Predicting plant microRNAs based on functional and biogenesis data

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How do sharks and roses develop from a single cell? This question has intrigued scientists for a long time because all plants and animals derive from a single cell, the fertilised egg. That single cell develops into an entire organism through many-many cell divisions but the genetic information does not change during those cell divisions. Therefore all our cells contain the same genetic information. However, there are many different tissues with specialised functions in our body. These tissues are different from each other because a different set of proteins are present in the cells that make up a certain tissue. The reason for this is that only a certain set of genes are active in each cell. Gene expression is a complex process therefore it can be regulated at several levels. First the chromosomal DNA is transcribed into mRNA and this step is regulated by various mechanisms. The mRNAs are then processed and translocated to the cytoplasm where they are translated into proteins. Accumulation of a protein can also be regulated by various mechanisms. One of the most recently discovered regulatory layers involves short RNAs to regulate the translation efficiency of mRNAs. A group of these short RNAs are called microRNAs (miRNAs) since these molecules are very short, only 21-24 nucleotides. Research in the last ten years found that miRNAs play a very important role in normal development.miRNAs are generated from a longer precursor molecule, which is folded into a characteristic stem-loop shape. This shape and a few other features of the precursor and mature miRNAs, which are all linked to how the miRNAs are produced (biogenesis) can be identified by computer programs. Several computer programs were developed that can predict miRNAs from a large number of short sequences captured from different tissues. These programs rely on different parameters such as the length of the stem and loop and many others but the value of these parameters are subjective and not experimentally proven. Naturally, programs using more stringent parameters predict fewer miRNAs than programs using more relaxed parameters. Until now, stringent parameters have been applied to ensure a low rate of false positive predictions. However, due to the strict criteria, miRNAs with a slightly shorter stem, bigger loop, etc. might have been missed. miRNAs recognise specific sequences on mRNAs they regulate and in plants they cause a cleavage between the 10th and 11th position within that sequence. The cleaved fragments can be captured providing functional information in addition to biogenesis features. Recently we developed a program that can compare all cleaved mRNA fragments to all small RNAs found in a sample. This program found more than 4000 fragments that were potentially cleaved by about 3500 small RNAs in the model plant Arabidopsis thaliana. Hundreds of those small RNAs showed similar features to miRNAs but marginally missed the stringent criteria of the prediction programs. Based on these we hypothesise that many miRNAs have been missed by computer programs and these could be identified using functional and biogenesis data together. The aim of the proposal is to test this hypothesis by developing a new program that can consider both biogenesis and functional data and identify many new miRNAs in the model species Arabidopsis and the crop species tomato. In the presence of functional data, we propose to use slightly less stringent biogenesis parameters to confidently predict new miRNAs. We will generate specific small RNA and cleaved mRNA data from normal plants and also from plants that contain reduced level of miRNAs or cleaved mRNA fragments. These will be used to identify experimentally validated parameters.