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In silico identification of novel antimicrobial peptides from the venom gland transcriptome of the spider Argiope bruennichi (Scopoli, 1772)open access

Authors
Shin, Min KyoungHwang, In-WookJang, Bo-YoungBu, Kyung-BinYoo, Jung SunSung, Jung-Suk
Issue Date
Jul-2023
Publisher
FRONTIERS MEDIA SA
Keywords
antimicrobial peptide; Argiope bruennichi; spider venom gland transcriptome; in silico analysis; Aranetoxin-Ab2a; Aranetoxin-Ab3a; multidrug-resistant Pseudomonas aeruginosa
Citation
Frontiers in Microbiology, v.14, pp 01 - 10
Pages
10
Indexed
SCIE
SCOPUS
Journal Title
Frontiers in Microbiology
Volume
14
Start Page
01
End Page
10
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/19975
DOI
10.3389/fmicb.2023.1249175
ISSN
1664-302X
1664-302X
Abstract
As the emergence and prevalence of antibiotic-resistant strains have resulted in a global crisis, there is an urgent need for new antimicrobial agents. Antimicrobial peptides (AMPs) exhibit inhibitory activity against a wide spectrum of pathogens and can be utilized as an alternative to conventional antibiotics. In this study, two novel AMPs were identified from the venom transcriptome of the spider Argiope bruennichi (Scopoli, 1772) using in silico methods, and their antimicrobial activity was experimentally validated. Aranetoxin-Ab2a (AATX-Ab2a) and Aranetoxin-Ab3a (AATX-Ab3a) were identified by homology analysis and were predicted to have high levels of antimicrobial activity based on in silico analysis. Both peptides were found to have antibacterial effect against Gram-positive and -negative strains, and, in particular, showed significant inhibitory activity against multidrug-resistant Pseudomonas aeruginosa isolates. In addition, AATX-Ab2a and AATX-Ab3a inhibited animal and vegetable fungal strains, while showing low toxicity to normal human cells. The antimicrobial activity of the peptides was attributed to the increased permeability of microbial membranes. The study described the discovery of novel antibiotic candidates, AATX-Ab2a and AATX-Ab3a, using the spider venom gland transcriptome, and validated an in silico-based method for identifying functional substances from biological resources.
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