The transcriptome analysis of the Arabidopsis thaliana in response to the Vibrio vulnificus by RNA-sequencing.
Yong-Soon ParkSeon-Kyu KimSeon-Young KimKyung Mo KimChoong Min RyuPublished in: PloS one (2019)
Because of the recent increase in the demand for fresh produce, contamination of raw food products has become an issue. Foodborne diseases are frequently caused by the infection of leguminous plants by human bacterial pathogens. Moreover, contamination by Vibrio cholerae, closely related with Vibrio vulnificus, has been reported in plants and vegetables. Here, we investigated the possibility of Vibrio vulnificus 96-11-17M, an opportunistic human pathogen, to infect and colonize Arabidopsis thaliana plants, resulting in typical disease symptoms at 5 and 7 days post-inoculation in vitro and in planta under artificial and favorable conditions, respectively. RNA-Seq analysis revealed 5,360, 4,204, 4,916 and 3,741 differentially expressed genes (DEGs) at 12, 24, 48 and 72 h post-inoculation, respectively, compared with the 0 h time point. Gene Ontology analysis revealed that these DEGs act in pathways responsive to chemical and hormone stimuli and plant defense. The expression of genes involved in salicylic acid (SA)-, jasmonic acid (JA)- and ethylene (ET)-dependent pathways was altered following V. vulnificus inoculation. Genetic analyses of Arabidopsis mutant lines verified that common pathogen-associated molecular pattern (PAMP) receptors perceive the V. vulnificus infection, thus activating JA and ET signaling pathways. Our data indicate that the human bacterial pathogen V. vulnificus 96-11-17M modulates defense-related genes and host defense machinery in Arabidopsis thaliana under favorable conditions.
Keyphrases
- arabidopsis thaliana
- single cell
- rna seq
- endothelial cells
- genome wide
- signaling pathway
- induced pluripotent stem cells
- risk assessment
- pluripotent stem cells
- candida albicans
- transcription factor
- poor prognosis
- health risk
- human health
- copy number
- gene expression
- cell proliferation
- physical activity
- electronic health record
- machine learning
- deep learning
- escherichia coli
- big data
- genome wide identification
- multidrug resistant
- innate immune
- artificial intelligence
- binding protein
- induced apoptosis
- heavy metals
- atomic force microscopy
- cystic fibrosis