The Mucilage Proteome of Maize (Zea mays L.) Primary Roots

Department of Plant Nutrition, China Agricultural University, Beijing, PR China.
Journal of Proteome Research (Impact Factor: 4.25). 06/2010; 9(6):2968-76. DOI: 10.1021/pr901168v
Source: PubMed


Maize (Zea mays L.) root cap cells secrete a large variety of compounds including proteins via an amorphous gel structure called mucilage into the rhizosphere. In the present study, mucilage secreted by primary roots of 3-4 day old maize seedlings was collected under axenic conditions, and the constitutively secreted proteome was analyzed. A total of 2848 distinct extracellular proteins were identified by nanoLC-MS/MS. Among those, metabolic proteins (approximately 25%) represented the largest class of annotated proteins. Comprehensive sets of proteins involved in cell wall metabolism, scavenging of reactive oxygen species, stress response, or nutrient acquisition provided detailed insights in functions required at the root-soil interface. For 85-94% of the mucilage proteins previously identified in the relatively small data sets of the dicot species pea, Arabidopsis, and rapeseed, a close homologue was identified in the mucilage proteome of the monocot model plant maize, suggesting a considerable degree of conservation between mono and dicot mucilage proteomes. Homologues of a core set of 12 maize proteins including three superoxide dismutases and four chitinases, which provide protection from fungal infections, were present in all three mucilage proteomes investigated thus far in the dicot species Arabidopsis, rapeseed, and pea and might therefore be of particular importance.

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Available from: Chunjian Li, Oct 23, 2015
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    • "Recent efforts to extend experimental data collations beyond Arabidopsis have been demonstrated for gene co-expression (Obayashi et al. 2009) by inclusion of data from soybean, rice and other plant species (Obayashi et al. 2014). A similar extension for subcellular localizations data has not yet been reported, but a significant body of experimental protein localization studies exists for barley (Endler et al. 2006; Ploscher et al. 2011), wheat (Kamal et al. 2012; Suliman et al. 2013), rice (Natera et al. 2008; Reiland et al. 2011) and maize (Huang et al. 2013; Ma et al. 2010; Majeran et al. 2012). These crops have also been the focus of large-scale genome sequencing projects (Chapman et al. "
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    ABSTRACT: Barley, wheat, rice and maize provide the bulk of human nutrition and have extensive industrial use as agricultural products. The genomes of these crops each contains >40,000 genes encoding proteins; however, the major genome databases for these species lack annotation information of protein subcellular location for >80% of these gene products. We address this gap, by constructing the compendium of crop protein subcellular locations called crop Proteins with Annotated Locations (cropPAL). Subcellular location is most commonly determined by fluorescent protein tagging of live cells or mass spectrometry detection in subcellular purifications, but can also be predicted from amino acid sequence or protein expression patterns. The cropPAL database collates 556 published studies, from >300 research institutes in >30 countries that have been previously published, as well as compiling eight pre-computed subcellular predictions for all Hordeum vulgare, Triticum aestivum, Oryza sativa and Zea mays protein sequences. The data collection including metadata for proteins and published studies can be accessed through a search portal The subcellular localization information housed in cropPAL helps to depict plant cells as compartmentalized protein networks that can be investigated for improving crop yield and quality, and developing new biotechnological solutions to agricultural challenges.
    Full-text · Article · Nov 2015 · Plant and Cell Physiology
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    • "These proteins are believed to play an important role in the rhizosphere and a relatively high number (54%) had predicted signal peptides. Ma et al. (2010) collected proteins secreted in the mucilage of primary maize roots. Using a combination of 1D SDS-PAGE and HPLC-MS/MS, the presence of 2848 proteins were reported, which is over 50 times more compared to earlier quantitative studies of root mucilage based on 2D-PAGE or MudPIT (Basu et al., 2006; Wen et al., 2007). "
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    ABSTRACT: The plant secretome refers to the set of proteins secreted out of the plant cell into the surrounding extracellular space commonly referred to as the apoplast. Secreted proteins maintain cell structure and acts in signaling and are crucial for stress responses where they can interact with pathogen effectors and control the extracellular environment. Typically, secreted proteins contain an N-terminal signal peptide and are directed through the endoplasmic reticulum/Golgi pathway. However, in plants many proteins found in the secretome lack such a signature and might follow alternative ways of secretion. This review covers techniques to isolate plant secretomes and how to identify and quantify their constituent proteins. Furthermore, bioinformatical tools to predict secretion signals and define the putative secretome are presented. Findings from proteomic studies and important protein families of plant secretomes, such as proteases and hydrolases, are highlighted.
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    • "Over the past decade, mass spectrometry (MS)-based shotgun proteomics has emerged as a high-throughput, unbiased method for the identification of proteins in complex samples [1], [2]. Its application holds great potential in identifying comprehensive proteins profile in all kinds of species [3], [4]. Brechenmacher, L. analyzed the proteome of isolated soybean root hair cells using shotgun proteomics approaches. "
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    ABSTRACT: Shotgun proteomics data analysis usually relies on database search. Because commonly employed protein sequence databases of most species do not contain sufficient protein information, the application of shotgun proteomics to the research of protein sequence profile remains a big challenge, especially to the species whose genome has not been sequenced yet. In this paper, we present a workflow with integrated database to partly address this problem. First, we downloaded the homologous species database. Next, we identified the transcriptome of the sample, created a protein sequence database based on the transcriptome data, and integtrated it with homologous species database. Lastly, we developed a workflow for identifying peptides simultaneously from shotgun proteomics data. We used datasets from orange leaves samples to demonstrate our workflow. The results showed that the integrated database had great advantage on orange shotgun proteomics data analysis compared to the homologous species database, an 18.5% increase in number of proteins identification.
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