OryzaPG-DB: rice proteome database based on shotgun proteogenomics

Conventional and Omics Approaches for Understanding the Abiotic Stress Response in Cereal Crops-An Updated Overview

Cereals have evolved various tolerance mechanisms to cope with abiotic stress. Understanding the abiotic stress response mechanism of cereal crops at the molecular level offers a path to high-yielding and stress-tolerant cultivars to sustain food and nutritional security. In this regard, enormous progress has been made in the omics field in the areas of genomics, transcriptomics, and proteomics. Omics approaches generate a massive amount of data, and adequate advancements in computational tools have been achieved for effective analysis. The combination of integrated omics and bioinformatics approaches has been recognized as vital to generating insights into genome-wide stress-regulation mechanisms. In this review, we have described the self-driven drought, heat, and salt stress-responsive mechanisms that are highlighted by the integration of stress-manipulating components, including transcription factors, co-expressed genes, proteins, etc. This review also provides a comprehensive catalog of available online omics resources for cereal crops and their effective utilization. Thus, the details provided in the review will enable us to choose the appropriate tools and techniques to reduce the negative impacts and limit the failures in the intensive crop improvement study.

Advances in Multi-Omics Approaches for Molecular Breeding of Black Rot Resistance in Brassica oleracea L

Brassica oleracea is one of the most important species of the Brassicaceae family encompassing several economically important vegetables produced and consumed worldwide. But its sustainability is challenged by a range of pathogens, among which black rot, caused by Xanthomonas campestris pv. campestris (Xcc), is the most serious and destructive seed borne bacterial disease, causing huge yield losses. Host-plant resistance could act as the most effective and efficient solution to curb black rot disease for sustainable production of B. oleracea. Recently, 'omics' technologies have emerged as promising tools to understand the host-pathogen interactions, thereby gaining a deeper insight into the resistance mechanisms. In this review, we have summarized the recent achievements made in the emerging omics technologies to tackle the black rot challenge in B. oleracea. With an integrated approach of the omics technologies such as genomics, proteomics, transcriptomics, and metabolomics, it would allow better understanding of the complex molecular mechanisms underlying black rot resistance. Due to the availability of sequencing data, genomics and transcriptomics have progressed as expected for black rot resistance, however, other omics approaches like proteomics and metabolomics are lagging behind, necessitating a holistic and targeted approach to address the complex questions of Xcc-Brassica interactions. Genomic studies revealed that the black rot resistance is a complex trait and is mostly controlled by quantitative trait locus (QTL) with minor effects. Transcriptomic analysis divulged the genes related to photosynthesis, glucosinolate biosynthesis and catabolism, phenylpropanoid biosynthesis pathway, ROS scavenging, calcium signalling, hormonal synthesis and signalling pathway are being differentially expressed upon Xcc infection. Comparative proteomic analysis in relation to susceptible and/or resistance interactions with Xcc identified the involvement of proteins related to photosynthesis, protein biosynthesis, processing and degradation, energy metabolism, innate immunity, redox homeostasis, and defence response and signalling pathways in Xcc-Brassica interaction. Specifically, most of the studies focused on the regulation of the photosynthesis-related proteins as a resistance response in both early and later stages of infection. Metabolomic studies suggested that glucosinolates (GSLs), especially aliphatic and indolic GSLs, its subsequent hydrolysis products, and defensive metabolites synthesized by jasmonic acid (JA)-mediated phenylpropanoid biosynthesis pathway are involved in disease resistance mechanisms against Xcc in Brassica species. Multi-omics analysis showed that JA signalling pathway is regulating resistance against hemibiotrophic pathogen like Xcc. So, the bonhomie between omics technologies and plant breeding is going to trigger major breakthroughs in the field of crop improvement by developing superior cultivars with broad-spectrum resistance. If multi-omics tools are implemented at the right scale, we may be able to achieve the maximum benefits from the minimum. In this review, we have also discussed the challenges, future prospects, and the way forward in the application of omics technologies to accelerate the breeding of B. oleracea for disease resistance. A deeper insight about the current knowledge on omics can offer promising results in the breeding of high-quality disease-resistant crops.

Toward Integrated Multi-Omics Intervention: Rice Trait Improvement and Stress Management

Rice (Oryza sativa) is an imperative staple crop for nearly half of the world's population. Challenging environmental conditions encompassing abiotic and biotic stresses negatively impact the quality and yield of rice. To assure food supply for the unprecedented ever-growing world population, the improvement of rice as a crop is of utmost importance. In this era, "omics" techniques have been comprehensively utilized to decipher the regulatory mechanisms and cellular intricacies in rice. Advancements in omics technologies have provided a strong platform for the reliable exploration of genetic resources involved in rice trait development. Omics disciplines like genomics, transcriptomics, proteomics, and metabolomics have significantly contributed toward the achievement of desired improvements in rice under optimal and stressful environments. The present review recapitulates the basic and applied multi-omics technologies in providing new orchestration toward the improvement of rice desirable traits. The article also provides a catalog of current scenario of omics applications in comprehending this imperative crop in relation to yield enhancement and various environmental stresses. Further, the appropriate databases in the field of data science to analyze big data, and retrieve relevant information vis-à-vis rice trait improvement and stress management are described.

Systems biology for crop improvement

In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

Proteogenomic Characterization of the Pathogenic Fungus Aspergillus flavus Reveals Novel Genes Involved in Aflatoxin Production

Aspergillus flavus (A. flavus), a pathogenic fungus, can produce carcinogenic and toxic aflatoxins that are a serious agricultural and medical threat worldwide. Attempts to decipher the aflatoxin biosynthetic pathway have been hampered by the lack of a high-quality genome annotation for A. flavus. To address this gap, we performed a comprehensive proteogenomic analysis using high-accuracy mass spectrometry data for this pathogen. The resulting high-quality data set confirmed the translation of 8724 previously predicted genes and identified 732 novel proteins, 269 splice variants, 447 single amino acid variants, 188 revised genes. A subset of novel proteins was experimentally validated by RT-PCR and synthetic peptides. Further functional annotation suggested that a number of the identified novel proteins may play roles in aflatoxin biosynthesis and stress responses in A. flavus. This comprehensive strategy also identified a wide range of posttranslational modifications (PTMs), including 3461 modification sites from 1765 proteins. Functional analysis suggested the involvement of these modified proteins in the regulation of cellular metabolic and aflatoxin biosynthetic pathways. Together, we provided a high-quality annotation of A. flavus genome and revealed novel insights into the mechanisms of aflatoxin production and pathogenicity in this pathogen.

Interactive proteogenomic exploration of response to Fusarium head blight in oat varieties with different resistance

Fusarium species are cereal pathogens that cause the Fusarium Head Blight (FHB) disease. FHB can reduce yield, cause mycotoxin accumulation in the grain and reduce germination efficiency of the harvested seeds. Understanding the biochemical interactions between the host plants and the pathogen is crucial for controlling the disease and for the development of cultivars with improved tolerance to FHB. Here, we studied morphological and proteomic differences between the susceptible oat variety Belinda and the more resistant variety Argamak using variety-specific transcriptome assemblies as references. Measurements of deoxynivalenol toxin levels confirmed the partial resistance in Argamak and the susceptibility in Belinda. To jointly investigate the proteomics- and sequence data, we developed an RShiny-based interface for interactive exploration of the dataset using univariate and multivariate statistics. When applying this interface to the dataset, quantitative protein differences between Belinda and Argamak were detected, and eighteen peptides were found uniquely in Argamak during infection, among them several lipoxygenases. Such proteins can be developed as markers for Fusarium resistance breeding. In conclusion, this study provides the first proteogenomic insight on molecular Fusarium-oat interactions at both morphological and molecular levels and the data are openly available through an interactive interface for further inspection. SIGNIFICANCE: Fusarium head blight causes widespread damage to crops, and chronic and acute toxicity to human and livestock due to the accumulation of toxins during infection. In the present study, two oat varieties with differing resistance were challenged with Fusarium to understand the disease better, and studied both at morphological and molecular levels, identifying proteins which could play a role in the defense mechanism. Furthermore, a proteogenomics approach allows joint profiling of expression and sequence level differences to identify potentially functionally differing mutations. Here such analysis is made openly available through an interactive interface which allows other scientists to draw further findings from the data. This study may both serve as a basis for understanding oat disease response and developing breeding markers for Fusarium resistant oat and future proteogenomic studies using the interactive approach described.

Large Scale Profiling of Protein Isoforms Using Label-Free Quantitative Proteomics Revealed the Regulation of Nonsense-Mediated Decay in Moso Bamboo (Phyllostachys edulis)

Moso bamboo is an important forest species with a variety of ecological, economic, and cultural values. However, the gene annotation information of moso bamboo is only based on the transcriptome sequencing, lacking the evidence of proteome. The lignification and fiber in moso bamboo leads to a difficulty in the extraction of protein using conventional methods, which seriously hinders research on the proteomics of moso bamboo. The purpose of this study is to establish efficient methods for extracting the total proteins from moso bamboo for following mass spectrometry-based quantitative proteome identification. Here, we have successfully established a set of efficient methods for extracting total proteins of moso bamboo followed by mass spectrometry-based label-free quantitative proteome identification, which further improved the protein annotation of moso bamboo genes. In this study, 10,376 predicted coding genes were confirmed by quantitative proteomics, accounting for 35.8% of all annotated protein-coding genes. Proteome analysis also revealed the protein-coding potential of 1015 predicted long noncoding RNA (lncRNA), accounting for 51.03% of annotated lncRNAs. Thus, mass spectrometry-based proteomics provides a reliable method for gene annotation. Especially, quantitative proteomics revealed the translation patterns of proteins in moso bamboo. In addition, the 3284 transcript isoforms from 2663 genes identified by Pacific BioSciences (PacBio) single-molecule real-time long-read isoform sequencing (Iso-Seq) was confirmed on the protein level by mass spectrometry. Furthermore, domain analysis of mass spectrometry-identified proteins encoded in the same genomic locus revealed variations in domain composition pointing towards a functional diversification of protein isoform. Finally, we found that part transcripts targeted by nonsense-mediated mRNA decay (NMD) could also be translated into proteins. In summary, proteomic analysis in this study improves the proteomics-assisted genome annotation of moso bamboo and is valuable to the large-scale research of functional genomics in moso bamboo. In summary, this study provided a theoretical basis and technical support for directional gene function analysis at the proteomics level in moso bamboo.

Improvements to the Rice Genome Annotation Through Large-Scale Analysis of RNA-Seq and Proteomics Data Sets

Rice (Oryza sativa) is one of the most important worldwide crops. The genome has been available for over 10 years and has undergone several rounds of annotation. We created a comprehensive database of transcripts from 29 public RNA sequencing data sets, officially predicted genes from Ensembl plants, and common contaminants in which to search for protein-level evidence. We re-analyzed nine publicly accessible rice proteomics data sets. In total, we identified 420K peptide spectrum matches from 47K peptides and 8,187 protein groups. 4168 peptides were initially classed as putative novel peptides (not matching official genes). Following a strict filtration scheme to rule out other possible explanations, we discovered 1,584 high confidence novel peptides. The novel peptides were clustered into 692 genomic loci where our results suggest annotation improvements. 80% of the novel peptides had an ortholog match in the curated protein sequence set from at least one other plant species. For the peptides clustering in intergenic regions (and thus potentially new genes), 101 loci were identified, for which 43 had a high-confidence hit for a protein domain. Our results can be displayed as tracks on the Ensembl genome or other browsers supporting Track Hubs, to support re-annotation of the rice genome.

RicyerDB: A Database For Collecting Rice Yield-related Genes with Biological Analysis

The Rice Yield-related Database (RicyerDB) was created to complement with related research of influence rice (Oryza sativa L.) yield in multiple traits by manually curating the related databases and literature, and genomics and proteomics information that could be useful for comprehensive understanding of the rice biology. RicyerDB provides a more valuable resource in which to efficiently investigate, browse and analyze yield-related genes. The whole data set can be easily queried and downloaded through the webpage. In addition, RicyerDB also constructed a protein-protein interaction network with biological analysis. The combined rice database opens a new path to facilitate researchers achieving information on rice gene in terms of their effects on traits important for rice breeding. The web server is freely available at: http://server.malab.cn/Ricyer/index.html.

Multi-Omics Driven Assembly and Annotation of the Sandalwood (Santalum album) Genome

Indian sandalwood (Santalum album) is an important tropical evergreen tree known for its fragrant heartwood-derived essential oil and its valuable carving wood. Here, we applied an integrated genomic, transcriptomic, and proteomic approach to assemble and annotate the Indian sandalwood genome. Our genome sequencing resulted in the establishment of a draft map of the smallest genome for any woody tree species to date (221 Mb). The genome annotation predicted 38,119 protein-coding genes and 27.42% repetitive DNA elements. In-depth proteome analysis revealed the identities of 72,325 unique peptides, which confirmed 10,076 of the predicted genes. The addition of transcriptomic and proteogenomic approaches resulted in the identification of 53 novel proteins and 34 gene-correction events that were missed by genomic approaches. Proteogenomic analysis also helped in reassigning 1,348 potential noncoding RNAs as bona fide protein-coding messenger RNAs. Gene expression patterns at the RNA and protein levels indicated that peptide sequencing was useful in capturing proteins encoded by nuclear and organellar genomes alike. Mass spectrometry-based proteomic evidence provided an unbiased approach toward the identification of proteins encoded by organellar genomes. Such proteins are often missed in transcriptome data sets due to the enrichment of only messenger RNAs that contain poly(A) tails. Overall, the use of integrated omic approaches enhanced the quality of the assembly and annotation of this nonmodel plant genome. The availability of genomic, transcriptomic, and proteomic data will enhance genomics-assisted breeding, germplasm characterization, and conservation of sandalwood trees.

Rice Functional Genomics Research: Past Decade and Future

Rice (Oryza sativa) is a major staple food crop for more than 3.5 billion people worldwide. Understanding the regulatory mechanisms of complex agronomic traits in rice is critical for global food security. Rice is also a model plant for genomics research of monocotyledons. Thanks to the rapid development of functional genomic technologies, over 2000 genes controlling important agronomic traits have been cloned, and their molecular biological mechanisms have also been partially characterized. Here, we briefly review the advances in rice functional genomics research during the past 10 years, including a summary of functional genomics platforms, genes and molecular regulatory networks that regulate important agronomic traits, and newly developed tools for gene identification. These achievements made in functional genomics research will greatly facilitate the development of green super rice. We also discuss future challenges and prospects of rice functional genomics research.

Recent advances in proteomics of cereals

Cereals contribute a major part of human nutrition and are considered as an integral source of energy for human diets. With genomic databases already available in cereals such as rice, wheat, barley, and maize, the focus has now moved to proteome analysis. Proteomics studies involve the development of appropriate databases based on developing suitable separation and purification protocols, identification of protein functions, and can confirm their functional networks based on already available data from other sources. Tremendous progress has been made in the past decade in generating huge data-sets for covering interactions among proteins, protein composition of various organs and organelles, quantitative and qualitative analysis of proteins, and to characterize their modulation during plant development, biotic, and abiotic stresses. Proteomics platforms have been used to identify and improve our understanding of various metabolic pathways. This article gives a brief review of efforts made by different research groups on comparative descriptive and functional analysis of proteomics applications achieved in the cereal science so far.

Cereal Crop Proteomics: Systemic Analysis of Crop Drought Stress Responses Towards Marker-Assisted Selection Breeding

Sustainable crop production is the major challenge in the current global climate change scenario. Drought stress is one of the most critical abiotic factors which negatively impact crop productivity. In recent years, knowledge about molecular regulation has been generated to understand drought stress responses. For example, information obtained by transcriptome analysis has enhanced our knowledge and facilitated the identification of candidate genes which can be utilized for plant breeding. On the other hand, it becomes more and more evident that the translational and post-translational machinery plays a major role in stress adaptation, especially for immediate molecular processes during stress adaptation. Therefore, it is essential to measure protein levels and post-translational protein modifications to reveal information about stress inducible signal perception and transduction, translational activity and induced protein levels. This information cannot be revealed by genomic or transcriptomic analysis. Eventually, these processes will provide more direct insight into stress perception then genetic markers and might build a complementary basis for future marker-assisted selection of drought resistance. In this review, we survey the role of proteomic studies to illustrate their applications in crop stress adaptation analysis with respect to productivity. Cereal crops such as wheat, rice, maize, barley, sorghum and pearl millet are discussed in detail. We provide a comprehensive and comparative overview of all detected protein changes involved in drought stress in these crops and have summarized existing knowledge into a proposed scheme of drought response. Based on a recent proteome study of pearl millet under drought stress we compare our findings with wheat proteomes and another recent study which defined genetic marker in pearl millet.

Ascribing Functions to Genes: Journey Towards Genetic Improvement of Rice Via Functional Genomics

Rice, one of the most important cereal crops for mankind, feeds more than half the world population. Rice has been heralded as a model cereal owing to its small genome size, amenability to easy transformation, high synteny to other cereal crops and availability of complete genome sequence. Moreover, sequence wealth in rice is getting more refined and precise due to resequencing efforts. This humungous resource of sequence data has confronted research fraternity with a herculean challenge as well as an excellent opportunity to functionally validate expressed as well as regulatory portions of the genome. This will not only help us in understanding the genetic basis of plant architecture and physiology but would also steer us towards developing improved cultivars. No single technique can achieve such a mammoth task. Functional genomics through its diverse tools viz. loss and gain of function mutants, multifarious omics strategies like transcriptomics, proteomics, metabolomics and phenomics provide us with the necessary handle. A paradigm shift in technological advances in functional genomics strategies has been instrumental in generating considerable amount of information w.r.t functionality of rice genome. We now have several databases and online resources for functionally validated genes but despite that we are far from reaching the desired milestone of functionally characterizing each and every rice gene. There is an urgent need for a common platform, for information already available in rice, and collaborative efforts between researchers in a concerted manner as well as healthy public-private partnership, for genetic improvement of rice crop better able to handle the pressures of climate change and exponentially increasing population.

OryzaGenome: Genome Diversity Database of Wild Oryza Species

The species in the genus Oryza, encompassing nine genome types and 23 species, are a rich genetic resource and may have applications in deeper genomic analyses aiming to understand the evolution of plant genomes. With the advancement of next-generation sequencing (NGS) technology, a flood of Oryza species reference genomes and genomic variation information has become available in recent years. This genomic information, combined with the comprehensive phenotypic information that we are accumulating in our Oryzabase, can serve as an excellent genotype-phenotype association resource for analyzing rice functional and structural evolution, and the associated diversity of the Oryza genus. Here we integrate our previous and future phenotypic/habitat information and newly determined genotype information into a united repository, named OryzaGenome, providing the variant information with hyperlinks to Oryzabase. The current version of OryzaGenome includes genotype information of 446 O. rufipogon accessions derived by imputation and of 17 accessions derived by imputation-free deep sequencing. Two variant viewers are implemented: SNP Viewer as a conventional genome browser interface and Variant Table as a text-based browser for precise inspection of each variant one by one. Portable VCF (variant call format) file or tab-delimited file download is also available. Following these SNP (single nucleotide polymorphism) data, reference pseudomolecules/scaffolds/contigs and genome-wide variation information for almost all of the closely and distantly related wild Oryza species from the NIG Wild Rice Collection will be available in future releases. All of the resources can be accessed through http://viewer.shigen.info/oryzagenome/.

Dual use of peptide mass spectra: Protein atlas and genome annotation

One of the objectives of genome science is the discovery and accurate annotation of all protein-coding genes. Proteogenomics has emerged as a methodology that provides orthogonal information to traditional forms of evidence used for genome annotation. By this method, peptides that are identified via tandem mass spectrometry are used to refine protein-coding gene models. Namely, these peptides are used to confirm the translation of predicted protein-coding genes, as evidence of novel genes or for correction of current gene models. Proteogenomics requires deep and broad sampling of the proteome in order to generate sufficient numbers of unique peptides. Therefore, we propose that proteogenomic projects are designed so that the generated peptides can also be used to create a comprehensive protein atlas that quantitatively catalogues protein abundance changes during development and in response to environmental stimulus.

Proteomic analysis of upland rice (Oryza sativa L.) exposed to intermittent water deficit

Rice is the most important crop consumed all over the world. In Brazil, irrigated rice covers 50 % of the rice producing area and is responsible for 75 % of the national production. Upland rice covers most of the remaining area, and is therefore, a very important production system in the country. In the present study, we have used the drought tolerant upland rice variety Três Meses Antigo to investigate the proteomic changes that occur during drought stress. Plants were submitted to drought by the reposition of 50 % of the water lost daily. Twenty days after the beginning of the drought stress period, leaves were harvested and used for protein extraction. The 2D maps obtained from treated and control plants revealed 408 reproducible spots, 44 of which were identified by mass spectrometry, including 15 differential proteins. Several unaltered proteins were also identified (39 spots) and were mainly involved in photosynthesis. Taken together, the results obtained suggest that the tolerant upland rice up-regulates anti-oxidant and energy production related proteins in order to cope with water deficit.

Rice proteomics: a model system for crop improvement and food security

Rice proteomics has progressed at a tremendous pace since the year 2000, and that has resulted in establishing and understanding the proteomes of tissues, organs, and organelles under both normal and abnormal (adverse) environmental conditions. Established proteomes have also helped in re-annotating the rice genome and revealing the new role of previously known proteins. The progress of rice proteomics had recognized it as the corner/stepping stone for at least cereal crops. Rice proteomics remains a model system for crops as per its exemplary proteomics research. Proteomics-based discoveries in rice are likely to be translated in improving crop plants and vice versa against ever-changing environmental factors. This review comprehensively covers rice proteomics studies from August 2010 to July 2013, with major focus on rice responses to diverse abiotic (drought, salt, oxidative, temperature, nutrient, hormone, metal ions, UV radiation, and ozone) as well as various biotic stresses, especially rice-pathogen interactions. The differentially regulated proteins in response to various abiotic stresses in different tissues have also been summarized, indicating key metabolic and regulatory pathways. We envision a significant role of rice proteomics in addressing the global ground level problem of food security, to meet the demands of the human population which is expected to reach six to nine billion by 2040.

Analysis of rice proteins using SDS-PAGE shotgun proteomics

In this chapter we describe the workflow used in our laboratory to analyze rice leaf samples using label-free shotgun proteomics based on SDS-PAGE fractionation of proteins. Rice proteomics has benefitted substantially from successful execution of shotgun proteomics techniques. We describe steps on how to proceed starting from rice protein extraction, SDS-PAGE, in-gel protein digestion with trypsin, nanoLC-MS/MS, and database searching using the GPM. Data from these experiments can be used for spectral counting, where simultaneous quantitation of several thousand proteins can be obtained.

Plant proteomics: from genome sequencing to proteome databases and repositories

Proteomic approaches are useful for the identification of functional proteins. These have been enhanced not only by the development of proteomic techniques but also in concert with genome sequencing. In this chapter, 30 databases and Web sites relating to plant proteomics are reviewed and recent technologies relating to data collection and annotation are surveyed.

A decade of plant proteomics and mass spectrometry: translation of technical advancements to food security and safety issues

Tremendous progress in plant proteomics driven by mass spectrometry (MS) techniques has been made since 2000 when few proteomics reports were published and plant proteomics was in its infancy. These achievements include the refinement of existing techniques and the search for new techniques to address food security, safety, and health issues. It is projected that in 2050, the world's population will reach 9-12 billion people demanding a food production increase of 34-70% (FAO, 2009) from today's food production. Provision of food in a sustainable and environmentally committed manner for such a demand without threatening natural resources, requires that agricultural production increases significantly and that postharvest handling and food manufacturing systems become more efficient requiring lower energy expenditure, a decrease in postharvest losses, less waste generation and food with longer shelf life. There is also a need to look for alternative protein sources to animal based (i.e., plant based) to be able to fulfill the increase in protein demands by 2050. Thus, plant biology has a critical role to play as a science capable of addressing such challenges. In this review, we discuss proteomics especially MS, as a platform, being utilized in plant biology research for the past 10 years having the potential to expedite the process of understanding plant biology for human benefits. The increasing application of proteomics technologies in food security, analysis, and safety is emphasized in this review. But, we are aware that no unique approach/technology is capable to address the global food issues. Proteomics-generated information/resources must be integrated and correlated with other omics-based approaches, information, and conventional programs to ensure sufficient food and resources for human development now and in the future.

Identification of new protein coding sequences and signal peptidase cleavage sites of Helicobacter pylori strain 26695 by proteogenomics

Correct annotation of protein coding genes is the basis of conventional data analysis in proteomic studies. Nevertheless, most protein sequence databases almost exclusively rely on gene finding software and inevitably also miss protein annotations or possess errors. Proteogenomics tries to overcome these issues by matching MS data directly against a genome sequence database. Here we report an in-depth proteogenomics study of Helicobacter pylori strain 26695. MS data was searched against a combined database of the NCBI annotations and a six-frame translation of the genome. Database searches with Mascot and X! Tandem revealed 1115 proteins identified by at least two peptides with a peptide false discovery rate below 1%. This represents 71% of the predicted proteome. So far this is the most extensive proteome study of Helicobacter pylori. Our proteogenomic approach unambiguously identified four previously missed annotations and furthermore allowed us to correct sequences of six annotated proteins. Since secreted proteins are often involved in pathogenic processes we further investigated signal peptidase cleavage sites. By applying a database search that accommodates the identification of semi-specific cleaved peptides, 63 previously unknown signal peptides were detected. The motif LXA showed to be the predominant recognition sequence for signal peptidases.

BIOLOGICAL SIGNIFICANCE

The results of MS-based proteomic studies highly rely on correct annotation of protein coding genes which is the basis of conventional data analysis. However, the annotation of protein coding sequences in genomic data is usually based on gene finding software. These tools are limited in their prediction accuracy such as the problematic determination of exact gene boundaries. Thus, protein databases own partly erroneous or incomplete sequences. Additionally, some protein sequences might also be missing in the databases. Proteogenomics, a combination of proteomic and genomic data analyses, is well suited to detect previously not annotated proteins and to correct erroneous sequences. For this purpose, the existing database of the investigated species is typically supplemented with a six-frame translation of the genome. Here, we studied the proteome of the major human pathogen Helicobacter pylori that is responsible for many gastric diseases such as duodenal ulcers and gastric cancer. Our in-depth proteomic study highly reliably identified 1115 proteins (FDR<0.01%) by at least two peptides (FDR<1%) which represent 71% of the predicted proteome deposited at NCBI. The proteogenomic data analysis of our data set resulted in the unambiguous identification of four previously missed annotations, the correction of six annotated proteins as well as the detection of 63 previously unknown signal peptides. We have annotated proteins of particular biological interest like the ferrous iron transport protein A, the coiled-coil-rich protein HP0058 and the lipopolysaccharide biosynthesis protein HP0619. For instance, the protein HP0619 could be a drug target for the inhibition of the LPS synthesis pathway. Furthermore it has been proven that the motif "LXA" is the predominant recognition sequence for the signal peptidase I of H. pylori. Signal peptidases are essential enzymes for the viability of bacterial cells and are involved in pathogenesis. Therefore signal peptidases could be novel targets for antibiotics. The inclusion of the corrected and new annotated proteins as well as the information of signal peptide cleavage sites will help in the study of biological pathways involved in pathogenesis or drug response of H. pylori.

Proteomics of nonmodel plant species

Until recently, large scale proteomic investigations in the plant field have only been possible for a few model species for which the whole genome sequence had been fully determined. In contrast, for many other species with a strong economic interest as sources of human food and animal feed, as well as industrial and pharmacological molecules, little was known about their genome sequence and identifying the proteome in these species was still considered challenging. However, progress has been made as a result of several recent advances in proteomics tools, e.g. in MS technology and data search programs, and the increasing availability of genomic and cDNA sequences from various species. Moreover, next-generation sequencing technologies now make it possible to rapidly determine, at a reasonable cost, the genome or RNA sequence of species not currently considered as models, thus considerably expanding the plant sequence databases. This review will show how these advances make it possible to identify a large set of proteins, even for species for which few sequences are currently available.

Proteomics of rice seed germination

Seed is a condensed form of plant. Under suitable environmental conditions, it can resume the metabolic activity from physiological quiescent status, and mobilize the reserves, biosynthesize new proteins, regenerate organelles, and cell membrane, eventually protrude the radicle and enter into seedling establishment. So far, how these activities are regulated in a coordinated and sequential manner is largely unknown. With the availability of more and more genome sequence information and the development of mass spectrometry (MS) technology, proteomics has been widely applied in analyzing the mechanisms of different biological processes, and proved to be very powerful. Regulation of rice seed germination is critical for rice cultivation. In recent years, a lot of proteomic studies have been conducted in exploring the gene expression regulation, reserves mobilization and metabolisms reactivation, which brings us new insights on the mechanisms of metabolism regulation during this process. Nevertheless, it also invokes a lot of questions. In this mini-review, we summarized the progress in the proteomic studies of rice seed germination. The current challenges and future perspectives were also discussed, which might be helpful for the following studies.

Effort and contribution of T-DNA Insertion mutant library for rice functional genomics research in China: review and perspective

With the completion of the rice (Oryza sativa L.) genome-sequencing project, the rice research community proposed to characterize the function of every predicted gene in rice by 2020. One of the most effective and high-throughput strategies for studying gene function is to employ genetic mutations induced by insertion elements such as T-DNA or transposons. Since 1999, with support from the Ministry of Science and Technology of China for Rice Functional Genomics Programs, large-scale T-DNA insertion mutant populations have been generated in Huazhong Agricultural University, the Chinese Academy of Sciences and the Chinese Academy of Agricultural Sciences. Currently, a total of 372,346 mutant lines have been generated, and 58,226 T-DNA or Tos17 flanking sequence tags have been isolated. Using these mutant resources, more than 40 genes with potential applications in rice breeding have already been identified. These include genes involved in biotic or abiotic stress responses, nutrient metabolism, pollen development, and plant architecture. The functional analysis of these genes will not only deepen our understanding of the fundamental biological questions in rice, but will also offer valuable gene resources for developing Green Super Rice that is high-yielding with few inputs even under the poor growth conditions of many regions of Africa and Asia.

The Rice Genome Knowledgebase (RGKbase): an annotation database for rice comparative genomics and evolutionary biology

Over the past 10 years, genomes of cultivated rice cultivars and their wild counterparts have been sequenced although most efforts are focused on genome assembly and annotation of two major cultivated rice (Oryza sativa L.) subspecies, 93-11 (indica) and Nipponbare (japonica). To integrate information from genome assemblies and annotations for better analysis and application, we now introduce a comparative rice genome database, the Rice Genome Knowledgebase (RGKbase, http://rgkbase.big.ac.cn/RGKbase/). RGKbase is built to have three major components: (i) integrated data curation for rice genomics and molecular biology, which includes genome sequence assemblies, transcriptomic and epigenomic data, genetic variations, quantitative trait loci (QTLs) and the relevant literature; (ii) User-friendly viewers, such as Gbrowse, GeneBrowse and Circos, for genome annotations and evolutionary dynamics and (iii) Bioinformatic tools for compositional and synteny analyses, gene family classifications, gene ontology terms and pathways and gene co-expression networks. RGKbase current includes data from five rice cultivars and species: Nipponbare (japonica), 93-11 (indica), PA64s (indica), the African rice (Oryza glaberrima) and a wild rice species (Oryza brachyantha). We are also constantly introducing new datasets from variety of public efforts, such as two recent releases—sequence data from ∼1000 rice varieties, which are mapped into the reference genome, yielding ample high-quality single-nucleotide polymorphisms and insertions–deletions.

The proteomic future: where mass spectrometry should be taking us

A newcomer to the -omics era, proteomics, is a broad instrument-intensive research area that has advanced rapidly since its inception less than 20 years ago. Although the 'wet-bench' aspects of proteomics have undergone a renaissance with the improvement in protein and peptide separation techniques, including various improvements in two-dimensional gel electrophoresis and gel-free or off-gel protein focusing, it has been the seminal advances in MS that have led to the ascension of this field. Recent improvements in sensitivity, mass accuracy and fragmentation have led to achievements previously only dreamed of, including whole-proteome identification, and quantification and extensive mapping of specific PTMs (post-translational modifications). With such capabilities at present, one might conclude that proteomics has already reached its zenith; however, 'capability' indicates that the envisioned goals have not yet been achieved. In the present review we focus on what we perceive as the areas requiring more attention to achieve the improvements in workflow and instrumentation that will bridge the gap between capability and achievement for at least most proteomes and PTMs. Additionally, it is essential that we extend our ability to understand protein structures, interactions and localizations. Towards these ends, we briefly focus on selected methods and research areas where we anticipate the next wave of proteomic advances.

Mass spectrum sequential subtraction speeds up searching large peptide MS/MS spectra datasets against large nucleotide databases for proteogenomics

We have developed a novel bioinformatics method called mass spectrum sequential subtraction (MSSS) to search large peptide spectra datasets produced by liquid chromatography/mass spectrometry (LC-MS/MS) against protein and large-sized nucleotide sequence databases. The main principle in MSSS is to search the peptide spectra set against the protein database, followed by removal of the spectra corresponding to the identified peptides to create a smaller set of the remaining peptide spectra for searching against the nucleotide sequences database. Therefore, we reduce the number of spectra to be searched to limit the peptide search space. Comparing MSSS and conventional search approach using a dataset of 27 LC-MS/MS runs of rice culture cells indicated that MSSS reduced the search queries to 50% and the search time to 75% on average. In addition, MSSS had no effect on the identification false-positive rate (FPR) or the novel peptide sequences identification ability. We used MSSS to analyze another dataset of 34 LC-MS/MS runs, resulting in identifying additional 74 novel peptides. Proteogenomic analysis with these additional peptides yielded 47 new genomic features in 24 rice genes plus 24 intergenic peptides. These results show that the utility of MSSS in searching large databases with large MS/MS datasets for proteogenomics.

Translational plant proteomics: a perspective

Translational proteomics is an emerging sub-discipline of the proteomics field in the biological sciences. Translational plant proteomics aims to integrate knowledge from basic sciences to translate it into field applications to solve issues related but not limited to the recreational and economic values of plants, food security and safety, and energy sustainability. In this review, we highlight the substantial progress reached in plant proteomics during the past decade which has paved the way for translational plant proteomics. Increasing proteomics knowledge in plants is not limited to model and non-model plants, proteogenomics, crop improvement, and food analysis, safety, and nutrition but to many more potential applications. Given the wealth of information generated and to some extent applied, there is the need for more efficient and broader channels to freely disseminate the information to the scientific community. This article is part of a Special Issue entitled: Translational Proteomics.

VESPA: software to facilitate genomic annotation of prokaryotic organisms through integration of proteomic and transcriptomic data

Background

The procedural aspects of genome sequencing and assembly have become relatively inexpensive, yet the full, accurate structural annotation of these genomes remains a challenge. Next-generation sequencing transcriptomics (RNA-Seq), global microarrays, and tandem mass spectrometry (MS/MS)-based proteomics have demonstrated immense value to genome curators as individual sources of information, however, integrating these data types to validate and improve structural annotation remains a major challenge. Current visual and statistical analytic tools are focused on a single data type, or existing software tools are retrofitted to analyze new data forms. We present Visual Exploration and Statistics to Promote Annotation (VESPA) is a new interactive visual analysis software tool focused on assisting scientists with the annotation of prokaryotic genomes though the integration of proteomics and transcriptomics data with current genome location coordinates.

Results

VESPA is a desktop Java™ application that integrates high-throughput proteomics data (peptide-centric) and transcriptomics (probe or RNA-Seq) data into a genomic context, all of which can be visualized at three levels of genomic resolution. Data is interrogated via searches linked to the genome visualizations to find regions with high likelihood of mis-annotation. Search results are linked to exports for further validation outside of VESPA or potential coding-regions can be analyzed concurrently with the software through interaction with BLAST. VESPA is demonstrated on two use cases (Yersinia pestis Pestoides F and Synechococcus sp. PCC 7002) to demonstrate the rapid manner in which mis-annotations can be found and explored in VESPA using either proteomics data alone, or in combination with transcriptomic data.

Conclusions

VESPA is an interactive visual analytics tool that integrates high-throughput data into a genomic context to facilitate the discovery of structural mis-annotations in prokaryotic genomes. Data is evaluated via visual analysis across multiple levels of genomic resolution, linked searches and interaction with existing bioinformatics tools. We highlight the novel functionality of VESPA and core programming requirements for visualization of these large heterogeneous datasets for a client-side application. The software is freely available at https://www.biopilot.org/docs/Software/Vespa.php.

The Rice Proteogenomics Database OryzaPG-DB: Development, Expansion, and New Features

Our recently developed rice proteogenomics database (OryzaPG-DB) is the first sustainable resource for rice shotgun-based proteogenomics, providing information on peptides identified in rice protein digested peptides measured by means of liquid chromatography-tandem mass spectrometry (LC-MS/MS), and mapping of the peptides to their genomic origins and the genomic novelty of each peptide. The sequences of the peptides, proteins, cDNAs and genes, and the gene annotations are available for download in FASTA and GFF3 formats, respectively. Further, an annotated visualization of the gene models, corresponding peptides, and genomic novelty is available for each gene, and MS/MS spectra are available for each peptide. In this article, we discuss the utilization of OryzaPG-DB and report on its development, recent content expansions, and newly added features in the current version (OryzaPG-DB v1.1).

Advances in omics and bioinformatics tools for systems analyses of plant functions

Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.

Advances in omics and bioinformatics tools for systems analyses of plant functions

Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.