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The genome can be considered the blueprint for an organism. Composed of DNA, it harbours all organism-specific instructions for the synthesis of all structural components and their associated functions. The role of carriers of actual molecular structure and functions was believed to be exclusively assumed by proteins encoded in particular segments of the genome, the genes. In the process of converting the information stored genes into functional proteins, RNA – a third major molecule class – was discovered early on to act a messenger by copying the genomic information and relaying it to the protein-synthesizing machinery. Furthermore, RNA molecules were identified to assist in the assembly of amino acids into native proteins. For a long time, these - rather passive - roles were thought to be the sole purpose of RNA. However, in recent years, new discoveries have led to a radical revision of this view. First, RNA molecules with catalytic functions - thought to be the exclusive domain of proteins - were discovered. Then, scientists realized that much more of the genomic sequence is transcribed into RNA molecules than there are proteins in cells begging the question what the function of all these molecules are. Furthermore, very short and altogether new types of RNA molecules seemingly playing a critical role in orchestrating cellular processes were discovered. Thus, RNA has become a central research topic in molecular biology, even to the extent that some researcher dub cells as “RNA machines”. This thesis aims to contribute towards our understanding of RNA-related phenomena by applying Bioinformatics means. First, we performed a genome-wide screen to identify sites at which the chemical composition of DNA (the genotype) critically influences phenotypic traits (the phenotype) of the model plant Arabidopsis thaliana. Whole genome hybridisation arrays were used and an informatics strategy developed, to identify polymorphic sites from hybridisation to genomic DNA. Following this approach, not only were genotype-phenotype associations discovered across the entire Arabidopsis genome, but also regions not currently known to encode proteins, thus representing candidate sites for novel RNA functional molecules. By statistically associating them with phenotypic traits, clues as to their particular functions were obtained. Furthermore, these candidate regions were subjected to a novel RNA-function classification prediction method developed as part of this thesis. While determining the chemical structure (the sequence) of candidate RNA molecules is relatively straightforward, the elucidation of its structure-function relationship is much more challenging. Towards this end, we devised and implemented a novel algorithmic approach to predict the structural and, thereby, functional class of RNA molecules. In this algorithm, the concept of treating RNA molecule structures as graphs was introduced. We demonstrate that this abstraction of the actual structure leads to meaningful results that may greatly assist in the characterization of novel RNA molecules. Furthermore, by using graph-theoretic properties as descriptors of structure, we indentified particular structural features of RNA molecules that may determine their function, thus providing new insights into the structure-function relationships of RNA. The method (termed Grapple) has been made available to the scientific community as a web-based service. RNA has taken centre stage in molecular biology research and novel discoveries can be expected to further solidify the central role of RNA in the origin and support of life on earth. As illustrated by this thesis, Bioinformatics methods will continue to play an essential role in these discoveries.
In a very simplified view, the plant leaf growth can be reduced to two processes, cell division and cell expansion, accompanied by expansion of their surrounding cell walls. The vacuole, as being the largest compartment of the plant cell, plays a major role in controlling the water balance of the plant. This is achieved by regulating the osmotic pressure, through import and export of solutes over the vacuolar membrane (the tonoplast) and by controlling the water channels, the aquaporins. Together with the control of cell wall relaxation, vacuolar osmotic pressure regulation is thought to play an important role in cell expansion, directly by providing cell volume and indirectly by providing ion and pH homestasis for the cytosoplasm. In this thesis the role of tonoplast protein coding genes in cell expansion in the model plant Arabidopsis thaliana is studied and genes which play a putative role in growth are identified. Since there is, to date, no clearly identified protein localization signal for the tonoplast, there is no possibility to perform genome-wide prediction of proteins localized to this compartment. Thus, a series of recent proteomic studies of the tonoplast were used to compile a list of cross-membrane tonoplast protein coding genes (117 genes), and other growth-related genes from notably the growth regulating factor (GRF) and expansin families were included (26 genes). For these genes a platform for high-throughput reverse transcription quantitative real time polymerase chain reaction (RT-qPCR) was developed by selecting specific primer pairs. To this end, a software tool (called QuantPrime, see http://www.quantprime.de) was developed that automatically designs such primers and tests their specificity in silico against whole transcriptomes and genomes, to avoid cross-hybridizations causing unspecific amplification. The RT-qPCR platform was used in an expression study in order to identify candidate growth related genes. Here, a growth-associative spatio-temporal leaf sampling strategy was used, targeting growing regions at high expansion developmental stages and comparing them to samples taken from non-expanding regions or stages of low expansion. Candidate growth related genes were identified after applying a template-based scoring analysis on the expression data, ranking the genes according to their association with leaf expansion. To analyze the functional involvement of these genes in leaf growth on a macroscopic scale, knockout mutants of the candidate growth related genes were screened for growth phenotypes. To this end, a system for non-invasive automated leaf growth phenotyping was established, based on a commercially available image capture and analysis system. A software package was developed for detailed developmental stage annotation of the images captured with the system, and an analysis pipeline was constructed for automated data pre-processing and statistical testing, including modeling and graph generation, for various growth-related phenotypes. Using this system, 24 knockout mutant lines were analyzed, and significant growth phenotypes were found for five different genes.