The metabolic signature related to high plant growth rate in Arabidopsis thaliana

Association mapping and candidate gene identification for yield traits in European hazelnut (Corylus avellana L.)

European hazelnut (Corylus avellana L.) is an important nut crop due to its nutritional benefits, culinary uses, and economic value. Türkiye is the leading producer of hazelnut, followed by Italy and the United States. Quantitative trait locus studies offer promising opportunities for breeders and geneticists to identify genomic regions controlling desirable traits in hazelnut. A genome-wide association analysis was conducted with 5,567 single nucleotide polymorphisms on a Turkish core set of 86 hazelnut accessions, revealing 189 quantitative trait nucleotides (QTNs) associated with 22 of 31 traits (p < 2.9E-07). These QTNs were associated with plant and leaf, phenological, reproductive, nut, and kernel traits. Based on the close physical distance of QTNs associated with the same trait, we identified 23 quantitative trait loci. Furthermore, we identified 23 loci of multiple QTs comprising chromosome locations associated with more than one trait at the same position or in close proximity. A total of 159 candidate genes were identified for 189 QTNs, with 122 of them containing significant conserved protein domains. Some candidate matches to known proteins/domains were highly significant, suggesting that they have similar functions as their matches. This comprehensive study provides valuable insights for the development of breeding strategies and the improvement of hazelnut and enhances the understanding of the genetic architecture of complex traits by proposing candidate genes and potential functions.

Long-term soil warming changes the profile of primary metabolites in fine roots of Norway spruce in a temperate montane forest

Climate warming poses major threats to temperate forests, but the response of tree root metabolism has largely remained unclear. We examined the impact of long-term soil warming (>14 years, +4°C) on the fine root metabolome across three seasons for 2 years in an old spruce forest, using a liquid chromatography-mass spectrometry platform for primary metabolite analysis. A total of 44 primary metabolites were identified in roots (19 amino acids, 12 organic acids and 13 sugars). Warming increased the concentration of total amino acids and of total sugars by 15% and 21%, respectively, but not organic acids. We found that soil warming and sampling date, along with their interaction, directly influenced the primary metabolite profiles. Specifically, in warming plots, concentrations of arginine, glycine, lysine, threonine, tryptophan, mannose, ribose, fructose, glucose and oxaloacetic acid increased by 51.4%, 19.9%, 21.5%, 19.3%, 22.1%, 23.0%, 38.0%, 40.7%, 19.8% and 16.7%, respectively. Rather than being driven by single compounds, changes in metabolite profiles reflected a general up- or downregulation of most metabolic pathway network. This emphasises the importance of metabolomics approaches in investigating root metabolic pathways and understanding the effects of climate change on tree root metabolism.

Transcriptome analysis reveals the key role of overdominant expression of photosynthetic and respiration-related genes in the formation of tobacco(Nicotiana tabacum L.) biomass heterosis

BACKGROUND

Leaves are the nutritional and economic organs of tobacco, and their biomass directly affects tobacco yield and the economic benefits of farmers. In the early stage, our research found that tobacco hybrids have more leaves and larger leaf areas, but the performance and formation reasons of biomass heterosis are not yet clear.

RESULTS

This study selected 5 parents with significant differences in tobacco biomass and paired them with hybrid varieties. It was found that tobacco hybrid varieties have a common biomass heterosis, and 45 days after transplantation is the key period for the formation of tobacco biomass heterosis; By analyzing the biomass heterosis of hybrids, Va116×GDH94 and its parents were selected for transcriptome analysis. 76.69% of the differentially expressed genes between Va116×GDH94 and its parents showed overdominant expression pattern, and these overdominant expression genes were significantly enriched in the biological processes of photosynthesis and TCA cycle; During the process of photosynthesis, the overdominant up-regulation of genes such as Lhc, Psa, and rbcl promotes the progress of photosynthesis, thereby increasing the accumulation of tobacco biomass; During the respiratory process, genes such as MDH, ACO, and OGDH are overedominantly down-regulated, inhibiting the TCA cycle and reducing substrate consumption in hybrid offspring; The photosynthetic characteristics of the hybrid and its parents were measured, and the net photosynthetic capacity of the hybrid was significantly higher than that of the parents.

CONCLUSION

These results indicate that the overdominant expression effect of differentially expressed genes in Va116×GDH94 and its parents plays a crucial role in the formation of tobacco biomass heterosis. The overdominant expression of genes related to photosynthesis and respiration enhances the photosynthetic ability of Va116×GDH94, reduces respiratory consumption, promotes the increase of biomass, and exhibits obvious heterosis.

OsLSC6 Regulates Leaf Sheath Color and Cold Tolerance in Rice Revealed by Metabolite Genome Wide Association Study

Plant metabolites including anthocyanins play an important role in the growth of plants, as well as in regulating biotic and abiotic stress responses to the environment. Here we report comprehensive profiling of 3315 metabolites and a further metabolic-based genome-wide association study (mGWAS) based on 292,485 SNPs obtained from 311 rice accessions, including 160 wild and 151 cultivars. We identified hundreds of common variants affecting a large number of secondary metabolites with large effects at high throughput. Finally, we identified a novel gene namely OsLSC6 (Oryza sativa leaf sheath color 6), which encoded a UDP 3-O-glucosyltransferase and involved in the anthocyanin biosynthesis of Cyanidin-3-Galc (sd1825) responsible for leaf sheath color, and resulted in significant different accumulation of sd1825 between wild (purple) and cultivars (green). The results of knockout transgenic experiments showed that OsLSC6 regulated the biosynthesis and accumulation of sd1825, controlled the purple leaf sheath. Our further research revealed that OsLSC6 also confers resistance to cold stress during the seedling stage in rice. And we identified that a SNP in OsLSC6 was responsible for the leaf sheath color and chilling tolerance, supporting the importance of OsLSC6 in plant adaption. Our study could not only demonstrate that OsLSC6 is a vital regulator during anthocyanin biosynthesis and abiotic stress responses, but also provide a powerful complementary tool based on metabolites-to-genes analysis by mGWAS for functional gene identification andpromising candidate in future rice breeding and improvement.

Metabolome plasticity in 241 Arabidopsis thaliana accessions reveals evolutionary cold adaptation processes

Acclimation and adaptation of metabolism to a changing environment are key processes for plant survival and reproductive success. In the present study, 241 natural accessions of Arabidopsis (Arabidopsis thaliana) were grown under two different temperature regimes, 16 °C and 6 °C, and growth parameters were recorded, together with metabolite profiles, to investigate the natural genome × environment effects on metabolome variation. The plasticity of metabolism, which was captured by metabolic distance measures, varied considerably between accessions. Both relative growth rates and metabolic distances were predictable by the underlying natural genetic variation of accessions. Applying machine learning methods, climatic variables of the original growth habitats were tested for their predictive power of natural metabolic variation among accessions. We found specifically habitat temperature during the first quarter of the year to be the best predictor of the plasticity of primary metabolism, indicating habitat temperature as the causal driver of evolutionary cold adaptation processes. Analyses of epigenome- and genome-wide associations revealed accession-specific differential DNA-methylation levels as potentially linked to the metabolome and identified FUMARASE2 as strongly associated with cold adaptation in Arabidopsis accessions. These findings were supported by calculations of the biochemical Jacobian matrix based on variance and covariance of metabolomics data, which revealed that growth under low temperatures most substantially affects the accession-specific plasticity of fumarate and sugar metabolism. Our findings indicate that the plasticity of metabolic regulation is predictable from the genome and epigenome and driven evolutionarily by Arabidopsis growth habitats.

Plant biochemical genetics in the multiomics era

Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel's milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies.

Tracking deuterium uptake in hydroponically grown maize roots using correlative helium ion microscopy and Raman micro-spectroscopy

BACKGROUND

Investigations into the growth and self-organization of plant roots is subject to fundamental and applied research in various areas such as botany, agriculture, and soil science. The growth activity of the plant tissue can be investigated by isotope labeling experiments with heavy water and subsequent detection of the deuterium in non-exchangeable positions incorporated into the plant biomass. Commonly used analytical methods to detect deuterium in plants are based on mass-spectrometry or neutron-scattering and they either suffer from elaborated sample preparation, destruction of the sample during analysis, or low spatial resolution. Confocal Raman micro-spectroscopy (CRM) can be considered a promising method to overcome the aforementioned challenges. The substitution of hydrogen with deuterium results in the measurable shift of the CH-related Raman bands. By employing correlative approaches with a high-resolution technique, such as helium ion microscopy (HIM), additional structural information can be added to CRM isotope maps and spatial resolution can be further increased. For that, it is necessary to develop a comprehensive workflow from sample preparation to data processing.

RESULTS

A workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed. The accuracy and linearity of deuterium detection by CRM were tested and confirmed with samples of deuterated glucose. A set of root samples taken from deuterated Zea mays in a time-series experiment was used to test the entire workflow. The deuterium content in the roots measured by CRM was close to the values obtained by isotope-ratio mass spectrometry. As expected, root tips being the most actively growing root zone had incorporated the highest amount of deuterium which increased with increasing time of labeling. Furthermore, correlative HIM-CRM analysis allowed for obtaining the spatial distribution pattern of deuterium and lignin in root cross-sections. Here, more active root zones with higher deuterium incorporation showed less lignification.

CONCLUSIONS

We demonstrated that CRM in combination with deuterium labeling can be an alternative and reliable tool for the analysis of plant growth. This approach together with the developed workflow has the potential to be extended to complex systems such as plant roots grown in soil.

Characterization of novel loci controlling seed oil content in Brassica napus by marker metabolite-based multi-omics analysis

BACKGROUND

Seed oil content is an important agronomic trait of Brassica napus (B. napus), and metabolites are considered as the bridge between genotype and phenotype for physical traits.

RESULTS

Using a widely targeted metabolomics analysis in a natural population of 388 B. napus inbred lines, we quantify 2172 metabolites in mature seeds by liquid chromatography mass spectrometry, in which 131 marker metabolites are identified to be correlated with seed oil content. These metabolites are then selected for further metabolite genome-wide association study and metabolite transcriptome-wide association study. Combined with weighted correlation network analysis, we construct a triple relationship network, which includes 21,000 edges and 4384 nodes among metabolites, metabolite quantitative trait loci, genes, and co-expression modules. We validate the function of BnaA03.TT4, BnaC02.TT4, and BnaC05.UK, three candidate genes predicted by multi-omics analysis, which show significant impacts on seed oil content through regulating flavonoid metabolism in B. napus.

CONCLUSIONS

This study demonstrates the advantage of utilizing marker metabolites integrated with multi-omics analysis to dissect the genetic basis of agronomic traits in crops.

Morphological and metabolomics profiling of intraspecific Arabidopsis hybrids in relation to biomass heterosis

Heterosis contributes greatly to the worldwide agricultural yield. However, the molecular mechanism underlying heterosis remains unclear. This study took advantage of Arabidopsis intraspecific hybrids to identify heterosis-related metabolites. Forty-six intraspecific hybrids were used to examine parental effects on seed area and germination time. The degree of heterosis was evaluated based on biomass: combinations showing high heterosis of F₁ hybrids exhibited a biomass increase from 6.1 to 44% over the better parent value (BPV), whereas that of the low- and no-heterosis hybrids ranged from - 19.8 to 9.8% over the BPV. Metabolomics analyses of F₁ hybrids with high heterosis and those with low one suggested that changes in TCA cycle intermediates are key factors that control growth. Notably, higher fumarate/malate ratios were observed in the high heterosis F₁ hybrids, suggesting they provide metabolic support associated with the increased biomass. These hybrids may produce more energy-intensive biomass by speeding up the efficiency of TCA fluxes. However, the expression levels of TCA-process-related genes in F₁ hybrids were not associated with the intensity of heterosis, suggesting that the post-transcriptional or post-translational regulation of these genes may affect the productivity of the intermediates in the TCA cycle.

The Metabolic Profile of Young, Watered Chickpea Plants Can Be Used as a Biomarker to Predict Seed Number under Terminal Drought

Chickpea is the second-most-cultivated legume globally, with India and Australia being the two largest producers. In both of these locations, the crop is sown on residual summer soil moisture and left to grow on progressively depleting water content, finally maturing under terminal drought conditions. The metabolic profile of plants is commonly, correlatively associated with performance or stress responses, e.g., the accumulation of osmoprotective metabolites during cold stress. In animals and humans, metabolites are also prognostically used to predict the likelihood of an event (usually a disease) before it occurs, e.g., blood cholesterol and heart disease. We sought to discover metabolic biomarkers in chickpea that could be used to predict grain yield traits under terminal drought, from the leaf tissue of young, watered, healthy plants. The metabolic profile (GC-MS and enzyme assays) of field-grown chickpea leaves was analysed over two growing seasons, and then predictive modelling was applied to associate the most strongly correlated metabolites with the final seed number plant^-1. Pinitol (negatively), sucrose (negatively) and GABA (positively) were significantly correlated with seed number in both years of study. The feature selection algorithm of the model selected a larger range of metabolites including carbohydrates, sugar alcohols and GABA. The correlation between the predicted seed number and actual seed number was R² adj = 0.62, demonstrating that the metabolic profile could be used to predict a complex trait with a high degree of accuracy. A previously unknown association between D-pinitol and hundred-kernel weight was also discovered and may provide a single metabolic marker with which to predict large seeded chickpea varieties from new crosses. The use of metabolic biomarkers could be used by breeders to identify superior-performing genotypes before maturity is reached.

Plant and microbial sciences as key drivers in the development of metabolomics research

This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.

Characterizing nutritional phenotypes using experimental nutrigenomics: Is there nutrient-specificity to different types of dietary stress?

The ability to directly measure and monitor poor nutrition in individual animals and ecological communities is hampered by methodological limitations. In this study, we use nutrigenomics to identify nutritional biomarkers in a freshwater zooplankter, Daphnia pulex, a ubiquitous primary consumer in lakes and a sentinel of environmental change. We grew animals in six ecologically relevant nutritional treatments: nutrient replete, low carbon (food), low phosphorus, low nitrogen, low calcium and high Cyanobacteria. We extracted RNA for transcriptome sequencing to identify genes that were nutrient responsive and capable of predicting nutritional status with a high degree of accuracy. We selected a list of 125 candidate genes, which were subsequently pruned to 13 predictive potential biomarkers. Using a nearest-neighbour classification algorithm, we demonstrate that these potential biomarkers are capable of classifying our samples into the correct nutritional group with 100% accuracy. The functional annotation of the selected biomarkers revealed some specific nutritional pathways and supported our hypothesis that animal responses to poor nutrition are nutrient specific and not simply different presentations of slow growth or energy limitation. This is a key step in uncovering the causes and consequences of nutritional limitation in animal consumers and their responses to small- and large-scale changes in biogeochemical cycles.

Plastid Phosphatidylglycerol Homeostasis Is Required for Plant Growth and Metabolism in Arabidopsis thaliana

A unique feature of plastid phosphatidylglycerol (PG) is a trans-double bond specifically at the sn-2 position of 16C fatty acid (16:1t- PG), which is catalyzed by FATTY ACID DESATURASE 4 (FAD4). To offer additional insights about the in vivo roles of FAD4 and its product 16:1t-PG, FAD4 overexpression lines (OX-FAD4s) were generated in Arabidopsis thaliana Columbia ecotype. When grown under continuous light condition, the fad4-2 and OX-FAD4s plants exhibited higher growth rates compared to WT control. Total lipids were isolated from Col, fad4-2, and OX-FAD4_2 plants, and polar lipids quantified by lipidomic profiling. We found that disrupting FAD4 expression altered prokaryotic and eukaryotic PG content and composition. Prokaryotic and eukaryotic monogalactosyl diacylglycerol (MGDG) was up-regulated in OX-FAD4 plants but not in fad4-2 mutant. We propose that 16:1t-PG homeostasis in plastid envelope membranes may coordinate plant growth and stress response by restricting photoassimilate export from the chloroplast.

Metabolomics analysis reveal the molecular responses of high CO2 concentration improve resistance to Pb stress of Oryza sativa L. seedlings

Rice seedlings were exposed to two CO₂ concentrations (400 ± 20 and 800 ± 20 μmol mol^-1) and three PbNO₃ concentrations (0, 50 and 100 µmol L^-1) for 10 days to explore the regulatory mechanisms of elevated CO₂ for Pb stress resistance. Electrical conductivity, MDA content, SOD, POD, CAT activities and metabolomics changes were studied. Results showed that: Pb stress damaged cell membrane system, electrical conductivity and MDA content increased 49.34 % and 73.27 %, respectively, and some antioxidant enzymes activities increased. Sugar, polyol, amino acid metabolism and fatty acid β-oxidation were all enhanced to improve osmotic adjustments, maintain cell membrane stability, supply energy, nitrogen assimilates and antioxidant capacity; Under composite treatments, cell membrane damage was reduced, activities of protective enzymes increased compared with only Pb stress, POD activity increased the most (49.14 %) under severe Pb composite treatment. High CO₂ caused the enhance of cells antioxidant capacity, TCA cycle intermediate products contents and fatty acid desaturation under mild Pb stress. Many sugars, polyols and amino acids contents were increased as osmotic regulatory substances by high CO₂ under severe Pb stress; Secondary metabolites played an important role under Pb stress and composite treatments. The object of this study is to provide a possible molecular mechanism of rice response to Pb stress under high CO₂ in the future.

Nitrogen-mediated metabolic patterns of susceptibility to Botrytis cinerea infection in tomato (Solanum lycopersicum) stems

Severe N stress allows an accumulation of C-based compounds but impedes that of N-based compounds required to lower the susceptibility of tomato stem to Botrytis cinerea. Botrytis cinerea, a necrotrophic filamentous fungus, forms potentially lethal lesions on the stems of infected plants. Contrasted levels of susceptibility to B. cinerea were obtained in a tomato cultivar grown on a range of nitrate concentration: low N supply resulted in high susceptibility while high N supply conferred a strong resistance. Metabolic deviations and physiological traits resulting from both infection and nitrogen limitation were investigated in the symptomless stem tissue surrounding the necrotic lesion. Prior to infection, nitrogen-deficient plants showed reduced levels of nitrogen-based compounds such as amino acids, proteins, and glutathione and elevated levels of carbon-based and defence compounds such as α-tomatine and chlorogenic acid. After B. cinerea inoculation, all plants displayed a few common responses, mainly alanine accumulation and galactinol depletion. The metabolome of resistant plants grown under high N supply showed no significant change after inoculation. On the contrary, the metabolome of susceptible plants grown under low N supply showed massive metabolic adjustments, including changes in central metabolism around glutamate and respiratory pathways, suggesting active resource mobilization and production of energy and reducing power. Redox and defence metabolisms were also stimulated by the infection in plants grown under low N supply; glutathione and chlorogenic acid accumulated, as well as metabolites with more controversial defensive roles, such as polyamines, GABA, branched-chain amino acids and phytosterols. Taken together, the results showed that nitrogen deficiency, although leading to an increase in secondary metabolites even before the pathogen attack, must have compromised the constitutive levels of defence proteins and delayed or attenuated the induced responses. The involvement of galactinol, alanine, cycloartenol and citramalate in the tomato stem response to B. cinerea is reported here for the first time.

Impairment of sugar transport in the vascular system acts on nitrogen remobilization and nitrogen use efficiency in Arabidopsis

Carbon (C) and nitrogen (N) metabolisms have long been known to be coupled, and this is required for adjusting nitrogen use efficiency (NUE). Despite this intricate relationship, it is still unclear how deregulation of sugar transport impacts N allocation. Here, we investigated in Arabidopsis the consequences of the simultaneous downregulation of the genes coding for the sugar transporters SWEET11, SWEET12, SWEET16, and SWEET17 on various anatomical and physiological traits ranging from the stem's vascular system development to plant biomass production, seed yield, and N remobilization and use efficiency. Our results show that intracellular sugar exchanges mediated by SWEET16 and SWEET17 proteins specifically impact vascular development but do not play a significant role in the distribution of N. Most importantly, we showed that the double mutant swt11 swt12, which has an impacted vascular development, displays an improved NUE and nitrogen remobilization to the seeds. In addition, a significant negative correlation between sugar and amino acids contents and the inflorescence stem radial growth exists, highlighting the complex interaction between the maintenance of C/N homeostasis and the inflorescence stem development. Our results thus deepen the link between sugar transport, C/N allocation, and vascular system development.

The field phenotyping platform's next darling: Dicotyledons

The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

VvTOR interacts with VvSnRK1.1 and regulates sugar metabolism in grape

VvTOR interacts with VvSnRK1.1 and regulates sugar accumulation and sugar-related genes expression in grape. Target of rapamycin (TOR) and sucrose-non-fermenting-related protein kinase 1.1 (SnRK1.1) both are critical proteins in plant sugar metabolism. Glucose-TOR signaling dictates transcriptional reprogramming of gene sets involved in central and secondary metabolism, cell cycle, transcription, signaling, transport and folding. SnRK1.1 is involved in sucrose-induced hypocotyl elongation. However, the relationship of TOR and SnRK1.1 in regulating sugar metabolism is unclear. In the study, we utilized grape (Vitis vinifera) calli to explore the relationship between TOR and SnRK1.1 in the sugar metabolism. We found that VvTOR interacted with VvSnRK1.1. By subcellular localization, VvTOR was found in the nucleus and cell membrane. Transgenic grape calli achieved by Agrobacterium-mediated transformation contained less glucose compared to WT calli. The fructose contents were markedly increased in the overexpressing VvTOR (OE-VvTOR), OE-VvTOR + RNAi-VvSnRK1.1 and RNAi-VvTOR + OE-VvSnRK1.1 transgenic calli. Sucrose contents were significantly increased in the OE-VvTOR transgenic calli and reduced in the OE-VvTOR + RNAi-VvSnRK1.1 transgenic calli, which implied that the pathway of VvTOR improving sucrose content might need the expression of VvSnRK1.1. VvTOR interacted with VvSnRK1.1 and regulated sugar metabolism in grape. These results suggest that there is a crosstalk between TOR and SnRK1.1 in plant sugar metabolism.

Crop genetic diversity uncovers metabolites, elements, and gene networks predicted to be associated with high plant biomass yields in maize

Rapid population growth and increasing demand for food, feed, and bioenergy in these times of unprecedented climate change require breeding for increased biomass production on the world's croplands. To accelerate breeding programs, knowledge of the relationship between biomass features and underlying gene networks is needed to guide future breeding efforts. To this end, large-scale multiomics datasets were created with genetically diverse maize lines, all grown in long-term organic and conventional cropping systems. Analysis of the datasets, integrated using regression modeling and network analysis revealed key metabolites, elements, gene transcripts, and gene networks, whose contents during vegetative growth substantially influence the build-up of plant biomass in the reproductive phase. We found that S and P content in the source leaf and P content in the root during the vegetative stage contributed the most to predicting plant performance at the reproductive stage. In agreement with the Gene Ontology enrichment analysis, the cis-motifs and identified transcription factors associated with upregulated genes under phosphate deficiency showed great diversity in the molecular response to phosphate deficiency in selected lines. Furthermore, our data demonstrate that genotype-dependent uptake, assimilation, and allocation of essential nutrient elements (especially C and N) during vegetative growth under phosphate starvation plays an important role in determining plant biomass by controlling root traits related to nutrient uptake. These integrative multiomics results revealed key factors underlying maize productivity and open new opportunities for efficient, rapid, and cost-effective plant breeding to increase biomass yield of the cereal crop maize under adverse environmental factors.

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.

High-throughput plant phenotyping: a role for metabolomics?

High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.

Metabolomic spectra for phenotypic prediction of malting quality in spring barley

We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

Multi-Omics Techniques for Soybean Molecular Breeding

Soybean is a major crop that provides essential protein and oil for food and feed. Since its origin in China over 5000 years ago, soybean has spread throughout the world, becoming the second most important vegetable oil crop and the primary source of plant protein for global consumption. From early domestication and artificial selection through hybridization and ultimately molecular breeding, the history of soybean breeding parallels major advances in plant science throughout the centuries. Now, rapid progress in plant omics is ushering in a new era of precision design breeding, exemplified by the engineering of elite soybean varieties with specific oil compositions to meet various end-use targets. The assembly of soybean reference genomes, made possible by the development of genome sequencing technology and bioinformatics over the past 20 years, was a great step forward in soybean research. It facilitated advances in soybean transcriptomics, proteomics, metabolomics, and phenomics, all of which paved the way for an integrated approach to molecular breeding in soybean. In this review, we summarize the latest progress in omics research, highlight novel findings made possible by omics techniques, note current drawbacks and areas for further research, and suggest that an efficient multi-omics approach may accelerate soybean breeding in the future. This review will be of interest not only to soybean breeders but also to researchers interested in the use of cutting-edge omics technologies for crop research and improvement.

Improvement of prediction ability by integrating multi-omic datasets in barley

Background

Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors.

Results

The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.

Conclusions

The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08337-7).

How do plants defend themselves against pathogens-Biochemical mechanisms and genetic interventions

In agro-ecosystem, plant pathogens hamper food quality, crop yield, and global food security. Manipulation of naturally occurring defense mechanisms in host plants is an effective and sustainable approach for plant disease management. Various natural compounds, ranging from cell wall components to metabolic enzymes have been reported to protect plants from infection by pathogens and hence provide specific resistance to hosts against pathogens, termed as induced resistance. It involves various biochemical components, that play an important role in molecular and cellular signaling events occurring either before (elicitation) or after pathogen infection. The induction of reactive oxygen species, activation of defensive machinery of plants comprising of enzymatic and non-enzymatic antioxidative components, secondary metabolites, pathogenesis-related protein expression (e.g. chitinases and glucanases), phytoalexin production, modification in cell wall composition, melatonin production, carotenoids accumulation, and altered activity of polyamines are major induced changes in host plants during pathogen infection. Hence, the altered concentration of biochemical components in host plants restricts disease development. Such biochemical or metabolic markers can be harnessed for the development of "pathogen-proof" plants. Effective utilization of the key metabolites-based metabolic markers can pave the path for candidate gene identification. This present review discusses the valuable information for understanding the biochemical response mechanism of plants to cope with pathogens and genomics-metabolomics-based sustainable development of pathogen proof cultivars along with knowledge gaps and future perspectives to enhance sustainable agricultural production.

cis-prenyltransferase 3 and α/β-hydrolase are new determinants of dolichol accumulation in Arabidopsis

Dolichols (Dols), ubiquitous components of living organisms, are indispensable for cell survival. In plants, as well as other eukaryotes, Dols are crucial for post-translational protein glycosylation, aberration of which leads to fatal metabolic disorders in humans and male sterility in plants. Until now, the mechanisms underlying Dol accumulation remain elusive. In this study, we have analysed the natural variation of the accumulation of Dols and six other isoprenoids among more than 120 Arabidopsis thaliana accessions. Subsequently, by combining QTL and GWAS approaches, we have identified several candidate genes involved in the accumulation of Dols, polyprenols, plastoquinone and phytosterols. The role of two genes implicated in the accumulation of major Dols in Arabidopsis-the AT2G17570 gene encoding a long searched for cis-prenyltransferase (CPT3) and the AT1G52460 gene encoding an α/β-hydrolase-is experimentally confirmed. These data will help to generate Dol-enriched plants which might serve as a remedy for Dol-deficiency in humans.

A Comparative Study of Flavonoids and Carotenoids Revealed Metabolite Responses for Various Flower Colorations Between Nicotiana tabacum L. and Nicotiana rustica L

Tobacco is a model plant for studying flower coloration. Flavonoids and carotenoids were reported to contribute to the flower color in many plants. We investigated the mechanism underlying flower color formation in tobacco by comparing the profiling flavonoids and carotenoids between various species Nicotiana tabacum L. and Nicotiana rustica L., as their flowers commonly presented red (pink) and yellow (orange), respectively. The metabolomes were conducted by UPLC-ESI-MS/MS system. The main findings were as follows: (1) A total of 31 flavonoids and 36 carotenoids were identified in all four cultivars involved in N. tabacum and N. rustica. (2) Flavonoids and carotenoids tended to concentrate in the red flowers (N. tabacum) and yellow flowers (N. rustica), respectively. (3) About eight flavonoids and 12 carotenoids were primarily screened out for metabolic biomarkers, such as the robust biomarker involving kaempferol-3-o-rut, quercetin-glu, rutin, lutein, and β-carotene. This is the first research of systematic metabolome involving both flavonoids and carotenoids in tobacco flower coloration. The metabolic mechanism concluded that flavonoids and carotenoids mainly contributed to red (pink) and yellow (orange) colors of the tobacco flowers, respectively. Our finding will provide essential insights into characterizing species and modifying flower color in tobacco breeding through genetic improvement or regulation of featured metabolic synthesis.

Biomass allocation in response to accession recognition in Arabidopsis thaliana depends on nutrient availability and plant age

Many organisms have evolved to identify and respond to differences in genetic relatedness between conspecifics, allowing them to select between competitive and facilitative strategies to improve fitness. Due to their sessile nature, plants frequently draw from the same pool of nutrients, and the ability to limit competition between closely related conspecifics would be advantageous. Studies with Arabidopsis thaliana have confirmed that plants can detect variations at the accession level and alter their root system architecture (RSA) in response, presumably for regulating nutrient uptake. The phenotypic impact of this accession-recognition on the RSA is influenced by nutrient availability, underscoring the importance of plant-plant recognition in their growth and fitness. Thus far, these observations have been limited to short-term studies (<21 days) of only the RSA of this model angiosperm. Here we exploit nutrient-mediated regulation of accession-recognition to observe how this plant-plant recognition phenomenon influences growth from germination to flowering in A. thaliana. Our work identifies root and shoot traits that are affected by nutrient-mediated accession recognition. By coupling phenotypic assays to mass spectrometry-based studies of primary metabolite distribution, we provide preliminary insight into the biochemical underpinnings of the changes observed during these plant-plant responses. Most notably that late-stage changes in sucrose metabolism in members of the same accession drove early flowering. This work underscores the need to evaluate accession-recognition under the context of nutrient availability and consider responses throughout the plant's life, not simply at the earliest stages of interaction.

Gains and Losses of Agricultural Food Production: Implications for the Twenty-First Century

The world food supply depends on a diminishing list of plant crops and animal livestock to not only feed the ever-growing human population but also improve its nutritional state and lower the disease burden. Over the past century or so, technological advances in agricultural and food processing have helped reduce hunger and poverty but have not adequately addressed sustainability targets. This has led to an erosion of agricultural biodiversity and balanced diets and contributed to climate change and rising rates of chronic metabolic diseases. Modern food supply chains have progressively lost dietary fiber, complex carbohydrates, micronutrients, and several classes of phytochemicals with high bioactivity and nutritional relevance. This review introduces the concept of agricultural food systems losses and focuses on improved sources of agricultural diversity, proteins with enhanced resilience, and novel monitoring, processing, and distribution technologies that are poised to improve food security, reduce food loss and waste, and improve health profiles in the near future. Expected final online publication date for the Annual Review of Food Science and Technology, Volume 13 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes

Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes.

Physiologic, metabolomic, and genomic investigations reveal distinct glutamine and mannose metabolism responses to ammonium toxicity in allotetraploid rapeseed genotypes

Ammonium (NH₄⁺) toxicity has become a serious ecological and agricultural issue owing to increasing soil nitrogen inputs and atmospheric nitrogen deposition. There is accumulating evidence for the mechanisms underlying NH₄⁺-tolerance in rice and Arabidopsis, but similar knowledge for dryland crops is currently limited. We investigated the responses of a natural population of allotetraploid rapeseed to NH₄⁺ and nitrate (NO₃^-) and screened one NH₄⁺-tolerant genotype (T5) and one NH₄⁺-sensitive genotype (S211). Determination of the shoot and root NH₄⁺ concentrations showed that levels were higher in S211 than in T5. ¹⁵NH₄⁺ uptake assays, glutamine synthetase (GS) activity quantification, and relative gene transcriptional analysis indicated that the significantly higher GS activity observed in T5 roots than that in S211 was the main reason for its NH₄⁺-tolerance. In-depth metabolomic analysis verified that Gln metabolism plays an important role in rapeseed NH₄⁺-tolerance. Furthermore, adaptive changes in carbon metabolism were much more active in T5 shoots than in S211. Interestingly, we found that N-glycosylation pathway was significantly induced by NH₄⁺, especially the mannose metabolism, which concentration was 2.75-fold higher in T5 shoots than in S211 with NH₄⁺ treatment, indicating that mannose may be a metabolomic marker which also confers physiological adaptations for NH₄⁺ tolerance in rapeseed. The corresponding amino acid and soluble sugar concentrations and gene expression in T5 and S211 were consistent with these results. Genomic sequencing identified variations in the GLN (encoding GS) and GMP1 (encoding the enzyme that provides GDP-mannose) gene families between the T5 and S211 lines. These genes will be utilized as candidate genes for future investigations of the molecular mechanisms underlying NH₄⁺ tolerance in rapeseed.

Genetic Improvement in Sunflower Breeding—Integrated Omics Approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

Plant design gets its details: Modulating plant architecture by phase transitions

Plants evolved different strategies to better adapt to the environmental conditions in which they live: the control of their body architecture and the timing of phase change are two important processes that can improve their fitness. As they age, plants undergo two major phase changes (juvenile to adult and adult to reproductive) that are a response to environmental and endogenous signals. These phase transitions are accompanied by alterations in plant morphology and also by changes in physiology and the behavior of gene regulatory networks. Six main pathways involving environmental and endogenous cues that crosstalk with each other have been described as responsible for the control of plant phase transitions: the photoperiod pathway, the autonomous pathway, the vernalization pathway, the temperature pathway, the GA pathway, and the age pathway. However, studies have revealed that sugar is also involved in phase change and the control of branching behavior. In this review, we discuss recent advances in plant biology concerning the genetic and molecular mechanisms that allow plants to regulate phase transitions in response to the environment. We also propose connections between phase transition and plant architecture control.

Metabolic Profile Discriminates and Predicts Arabidopsis Susceptibility to Virus under Field Conditions

As obligatory parasites, plant viruses alter host cellular metabolism. There is a lack of information on the variability of virus-induced metabolic responses among genetically diverse plants in a natural context with daily changing conditions. To decipher the metabolic landscape of plant-virus interactions in a natural setting, twenty-six and ten accessions of Arabidopsis thaliana were inoculated with Turnip mosaic virus (TuMV), in two field experiments over 2 years. The accessions were measured for viral accumulation, above-ground biomass, targeted and untargeted metabolic profiles. The phenotypes of the accessions ranged from susceptibility to resistance. Susceptible and resistant accessions were shown to have different metabolic routes after inoculation. Susceptible genotypes accumulate primary and secondary metabolites upon infection, at the cost of hindered growth. Twenty-one metabolic signatures significantly accumulated in resistant accessions whereas they maintained their growth as mock-inoculated plants without biomass penalty. Metabolic content was demonstrated to discriminate and be highly predictive of the susceptibility of inoculated Arabidopsis. This study is the first to describe the metabolic landscape of plant-virus interactions in a natural setting and its predictive link to susceptibility. It provides new insights on plant-virus interactions. In this undomesticated species and in ecologically realistic conditions, growth and resistance are in a permanent conversation.

Multi-omics-based prediction of hybrid performance in canola

Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F₁ hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.

Integrated transcript and metabolite profiling reveals coordination between biomass size and nitrogen metabolism in Arabidopsis F1 hybrids

Heterosis refers to the improved agronomic performance of F₁ hybrids relative to their parents. Although this phenomenon is widely employed to increase biomass, yield, and stress tolerance of plants, the underlying molecular mechanisms remain unclear. To dissect the metabolic fluctuations derived from genomic and/or environmental differences contributing to the improved biomass of F₁ hybrids relative to their parents, we optimized the growth condition for Arabidopsis thaliana F₁ hybrids and their parents. Modest but statistically significant increase in the biomass of F₁ hybrids was observed. Plant samples grown under the optimized condition were also utilized for integrated omics analysis to capture specific changes in the F₁ hybrids. Metabolite profiling of F₁ hybrids and parent plants was performed using gas chromatography-mass spectrometry. Among the detected 237 metabolites, 2-oxoglutarate (2-OG) and malate levels were lower and the level of aspartate was higher in the F₁ hybrids than in each parent. In addition, microarray analysis revealed that there were 44 up-regulated and 12 down-regulated genes with more than 1.5-fold changes in expression levels in the F₁ hybrid compared to each parent. Gene ontology (GO) analyses indicated that genes up-regulated in the F₁ hybrids were largely related to organic nitrogen (N) process. Quantitative PCR verified that glutamine synthetase 2 (AtGLN2) was upregulated in the F₁ hybrids, while other genes encoding enzymes in the GS-GOGAT cycle showed no significant differences between the hybrid and parent lines. These results suggested the existence of metabolic regulation that coordinates biomass and N metabolism involving AtGLN2 in F₁ hybrids.

Network Analysis of Gene Transcriptions of Arabidopsis thaliana in Spaceflight Microgravity

The transcriptomic datasets of the plant model organism Arabidopsis thaliana grown in the International Space Station provided by GeneLab have been mined to isolate the impact of spaceflight microgravity on gene expressions related to root growth. A set of computational tools is used to identify the hub genes that respond differently in spaceflight with controlled lighting compared to on the ground. These computational tools based on graph-theoretic approaches are used to infer gene regulatory networks from the transcriptomic datasets. The three main algorithms used for network analyses are Least Absolute Shrinkage and Selection Operator (LASSO), Pearson correlation, and the Hyperlink-Induced Topic Search (HITS) algorithm. Graph-based spectral analyses reveal distinct properties of the spaceflight microgravity networks for the Wassilewskija (WS), Columbia (Col)-0, and mutant phytochromeD (phyD) ecotypes. The set of hub genes that are significantly altered in spaceflight microgravity are mainly involved in cell wall synthesis, protein transport, response to auxin, stress responses, and catabolic processes. Network analysis highlights five important root growth-regulating hub genes that have the highest outdegree distribution in spaceflight microgravity networks. These concerned genes coding for proteins are identified from the Gene Regulatory Networks (GRNs) corresponding to spaceflight total light environment. Furthermore, network analysis uncovers genes that encode nucleotide-diphospho-sugar interconversion enzymes that have higher transcriptional regulation in spaceflight microgravity and are involved in cell wall biosynthesis.

Developmental stage-specific metabolite signatures in Arabidopsis thaliana under optimal and mild nitrogen limitation

Metabolites influence flowering time, and thus are among the major determinants of yield. Despite the reported role of trehalose 6-phosphate and nitrate signaling on the transition from the vegetative to the reproductive phase, little is known about other metabolites contributing and responding to developmental phase changes. To increase our understanding which metabolic traits change throughout development in Arabidopsis thaliana and to identify metabolic markers for the vegetative and reproductive phases, especially among individual amino acids (AA), we profiled metabolites of plants grown in optimal (ON) and limited nitrogen (N) (LN) conditions, the latter providing a mild but consistent limitation of N. We found that although LN plants adapt their growth to a decreased level of N, their metabolite profiles are strongly distinct from ON plant profiles, with N as the driving factor for the observed differences. We demonstrate that the vegetative and the reproductive phase are not only marked by growth parameters such as biomass and rosette area, but also by specific metabolite signatures including specific single AA. In summary, we identified N-dependent and -independent indicators manifesting developmental stages, indicating that the plant's metabolic status also reports on the developmental phases.

Metabolomic and Transcriptomic Profiling Provide Novel Insights into Fruit Ripening and Ripening Disorder Caused by 1-MCP Treatments in Papaya

Treatment with 1-methylcyclopropylene (1-MCP) is an effective technique to preserve fruits, but inappropriate treatment with 1-MCP causes a ripening disorder (rubbery texture) in papaya fruit. In this study, a combined metabolomic and transcriptomic analysis was conducted to reveal the possible mechanism of the ripening disorder caused by unsuitable 1-MCP in papaya. A total of 203 differential accumulated metabolites (DAMs) were identified in the metabolome analysis. Only 24 DAMs were identified in the control (CK) vs. the 1-MCP 2 h group, and they were primarily flavonoids. Ninety and 89 DAMs were identified in the CK vs. 1-MCP 16 h and 1-MCP 2 h vs. 1-MCP 16 h groups, respectively, indicating that long-term 1-MCP treatment severely altered the metabolites during fruit ripening. 1-MCP 16 h treatment severely reduced the number of metabolites, which primarily consisted of flavonoids, lipids, phenolic acids, alkaloids, and organic acids. An integrated analysis of RNA-Seq and metabolomics showed that various energy metabolites for the tricarboxylic acid cycle were reduced by long-term treatment with 1-MCP, and the glycolic acid cycle was the most significantly affected, as well as the phenylpropane pathway. These results provide valuable information for fruit quality control and new insight into the ripening disorder caused by unsuitable treatment with 1-MCP in papaya.

Using Metabolomics to Assist Plant Breeding

Recent methodological advances in both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) have provided a deep understanding of metabolic regulation occurring in plant cells. The application of these techniques to agricultural systems is, however, subject to more complex interactions. Here we summarize a step-by-step modern metabolomics methodology that generates metabolome data toward the implementation of metabolomics in crop breeding. We describe a metabolic workflow, and provide guidelines for handling large sample numbers for the specific purpose of metabolic quantitative trait loci approaches.

Applying Molecular Phenotyping Tools to Explore Sugarcane Carbon Potential

Sugarcane (Saccharum spp.), a C₄ grass, has a peculiar feature: it accumulates, gradient-wise, large amounts of carbon (C) as sucrose in its culms through a complex pathway. Apart from being a sustainable crop concerning C efficiency and bioenergetic yield per hectare, sugarcane is used as feedstock for producing ethanol, sugar, high-value compounds, and products (e.g., polymers and succinate), and bioelectricity, earning the title of the world's leading biomass crop. Commercial cultivars, hybrids bearing high levels of polyploidy, and aneuploidy, are selected from a large number of crosses among suitable parental genotypes followed by the cloning of superior individuals among the progeny. Traditionally, these classical breeding strategies have been favoring the selection of cultivars with high sucrose content and resistance to environmental stresses. A current paradigm change in sugarcane breeding programs aims to alter the balance of C partitioning as a means to provide more plasticity in the sustainable use of this biomass for metabolic engineering and green chemistry. The recently available sugarcane genetic assemblies powered by data science provide exciting perspectives to increase biomass, as the current sugarcane yield is roughly 20% of its predicted potential. Nowadays, several molecular phenotyping tools can be applied to meet the predicted sugarcane C potential, mainly targeting two competing pathways: sucrose production/storage and biomass accumulation. Here we discuss how molecular phenotyping can be a powerful tool to assist breeding programs and which strategies could be adopted depending on the desired final products. We also tackle the advances in genetic markers and mapping as well as how functional genomics and genetic transformation might be able to improve yield and saccharification rates. Finally, we review how "omics" advances are promising to speed up plant breeding and reach the unexplored potential of sugarcane in terms of sucrose and biomass production.

Metabolite Profiling and Network Analysis Reveal Coordinated Changes in Low-N Tolerant and Low-N Sensitive Maize Genotypes under Nitrogen Deficiency and Restoration Conditions

Nitrogen (N), applied in the form of a nitrogenous fertilizer, is one of the main inputs for agricultural production. Food production is closely associated with the application of N. However, the application of nitrogenous fertilizers to agricultural fields is associated with heavy production of nitrous oxide because agricultural crops can only utilize 30-40% of applied N, leaving behind unused 60-70% N in the environment. The global warming effect of this greenhouse gas is approximately 300 times more than of carbon dioxide. Under the present scenario of climate change, it is critical to maintain the natural balance between food production and environmental sustainability by targeting traits responsible for improving nitrogen-use-efficiency (NUE). Understanding of the molecular mechanisms behind the metabolic alterations due to nitrogen status needs to be addressed. Additionally, mineral nutrient deficiencies and their associated metabolic networks have not yet been studied well. Given this, the alterations in core metabolic pathways of low-N tolerant (LNT) and low-N sensitive (LNS) genotypes of maize under N-deficiency and their efficiency of recovering the changes upon resupplying N were investigated by us, using the GC-MS and LC-MS based metabolomic approach. Significant genotype-specific changes were noted in response to low-N. The N limitation affected the whole plant metabolism, most significantly the precursors of primary metabolic pathways. These precursors may act as important targets for improving the NUE. Limited availability of N reduced the levels of N-containing metabolites, organic acids and amino acids, but soluble sugars increased. Major variations were encountered in LNS, as compared to LNT. This study has revealed potential metabolic targets in response to the N status, which are indeed the prospective targets for crop improvement.

Genetic Variance of Metabolomic Features and Their Relationship With Malting Quality Traits in Spring Barley

Barley is the most common source for malt to be used in brewing beer and other alcoholic beverages. This involves converting the starch of barley into fermentable sugars a process that involves malting, that is germinating of the grains, and mashing, which is an enzymatic process. Numerous metabolic processes are involved in germination, where distinct and time-dependent alterations at the metabolite levels happen. In this study, 2,628 plots of 565 spring malting barley lines from Nordic Seed A/S were investigated. Phenotypic records were available for six malting quality (MQ) traits: filtering speed (FS), wort clearness (WCL), extract yield (EY), wort color (WCO), beta glucan (BG), and wort viscosity (WV). Each line had a set of dense genomic markers. In addition, 24,018 metabolomic features (MFs) were obtained for each sample from nuclear magnetic resonance (NMR) spectra for wort samples produced from each experimental plot. The genetic variation in the MFs was investigated using a univariate model, and the relationship between MFs and the MQ traits was studied using a bivariate model. Results showed that a total of 8,604 MFs had heritability estimates significantly larger than 0 and for all MQ traits, there were genetic correlations with up to 86.77% and phenotypic correlations with up to 90.07% of the significant heritable MFs. In conclusion, around one third of all MFs were significantly heritable, among which a considerable proportion had significant additive genetic and/or phenotypic correlations with the MQ traits (WCO, WV, and BG) in spring barley. The results from this study indicate that many of the MFs are heritable and MFs have great potential to be used in breeding barley for high MQ.

Biomass composition explains fruit relative growth rate and discriminates climacteric from non-climacteric species

Fleshy fruits are very varied, whether in terms of their composition, physiology, or rate and duration of growth. To understand the mechanisms that link metabolism to phenotypes, which would help the targeting of breeding strategies, we compared eight fleshy fruit species during development and ripening. Three herbaceous (eggplant, pepper, and cucumber), three tree (apple, peach, and clementine) and two vine (kiwifruit and grape) species were selected for their diversity. Fruit fresh weight and biomass composition, including the major soluble and insoluble components, were determined throughout fruit development and ripening. Best-fitting models of fruit weight were used to estimate relative growth rate (RGR), which was significantly correlated with several biomass components, especially protein content (R=84), stearate (R=0.72), palmitate (R=0.72), and lignocerate (R=0.68). The strong link between biomass composition and RGR was further evidenced by generalized linear models that predicted RGR with R-values exceeding 0.9. Comparison of the fruit also showed that climacteric fruit (apple, peach, kiwifruit) contained more non-cellulosic cell-wall glucose and fucose, and more starch, than non-climacteric fruit. The rate of starch net accumulation was also higher in climacteric fruit. These results suggest that the way biomass is constructed has a major influence on performance, especially growth rate.

Metabolomics analysis and metabolite-agronomic trait associations using kernels of wheat (Triticum aestivum) recombinant inbred lines

Plants produce numerous metabolites that are important for their development and growth. However, the genetic architecture of the wheat metabolome has not been well studied. Here, utilizing a high-density genetic map, we conducted a comprehensive metabolome study via widely targeted LC-MS/MS to analyze the wheat kernel metabolism. We further combined agronomic traits and dissected the genetic relationship between metabolites and agronomic traits. In total, 1260 metabolic features were detected. Using linkage analysis, 1005 metabolic quantitative trait loci (mQTLs) were found distributed unevenly across the genome. Twenty-four candidate genes were found to modulate the levels of different metabolites, of which two were functionally annotated by in vitro analysis to be involved in the synthesis and modification of flavonoids. Combining the correlation analysis of metabolite-agronomic traits with the co-localization of methylation quantitative trait locus (mQTL) and phenotypic QTL (pQTL), genetic relationships between the metabolites and agronomic traits were uncovered. For example, a candidate was identified using correlation and co-localization analysis that may manage auxin accumulation, thereby affecting number of grains per spike (NGPS). Furthermore, metabolomics data were used to predict the performance of wheat agronomic traits, with metabolites being found that provide strong predictive power for NGPS and plant height. This study used metabolomics and association analysis to better understand the genetic basis of the wheat metabolism which will ultimately assist in wheat breeding.

Can metabolic prediction be an alternative to genomic prediction in barley?

Like other crop species, barley, the fourth most important crop worldwide, suffers from the genetic bottleneck effect, where further improvements in performance through classical breeding methods become difficult. Therefore, indirect selection methods are of great interest. Here, genomic prediction (GP) based on 33,005 SNP markers and, alternatively, metabolic prediction (MP) based on 128 metabolites with sampling at two different time points in one year, were applied to predict multi-year agronomic traits in the nested association mapping (NAM) population HEB-25. We found prediction abilities of up to 0.93 for plant height with SNP markers and of up to 0.61 for flowering time with metabolites. Interestingly, prediction abilities in GP increased after reducing the number of incorporated SNP markers. The estimated effects of GP and MP were highly concordant, indicating MP as an interesting alternative to GP, being able to reflect a stable genotype-specific metabolite profile. In MP, sampling at an early developmental stage outperformed sampling at a later stage. The results confirm the value of GP for future breeding. With MP, an interesting alternative was also applied successfully. However, based on our results, usage of MP alone cannot be recommended in barley. Nevertheless, MP can assist in unravelling physiological pathways for the expression of agronomically important traits.

What is the role of putrescine accumulated under potassium deficiency?

Biomarker metabolites are of increasing interest in crops since they open avenues for precision agriculture, whereby nutritional needs and stresses can be monitored optimally. Putrescine has the potential to be a useful biomarker to reveal potassium (K⁺ ) deficiency. In fact, although this diamine has also been observed to increase during other stresses such as drought, cold or heavy metals, respective changes are comparably low. Due to its multifaceted biochemical properties, several roles for putrescine under K⁺ deficiency have been suggested, such as cation balance, antioxidant, reactive oxygen species mediated signalling, osmolyte or pH regulator. However, the specific association of putrescine build-up with low K⁺ availability in plants remains poorly understood, and possible regulatory roles must be consistent with putrescine concentration found in plant tissues. We hypothesize that the massive increase of putrescine upon K⁺ starvation plays an adaptive role. A distinction of putrescine function from that of other polyamines (spermine, spermidine) may be based either on its specificity or (which is probably more relevant under K⁺ deficiency) on a very high attainable concentration of putrescine, which far exceeds those for spermidine and spermine. putrescine and its catabolites appear to possess a strong potential in controlling cellular K⁺ and Ca²⁺ , and mitochondria and chloroplasts bioenergetics under K⁺ stress.