins in the human fungal pathogen cryptococcus neoformans. Int J Mol Sci 21(4):1–15. https://doi.org/10.3390/ijms21041361 Albataineh MT, Kadosh D (2016) Regulatory roles of phosphorylation in model and pathogenic fungi. Med Mycol 54(4):333–352. https://doi.org/10.1093/mmy/myv098 Wu G, Feng X, Stein L (2010) A human functional protein interaction network and its application to cancer data analysis. Genome Biol 11:1–23. https://doi.org/10.1186/gb2010-11-5-r53 Ali A et al (2018) Identification of novel therapeutic targets in myelodysplastic syndrome using protein-protein interaction approach and neural networks computer science & systems biology. J Comput Sci Syst Biol 11(2):184–189. https://doi.org/10.4172/jcsb.1000270 Nguyen NTT, Contreras-Moreira B, Castro-Mondragon JA, Santana-Garcia W, Ossio R, Robles-Espinoza CD, Bahin M, Collombet S, Vincens P, Thieffry D, van Helden J, Medina-Rivera A, Thomas-Chollier M (2018) RSAT: regulatory sequence analysis tools 20th anniversary. Nucleic Acids Res 46(W1):1–7. https://doi.org/10.1093/nar/gky317 Macpherson S, Larochelle M, Turcotte B (2006) A fungal family of transcriptional regulators: the zinc cluster proteins. Microbiol Mol Biol Rev 70(3):583–604. https://doi.org/10.1128/MMBR.00015-06 Chen C, Li Q, Fu R, Wang J, Xiong C, Fan Z, Hu R, Zhang H, Lu D (2019) Characterization of the mitochondrial genome of the pathogenic fungus Scytalidium auriculariicola (Leotiomycetes) and insights into its phylogenetics. Sci Rep 9(1):1–12. https://doi.org/10.1038/s41598-019-53941-5 Sharif J, Endo TA, Toyoda T, Koseki H (2010) Divergence of CpG island promoters : a consequence or cause of evolution? Develop Growth Differ 52(6):545–554. https://doi.org/10.1111/j.1440-169X.2010.01193.x Mishra PK, Baum M, Carbon J (2011) DNA methylation regulates phenotype-dependent transcriptional activity in Candida albicans. Proc Natl Acad Sci U S A 108(29):11965–11970. https://doi.org/10.1073/pnas.1109631108 Lin R, Liu C, Shen B, Bai M, Ling J, Chen G, Mao Z, Cheng X, Xie B (2015) Analysis of the complete mitochondrial genome of Pochonia chlamydosporia suggests a close relationship to the invertebrate-pathogenic fungi in Hypocreales. BMC Microbiol 15(1):1–15. https://doi.org/10.1186/s12866-015-0341-8 Teissandier A, Bourc’his D (2017) Gene body DNA methylation conspires with H3K36me3 to preclude aberrant transcription. EMBO J 36:1471–1473. https://doi.org/10.15252/embj.201796812 Sloan DB, Oxelman B, Rautenberg A, Taylor DR (2009) Phylogenetic analysis of mitochondrial substitution rate variation in the angiosperm tribe Sileneae. BMC Evol Biol 16(1):1–16. https://doi.org/10.1186/1471-2148-9-260