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Background: Drought stress restricts the growth, distribution and productivity of alfalfa (Medicago sativa L.). In order to study the response differences of alfalfa cultivars to drought stress, we previously carried out physiological and molecular comparative analysis on two alfalfa varieties with contrasting drought resistance (relatively drought-tolerant Longdong and drought-sensitive Algonquin). However, the differences in proteomic factors of the two varieties in response to drought stress still need to be further studied. Therefore, TMT-based quantitative proteomic analysis was performed using leaf tissues of the two alfalfa cultivars to identify and uncover differentially abundant proteins (DAPs)., Results: In total, 677 DAPs were identified in Algonquin and 277 in Longdong under drought stress. Subsequently, we conducted various bioinformatics analysis on these DAPs, including subcellular location, functional classification and biological pathway enrichment. The first two main COG functional categories of DAPs in both alfalfa varieties after drought stress were 'Translation, ribosomal structure and biogenesis' and 'Posttranslational modification, protein turnover, chaperones'. According to KEGG database, the DAPs of the two alfalfa cultivars after drought treatment were differentially enriched in different biological pathways. The DAPs from Algonquin were enriched in 'photosynthesis' and 'ribosome'. The pathways of 'linoleic acid metabolism', 'protein processing in endoplasmic reticulum' and 'RNA transport' in Longdong were significantly enriched. Finally, we found significant differences in DAP enrichment and expression patterns between Longdong and Algonquin in glycolysis/glycogenesis, TCA cycle, photosynthesis, protein biosynthesis, flavonoid and isoflavonoid biosynthesis, and plant-pathogen interaction pathway after drought treatment., Conclusions: The differences of DAPs involved in various metabolic pathways may explain the differences in the resistance of the two varieties to drought stress. These DAPs can be used as candidate proteins for molecular breeding of alfalfa to cultivate new germplasm with more drought tolerance to adapt to unfavorable environments., (© 2024. The Author(s).)

Pollination is a crucial stage in plant reproductive process. The self-compatibility (SC) and self-incompatibility (SI) mechanisms determined the plant genetic diversity and species survival. D. chrysanthum is a highly valued ornamental and traditional herbal orchid in Asia but has been declared endangered. The sexual reproduction in D. chrysanthum relies on the compatibility of pollination. To provide a better understanding of the mechanism of pollination, the differentially expressed proteins (DEP) between the self-pollination (SP) and cross-pollination (CP) pistil of D. chrysanthum were investigated using proteomic approaches-two-dimensional electrophoresis (2-DE) coupled with tandem mass spectrometry technique. A total of 54 DEP spots were identified in the two-dimensional electrophoresis (2-DE) maps between the SP and CP. Gene ontology analysis revealed an array of proteins belonging to following different functional categories: metabolic process (8.94%), response to stimulus (5.69%), biosynthetic process (4.07%), protein folding (3.25%) and transport (3.25%). Identification of these DEPs at the early response stage of pollination will hopefully provide new insights in the mechanism of pollination response and help for the conservation of the orchid species., Competing Interests: The authors declare no conflict of interest.

Angiosperms have developed self-incompatibility (SI) systems to reject self-pollen, thereby promoting outcrossing. The Brassicaceae belongs to typical sporophytic system, having a single S-locus controlled SI response, and was chosen as a model system to study SI-related intercellular signal transduction. In this regard, the downstream factor of EXO70A1 was unknown. Here, protein two-dimensional electrophoresis (2-DE) method and coupled with matrix-assisted laser desorption ionization/time of flight of flight mass spectrometry (MALDI-TOF -MS) and peptide mass fingerprinting (PMF) was used to further explore the mechanism of SI responses in Brassica oleracea L. var. capitata L. at protein level. To further confirm the time point of protein profile change, total proteins were collected from B. oleracea pistils at 0 min, 1 h, and 2 h after self-pollination. In total 902, 1088 and 1023 protein spots were separated in 0 min, 1 h and 2 h 2-DE maps, respectively. Our analyses of self-pollination profiles indicated that proteins mainly changed at 1 h post-pollination in B. oleracea. Moreover, 1077 protein spots were separated in cross-pollinated 1 h (CP) pistil 2-DE map. MALDI-TOF-MS and PMF successfully identified 34 differentially-expressed proteins (DEPs) in SP and CP 1 h 2-DE maps. Gene ontology and KEGG analysis revealed an array of proteins grouped in the following categories: stress and defense response (35%), protein metabolism (18%), carbohydrate and energy metabolism (12%), regulation of translation (9%), pollen tube development (12%), transport (9%) and cytoskeletal (6%). Sets of DEPs identified specifically in SP or only up-regulated expressed in CP pistils were chosen for funther investigating in floral organs and during the process of self- and cross-pollination. The function of these DEPs in terms of their potential involvement in SI in B. oleracea is discussed.

Bioinformatic tools have become part of the way plant researchers undertake investigations. Large data sets encompassing genomes, transcriptomes, proteomes, epigenomes, and other "-omes" that have been generated in the past decade may be easily accessed with such tools, such that hypotheses may be generated at the click of a mouse. In this chapter, we'll cover the use of bioinformatic tools available at the Bio-Analytic Resource for Plant Biology at http://bar.utoronto.ca for exploring gene expression and coexpression patterns, undertaking promoter analyses, performing functional classification enrichment analyses for sets of genes, and examining protein-protein interactions. We also touch on some newer bioinformatic tools that allow integration of data from several sources for improved hypothesis generation, both for Arabidopsis and translationally. Most of the data sets come from Arabidopsis, but useful BAR tools for other species will be mentioned where appropriate.

Tandem repeats (TR) in proteins are common in nature and have several unique functions. They come in various forms that are frequently difficult to recognize from a sequence. A previously proposed structural classification has been recently implemented in the RepeatsDB database. This defines five main classes, mainly based on repeat unit length, with subclasses representing specific folds. Sequence-based classifications, such as Pfam, provide an alternative classification based on evolutionarily conserved repeat families. Here, we discuss a detailed comparison between the structural classes in RepeatsDB and the corresponding Pfam repeat families and clans. Most instances are found to map one-to-one between structure and sequence. Some notable exceptions such as leucine-rich repeats (LRRs) and α-solenoids are discussed., (© 2015 Authors; published by Portland Press Limited.)

Proteomics techniques generate an avalanche of data and promise to satisfy biologists' long-held desire to measure absolute protein abundances on a genome-wide scale. However, can this knowledge be translated into a clearer picture of how cells invest their protein resources? This article aims to give a broad perspective on the composition of proteomes as gleaned from recent quantitative proteomics studies. We describe proteomaps, an approach for visualizing the composition of proteomes with a focus on protein abundances and functions. In proteomaps, each protein is shown as a polygon-shaped tile, with an area representing protein abundance. Functionally related proteins appear in adjacent regions. General trends in proteomes, such as the dominance of metabolism and protein production, become easily visible. We make interactive visualizations of published proteome datasets accessible at www.proteomaps.net. We suggest that evaluating the way protein resources are allocated by various organisms and cell types in different conditions will sharpen our understanding of how and why cells regulate the composition of their proteomes.