artículo
Dual transcriptomic analysis reveals metabolic changes associated with differential persistence of human pathogenic bacteria in leaves of Arabidopsis and lettuce
Fecha
2021Registro en:
10.1093/g3journal/jkab331
21601836
21601836
34550367
SCOPUS_ID:85120165825
Autor
Jacob C.
Melotto M.
Jacob C.
Jacob C.
Velasquez A.C.
He S.Y.
Josh N.A.
Settles M.
He S.Y.
Institución
Resumen
© The Author(s) 2021.Understanding the molecular determinants underlying the interaction between the leaf and human pathogenic bacteria is key to provide the foundation to develop science-based strategies to prevent or decrease the pathogen contamination of leafy greens. In this study, we conducted a dual RNA-sequencing analysis to simultaneously define changes in the transcriptomic profiles of the plant and the bacterium when they come in contact. We used an economically relevant vegetable crop, lettuce (Lactuca sativa L. cultivar Salinas), and a model plant, Arabidopsis thaliana Col-0, as well as two pathogenic bacterial strains that cause disease outbreaks associated with fresh produce, Escherichia coli O157:H7 and Salmonella enterica serovar Typhimurium 14028s (STm 14028s). We observed commonalities and specificities in the modulation of biological processes between Arabidopsis and lettuce and between O157:H7 and STm 14028s during early stages of the interaction. We detected a larger alteration of gene expression at the whole transcriptome level in lettuce and Arabidopsis at 24 h post inoculation with STm 14028s compared to that with O157:H7. In addition, bacterial transcriptomic adjustments were substantially larger in Arabidopsis than in lettuce. Bacterial transcriptome was affected at a larger extent in the first 4 h compared to the subsequent 20 h after inoculation. Overall, we gained valuable knowledge about the responses and counter-responses of both bacterial pathogen and plant host when these bacteria are residing in the leaf intercellular space. These findings and the public genomic resources generated in this study are valuable for additional data mining.