Peptide separation methodologies for in-depth proteomics in Arabidopsis.

In the post-genome era, several tools that have increased our global understanding of the molecular basis of several cell-based phenomena have been developed. However, proteomics has not been efficiently integrated with the other 'omics' (e.g. transcriptomics and metabolomics), because of the relatively low number of proteins identified by mass spectrometry (MS). Peptides from low-abundance proteins are often not detected by MS due to ionization suppression. To improve the number of peptide identifications in MS analyses, we propose three separation methodologies; namely, OFFGEL electrophoresis, 2D-liquid chromatography (LC) and the long monolithic silica-C18 capillary column method, with the common aim to decrease peptide complexity prior to MS analyses. Proteomics using the above three peptide separation methods were separately applied to protoplasts collected from the epidermal cell layer of Arabidopsis roots using fluorescence-activated cell sorting. In each method alone, 1,132, 836 and 795 proteins were specifically identified, respectively. This has allowed the identification of 1,493 proteins with no redundancy and with <1.0% false discovery rate. Moreover, approximately two-thirds of these proteins are identified here for the first time in the epidermal cell layer. These results show that use of different proteomic approaches can increase the total number of proteins identified. We propose that the integration of data from these methodologies represents a powerful tool for generation of proteome maps by enabling identification of low-abundance proteins in the various Arabidopsis root cell layers.

A GAL4-based targeted activation tagging system in Arabidopsis thaliana.

Activation tagging is a powerful tool for discovering novel genes that are not easily identified by loss-of-function (lof) screening due to genetic redundancy or lethality. Although the current activation tagging system, which involves a viral enhancer sequence, has been used for a decade, alternative methods that allow organ- or tissue-specific activation are required to identify genes whose strong activation leads to loss of fertility or viability. Here, we established a GAL4/UAS activation-tagging system in Arabidopsis thaliana. Host plants that express a synthetic transcription activator GAL4:VP16 (GV) in an organ- or tissue-specific manner were transformed with a T-DNA harboring tandem copies of UAS, a GAL4-binding sequence. Using a post-embryonic and root-specific GV-expressing line as the host plant, we isolated several dominant mutants with abnormal root tissue patterns, designated as uas-tagged root patterning (urp) mutants, and identified their causal genes. Notably, most URP genes encoded putative transcription factors, indicating that the GAL4/UAS activation tagging system effectively identifies genes with regulatory functions. lof phenotypes of most URP genes were either local patterning defects or visible only if homologous genes were disrupted simultaneously or independently. Systemic overexpression of some URP genes resulted in seedling lethality. These results indicate that GAL4/UAS activation tagging is a powerful method for identifying genes with biological functions that are not readily identified by conventional screening methods.