Systematic structural characterization of metabolites in Arabidopsis via candidate substrate-product pair networks

Integrating physiological and multi-omics methods to elucidate heat stress tolerance for sustainable rice production

Heat stress presents unique challenges compared to other environmental stressors, as predicting crop responses and understanding the mechanisms for heat tolerance are complex tasks. The escalating impact of devastating climate changes heightens the frequency and intensity of heat stresses, posing a noteworthy threat to global agricultural productivity, especially in rice-dependent regions of the developing world. Humidity has been demonstrated to negatively affect rice yields worldwide. Plants have evolved intricate biochemical adaptations, involving intricate interactions among genes, proteins, and metabolites, to counter diverse external signals and ensure their survival. Modern-omics technologies, encompassing transcriptomics, metabolomics, and proteomics, have revolutionized our comprehension of the intricate biochemical and cellular shifts that occur in stressed agricultural plants. Integrating these multi-omics approaches offers a comprehensive view of cellular responses to heat stress and other challenges, surpassing the insights gained from multi-omics analyses. This integration becomes vital in developing heat-tolerant crop varieties, which is crucial in the face of increasingly unpredictable weather patterns. To expedite the development of heat-resistant rice varieties, aiming at sustainability in terms of food production and food security globally, this review consolidates the latest peer-reviewed research highlighting the application of multi-omics strategies.

Constructing the metabolic network of wheat kernels based on structure-guided chemical modification and multi-omics data

Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities, although it often proves to be challenging and labor-intensive, particularly with non-model organisms. In this study, we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat. This construction results in a comprehensive structure-guided network, including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database. Using a combination of gene annotation, reaction classification, structure similarity, and correlations from transcriptome and metabolome analysis, a total of 229 potential genes related to these reactions are identified within this network. To validate the network, the functionality of a hydroxycinnamoyltransferase (TraesCS3D01G314900) for the synthesis of polyphenols and a rhamnosyltransferase (TraesCS2D01G078700) for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests, respectively. Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.

Depicting the genetic and metabolic panorama of chemical diversity in the tea plant

As a frequently consumed beverage worldwide, tea is rich in naturally important bioactive metabolites. Combining genetic, metabolomic and biochemical methodologies, here, we present a comprehensive study to dissect the chemical diversity in tea plant. A total of 2837 metabolites were identified at high-resolution with 1098 of them being structurally annotated and 63 of them were structurally identified. Metabolite-based genome-wide association mapping identified 6199 and 7823 metabolic quantitative trait loci (mQTL) for 971 and 1254 compounds in young leaves (YL) and the third leaves (TL), respectively. The major mQTL (i.e., P < 1.05 × 10-5, and phenotypic variation explained (PVE) > 25%) were further interrogated. Through extensive annotation of the tea metabolome as well as network-based analysis, this study broadens the understanding of tea metabolism and lays a solid foundation for revealing the natural variations in the chemical composition of the tea plant. Interestingly, we found that galloylations, rather than hydroxylations or glycosylations, were the largest class of conversions within the tea metabolome. The prevalence of galloylations in tea is unusual, as hydroxylations and glycosylations are typically the most prominent conversions of plant specialized metabolism. The biosynthetic pathway of flavonoids, which are one of the most featured metabolites in tea plant, was further refined with the identified metabolites. And we demonstrated the further mining and interpretation of our GWAS results by verifying two identified mQTL (including functional candidate genes CsUGTa, CsUGTb, and CsCCoAOMT) and completing the flavonoid biosynthetic pathway of the tea plant.

Chemical composition, antifungal, and anti-virulence action of the stem bark of Hancornia speciosa Gomes (Apocynaceae) against Candida spp

ETHNOPHARMACOLOGICAL RELEVANCE

Hancornia speciosa Gomes is a fruit and medicinal species used for treating infectious diseases of the genitourinary system. However, its mechanism of action against microbes is still not fully understood. Infections in the genitourinary system caused by Candida spp. are associated with its fungal resistance and pathogenicity. New plant-derived compounds are an alternative to fight these Candida infections.

AIM OF THE STUDY

The objective of this study was to evaluate the anti-Candida effects of extracts of the stem bark of H. speciosa. This research investigated the chemical composition of sulfuric ether (EEHS) and methanolic (MEHS) extracts, their drug-modifying action on fluconazole, and their anti-virulence action on the morphological transition of Candida species.

MATERIALS AND METHODS

The extracts (EEHS and MEHS) of the stem bark of H. speciosa were chemically characterized via qualitative phytochemical screening and by liquid chromatography coupled with mass spectrometry (UPLC-MS-ESI-QTOF). The extracts were evaluated regarding their antifungal effects and fluconazole-modifying activity against Candida albicans, Candida krusei, and Candida tropicalis using the broth microdilution method. Additionally, the study evaluated the inhibition of fungal virulence in Candida species through morphological transition assays.

RESULTS

The phytochemical screening revealed the presence of anthocyanidins, anthocyanins, aurones, catechins, chalcones, flavones, flavonols, flavanones, leucoanthocyanidins, tannins (condensed and pyrogallic), and xanthones in both extracts of the stem bark of H. speciosa. The UPLC-MS-ESI-QTOF analysis identified the same compounds in both extracts, predominating phenolic compounds. Some compounds were first time recorded in this species: gluconic acid, cinchonain IIb, cinchonain Ib isomer, and lariciresinol hexoside isomers. Most of the intrinsic antifungal activity was observed for the MEHS against C. krusei (IC50: 58.41 μg/mL). At subinhibitory concentrations (MC/8), the EEHS enhanced the action of fluconazole against all Candida strains. The MEHS exhibited greater efficacy than fluconazole inhibiting C. krusei growth. The EEHS completely inhibited hyphae appearance and reduced pseudohyphae formation in C. albicans.

CONCLUSION

The stem bark of H. speciosa is a rich source of bioactive compounds, especially phenolic. Phenolic compounds can have important roles in fighting infectious diseases of the genitourinary system, such as candidiasis. The extracts of H. speciosa improved the action of the drug fluconazole against Candida species, inhibited hyphae appearance, and reduced pseudohyphae formation. The results of this study can support the development of new therapeutics against resistant strains of Candida.

Chemical-tag-based semi-annotated metabolomics facilitates gene identification and specialized metabolic pathway elucidation in wheat

The importance of metabolite modification and species-specific metabolic pathways has long been recognized. However, linking the chemical structure of metabolites to gene function in order to explore the genetic and biochemical basis of metabolism has not yet been reported in wheat (Triticum aestivum). Here, we profiled metabolic fragment enrichment in wheat leaves and consequently applied chemical-tag-based semi-annotated metabolomics in a genome-wide association study in accessions of wheat. The studies revealed that all 1,483 quantified metabolites have at least one known functional group whose modification is tailored in an enzyme-catalyzed manner and eventually allows efficient candidate gene mining. A Triticeae crop-specific flavonoid pathway and its underlying metabolic gene cluster were elucidated in further functional studies. Additionally, upon overexpressing the major effect gene of the cluster TraesCS2B01G460000 (TaOMT24), the pathway was reconstructed in rice (Oryza sativa), which lacks this pathway. The reported workflow represents an efficient and unbiased approach for gene mining using forward genetics in hexaploid wheat. The resultant candidate gene list contains vast molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets and will ultimately aid in achieving wheat crop improvement.

Pour some sugar on me: The diverse functions of phenylpropanoid glycosylation

The phenylpropanoid metabolism is the source of a vast array of specialized metabolites that play diverse functions in plant growth and development and contribute to all aspects of plant interactions with their surrounding environment. These compounds protect plants from damaging ultraviolet radiation and reactive oxygen species, provide mechanical support for the plants to stand upright, and mediate plant-plant and plant-microorganism communications. The enormous metabolic diversity of phenylpropanoids is further expanded by chemical modifications known as "decorative reactions", including hydroxylation, methylation, glycosylation, and acylation. Among these modifications, glycosylation is the major driving force of phenylpropanoid structural diversification, also contributing to the expansion of their properties. Phenylpropanoid glycosylation is catalyzed by regioselective uridine diphosphate (UDP)-dependent glycosyltransferases (UGTs), whereas glycosyl hydrolases known as β-glucosidases are the major players in deglycosylation. In this article, we review how the glycosylation process affects key physicochemical properties of phenylpropanoids, such as molecular stability and solubility, as well as metabolite compartmentalization/storage and biological activity/toxicity. We also summarize the recent knowledge on the functional implications of glycosylation of different classes of phenylpropanoid compounds. A balance of glycosylation/deglycosylation might represent an essential molecular mechanism to regulate phenylpropanoid homeostasis, allowing plants to dynamically respond to diverse environmental signals.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Decreased metabolic diversity in common beans associated with domestication revealed by untargeted metabolomics, information theory, and molecular networking

The process of crop domestication leads to a dramatic reduction in the gene expression associated with metabolic diversity. Genes involved in specialized metabolism appear to be particularly affected. Although there is ample evidence of these effects at the genetic level, a reduction in diversity at the metabolite level has been taken for granted despite having never been adequately accessed and quantified. Here we leveraged the high coverage of ultra high performance liquid chromatography-high-resolution mass spectrometry based metabolomics to investigate the metabolic diversity in the common bean (Phaseolus vulgaris). Information theory highlights a shift towards lower metabolic diversity and specialization when comparing wild and domesticated bean accessions. Moreover, molecular networking approaches facilitated a broader metabolite annotation than achieved to date, and its integration with gene expression data uncovers a metabolic shift from specialized metabolism towards central metabolism upon domestication of this crop.

QT-GWAS: A novel method for unveiling biosynthetic loci affecting qualitative metabolic traits

Although the plant kingdom provides an enormous diversity of metabolites with potentially beneficial applications for humankind, a large fraction of these metabolites and their biosynthetic pathways remain unknown. Resolving metabolite structures and their biosynthetic pathways is key to gaining biological understanding and to allow metabolic engineering. In order to retrieve novel biosynthetic genes involved in specialized metabolism, we developed a novel untargeted method designated as qualitative trait GWAS (QT-GWAS) that subjects qualitative metabolic traits to a genome-wide association study, while the conventional metabolite GWAS (mGWAS) mainly considers the quantitative variation of metabolites. As a proof of the validity of QT-GWAS, 23 and 15 of the retrieved associations identified in Arabidopsis thaliana by QT-GWAS and mGWAS, respectively, were supported by previous research. Furthermore, seven gene-metabolite associations retrieved by QT-GWAS were confirmed in this study through reverse genetics combined with metabolomics and/or in vitro enzyme assays. As such, we established that CYTOCHROME P450 706A5 (CYP706A5) is involved in the biosynthesis of chroman derivatives, UDP-GLYCOSYLTRANSFERASE 76C3 (UGT76C3) is able to hexosylate guanine in vitro and in planta, and SULFOTRANSFERASE 202B1 (SULT202B1) catalyzes the sulfation of neolignans in vitro. Collectively, our study demonstrates that the untargeted QT-GWAS method can retrieve valid gene-metabolite associations at the level of enzyme-encoding genes, even new associations that cannot be found by the conventional mGWAS, providing a new approach for dissecting qualitative metabolic traits.

Multilayered regulation of secondary metabolism in medicinal plants

Medicinal plants represent a huge reservoir of secondary metabolites (SMs), substances with significant pharmaceutical and industrial potential. However, obtaining secondary metabolites remains a challenge due to their low-yield accumulation in medicinal plants; moreover, these secondary metabolites are produced through tightly coordinated pathways involving many spatiotemporally and environmentally regulated steps. The first regulatory layer involves a complex network of transcription factors; a second, more recently discovered layer of complexity in the regulation of SMs is epigenetic modification, such as DNA methylation, histone modification and small RNA-based mechanisms, which can jointly or separately influence secondary metabolites by regulating gene expression. Here, we summarize the findings in the fields of genetic and epigenetic regulation with a special emphasis on SMs in medicinal plants, providing a new perspective on the multiple layers of regulation of gene expression.

Expanding our coverage: Strategies to detect a greater range of metabolites

Whilst the study of metabolites can arguably be traced back several hundred years it began in earnest in the 20th century with studies based on single metabolites or simple metabolic pathways. The advent of metabolomics and in particular the adoption of high-resolution mass spectrometry now means we can faithfully annotate and quantify in excess of 1000 plant metabolites. Whilst this is an impressive leap it falls well short of the estimated number of metabolites in the plant kingdom. This, whilst considerable and important insights have been achieved using commonly utilized approaches, there is a need to improve the coverage of the metabolome. Here, we review three largely complementary strategies (i) methods based on using chemical libraries (ii) methods based on molecular networking and (iii) approaches that link metabolomics and genetic variance. It is our contention that using all three approaches in tandem represents the best approach to tackle this challenge.

A liquid chromatography-mass spectrometry-based metabolomics strategy to explore plant metabolic diversity

Plants are expert chemists producing millions of metabolites, only a fraction of which are known to date. Plant metabolomics explores the rationale for highly diverse metabolites evolved and synthesized by plants. Over two-thirds of modern medicines are somehow inspired and/or derived from plants, making the identification of phytochemicals a means of discovering new medicines to challenge existing and emerging diseases. This chapter introduces our established liquid chromatography-tandem mass spectrometry-based untargeted metabolomics approach centered around discovering specialized metabolites (so-called secondary metabolites) across broad lineages of nonmodel plant species. Detecting hundreds to thousands of metabolite peaks, including assigning chemical identity, makes metabolomics data generation and analysis a very complex process. Various mass spectrometry techniques are currently being developed to approach the comprehensive metabolome. Among them, untargeted metabolomics can provide new biological insights by simultaneously and unbiasedly measuring and analyzing all detected metabolites. We have provided a hands-on modular account for untargeted plant metabolomics, from preparing plant biological samples to data analysis and processing using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The methods described here offer a foundation and expert opinion on plant metabolome analysis.

Mass Spectroscopy as an Analytical Tool to Harness the Production of Secondary Plant Metabolites: The Way Forward for Drug Discovery

The molecular map of diverse biological molecules linked with structure, function, signaling, and regulation within a cell can be elucidated using an analytically demanding omic approach. The latest trend of using "metabolomics" technologies has explained the natural phenomenon of opening a new avenue to understand and enhance bioactive compounds' production. Examination of sequenced plant genomes has revealed that a considerable portion of these encodes genes of secondary metabolism. In addition to genetic and molecular tools developed in the current era, the ever-increasing knowledge about plant metabolism's biochemistry has initiated an approach for wisely designed, more productive genetic engineering of plant secondary metabolism for improved defense systems and enhanced biosynthesis of beneficial metabolites. Secondary plant metabolites are natural products synthesized by plants that are not directly involved with their average growth and development but play a vital role in plant defense mechanisms. Plant secondary metabolites are classified into four major classes: terpenoids, phenolic compounds, alkaloids, and sulfur-containing compounds. More than 200,000 secondary metabolites are synthesized by plants having a unique and complex structure. Secondary plant metabolites are well characterized and quantified by omics approaches and therefore used by humans in different sectors such as agriculture, pharmaceuticals, chemical industries, and biofuel. The aim is to establish metabolomics as a comprehensive and dynamic model of diverse biological molecules for biomarkers and drug discovery. In this chapter, we aim to illustrate the role of metabolomic technology, precisely liquid chromatography-mass spectrometry, capillary electrophoresis mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy, specifically as a research tool in the production and identification of novel bioactive compounds for drug discovery and to obtain a unified insight of secondary metabolism in plants.

Metabolic diversity in a collection of wild and cultivated Brassica rapa subspecies

Brassica rapa (B. rapa) and its subspecies contain many bioactive metabolites that are important for plant defense and human health. This study aimed at investigating the metabolite composition and variation among a large collection of B. rapa genotypes, including subspecies and their accessions. Metabolite profiling of leaves of 102 B. rapa genotypes was performed using ultra-performance liquid chromatography coupled with a photodiode array detector and quadrupole time-of-flight mass spectrometry (UPLC-PDA-QTOF-MS/MS). In total, 346 metabolites belonging to different chemical classes were tentatively identified; 36 out of them were assigned with high confidence using authentic standards and 184 were those reported in B. rapa leaves for the first time. The accumulation and variation of metabolites among genotypes were characterized and compared to their phylogenetic distance. We found 47 metabolites, mostly representing anthocyanins, flavonols, and hydroxycinnamic acid derivatives that displayed a significant correlation to the phylogenetic relatedness and determined four major phylometabolic branches; 1) Chinese cabbage, 2) yellow sarson and rapid cycling, 3) the mizuna-komatsuna-turnip-caitai; and 4) a mixed cluster. These metabolites denote the selective pressure on the metabolic network during B. rapa breeding. We present a unique study that combines metabolite profiling data with phylogenetic analysis in a large collection of B. rapa subspecies. We showed how selective breeding utilizes the biochemical potential of wild B. rapa leading to highly diverse metabolic phenotypes. Our work provides the basis for further studies on B. rapa metabolism and nutritional traits improvement.

Transcriptional and metabolic changes associated with internode development and reduced cinnamyl alcohol dehydrogenase activity in sorghum

The molecular mechanisms associated with secondary cell wall (SCW) deposition in sorghum remain largely uncharacterized. Here, we employed untargeted metabolomics and large-scale transcriptomics to correlate changes in SCW deposition with variation in global gene expression profiles and metabolite abundance along an elongating internode of sorghum, with a major focus on lignin and phenolic metabolism. To gain deeper insight into the metabolic and transcriptional changes associated with pathway perturbations, a bmr6 mutant [with reduced cinnamyl alcohol dehydrogenase (CAD) activity] was analyzed. In the wild type, internode development was accompanied by an increase in the content of oligolignols, p-hydroxybenzaldehyde, hydroxycinnamate esters, and flavonoid glucosides, including tricin derivatives. We further identified modules of genes whose expression pattern correlated with SCW deposition and the accumulation of these target metabolites. Reduced CAD activity resulted in the accumulation of hexosylated forms of hydroxycinnamates (and their derivatives), hydroxycinnamaldehydes, and benzenoids. The expression of genes belonging to one specific module in our co-expression analysis correlated with the differential accumulation of these compounds and contributed to explaining this metabolic phenotype. Metabolomics and transcriptomics data further suggested that CAD perturbation activates distinct detoxification routes in sorghum internodes. Our systems biology approach provides a landscape of the metabolic and transcriptional changes associated with internode development and with reduced CAD activity in sorghum.

A novel method for efficient screening and annotation of important pathway-associated metabolites based on the modified metabolome and probe molecules

植物次生代谢物在抵御生物/非生物胁迫、生物间互作以及信息传递等方面发挥重要作用,次生代谢途径解析对植物分子育种、天然产物合成等方面具有重要意义。液相色谱-高分辨串联质谱(LC-HRMS/MS)为次生代谢物鉴定及途径表征提供了技术手段。非靶向LC-HRMS/MS方法可获得丰富的质谱信号,包括一级质谱和二级质谱(MS, MS/MS),但受质谱数据库规模以及次生代谢物复杂性的制约,次生代谢物注释十分困难。该研究以玉米叶片中苯丙烷途径代谢物为例,发展用于非靶向代谢组数据中重要途径代谢物的高效筛选和注释新方法。首先,利用公共代谢途径数据库及文献获取参与苯丙烷代谢途径的61种修饰反应类型,进而从非靶向实验数据中筛选出修饰代谢组。其次,获取开源串联质谱数据中的苯丙烷类化合物作为探针分子,构建探针分子质谱数据库。将探针分子与修饰代谢组共建分子网络,锁定目标途径代谢物并注释结构。该方法在正、负离子模式下分别筛选出玉米叶片中392个和417个苯丙烷途径候选代谢物,去冗余后共注释出129个代谢物,涉及苯丙烷代谢的主要分支途径,如黄酮途径的8个类黄酮、19个氧苷类黄酮和32个碳苷类黄酮,31个羟基肉桂酸途径代谢物以及22个木脂素途径代谢物;其中26个在PubChem和SciFinder数据库中未见收录。该研究利用探针分子结合修饰组可快速锁定途径代谢物,且有助于快速、准确的网络传播注释,可显著提高目标途径代谢物筛选与注释效率,为植物次生代谢途径的深入解析提供分析手段。

Expanding the Coverage of Metabolic Landscape in Cultivated Rice with Integrated Computational Approaches

Genome-scale metabolomics analysis is increasingly used for pathway and function discovery in the post-genomics era. The great potential offered by developed mass spectrometry (MS)-based technologies has been hindered, since only a small portion of detected metabolites were identifiable so far. To address the critical issue of low identification coverage in metabolomics, we adopted a deep metabolomics analysis strategy by integrating advanced algorithms and expanded reference databases. The experimental reference spectra and in silico reference spectra were adopted to facilitate the structural annotation. To further characterize the structure of metabolites, two approaches were incorporated into our strategy, i.e., structural motif search combined with neutral loss scanning and metabolite association network. Untargeted metabolomics analysis was performed on 150 rice cultivars using ultra-performance liquid chromatography coupled with quadrupole-Orbitrap MS. Consequently, a total of 1939 out of 4491 metabolite features in the MS/MS spectral tag (MS2T) library were annotated, representing an extension of annotation coverage by an order of magnitude in rice. The differential accumulation patterns of flavonoids between indica and japonica cultivars were revealed, especially O-sulfated flavonoids. A series of closely-related flavonolignans were characterized, adding further evidence for the crucial role of tricin-oligolignols in lignification. Our study provides an important protocol for exploring phytochemical diversity in other plant species.

Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics

Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis.

Flexible and digestible wood caused by viral-induced alteration of cell wall composition

Woody plant material represents a vast renewable resource that has the potential to produce biofuels and other bio-based products with favorable net CO₂ emissions.^1,2 Its potential has been demonstrated in a recent study that generated novel structural materials from flexible moldable wood.³ Apple rubbery wood (ARW) disease is the result of a viral infection that causes woody stems to exhibit increased flexibility.⁴ Although ARW disease is associated with the presence of an RNA virus⁵ known as apple rubbery wood virus (ARWV), how the unique symptoms develop is unknown. We demonstrate that the symptoms of ARWV infections arise from reduced lignification within the secondary cell wall of xylem fibers and result in increased wood digestibility. In contrast, the mid-lamellae region and xylem ray cells are largely unaffected by the infection. Gene expression and proteomic data from symptomatic xylem clearly show the downregulation of phenylalanine ammonia lyase (PAL), the enzyme catalyzing the first committed step in the phenylpropanoid pathway leading to lignin biosynthesis. A large increase in soluble phenolics in symptomatic xylem, including the lignin precursor phenylalanine, is also consistent with PAL downregulation. ARWV infection results in the accumulation of many host-derived virus-activated small interfering RNAs (vasiRNAs). PAL-derived vasiRNAs are among the most abundant vasiRNAs in symptomatic xylem and are likely the cause of reduced PAL activity. Apparently, the mechanism used by the virus to alter lignin exhibits similarities to the RNAi strategy used to alter lignin in genetically modified trees to generate comparable improvements in wood properties.6, 7, 8

Video abstract

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Apple rubbery wood (ARW) symptoms are caused by decreased lignin in woody tissue

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RNA-seq, proteomics, and metabolomics suggest phenylalanine levels decrease

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Virus-activated small interfering RNAs (vasiRNAs) are generated in response to ARWV infection

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VasiRNAs cause siRNA-based downregulation of phenylalanine ammonia

Past accomplishments and future challenges of the multi-omics characterization of leaf growth

The advent of omics technologies has revolutionized biology and advanced our understanding of all biological processes, including major developmental transitions in plants and animals. Here, we review the vast knowledge accumulated concerning leaf growth in terms of transcriptional regulation before turning our attention to the historically less well-characterized alterations at the protein and metabolite level. We will then discuss how the advent of biochemical methods coupled with metabolomics and proteomics can provide insight into the protein-protein and protein-metabolite interactome of the growing leaves. We finally highlight the substantial challenges in detection, spatial resolution, integration, and functional validation of the omics results, focusing on metabolomics as a prerequisite for a comprehensive understanding of small-molecule regulation of plant growth.

Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration

Human Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we use a systems biology framework that integrates glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we construct models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. Our model aggregation approach recovers 2 of 2 previously known gene-enzyme relations and 2 of 3 empirically confirmed gene-enzyme relations. The top genes we propose for the remaining 5 linkage reactions are consistent with previously published literature. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the goal of understanding and improving infant health and development.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Polyphenol-Enriched Extracts of Prunus spinosa Fruits: Anti-Inflammatory and Antioxidant Effects in Human Immune Cells Ex Vivo in Relation to Phytochemical Profile

The fresh fruits of Prunus spinosa L., a wild plum species, are traditionally used for dietary purposes and medicinal applications in disorders related to inflammation and oxidative stress. This study aimed to investigate the phytochemical composition of the fruits in the function of fractionated extraction and evaluate the biological potential of the extracts as functional products in two models of human immune cells ex vivo. Fifty-seven phenolic components were identified in the extracts by UHPLC-PDA-ESI-MS3, including twenty-eight new for the analysed fruits. Fractionation enabled the enrichment of polyphenols in the extracts up to 126.5 mg gallic acid equivalents/g dw total contents, 91.3 mg/g phenolic acids (caffeoyl-, coumaroyl-, and feruloylquinic acids), 41.1 mg/g flavonoids (mostly quercetin mono-, di- and triglycosides), 44.5 mg/g condensed proanthocyanidins, and 9.2 mg/g anthocyanins (cyanidin and peonidin glycosides). The hydroalcoholic extract and phenolic-enriched fractions of the fruits revealed significant ability to modulate pro-oxidant, pro-inflammatory, and anti-inflammatory functions of human neutrophils and peripheral blood mononuclear cells (PBMCs): they strongly downregulated the release of reactive oxygen species, TNF-α, and neutrophils elastase, upregulated the secretion of IL-10, and slightly inhibited the production of IL-8 and IL-6 in the cells stimulated by fMLP, fMLP+cytochalasin B, and LPS, depending on the test. Correlation studies and experiments on the pure compounds indicated a significant contribution of polyphenols to these effects. Moreover, cellular safety was confirmed for the extracts by flow cytometry in a wide range of concentrations. The results support the traditional use of fresh blackthorn fruits in inflammatory disorders and indicate extracts that are most promising for functional applications.

Hydroxycinnamic acid-modified xylan side chains and their cross-linking products in rice cell walls are reduced in the Xylosyl arabinosyl substitution of xylan 1 mutant

The intricate architecture of cell walls and the complex cross-linking of their components hinders some industrial and agricultural applications of plant biomass. Xylan is a key structural element of grass cell walls, closely interacting with other cell wall components such as cellulose and lignin. The main branching points of grass xylan, 3-linked l-arabinosyl substitutions, can be modified by ferulic acid (a hydroxycinnamic acid), which cross-links xylan to other xylan chains and lignin. XAX1 (Xylosyl arabinosyl substitution of xylan 1), a rice (Oryza sativa) member of the glycosyltransferase family GT61, has been described to add xylosyl residues to arabinosyl substitutions modified by ferulic acid. In this study, we characterize hydroxycinnamic acid-decorated arabinosyl substitutions present on rice xylan and their cross-linking, in order to decipher the role of XAX1 in xylan synthesis. Our results show a general reduction of hydroxycinnamic acid-modified 3-linked arabinosyl substitutions in xax1 mutant rice regardless of their modification with a xylosyl residue. Moreover, structures resembling the direct cross-link between xylan and lignin (ferulated arabinosyl substitutions bound to lignin monomers and dimers), together with diferulates known to cross-link xylan, are strongly reduced in xax1. Interestingly, apart from feruloyl and p-coumaroyl modifications on arabinose, putative caffeoyl and oxalyl modifications were characterized, which were also reduced in xax1. Our results suggest an alternative function of XAX1 in the transfer of hydroxycinnamic acid-modified arabinosyl substitutions to xylan, rather than xylosyl transfer to arabinosyl substitutions. Ultimately, XAX1 plays a fundamental role in cross-linking, providing a potential target for the improvement of use of grass biomass.

Rice metabolic regulatory network spanning the entire life cycle

As one of the most important crops in the world, rice (Oryza sativa) is a model plant for metabolome research. Although many studies have focused on the analysis of specific tissues, the changes in metabolite abundance across the entire life cycle have not yet been determined. In this study, combining both targeted and nontargeted metabolite profiling methods, a total of 825 annotated metabolites were quantified in rice samples from different tissues covering the entire life cycle. The contents of metabolites in different tissues of rice were significantly different, with various metabolites accumulating in the plumule and radicle during seed germination. Combining these data with transcriptome data obtained from the same time period, we constructed the Rice Metabolic Regulation Network. The metabolites and co-expressed genes were further divided into 12 clusters according to their accumulation patterns, with members within each cluster displaying a uniform and clear pattern of abundance across development. Using this dataset, we established a comprehensive metabolic profile of the rice life cycle and used two independent strategies to identify novel transcription factors-namely the use of known regulatory genes as bait to screen for new networks underlying lignin metabolism and the unbiased identification of new glycerophospholipid metabolism regulators on the basis of tissue specificity. This study thus demonstrates how guilt-by-association analysis of metabolome and transcriptome data spanning the entire life cycle in cereal crops provides novel resources and tools to aid in understanding the mechanisms underlying important agronomic traits.

Physiological and Multi-Omics Approaches for Explaining Drought Stress Tolerance and Supporting Sustainable Production of Rice

Drought differs from other natural disasters in several respects, largely because of the complexity of a crop's response to it and also because we have the least understanding of a crop's inductive mechanism for addressing drought tolerance among all abiotic stressors. Overall, the growth and productivity of crops at a global level is now thought to be an issue that is more severe and arises more frequently due to climatic change-induced drought stress. Among the major crops, rice is a frontline staple cereal crop of the developing world and is critical to sustaining populations on a daily basis. Worldwide, studies have reported a reduction in rice productivity over the years as a consequence of drought. Plants are evolutionarily primed to withstand a substantial number of environmental cues by undergoing a wide range of changes at the molecular level, involving gene, protein and metabolite interactions to protect the growing plant. Currently, an in-depth, precise and systemic understanding of fundamental biological and cellular mechanisms activated by crop plants during stress is accomplished by an umbrella of -omics technologies, such as transcriptomics, metabolomics and proteomics. This combination of multi-omics approaches provides a comprehensive understanding of cellular dynamics during drought or other stress conditions in comparison to a single -omics approach. Thus a greater need to utilize information (big-omics data) from various molecular pathways to develop drought-resilient crop varieties for cultivation in ever-changing climatic conditions. This review article is focused on assembling current peer-reviewed published knowledge on the use of multi-omics approaches toward expediting the development of drought-tolerant rice plants for sustainable rice production and realizing global food security.

Sample Preparation, Data Acquisition, and Data Analysis for 15N-Labeled and Nonlabeled Monoterpene Indole Alkaloids in Catharanthus roseus

Recent approaches developed in metabolomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) enabled us to assign a part of specialized metabolites in plants. However, the approaches are not good enough for the rest of the metabolites, which are still unknown. To characterize the unknown metabolites, more appropriate and precise approaches need to be developed. Here, a procedure to analyze ¹⁵N-labeled and nonlabeled LC-MS/MS data for identification of monoterpene indole alkaloids was developed.

Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples

LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.

Origin and Function of Structural Diversity in the Plant Specialized Metabolome

The plant specialized metabolome consists of a multitude of structurally and functionally diverse metabolites, variable from species to species. The specialized metabolites play roles in the response to environmental changes and abiotic or biotic stresses, as well as in plant growth and development. At its basis, the specialized metabolism is built of four major pathways, each starting from a few distinct primary metabolism precursors, and leading to distinct basic carbon skeleton core structures: polyketides and fatty acid derivatives, terpenoids, alkaloids, and phenolics. Structural diversity in specialized metabolism, however, expands exponentially with each subsequent modification. We review here the major sources of structural variety and question if a specific role can be attributed to each distinct structure. We focus on the influences that various core structures and modifications have on flavonoid antioxidant activity and on the diversity generated by oxidative coupling reactions. We suggest that many oxidative coupling products, triggered by initial radical scavenging, may not have a function in se, but could potentially be enzymatically recycled to effective antioxidants. We further discuss the wide structural variety created by multiple decorations (glycosylations, acylations, prenylations), the formation of high-molecular weight conjugates and polyesters, and the plasticity of the specialized metabolism. We draw attention to the need for untargeted methods to identify the complex, multiply decorated and conjugated compounds, in order to study the functioning of the plant specialized metabolome.

CRISPR-Cas9 editing of CAFFEOYL SHIKIMATE ESTERASE 1 and 2 shows their importance and partial redundancy in lignification in Populus tremula × P. alba

Lignins are cell wall-located aromatic polymers that provide strength and hydrophobicity to woody tissues. Lignin monomers are synthesized via the phenylpropanoid pathway, wherein CAFFEOYL SHIKIMATE ESTERASE (CSE) converts caffeoyl shikimate into caffeic acid. Here, we explored the role of the two CSE homologs in poplar (Populus tremula × P. alba). Reporter lines showed that the expression conferred by both CSE1 and CSE2 promoters is similar. CRISPR-Cas9-generated cse1 and cse2 single mutants had a wild-type lignin level. Nevertheless, CSE1 and CSE2 are not completely redundant, as both single mutants accumulated caffeoyl shikimate. In contrast, the cse1 cse2 double mutants had a 35% reduction in lignin and associated growth penalty. The reduced-lignin content translated into a fourfold increase in cellulose-to-glucose conversion upon limited saccharification. Phenolic profiling of the double mutants revealed large metabolic shifts, including an accumulation of p-coumaroyl, 5-hydroxyferuloyl, feruloyl and sinapoyl shikimate, in addition to caffeoyl shikimate. This indicates that the CSEs have a broad substrate specificity, which was confirmed by in vitro enzyme kinetics. Taken together, our results suggest an alternative path within the phenylpropanoid pathway at the level of the hydroxycinnamoyl-shikimates, and show that CSE is a promising target to improve plants for the biorefinery.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.

Learning graph representations of biochemical networks and its application to enzymatic link prediction

Motivation

The complete characterization of enzymatic activities between molecules remains incomplete, hindering biological engineering and limiting biological discovery. We develop in this work a technique, enzymatic link prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules. ELP models enzymatic reactions cataloged in the KEGG database as a graph. ELP is innovative over prior works in using graph embedding to learn molecular representations that capture not only molecular and enzymatic attributes but also graph connectivity.

Results

We explore transductive (test nodes included in the training graph) and inductive (test nodes not part of the training graph) learning models. We show that ELP achieves high AUC when learning node embeddings using both graph connectivity and node attributes. Further, we show that graph embedding improves link prediction by 30% in area under curve over fingerprint-based similarity approaches and by 8% over support vector machines. We compare ELP against rule-based methods. We also evaluate ELP for predicting links in pathway maps and for reconstruction of edges in reaction networks of four common gut microbiota phyla: actinobacteria, bacteroidetes, firmicutes and proteobacteria. To emphasize the importance of graph embedding in the context of biochemical networks, we illustrate how graph embedding can guide visualization.

Availability and implementation

The code and datasets are available through https://github.com/HassounLab/ELP.

Metabolic source isotopic pair labeling and genome-wide association are complementary tools for the identification of metabolite-gene associations in plants

The optimal extraction of information from untargeted metabolomics analyses is a continuing challenge. Here, we describe an approach that combines stable isotope labeling, liquid chromatography- mass spectrometry (LC-MS), and a computational pipeline to automatically identify metabolites produced from a selected metabolic precursor. We identified the subset of the soluble metabolome generated from phenylalanine (Phe) in Arabidopsis thaliana, which we refer to as the Phe-derived metabolome (FDM) In addition to identifying Phe-derived metabolites present in a single wild-type reference accession, the FDM was established in nine enzymatic and regulatory mutants in the phenylpropanoid pathway. To identify genes associated with variation in Phe-derived metabolites in Arabidopsis, MS features collected by untargeted metabolite profiling of an Arabidopsis diversity panel were retrospectively annotated to the FDM and natural genetic variants responsible for differences in accumulation of FDM features were identified by genome-wide association. Large differences in Phe-derived metabolite accumulation and presence/absence variation of abundant metabolites were observed in the nine mutants as well as between accessions from the diversity panel. Many Phe-derived metabolites that accumulated in mutants also accumulated in non-Col-0 accessions and was associated to genes with known or suspected functions in the phenylpropanoid pathway as well as genes with no known functions. Overall, we show that cataloguing a biochemical pathway's products through isotopic labeling across genetic variants can substantially contribute to the identification of metabolites and genes associated with their biosynthesis.

Two chemically distinct root lignin barriers control solute and water balance

Lignin is a complex polymer deposited in the cell wall of specialised plant cells, where it provides essential cellular functions. Plants coordinate timing, location, abundance and composition of lignin deposition in response to endogenous and exogenous cues. In roots, a fine band of lignin, the Casparian strip encircles endodermal cells. This forms an extracellular barrier to solutes and water and plays a critical role in maintaining nutrient homeostasis. A signalling pathway senses the integrity of this diffusion barrier and can induce over-lignification to compensate for barrier defects. Here, we report that activation of this endodermal sensing mechanism triggers a transcriptional reprogramming strongly inducing the phenylpropanoid pathway and immune signaling. This leads to deposition of compensatory lignin that is chemically distinct from Casparian strip lignin. We also report that a complete loss of endodermal lignification drastically impacts mineral nutrients homeostasis and plant growth.

Evaluation of Antioxidant and Enzyme Inhibition Properties of Croton hirtus L'Hér. Extracts Obtained with Different Solvents

Croton hirtus L'Hér methanol extract was studied by NMR and two different LC-DAD-MSn using electrospray (ESI) and atmospheric pressure chemical ionization (APCI) sources to obtain a quali-quantitative fingerprint. Forty different phytochemicals were identified, and twenty of them were quantified, whereas the main constituents were dihydro α ionol-O-[arabinosil(1-6) glucoside] (133 mg/g), dihydro β ionol-O-[arabinosil(1-6) glucoside] (80 mg/g), β-sitosterol (49 mg/g), and isorhamnetin-3-O-rutinoside (26 mg/g). C. hirtus was extracted with different solvents-namely, water, methanol, dichloromethane, and ethyl acetate-and the extracts were assayed using different in vitro tests. The methanolic extracts presented the highest 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2'-azino-bis(3-ethylbenzothiazoline)-6-sulfonic acid (ABTS), and ferric reducing antioxidant power (FRAP) values. All the tested extracts exhibited inhibitory effects on acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with a higher activity observed for dichloromethane (AChE: 5.03 and BChE: 16.41 mgGALAE/g), while the methanolic extract showed highest impact against tyrosinase (49.83 mgKAE/g). Taken together, these findings suggest C. hirtus as a novel source of bioactive phytochemicals with potential for commercial development.

Rewired phenolic metabolism and improved saccharification efficiency of a Zea mays cinnamyl alcohol dehydrogenase 2 (zmcad2) mutant

Lignocellulosic biomass is an abundant byproduct from cereal crops that can potentially be valorized as a feedstock to produce biomaterials. Zea mays CINNAMYL ALCOHOL DEHYDROGENASE 2 (ZmCAD2) is involved in lignification, and is a promising target to improve the cellulose-to-glucose conversion of maize stover. Here, we analyzed a field-grown zmcad2 Mutator transposon insertional mutant. Zmcad2 mutant plants had an 18% lower Klason lignin content, whereas their cellulose content was similar to that of control lines. The lignin in zmcad2 mutants contained increased levels of hydroxycinnamaldehydes, i.e. the substrates of ZmCAD2, ferulic acid and tricin. Ferulates decorating hemicelluloses were not altered. Phenolic profiling further revealed that hydroxycinnamaldehydes are partly converted into (dihydro)ferulic acid and sinapic acid and their derivatives in zmcad2 mutants. Syringyl lactic acid hexoside, a metabolic sink in CAD-deficient dicot trees, appeared not to be a sink in zmcad2 maize. The enzymatic cellulose-to-glucose conversion efficiency was determined after 10 different thermochemical pre-treatments. Zmcad2 yielded significantly higher conversions compared with controls for almost every pre-treatment. However, the relative increase in glucose yields after alkaline pre-treatment was not higher than the relative increase when no pre-treatment was applied, suggesting that the positive effect of the incorporation of hydroxycinnamaldehydes was leveled off by the negative effect of reduced p-coumarate levels in the cell wall. Taken together, our results reveal how phenolic metabolism is affected in CAD-deficient maize, and further support mutating CAD genes in cereal crops as a promising strategy to improve lignocellulosic biomass for sugar-platform biorefineries.

Integrating multi-omics data for crop improvement

Our agricultural systems are now in urgent need to secure food for a growing world population. To meet this challenge, we need a better characterization of plant genetic and phenotypic diversity. The combination of genomics, transcriptomics and metabolomics enables a deeper understanding of the mechanisms underlying the complex architecture of many phenotypic traits of agricultural relevance. We review the recent advances in plant genomics to see how these can be integrated with broad molecular profiling approaches to improve our understanding of plant phenotypic variation and inform crop breeding strategies.

Maize specialized metabolome networks reveal organ-preferential mixed glycosides

Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/).

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Cell wall remodeling under salt stress: Insights into changes in polysaccharides, feruloylation, lignification, and phenolic metabolism in maize

Although cell wall polymers play important roles in the tolerance of plants to abiotic stress, the effects of salinity on cell wall composition and metabolism in grasses remain largely unexplored. Here, we conducted an in-depth study of changes in cell wall composition and phenolic metabolism induced upon salinity in maize seedlings and plants. Cell wall characterization revealed that salt stress modulated the deposition of cellulose, matrix polysaccharides and lignin in seedling roots, plant roots and stems. The extraction and analysis of arabinoxylans by size-exclusion chromatography, 2D-NMR spectroscopy and carbohydrate gel electrophoresis showed a reduction of arabinoxylan content in salt-stressed roots. Saponification and mild acid hydrolysis revealed that salinity also reduced the feruloylation of arabinoxylans in roots of seedlings and plants. Determination of lignin content and composition by nitrobenzene oxidation and 2D-NMR confirmed the increased incorporation of syringyl units in lignin of maize roots. Salt stress also induced the expression of genes and the activity of enzymes enrolled in phenylpropanoid biosynthesis. The UHPLC-MS-based metabolite profiling confirmed the modulation of phenolic profiling by salinity and the accumulation of ferulate and its derivatives 3- and 4-O-feruloyl quinate. In conclusion, we present a model for explaining cell wall remodeling in response to salinity.

Exploiting Natural Variation in Tomato to Define Pathway Structure and Metabolic Regulation of Fruit Polyphenolics in the Lycopersicum Complex

While the structures of plant primary metabolic pathways are generally well defined and highly conserved across species, those defining specialized metabolism are less well characterized and more highly variable across species. In this study, we investigated polyphenolic metabolism in the lycopersicum complex by characterizing the underlying biosynthetic and decorative reactions that constitute the metabolic network of polyphenols across eight different species of tomato. For this purpose, GC-MS- and LC-MS-based metabolomics of different tissues of Solanum lycopersicum and wild tomato species were carried out, in concert with the evaluation of cross-hybridized microarray data for MapMan-based transcriptomic analysis, and publicly available RNA-sequencing data for annotation of biosynthetic genes. The combined data were used to compile species-specific metabolic networks of polyphenolic metabolism, allowing the establishment of an entire pan-species biosynthetic framework as well as annotation of the functions of decoration enzymes involved in the formation of metabolic diversity of the flavonoid pathway. The combined results are discussed in the context of the current understanding of tomato flavonol biosynthesis as well as a global view of metabolic shifts during fruit ripening. Our results provide an example as to how large-scale biology approaches can be used for the definition and refinement of large specialized metabolism pathways.

Specialized phenolic compounds in seeds: structures, functions, and regulations

Plants produce a huge diversity of specialized metabolites (SM) throughout their life cycle that play important physiological and ecological functions. SM can protect plants and seeds against diseases, predators, and abiotic stresses, or support their interactions with beneficial or symbiotic organisms. They also have strong impacts on human nutrition and health. Despite this importance, the biosynthesis and biological functions of most of the SM remain elusive and their diversity and/or quantity have been reduced in most crops during domestication. Seeds present a large number of SM that are important for their physiological, agronomic, nutritional or industrial qualities and hence, provide interesting models for both studying biosynthesis and producing large amounts of specialized metabolites. For instance, phenolics are abundant and widely distributed in seeds. More specifically, flavonoid pathway has been instrumental for understanding environmental or developmental regulations of specialized metabolic pathways, at the molecular and cellular levels. Here, we summarize current knowledge on seed phenolics as model, and discuss how recent progresses in omics approaches could help to further characterize their diversity, regulations, and the underlying molecular mechanisms involved.

Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

INTRODUCTION

Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system.

AREAS COVERED

This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches.

EXPERT OPINION

Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.

Higher dimensional metabolomics using stable isotope labeling for identifying the missing specialized metabolism in plants

The exact mechanics of specialized metabolism and its importance throughout plant evolution remain mysterious. Specialized metabolites and their corresponding biosynthetic genes are crucial to understand the reason for the prevalence of certain metabolism. Even though mass spectrometry-based metabolomics has enabled us to acquire data about the structural properties of unknown specialized metabolites as well as known metabolites and their corresponding isomers/analogs, extensive analytical approaches are still required. Herein, we review the most advanced analytical approaches using stable isotope labeling that can be used to identify the unknown specialized metabolites.

An untargeted liquid chromatography-mass spectrometry-based workflow for the structural characterization of plant polyesters

Cell wall localized heterogeneous polyesters are widespread in land plants. The composition of these polyesters, such as cutin, suberin, or more plant-specific forms such as the flax seed coat lignan macromolecule, can be determined after total hydrolysis of the ester linkages. The main bottleneck in the structural characterization of these macromolecules, however, resides in the determination of the higher order monomer sequences. Partial hydrolysates of the polyesters release a complex mixture of fragments of different lengths, each present in low abundance and therefore are challenging to structurally characterize. Here, a method is presented by which liquid chromatography-mass spectrometry (LC-MS) profiles of such partial hydrolysates are searched for pairs of related fragments. LC-MS peaks that show a mass difference corresponding to the addition of one or more macromolecule monomers were connected in a network. Starting from the lowest molecular weight peaks in the network, the annotation of the connections as the addition of one or more polyester monomers allows the prediction of consecutive and increasingly complex adjacent peaks. Multi-stage MS (MSn) experiments further helped to reject, corroborate, and sometimes refine the structures predicted by the network. As a proof of concept, this procedure was applied to partial hydrolysates of the flax seed coat lignan macromolecule, and allowed to characterize 120 distinct oligo-esters, consisting of up to six monomers, and containing monomers and linkages for which incorporation in the lignan macromolecule had not been described before. These results showed the capacity of the approach to advance the structural elucidation of complex plant polyesters.

Silver nanoparticles affect phenolic and phytoalexin composition of Arabidopsis thaliana

Silver nanoparticles are widely used in industry, medicine, biotechnology and agriculture. As a consequence, these nanoparticles are reaching the environment as waste products, which might have a negative impact on the environment, especially on plants. This includes the elicitation of various biochemical processes in plants. In this article, we report on the changes in secondary metabolic profile of Arabidopsis thaliana seedlings subjected to silver nanoparticle treatment in vitro. Briefly, various sizes (10 nm, 40 nm and 100 nm in diameter) and concentrations (0.5-5.0 ppm) of silver nanoparticles were tested. Ultraperformance liquid chromatography coupled with ultraviolet and fluorescence detectors as well as hyphenated to a high-resolution mass spectrometer (UPLC-PDA-FLR, UPLC-HESI-HRMS) and HPLC - ion trap mass spectrometer (HPLC-ESI-MS/MS), were applied to identify and quantify secondary metabolites. To understand whether silver ions could induce changes in the secondary metabolite profile, seedlings treated with silver nitrate in concentrations equivalent to these of nanoparticles were also analysed. The results showed significant differences in the accumulation of phenolic and indole compounds between treatments. Silver nanoparticles and silver ions induced the biosynthesis of camalexin, hydroxycamalexin O-hexoside and hydroxycamalexin malonyl-hexoside. These compounds are important phytoalexins for Brassicaceae family (especially for Camelinae clad) and are also synthetized in response to biotic and abiotic stresses. Statistically significant changes have been also observed for five phenolic compounds and 5'Glucosyl-dihydroneoascorbigen in different treatment conditions.

Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics

Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC-MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model.

Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics

High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.