Zhao Song

Bio: Zhao Song is an academic researcher from University of Missouri. The author has contributed to research in topics: Peptide mass fingerprinting & Proteomics. The author has an hindex of 5, co-authored 9 publications receiving 414 citations. Previous affiliations of Zhao Song include United States Department of Agriculture.

Proteomic analysis of seed filling in Brassica napus. Developmental characterization of metabolic isozymes using high-resolution two-dimensional gel electrophoresis.

Abstract: 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.

Systems analysis of seed filling in Arabidopsis: using general linear modeling to assess concordance of transcript and protein expression

Abstract: 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.

Establishment of a Protein Reference Map for Soybean Root Hair Cells

Abstract: Root hairs are single tubular cells formed from the differentiation of epidermal cells on roots. They are involved in water and nutrient uptake and represent the infection site on leguminous roots by rhizobia, soil bacteria that establish a nitrogen-fixing symbiosis. Root hairs develop by polar cell expansion or tip growth, a unique mode of plant growth shared only with pollen tubes. A more complete characterization of root hair cell biology will lead to a better understanding of tip growth, the rhizobial infection process, and also lead to improvements in plant water and nutrient uptake. We analyzed the proteome of isolated soybean (Glycine max) root hair cells using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and shotgun proteomics (1D-PAGE-liquid chromatography and multidimensional protein identification technology) approaches. Soybean was selected for this study due to its agronomic importance and its root size. The resulting soybean root hair proteome reference map identified 1,492 different proteins. 2D-PAGE followed by mass spectrometry identified 527 proteins from total cell contents. A complementary shotgun analysis identified 1,134 total proteins, including 443 proteins that were specific to the microsomal fraction. Only 169 proteins were identified by the 2D-PAGE and shotgun methods, which highlights the advantage of using both methods. The proteins identified are involved not only in basic cell metabolism but also in functions more specific to the single root hair cell, including water and nutrient uptake, vesicle trafficking, and hormone and secondary metabolism. The data presented provide useful insight into the metabolic activities of a single, differentiated plant cell type.

Development and assessment of scoring functions for protein identification using PMF data.

Abstract: PMF is one of the major methods for protein identification using the MS technology. It is faster and cheaper than MS/MS. Although PMF does not differentiate trypsin-digested peptides of identical mass, which makes it less informative than MS/MS, current computational methods for PMF have the potential to improve its detection accuracy by better use of the information content in PMF spectra. We developed a number of new probability-based scoring functions for PMF protein identification based on the MOWSE algorithm. We considered a detailed distribution of matching masses in a protein database and peak intensity, as well as the likelihood of peptide matches to be close to each other in a protein sequence. Our computational methods are assessed and compared with other methods using PMF data of 52 gel spots of known protein standards. The comparison shows that our new scoring schemes have higher or comparable accuracies for protein identification in comparison to the existing methods. Our software is freely available upon request. The scoring functions can be easily incorporated into other proteomics software packages.

Confidence assessment for protein identification by using peptide-mass fingerprinting data.

Abstract: Protein identification using Peptide Mass Fingerprinting (PMF) data remains an important yet only partially solved problem. Current computational methods may lead to false positive identification since the top hit from a database search may not be the target protein. In addition, the identification scores assigned singly by a scoring function (raw scores) are not normalized. Therefore, the ranking based on raw scores may be biased. To address the above issue, we have developed a statistical model to evaluate the confidence of the raw score and to improve the ranking of proteins for identification. The results show that the statistical model better ranks the correct protein than the raw scores. Our study provides a new method to enhance the accuracy of protein identification by using PMF data. We incorporated the method into our software package "Protein-Decision" together with a user-friendly graphical interface. A standalone version of Protein-Decision is freely available at http://digbio. missouri.edu/ProteinDecision/.

The Root Hair “Infectome” of Medicago truncatula Uncovers Changes in Cell Cycle Genes and Reveals a Requirement for Auxin Signaling in Rhizobial Infection

Abstract: Nitrogen-fixing rhizobia colonize legume roots via plant-made intracellular infection threads. Genetics has identified some genes involved but has not provided sufficient detail to understand requirements for infection thread development. Therefore, we transcriptionally profiled Medicago truncatula root hairs prior to and during the initial stages of infection. This revealed changes in the responses to plant hormones, most notably auxin, strigolactone, gibberellic acid, and brassinosteroids. Several auxin responsive genes, including the ortholog of Arabidopsis thaliana Auxin Response Factor 16, were induced at infection sites and in nodule primordia, and mutation of ARF16a reduced rhizobial infection. Associated with the induction of auxin signaling genes, there was increased expression of cell cycle genes including an A-type cyclin and a subunit of the anaphase promoting complex. There was also induction of several chalcone O-methyltransferases involved in the synthesis of an inducer of Sinorhizobium meliloti nod genes, as well as a gene associated with Nod factor degradation, suggesting both positive and negative feedback loops that control Nod factor levels during rhizobial infection. We conclude that the onset of infection is associated with reactivation of the cell cycle as well as increased expression of genes required for hormone and flavonoid biosynthesis and that the regulation of auxin signaling is necessary for initiation of rhizobial infection threads.

Integrating omic approaches for abiotic stress tolerance in soybean.

Abstract: Soybean production is greatly influenced by abiotic stresses imposed by environmental factors such as drought, water submergence, salt, and heavy metals. A thorough understanding of plant response to abiotic stress at the molecular level is a prerequisite for its effective management. The molecular mechanism of stress tolerance is complex and requires information at the omic level to understand it effectively. In this regard, enormous progress has been made in the omics field in the areas of genomics, transcriptomics, and proteomics. The emerging field of ionomics is also being employed for investigating abiotic stress tolerance in soybean. Omic approaches generate a huge amount of data, and adequate advancements in computational tools have been achieved for effective analysis. However, the integration of omic-scale information to address complex genetics and physiological questions is still a challenge. In this review, we have described advances in omic tools in the view of conventional and modern approaches being used to dissect abiotic stress tolerance in soybean. Emphasis was given to approaches such as quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection (GS). Comparative genomics and candidate gene approaches are also discussed considering identification of potential genomic loci, genes, and biochemical pathways involved in stress tolerance mechanism in soybean. This review also provides a comprehensive catalog of available online omic resources for soybean and its effective utilization. We have also addressed the significance of phenomics in the integrated approaches and recognized high-throughput multi-dimensional phenotyping as a major limiting factor for the improvement of abiotic stress tolerance in soybean.

Seed-Development Programs: A Systems Biology–Based Comparison Between Dicots and Monocots

Abstract: Seeds develop differently in dicots and monocots, especially with respect to the major storage organs. High-resolution transcriptome data have provided the first insights into the molecular networks and pathway interactions that function during the development of individual seed compartments. Here, we review mainly recent data obtained by systems biology–based approaches, which have allowed researchers to construct and model complex metabolic networks and fluxes and identify key limiting steps in seed development. Comparative coexpression network analyses define evolutionarily conservative (FUS3/ABI3/LEC1) and divergent (LEC2) networks in dicots and monocots. Finally, we discuss the determination of seed size—an important yield-related characteristic—as mediated by a number of processes (maternal and epigenetic factors, fine-tuned regulation of cell death in distinct seed compartments, and endosperm growth) and underlying genes defined through mutant analyses. Altogether, systems approaches can make impor...

Comparative Transcriptome Analysis of Three Oil Palm Fruit and Seed Tissues That Differ in Oil Content and Fatty Acid Composition

Abstract: Oil palm (Elaeis guineensis) produces two oils of major economic importance, commonly referred to as palm oil and palm kernel oil, extracted from the mesocarp and the endosperm, respectively. While lauric acid predominates in endosperm oil, the major fatty acids (FAs) of mesocarp oil are palmitic and oleic acids. The oil palm embryo also stores oil, which contains a significant proportion of linoleic acid. In addition, the three tissues display high variation for oil content at maturity. To gain insight into the mechanisms that govern such differences in oil content and FA composition, tissue transcriptome and lipid composition were compared during development. The contribution of the cytosolic and plastidial glycolytic routes differed markedly between the mesocarp and seed tissues, but transcriptional patterns of genes involved in the conversion of sucrose to pyruvate were not related to variations for oil content. Accumulation of lauric acid relied on the dramatic up-regulation of a specialized acyl-acyl carrier protein thioesterase paralog and the concerted recruitment of specific isoforms of triacylglycerol assembly enzymes. Three paralogs of the WRINKLED1 (WRI1) transcription factor were identified, of which EgWRI1-1 and EgWRI1-2 were massively transcribed during oil deposition in the mesocarp and the endosperm, respectively. None of the three WRI1 paralogs were detected in the embryo. The transcription level of FA synthesis genes correlated with the amount of WRI1 transcripts and oil content. Changes in triacylglycerol content and FA composition of Nicotiana benthamiana leaves infiltrated with various combinations of WRI1 and FatB paralogs from oil palm validated functions inferred from transcriptome analysis.