Highly Sensitive and Specific Detection of Staphylococcal Enterotoxins SEA, SEG, SEH, and SEI by Immunoassay.

Staphylococcal food poisoning (SFP) is one of the most common foodborne diseases worldwide, resulting from the ingestion of staphylococcal enterotoxins (SEs), primarily SE type A (SEA), which is produced in food by enterotoxigenic strains of staphylococci, mainly S. aureus. Since newly identified SEs have been shown to have emetic properties and the genes encoding them have been found in food involved in poisoning outbreaks, it is necessary to have reliable tools to prove the presence of the toxins themselves, to clarify the role played by these non-classical SEs, and to precisely document SFP outbreaks. We have produced and characterized monoclonal antibodies directed specifically against SE type G, H or I (SEG, SEH or SEI respectively) or SEA. With these antibodies, we have developed, for each of these four targets, highly sensitive, specific, and reliable 3-h sandwich enzyme immunoassays that we evaluated for their suitability for SE detection in different matrices (bacterial cultures of S. aureus, contaminated food, human samples) for different purposes (strain characterization, food safety, biological threat detection, diagnosis). We also initiated and described for the first time the development of monoplex and quintuplex (SEA, SE type B (SEB), SEG, SEH, and SEI) lateral flow immunoassays for these new staphylococcal enterotoxins. The detection limits in buffer were under 10 pg/mL (0.4 pM) by enzyme immunoassays and at least 300 pg/mL (11 pM) by immunochromatography for all target toxins with no cross-reactivity observed. Spiking studies and/or bacterial supernatant analysis demonstrated the applicability of the developed methods, which could become reliable detection tools for the routine investigation of SEG, SEH, and SEI.

Management and Characterization of Abiotic Stress via PhénoField®, a High-Throughput Field Phenotyping Platform.

In order to evaluate the impact of water deficit in field conditions, researchers or breeders must set up large experiment networks in very restrictive field environments. Experience shows that half of the field trials are not relevant because of climatic conditions that do not allow the stress scenario to be tested. The PhénoField® platform is the first field based infrastructure in the European Union to ensure protection against rainfall for a large number of plots, coupled with the non-invasive acquisition of crops' phenotype. In this paper, we will highlight the PhénoField® production capability using data from 2017-wheat trial. The innovative approach of the PhénoField® platform consists in the use of automatic irrigating rainout shelters coupled with high throughput field phenotyping to complete conventional phenotyping and micrometeorological densified measurements. Firstly, to test various abiotic stresses, automatic mobile rainout shelters allow fine management of fertilization or irrigation by driving daily the intensity and period of the application of the desired limiting factor on the evaluated crop. This management is based on micro-meteorological measurements coupled with a simulation of a carbon, water and nitrogen crop budget. Furthermore, as high-throughput plant-phenotyping under controlled conditions is well advanced, comparable evaluation in field conditions is enabled through phenotyping gantries equipped with various optical sensors. This approach, giving access to either similar or innovative variables compared manual measurements, is moreover distinguished by its capacity for dynamic analysis. Thus, the interactions between genotypes and the environment can be deciphered and better detailed since this gives access not only to the environmental data but also to plant responses to limiting hydric and nitrogen conditions. Further data analyses provide access to the curve parameters of various indicator kinetics, all the more integrative and relevant of plant behavior under stressful conditions. All these specificities of the PhénoField® platform open the way to the improvement of various categories of crop models, the fine characterization of variety behavior throughout the growth cycle and the evaluation of particular sensors better suited to a specific research question.