Abstract
BACKGROUND: 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. RESULTS: 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 0.8(a), 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%. CONCLUSION: 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.