Sustainable Agriculture: Advances in Plant Metabolome and Microbiome

By Javid Ahmad Parray and Nowsheen Shameem

Sustainable Agriculture: Advances in Plant Metabolome and Microbiome focuses on the advancement of basic and applied research related to plant-microbe interaction and their implementation in progressive agricultural sustainability. The book also highlights the developing area of bioinformatics tools for the interpretation of metabolome, the integration of statistical and bioinformatics tools to manage huge generating data, metabolite profiling, and key signaling-driven substances, along with a section on the role of key biosynthetic pathways. Focused on selecting positive and effective interactive core-microbiome which will be adaptive and sustainable, this book will help researchers further improve the quality and productivity of crops through sustainable agriculture.

• Details the two-way interactive approach to both plants and microbes
• Describes setting up core and functional microbiomes
• Presents the relationship of metabolomics and biocontrol

• Language: English
• Publisher: Academic Press
• Release date: Nov 21, 2019
• ISBN: 9780128173749

Javid Ahmad Parray

Javid A Parray is currently teaching at the Department of Environmental Science, GDC Eidgah, affiliated to Cluster University, Srinagar. His research interests include ecological and agricultural microbiology, climate change, microbial biotechnology, and environmental microbiomes. He has also done his Post Doctorate Research from the University of Kashmir. Dr Parray was also awarded a Fast Track Young Scientist Project by SERB – DST, GoI New Delhi. Dr Javid was also awarded as “Emerging scientist year Gold Medal” for the year 2018 by Indian Academy of Environmental Science. Dr Parray is the course coordinator for the UG- Programmes for CeC-MOOCS Swayam in Environmental Science. Dr Parray is an expert review member in the field of environmental science in the CSTT, Ministry of Education, GoI New Delhi.

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Preface

Studies on plant root microbiomes indicate that soil type generally has a stronger influence on microbial communities than host phylogeny, as depicted by model systems. Metabolites or metabolome are the band of molecules produced by various processes within cells or the end-products of a biological system in response to outer or inner changes. A new obligatory approach of microbial miscellany besides innovative developments in generation of ample datasets of biological processes of all basic life molecules like genes, proteins, and metabolites, and other analytical techniques are contributing to the swift progress in microbiome research. The driving motivation regarding harnessing beneficial microbes and analyzing the metabolome is to improve the quality and productivity of crop plants. Therefore, a multiomic and coordinated research effort for plant microbes and metabolome and communication signaling pathways is need of hour to put it into modern agricultural practices for sustainable agriculture. Omic studies are necessary to determine the role and understanding of beneficial strains and inoculation with crop varieties to improve plant architect and other traits such as stress tolerance, host pathogen interaction, etc. In addition, multigenome analysis is required to determine effector genes in order to understand the extent of pathogenicity and remedial measures vis-à-vis control. The delineation of core microbiomes and functional core will not only help to screen out temporary microbial plant associations and focus on stable taxa that affect the plant phenotype, but also assist in identifying the predictive and adaptive functions for the studied plants using various emerging tools such as targeted and shotgun metagenomic approaches for functional potential, while other techniques like metatranscriptomics, proteomics, and metabolomics reveal the community phenotype. Another aspect of research is to focus on advancing basic and applied research related to plant microbe interaction and their implementation regarding progressive agricultural sustainability. Simultaneously, metabolome signaling or two-way communication study is also a challenging area that will offer new opportunities for providing insights and understanding in terms of plant microbe interaction. Comparing the metabolome or microbiome among some key groups of same phylas could reveal host-driven differences in plant microbe symbiosis; however, nowadays these interactions can be engineered using sophisticated and advanced techniques and tools to increase heritability as well as target breeding approaches to boost the agricultural industry. The proposal will also highlight the new, developing area of bioinformatics tools for interpretation of the metabolome. Further, the assimilation of metabolome data with metagenomics through a systemic approach will provide us with a holistic overview of the total microbiome and metabolome assembly. Therefore the current need is to integrate thoroughly all statistical and bioinformatics tools to manage huge generating data, metabolite profiling, and key signaling driven substances, develop a sustainable consortia, and determine the knowhow of key biosynthetic pathways. This book will focus primarily on selecting positive and effective interactive core-microbiomes which will be both phenotypically and genotypically very adaptive and sustainable; this will aid in improving further the quality and productivity of crops vis-à-vis sustainable agriculture.

Chapter 1

Metabolomics for crop improvement: Quality and productivity

Abstract

Metabolomics is a critical instrument for the determination of the molecular foundations of macroscopic biological phenomena. It is quite useful to analyze the property of plant species before their genomes were fully sequenced due to its applicability for species with incomplete genomic data. Metabolomics is a fast-growing interdisciplinary field of science by the combination of biochemistry, analytical chemistry, bioinformatics, medicine, and so on. It enables a high degree of biological system information and is highly promising to develop new diagnostic tests and therapies, including personalized medicine. Though analytical and computer systems are powerfully integrated, many metabolic and analytical challenges remain. Metabolomics, in combination with other technologies, enables us to solve key agricultural performance issues, which have been unresolved to date. It also becomes a valuable instrument for monitoring and evaluating the gene function and for the broad characterization of postgenomic processes. Many efforts can be concentrated on crop plants with detailed performance information in various environments. In addition to various networking approaches, this chapter details the role of metabolomics in improving different agricultural crops, including GMO varieties. The role of plant bioactive substances in soil microbial communities is also elaborated in the final section.

Keywords

Metabolome; Network models; GMO crops; Food crops

1.1 Introduction

In any biological system, the metabolome indicates a complete set of metabolites [1]. The concept has come from structural biology, which strives to generate the necessary knowledge about the various inter/intra-molecular interactions providing a molecular basis for the macroscopic properties of these systems [2] in many features of molecular mechanisms, i.e., genes, transcripts, proteins, and metabolites. It is currently challenging, even for models like Arabidopsis thaliana with complete and extensively annotated genomes, to measure the entire metabolome of any simplest biological system. Specifically, the metabolite content in particular animals has not been specifically determined, but the genome size varies between 5000 and 25,000 in yeast, humans, and plants [3–5]. In the meantime, a large range of analytical strategies for enhancing the evaluation of plant metabolites have been framed. They were typically described in metabolomics verbally, which is inefficient when defining experimental methods such as metabolite profiling, metabolism, metabolic fingerprinting, metabolite footage, etc. [6]. Signals associated with a substrate or medium growth of an organism are examined by metabolite foot-printing through the examination of the substrate of the chemical [7, 8]. Experiments in metabolite profiling for more effective metabolite analysis have been carried out for many decades [9]. Before the high-performance strategies used for metabolite analysis and DNA/RNA characterization were developed, other techniques like nucleic acid microarrays and mass spectrometry were employed. Although metabolomics showed far fewer dependencies than other substances such as nuclear acids or proteins from any technological intervention due to a vibrant chemical property of metabolites, DNA, and RNA, protein associated with it. In addition to the use of NMR, IR spectroscopy and MS techniques [10–12] were advanced in metabolomics. This is all possible for a broad spectrum of metabolites, which show both benefits and disadvantages in terms of the kind of information provided, susceptibility, and interferences. The metabolome analysis challenges and perspectives of different plant species will be detailed in the following section [6].

1.2 Metabolome studies: Challenges and perspectives

Metabolomics is the latest omic technique involving a full analysis of small organism or a biological sample metabolite. Metabolomics generally stimulates a perception regarding the cell condition vis-à-vis the health condition of an organism. It specifies an exclusive prospect to study the effect of genetic variations, disease, treatment, or diet on organisms’ endogenous metabolism [13]. Many fields could benefit from metabolomic research, and biomarker discovery is one of the most popular objectives of metabolomic studies. Researchers face challenges such as technical limitations, bioinformatics problems, and integration with other omic sciences in the field of analysis of metabolome. The problem of analysis of data, which is likely to be the most time-consuming stage of metabolomics workflows and requires close collaboration between analysts, clinicians, and experts on chemometrics, is one of the great challenges for metabolomics studies. The application in clinical practice of metabolomics depends on the development of standardized protocols of analytical performance and data analyzes with respect to the validation of biomarkers [14]. The organism's final response to environmental factors can include metabolite levels, genetic modifications, changes in intestinal micro flora, and changes in enzyme kinetic activity [15]. Many examples of a successful metabolomics study within existing analytical limitations and capability include an assessment of significant equality for genetically modified plant (GMO) safety assessment [16].

The genetic knockouts of plant axis [17], circadian clock functions, natural product biosynthesis [18], biological phytofoam [19, 20], and so forth are recognized for metabolomics in genome annotation [21]. The metabolite profiling of phyto-medicinal species has proved a valuable instrument for quality control and efficiency optimization. The unexpected bioactive compounds in interactions between plant herbivores, endophyte molecular signatures, and from fungal infections were detected. Metabolite fingerprints are also used in herbal breeding programs to determine compounds associated with certain quality features [22]. Wherever transcriptomics and proteomics do not exist, metabolomics may be effective for analysis of species with a sequenced genome that strengthens the case for species analysis through metabolomics [23]. The three major approaches used in the field of metabolism are metabolic fingerprinting, metabolite profiling, and targeted metabolism:

(i)Metabolite fingerprinting is a worldwide, rapid, and ready analysis of any biological sample's replicable metabolite fingerprint where the identification of metabolites is not necessary and represents many distinct classes of potential compounds. Neither preparation nor ultimate chromatographic resolution techniques are required for metabolic fingerprinting. Instead, it uses techniques that provide less complex and replicable information. Metabolic fingerprints are mainly used to classify a sample (qualitative analysis). The objective is to distinguish specimens from biologically different levels, e.g., disease/health, from a single pattern that characterizes a metabolic state in a given tissue or biological fluid [24].

(ii)Metabolite profiling is also an unspecified method for analyzing various amino acids, sugars, lipids, bile acids, etc. By comparison, the goal of metabolite profiling is to identify and quantify as many compounds as possible by means of metabolite high performance measurements involving chromatographically highly resolved separation and MS detection. This approach allows changes to unforeseen metabolome to be detected and can be addressed in particular metabolic ways. This technique thus leads to new scientific hypotheses being articulated and new metabolic biomarkers being identified [25, 26].

(iii)Targeted metabolism focus on the monitoring, usually for definite identification and exact quantification of one or more predefined metabolites. The compounds are a priori selected based on known metabolic trajectories or biomarkers and are associated with an organism's specific reaction. For targeted metabolomics, analysis techniques, including the preparation of samples and clear methods for detection, are designed to provide maximum sensitivity and selectivity to achieve low detection and metabolite quantitative limitations [27, 28].

1.3 Analytical challenges in plant metabolomics

Cells, fabrics, or their exudates may be focused on metabolomic research. The core objective of metabolomics could be defined as the unique features of the metabolome of the organisms [29]. The current practical impossibility of comprehensive metabolomics starts to be appreciated when looking at the methodical trials convoluted in metabolite analysis and in divergence to genome sequence, i.e., the chemical diversity of a characteristic microbial metabolite concentrate [30], the spatial distribution including specific organ, cellular, subcellular domain, extra cellular, and occasionally external environments as well [31], and temporary distribution [32]. In contrast to proteomics and transcriptional studies, genomic information cannot be used as a constraint in identifying molecular species. Although the aim of all the work of documentation, quantification, and localization of each metabolite is not to be concise, there are many challenges for metabolome analysis. Genetic information cannot be used as a restriction for the identification of molecular species as opposed to proteomics and transcription studies. Although the ultimate objective of documenting, quantifying, and finding each metabolite is not to be crisp, but the analysis of the metabolism presents many challenges [33]. However, cellular homogenization followed by further extraction should be used in metabolomic studies for the removal of various substances from cells to be accepted for analysis, preferably ensuing the steps outlined as (a) metabolism should be quenched and (b) the metabolite's chemical identity should be sustained and reproductive, and preferably complete metabolite solubilization should be done.

Some external factors, as well as the extremes of temperature or pH, influence metabolome analyses, causing a significant degradation in numerous metabolites. Freezing assists in the stabilization of volatile substances; however, tissue expurgation with freezing of the tissues must be carried out quickly in order to avoid injury responses. Quick freeze methods include many options such as liquid N, isopentane liquid nitrogen [32, 33], and freeze clamping [34]. Freeze drying may contribute to the stability of frozen samples, but the loss of volatile metabolites can result in the reabsorption of airborne water by the lyophilized sample and the recovery of certain enzyme activity. One strategy is to remove metabolites from enzymes and cellular debris by extracting polar organic solvents like ethyl alcohol and acetonitrile from these freeze samples at very low temperatures (− 72°C). Preliminary extractions remove water efficiently from enzymes so that the extraction temperature (4°C) can be improved in subsequent rounds, because extraction is highly inefficient at low temperatures and requires multiple repetitions [35]. Several studies have shown that a ratio of 9:1 methanol:water followed by an isopropanol water mixture is considered to be the best solvent mixture for effective extraction. Other studies examined the various LC-MS and GC-MS extraction protocols and NMR analysis for multiple extraction [36–38]. Fig. 1.1 shows the diagrammatic interpretation of the process of high-throughput metabolome analysis.

Fig. 1.1 Graphic illustration of high throughput data analysis process for metabolome analysis.

Although each technique has its own limitations in terms of detection, sensitivity, dynamic range, and estimation, are all capable of measuring the wide range of metabolites of all classes via analytical techniques commonly used in metabolomics research. In addition, the GC/MS is restricted to heat-stable and volatile analyses, but it has a more sensitive magnitude than the NMR to show a linear signal to a concentration relationship. For almost every biomolecule, the NMR technique provides extensive structural information, and the signal strength is directly associated to the richness of analyte, although it is far less sensitive for superficial quantification than the MS method [38]. However, many types of NMR tests can be used to solve overlap peaks in a complex NMR spectrum [39]. LC-MS is even more sensitive than GC-MS, as volatility and thermal labiality are unnecessary. The process of ESI ionization is reasonable and cannot produce a linear signal, as the concentration curve numbers of the compounds are simultaneously ionized [40].

Semiquantitative approaches in SD metabolomics, such as comparative identifications, where abundance alterations are quantified instead of absolute measurements [41], are chosen because of the calibration curve requirements or internal standard curves using reference compound samples [42]. GC-MS is generally acceptable to relatively quantify prudently controlled and randomized samples through a spectral comparison. Relative quantification of LC-MS due to ion suppression phenomena [43] is significantly less reliable. In LC-MS analysis of complex samples, this method can be used to prevent the perplexing effects of the ESI ion suppression. The isotope dilution analysis is based on the principle that the two compounds, which only diverge by mass, can be effectively purified by substituting one or more resilient, heavy atom isotopes enriched after isolation [44]. Absolute quantification can be adjusted before work and analysis by adding a reference substance. Finding sources of etiquette blends can be difficult with this approach, which is used comprehensively in proteomics to improve proteomics, and continues to be used for the purpose of prevention of these metabolic indentations in plant suspension and in whole plants [45]. This approach is also difficult to use in the measurement of numerous compounds. This method can easily be used for comparative measurement of several compounds by blending labeled and unlabeled substances for each experimental sample, or by producing a large amount of plant material labeled that can be spiked into a large number of unlabeled experimental samples. A complete quantification of the specific metabolites of labeled plant material can be achieved by means of a modified approach using reverse dilution [46]. The identification of high throughput compounds is an important challenge for metabolomics with multiple vagueness dimensions in the most commonly applied technology. The usage of standard compounds is certainly often practicable since no reference compounds exist. Different methods vary significantly, depending on the standardization of the methodology, how useful these comparisons are, and the resources available for comparisons from a publicly accessible database. In order to permit the similar match to chromatographic or spectral data, high confidence identification typically depends on the direct comparison with standard reference combinations. For example, the GC/MS can be very consistent between various instruments and several laboratories with both fragmentations from electron ionization (EI) to indexed retention times (IRT) [47–50].

MS-MS spectrums can theoretically provide appropriate fragmentation data for the development of a spectrum database matching strategy for GC-MS identification. However, the MS-MS spectrum varies from instrument to instrument, making it less efficient to match MS-MS spectral library strategies. NIST has taken the lead in providing reliable information through a collection of 14,802 positive and 1410 negative ions. Meticulous advances have also been made in the METLIN database, in which 573 positive ions and 587 negative spectrums with 881 metabolites [49] were reported. This database could be a foundation for standardization of MS-MS and could make research into LC-MS/MS libraries feasible. Even if the results are not always precise, the NMR and IR spectrums can often be determined. A NMR spectral database for over 19,700 compounds including several simulations beside spectrums collected from standard compounds [51] is available at the Madison Metabolomics Consortium Database.

Metabolite removal usually involves homogenization and extraction processes that interfere with analysis from other plant components, while homogenization totally destroys valuable information on the distribution of metabolites in a sample [31]. However, there are several techniques such as brute strength generally used in melon dissecting, homogenizing, and analyzing particular zones of larger plant organs [52], which are not so promising for smaller tissues. Laser-based micro dissection techniques are also vital in the testing of few types of cells and do not allow a simple, comprehensive view of metabolite availability [53]. Many mass spectrometry (MS) approaches provide spatially relatively low space measurements to multiple species [54]. The number is limited to Ionization of the Electrospray (DESI) images [55] and Ionization of Matrix Assisted Laser Desorption and Secondary Ion Mass Spectrometry (SIMS) images [56]. The best-quality resolution image (1 nm) is provided by SIMS Image analysis though high-energy ionization molecular fragmentation, which can reduce that approach. DESI is a notable lower-resolution approach (> 250 μm), but it is also a far lower power approach that makes intact molecular ions possible.

A 20–200 μm resolution of the MALDI image is outstanding [57]. MALDI does not use an ion beam, but uses the laser to use the matrix as a tool for ionization. MALDI is a smooth ionization process, like DESI, which leads to intact molecular ions with one charge. The extreme degree of spectral overlap around small molecules vis-à-vis the signal is one of the major challenges in MALDI imagery. MALDI is equipped with an AP laser, exciting the sample water matrix [58] while the C-60 and colloidal graphite [58] have been used to deal with any interference with a high peak molecular matrix. Next, the MS-tandem is monitored to reduce the background noise of selected solid-fragmentation reaction products [59]; the default UV laser matrix is then applied and special surfaces such as porous silicone guns [60] are applied. Zhang and colleagues attributed MALDI images with standard MALDI matrix methods with the use of colloidal graphite in the mature apple and strawberry areas. This survey showed colloidal graphite that produced superior results with reduced interference in the matrix. MS-MS supports the assignment of compounds, but MS data has only been generated for apples, strawberries, and compound distributors. MS-MS transitions were shown for signal improvement in strawberry samples. This colloidal approach was used to study cuticular waxes and flavonoids on various surfaces, and across parts of Arabidopsis aerial tissue. Strawberries, bananas, grape, and organic acids were examined, and many sugars with lower fatty acids were observed. In the lipid matrix instead of lipids, hydrophilic analytes can be used. K-adducts and other amino acids were mostly found in oligosaccharide. This is the way to avoid exogenous matrix [61].

1.4 Metabolic perspectives

Metabolomics became one of the great achievements of the present scientific research and has set the stage for the accurate profiling of metabolites in every species [1]. The quantitative plant metabolomics provides us with complete plant metabolism knowledge to improve crop yields [62]. A wide range of metabolites can be detected from one unique extract and can be used for quick and accurate analysis. Since metabolomics progresses quickly, transgenic line and mutant metabolite studies can be used to understand the metabolic networks and define the fundamental genes in addition to resolving the gene function and its impact on metabolic pathways, and regulating and intercepting, which is difficult to accomplish with conventional assays [5]. An integrated approach that accepts genomics, transcriptomics, proteomics, and metabolomics inferences enable scientists to mark and chart out genes to improve key characteristics for crop plants. The above omical studies have also been extended to include relevant regulatory measures such as epigenetic regulation, posttranscription, and posttranslation changes [63]. The plants are progressively seen as a key basis for a plant-related economy, providing food with improved nutrient supply, safety, stability, process capacity, and other features to meet existing and projected global consumer demands. For many crop species, the availability of transgenic systems increases the use of metabolomics that can rapidly up the selection and improvement of superior characteristics [64]. Methods and instruments for studying metabolomics, including the spectroscopic methods of metabolomics MS and NMR as discussed above, have made substantial progress. The metabolic platforms available at the moment are capable of allowing large-scale metabolite surveys covering both known and unknown metabolites. Bioinformatics and metabolomic databases are becoming more powerful, as is the one for the Arabidopsis model plant and other species as well [65]. A significant quantity of metabolic survey data is very useful for improving plants such as yield, resistance to disease, and tolerance of stress. In addition to the rapid generation of data on the genome scale through sequencing of DNA/RNA and the quantification of other metabolites by MS, it is necessary to collate this knowledge to formulate a complete chart for improving plant characteristics [66]. However, numerous current studies are being carried out in well-established model systems and these studies can also be followed in other plant species. The scientific community is currently facing a phenomenal task of management of huge multiomics data for framework-level analysis [9]. In such situations, the analysis of these data sets in a stronger fusion, which eventually can be translated into better fusion, will require emerging tools in statistics and bioinformatics to improve the functioning and performance of the plant (Fig. 1.2).

Fig. 1.2 Overview of crop enhancement omics approaches.

1.5 Metabolomics and crop improvement

Genetic plot or genomic selection by genetic markers is directly related to crop breeding [67]. Combined with other technologies, metabolomics enables us to solve key agronomic performance issues that have remained unsettled so far. Many efforts can be focused on crops with detailed performance information in a variety of environments [65]. The plant metabolomics technique can stimulate not only information on the number of identified metabolites but also their correlation with agricultural vital attributes, making it apparent for more rational designs to connect specific metabolites or pathways to characteristics associated with yield or quality. A more encouraging relationship between metabolite changes and the resulting phenotypes is, however, more likely [62]. The continuous efforts to explicate metabolic responses to different stresses also make metabolome-based breeding helpful and appropriate in obtaining stress-resistant plants [68].

Effective metabolic pathways of plant engineering with contemporary technologies will bring greater assistances to human survival by serving them with food and medicine [64]. For instance, vitamin A is accumulated at higher levels in Golden Rice, showing that metabolic engineering can improve the crop nutrient level [69]. Although the absence of collaborating information makes it difficult to identify a protective part of a particular metabolite, a comparison of stressed and unfit species or cultivars is one way to recognize adaptive changes in metabolites. In the forage legume Lotus corniculatus, a low degree of super lapse in dry metabolism was discovered, indicating a high degree of bio-functionality between small MW metabolites species [70, 71]. The degree of change between the glycophyte and halophyte species nevertheless had a similar propensity. In accordance with the preadaptation model, those changes might remain connected with diverse base levels. An NMR-based study of maize profiling metabolites found that the osmotic component of salinity is linked to early saltiness effects. In addition, the observations were consistently stronger in shoots than in roots with a stronger osmotic effect [72]. Recently, sugars and polyamines have been observed to participate in cold adaptation mechanisms in inappropriate Thellungiella accessions [73, 74]. Similar metabolite fingerprints were observed, demonstrating that the mechanisms for cold adjustments could also be the same among kingdoms. Moreover, the important metabolic changes taking place during cold acclimatization reinforce the idea that the synthesis of cryo-protective molecules like sugars and other associated substances are vital. The accumulation of these molecules (maltose, sucrose, trehalose, amino acids, glycerols) in adapted persons could therefore increase the tolerance of cold stress [75].

Samples from the maize hybrid were analyzed with GC-MS, indicating the metabolic differences between the greenhouse and field conditions in a recent study that were different in drought tolerance and dehydrated in greenhouse conditions. A definite peculiarity among the tolerant and sensitive genotypes for plant phenotyping, metabolite response related to tolerance could be observed [76], showing the power of metabolite-profiling techniques to demonstrate environmentally masked differences. However, the importance of an adequate phenotype assessment for metabolic marker development and stress tolerance is important to emphasize [77]. A tolerance feature in crops was regarded as tolerance to salt stress in the ability to maintain high salinity growth even at high Na + levels. In addition, the salt stress mechanisms of other glycophytes in citrus plants are certainly different where tolerance is the ability to decrease the Cl− absorption of the aerial section. Furthermore, the ability to reduce the metal intake in photosynthetic organs is considered a tolerance feature in other stress conditions, such as heavy metal contamination. Phytochelatin biosynthesis and glutathione metabolism are exceptionally upregulated in these species, when cultivated at high metal concentrations. Some hyperaccumulators can accumulate metals; however, a direct correlation among citrate and accumulation of metal ions in the analyzed species was found in studies and high levels of malonate in leaves, particularly in hyperaccumulators, which were probably found to act as a mechanism for storing metal [78]. Each of these findings suggests the need for additional physiological reporting to understand in what way plants react to abiotic stress and not to use physiological answers as markers of stress tolerance; further assessment under various stress conditions is required [79].

1.5.1 High CO2 stress: Its quality and yield characteristics

A major challenge for agriculture in the 21st century remains sustainable cultivation against increasing levels of CO2. However, the availability of water and carbon dioxide are directly linked to plant photosynthesis with respect to visual growth, C sequestration, and that helps to preserve terrestrial ecosystems [80]. Land-based plants and aquatic phyto plants use increased concentration of CO2 in significant ways to increase their biomass [81]. As high levels of CO2 are documented, growth of grass species may be promoted, which is a positive finding for food crops like cereals. The ultimate sink of the plant is fruit, grain, and tuber. The growth of this sink organ depends directly on the division between the source and sink organs. A number of metabolites relying on other components are stored in sink organs such as species type, source power, photosynthesis composition, and plant demand [82]. Several reports have documented the correlation between high CO2 and yield in many commercial crop species. Excessive CO2, for example, has been reported to increase production significantly. More reports in wheat or rice have been validated for a higher level of atmospheric CO2 yield stimulation [83]. Also, in potatoes, the results were comparable, where enriched CO2 agriculture resulted in a 54% increase in tuber yield. Likewise, the increased CO2 level recorded a higher cotton yield but was lower than the yield at high temperatures [84].

Since most reports in carbon-rich cereals have increased yields of high CO2 and the significant requirement for food quality and safety to meet demand has remained, wheat grown in higher CO2-open conditions shows a slower nitrate metabolism. Sustainable food safety and nutritional quality demands have been shown. The nitrogen decline in cereals is due to higher carbohydrate levels [85]. The total content of amino acids was found to be at higher levels in soyabean leaves at the beginning of the season, but later they began to increase again [86]. The amino acid level of Chinese root is also effectively reduced by a combination of a higher temperature and CO2, and in the strawberry fruit they increase the index of sugar and sweetness, and decrease the amount of antioxidants and nitrogen [85]. More CO2 has proven to have a huge impact on the quality of mustard oil due to a rise in the content of starch and oil on the cost of proteins. The elevated levels of carbohydrates induce the increasing levels of the oleic acid in lipids and reducing levels of linolenic acid and nervous acid [87] which have their effects on the lipid composition of mosquitos [87]. CO2 enrichment has decreased while the quality of mustard seeds has improved. It is documented that crops grown under prominent CO2 produce higher yields, but can significantly influence the nutritional content of crops, notably amino acids [88].

1.5.2 Horticultural crops (fruits)

In particular for ripening and quality, metabolomics have indicated higher levels of understanding in fruit biology. Metabolome is useful for distinguishing a correlation with transcriptome of the fruits; it is also used in genome wide-ranging metabolic studies to explain diverse and differential biochemical pathways of tomatoes and ecotypes and ancestral species [21]. Organic acid, sugars, flavonoids, and carotenoids are well established in Citrus fruits. Metabolic studies have identified 130 metabolites like acids, sugars, flavonoids, alkaline, limonoids, coumarins, and other plant hormones with greater levels of lycopene and that are sweeter than the wild type [89]. Higher levels of soluble sugars, lower organic acid levels, and the differential levels of flavonoids at a maturing stage determined the taste and flavor. The Candidatus Liberibacter asiaticus infection, which causes Citrus Huanglongbing, impairs the quality of the species’ juice [90]. Alanine, arginine, isoleucine, leucine, proline, threonine, and valinic acids are increased in the infection although citrates and phenylalanine levels improve. Fruit thermal treatment is widely used for fruit prevention, which has strong metabolomics storage support during the postharvest period. ABA is also reportedly used during fruit development as a biosynthesis regulator for citrus cuticular wax. Heat therapy reduces organic and amino acid content significantly, although certain metabolites are accreted, such as 2-keto-d gluconic, oleic acid, ornithine, succinic acid, myo-inositol, fructose, and so on [91].

In its peel and flesh, apples include a number of beneficial antioxidant substances to lessen the risk of various diseases like asthma, cancer, and diabetes. In order to distinguish between commercially important cultivars [92], the metabolite content of apples is often used. One variety called Golden Delicious contains elevated amounts of succinic acid and myo inositol but there are higher amounts of triterpene, flavonoids, stearic, and carbohydrates available in Red Delicious varieties. The apple fruit metabolome analysis and [93] three-dimensional allocation of metabolites are well explained. Metabolomics on stored apples demonstrated a variation in primary metabolites with different lengths of time. Increased mannose and xylose levels due to cell wall hemicellulose breakdown and a well-established correlation with regulation of metabolome were used for fruit senescence during the postharvest period [27].

Kiwifruit offers huge health-related nutrients such as fiber and vitamin C. Nardozza et al. [94] identified 51 metabolites during kiwi improvement besides the ripening process as well. During the ripening process, however, the concentration of soluble sugars significantly changes and finally, the quality and taste of the fruit is determined. Synthetic cytokine substantially upsurges the size of the fruit and influences the ripening process in metabolites such as amino acids, sugars, organic acids, etc. [95]. Likewise in Vitis vinifera grapes, the creation of fruit relies on plenty of metabolites, and the expression of hormones and pathways of metabolism of sugar can be controlled. In regions with high sunlight and low rainfall, the grape metabolite content varies geographically, as the grapes produced have enriched sugar content and amino acids, Na, and Ca, and the low levels of organic acids, which play the role of external factors in the quality of grapes. The abundance of metabolites in grapes is described as stage specific and culture specific, and regulates the ripening process [96]. The main reason for grape metabolomics was the identification of some 100 metabolites used to construct a new product database [95, 96].

The metabolomics analysis of Pyrus communis confirmed that approximately 250 metabolites were accumulated during fruit development and maturation [96]. Mature potato fruit has manifested sugar accumulation and amino acids containing S, phytohormones like ABA and brassinoid, and 15 phytohormones have been detected including ABA, auxin, brassinoid acids, gibberellin, JA, and SA. The flowering stage shows significant increases in metabolites like amino and organic acids, which further decrease with the development of fruit. Similarly in strawberry, Aharoni et al. have demonstrated the process of gaining and losing aromas of strawberries during development and domesticating [97]. Most of the terpenoides, like mono- and Sesqui-terpenes, are frequently comprised in cropped strawberry species. However, olefin monoterpenes and myrtenyl acetate are rich in wild species [98]. During fruit development and fruit maturation, GC/MS and HPLC showed a change in the metabolite content in strawberry. Maturation of strawberries led to increased amino acid content, including substantial sugar changes, including ester biosynthesis, shikimates and tricarboxylic acid [99]. As a result, the sugar content has changed significantly with reference to biotic stress and fungicide on the quality of strawberry. Infection with Colletotrichum nymphaeae induces sugar build-up and lowers biologic acid levels. The fruits showed modified metabolite content, like flavanols and phenolic substances. The increase by fungal pathogens viz., in the levels of polyphenol of white-fruited strawberry species by Botrytis cinerea, was reported, as well as Colletotrichum acutatum [100].

1.5.3 Cereal and legume crops

The qualitative and quantitative metabolomic analysis of numerous cereals, i.e., rice, maize, wheat, barley, and oat, reveals the specific cereal metabolome [101]. Early chemical cereal analysis focused on measuring compounds of N, P, dietary fiber, sugars, and protein content [102]. A substantial study of the regulation of proteins, carbohydrates, and energy consumption systems were determined by 14C isotope labeling in wheat plants, and a phenolic profiling of several cereal plants was reported as early as the 1960s [103]. Until 2000, the bulk of the cereal metabolomic studies relating to different biotic and abiotic stresses directly targeted analysis of vitamins, sterols, phenolics, volatile compositions, and metabolites [104]. Metabolomic fingerprints of transgenic field samples of field-grown wheat were the first completion of the cereal metabolomic analysis with the use of 1D ¹H NMR and GC-MS [105] as well as rice plant metabolomics during plant production using GC-MS [106]. In continuous development and progress in analysis, and ensuing elucidation of highly intricate metabolomic datasets, the role of metabolomics in cereal science has been appreciably expanded. The constituents present are starch, nutritional fiber, proteins, and sugars in grains of cereals. However, small metabolites, such as phenolic acids, sterols, and flavonoids, contribute considerably to the features of the seed [107]. In particular, polyphenols have been very carefully considered recently because of their tolerance mechanisms regarding abiotic/biotic stresses [108]. One of the most highly prized sources of polyphenols used in the human diets is a grain and cereal product that includes 4-hydroxybenzoic acid compounds and hydroxycinnamic acids, i.e., vanilic, gallic, and other coumaric acids. Cereal phenolics contain sugars and other ingredients that change their solubility or bioactivity in free and combined forms. The main components of cereal and cereal products are phenolic acids [109]. In addition, it was argued that polyphene and antioxidant contents might be highly dependent on the β-glucan content in barley. Due to growing conditions and geographical region, the phytochemical composition of one cereal variety can vary greatly