Anti-Salmonella Monoclonal antibody, Clone KHU0223

High-throughput genomic sequencing has helped to reveal the plasmidome ofAeromonas salmonicida. This literature review provides an overview ofA. salmonicida's rich plasmidome by presenting all the plasmids identified so far, addressing their biological importance and the functional links between them. The plasmids ofA. salmonicida, especially those bearing antibiotic resistance genes, can provide clues about interactions of this species with other pathogens (animals and humans), as is the case for pRAS3-3432 andChlamydia suisor pSN254b andSalmonella enterica. In addition to antibiotic resistance, plasmids play an important role in the virulence ofA. salmonicida, particularly for the subspeciessalmonicidaand the plasmid pAsa5, which carries genes for the type-three secretion system, a virulence factor essential for the bacterium. TheA. salmonicidaplasmidome also has many cryptic plasmids with no known biological function, but which can be used for the acquisition of new genetic elements. Striking examples are pAsa7 and pAsaXII that provide, respectively, resistance to chloramphenicol and formaldehyde and are derivatives of cryptic pAsa2.

Surface-enhanced Raman spectroscopy (SERS) with subsequent chemometric evaluation was performed for the rapid and non-destructive differentiation of seven important meat-associated microorganisms, namely Brochothrix thermosphacta DSM 20171(T), Pseudomonas fluorescens DSM 4358, Salmonella enterica subsp. enterica sv. Enteritidis DSM 14221, Listeria monocytogenes DSM 19094, Micrococcus luteus DSM 20030(T), Escherichia coli HB101 and Bacillus thuringiensis sv. israelensis DSM 5724. A simple method for collecting spectra from commercial paper-based SERS substrates without any laborious pre-treatments was used. In order to prepare the spectroscopic data for classification at genera level with a subsequent chemometric evaluation consisting of principal component analysis and discriminant analysis, a data pre-processing method with spike correction and sum normalisation was performed. Because of the spike correction rather than exclusion, and therefore the use of a balanced data set, the multivariate analysis of the data is significantly resilient and meaningful. The analysis showed that the differentiation of meat-associated microorganisms and thereby the detection of important meatrelated pathogenic bacteria was successful on genera level and a cross-validation as well as a classification of ungrouped data showed promising results, with 99.5% and 97.5%, respectively.