Plant Proteomics Research Articles

Navigating the landscape of plant proteomics.

In plants, proteins are fundamental to virtually all biological processes, such as photosynthesis, signal transduction, metabolic regulation, and stress responses. Studying protein distribution, function, modifications, and interactions at the cellular and tissue levels is critical for unraveling the complexities of these biological pathways. Protein abundance and localization are highly dynamic and vary widely across the proteome, presenting a challenge for global protein quantification and analysis. Mass spectrometry-based proteomics approaches have proven to be powerful tools for addressing this complex issue. In this review, we summarize recent advancements in proteomics research and their applications in plant biology, with an emphasis on the current state and challenges of studying post-translational modifications, single-cell proteomics, and protein-protein interactions. Additionally, we discuss future prospects for plant proteomics, highlighting potential opportunities that proteomics technologies offer in advancing plant biology research.

Diverse roles of phytohormonal signaling in modulating plant-virus interaction.

Virus infection brings about changes in the transcriptome, proteome and metabolome status of the infected plant wherein substantial alterations in the abundance of phytohormones and associated components involved in their signaling pathways have been observed. In the recent years, extensive research in the field of plant virology has showcased the undisputable significance of phytohormone signaling during plant-virus interactions. Apart from acting as growth regulators, phytohormones elicit robust immune response, which restricts the viral multiplication within the plant as well as its propagation by vector. Interestingly, these pathways have been shown to not only act as isolated mechanisms but as complex intertwined regulatory cascades where, the cross-talk among different phytohormones and with other antiviral pathways takes place during plant-virus interplay. Viruses cleverly disrupt phytohormone homeostasis via their multifunctional effectors that seems to be smart approach adopted by viruses to circumvent phytohormone-mediated plant immune responses. In this review, we summarize the current understanding of role of phytohormone signaling pathways during plant-virus interaction in activating antiviral immune responses of plant and also, how viruses exploit these signaling pathways favoring their pathogenesis.

The Present State and Impact of AI-Driven Computational Tools for Predicting Plant Protein Structures.

Several key functions of plants, such as photosynthesis, nutrient transport, disease resistance, and abiotic tolerance, are manifested by several classes of proteins. Prediction of 3- dimensional (3-D) structures of proteins and their working mechanisms can have a profound impact on plant proteomics research and could help improve key agricultural traits in crop plants. This review aims to present the current status of plant protein structure determination and discuss the way forward. Most experimentally proven protein structures are available only for the model plant Arabidopsis thaliana. Most of the key crop plants have only a few hundred or fewer experimentally proven 3-D structures. Fewer than 1% of the protein sequences in the majority of plants have had their 3D structures experimentally determined, and A. thaliana is the sole plant with the highest percentage of 1.4 % of protein sequences with experimentally determined structures. AI-based protein structure prediction tool AlphaFold has predicted models of several thousand proteins for many crop plants. In AlphaFold predicted protein models, soybean has the highest percentage (65%) of its UniProt protein sequences with predicted models, and a few other crop plants have also a considerable percentage of its UniProt sequences with AlphaFold predicted models. AlphaFold might help predict models and bridge the gap in plant structure determination studies. Protein structure information might lead to engineering key residues to improve the agronomical performance of crop plants.

Inhibition of sulfur assimilation by S-benzyl-L-cysteine: Impacts on growth, photosynthesis, and leaf proteome of maize plants

Sulfur is an essential nutrient for various physiological processes, including protein synthesis and enzyme activation. We aimed to evaluate how S-benzyl-L-cysteine (SBC), an inhibitor of the sulfur assimilation pathway, affects maize plants' growth, photosynthesis, and leaf proteomic profile. Thus, maize plants were grown for 14 days in vermiculite supplemented with SBC. Photosynthesis was assessed using light and CO2 response curves and chlorophyll a fluorescence. Leaf proteome analysis was conducted to evaluate photosynthetic protein biosynthesis, and ROS content was quantified to assess oxidative stress. Applying SBC resulted in a significant decrease in the growth of maize plants. The gas exchange analysis revealed that maize plants exhibited a diminished rate of CO2 assimilation attributable to both stomatal and non-stomatal limitations. Furthermore, SBC suppressed the activity of important elements involved in the photosynthetic electron transport chain (including photosystems I and II, cytochrome b6f, and ATP synthase) and enzymes responsible for the Calvin cycle, some of which have sulfur-containing prosthetic groups. Consequently, the diminished electron flow rate resulted in a substantial increase in the levels of ROS within the leaves. Our research highlights the crucial role of SBC in disrupting maize photosynthesis by limiting L-cysteine and assimilated sulfur availability, which are essential for the synthesis of protein and prosthetic groups and photosynthetic processes, emphasizing the potential of OAS-TL as a new herbicide site of action.

Seqrutinator: scrutiny of large protein superfamily sequence datasets for the identification and elimination of non-functional homologues

Seqrutinator is an objective, flexible pipeline that removes sequences with sequencing and/or gene model errors and sequences from pseudogenes from complex, eukaryotic protein superfamilies. Testing Seqrutinator on major superfamilies BAHD, CYP, and UGT removes only 1.94% of SwissProt entries, 14% of entries from the model plant Arabidopsis thaliana, but 80% of entries from Pinus taeda’s recent complete proteome. Application of Seqrutinator on crude BAHDomes, CYPomes, and UGTomes obtained from 16 plant proteomes shows convergence of the numbers of paralogues. MSAs, phylogenies, and particularly functional clustering improve drastically upon Seqrutinator application, indicating good performance.

The Influence of Chitosan Derivatives in Combination with Bacillus subtilis Bacteria on the Development of Systemic Resistance in Potato Plants with Viral Infection and Drought.

Viral diseases of potatoes are among the main problems causing deterioration in the quality of tubers and loss of yield. The growth and development of potato plants largely depend on soil moisture. Prevention strategies require comprehensive protection against pathogens and abiotic stresses, including modeling the beneficial microbiome of agroecosystems combining microorganisms and immunostimulants. Chitosan and its derivatives have great potential for use in agricultural engineering due to their ability to induce plant immune responses. The effect of chitosan conjugate with caffeic acid (ChCA) in combination with Bacillus subtilis 47 on the transcriptional activity of PR protein genes and changes in the proteome of potato plants during potato virus Y (PVY) infection and drought was studied. The mechanisms of increasing the resistance of potato plants to PVY and lack of moisture are associated with the activation of transcription of genes encoding PR proteins: the main protective protein (PR-1), chitinase (PR-3), thaumatin-like protein (PR-5), protease inhibitor (PR-6), peroxidase (PR-9), and ribonuclease (PR-10), as well as qualitative and quantitative changes in the plant proteome. The revealed activation of the expression of marker genes of systemic acquired resistance and induced systemic resistance under the influence of combined treatment with B. subtilis and chitosan conjugate indicate that, in potato plants, the formation of resistance to viral infection in drought conditions proceeds synergistically. By two-dimensional electrophoresis of S. tuberosum leaf proteins followed by MALDI-TOF analysis, 10 proteins were identified, the content and composition of which differed depending on the experiment variant. In infected plants treated with ChCA, the synthesis of proteinaceous RNase P 1 and oxygen-evolving enhancer protein 2 was enhanced in conditions of normal humidity, and 20 kDa chaperonin and TMV resistance protein N-like was enhanced in conditions of lack of moisture. The virus coat proteins were detected, which intensively accumulated in the leaves of plants infected with potato Y-virus. ChCA treatment reduced the content of these proteins in the leaves, and in plants treated with ChCA in combination with Bacillus subtilis, viral proteins were not detected at all, both in conditions of normal humidity and lack of moisture, which suggests the promising use of chitosan derivatives in combination with B. subtilis bacteria in the regulation of plant resistance.

A combined lipidomic and proteomic profiling of Arabidopsis thaliana plasma membrane.

The plant plasma membrane (PM) plays a key role in perception of environmental signals, and set-up of adaptive responses. An exhaustive and quantitative description of the whole set of lipids and proteins constituting the PM is necessary to understand how these components allow to fulfill such essential physiological functions. Here we provide by state-of-the-art approaches the first combined reference of the plant PM lipidome and proteome from Arabidopsis thaliana suspension cell culture. We identified and quantified a reproducible core set of 2165 proteins, which is by far the largest set of available data concerning this plant PM proteome. Using the same samples, combined lipidomic approaches, allowing the identification and quantification of an unprecedented repertoire of 414 molecular species of lipids showed that sterols, phospholipids, and sphingolipids are present in similar proportions in the plant PM. Within each lipid class, the precise amount of each lipid family and the relative proportion of each molecular species were further determined, allowing to establish the complete lipidome of Arabidopsis PM, and highlighting specific characteristics of the different molecular species of lipids. Results obtained point to a finely tuned adjustment of the molecular characteristics of lipids and proteins. More than a hundred proteins related to lipid metabolism, transport, or signaling have been identified and put in perspective of the lipids with which they are associated. This set of data represents an innovative resource to guide further research relative to the organization and functions of the plant PM.

The fungus Acremonium alternatum enhances salt stress tolerance by regulating host redox homeostasis and phytohormone signaling.

While endophytic fungi offer promising avenues for bolstering plant resilience against abiotic stressors, the molecular mechanisms behind this biofortification remain largely unknown. This study employed a multifaceted approach, combining plant physiology, proteomic, metabolomic, and targeted hormonal analyses to illuminate the early response of Brassica napus to Acremonium alternatum during the nascent stages of their interaction. Notably, under optimal growth conditions, the initial reaction to fungus was relatively subtle, with no visible alterations in plant phenotype and only minor impacts on the proteome and metabolome. Interestingly, the identified proteins associated with the Acremonium response included TUDOR 1, Annexin D4, and a plastidic K+ efflux antiporter, hinting at potential processes that could counter abiotic stressors, particularly salt stress. Subsequent experiments validated this hypothesis, showcasing significantly enhanced growth in Acremonium-inoculated plants under salt stress. Molecular analyses revealed a profound impact on the plant's proteome, with over 50% of salt stress response proteins remaining unaffected in inoculated plants. Acremonium modulated ribosomal proteins, increased abundance of photosynthetic proteins, enhanced ROS metabolism, accumulation of V-ATPase, altered abundances of various metabolic enzymes, and possibly promoted abscisic acid signaling. Subsequent analyses validated the accumulation of this hormone and its enhanced signaling. Collectively, these findings indicate that Acremonium promotes salt tolerance by orchestrating abscisic acid signaling, priming the plant's antioxidant system, as evidenced by the accumulation of ROS-scavenging metabolites and alterations in ROS metabolism, leading to lowered ROS levels and enhanced photosynthesis. Additionally, it modulates ion sequestration through V-ATPase accumulation, potentially contributing to the observed decrease in chloride content.

Characterization of the High-Quality Genome Sequence and Virulence Factors of Fusarium oxysporum f. sp. vasinfectum Race 7.

Fusarium oxysporum f. sp. vasinfectum (Fov) is a common soilborne fungal pathogen that causes Fusarium wilt (FW) disease in cotton. Although considerable progress has been made in cotton disease-resistance breeding against FW in China, and the R gene conferring resistance to Fov race 7 (FOV) in Upland cotton (Gossypium hirsutum) has been identified, knowledge regarding the evolution of fungal pathogenicity and virulence factors in Fov remains limited. In this study, we present a reference-scale genome assembly and annotation for FOV7, created through the integration of single-molecule real-time sequencing (PacBio) and high-throughput chromosome conformation capture (Hi-C) techniques. Comparative genomics analysis revealed the presence of six supernumerary scaffolds specific to FOV7. The genes or sequences within this region can potentially serve as reliable diagnostic markers for distinguishing Fov race 7. Furthermore, we conducted an analysis of the xylem sap proteome of FOV7-infected cotton plants, leading to the identification of 19 proteins that are secreted in xylem (FovSIX). Through a pathogenicity test involving knockout mutants, we demonstrated that FovSIX16 is crucial for the full virulence of FOV7. Overall, this study sheds light on the underlying mechanisms of Fov's pathogenicity and provides valuable insights into potential management strategies for controlling FW.

Advanced Biotechnological Interventions in Mitigating Drought Stress in Plants.

This comprehensive article critically analyzes the advanced biotechnological strategies to mitigate plant drought stress. It encompasses an in-depth exploration of the latest developments in plant genomics, proteomics, and metabolomics, shedding light on the complex molecular mechanisms that plants employ to combat drought stress. The study also emphasizes the significant advancements in genetic engineering techniques, particularly CRISPR-Cas9 genome editing, which have revolutionized the creation of drought-resistant crop varieties. Furthermore, the article explores microbial biotechnology's pivotal role, such as plant growth-promoting rhizobacteria (PGPR) and mycorrhizae, in enhancing plant resilience against drought conditions. The integration of these cutting-edge biotechnological interventions with traditional breeding methods is presented as a holistic approach for fortifying crops against drought stress. This integration addresses immediate agricultural needs and contributes significantly to sustainable agriculture, ensuring food security in the face of escalating climate change challenges.

Abstract 2348 Deployment of Quantitative Proteomics Approaches in Defining Dynamic Changes the Diel Plant Proteome

Abstract 2348 Deployment of Quantitative Proteomics Approaches in Defining Dynamic Changes the Diel Plant Proteome

Development and applications of metabolic models in plant multi-omics research.

Plant growth and development are characterized by systematic and continuous processes, each involving intricate metabolic coordination mechanisms. Mathematical models are essential tools for investigating plant growth and development, metabolic regulation networks, and growth patterns across different stages. These models offer insights into secondary metabolism patterns in plants and the roles of metabolites. The proliferation of data related to plant genomics, transcriptomics, proteomics, and metabolomics in the last decade has underscored the growing importance of mathematical modeling in this field. This review aims to elucidate the principles and types of metabolic models employed in studying plant secondary metabolism, their strengths, and limitations. Furthermore, the application of mathematical models in various plant systems biology subfields will be discussed. Lastly, the review will outline how mathematical models can be harnessed to address research questions in this context.

Proteomic alterations in various plant tissues of maize under induced chromium stress

Heavy metal contamination is becoming a major cause of pollution in the environment, impacting humans, animals, and plants directly. Because of its widespread use in the tanning industry, chromium (Cr) regarded as a highly dangerous environmental toxin. The goal of this study was to investigate growth and proteins changes in different plant tissues (leaves, shoots, and seeds) of two maize cultivars (NMH-360 and DKC 61-42) under the stress of Cr (0, 50, and 150 ppm Cr). Sodium dodecyl polyacrylamide gel electrophoresis (SDS-PAGE) was used to observe plant proteome modification in response to Cr stress. Results revealed that at 150 ppm, both maize cultivars showed a decrease in plant growth attributes. Furthermore, it was noticed that the plant proteome changed in response to Cr stress in leaf and shoot tissues. A few proteins were up-regulated (70 kDa in C1 variety), while others were down-regulated (154, 140, 115, 80, 70 and 53 kDa in C1 cultivar shoots while 154, 65, 60, 17 kDa bands in shoots of C2 cultivar). Some protein bands were induced at 50 ppm, some at 150 ppm, and yet others at both concentrations. The findings of this study could aid in the selection of Cr-tolerant plant cultivars as well as the design of new protein biomarkers that can be utilized as a monitoring tool in heavy metal stress responses.

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection.

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model's resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.

Suboptimal zinc supply affects the S-nitrosoglutathione reductase enzyme and nitric oxide signaling in Arabidopsis

The S-nitrosoglutathione reductase (GSNOR), a key regulator of nitric oxide (NO) signaling, requires zinc (Zn) cofactors for its catalytic activity. Possible changes in GSNOR function and NO signaling due to limited Zn supply and participation of GSNOR in response to Zn deficiency have not yet been examined. The wild-type Arabidopsis thaliana seedlings (Col-0) and the overproducer of GSNOR (35S::FLAG-GSNOR1) were grown in agar medium supplemented with 15 µM (full Zn), 1.5 µM (Zn/10) and 0 µM (Zn0) zinc sulfate. Limited availability of Zn in the nutrient medium during the experimental period resulted in a significantly reduced Zn content in the roots and shoots of Arabidopsis and altered the expression of Zn deficiency marker genes; however, the Zn content did not reflect Zn deficiency in the seedlings. GSNOR has no effect on the Zn limitation-induced reduction in the in planta Zn content. Suboptimal Zn supply results in a smaller but more active GSNOR protein pool in Col-0 A. thaliana. In 35S::FLAG-GSNOR1, overproduction possibly compensates for the negative effects of reduced Zn supply, resulting in unchanged GSNOR expression, protein abundance, and activity. Suboptimal Zn supply alters NO but did not modify peroxynitrite and hydrogen peroxide levels and exerts a pronitrant effect in the plant proteome. Overall, this is the first report to show that limited Zn supply affects the GSNOR enzyme at the gene and protein levels but not at the activity level and modifies reactive species levels and physiological nitroproteome in Arabidopsis.

Systematic thermal analysis of the Arabidopsis proteome: Thermal tolerance, organization, and evolution.

Understanding the thermal stability of the plant proteome in the context of the native cellular environment would aid the design of crops with high thermal tolerance, but only limited such data are available. Here, we applied quantitative mass spectrometry to profile the thermal stability of the Arabidopsis proteome and identify thermo-sensitive and thermo-resilient protein networks in Arabidopsis, providing a basis for understanding heat-induced damage. We also show that the similarities of the protein-melting curves can be used as a proxy to evaluate system-wide protein-protein interactions in non-engineered plants and enable the identification of transient interactions exhibited by metabolons in the context of the cellular environment. Finally, we report a systematic comparison of the thermal stability of paralogs in Arabidopsis to aid the investigation and understanding of gene duplication and protein evolution. Taken together, our results could have broad implications for the fields of plant thermal tolerance, plant protein assemblies, and evolution.

Biochemical and Structural Characterization of a Uronic Acid Oxidase from Citrus sinensis

AbstractAldaric acids are attractive diacids that can be prepared by selective oxidation of carbohydrates. For this, effective biocatalysts are in demand. This work reports on the discovery, biochemical and structural characterization of a VAO‐type flavin‐containing carbohydrate oxidase from Citrus sinensis: URAOCs3. URAOCs3 could be overexpressed using prokaryotic and eukaryotic expression systems. Extensive biochemical characterization revealed that the enzyme displays a high thermostability and an exquisite selectivity for uronic acids, galacturonic acid and glucuronic acid. The enzyme was further investigated by determining the crystal structure. The selective oxidation of D‐galacturonic acid in a complex mixture was demonstrated, showing how URAOCs3 was found to be highly effective in selectively producing galactaric acid while leaving other carbohydrates untouched. In addition to the specific discovery of URAOCs3, these findings suggest that plant proteomes can be an interesting source for new biocatalysts.

Proteomes reveal the lipid metabolic network in the complex plastid of Phaeodactylum tricornutum.

Phaeodactylum tricornutum plastid is surrounded by four membranes, and its protein composition and function remain mysterious. In this study, the P. tricornutum plastid-enriched fraction was obtained and 2850 proteins were identified, including 92 plastid-encoded proteins, through label-free quantitative proteomic technology. Among them, 839 nuclear-encoded proteins were further determined to be plastidial proteins based on the BLAST alignments within Plant Proteome DataBase and subcellular localization prediction, in spite of the strong contamination by mitochondria-encoded proteins and putative plasma membrane proteins. According to our proteomic data, we reconstructed the metabolic pathways and highlighted the hybrid nature of this diatom plastid. Triacylglycerol (TAG) hydrolysis and glycolysis, as well as photosynthesis, glycan metabolism, and tocopherol and triterpene biosynthesis, occur in the plastid. In addition, the synthesis of long-chain acyl-CoAs, elongation, and desaturation of fatty acids (FAs), and synthesis of lipids including TAG are confined in the four-layered-membrane plastid based on the proteomic and GFP-fusion localization data. The whole process of generation of docosahexaenoic acid (22:6) from palmitic acid (16:0), via elongation and desaturation of FAs, occurs in the chloroplast endoplasmic reticulum membrane, the outermost membrane of the plastid. Desaturation that generates 16:4 from 16:0 occurs in the plastid stroma and outer envelope membrane. Quantitative analysis of glycerolipids between whole cells and isolated plastids shows similar composition, and the FA profile of TAG was not different. This study shows that the diatom plastid combines functions usually separated in photosynthetic eukaryotes, and differs from green alga and plant chloroplasts by undertaking the whole process of lipid biosynthesis.

Plant adenylate cyclases have come full circle.

In bacteria, fungi and animals, 3'-5'-cyclic adenosine monophosphate (cAMP) and adenylate cyclases (ACs), enzymes that catalyse the formation of 3',5'-cAMP from ATP, are recognized as key signalling components. In contrast, the presence of cAMP and its biological roles in higher plants have long been a matter of controversy due to the generally lower amounts in plant tissues compared with that in animal and bacterial cells, and a lack of clarity on the molecular nature of the generating and degrading enzymes, as well as downstream effectors. While treatment with 3',5'-cAMP elicited many plant responses, ACs were, however, somewhat elusive. This changed when systematic searches with amino acid motifs deduced from the conserved catalytic centres of annotated ACs from animals and bacteria identified candidate proteins in higher plants that were subsequently shown to have AC activities in vitro and in vivo. The identification of active ACs moonlighting within complex multifunctional proteins is consistent with their roles as molecular tuners and regulators of cellular and physiological functions. Furthermore, the increasing number of ACs identified as part of proteins with different domain architectures suggests that there are many more hidden ACs in plant proteomes and they may affect a multitude of mechanisms and processes at the molecular and systems levels.

Systems-level proteomics and metabolomics reveals the diel molecular landscape of diverse kale cultivars.

Kale is a group of diverse Brassicaceae species that are nutritious leafy greens consumed for their abundance of vitamins and micronutrients. Typified by their curly, serrated and/or wavy leaves, kale varieties have been primarily defined based on their leaf morphology and geographic origin, despite having complex genetic backgrounds. Kale is a very promising crop for vertical farming due to its high nutritional content; however, being a non-model organism, foundational, systems-level analyses of kale are lacking. Previous studies in kale have shown that time-of-day harvesting can affect its nutritional composition. Therefore, to gain a systems-level diel understanding of kale across its wide-ranging and diverse genetic landscape, we selected nine publicly available and commercially grown kale cultivars for growth under near-sunlight LED light conditions ideal for vertical farming. We then analyzed changes in morphology, growth and nutrition using a combination of plant phenotyping, proteomics and metabolomics. As the diel molecular activities of plants drive their daily growth and development, ultimately determining their productivity as a crop, we harvested kale leaf tissue at both end-of-day (ED) and end-of-night (EN) time-points for all molecular analyses. Our results reveal that diel proteome and metabolome signatures divide the selected kale cultivars into two groups defined by their amino acid and sugar content, along with significant proteome differences involving carbon and nitrogen metabolism, mRNA splicing, protein translation and light harvesting. Together, our multi-cultivar, multi-omic analysis provides new insights into the molecular underpinnings of the diel growth and development landscape of kale, advancing our fundamental understanding of this nutritious leafy green super-food for horticulture/vertical farming applications.