Codon optimization of chicken β Gallinacin-3 gene results in constitutive expression and enhanced antimicrobial activity in transgenic Medicago sativa L

Gallinacin-3 (Gal-3) is a newly discovered epithelial beta-defensin that acts as cationic antimicrobial peptides, and plays an important role in chicken innate immunity. However, the gallinacin-3 precursor containeda lengthy C-terminal region, which often hindered itsexpression. After codon optimization of Gal-3 and construction of an expression vector, the transgenic plants of Medicago sativa were obtained. Transgenic plants were validated and expression of proteins was detected. The antimicrobial activity of chicken β Gal-3 was analyzed and effects of chicken β Gal-3 on the body weight and intestinal microflora of mice were described. Our results demonstrated that the codon optimized chicken Gal-3 was stably expressed in transgenic Medicago sativa using the pCAMBIA3301 expression vector under the control of protein phosphatase (Ppha) promoter. Five transgenic plants with the highest expression of chicken β Gal-3 were selected, and were evaluated for the in vitro antimicrobial activity against Escherichia coli, Staphylococcus aureus and Salmonella typhi. Our findings confirmed that the Minimum Inhibitory Concentration (MIC) of the three bacterial strains were 32, 16 and 128 μg/mL, respectively. In addition, the effect of chicken Gal-3 on the body weight of mice fed with transgenic plants showed no significant deviation compared with that of the control group. Similarly, no loss of intestinal microflora was evident in the experimental group compared with the control group. Together, our findings demonstrate an alternative method for the stable expression of chicken Gal-3 withsignificant antibacterial effects and potential probiotics uses. In addition, this study may also be useful in the development of resistant M. sativa plants against pathogenic bacteria in future studies.

The Role of Antimicrobial Peptides as Antimicrobial and Antibiofilm Agents in Tackling the Silent Pandemic of Antimicrobial Resistance

Just over a million people died globally in 2019 due to antibiotic resistance caused by ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species). The World Health Organization (WHO) also lists antibiotic-resistant Campylobacter and Helicobacter as bacteria that pose the greatest threat to human health. As it is becoming increasingly difficult to discover new antibiotics, new alternatives are needed to solve the crisis of antimicrobial resistance (AMR). Bacteria commonly found in complex communities enclosed within self-produced matrices called biofilms are difficult to eradicate and develop increased stress and antimicrobial tolerance. This review summarises the role of antimicrobial peptides (AMPs) in combating the silent pandemic of AMR and their application in clinical medicine, focusing on both the advantages and disadvantages of AMPs as antibiofilm agents. It is known that many AMPs display broad-spectrum antimicrobial activities, but in a variety of organisms AMPs are not stable (short half-life) or have some toxic side effects. Hence, it is also important to develop new AMP analogues for their potential use as drug candidates. The use of one health approach along with developing novel therapies using phages and breakthroughs in novel antimicrobial peptide synthesis can help us in tackling the problem of AMR.

Antimicrobial Peptides: An Update on Classifications and Databases

Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.