Back

Non-pathogenic leaf-colonising bacteria elicit pathogen-like responses in a colonisation density-dependent manner

Miebach, M.; Faivre, L.; Schubert, D.; Jameson, P.; Remus-Emsermann, M. N. P.

2023-05-05 plant biology
10.1101/2023.05.04.539505 bioRxiv
Show abstract

Leaves are colonised by a complex mix of microbes, termed the leaf microbiota. Even though the leaf microbiota is increasingly recognised as an integral part of plant life and health, our understanding of its interactions with the plant host is still limited. Here, mature, axenically grown Arabidopsis thaliana plants were spray-inoculated with six diverse leaf-colonising bacteria. The transcriptomic changes in leaves were tracked over time and significant changes in ethylene marker (ARL2) expression were observed only two to four days after spray-inoculation. Whole transcriptome sequencing revealed that four days after inoculation, leaf transcriptional changes to colonisation by non-pathogenic and pathogenic bacteria differed in strength but not in the type of response. Inoculation of plants with different densities of the non-pathogenic bacterium Williamsia sp. Leaf354 showed that high bacterial titers caused disease phenotypes and led to severe transcriptional reprogramming with a strong focus on plant defence. An in silico epigenetic analysis of the data was congruent with the transcriptomic analysis. These findings suggest (1) that plant responses are not rapid after spray-inoculation, (2) that plant responses only differ in strength and (3) that plants respond to high titers of non-pathogenic bacteria with pathogen-like responses. Plain Language SummaryPlants are colonised by diverse bacteria affecting many aspects of plant life. Here we show that plants do not differentiate between different bacteria but measure their quantities to keep bacterial numbers in check.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.