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.
If molecular profiling of the defence response gives us the fine detail, these data are a sketched outline. The gaps will be filled in as the data are mined over and over again by scientists searching for parts of the defence response to improve and understand better, and the pathway interactions implied by the data are investigated. You can see the data underlying the models the team made on GEO.
Katherine Denby, the corresponding author on this paper, said, ‘For me the exciting thing about the paper is the scale of the response. By generating a long time series of expression data, we can work out what are the early events in the defence response and start to identify regulatory events between genes. We want to go beyond the identification of single components of the defence response, and start to elucidate the network of regulatory interactions that occurs in response to pathogen infection. The use of high-resolution time series data with modelling algorithms is a powerful approach to do this. We have started this in this paper but want to develop this much further and also look for shared regulatory interactions between different stress responses.’
Windram et al. used CATMA microarrays, the MAANOVA microarray data analysis package to sift through the data and generate expression profiles, the GP2S method (Stegle et al., 2010) to identify differentially expressed genes, Splinecluster (Heard et al., 2005) to cluster them, and BiNGO (Maere et al., 2005) to assign gene ontology labels to them.
Highlighted article: Oliver Windram, Priyadharshini Madhou, Stuart McHattie, Claire Hill, Richard Hickman, Emma Cooke, Dafyd J. Jenkins, Christopher A. Penfold, Laura Baxter, Emily Breeze, Steven J. Kiddle, Johanna Rhodes, Susanna Atwell, Daniel J. Kliebenstein, Youn-sung Kim, Oliver Stegle, Karsten Borgwardt, Cunjin Zhang, Alex Tabrett, Roxane Legaie, Jonathan Moore, Bärbel Finkenstadt, David L. Wild, Andrew Mead, David Rand, Jim Beynon, Sascha Ott, Vicky Buchanan-Wollastonand Katherine J. Denby (2012). Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis. The Plant Cell 24:3530-3557. DOI: http://dx.doi.org/10.1105/tpc.112.102046
Image credits: Arabidopsis leaf infected with Botrytis cinerea, courtesy of Katherine Denby. Strawberry fruit rot Botrytis cinerea by Aardbei Lambada vruchtrot via Wikipedia.