@phdthesis{Arvidsson2010,
  author    = {Arvidsson, Samuel Janne},
  title     = {Identification of growth-related tonoplast proteins in Arabidopsis thaliana},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-52408},
  school      = {Universit{\"a}t Potsdam},
  year      = {2010},
  abstract  = {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.},
  language  = {en}
}