Most of the RNA molecules in cells are involved in protein production (ribosomal, transfer or messenger RNAs), however there are RNA molecules with other functions. A recently discovered class of non-coding short RNAs (sRNA) regulate the level of protein production in a gene specific manner. These sRNAs can recognise specific mRNAs or DNA sequences because they have partially complementary sequences to them. As a result of this interaction expression of the targeted mRNAs is significantly reduced or transcription of the targeted DNA is suppressed. More than 70 000 different sRNAs were found in the model plant species Arabidopsis and we showed that these sRNAs are derived from more than 4000 clusters. We found that sRNAs are also produced from clusters in tomato fruits and that the expression of clusters was consistent between different samples of the same tomato type. Crop species have different cultivars that can be cross-fertilised but have different characteristics (e.g. fruit/seed size, colour, taste, texture, etc.). Arabidopsis - a non-cultivated model plant / also has different forms, which are called ecotypes. We tested the hypothesis that different clusters are active in different cultivars of tomato and different Arabidopsis ecotypes. In a preliminary experiment we found several sRNA clusters that accumulated at a different level between four Arabidopsis ecotypes and also between three tomato cultivars. Based on these data we propose to develop a tool that uses sRNAs as molecular markers of valuable characteristics in tomato. In the first phase of the project we will identify all the sRNA clusters in the tomato genome. Probes complementary to the identified sRNA clusters will be spotted on small glass plates. These microarrays will be used to profile sRNAs in a large number of tomato cultivars. Statistical analysis will then be used to establish the correlation between the phenotypes and production of specific sRNAs. Array based expression profile of sRNAs will identify a subset of sRNA clusters that are predictors of phenotypes. The proposed work will also evaluate the possibility that important agronomic traits can be modified when selected sRNAs are overexpressed or suppressed transgenically. These analyses and manipulations of sRNAs have the potential to establish a novel approach to crop improvement that is based on variation of regulatory RNAs rather than of protein coding genes.

Mutlicellular organisms possess tissues containing various cell types. Each cell type differentiates to perform defined functions. We are interested in processes that occur in specific cells. The aim is to identify mechanisms allowing differential gene expression in particular cells, and the consequences of this differential expression in those cells. For example, Applicant 1 aims to identify differences in methylation of promoter elements between cell-types, applicants 3-5 wish to determine transcript abundance from specific cell-types, and then use that information to generate developmental and circadian networks, or understand responses to pathogen attack or cell signalling. As few as ten plant cells have been collected via LCM prior to analysis of gene expression, and so all the work proposed is feasible and we should make significant advances in each of our fields.

Most of the RNA molecules in cells are involved in protein production (ribosomal, transfer or messenger RNAs), however there are RNA molecules with other functions. A recently discovered class of non-coding short RNAs (sRNA) regulate the level of protein production in a gene specific manner. These sRNAs can recognise specific mRNAs or DNA sequences because they have partially complementary sequences to them. As a result of this interaction expression of the targeted mRNAs is significantly reduced or transcription of the targeted DNA is suppressed. More than 70 000 different sRNAs were found in the model plant species Arabidopsis and we showed that these sRNAs are derived from more than 4000 clusters. We found that sRNAs are also produced from clusters in tomato fruits and that the expression of clusters was consistent between different samples of the same tomato type. Crop species have different cultivars that can be cross-fertilised but have different characteristics (e.g. fruit/seed size, colour, taste, texture, etc.). Arabidopsis - a non-cultivated model plant / also has different forms, which are called ecotypes. We tested the hypothesis that different clusters are active in different cultivars of tomato and different Arabidopsis ecotypes. In a preliminary experiment we found several sRNA clusters that accumulated at a different level between four Arabidopsis ecotypes and also between three tomato cultivars. Based on these data we propose to develop a tool that uses sRNAs as molecular markers of valuable characteristics in tomato. In the first phase of the project we will identify all the sRNA clusters in the tomato genome. Probes complementary to the identified sRNA clusters will be spotted on small glass plates. These microarrays will be used to profile sRNAs in a large number of tomato cultivars. Statistical analysis will then be used to establish the correlation between the phenotypes and production of specific sRNAs. Array based expression profile of sRNAs will identify a subset of sRNA clusters that are predictors of phenotypes. The proposed work will also evaluate the possibility that important agronomic traits can be modified when selected sRNAs are overexpressed or suppressed transgenically. These analyses and manipulations of sRNAs have the potential to establish a novel approach to crop improvement that is based on variation of regulatory RNAs rather than of protein coding genes.

Peatlands store more carbon than any other terrestrial ecosystem, both in the UK and globally. As a result of human disturbance they are rapidly losing this carbon to the atmosphere, contributing significantly to global greenhouse gas emissions and climate change. We propose to turn this problem into a solution, by re-establishing and augmenting the unique natural capacity of peatlands to remove CO2 from the atmosphere and to store it securely for millennia. We will do this by working with natural processes to recreate, and where possible enhance, the environmental conditions that lead to peat formation, in both lowland and upland Britain. At the same time, we will optimise conditions to avoid emissions of methane and nitrous oxide that could offset the benefits of CO2 removal; develop innovative cropping and management systems to augment rates of CO2 uptake; evaluate whether we can further increase peat carbon accumulation through the formation and addition of biomass and biochar; and develop new economic models to support greenhouse gas removal by peatlands as part of profitable and sustainable farming and land management systems. Implementation of these new approaches to the 2.3 million hectares of degraded upland and lowland peat in the UK has the potential to remove significant quantities of greenhouse gases from the atmosphere, to secure carbon securely and permanently within a productive, biodiverse and self-sustaining ecosystem, and thereby to help the UK to achieve its ambition of having net zero greenhouse gas emissions by 2050.