Targeted Proteomics Approach for Precision Plant Breeding

Enhanced stress resilience in potato by deletion of Parakletos

Continued climate change impose multiple stressors on crops, including pathogens, salt, and drought, severely impacting agricultural productivity. Innovative solutions are necessary to develop resilient crops. Here, using quantitative potato proteomics, we identify Parakletos, a thylakoid protein that contributes to disease susceptibility. We show that knockout or silencing of Parakletos enhances resistance to oomycete, fungi, bacteria, salt, and drought, whereas its overexpression reduces resistance. In response to biotic stimuli, Parakletos-overexpressing plants exhibit reduced amplitude of reactive oxygen species and Ca2+ signalling, and silencing Parakletos does the opposite. Parakletos homologues have been identified in all major crops. Consecutive years of field trials demonstrate that Parakletos deletion enhances resistance to Phytophthora infestans and increases yield. These findings demark a susceptibility gene, which can be exploited to enhance crop resilience towards abiotic and biotic stresses in a low-input agriculture.

From grain to malt: Tracking changes of ultra-low-gluten barley storage proteins after malting

Barley (Hordeum vulgare L.) is a major cereal crop produced globally. Hordeins, the major storage proteins in barley, can trigger immune responses leading to celiac disease or symptoms associated with food allergy. Here, proteomics approaches were employed to investigate the proteome level changes of grain and malt from the malting barley cultivar, Sloop, and single-, double- and triple hordein-reduced lines. The triple hordein-reduced line is an ultra-low gluten barley cultivar, Kebari®. Using discovery proteomics, 2,688 and 3,034 proteins in the barley and malt samples were detected respectively. Through the application of targeted proteomics, a significant reduction in the quantity of B-, D-, and γ-hordeins, as well as avenin-like proteins, was observed in the ultra-low gluten malt sample. A compensation mechanism was observed evidenced by increased biosynthesis of seed storage globulins, specifically vicilin-like globulins. Overall, this study has provided insights into protein compositional changes after malting in celiac-friendly barley varieties.

Breeding strategies for late blight resistance in potato crop: recent developments

Late blight (LB) is a serious disease that affects potato crop and is caused by Phytophthora infestans. Fungicides are commonly used to manage this disease, but this practice has led to the development of resistant strains and it also poses serious environmental and health risks. Therefore, breeding for resistance development can be the most effective strategies to control late blight. Various Solanum species have been utilized as a source of resistance genes to combat late blight disease. Several potential resistance genes and quantitative resistance loci (QRLs) have been identified and mapped through the application of molecular techniques. Furthermore, molecular markers closely linked to resistance genes or QRLs have been utilized to hasten the breeding process. However, the use of single-gene resistance can lead to the breakdown of resistance within a short period. To address this, breeding programs are now being focused on development of durable and broad-spectrum resistant cultivars by combining multiple resistant genes and QRLs using advanced molecular breeding tools such as marker-assisted selection (MAS) and cis-genic approaches. In addition to the strategies mentioned earlier, somatic hybridization has been utilized for the development and characterization of interspecific somatic hybrids. To further broaden the scope of late blight resistance breeding, approaches such as genomic selection, RNAi silencing, and various genome editing techniques can be employed. This study provides an overview of recent advances in various breeding strategies and their applications in improving the late blight resistance breeding program.

Optimization of Targeted Plant Proteomics Using Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS)

This study was conducted to optimize a targeted plant proteomics approach from signature peptide selection and liquid chromatography with tandem mass spectrometry (LC-MS/MS) analytical method development and optimization to sample preparation method optimization. Three typical protein extraction and precipitation methods, including trichloroacetic acid (TCA)/acetone method, phenol method, and TCA/acetone/phenol method, and two digestion methods, including trypsin digestion and LysC/trypsin digestion, were evaluated for selected proteins related to the impact of engineered nanomaterials (ENMs) on wheat (Triticum aestivum) plant growth. In addition, we compared two plant tissue homogenization methods: grinding freeze-dried tissue and fresh tissue into a fine powder using a mortar and pestle aided with liquid nitrogen. Wheat plants were grown under a 16 h photoperiod (light intensity 150 μmol·m^-2·s^-1) for 4 weeks at 22 °C with a relative humidity of 60% and were watered daily to maintain a 70-90% water content in the soil. Processed samples were analyzed with an optimized LC-MS/MS method. The concentration of selected signature peptides for the wheat proteins of interest indicated that the phenol extraction method using fresh plant tissue, coupled with trypsin digestion, was the best sample preparation method for the targeted proteomics study. Overall, the optimized approach yielded the highest total peptide concentration (68,831 ng/g, 2.4 times the lowest concentration) as well as higher signature peptide concentrations for most peptides (19 out of 28). In addition, three of the signature peptides could only be detected using the optimized approach. This study provides a workflow for optimizing targeted proteomics studies.

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

Recent developments allowed generating multiple high-quality 'omics' data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

Harnessing genetic resistance to rusts in wheat and integrated rust management methods to develop more durable resistant cultivars

Wheat is one of the most important staple foods on earth. Leaf rust, stem rust and stripe rust, caused by Puccini triticina, Puccinia f. sp. graminis and Puccinia f. sp. striiformis, respectively, continue to threaten wheat production worldwide. Utilization of resistant cultivars is the most effective and chemical-free strategy to control rust diseases. Convectional and molecular biology techniques identified more than 200 resistance genes and their associated markers from common wheat and wheat wild relatives, which can be used by breeders in resistance breeding programmes. However, there is continuous emergence of new races of rust pathogens with novel degrees of virulence, thus rendering wheat resistance genes ineffective. An integration of genomic selection, genome editing, molecular breeding and marker-assisted selection, and phenotypic evaluations is required in developing high quality wheat varieties with resistance to multiple pathogens. Although host genotype resistance and application of fungicides are the most generally utilized approaches for controlling wheat rusts, effective agronomic methods are required to reduce disease management costs and increase wheat production sustainability. This review gives a critical overview of the current knowledge of rust resistance, particularly race-specific and non-race specific resistance, the role of pathogenesis-related proteins, non-coding RNAs, and transcription factors in rust resistance, and the molecular basis of interactions between wheat and rust pathogens. It will also discuss the new advances on how integrated rust management methods can assist in developing more durable resistant cultivars in these pathosystems.

Functional phenomics for improved climate resilience in Nordic agriculture

The five Nordic countries span the most northern region for field cultivation in the world. This presents challenges per se, with short growing seasons, long days, and a need for frost tolerance. Climate change has additionally increased risks for micro-droughts and water logging, as well as pathogens and pests expanding northwards. Thus, Nordic agriculture demands crops that are adapted to the specific Nordic growth conditions and future climate scenarios. A focus on crop varieties and traits important to Nordic agriculture, including the unique resource of nutritious wild crops, can meet these needs. In fact, with a future longer growing season due to climate change, the region could contribute proportionally more to global agricultural production. This also applies to other northern regions, including the Arctic. To address current growth conditions, mitigate impacts of climate change, and meet market demands, the adaptive capacity of crops that both perform well in northern latitudes and are more climate resilient has to be increased, and better crop management systems need to be built. This requires functional phenomics approaches that integrate versatile high-throughput phenotyping, physiology, and bioinformatics. This review stresses key target traits, the opportunities of latitudinal studies, and infrastructure needs for phenotyping to support Nordic agriculture.

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

Omics Path to Increasing Productivity in Less-Studied Crops Under Changing Climate-Lentil a Case Study

Conventional breeding techniques for crop improvement have reached their full potential, and hence, alternative routes are required to ensure a sustained genetic gain in lentils. Although high-throughput omics technologies have been effectively employed in major crops, less-studied crops such as lentils have primarily relied on conventional breeding. Application of genomics and transcriptomics in lentils has resulted in linkage maps and identification of QTLs and candidate genes related to agronomically relevant traits and biotic and abiotic stress tolerance. Next-generation sequencing (NGS) complemented with high-throughput phenotyping (HTP) technologies is shown to provide new opportunities to identify genomic regions and marker-trait associations to increase lentil breeding efficiency. Recent introduction of image-based phenotyping has facilitated to discern lentil responses undergoing biotic and abiotic stresses. In lentil, proteomics has been performed using conventional methods such as 2-D gel electrophoresis, leading to the identification of seed-specific proteome. Metabolomic studies have led to identifying key metabolites that help differentiate genotypic responses to drought and salinity stresses. Independent analysis of differentially expressed genes from publicly available transcriptomic studies in lentils identified 329 common transcripts between heat and biotic stresses. Similarly, 19 metabolites were common across legumes, while 31 were common in genotypes exposed to drought and salinity stress. These common but differentially expressed genes/proteins/metabolites provide the starting point for developing high-yielding multi-stress-tolerant lentils. Finally, the review summarizes the current findings from omic studies in lentils and provides directions for integrating these findings into a systems approach to increase lentil productivity and enhance resilience to biotic and abiotic stresses under changing climate.

Plant proteomic research for improvement of food crops under stresses: a review

Crop improvement approaches have been changed due to technological advancements in traditional plant-breeding methods. Abiotic and biotic stresses limit plant growth and development, which ultimately lead to reduced crop yield. Proteins encoded by genomes have a considerable role in the endurance and adaptation of plants to different environmental conditions. Biotechnological applications in plant breeding depend upon the information generated from proteomic studies. Proteomics has a specific advantage to contemplate post-translational modifications, which indicate the functional effects of protein modifications on crop production. Subcellular proteomics helps in exploring the precise cellular responses and investigating the networking among subcellular compartments during plant development and biotic/abiotic stress responses. Large-scale mass spectrometry-based plant proteomic studies with a more comprehensive overview are now possible due to dramatic improvements in mass spectrometry, sample preparation procedures, analytical software, and strengthened availability of genomes for numerous plant species. Development of stress-tolerant or resilient crops is essential to improve crop productivity and growth. Use of high throughput techniques with advanced instrumentation giving efficient results made this possible. In this review, the role of proteomic studies in identifying the stress-response processes in different crops is summarized. Advanced techniques and their possible utilization on plants are discussed in detail. Proteomic studies accelerate marker-assisted genetic augmentation studies on crops for developing high yielding stress-tolerant lines or varieties under stresses.

Leaf Apoplast of Field-Grown Potato Analyzed by Quantitative Proteomics and Activity-Based Protein Profiling

Multiple biotic and abiotic stresses challenge plants growing in agricultural fields. Most molecular studies have aimed to understand plant responses to challenges under controlled conditions. However, studies on field-grown plants are scarce, limiting application of the findings in agricultural conditions. In this study, we investigated the composition of apoplastic proteomes of potato cultivar Bintje grown under field conditions, i.e., two field sites in June-August across two years and fungicide treated and untreated, using quantitative proteomics, as well as its activity using activity-based protein profiling (ABPP). Samples were clustered and some proteins showed significant intensity and activity differences, based on their field site and sampling time (June-August), indicating differential regulation of certain proteins in response to environmental or developmental factors. Peroxidases, class II chitinases, pectinesterases, and osmotins were among the proteins more abundant later in the growing season (July-August) as compared to early in the season (June). We did not detect significant differences between fungicide Shirlan treated and untreated field samples in two growing seasons. Using ABPP, we showed differential activity of serine hydrolases and β-glycosidases under greenhouse and field conditions and across a growing season. Furthermore, the activity of serine hydrolases and β-glycosidases, including proteins related to biotic stress tolerance, decreased as the season progressed. The generated proteomics data would facilitate further studies aiming at understanding mechanisms of molecular plant physiology in agricultural fields and help applying effective strategies to mitigate biotic and abiotic stresses.

Proteomics Data Analysis for the Identification of Proteins and Derived Proteotypic Peptides of Potential Use as Putative Drought Tolerance Markers for Quercus ilex

Drought is one of the main causes of mortality in holm oak (Quercus ilex) seedlings used in reforestation programs. Although this species shows high adaptability to the extreme climate conditions prevailing in Southern Spain, its intrinsic genetic variability may play a role in the differential response of some populations and individuals. The aim of this work was to identify proteins and derived proteotypic peptides potentially useful as putative markers for drought tolerance in holm oak by using a targeted post-acquisition proteomics approach. For this purpose, we used a set of proteins identified by shotgun (LC-MSMS) analysis in a drought experiment on Q. ilex seedlings from four different provenances (viz. the Andalusian provinces Granada, Huelva, Cadiz and Seville). A double strategy involving the quantification of proteins and target peptides by shotgun analysis and post-acquisition data analysis based on proteotypic peptides was used. To this end, an initial list of proteotypic peptides from proteins highly represented under drought conditions was compiled that was used in combination with the raw files from the shotgun experiment to quantify the relative abundance of the fragment’s ion peaks with the software Skyline. The most abundant peptides under drought conditions in at least two populations were selected as putative markers of drought tolerance. A total of 30 proteins and 46 derived peptides belonging to the redox, stress-related, synthesis,-folding and degradation, and primary and secondary metabolism functional groups were thus identified. Two proteins (viz., subtilisin and chaperone GrpE protein) were found at increased levels in three populations, which make them especially interesting for validation drought tolerance markers in subsequent experiments.

An integrated transcriptomic- and proteomic-based approach to evaluate the human skin sensitization potential of glyphosate and its commercial agrochemical formulations

We investigated the skin sensitization hazard of glyphosate, the surfactant polyethylated tallow amine (POEA) and two commercial glyphosate-containing formulations using different omics-technologies based on a human dendritic cell (DC)-like cell line. First, the GARD™skin assay, investigating changes in the expression of 200 transcripts upon cell exposure to xenobiotics, was used for skin sensitization prediction. POEA and the formulations were classified as skin sensitizers while glyphosate alone was classified as a non-sensitizer. Interestingly, the mixture of POEA together with glyphosate displayed a similar sensitizing prediction as POEA alone, indicating that glyphosate likely does not increase the sensitizing capacity when associated with POEA. Moreover, mass spectrometry analysis identified differentially regulated protein groups and predicted molecular pathways based on a proteomic approach in response to cell exposures with glyphosate, POEA and the glyphosate-containing formulations. Based on the protein expression data, predicted pathways were linked to immunologically relevant events and regulated proteins further to cholesterol biosynthesis and homeostasis as well as to autophagy, identifying novel aspects of DC responses after exposure to xenobiotics. In summary, we here present an integrative analysis involving advanced technologies to elucidate the molecular mechanisms behind DC activation in the skin sensitization process triggered by the investigated agrochemical materials. SIGNIFICANCE: The use of glyphosate has increased worldwide, and much effort has been made to improve risk assessments and to further elucidate the mechanisms behind any potential human health hazard of this chemical and its agrochemical formulations. In this context, omics-based techniques can provide a multiparametric approach, including several biomarkers, to expand the mechanistic knowledge of xenobiotics-induced toxicity. Based on this, we performed the integration of GARD™skin and proteomic data to elucidate the skin sensitization hazard of POEA, glyphosate and its two commercial mixtures, and to investigate cellular responses more in detail on protein level. The proteomic data indicate the regulation of immune response-related pathways and proteins associated with cholesterol biosynthesis and homeostasis as well as to autophagy, identifying novel aspects of DC responses after exposure to xenobiotics. Therefore, our data show the applicability of a multiparametric integrated approach for the mechanism-based hazard evaluation of xenobiotics, eventually complementing decision making in the holistic risk assessment of chemicals regarding their allergenic potential in humans.

Linking crop traits to transcriptome differences in a progeny population of tetraploid potato

BACKGROUND

Potato is the third most consumed crop in the world. Breeding for traits such as yield, product quality and pathogen resistance are main priorities. Identifying molecular signatures of these and other important traits is important in future breeding efforts. In this study, a progeny population from a cross between a breeding line, SW93-1015, and a cultivar, Désirée, was studied by trait analysis and RNA-seq in order to develop understanding of segregating traits at the molecular level and identify transcripts with expressional correlation to these traits. Transcript markers with predictive value for field performance applicable under controlled environments would be of great value for plant breeding.

RESULTS

A total of 34 progeny lines from SW93-1015 and Désirée were phenotyped for 17 different traits in a field in Nordic climate conditions and controlled climate settings. A master transcriptome was constructed with all 34 progeny lines and the parents through a de novo assembly of RNA-seq reads. Gene expression data obtained in a controlled environment from the 34 lines was correlated to traits by different similarity indices, including Pearson and Spearman, as well as DUO, which calculates the co-occurrence between high and low values for gene expression and trait. Our study linked transcripts to traits such as yield, growth rate, high laying tubers, late and tuber blight, tuber greening and early flowering. We found several transcripts associated to late blight resistance and transcripts encoding receptors were associated to Dickeya solani susceptibility. Transcript levels of a UBX-domain protein was negatively associated to yield and a GLABRA2 expression modulator was negatively associated to growth rate.

CONCLUSION

In our study, we identify 100's of transcripts, putatively linked based on expression with 17 traits of potato, representing both well-known and novel associations. This approach can be used to link the transcriptome to traits. We explore the possibility of associating the level of transcript expression from controlled, optimal environments to traits in a progeny population with different methods introducing the application of DUO for the first time on transcriptome data. We verify the expression pattern for five of the putative transcript markers in another progeny population.

Quantitative Analysis of Target Peptides Related to Resistance Against Ascochyta Blight (Peyronellaea pinodes) in Pea

Peyronellaea pinodes causes Ascochyta blight, one of the major diseases in pea worldwide. Cultivated pea plants have a low resistance to this disease. Although quantitative trait loci (QTLs) involved in the resistance to Ascochyta blight have been identified, the specific genes associated with these QTLs remain unknown, which makes marker-assisted selection difficult. Complex traits alter proteins and their abundance. Quantitative estimation of proteins in pea might therefore be useful in selecting potential markers for breeding. In this work, we developed a strategy using a combination of shotgun proteomics (viz., high performance liquid chromatography-mass spectrometry data-dependent acquisition) and data-independent acquisition (DIA) analysis, to identify putative protein markers associated with resistance to Ascochyta blight and explored its use for breeding selection. For this purpose, an initial list of target peptides based on proteins closely related to resistance to P. pinodes was compiled by using two genotypes with contrasting responses to the disease. Then, targeted data analysis (viz., shotgun proteomics-DIA) was used for constitutive quantification of the target peptides in a representative number of the recombinant inbred line population segregated for resistance as derived from a cross between the two genotypes. Finally, a peptide panel of potential markers for resistance to P. pinodes was built. The results thus obtained are discussed and compared with those of previous gene expression studies using the same parental pea genotypes responding to the pathogen. Also, a molecular defense mechanism against Ascochyta blight in pea is proposed. To the authors' knowledge, this is the first time a targeted proteomics approach based on data analysis has been used to identify peptides associated with resistance to this disease.

What Is New in (Plant) Proteomics Methods and Protocols: The 2015-2019 Quinquennium

The third edition of "Plant Proteomics Methods and Protocols," with the title "Advances in Proteomics Techniques, Data Validation, and Integration with Other Classic and -Omics Approaches in the Systems Biology Direction," was conceived as being based on the success of the previous editions, and the continuous advances and improvements in proteomic techniques, equipment, and bioinformatics tools, and their uses in basic and translational plant biology research that has occurred in the past 5 years (in round figures, of around 22,000 publications referenced in WoS, 2000 were devoted to plants).The monograph contains 29 chapters with detailed proteomics protocols commonly employed in plant biology research. They present recent advances at all workflow stages, starting from the laboratory (tissue and cell fractionation, protein extraction, depletion, purification, separation, MS analysis, quantification) and ending on the computer (algorithms for protein identification and quantification, bioinformatics tools for data analysis, databases and repositories). At the end of each chapter there are enough explanatory notes and comments to make the protocols easily applicable to other biological systems and/or studies, discussing limitations, artifacts, or pitfalls. For that reason, as with the previous editions, it would be especially useful for beginners or novices.Out of the 29 chapters, six are devoted to descriptive proteomics, with a special emphasis on subcellular protein profiling (Chapters 5 - 10 ), six to PTMs (Chapters 11 , and 14 - 18 ), three to protein interactions (Chapters 19 - 21 ), and two to specific proteins, peroxidases (Chapter 24 ) and proteases and protease inhibitors (Chapter 26 ). The book reflects the new trajectory in MS-based protein identification and quantification, moving from the classic gel-based approaches to the most recent labeling (Chapters 10 , 11 , 29 ), shotgun (Chapters 5 , 7 , 12 , 15 ), parallel reaction monitoring (Chapter 16 ), and targeted data acquisition (Chapter 13 ). MS imaging (Chapter 25 ), the only in vivo MS-based proteomics strategy, is far from being fully optimized and exploited in plant biology research. A confident protein identification and quantitation, especially in orphan species, of low-abundance proteins, is still a challenging task (Chapters 4 , 28 ).What is really new is the use of different techniques for proteomics data validation and their integration into other classic and -omics approaches in the systems biology direction. Chapter 2 reports on multiple extractions in a single experiment of the different biomolecules, nucleic acids, proteins, and metabolites. Chapter 27 describes how metabolic pathways can be reconstructed from multiple -omics data, and Chapter 3 network building. Finally, Chapters 22 and 23 deal with, respectively, the search for allele-specific proteins and proteogenomics.Around 200 groups were, almost 1 year ago, invited to take part in this edition. Unfortunately, only 10% of them kindly accepted. My gratitude to those who accepted our invitation but also to those who did not, as all of them have contributed to the plant proteomics field. I will enlist, in this introductory chapter, following my own judgment, some of the relevant papers published in the past 5 years, those that have shown us how to enhance and exploit the potential of proteomics in plant biology research, without aiming at giving a too exhaustive list.

Proteomics of PTI and Two ETI Immune Reactions in Potato Leaves

Plants have a variety of ways to defend themselves against pathogens. A commonly used model of the plant immune system is divided into a general response triggered by pathogen-associated molecular patterns (PAMPs), and a specific response triggered by effectors. The first type of response is known as PAMP triggered immunity (PTI), and the second is known as effector-triggered immunity (ETI). To obtain better insight into changes of protein abundance in immunity reactions, we performed a comparative proteomic analysis of a PTI and two different ETI models (relating to Phytophthora infestans) in potato. Several proteins showed higher abundance in all immune reactions, such as a protein annotated as sterol carrier protein 2 that could be interesting since Phytophthora species are sterol auxotrophs. RNA binding proteins also showed altered abundance in the different immune reactions. Furthermore, we identified some PTI-specific changes of protein abundance, such as for example, a glyoxysomal fatty acid beta-oxidation multifunctional protein and a MAR-binding protein. Interestingly, a lysine histone demethylase was decreased in PTI, and that prompted us to also analyze protein methylation in our datasets. The proteins upregulated explicitly in ETI included several catalases. Few proteins were regulated in only one of the ETI interactions. For example, histones were only downregulated in the ETI-Avr2 interaction, and a putative multiprotein bridging factor was only upregulated in the ETI-IpiO interaction. One example of a methylated protein that increased in the ETI interactions was a serine hydroxymethyltransferase.

Concepts and strategies of soybean seed proteomics using the shotgun proteomics approach

Introduction: The last decade has yielded significant developments in the field of proteomics, especially in mass spectrometry (MS) and data analysis tools. In particular, a shift from gel-based to MS-based proteomics has been observed, thereby providing a platform with which to construct proteome atlases for all life forms. Nevertheless, the analysis of plant proteomes, especially those of samples that contain high-abundance proteins (HAPs), such as soybean seeds, remains challenging. Areas covered: Here, we review recent progress in soybean seed proteomics and highlight advances in HAPs depletion methods and peptide pre-fractionation, identification, and quantification methods. We also suggest a pipeline for future proteomic analysis, in order to increase the dynamic coverage of the soybean seed proteome. Expert opinion: Because HAPs limit the dynamic resolution of the soybean seed proteome, the depletion of HAPs is a prerequisite of high-throughput proteome analysis, and owing to the use of two-dimensional gel electrophoresis-based proteomic approaches, few soybean seed proteins have been identified or characterized. Recent advances in proteomic technologies, which have significantly increased the proteome coverage of other plants, could be used to overcome the current complexity and limitation of soybean seed proteomics.

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

High-throughput field phenotyping has garnered major attention in recent years leading to the development of several new protocols for recording various plant traits of interest. Phenotyping of plants for breeding and for precision agriculture have different requirements due to different sizes of the plots and fields, differing purposes and the urgency of the action required after phenotyping. While in plant breeding phenotyping is done on several thousand small plots mainly to evaluate them for various traits, in plant cultivation, phenotyping is done in large fields to detect the occurrence of plant stresses and weeds at an early stage. The aim of this review is to highlight how various high-throughput phenotyping methods are used for plant breeding and farming and the key differences in the applications of such methods. Thus, various techniques for plant phenotyping are presented together with applications of these techniques for breeding and cultivation. Several examples from the literature using these techniques are summarized and the key technical aspects are highlighted.

Proteomic analysis of the rice (Oryza officinalis) provides clues on molecular tagging of proteins for brown planthopper resistance

BACKGROUND

Among various pests, the brown planthopper (BPH) that damages rice is the major destructive pests. Understanding resistance mechanisms is a critical step toward effective control of BPH. This study investigates the proteomics of BPH interactions with three rice cultivars: the first resistant (PR) to BPH, the second susceptible (PS), and the third hybrid (HR) between the two, in order to understand mechanisms of BPH resistance in rice.

RESULTS

Over 4900 proteins were identified from these three rice cultivars using iTRAQ proteomics study. A total of 414, 425 and 470 differentially expressed proteins (DEPs) were detected from PR, PS and HR, respectively, after BPH infestation. Identified DEPs are mainly enriched in categories related with biosynthesis of secondary metabolites, carbon metabolism, and glyoxylate and dicarboxylate metabolism. A two-component response regulator protein (ORR22) may participate in the early signal transduction after BPH infestation. In the case of the resistant rice cultivar (PR), 6 DEPs, i.e. two lipoxygenases (LOXs), a lipase, two dirigent proteins (DIRs) and an Ent-cassa-12,15-diene synthase (OsDTC1) are related to inheritable BPH resistance. A heat shock protein (HSP20) may take part in the physiological response to BPH infestation, making it a potential target for marker-assisted selection (MAS) of rice. Quantitative real-time polymerase chain reaction (qRT-PCR) revealed eight genes encoding various metabolic proteins involved in BPH resistance. During grain development the expressions of these genes varied at the transcriptional and translational levels.

CONCLUSIONS

This study provides comprehensive details of key proteins under compatible and incompatible interactions during BPH infestation, which will be useful for further investigation of the molecular basis of rice resistance to BPH and for breeding BPH-resistant rice cultivars.

A transnational and holistic breeding approach is needed for sustainable wheat production in the Baltic Sea region

The Baltic Sea is one of the largest brackish water bodies in the world. Eutrophication is a major concern in the Baltic Sea due to the leakage of nutrients to the sea with agriculture being the primary source. Wheat (Triticum aestivum L.) is the most widely grown crop in the countries surrounding the Baltic Sea and thus promoting sustainable agriculture practices for wheat cultivation will have a major impact on reducing pollution in the Baltic Sea. This approach requires identifying and addressing key challenges for sustainable wheat production in the region. Implementing new technologies for climate-friendly breeding and digital farming across all surrounding countries should promote sustainable intensification of agriculture in the region. In this review, we highlight major challenges for wheat cultivation in the Baltic Sea region and discuss various solutions integrating transnational collaboration for pre-breeding and technology sharing to accelerate development of low input wheat cultivars with improved host plant resistance to pathogen and enhanced adaptability to the changing climate.

Association of Patatin-Based Proteomic Distances with Potato ( Solanum tuberosum L.) Quality Traits

Patatin is the major tuber storage protein constituted by multiple isoforms highly variable across potato ( S. tuberosum) varieties. Here, we report a first association study of the variability of patatin isoforms between cultivars with their differences in tuber quality traits. Patatin-based proteomic distances were assessed between 15 table and/or processing potato cultivars from profiles of patatin obtained by two-dimensional electrophoresis. The content of ash, dry matter, reducing sugars, starch, total protein, and amino acid composition was also evaluated in tubers of each cultivar. Results showed that proteomic distances were significantly ( P < 0.05) associated with differences in the content of ash, dry matter, and essential amino acids. Proteomic distances were also able to identify outlier cultivars regarding the content of dry matter, content of protein, and protein quality. In conclusion, patatin-based proteomic distances can shorten the screening and selection processes of potato cultivars with advantageous characteristics in molecular breeding.

Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat

Phenotyping with proximal sensors allow high-precision measurements of plant traits both in the controlled conditions and in the field. In this work, using machine learning, an integrated analysis was done from the data obtained from spectroradiometer, infrared thermometer, and chlorophyll fluorescence measurements to identify most predictive proxy measurements for studying Septoria tritici blotch (STB) disease of wheat. The random forest (RF) models for chlorosis and necrosis identified photosystem II quantum yield (QY) and vegetative indices (VIs) associated with the biochemical composition of leaves as the top predictive variables for identifying disease symptoms. The RF model for chlorosis was validated with a validation set (R2: 0.80) and in an independent test set (R2: 0.55). Based on the results, it can be concluded that the proxy measurements for photosystem II, chlorophyll content, carotenoid, and anthocyanin levels and leaf surface temperature can be successfully used to detect STB. Further validation of these results in the field will enable application of these predictive variables for detection of STB in the field.

Metabolite and transcript markers for the prediction of potato drought tolerance

Potato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker-assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT-PCR and GC-MS profiling, respectively. Transcript marker candidates were selected from a published RNA-Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6% and 9%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions.

Comparative Membrane-Associated Proteomics of Three Different Immune Reactions in Potato

Plants have evolved different types of immune reactions but large-scale proteomics about these processes are lacking, especially in the case of agriculturally important crop pathosystems. We have established a system for investigating PAMP-triggered immunity (PTI) and two different effector-triggered immunity (ETI; triggered by Avr2 or IpiO) responses in potato. The ETI responses are triggered by molecules from the agriculturally important Phytophthora infestans interaction. To perform large-scale membrane protein-based comparison of these responses, we established a method to extract proteins from subcellular compartments in leaves. In the membrane fractions that were subjected to quantitative proteomics analysis, we found that most proteins regulated during PTI were also regulated in the same way in ETI. Proteins related to photosynthesis had lower abundance, while proteins related to oxidative and biotic stress, as well as those related to general antimicrobial defense and cell wall degradation, were found to be higher in abundance. On the other hand, we identified a few proteins—for instance, an ABC transporter-like protein—that were only found in the PTI reaction. Furthermore, we also identified proteins that were regulated only in ETI interactions. These included proteins related to GTP binding and heterotrimeric G-protein signaling, as well as those related to phospholipase signaling.

Genetical genomics of quality related traits in potato tubers using proteomics

BACKGROUND

Recent advances in ~omics technologies such as transcriptomics, metabolomics and proteomics along with genotypic profiling have permitted the genetic dissection of complex traits such as quality traits in non-model species. To get more insight into the genetic factors underlying variation in quality traits related to carbohydrate and starch metabolism and cold sweetening, we determined the protein content and composition in potato tubers using 2D-gel electrophoresis in a diploid potato mapping population. Upon analyzing we made sure that the proteins from the patatin family were excluded to ensure a better representation of the other proteins.

RESULTS

We subsequently performed pQTL analyses for all other proteins with a sufficient representation in the population and established a relationship between proteins and 26 potato tuber quality traits (e.g. flesh colour, enzymatic discoloration) by co-localization on the genetic map and a direct correlation study of protein abundances and phenotypic traits. Over 1643 unique protein spots were detected in total over the two harvests. We were able to map pQTLs for over 300 different protein spots some of which co-localized with traits such as starch content and cold sweetening. pQTLs were observed on every chromosome although not evenly distributed over the chromosomes. The largest number of pQTLs was found for chromosome 8 and the lowest for chromosome number 10. For some 20 protein spots multiple QTLs were observed.

CONCLUSIONS

From this analysis, hotspot areas for protein QTLs were identified on chromosomes three, five, eight and nine. The hotspot on chromosome 3 coincided with a QTL previously identified for total protein content and had more than 23 pQTLs in the region from 70 to 80 cM. Some of the co-localizing protein spots associated with some of the most interesting tuber quality traits were identified, albeit far less than we had anticipated at the onset of the experiments.

Mass Spectrometric-Based Selected Reaction Monitoring of Protein Phosphorylation during Symbiotic Signaling in the Model Legume, Medicago truncatula

Unlike the major cereal crops corn, rice, and wheat, leguminous plants such as soybean and alfalfa can meet their nitrogen requirement via endosymbiotic associations with soil bacteria. The establishment of this symbiosis is a complex process playing out over several weeks and is facilitated by the exchange of chemical signals between these partners from different kingdoms. Several plant components that are involved in this signaling pathway have been identified, but there is still a great deal of uncertainty regarding the early events in symbiotic signaling, i.e., within the first minutes and hours after the rhizobial signals (Nod factors) are perceived at the plant plasma membrane. The presence of several protein kinases in this pathway suggests a mechanism of signal transduction via posttranslational modification of proteins in which phosphate is added to the hydroxyl groups of serine, threonine and tyrosine amino acid side chains. To monitor the phosphorylation dynamics and complement our previous untargeted 'discovery' approach, we report here the results of experiments using a targeted mass spectrometric technique, Selected Reaction Monitoring (SRM) that enables the quantification of phosphorylation targets with great sensitivity and precision. Using this approach, we confirm a rapid change in the level of phosphorylation in 4 phosphosites of at least 4 plant phosphoproteins that have not been previously characterized. This detailed analysis reveals aspects of the symbiotic signaling mechanism in legumes that, in the long term, will inform efforts to engineer this nitrogen-fixing symbiosis in important non-legume crops such as rice, wheat and corn.