Multi-platform metabolomics analyses of a broad collection of fragrant and non-fragrant rice varieties reveals the high complexity of grain quality characteristics

Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought

The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q2 = 0.54-0.56 versus 0.35) and for stress-induced GY loss (Q2 = 0.59-0.64 versus 0.03-0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate-glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.

Polyploidization Increases the Lipid Content and Improves the Nutritional Quality of Rice

Plant polyploidization is frequently associated with changes in nutrient contents. However, the possible contribution of metabolites to this change has not been investigated by characterizing the metabolite contents of diploid and tetraploid forms of rice (Oryza sativa L.). We compared the metabolites of a group of diploid-tetraploid japonica brown rice and a group of diploid-tetraploid indica brown rice based on liquid chromatography-tandem mass spectrometry. In total, 401 metabolites were identified; of these, between the two diploid-tetraploid groups, 180 showed opposite expression trends, but 221 showed the same trends (147 higher abundance vs. 74 lower abundance). Hierarchical cluster analysis of differential metabolites between diploid and tetraploid species showed a clear grouping pattern, in which the expression abundance of lipids, amino acids and derivatives, and phenolic acids increased in tetraploids. Further analysis revealed that the lipids in tetraploid rice increased significantly, especially unsaturated fatty acids and phospholipids. This study provides further basis for understanding the changes in rice nutritional quality following polyploidization and may serve as a new theoretical reference for breeding eutrophic or functional rice varieties via polyploidization.

Multi-Platform Metabolomics Analyses Revealed the Complexity of Serum Metabolites in LPS-Induced Neuroinflammed Rats Treated with Clinacanthus nutans Aqueous Extract

The use of metabolomics as a comprehensive tool in the analysis of metabolic profiles in disease progression and therapeutic intervention is rapidly advancing. Yet, a single analytical platform could not be applied to cover the entire spectrum of a biological sample’s metabolome. In the present paper, multi-platform metabolomics approaches were explored to determine the diverse rat sera metabolites extracted from intracerebroventricular lipopolysaccharides (LPS)-induced neuroinflammed rats treated with oral therapeutic interventions of positive drug (dextromethorphan, 5 mg/kg BW); with Clinacanthus nutans (CN) aqueous extract (CNE, 500 mg/kg BW); and with phosphate buffer saline (PBS) as the control group for 14 days. Analyzed by nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC-MS) techniques, this study depicted the potential of metabolites associated with neuroinflammation and verified by MetDisease. The key observations in the perturbed metabolic pathways that showed ameliorative effects were linked to the class of amino acid and peptide metabolism involving valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; and phenylalanine metabolism. Lipid metabolism of arachidonic acid metabolism, glycerophospholipid metabolism, terpenoid backbone biosynthesis, and glycosphingolipid metabolism were also affected. Current findings suggested that the putative biomarkers, especially lysophosphatidic acid (LPA) and 5-diphosphomevalonic acid from glycerophospholipid and squalene/terpenoid and cholesterol biosynthesis, respectively, showed the ameliorative effects of the drug and CN treatments by controlling cell differentiation and proliferation. Our study proved that the complex and dynamic sera profiling affected during the CN treatment was greatly influenced by the analytical platform selection as integration between the two data yielded a more holistic summary of the metabolite pattern changes. Hence, an evidence-based herb, such as CN, can be used for novel diagnostic tools in the quest for ethnopharmacological studies.

Metabolic Fingerprinting for Identifying the Mode of Action of the Fungicide SYP-14288 on Rhizoctonia solani

The fungicide SYP-14288 has a high efficiency, low toxicity, and broad spectrum in inhibiting both fungi and oomycetes, but its mode of action (MoA) remains unclear on inhibiting fungi. In this study, the MoA was determined by analyzing the metabolism and respiratory activities of Rhizoctonia solani treated by SYP-14288. Wild-type strains and SYP-14288-resistant mutants of R. solani were incubated on potato dextrose agar amended with either SYP-14288 or one of select fungicides acting on fungal respiration, including complex I, II, and III inhibitors; uncouplers; and ATP synthase inhibitors. Mycelial growth was measured under fungicides treatments. ATP content was determined using an ATP assay kit, membrane potential of mitochondria was detected with the JC-1 kit, and respiratory rate was calculated based on the measurement of oxygen consumption of R. solani. A model of metabolic fingerprinting cluster was established to separate oxidation inhibitors and phosphorylation inhibitors. All the results together displayed a clear discrimination between oxidation inhibitors and phosphorylation inhibitors, and the latter inhibited ATP synthase production having or uncoupling activities. Based on the model, SYP-14288 was placed in phosphorylation inhibitor group, because it significantly reduced ATP content and membrane potential of mitochondria while increasing respiratory rate in R. solani. Therefore, the MoA of SYP-14288 on R. solani was confirmed to involve phosphorylation inhibition and possibly uncoupling activity.

Towards superior plant-based foods using metabolomics

Metabolomics is proving a useful approach for many of the main future goals in agronomy and food production such as sustainability/crop resilience, food quality, safety, storage, and nutrition. Targeted and/or untargeted small-molecule analysis, coupled to chemometric analysis, has already unveiled a great deal of the complexity of plant-based foods, but there is still 'dark matter' to be discovered. Moreover, state-of-the-art food metabolomics offers insights into the molecular mechanisms underlying sensorial and nutritional characteristics of foods and thus enables higher precision and speed. This review describes recent applications of food metabolomics from fork to farm and focuses on the opportunities these bring to continue food innovation and support the shift to plant-based foods.

Lipidomics reveals associations between rice quality traits

INTRODUCTION

Lipids are a diverse group of macromolecules that occur in rice grains and are known to impact rice grain properties. Identifying the relationships between specific lipids and traits of quality is important to improve varietal selection for high quality rice.

OBJECTIVES

Using untargeted lipidomics, this study aims to understand the role of lipids on different traits of quality by identifying the genotypic effect of lipids and their impact on traits of cooking and eating quality of a rice mapping population.

METHODS

Lipids from milled rice grains of three sets of rice samples were screened by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) in the positive ionisation mode. Lipid features were putatively identified using analytical standards and online databases. Multivariate statistics were carried out to identify the lipid profile of varieties across three experiments. Correlation analysis was carried out between lipid features and 12 quality traits across a rice mapping population that segregates for grain physical and texture-associated traits.

RESULTS

Thousands of features in rice grain lipids were detected, and were grouped into six categories-fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids and prenol lipids. A strong genotypic basis for the lipid profile was observed among the four varieties grown under five nitrogen treatments. Clear differentiation in lipid profiles between waxy and non-waxy rice was observed. Strong correlations were observed for putative lipids that form the amylose-lipid complex and with amylose content and viscosity parameters.

CONCLUSIONS

This study demonstrates the strength of untargeted lipidomics in putatively determining features that differentiate varieties from each other, and reveals the role of specific lipids on the physical and textural quality of rice.

Biomarkers for grain yield stability in rice under drought stress

Crop yield stability requires an attenuation of the reduction of yield losses caused by environmental stresses such as drought. Using a combination of metabolomics and high-throughput colorimetric assays, we analysed central metabolism and oxidative stress status in the flag leaf of 292 indica rice (Oryza sativa) accessions. Plants were grown in the field and were, at the reproductive stage, exposed to either well-watered or drought conditions to identify the metabolic processes associated with drought-induced grain yield loss. Photorespiration, protein degradation, and nitrogen recycling were the main processes involved in the drought-induced leaf metabolic reprogramming. Molecular markers of drought tolerance and sensitivity in terms of grain yield were identified using a multivariate model based on the values of the metabolites and enzyme activities across the population. The model highlights the central role of the ascorbate-glutathione cycle, particularly dehydroascorbate reductase, in minimizing drought-induced grain yield loss. In contrast, malondialdehyde was an accurate biomarker for grain yield loss, suggesting that drought-induced lipid peroxidation is the major constraint under these conditions. These findings highlight new breeding targets for improved rice grain yield stability under drought.

Defense of pyrethrum flowers: repelling herbivores and recruiting carnivores by producing aphid alarm pheromone

(E)-β-Farnesene (EβF) is the predominant constituent of the alarm pheromone of most aphid pest species. Moreover, natural enemies of aphids use EβF to locate their aphid prey. Some plant species emit EβF, potentially as a defense against aphids, but field demonstrations are lacking. Here, we present field and laboratory studies of flower defense showing that ladybird beetles are predominantly attracted to young stage-2 pyrethrum flowers that emitted the highest and purest levels of EβF. By contrast, aphids were repelled by EβF emitted by S2 pyrethrum flowers. Although peach aphids can adapt to pyrethrum plants in the laboratory, aphids were not recorded in the field. Pyrethrum's (E)-β-farnesene synthase (EbFS) gene is strongly expressed in inner cortex tissue surrounding the vascular system of the aphid-preferred flower receptacle and peduncle, leading to elongated cells filled with EβF. Aphids that probe these tissues during settlement encounter and ingest plant EβF, as evidenced by the release in honeydew. These EβF concentrations in honeydew induce aphid alarm responses, suggesting an extra layer of this defense. Collectively, our data elucidate a defensive mimicry in pyrethrum flowers: the developmentally regulated and tissue-specific EβF accumulation and emission both prevents attack by aphids and recruits aphid predators as bodyguards.

The genetics and biosynthesis of 2-acetyl-1-pyrroline in fragrant rice

Rice (Oryza sativa L.) is a staple food for the majority of the world's population. Rice fragrance, aroma, or scent is not just a cooking quality, but also an eating quality of rice. Rice fragrance is a trait that is widely desired among rice consumers. Consequently, rice producers are sorting for rice cultivars with strong fragrance. High demand for fragrant rice cultivars has prompted rice breeders and researchers to investigate the genetics and the ways to improve fragrance in rice. It has been established by many researches that fgr gene on the chromosome 8 of rice controls its fragrance. As with other plants, rice contains BADH but because rice does not accumulate GB, a catalyst for BADH coding, BADH1 on chromosome 4 of rice and BADH2 on chromosome 8 of rice have been widely reported to be responsible for encoding BADH. badh2, a recessive allele of BADH2 has been confirmed to be responsible for fragrance in rice. badh2 and its alleles have been associated with the accumulation and synthesis of 2AP. Proline, ornithine, glutamate, methylglyoxal, Δ1-pyrroline-5-carboxylate synthetase and glyceraldehyde-3-phosphate dehydrogenase have all been identified as the precursors for the synthesis and accumulation of 2AP. By reviewing and summarising the main results of various researchers, we have been able to elucidate how various genes and metabolites influence 2AP accumulation in fragrant rice. It is our hope that this paper will be beneficial to researchers, who are working on the improvement of rice fragrance.

Quality evaluation, fatty acid analysis and untargeted profiling of volatiles in Cambodian rice

This study provides the first investigation of the physical traits, pasting properties and volatile compounds of Cambodian rice cultivars, including traditional, improved, and improved traditional varieties, allowing for their differentiation as high and low quality rice. Analysis of the grain quality traits illustrates interesting features of traditional varieties and correlations between traits that assist with understanding texture. Untargeted profiling of volatile compounds shows that high quality fragrant varieties not only contain 2-acetyl-1-pyrroline but also several other compounds, including aldehydes, alcohols and 2-alkylfurans that contribute to overall aroma. Moreover, low odour threshold volatile compounds, which can be derived from the oxidation of unsaturated fatty acids, were more abundant in the fragrant varieties. The percentage area of both oleic and linoleic acid were found to be significantly different among the rice varieties tested. Such findings suggest that unsaturated fatty acids in milled rice contribute to rice fragrance, and thereby to overall quality.

A Multidisciplinary Phenotyping and Genotyping Analysis of a Mapping Population Enables Quality to Be Combined with Yield in Rice

In this study a mapping population (F₈) of ca 200 progeny from a cross between the commercial rice varieties Apo and IR64 has been both genotyped and phenotyped. A genotyping-by-sequencing approach was first used to identify 2,681 polymorphic SNP markers which gave dense coverage of the genome with a good distribution across all 12 chromosomes. The coefficient of parentage was also low, at 0.13, confirming that the parents are genetically distant from each other. The progeny, together with both parents, were grown under irrigated and water restricted conditions in a randomised block design. All grain was harvested to determine variation in yield across the population. The grains were then polished following standard procedures prior to performing the phenotyping analyses. A Gas Chromatography—Mass Spectrometry approach was used to determine the volatile biochemical profiles of each line and after data curation and processing, discriminatory metabolites were putatively identified based on in-house and commercial spectral libraries. These data were used to predict the potential role of these metabolites in determining differences in aroma between genotypes. A number of QTLs for yield and for individual metabolites have been identified. Following these combined multi-disciplinary analyses, it proved possible to identify a number of lines which appeared to combine the favourable aroma attributes of IR64 with the favourable (higher) yield potential of Apo. As such, these lines are excellent candidates to assess further as potential genotypes to work up into a new variety of rice which has both good yield and good quality, thus meeting the needs of both farmer and consumer alike.

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.

How close are we to complete annotation of metabolomes?

The metabolome describes the full complement of the tens to hundreds of thousands of low molecular weight metabolites present within a biological system. Identification of the metabolome is critical for discovering the maximum amount of biochemical knowledge from metabolomics datasets. Yet no exhaustive experimental characterisation of any organismal metabolome has been reported to date, dramatically contrasting with the genome sequencing of thousands of plants, animals and microbes. Here, we review the status of metabolome annotation and describe advances in the analytical methodologies being applied. In part through new international coordination, we conclude that we are now entering a new era of metabolome annotation.

Improved batch correction in untargeted MS-based metabolomics

INTRODUCTION

Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account.

OBJECTIVES

This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects.

METHODS

Batch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC-MS and GC-MS data sets of samples from Arabidopsis thaliana.

RESULTS

The three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects-replacing them with very small numbers such as zero seems the worst of the approaches considered.

CONCLUSION

The use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections.