Systems of plant defence

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Published on: November 8, 2012
Arabidopsis leaf cells, stained to visualise the cell wall and Botrytis cinerea mycelia

Today’s highlighted paper demonstrates the scale of the pathogen response in greater detail than has been published previously. Windram et al. (2012) profiled gene expression in Arabidopsis thaliana leaves every two hours after infection with Botrytis cinerea, until the fungus was truly established 48 hours after infection.

On the whole, until now research into the pathogen response has been at the pathway-level. Many details are known about the plant pathogen response, for example it is possible to identify loci responsible for resistance, as highlighted on this blog last week, and the intricacies of the oxidative burst are being discovered. When we understand these kinds of details, it is possible to make aphid-repellant crops, and harness the TALE tools used by Xanthomonas spp. to make disease resistant rice. On the other hand, they are just details – a close-up, zoomed in fraction of the whole, and broadly speaking it is luck if a piece of research provides anything of commercial worth.

A systems biology approach allows us to see the whole picture rather than the details of a close-up. From the data in Windram et al., we now know that a third of the Arabidopsis genome is differentially expressed in leaves infected with Botrytis compared to mock-inoculated controls. This represents a huge chunk of defence-related pathways, not previously studied, which could be affected by any attempts to improve pathogen resistance in plants.

This experiment was a timecourse, which allowed the team to record the timings of defence response pathways to two-hour time slots, like ethylene synthesis at 14 hours and response to jasmonic acid at 16 hours post-infection. Additionally, it showed that pathways including translation, photosynthesis, and protein phosphorylation were all down-regulated, and the order and timing in which they occurred. The ability to assign each process a time is important for modelling and predicting regulatory mechanisms.  (more…)

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