ChitinOMix – A multidisciplinary project to understand the effect of chitin soil amendment on the plant response, natural microbial community and the fate of human pathogenic bacteria
At a glance
- Project leader : Dr. Joël Pothier
- Co-project leader : Prof. Dr. Mieke Uyttendaele, Prof. Dr. Cyril Zipfel
- Project team : Dr. Bart Cottyn, Prof. Dr. Marc Heyndrickx, Moritz Kaufmann, Dr. Leilei Li, Moffat Makechemu
- Project budget : CHF 697'093
- Project status : ongoing
- Funding partner : SNSF (SNF-Projektförderung / Projekt-Nr. 189340)
- Project partner : Universität Zürich / Department of Plant and Microbial Biology, Ghent University, Institute for Agricultural, Fisheries and Food Research ILVO
- Contact person : Joël Pothier
The consumption of fruit and vegetables are considered as important for a healthy and balanced diet and recognized as a significant source of vitamins and fibre. Authorities encourage the consumption of fresh plant produce, but food safety of fresh fruits and vegetables continues to be a major concern. By now it is a well-accepted fact that human pathogenic can survive in plants. Soil amendment with chitin has been shown to be promising for improving soil quality, plant growth, and plant resilience. The suppressive effect of chitin on human pathogens in the phyllosphere was only recently described. The objective of this project is to gain deeper understanding of the mechanisms behind pathogen suppression in plants. The results can offer an alternative to reduce the contamination risk of fresh produce during preharvest, the most important production step to reduce the human health risk. This SNSF-FWO project focus on finding and consolidating the causal factors of the suppressive effect by using innovative and challenging OMICS approaches (e.g. metagenomics, dual RNA-seq combined with metabolomics) in combination with classical culture dependent microbiology and plant mutant studies. Salmonella is chosen as model pathogen, given the high number of outbreaks related to fresh produce. Lettuce is chosen as a model for an edible crop, it is a susceptible crop given the conducive wet conditions between the leaves of the plant. Experiments will also be performed on the plant model Arabidopsis in order to compare our results with the fundamental knowledge regarding the plant immunity system.
Schilling, Frank-Peter; Stadelmann, Thilo, eds.,
Artificial Neural Networks in Pattern Recognition.
9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020.
Lecture Notes in Computer Science ; 12294.
Available from: https://doi.org/10.1007/978-3-030-58309-5_16