Plant proteomics methods to reach low-abundance proteins

iTRAQ-based comparative proteomic analysis of differences in the protein profiles of stems and leaves from two alfalfa genotypes

BACKGROUND

To explore the molecular regulatory mechanisms of early stem and leaf development, proteomic analysis was performed on leaves and stems of F genotype alfalfa, with thin stems and small leaves, and M genotype alfalfa, with thick stems and large leaves.

RESULTS

Based on fold-change thresholds of > 1.20 or < 0.83 (p < 0.05), a large number of proteins were identified as being differentially enriched between the M and F genotypes: 249 downregulated and 139 upregulated in stems and 164 downregulated and 134 upregulated in leaves. The differentially enriched proteins in stems were mainly involved in amino acid biosynthesis, phenylpropanoid biosynthesis, carbon fixation, and phenylalanine metabolism. The differentially enriched proteins in leaves were mainly involved in porphyrin and chlorophyll metabolism, phenylpropanoid biosynthesis, starch and sucrose metabolism, and carbon fixation in photosynthetic organisms. Six differentially enriched proteins were mapped onto the porphyrin and chlorophyll metabolism pathway in leaves of the M genotype, including five upregulated proteins involved in chlorophyll biosynthesis and one downregulated protein involved in chlorophyll degradation. Eleven differentially enriched proteins were mapped onto the phenylpropanoid pathway in stems of the M genotype, including two upregulated proteins and nine downregulated proteins.

CONCLUSION

Enhanced chlorophyll synthesis and decreased lignin synthesis provided a reasonable explanation for the larger leaves and lower levels of stem lignification in M genotype alfalfa. This proteomic study aimed to classify the functions of differentially enriched proteins and to provide information on the molecular regulatory networks involved in stem and leaf development.

Proteome Map of Pea (Pisum sativum L.) Embryos Containing Different Amounts of Residual Chlorophylls

Due to low culturing costs and high seed protein contents, legumes represent the main global source of food protein. Pea (Pisum sativum L.) is one of the major legume crops, impacting both animal feed and human nutrition. Therefore, the quality of pea seeds needs to be ensured in the context of sustainable crop production and nutritional efficiency. Apparently, changes in seed protein patterns might directly affect both of these aspects. Thus, here, we address the pea seed proteome in detail and provide, to the best of our knowledge, the most comprehensive annotation of the functions and intracellular localization of pea seed proteins. To address possible intercultivar differences, we compared seed proteomes of yellow- and green-seeded pea cultivars in a comprehensive case study. The analysis revealed totally 1938 and 1989 nonredundant proteins, respectively. Only 35 and 44 proteins, respectively, could be additionally identified after protamine sulfate precipitation (PSP), potentially indicating the high efficiency of our experimental workflow. Totally 981 protein groups were assigned to 34 functional classes, which were to a large extent differentially represented in yellow and green seeds. Closer analysis of these differences by processing of the data in KEGG and String databases revealed their possible relation to a higher metabolic status and reduced longevity of green seeds.

Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics

In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

Shotgun proteome analysis of honeybee venom using targeted enrichment strategies

The aim of this study was to explore the honeybee venom proteome applying a shotgun proteomics approach using different enrichment strategies (combinatorial peptide ligand libraries and solid phase extraction). The studies were conducted using nano-LC/MALDI-TOF/TOF-MS system. The MS analysis of peptide profiles (in the range of 900-4500 Da) and virtual gel-image of proteins from Lab-on-Chip assay (in the range of 10-250 kDa) confirm that use of targeted enrichment strategies increase detection of honeybee venom components. The gel-free shotgun strategy and sophisticated instrumentation led to a significant increase of the sensitivity and higher number of identified peptides in honeybee venom samples, comparing with the current literature. Moreover, 11 of 12 known honeybee venom allergens were acknowledged and 4 new, so far uncharacterized proteins were identified. In addition, similarity searches were performed in order to investigate biological relations and homology between newly identified proteins sequences from Apis mellifera and other Hymenoptera.