Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Integrated microbiome and metabolome analysis reveals a novel interplay between commensal bacteria and metabolites in colorectal cancer

Rationale: Colorectal cancer (CRC) is a malignant tumor with the third highest morbidity rate among all cancers. Driven by the host's genetic makeup and environmental exposures, the gut microbiome and its metabolites have been implicated as the causes and regulators of CRC pathogenesis. We assessed human fecal samples as noninvasive and unbiased surrogates to catalog the gut microbiota and metabolome in patients with CRC. Methods: Fecal samples collected from CRC patients (CRC group, n = 50) and healthy volunteers (H group, n = 50) were subjected to microbiome (16S rRNA gene sequencing) and metabolome (gas chromatography-mass spectrometry, GC-MS) analyses. The datasets were analyzed individually and integrated for combined analysis using various bioinformatics approaches. Results: Fecal metabolomic analysis led to the identification of 164 metabolites spread across 40 metabolic pathways in both groups. In addition, there were 42 and 17 metabolites specific to the H and CRC groups, respectively. Sequencing of microbial diversity revealed 1084 operational taxonomic units (OTUs) across the two groups, and there was less species diversity in the CRC group than in the H group. Seventy-six discriminatory OTUs were identified for the microbiota of H volunteers and CRC patients. Integrated analysis correlated CRC-associated microbes with metabolites, such as polyamines (cadaverine and putrescine). Conclusions: Our results provide substantial evidence of a novel interplay between the gut microbiome and metabolome (i.e., polyamines), which is drastically perturbed in CRC. Microbe-associated metabolites can be used as diagnostic biomarkers in therapeutic explorations.

State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods' limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.

Draft genome sequence of the rubber tree Hevea brasiliensis

Background

Hevea brasiliensis, a member of the Euphorbiaceae family, is the major commercial source of natural rubber (NR). NR is a latex polymer with high elasticity, flexibility, and resilience that has played a critical role in the world economy since 1876.

Results

Here, we report the draft genome sequence of H. brasiliensis. The assembly spans ~1.1 Gb of the estimated 2.15 Gb haploid genome. Overall, ~78% of the genome was identified as repetitive DNA. Gene prediction shows 68,955 gene models, of which 12.7% are unique to Hevea. Most of the key genes associated with rubber biosynthesis, rubberwood formation, disease resistance, and allergenicity have been identified.

Conclusions

The knowledge gained from this genome sequence will aid in the future development of high-yielding clones to keep up with the ever increasing need for natural rubber.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Challenges and Opportunities in Cancer Metabolomics

Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture-based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.

Software tools, databases and resources in metabolomics: updates from 2018 to 2019

Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.

Jasmonate-mediated stomatal closure under elevated CO2 revealed by time-resolved metabolomics

Foliar stomatal movements are critical for regulating plant water loss and gas exchange. Elevated carbon dioxide (CO₂ ) levels are known to induce stomatal closure. However, the current knowledge on CO₂ signal transduction in stomatal guard cells is limited. Here we report metabolomic responses of Brassica napus guard cells to elevated CO₂ using three hyphenated metabolomics platforms: gas chromatography-mass spectrometry (MS); liquid chromatography (LC)-multiple reaction monitoring-MS; and ultra-high-performance LC-quadrupole time-of-flight-MS. A total of 358 metabolites from guard cells were quantified in a time-course response to elevated CO₂ level. Most metabolites increased under elevated CO₂ , showing the most significant differences at 10 min. In addition, reactive oxygen species production increased and stomatal aperture decreased with time. Major alterations in flavonoid, organic acid, sugar, fatty acid, phenylpropanoid and amino acid metabolic pathways indicated changes in both primary and specialized metabolic pathways in guard cells. Most interestingly, the jasmonic acid (JA) biosynthesis pathway was significantly altered in the course of elevated CO₂ treatment. Together with results obtained from JA biosynthesis and signaling mutants as well as CO₂ signaling mutants, we discovered that CO₂ -induced stomatal closure is mediated by JA signaling.

Plant single-cell and single-cell-type metabolomics

In conjunction with genomics, transcriptomics, and proteomics, plant metabolomics is providing large data sets that are paving the way towards a comprehensive and holistic understanding of plant growth, development, defense, and productivity. However, dilution effects from organ- and tissue-based sampling of metabolomes have limited our understanding of the intricate regulation of metabolic pathways and networks at the cellular level. Recent advances in metabolomics methodologies, along with the post-genomic expansion of bioinformatics knowledge and functional genomics tools, have allowed the gathering of enriched information on individual cells and single cell types. Here we review progress, current status, opportunities, and challenges presented by single cell-based metabolomics research in plants.

Data normalization strategies in metabolomics: Current challenges, approaches, and tools

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from - either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.

Evaluation of in vivo anti-hyperglycemic and antioxidant potentials of α-santalol and sandalwood oil

Sandalwood finds numerous mentions across diverse traditional medicinal systems in use worldwide. The objective of this study was to evaluate the in vivo anti-hyperglycemic and antioxidant potential of sandalwood oil and its major constituent α-santalol. The in vivo anti-hyperglycemic experiment was conducted in alloxan-induced diabetic male Swiss albino mice models. The in vivo antioxidant experiment was performed in d-galactose mediated oxidative stress induced male Swiss albino mice models. Intraperitoneal administration of α-santalol (100mg/kg BW) and sandalwood oil (1g/kg BW) for an week modulated parameters such as body weight, blood glucose, serum bilirubin, liver glycogen, and lipid peroxides contents to normoglycemic levels in the alloxan-induced diabetic mice. Similarly, intraperitoneal administration of α-santalol (100mg/kg BW) and sandalwood oil (1g/kg BW) for two weeks modulated parameters such as serum aminotransferases, alkaline phosphatase, bilirubin, superoxide dismutase, catalase, free sulfhydryl, protein carbonyl, nitric oxide, liver lipid peroxide contents, and antioxidant capacity in d-galactose mediated oxidative stress induced mice. Besides, it was observed that the beneficial effects of α-santalol were well complimented, differentially by other constituents present in sandalwood oil, thus indicating synergism in biological activity of this traditionally used bioresource.

New tools and resources in metabolomics: 2016-2017

Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.

Comparative phytochemical analysis and antibacterial efficacy of in vitro and in vivo extracts from East Indian sandalwood tree (Santalum album L.)

Sandalwood oil has been found in numerous therapeutic applications in traditional medicines such as Chinese traditional medicine and Ayurveda. However, there are no comparative accounts available in the literature that focused on in vitro and in vivo tree sample-derived extracts. Combined dichloromethane and methanol extracts were obtained from in vitro samples, that is, callus, somatic embryo and seedlings, and in vivo from leaves of non-oil-yielding young and oil-yielding matured trees. Phytochemical evaluation of the extracts reveals that the tree is rich in terpenoids, saponin, phenolics and tannins. The antibacterial properties of the five extracts were compared with sandalwood oil by screening against nine Gram-negative and five Gram-positive bacterial strains by disc diffusion, agar spot and TLC bioautography methods. Minimum inhibitory concentration (MIC) for sandalwood oil was determined to be in the range of 0·078-5 μg ml(-1) for most of the test micro-organisms screened. Bioautography results indicated the presence of potential antimicrobial constituents in somatic embryo extracts and sandalwood oil. Among the extracts screened, the somatic embryo extracts showed the strongest antibacterial activity comparable only with sandalwood oil and matured tree leaves' extract. The findings presented here also suggest that apart from sandalwood oil, other parts of this tree across developmental stages are also enriched with antibacterial principles.

SIGNIFICANCE AND IMPACT OF STUDY

This study constitutes the first systematic investigation on phytochemical composition and antimicrobial efficacy of sandalwood tree across in vitro and in vivo developmental stages screened against thirteen bacterial strains by four methods. Using a battery of antimicrobial assay techniques, it is possible to follow the differential bioactive metabolic richness of plant parts, to decipher, for example comparable efficacy of somatic embryo extracts and sandalwood oil.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

Polyploidy and the proteome

Although major advances have been made during the past 20 years in our understanding of the genetic and genomic consequences of polyploidy, our knowledge of polyploidy and the proteome is in its infancy. One of our goals is to stimulate additional study, particularly broad-scale proteomic analyses of polyploids and their progenitors. Although it may be too early to generalize regarding the extent to which transcriptomic data are predictive of the proteome of polyploids, it is clear that the proteome does not always reflect the transcriptome. Despite limited data, important observations on the proteomes of polyploids are emerging. In some cases, proteomic profiles show qualitatively and/or quantitatively non-additive patterns, and proteomic novelty has been observed. Allopolyploids generally combine the parental contributions, but there is evidence of parental dominance of one contributing genome in some allopolyploids. Autopolyploids are typically qualitatively identical to but quantitatively different from their parents. There is also evidence of parental legacy at the proteomic level. Proteomes clearly provide insights into the consequences of genomic merger and doubling beyond what is obtained from genomic and/or transcriptomic data. Translating proteomic changes in polyploids to differences in morphology and physiology remains the holy grail of polyploidy--this daunting task of linking genotype to proteome to phenotype should emerge as a focus of polyploidy research in the next decade. This article is part of a Special Issue entitled: Plant Proteomics--a bridge between fundamental processes and crop production, edited by Dr. Hans-Peter Mock.

The chemical exposome of type 2 diabetes mellitus: Opportunities and challenges in the omics era

Type 2 diabetes mellitus (T2DM) is a global silent killer, with > 450 million affected adults worldwide. A diverse array of non-modifiable risk factors such as family history, age (> 45 yrs), race/ethnicity, genetics, and history of gestational diabetes and modifiable risk factors such as physical inactivity, high body fat, body weight, high blood pressure, and high cholesterol for progression of prediabetes to T2DM. Given, that the modern world human population is constantly exposed to multiple stressors in the form of physical (i.e., sound, weather etc.) and chemical environment (i.e., diet, pollutants etc.), industrialization, and modernization has led to form a basis for exposomal correlation with T2DM incidence. Over the past decade, there have been emerging reports on association of levels of persistent organic pollutants (POPs), phthalates, antibiotics, drugs, air pollution, pesticides, and heavy metals with T2DM. In this review, we discuss the well known chemical exposome that has been associated with T2DM; the tools and approaches to capture this chemical exposome, and future opportunities and challenges in this exciting area of research. We further provide a window of thoughts, whether omics technologies can help fill in the gaps to help provide high throughput exposomics datasets in an unbiased manner to help understand T2DM pathophysiology in the context of industrialization, drastic lifestyle changes, urbanization, and pollution. We also discuss and provide guidelines/call to action for future exposomics studies investigating the association of T2DM with exposomes in the context of both epidemiological and experimental approaches.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

Advances in high resolution GC-MS technology: a focus on the application of GC-Orbitrap-MS in metabolomics and exposomics for FAIR practices

Gas chromatography-mass spectrometry (GC-MS) provides a complementary analytical platform for capturing volatiles, non-polar and (derivatized) polar metabolites and exposures from a diverse array of matrixes. High resolution (HR) GC-MS as a data generation platform can capture data on analytes that are usually not detectable/quantifiable in liquid chromatography mass-spectrometry-based solutions. With the rise of high-resolution accurate mass (HRAM) GC-MS systems such as GC-Orbitrap-MS in the last decade after the time-of-flight (ToF) renaissance, numerous applications have been found in the fields of metabolomics and exposomics. In a short span of time, a multitude of studies have used GC-Orbitrap-MS to generate exciting new high throughput data spanning from diverse basic to applied research areas. The GC-Orbitrap-MS has found application in both targeted and untargeted efforts for capturing metabolomes and exposomes across diverse studies. In this review, I capture and summarize all the reported studies to date, and provide a snapshot of the milieu of commercial and open-source software solutions, spectral libraries, and informatics solutions available to a GC-Orbitrap-MS system instrument user or a data analyst dealing with these datasets. Lastly, but importantly, I provide an account on data sharing and meta-data capturing solutions that are available to make HRAM GC-MS based metabolomics and exposomics studies findable, accessible, interoperable, and reproducible (FAIR). These FAIR practices would allow data generators and users of GC-HRMS instruments to help the community of GC-MS researchers to collaborate and co-develop exciting tools and algorithms in the future.

The guard cell metabolome: functions in stomatal movement and global food security

Guard cells represent a unique single cell-type system for the study of cellular responses to abiotic and biotic perturbations that affect stomatal movement. Decades of effort through both classical physiological and functional genomics approaches have generated an enormous amount of information on the roles of individual metabolites in stomatal guard cell function and physiology. Recent application of metabolomics methods has produced a substantial amount of new information on metabolome control of stomatal movement. In conjunction with other "omics" approaches, the knowledge-base is growing to reach a systems-level description of this single cell-type. Here we summarize current knowledge of the guard cell metabolome and highlight critical metabolites that bear significant impact on future engineering and breeding efforts to generate plants/crops that are resistant to environmental challenges and produce high yield and quality products for food and energy security.

High-resolution gas chromatography/mass spectrometry metabolomics of non-human primate serum

RATIONALE

Metabolomics analyses using gas chromatography/mass spectrometry (GC/MS)-based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high-resolution (HR) platforms such as 1D-GC/Orbitrap-MS.

METHODS

We used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HRMS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using two-dimensional gas chromatography/time-of-flight mass spectrometry (2D-GC/TOF-MS) for metabolite identification and quantification following established standard protocols.

RESULTS

Overall, the 2D-GC/TOF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim  ≥ 900 and signal-to-noise (S/N) ratio of >25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/TOF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30-70% of the peaks remain unidentified or un-annotated across both platforms.

CONCLUSIONS

Our study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.

New nodes and edges in the glucosinolate molecular network revealed by proteomics and metabolomics of Arabidopsis myb28/29 and cyp79B2/B3 glucosinolate mutants

Glucosinolates present in Brassicales are important for human health and plant defense against insects and pathogens. Here we investigate the proteomes and metabolomes of Arabidopsis myb28/29 and cyp79B2/B3 mutants deficient in aliphatic glucosinolates and indolic glucosinolates, respectively. Quantitative proteomics of the myb28/29 and cyp79B2/B3 mutants led to the identification of 2785 proteins, of which 142 proteins showed significant changes in the two mutants compared to wild type (WT). By mapping the differential proteins using STRING, we detected 59 new edges in the glucosinolate metabolic network. These connections can be classified as primary with direct roles in glucosinolate metabolism, secondary related to plant stress responses, and tertiary involved in other biological processes. Gene Ontology analysis of the differential proteins showed high level of enrichment in the nodes belonging to metabolic process including glucosinolate biosynthesis and response to stimulus. Using metabolomics, we quantified 292 metabolites covering a broad spectrum of metabolic pathways, and 89 exhibited differential accumulation patterns between the mutants and WT. The changing metabolites (e.g., γ-glutamyl amino acids, auxins and glucosinolate hydrolysis products) complement our proteomics findings. This study contributes toward engineering and breeding of glucosinolate profiles in plants in efforts to improve human health, crop quality and productivity.

BIOLOGICAL SIGNIFICANCE

Glucosinolates in Brassicales constitute an important group of natural metabolites important for plant defense and human health. Its biosynthetic pathways and transcriptional regulation have been well-studied. Using Arabidopsis mutants of important genes in glucosinolate biosynthesis, quantitative proteomics and metabolomics led to identification of many proteins and metabolites that are potentially related to glucosinolate metabolism. This study provides a comprehensive insight into the molecular networks of glucosinolate metabolism, and will facilitate efforts toward engineering and breeding of glucosinolate profiles for enhanced crop defense, and nutritional value.

Chemodiversity of the Glucosinolate-Myrosinase System at the Single Cell Type Resolution

Glucosinolates (GLSs) are a well-defined group of specialized metabolites, and like any other plant specialized metabolites, their presence does not directly affect the plant survival in terms of growth and development. However, specialized metabolites are essential to combat environmental stresses, such as pathogens and herbivores. GLSs naturally occur in many pungent plants in the order of Brassicales. To date, more than 200 different GLS structures have been characterized and their distribution differs from species to species. GLSs co-exist with classical and atypical myrosinases, which can hydrolyze GLS into an unstable aglycone thiohydroximate-O-sulfonate, which rearranges to produce different degradation products. GLSs, myrosinases, myrosinase interacting proteins, and GLS degradation products constitute the GLS-myrosinase (GM) system ("mustard oil bomb"). This review discusses the cellular and subcellular organization of the GM system, its chemodiversity, and functions in different cell types. Although there are many studies on the functions of GLSs and/or myrosinases at the tissue and whole plant levels, very few studies have focused on different single cell types. Single cell type studies will help to reveal specific functions that are missed at the tissue and organismal level. This review aims to highlight (1) recent progress in cellular and subcellular compartmentation of GLSs, myrosinases, and myrosinase interacting proteins; (2) molecular and biochemical diversity of GLSs and myrosinases; and (3) myrosinase interaction with its interacting proteins, and how it regulates the degradation of GLSs and thus the biological functions (e.g., plant defense against pathogens). Future prospects may include targeted approaches for engineering/breeding of plants and crops in the cell type-specific manner toward enhanced plant defense and nutrition.