BING, a novel antimicrobial peptide isolated from Japanese medaka plasma, targets bacterial envelope stress response by suppressing cpxR expression.

Antimicrobial peptides (AMPs) have emerged as a promising alternative to small molecule antibiotics. Although AMPs have previously been isolated in many organisms, efforts on the systematic identification of AMPs in fish have been lagging. Here, we collected peptides from the plasma of medaka (Oryzias latipes) fish. By using mass spectrometry, 6399 unique sequences were identified from the isolated peptides, among which 430 peptides were bioinformatically predicted to be potential AMPs. One of them, a thermostable 13-residue peptide named BING, shows a broad-spectrum toxicity against pathogenic bacteria including drug-resistant strains, at concentrations that presented relatively low toxicity to mammalian cell lines and medaka. Proteomic analysis indicated that BING treatment induced a deregulation of periplasmic peptidyl-prolyl isomerases in gram-negative bacteria. We observed that BING reduced the RNA level of cpxR, an upstream regulator of envelope stress responses. cpxR is known to play a crucial role in the development of antimicrobial resistance, including the regulation of genes involved in drug efflux. BING downregulated the expression of efflux pump components mexB, mexY and oprM in P. aeruginosa and significantly synergised the toxicity of antibiotics towards these bacteria. In addition, exposure to sublethal doses of BING delayed the development of antibiotic resistance. To our knowledge, BING is the first AMP shown to suppress cpxR expression in Gram-negative bacteria. This discovery highlights the cpxR pathway as a potential antimicrobial target.

Field test of beach litter assessment by commercial aerial drone.

The visual survey is the most common method to quantify and characterize beach litter. However, it is very labor intensive and difficult to carry out on beaches which are remote or difficult to access. We suggest an alternative approach for assessing beach litter using an unmanned aerial vehicle (UAV), or aerial drone, with automated image requisition and processing. Litter of different sizes, colours, and materials were placed randomly on two beaches. Images of beaches with different substrates were obtained by the drone at different operating heights and light conditions and litter on the beaches was identified from the photos by untrained personnel. The quantification of beach litter using the drone was three times faster than that by visual census. This study has demonstrated the potential of using the drone as a cost-effective and an efficient sampling method in routine beach litter monitoring programs.