Dynamic Proteomic Characteristics and Network Integration Revealing Key Proteins for Two Kernel Tissue Developments in Popcorn

Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of analytical, biochemical, and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes accompanying plant development, senescence, and response to biotic or abiotic stress. Among individual post-genomic methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. During the recent decades, multiple methodological advances were introduced in different branches of life science, although only some of them were established in seed proteomics so far. Therefore, here we discuss main methodological approaches already employed in seed proteomics, as well as those still waiting for implementation in this field of plant research, with a special emphasis on sample preparation, data acquisition, processing, and post-processing. Thereby, the overall goal of this review is to bring new methodologies emerging in different areas of proteomics research (clinical, food, ecological, microbial, and plant proteomics) to the broad society of seed biologists.

Highly interwoven communities of a gene regulatory network unveil topologically important genes for maize seed development

The complex interactions between transcription factors (TFs) and their target genes in a spatially and temporally specific manner are crucial to all cellular processes. Reconstruction of gene regulatory networks (GRNs) from gene expression profiles can help to decipher TF-gene regulations in a variety of contexts; however, the inevitable prediction errors of GRNs hinder optimal data mining of RNA-Seq transcriptome profiles. Here we perform an integrative study of Zea mays (maize) seed development in order to identify key genes in a complex developmental process. First, we reverse engineered a GRN from 78 maize seed transcriptome profiles. Then, we studied collective gene interaction patterns and uncovered highly interwoven network communities as the building blocks of the GRN. One community, composed of mostly unknown genes interacting with opaque2, brittle endosperm1 and shrunken2, contributes to seed phenotypes. Another community, composed mostly of genes expressed in the basal endosperm transfer layer, is responsible for nutrient transport. We further integrated our inferred GRN with gene expression patterns in different seed compartments and at various developmental stages and pathways. The integration facilitated a biological interpretation of the GRN. Our yeast one-hybrid assays verified six out of eight TF-promoter bindings in the reconstructed GRN. This study identified topologically important genes in interwoven network communities that may be crucial to maize seed development.

iTRAQ-Based Proteomics Analysis and Network Integration for Kernel Tissue Development in Maize

Grain weight is one of the most important yield components and a developmentally complex structure comprised of two major compartments (endosperm and pericarp) in maize (Zea mays L.), however, very little is known concerning the coordinated accumulation of the numerous proteins involved. Herein, we used isobaric tags for relative and absolute quantitation (iTRAQ)-based comparative proteomic method to analyze the characteristics of dynamic proteomics for endosperm and pericarp during grain development. Totally, 9539 proteins were identified for both components at four development stages, among which 1401 proteins were non-redundant, 232 proteins were specific in pericarp and 153 proteins were specific in endosperm. A functional annotation of the identified proteins revealed the importance of metabolic and cellular processes, and binding and catalytic activities for the tissue development. Three and 76 proteins involved in 49 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were integrated for the specific endosperm and pericarp proteins, respectively, reflecting their complex metabolic interactions. In addition, four proteins with important functions and different expression levels were chosen for gene cloning and expression analysis. Different concordance between mRNA level and the protein abundance was observed across different proteins, stages, and tissues as in previous research. These results could provide useful message for understanding the developmental mechanisms in grain development in maize.

Repeatability and Agreement of Central Corneal Thickness and Keratometry Measurements between Four Different Devices

Background. To estimate repeatability and comparability of central corneal thickness (CCT) and keratometry measurements obtained by four different devices in healthy eyes. Methods. Fifty-five healthy eyes from 55 volunteers were enrolled in this study. CCT (IOLMaster 700, Pentacam HR, and Cirrus HD-OCT) and keratometry readings (IOLMaster 700, Pentacam HR, and iDesign) were measured. For statistical analysis, the corneal spherocylinder was converted into power vectors (J0, J45). Repeatability was assessed by intraclass correlation coefficient (ICC). Agreement of measurements between the devices was evaluated by the Bland-Altman method. Results. The analysis of repeatability of CCT data of IOLMaster 700, Pentacam HR, and Cirrus HD-OCT showed high ICCs (range 0.995 to 0.999). The comparison of CCT measurements revealed statistically significant differences between Pentacam HR versus IOLMaster 700 (p < 0.0001) and Pentacam HR versus Cirrus HD-OCT (p < 0.0001), respectively. There was no difference in CCT measurements between IOLMaster 700 and Cirrus HD-OCT (p = 0.519). The repeatability of keratometry readings (J0 and J45) of IOLMaster 700, Pentacam HR, and iDesign was also high with ICCs ranging from 0.974 to 0.999. The Pentacam HR revealed significantly higher J0 in comparison to IOLMaster 700 (p = 0.009) and iDesign (p = 0.041); however, no significant difference was between IOLMaster 700 and iDesign (p = 0.426). Comparison of J45 showed no significant difference between IOLMaster 700, Pentacam HR, and iDesign. These results were in accordance with Bland-Altman plots. Conclusion. In clinical practice, the devices analyzed should not be used interchangeably due to low agreement regarding CCT as well as keratometry readings.

QTL Mapping of Low-Temperature Germination Ability in the Maize IBM Syn4 RIL Population

Low temperature is the primary factor to affect maize sowing in early spring. It is, therefore, vital for maize breeding programs to improve tolerance to low temperatures at seed germination stage. However, little is known about maize QTL involved in low-temperature germination ability. 243 lines of the intermated B73×Mo17 (IBM) Syn4 recombinant inbred line (RIL) population was used for QTL analysis of low-temperature germination ability. There were significant differences in germination-related traits under both conditions of low temperature (12°C/16h, 18°C/8h) and optimum temperature (28°C/24h) between the parental lines. Only three QTL were identified for controlling optimum-temperature germination rate. Six QTL controlling low-temperature germination rate were detected on chromosome 4, 5, 6, 7 and 9, and contribution rate of single QTL explained between 3.39%~11.29%. In addition, six QTL controlling low-temperature primary root length were detected in chromosome 4, 5, 6, and 9, and the contribution rate of single QTL explained between 3.96%~8.41%. Four pairs of QTL were located at the same chromosome position and together controlled germination rate and primary root length under low temperature condition. The nearest markers apart from the corresponding QTL (only 0.01 cM) were umc1303 (265.1 cM) on chromosome 4, umc1 (246.4 cM) on chromosome 5, umc62 (459.1 cM) on chromosome 6, bnl14.28a (477.4 cM) on chromosome 9, respectively. A total of 3155 candidate genes were extracted from nine separate intervals based on the Maize Genetics and Genomics Database (http://www.maizegdb.org). Five candidate genes were selected for analysis as candidates putatively affecting seed germination and seedling growth at low temperature. The results provided a basis for further fine mapping, molecular marker assisted breeding and functional study of cold-tolerance at the stage of seed germination in maize.