Bioactivity assessment of peptide QUB1471 from the skin secretion of the Chinese torrent frog (Amolops wuyiensis) 

  • Ming Yuan

Student thesis: Masters ThesisMasters

Abstract

Amphibians have developed successful defensive strategies for combating predators and invasive microorganisms encountered in their broad range of living environments, which involves secretion of complex cocktails of noxious, toxic and diverse bioactive molecules from the skins. Since ancient times, substances derived from amphibian skins have been recognised to possess various medicinal properties. In recent years, bioactive peptides from amphibian skin secretions have attracted much attention for their profound significance in providing clues directly towards novel drug development, for better understanding of miscellaneous physiological and pathological processes, for elucidation of phylogenetic relationships, and for improved taxonomy.
In this study, the isolation and identification of bioactive peptides from the skin secretion of Chinese torrent frog, Amolops wuyiensis, were performed through the genomic approach shotgun cloning. The mRNA was isolated from the skin secretion and was used to construct cDNA library by reverse transcription. The target cDNA of interest was amplified and sequenced using molecular cloning. Once the sequence was confirmed, the peptide was chemically synthesised, and biological activity screening was performed subsequently. In this thesis, one bioactive peptide, named QUB1471, was identified. Trypsin inhibitory assay, haemolysis assay and antimicrobial assays were performed to evaluate the biological activities of QUB1471. Based on the results, QUB1471 was an effective trypsin inhibitor and its Ki value was 0.8657 µM. However, QUB1471 did not inhibit the growth of the three test microorganisms and was not capable of inducing haemolysis at concentrations up to 512 μM.
Date of AwardJul 2018
Original languageEnglish
Awarding Institution
  • Queen's University Belfast
SupervisorYuxin Wu (Supervisor), Tianbao Chen (Supervisor), Mei Zhou (Supervisor), Lei Wang (Supervisor) & Christopher Shaw (Supervisor)

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