Jay J. Thelen

Genome sequence of the palaeopolyploid soybean

Soybean (Glycine max) is one of the most important crop plants for seed protein and oil content, and for its capacity to fix atmospheric nitrogen through symbioses with soil-borne microorganisms. We sequenced the 1.1-gigabase genome by a whole-genome shotgun approach and integrated it with physical and high-density genetic maps to create a chromosome-scale draft sequence assembly. We predict 46,430 protein-coding genes, 70% more than Arabidopsis and similar to the poplar genome which, like soybean, is an ancient polyploid (palaeopolyploid). About 78% of the predicted genes occur in chromosome ends, which comprise less than one-half of the genome but account for nearly all of the genetic recombination. Genome duplications occurred at approximately 59 and 13 million years ago, resulting in a highly duplicated genome with nearly 75% of the genes present in multiple copies. The two duplication events were followed by gene diversification and loss, and numerous chromosome rearrangements. An accurate soybean genome sequence will facilitate the identification of the genetic basis of many soybean traits, and accelerate the creation of improved soybean varieties.

A Systematic Proteomic Study of Seed Filling in Soybean. Establishment of High-Resolution Two-Dimensional Reference Maps, Expression Profiles, and an Interactive Proteome Database

A high-throughput proteomic approach was employed to determine the expression profile and identity of hundreds of proteins during seed filling in soybean (Glycine max) cv Maverick. Soybean seed proteins were analyzed at 2, 3, 4, 5, and 6 weeks after flowering using two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. This led to the establishment of high-resolution proteome reference maps, expression profiles of 679 spots, and corresponding matrix-assisted laser desorption ionization time-of-flight mass spectrometry spectra for each spot. Database searching with these spectra resulted in the identification of 422 proteins representing 216 nonredundant proteins. These proteins were classified into 14 major functional categories. Proteins involved in metabolism, protein destination and storage, metabolite transport, and disease/defense were the most abundant. For each functional category, a composite expression profile is presented to gain insight into legume seed physiology and the general regulation of proteins associated with each functional class. Using this approach, an overall decrease in metabolism-related proteins versus an increase in proteins associated with destination and storage was observed during seed filling. The accumulation of unknown proteins, sucrose transport and cleavage enzymes, cysteine and methionine biosynthesis enzymes, 14-3-3-like proteins, lipoxygenases, storage proteins, and allergenic proteins during seed filling is also discussed. A user-intuitive database (http://oilseedproteomics.missouri.edu) was developed to access these data for soybean and other oilseeds currently being investigated.

Proteomic Analysis of Seed Filling in Brassica napus. Developmental Characterization of Metabolic Isozymes Using High-Resolution Two-Dimensional Gel Electrophoresis

Brassica napus (cultivar Reston) seed proteins were analyzed at 2, 3, 4, 5, and 6 weeks after flowering in biological quadruplicate using two-dimensional gel electrophoresis. Developmental expression profiles for 794 protein spot groups were established and hierarchical cluster analysis revealed 12 different expression trends. Tryptic peptides from each spot group were analyzed in duplicate using matrix-assisted laser desorption ionization time-of-flight mass spectrometry and liquid chromatography-tandem mass spectrometry. The identity of 517 spot groups was determined, representing 289 nonredundant proteins. These proteins were classified into 14 functional categories based upon the Arabidopsis (Arabidopsis thaliana) genome classification scheme. Energy and metabolism related proteins were highly represented in developing seed, accounting for 24.3% and 16.8% of the total proteins, respectively. Analysis of subclasses within the metabolism group revealed coordinated expression during seed filling. The influence of prominently expressed seed storage proteins on relative quantification data is discussed and an in silico subtraction method is presented. The preponderance of energy and metabolic proteins detected in this study provides an in-depth proteomic view on carbon assimilation in B. napus seed. These data suggest that sugar mobilization from glucose to coenzyme A and its acyl derivative is a collaboration between the cytosol and plastids and that temporal control of enzymes and pathways extends beyond transcription. This study provides a systematic analysis of metabolic processes operating in developing B. napus seed from the perspective of protein expression. Data generated from this study have been deposited into a web database (http://oilseedproteomics.missouri.edu) that is accessible to the public domain.

Musite, a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites

Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models (including condition-specific models) from users own data. In addition, with its easily extensible open source application programming interface, Musite is aimed at being an open platform for community-based development of machine learning-based phosphorylation site prediction applications. Musite is available at http://musite.sourceforge.net/.

Proteomic Analysis of Soybean Root Hairs After Infection by Bradyrhizobium japonicum

Infection of soybean root hairs by Bradyrhizobium japonicum is the first of several complex events leading to nodulation. In the current proteomic study, soybean root hairs after inoculation with B. japonicum were separated from roots. Total proteins were analyzed by two-dimensional (2-D) polyacrylamide gel electrophoresis. In one experiment, 96 protein spots were analyzed by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) to compare protein profiles between uninoculated roots and root hairs. Another 37 spots, derived from inoculated root hairs over different timepoints, were also analyzed by tandem MS (MS/MS). As expected, some proteins were differentially expressed in root hairs compared with roots (e.g., a chitinase and phosphoenolpyruvate carboxylase). Out of 37 spots analyzed by MS/MS, 27 candidate proteins were identified by database comparisons. These included several proteins known to respond to rhizobial inoculation (e.g., peroxidase and phenylalanine-ammonia lyase). However, novel proteins were also identified (e.g., phospholipase D and phosphoglucomutase). This research establishes an excellent system for the study of root-hair infection by rhizobia and, in a more general sense, the functional genomics of a single, plant cell type. The results obtained also indicate that proteomic studies with soybean, lacking a complete genome sequence, are practical.

Quantitative Proteomics in Plants: Choices in Abundance

The field of proteomics is evolving from cataloguing proteins under static conditions to comparative analyses. Defining proteins that change in abundance, form, location, or activity may indicate which proteins are involved in developmental changes or responses to alterations in environmental conditions. Such studies are facilitated by an increasing number of complementary technical options for performing quantitative proteomic comparisons. As with any developing field, however, rapid expansion in new techniques introduces concerns about choosing the appropriate approach. The goals of this perspective essay, therefore, are both to introduce the various options that are available (or nearly so) for quantitative proteomics and to discuss considerations related to applying these methods in the laboratory.

Large Scale Identification and Quantitative Profiling of Phosphoproteins Expressed during Seed Filling in Oilseed Rape

Seed filling is a dynamic, temporally regulated phase of seed development that determines the composition of storage reserves in mature seeds. Although the metabolic pathways responsible for storage reserve synthesis such as carbohydrates, oils, and proteins are known, little is known about their regulation. Protein phosphorylation is a ubiquitous form of regulation that influences many aspects of dynamic cellular behavior in plant biology. Here a systematic study has been conducted on five sequential stages (2, 3, 4, 5, and 6 weeks after flowering) of seed development in oilseed rape (Brassica napus L. Reston) to survey the presence and dynamics of phosphoproteins. High resolution two-dimensional gel electrophoresis in combination with a phosphoprotein-specific Pro-Q Diamond phosphoprotein fluorescence stain revealed ∼300 phosphoprotein spots. Of these, quantitative expression profiles for 234 high quality spots were established, and hierarchical cluster analyses revealed the occurrence of six principal expression trends during seed filling. The identity of 103 spots was determined using LC-MS/MS. The identified spots represented 70 non-redundant phosphoproteins belonging to 10 major functional categories including energy, metabolism, protein destination, and signal transduction. Furthermore phosphorylation within 16 non-redundant phosphoproteins was verified by mapping the phosphorylation sites by LC-MS/MS. Although one of these sites was postulated previously, the remaining sites have not yet been reported in plants. Phosphoprotein data were assembled into a web database. Together this study provides evidence for the presence of a large number of functionally diverse phosphoproteins, including global regulatory factors like 14-3-3 proteins, within developing B. napus seed.

In-Depth Investigation of the Soybean Seed-Filling Proteome and Comparison with a Parallel Study of Rapeseed

To better understand the metabolic processes of seed filling in soybean (Glycine max), two complementary proteomic approaches, two-dimensional gel electrophoresis (2-DGE) and semicontinuous multidimensional protein identification technology (Sec-MudPIT) coupled with liquid chromatography-mass spectrometry, were employed to analyze whole seed proteins at five developmental stages. 2-DGE and Sec-MudPIT analyses collectively identified 478 nonredundant proteins with only 70 proteins common to both datasets. 2-DGE data revealed that 38% of identified proteins were represented by multiple 2-DGE species. Identified proteins belonged to 13 (2-DGE) and 15 (Sec-MudPIT) functional classes. Proteins involved in metabolism, protein destination and storage, and energy were highly represented, collectively accounting for 61.1% (2-DGE) and 42.2% (Sec-MudPIT) of total identified proteins. Membrane proteins, based upon transmembrane predictions, were 3-fold more prominent in Sec-MudPIT than 2-DGE. Data were integrated into an existing soybean proteome database (www.oilseedproteomics.missouri.edu). The integrated quantitative soybean database was compared to a parallel study of rapeseed (Brassica napus) to further understand the regulation of intermediary metabolism in protein-rich versus oil-rich seeds. Comparative analyses revealed (1) up to 3-fold higher expression of fatty acid biosynthetic proteins during seed filling in rapeseed compared to soybean; and (2) approximately a 48% higher number of protein species and a net 80% higher protein abundance for carbon assimilatory and glycolytic pathways leading to fatty acid synthesis in rapeseed versus soybean. Increased expression of glycolytic and fatty acid biosynthetic proteins in rapeseed compared to soybean suggests that a possible mechanistic basis for higher oil in rapeseed involves the concerted commitment of hexoses to glycolysis and eventual de novo fatty acid synthesis pathways.

Systems Analysis of Seed Filling in Arabidopsis: Using General Linear Modeling to Assess Concordance of Transcript and Protein Expression

Previous systems analyses in plants have focused on a single developmental stage or time point, although it is often important to additionally consider time-index changes. During seed development a cascade of events occurs within a relatively brief time scale. We have collected protein and transcript expression data from five sequential stages of Arabidopsis (Arabidopsis thaliana) seed development encompassing the period of reserve polymer accumulation. Protein expression profiling employed two-dimensional gel electrophoresis coupled with tandem mass spectrometry, while transcript profiling used oligonucleotide microarrays. Analyses in biological triplicate yielded robust expression information for 523 proteins and 22,746 genes across the five developmental stages, and established 319 protein/transcript pairs for subsequent pattern analysis. General linear modeling was used to evaluate the protein/transcript expression patterns. Overall, application of this statistical assessment technique showed concurrence for a slight majority (56%) of expression pairs. Many specific examples of discordant protein/transcript expression patterns were detected, suggesting that this approach will be useful in revealing examples of posttranscriptional regulation.

P3DB: a plant protein phosphorylation database

P3DB (http://www.p3db.org/) provides a resource of protein phosphorylation data from multiple plants. The database was initially constructed with a dataset from oilseed rape, including 14 670 nonredundant phosphorylation sites from 6382 substrate proteins, representing the largest collection of plant phosphorylation data to date. Additional protein phosphorylation data are being deposited into this database from large-scale studies of Arabidopsis thaliana and soybean. Phosphorylation data from current literature are also being integrated into the P3DB. With a web-based user interface, the database is browsable, downloadable and searchable by protein accession number, description and sequence. A BLAST utility was integrated and a phosphopeptide BLAST browser was implemented to allow users to query the database for phosphopeptides similar to protein sequences of their interest. With the large-scale phosphorylation data and associated web-based tools, P3DB will be a valuable resource for both plant and nonplant biologists in the field of protein phosphorylation.