ABSTRACT
Plant and microbial toxins such as ricin, staphylococcal enterotoxin B (SEB), and the botulinum neurotoxins (BoNT) are considered as potential biological warfare agents. Specific screening methods are, therefore, required that enable unambiguous and sensitive identification of these biohazards, particularly for the occurrence of the toxins in complex sample matrixes. The present study describes a combination of a multiplex-immunoaffinity purification approach, followed by matrix-assisted laser desorption/ionization (MALDI)-based detection for the simultaneous identification of ricin, SEB, BoNT/A, and BoNT/B. The method comprises an affinity enrichment step, using specific monoclonal antibodies for each of the four toxins which have been selected from a pool of antibodies. The selected antibodies allow for specific and simultaneous capture of ricin, SEB, BoNT/A, BoNT/B, and the corresponding BoNT complexes. These were subsequently identified by MALDI time-of-flight (TOF) mass spectrometry (MS), following tryptic digest. The sensitivity of the technique was approximately 500 fmol for each of the toxins. These toxins were detectable within 8 h, even when present in complex matrixes such as milk or juice. Furthermore, the MALDI-based multiplex assay allowed for the discrimination of closely related BoNT sero- and subtypes, including a real case of food-borne botulism in Germany.
Subject(s)
Botulinum Toxins/chemistry , Enterotoxins/chemistry , Immunomagnetic Separation/methods , Ricin/chemistry , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods , Amino Acid Sequence , Animals , Antibodies, Immobilized/immunology , Antibodies, Immobilized/metabolism , Antibodies, Monoclonal/immunology , Antibodies, Monoclonal/metabolism , Botulinum Toxins/analysis , Enterotoxins/analysis , Food Microbiology , Milk/microbiology , Molecular Sequence Data , Ricin/analysis , Time FactorsABSTRACT
Ontologies are one of the key technologies for data integration and meta-databases, by connecting databases at a semantical level. Still, the database has to be connected to the ontology and vice versa. To achieve this, we propose a two step process. First, we automatically generate an ontology directly from the database schema. Next, we annotate this with concepts from a domain specific ontology. We demonstrate this for signal transduction pathways, which describe how cells can react to external stimuli. In this paper we present our mapping of database schemas to ontologies and describe how these ontologies can be enriched by semantical information.
