Metabolite and transcript markers for the prediction of potato drought tolerance

Whole-genome sequencing of tetraploid potato varieties reveals different strategies for drought tolerance

Climate changes leading to increasingly longer seasonal drought periods in large parts of the world increase the necessity for breeding drought-tolerant crops. Cultivated potato (Solanum tuberosum), the third most important vegetable crop worldwide, is regarded as drought-sensitive due to its shallow root architecture. Two German tetraploid potato cultivars differing in drought tolerance and their F1-progeny were evaluated under various drought scenarios. Bulked segregant analyses were combined with whole-genome sequencing (BSA-Seq) using contrasting bulks of drought-tolerant and drought-sensitive F1-clones. Applying QTLseqr, 15 QTLs comprising 588,983 single nucleotide polymorphisms (SNPs) in 2325 genes associated with drought stress tolerance were identified. SeqSNP analyses in an association panel of 34 mostly starch potato varieties using 1-8 SNPs for each of 188 selected genes narrowed the number of candidate genes down to 10. In addition, ent-kaurene synthase B was the only gene present under QTL 10. Eight of the identified genes (StABP1, StBRI1, StKS, StLEA, StPKSP1, StPKSP2, StYAB5, and StZOG1) address plant development, the other three genes (StFATA, StHGD and StSYP) contribute to plant protection under drought stress. Allelic variation in these genes might be explored in future breeding for drought-tolerant potato varieties.

Data-driven approaches to improve water-use efficiency and drought resistance in crop plants

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

Abiotic Stress in Crop Production

The vast majority of agricultural land undergoes abiotic stress that can significantly reduce agricultural yields. Understanding the mechanisms of plant defenses against stresses and putting this knowledge into practice is, therefore, an integral part of sustainable agriculture. In this review, we focus on current findings in plant resistance to four cardinal abiotic stressors—drought, heat, salinity, and low temperatures. Apart from the description of the newly discovered mechanisms of signaling and resistance to abiotic stress, this review also focuses on the importance of primary and secondary metabolites, including carbohydrates, amino acids, phenolics, and phytohormones. A meta-analysis of transcriptomic studies concerning the model plant Arabidopsis demonstrates the long-observed phenomenon that abiotic stressors induce different signals and effects at the level of gene expression, but genes whose regulation is similar under most stressors can still be traced. The analysis further reveals the transcriptional modulation of Golgi-targeted proteins in response to heat stress. Our analysis also highlights several genes that are similarly regulated under all stress conditions. These genes support the central role of phytohormones in the abiotic stress response, and the importance of some of these in plant resistance has not yet been studied. Finally, this review provides information about the response to abiotic stress in major European crop plants—wheat, sugar beet, maize, potatoes, barley, sunflowers, grapes, rapeseed, tomatoes, and apples.

Potential of desiccation-tolerant plant growth-promoting rhizobacteria in growth augmentation of wheat (Triticum aestivum L.) under drought stress

Wheat (Triticum aestivum L.) yield and physiology are adversely affected due to limited water availability. However, desiccation-tolerant plant growth-promoting rhizobacteria (DT-PGPR) are potential candidates that can overcome the negative impacts of water stress. In the present study, a total of 164 rhizobacterial isolates were screened for desiccation tolerance up to -0.73 MPa osmotic pressure, of which five isolates exhibited growth and expression of plant growth properties under the influence of desiccation stress of -0.73 MPa. These five isolates were identified as Enterobacter cloacae BHUAS1, Bacillus cereus BHUAS2, Bacillus megaterium BHUIESDAS3, Bacillus megaterium BHUIESDAS4, and Bacillus megaterium BHUIESDAS5. All five isolates exhibited plant growth-promoting properties and production of exopolysaccharide (EPS) under the impact of desiccation stress. Furthermore, a pot experiment on wheat (variety HUW-234) inoculated with the isolates Enterobacter cloacae BHUAS1, Bacillus cereus BHUAS2, and Bacillus megaterium BHUIESDAS3 exhibited a positive influence on the growth of wheat under the condition of water stress. A significant improvement in plant height, root length, biomass, chlorophyll and carotenoid content, membrane stability index (MSI), leaf relative water content (RWC), total soluble sugar, total phenol, proline, and total soluble protein, were recorded under limited water-induced drought stress in treated plants as compared with non-treated plants. Moreover, plants treated with Enterobacter cloacae BHUAS1, Bacillus cereus BHUAS2, and Bacillus megaterium BHUIESDAS3 depicted improvement in enzymatic activities of several antioxidant enzymes such as guaiacol peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX). Beside this significant decrease in electrolyte leakage, H₂O₂ and malondialdehyde (MDA) contents were also recorded in treated plants. From the results obtained, it is evident that E. cloacae BHUAS1, B. megaterium BHUIESDAS3, and B. cereus BHUAS2 are the potential DT-PGPR having the capability to sustain growth and yield, alleviating the deleterious effect of water stress in wheat.

Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species

In Populus, drought is a major problem affecting plant growth and development which can be closely reflected by corresponding transcriptomic changes. Nevertheless, how these changes in Populus are not fully understood. Here, we first used meta-analysis and machine learning methods to identify water stress-responsive genes and then performed a systematic approach to discover important gene networks. Our analysis revealed that large transcriptional variations occur during drought stress. These changes were more associated with the response to stress, cellular catabolic process, metabolic pathways, and hormone-related genes. The differential gene coexpression analysis highlighted two acetyltransferase NATA1-like and putative cytochrome P450 genes that have a special contribution in response to drought stress. In particular, the findings showed that MYBs and MAPKs have a prominent role in the drought stress response that could be considered to improve the drought tolerance of Populus. We also suggest ARF2-like and PYL4-like genes as potential markers for use in breeding programs. This study provides a better understanding of how Populus responses to drought that could be useful for improving tolerance to stress in Populus.

Continuous monitoring of chemical signals in plants under stress

Time is an often-neglected variable in biological research. Plants respond to biotic and abiotic stressors with a range of chemical signals, but as plants are non-equilibrium systems, single-point measurements often cannot provide sufficient temporal resolution to capture these time-dependent signals. In this article, we critically review the advances in continuous monitoring of chemical signals in living plants under stress. We discuss methods for sustained measurement of the most important chemical species, including ions, organic molecules, inorganic molecules and radicals. We examine analytical and modelling approaches currently used to identify and predict stress in plants. We also explore how the methods discussed can be used for applications beyond a research laboratory, in agricultural settings. Finally, we present the current challenges and future perspectives for the continuous monitoring of chemical signals in plants.

Transcriptome Analysis of Two Tetraploid Potato Varieties under Water-Stress Conditions

Potato (Solanum tuberosum L.) is one of the most important crops worldwide, but due to its sensitivity to drought, its production can be affected by water availability. In this study, the varieties Agria and Zorba were used to determine the expression differences between control and water-stressed plants. For this purpose, they were sequenced by RNAseq, obtaining around 50 million transcripts for each variety and treatment. When comparing the significant transcripts obtained from control and drought-stressed plants of the Agria variety, we detected 931 genes that were upregulated and 2077 genes that were downregulated under stress conditions. When both treatments were compared in Zorba plants, 735 genes were found to be upregulated and 923 genes were found to be downregulated. Significantly more DEGs were found in the Agria variety, indicating a good stress response of this variety. "Abscisic acid and environmental stress-inducible protein TAS14-like" was the most overexpressed gene under drought conditions in both varieties, but expression differences were also found in numerous transcription factors and heat shock proteins. The principal GO term found was "cellular components", more specifically related to the cell membrane and the cell wall, but other metabolic pathways such as carbohydrate metabolism and osmotic adjustment were also identified. These results provide valuable information related to the molecular mechanisms of tolerance to water stress in order to establish the basis for breeding new, more tolerant varieties.

Comparative physiological, transcriptomic, and WGCNA analyses reveal the key genes and regulatory pathways associated with drought tolerance in Tartary buckwheat

Drought stress is one of the major abiotic stress factors that affect plant growth and crop productivity. Tartary buckwheat is a nutritionally balanced and flavonoid-rich pseudocereal crop and also has strong adaptability to different adverse environments including drought. However, little is known about its drought tolerance mechanism. In this study, we performed comparative physiological and transcriptomic analyses of two contrasting drought-resistant Tartary buckwheat genotypes under nature drought treatment in the reproductive stage. Under drought stress, the drought-tolerant genotype XZSN had significantly higher contents of relative water, proline, and soluble sugar, as well as lower relative electrolyte leakage in the leaves than the drought-susceptible LK3. A total of 5,058 (2,165 upregulated and 2,893 downregulated) and 5,182 (2,358 upregulated and 2,824 downregulated) potential drought-responsive genes were identified in XZSN and LK3 by transcriptome sequencing analysis, respectively. Among the potential drought-responsive genes of XZSN, 1,206 and 1,274 genes were identified to be potential positive and negative contributors for XZSN having higher drought resistance ability than LK3. Furthermore, 851 out of 1,206 positive drought-resistant genes were further identified to be the core drought-resistant genes of XZSN based on WGCNA analysis, and most of them were induced earlier and quicker by drought stress than those in LK3. Functional annotation of the 851 core drought-resistant genes found that a large number of stress-responsive genes were involved in TFs, abscisic acid (ABA) biosynthesis, signal transduction and response, non-ABA signal molecule biosynthesis, water holding, oxygen species scavenging, osmotic adjustment, cell damage prevention, and so on. Transcriptional regulatory network analyses identified the potential regulators of these drought-resistant functional genes and found that the HD-ZIP and MYB TFs might be the key downstream TFs of drought resistance in Tartary buckwheat. Taken together, these results indicated that the XZSN genotype was more drought-tolerant than the LK3 genotype as evidenced by triggering the rapid and dramatic transcriptional reprogramming of drought-resistant genes to reduce water loss, prevent cell damage, and so on. This research expands our current understanding of the drought tolerance mechanisms of Tartary buckwheat and provides important information for its further drought resistance research and variety breeding.

Construction of drought stress regulation networks in potato based on SMRT and RNA sequencing data

BACKGROUND

Potato (Solanum tuberosum) is the fourth most important food crop in the world and plays an important role in food security. Drought stress has a significantly negative impact on potato growth and production. There are several publications involved drought stress in potato, this research contributes to enrich the knowledge.

RESULTS

In this study, next-generation sequencing (NGS) and single-molecule real-time (SMRT) sequencing technology were used to study the transcription profiles in potato in response to 20%PEG6000 simulates drought stress. The leaves of the variety "Désirée" from in vitro plantlets after drought stress at six time points from 0 to 48 hours were used to perform NGS and SMRT sequencing. According to the sequencing data, a total of 12,798 differentially expressed genes (DEGs) were identified in six time points. The real-time (RT)-PCR results are significantly correlated with the sequencing data, confirming the accuracy of the sequencing data. Gene ontology and KEGG analysis show that these DEGs participate in response to drought stress through galactose metabolism, fatty acid metabolism, plant-pathogen interaction, glutathione metabolism and other pathways. Through the analysis of alternative splicing of 66,888 transcripts, the functional pathways of these transcripts were enriched, and 51,098 transcripts were newly discovered from alternative splicing events and 47,994 transcripts were functionally annotated. Moreover, 3445 lncRNAs were predicted and enrichment analysis of corresponding target genes was also performed. Additionally, Alternative polyadenylation was analyzed by TADIS, and 26,153 poly (A) sites from 13,010 genes were detected in the Iso-Seq data.

CONCLUSION

Our research greatly enhanced potato drought-induced gene annotations and provides transcriptome-wide insights into the molecular basis of potato drought resistance.

Emerging Roles of Plant DNA-Binding With One Finger Transcription Factors in Various Hormone and Stress Signaling Pathways

Coordinated transcriptional regulation of stress-responsive genes orchestrated by a complex network of transcription factors (TFs) and the reprogramming of metabolism ensure a plant's continued growth and survival under adverse environmental conditions (e.g., abiotic stress). DNA-binding with one finger (Dof) proteins, a group of plant-specific TF, were identified as one of several key components of the transcriptional regulatory network involved in abiotic stress responses. In many plant species, Dofs are often activated in response to a wide range of adverse environmental conditions. Dofs play central roles in stress tolerance by regulating the expression of stress-responsive genes via the DOFCORE element or by interacting with other regulatory proteins. Moreover, Dofs act as a key regulatory hub of several phytohormone pathways, integrating abscisic acid, jasmonate, SA and redox signaling in response to many abiotic stresses. Taken together, we highlight a unique role of Dofs in hormone and stress signaling that integrates plant response to adverse environmental conditions with different aspects of plant growth and development.

1H-NMR-Based Metabolomic Study of Potato Cultivars, Markies and Fianna, Exposed to Different Water Regimes

This study investigated the effects of varying soil moisture conditions (through either flooding, drought, or provision of a moderate water supply) on the metabolomic profile of two potato cultivars, namely, Markies and Fianna. Representative tubers of the treated plants were collected 91 days after planting. The samples were freeze-dried, and ground to a fine powder in liquid nitrogen. The fine powder of the tuber samples was analyzed by nuclear magnetic resonance spectroscopy (NMR) to identify their metabolomic profiles. The NMR data was analyzed using principal component analysis and orthogonal partial least square-discriminant analysis to identify any variations between the treatments. In both models, plants exposed to drought clearly separated from the plants that received either excess or moderate water (control). The potato tubers that experienced drought and flood treatments had the highest quantities of aspartic acid, asparagine, and isoleucine. Furthermore, the potatoes exposed to either drought or flood had higher levels of valine and leucine (which are essential for plant defense and resistance against plant pathogens). Potato plants can respond metabolically to varying soil moisture stress.

Classification of Potato Varieties Drought Stress Tolerance Using Supervised Learning

The presented study was aimed at investigating the variability for drought tolerance among potato cultivars. To achieve this, the stability of drought tolerance of potato cultivars under different water regime conditions was inspected during 11 years of consecutive experiments. The data on 50 potato cultivars’ responses to drought stress, based on the morphological features of plants, i.e., leaf and stem mass and size of the assimilation area, have been collected. The tuber yield, as well as calculated plant tolerance indexes and Climatic Water Balance for each growing season, were analyzed. The studied cultivars were later assigned into one of three tolerance groups for soil drought. The highest linear relationship was found between the mass of leaves and stems and the tuber yield but was found too weak to raise any conclusions. Thus, the ensemble learning models have been evaluated and returned better performance results, and the final classifier is the implementation of extreme gradient boosting. The final classifier of the 96.7% accuracy, which used several measured potato parameters (Relative yield decrease, Stem mass, Maturity, Assimilation area, Leaves mass, Yield per plant, calculated Climatic water balance, and indices: MSTI and DSI) that could distinguish the different tolerance groups were evaluated in the study.

Differential Physio-Biochemical and Metabolic Responses of Peanut (Arachis hypogaea L.) under Multiple Abiotic Stress Conditions

The frequency and severity of extreme climatic conditions such as drought, salinity, cold, and heat are increasing due to climate change. Moreover, in the field, plants are affected by multiple abiotic stresses simultaneously or sequentially. Thus, it is imperative to compare the effects of stress combinations on crop plants relative to individual stresses. This study investigated the differential regulation of physio-biochemical and metabolomics parameters in peanut (Arachis hypogaea L.) under individual (salt, drought, cold, and heat) and combined stress treatments using multivariate correlation analysis. The results showed that combined heat, salt, and drought stress compounds the stress effect of individual stresses. Combined stresses that included heat had the highest electrolyte leakage and lowest relative water content. Lipid peroxidation and chlorophyll contents did not significantly change under combined stresses. Biochemical parameters, such as free amino acids, polyphenol, starch, and sugars, significantly changed under combined stresses compared to individual stresses. Free amino acids increased under combined stresses that included heat; starch, sugars, and polyphenols increased under combined stresses that included drought; proline concentration increased under combined stresses that included salt. Metabolomics data that were obtained under different individual and combined stresses can be used to identify molecular phenotypes that are involved in the acclimation response of plants under changing abiotic stress conditions. Peanut metabolomics identified 160 metabolites, including amino acids, sugars, sugar alcohols, organic acids, fatty acids, sugar acids, and other organic compounds. Pathway enrichment analysis revealed that abiotic stresses significantly affected amino acid, amino sugar, and sugar metabolism. The stress treatments affected the metabolites that were associated with the tricarboxylic acid (TCA) and urea cycles and associated amino acid biosynthesis pathway intermediates. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and heatmap analysis identified potential marker metabolites (pinitol, malic acid, and xylopyranose) that were associated with abiotic stress combinations, which could be used in breeding efforts to develop peanut cultivars that are resilient to climate change. The study will also facilitate researchers to explore different stress indicators to identify resistant cultivars for future crop improvement programs.

Genotype determines Arbutus unedo L. physiological and metabolomic responses to drought and recovery

Strawberry tree (Arbutus unedo) is a small resilient species with a circum-Mediterranean distribution, high ecological relevance in southern European forests and with several economical applications. As most orchards are usually installed on marginal lands where plants usually face severe drought, selecting plants that can better cope with water restriction is critical, and a better understanding of the tolerance mechanisms is required. Strawberry tree plants under drought follow a typical isohydric strategy, by limiting transpiration through stomata closure. However, the contribution of genotype and its bio-geographic origin on plant performance needs clarification, as well as the involvement of a specific metabolic reactions associated with the mechanical response. To test this hypothesis, several eco-physiological and biochemical parameters were assessed on different genotypes, and the metabolic profiles studied, including important stress-related phytohormones, on plants under different water regimes (plants watered to 70% and 18% field capacity) and a recovery assay. A contrasting drought tolerance was found in plants from different genotypes, associated with physiological and metabolic responses. Metabolomics revealed more than 500 metabolic features were differentially accumulated, including abscisic and salicylic acids, for the genotype with better performance under drought (A4). This genotype also recovered faster when the imposed stress was interrupted, thus indicating the relevance of metabolic adaptation under water deficit conditions. By correlating carbon assimilation with identified metabolites, some proved to be satisfactory predictors of plant performance under drought and might be used for marker assisted breeding. Therefore, our study proves the importance of genotype as a major selection criterion of resistant plants to drought and provides empirical knowledge of the metabolic response involved. We also hypothesized the involvement of phenolics on response mechanisms under drought, which is worth to be explored to shed light on the metabolic pathways involved in plant response to water stress.

Transcriptome-Based Identification and Functional Characterization of NAC Transcription Factors Responsive to Drought Stress in Capsicum annuum L

Capsicum annuum L. is one of the most cultivated Solanaceae species, and in the open field, water limitation leading to drought stress affects its fruit quality, fruit setting, fruit size and ultimately yield. We identified stage-specific and a common core set of differentially expressed genes, following RNA-seq transcriptome analyses of a breeding line subjected to acute drought stress followed by recovery (rewatering), at three stages of plant development. Among them, two NAC transcription factor (TF) genes, i.e., CaNAC072 and CaNAC104, were always upregulated after drought stress and downregulated after recovery. The two TF proteins were observed to be localized in the nucleus following their transient expression in Nicotiana benthamiana leaves. The expression of the two NACs was also induced by NaCl, polyethylene glycol (PEG) and abscisic acid (ABA) treatments, suggesting that CaNAC072 is an early, while CaNAC104 is a late abiotic stress-responsive gene. Virus-induced gene silencing (VIGS) of CaNAC104 did not affect the pepper plantlet’s tolerance to drought stress, while VIGS of CaNAC072 increased drought tolerance. Heterologous expression of CaNAC072 in Arabidopsis thaliana as well as in plants mutated for its homolog ANAC072 did not increase drought stress tolerance. This highlights a different role of the two NAC homologs in the two species. Here, we discuss the complex role of NACs as transcriptional switches in the response to drought stress in bell pepper.

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.

Natural variation in wild tomato trichomes; selecting metabolites that contribute to insect resistance using a random forest approach

BACKGROUND

Plant-produced specialised metabolites are a powerful part of a plant's first line of defence against herbivorous insects, bacteria and fungi. Wild ancestors of present-day cultivated tomato produce a plethora of acylsugars in their type-I/IV trichomes and volatiles in their type-VI trichomes that have a potential role in plant resistance against insects. However, metabolic profiles are often complex mixtures making identification of the functionally interesting metabolites challenging. Here, we aimed to identify specialised metabolites from a wide range of wild tomato genotypes that could explain resistance to vector insects whitefly (Bemisia tabaci) and Western flower thrips (Frankliniella occidentalis). We evaluated plant resistance, determined trichome density and obtained metabolite profiles of the glandular trichomes by LC-MS (acylsugars) and GC-MS (volatiles). Using a customised Random Forest learning algorithm, we determined the contribution of specific specialised metabolites to the resistance phenotypes observed.

RESULTS

The selected wild tomato accessions showed different levels of resistance to both whiteflies and thrips. Accessions resistant to one insect can be susceptible to another. Glandular trichome density is not necessarily a good predictor for plant resistance although the density of type-I/IV trichomes, related to the production of acylsugars, appears to correlate with whitefly resistance. For type VI-trichomes, however, it seems resistance is determined by the specific content of the glands. There is a strong qualitative and quantitative variation in the metabolite profiles between different accessions, even when they are from the same species. Out of 76 acylsugars found, the random forest algorithm linked two acylsugars (S3:15 and S3:21) to whitefly resistance, but none to thrips resistance. Out of 86 volatiles detected, the sesquiterpene α-humulene was linked to whitefly susceptible accessions instead. The algorithm did not link any specific metabolite to resistance against thrips, but monoterpenes α-phellandrene, α-terpinene and β-phellandrene/D-limonene were significantly associated with susceptible tomato accessions.

CONCLUSIONS

Whiteflies and thrips are distinctly targeted by certain specialised metabolites found in wild tomatoes. The machine learning approach presented helped to identify features with efficacy toward the insect species studied. These acylsugar metabolites can be targets for breeding efforts towards the selection of insect-resistant cultivars.

Genome-Wide Approach to Identify Quantitative Trait Loci for Drought Tolerance in Tetraploid Potato (Solanum tuberosum L.)

Drought represents a major abiotic stress factor negatively affecting growth, yield and tuber quality of potatoes. Quantitative trait locus (QTL) analyses were performed in cultivated potatoes for drought tolerance index DRYM (deviation of relative starch yield from the experimental median), tuber starch content, tuber starch yield, tuber fresh weight, selected transcripts and metabolites under control and drought stress conditions. Eight genomic regions of major interest for drought tolerance were identified, three representing standalone DRYM QTL. Candidate genes, e.g., from signaling pathways for ethylene, abscisic acid and brassinosteroids, and genes encoding cell wall remodeling enzymes were identified within DRYM QTL. Co-localizations of DRYM QTL and QTL for tuber starch content, tuber starch yield and tuber fresh weight with underlying genes of the carbohydrate metabolism were observed. Overlaps of DRYM QTL with metabolite QTL for ribitol or galactinol may indicate trade-offs between starch and compatible solute biosynthesis. Expression QTL confirmed the drought stress relevance of selected transcripts by overlaps with DRYM QTL. Bulked segregant analyses combined with next-generation sequencing (BSAseq) were used to identify mutations in genes under the DRYM QTL on linkage group 3. Future analyses of identified genes for drought tolerance will give a better insight into drought tolerance in potatoes.

Unravelling Differences in Candidate Genes for Drought Tolerance in Potato (Solanum tuberosum L.) by Use of New Functional Microsatellite Markers

Potato is regarded as drought sensitive and most vulnerable to climate changes. Its cultivation in drought prone regions or under conditions of more frequent drought periods, especially in subtropical areas, requires intensive research to improve drought tolerance in order to guarantee high yields under limited water supplies. A candidate gene approach was used to develop functional simple sequence repeat (SSR) markers for association studies in potato with the aim to enhance breeding for drought tolerance. SSR primer combinations, mostly surrounding interrupted complex and compound repeats, were derived from 103 candidate genes for drought tolerance. Validation of the SSRs was performed in an association panel representing 34 mainly starch potato cultivars. Seventy-five out of 154 SSR primer combinations (49%) resulted in polymorphic, highly reproducible banding patterns with polymorphic information content (PIC) values between 0.11 and 0.90. Five SSR markers identified allelic differences between the potato cultivars that showed significant associations with drought sensitivity. In all cases, the group of drought-sensitive cultivars showed predominantly an additional allele, indicating that selection against these alleles by marker-assisted breeding might confer drought tolerance. Further studies of these differences in the candidate genes will elucidate their role for an improved performance of potatoes under water-limited conditions.

A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications

Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.

Machine learning in plant science and plant breeding

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

Plant growth promoting rhizobacteria alleviates drought stress in potato in response to suppressive oxidative stress and antioxidant enzymes activities

Maintenance of plant physiological functions under drought stress is normally considered a positive feature as it indicates sustained plant health and growth. This study was conducted to investigate whether plant growth-promoting rhizobacteria (PGPR) Bacillus subtilis HAS31 has potential to maintain potato growth and yield under drought stress. We analyzed trends of chlorophyll concentration, photosynthesis process, relative water content, osmolytes, antioxidants enzymes and oxidative stress, relative growth rate, tuber and aboveground biomass production in two potato varieties, Santae (drought-tolerant) and PRI-Red (drought-sensitive). Plants of both genotypes were treated with 100 g of HAS31 inoculant at 10 days after germination and exposed to different soil relative water contents (SRWC), including 80 ± 5% (well watered), 60 ± 5% (moderate stress) and 40 ± 5% SRWC (severe stress) for 7 days at tuber initiation stage (30 days after germination). The drought stress reduced plant relative growth rate, biomass production, leaf area, number of leaves and tubers, tuber weight, and final yield. The drought-stressed plants showed decline in chlorophyll contents, membrane stability, leaf relative water contents and photosynthetic rate. Under drought stress, enzymatic activity of catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD), contents of total soluble sugars, soluble proteins and proline increased. The application of PGPR reduced the impact of drought and maintained higher growth and physio-chemical traits of the plants. The plants with PGPR application showed higher relative growth rate, dry matter production, leaf area, number of tubers, tuber weight and yield as compared to plants without PGPR. The PGPR-HAS31 treated plants maintained higher photosynthetic process, contents of chlorophyll, soluble proteins, total soluble sugars, and enzymatic activities of CAT, POD and SOD as compared to plants without PGPR. The results of the study suggest that plant growth regulators have ability to sustain growth and yield of potato under drought stress by maintaining physiological functions of the plants.

Drought-Induced Regulatory Cascades and Their Effects on the Nutritional Quality of Developing Potato Tubers

Competition for scarce water resources and the continued effects of global warming exacerbate current constraints on potato crop production. While plants’ response to drought in above-ground tissues has been well documented, the regulatory cascades and subsequent nutritive changes in developing tubers have been largely unexplored. Using the commercial Canadian cultivar “Vigor”, plants were subjected to a gradual drought treatment under high tunnels causing a 4 °C increase in the canopy temperature. Tubers were sampled for RNAseq and metabolite analysis. Approximately 2600 genes and 3898 transcripts were differentially expressed by at least 4-fold in drought-stressed potato tubers, with 75% and 69% being down-regulated, respectively. A further 229 small RNAs were implicated in gene regulation during drought. Expression of several small RNA clusters negatively correlated with expression of their six target patatin genes, suggesting involvement in the regulation of storage proteins during drought. The comparison of protein homologues between Solanum tuberosum L. and Arabidopsis thaliana L. indicated that down-regulated genes were associated with phenylpropanoid and carotenoid biosynthesis. As is indicative of reduced flow through the phenylpropanoid pathway, phenylalanine accumulated in drought-stressed tubers. This suggests that there may be nutritive implications to drought stress occurring during the potato tuber bulking phase in sensitive cultivars.

Machine learning: A powerful tool for gene function prediction in plants

Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule-based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning-based approaches in plants.

Linking crop traits to transcriptome differences in a progeny population of tetraploid potato

BACKGROUND

Potato is the third most consumed crop in the world. Breeding for traits such as yield, product quality and pathogen resistance are main priorities. Identifying molecular signatures of these and other important traits is important in future breeding efforts. In this study, a progeny population from a cross between a breeding line, SW93-1015, and a cultivar, Désirée, was studied by trait analysis and RNA-seq in order to develop understanding of segregating traits at the molecular level and identify transcripts with expressional correlation to these traits. Transcript markers with predictive value for field performance applicable under controlled environments would be of great value for plant breeding.

RESULTS

A total of 34 progeny lines from SW93-1015 and Désirée were phenotyped for 17 different traits in a field in Nordic climate conditions and controlled climate settings. A master transcriptome was constructed with all 34 progeny lines and the parents through a de novo assembly of RNA-seq reads. Gene expression data obtained in a controlled environment from the 34 lines was correlated to traits by different similarity indices, including Pearson and Spearman, as well as DUO, which calculates the co-occurrence between high and low values for gene expression and trait. Our study linked transcripts to traits such as yield, growth rate, high laying tubers, late and tuber blight, tuber greening and early flowering. We found several transcripts associated to late blight resistance and transcripts encoding receptors were associated to Dickeya solani susceptibility. Transcript levels of a UBX-domain protein was negatively associated to yield and a GLABRA2 expression modulator was negatively associated to growth rate.

CONCLUSION

In our study, we identify 100's of transcripts, putatively linked based on expression with 17 traits of potato, representing both well-known and novel associations. This approach can be used to link the transcriptome to traits. We explore the possibility of associating the level of transcript expression from controlled, optimal environments to traits in a progeny population with different methods introducing the application of DUO for the first time on transcriptome data. We verify the expression pattern for five of the putative transcript markers in another progeny population.

Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops

Roots, tubers, and bananas (RTB) are vital staples for food security in the world's poorest nations. A major constraint to current RTB breeding programmes is limited knowledge on the available diversity due to lack of efficient germplasm characterization and structure. In recent years large-scale efforts have begun to elucidate the genetic and phenotypic diversity of germplasm collections and populations and, yet, biochemical measurements have often been overlooked despite metabolite composition being directly associated with agronomic and consumer traits. Here we present a compound database and concentration range for metabolites detected in the major RTB crops: banana (Musa spp.), cassava (Manihot esculenta), potato (Solanum tuberosum), sweet potato (Ipomoea batatas), and yam (Dioscorea spp.), following metabolomics-based diversity screening of global collections held within the CGIAR institutes. The dataset including 711 chemical features provides a valuable resource regarding the comparative biochemical composition of each RTB crop and highlights the potential diversity available for incorporation into crop improvement programmes. Particularly, the tropical crops cassava, sweet potato and banana displayed more complex compositional metabolite profiles with representations of up to 22 chemical classes (unknowns excluded) than that of potato, for which only metabolites from 10 chemical classes were detected. Additionally, over 20% of biochemical signatures remained unidentified for every crop analyzed. Integration of metabolomics with the on-going genomic and phenotypic studies will enhance 'omics-wide associations of molecular signatures with agronomic and consumer traits via easily quantifiable biochemical markers to aid gene discovery and functional characterization.

Aroma and quality of breads baked from old and modern wheat varieties and their prediction from genomic and flour-based metabolite profiles

Bread aroma is the principal characteristic perceived by the consumer yet it is mostly disregarded in the product chain. The main aim of this study was to evaluate the potential to include bread aroma as a new target criterion into the wheat product chain. The objectives of our study were to (i) quantify the influence of genetic versus environmental factors on the bread aroma and quality characteristics, (ii) evaluate whether bread baked from modern wheat varieties differ in terms of aroma from those baked from old varieties, and (iii) compare genomic and metabolomic approaches for their efficiency to predict bread aroma and quality characteristics in a wheat breeding program. Agronomic characters as well as bread aroma and quality traits were assessed for 18 old and 22 modern winter wheat varieties evaluated at up to three locations in Germany. Metabolite profiles of all 120 flour samples were collected using a 7200 GC-QTOF. Considerable differences in the adjusted entry means for all examined bread aroma and quality characters were observed. For aroma, which was rated on a scale from 1 to 9, the adjusted entry means varied for the 40 wheat varieties between 3 and 8. In contrast, the aroma of bread prepared from old and modern wheat varieties did not differ significantly (P < 0.05). Bread aroma was not significantly (P < 0.05) correlated with grain yield, which suggested that it is possible to select for the former character in wheat breeding programs without reducing the gain of selection for the latter. Finally, we have shown that bread aroma can be better predicted using a combination of metabolite and SNP genotyping profiles instead of the SNP genotyping profile only. In conclusion, we have illustrated possibilities to increase the quality of wheat for consumers in the product chain.

A potato RING-finger protein gene StRFP2 is involved in drought tolerance

The ubiquitin/26S proteasome pathway is widely related to plant growth and metabolism and response to treatment by specifically degrading ubiquitin-modified proteins, including RING-finger-type E3 ubiquitin ligase (RING). The RING finger protein (RFP) gene family, determining the specificity of the ubiquitination process, is numerous and complex in function. In this study, we constructed a pCEGFP-StRFP2 fusion protein expression vector and transformed it into tobacco to achieve transient expression, thereby confirming that StRFP2 is localized in the cell membrane and cytoplasm. The result of qRT-PCR analysis showed that StRFP2 gene was significantly expressed in potato leaves, and the expression level of StRFP2 was significantly up-regulated under drought treatment. The transgenic plants of overexpressing StRFP2 gene were obtained with Agrobacterium tumefaciens-mediated transformation. Plant height, stem diameter, root length, fresh weight and root-shoot ratio of transgenic plants were significantly higher than those of non-transgenic plants (WT), indicating that the growth of plants was significantly promoted after overexpression of StRFP2 gene. Under PEG osmotic stress, the expressional level of StRFP2 in transgenic potato plants was significantly higher than that of WT. Furthermore, the free proline content and CAT activity in transgenic plants were higher than WT, on the contrary, MDA was lower than WT, and transgenic plants have stronger water retention capacity under simulated drought stress treatment, which indicated that StRFP2 could strengthen the tolerance of plants responding to drought stress. The above evidence strongly suggested that the StRFP2 gene is obviously up-regulated expression by drought stress, thereby enhancing the drought tolerance of the potato.

Physiological, Biochemical, and Transcriptional Responses to Single and Combined Abiotic Stress in Stress-Tolerant and Stress-Sensitive Potato Genotypes

Potato production is often constrained by abiotic stresses such as drought and high temperatures which are often present in combination. In the present work, we aimed to identify key mechanisms and processes underlying single and combined abiotic stress tolerance by comparative analysis of tolerant and susceptible cultivars. Physiological data indicated that the cultivars Desiree and Unica were stress tolerant while Agria and Russett Burbank were stress susceptible. Abiotic stress caused a greater reduction of photosynthetic carbon assimilation in the susceptible cultivars which was associated with a lower leaf transpiration rate. Oxidative stress, as estimated by the accumulation of malondialdehyde was not induced by stress treatments in any of the genotypes with the exception of drought stress in Russett Burbank. Stress treatment resulted in increases in ascorbate peroxidase activity in all cultivars except Agria which increased catalase activity in response to stress. Transcript profiling highlighted a decrease in the abundance of transcripts encoding proteins associated with PSII light harvesting complex in stress tolerant cultivars. Furthermore, stress tolerant cultivars accumulated fewer transcripts encoding a type-1 metacaspase implicated in programmed cell death. Stress tolerant cultivars exhibited stronger expression of genes associated with plant growth and development, hormone metabolism and primary and secondary metabolism than stress susceptible cultivars. Metabolite profiling revealed accumulation of proline in all genotypes following drought stress that was partially suppressed in combined heat and drought. On the contrary, the sugar alcohols inositol and mannitol were strongly accumulated under heat and combined heat and drought stress while galactinol was most strongly accumulated under drought. Combined heat and drought also resulted in the accumulation of Valine, isoleucine, and lysine in all genotypes. These data indicate that single and multiple abiotic stress tolerance in potato is associated with a maintenance of CO₂ assimilation and protection of PSII by a reduction of light harvesting capacity. The data further suggests that stress tolerant cultivars suppress cell death and maintain growth and development via fine tuning of hormone signaling, and primary and secondary metabolism. This study highlights potential targets for the development of stress tolerant potato cultivars.

Can Metabolite- and Transcript-Based Selection for Drought Tolerance in Solanum tuberosum Replace Selection on Yield in Arid Environments?

Climate models predict an increased likelihood of drought, demanding efficient selection for drought tolerance to maintain yield stability. Classic tolerance breeding relies on selection for yield in arid environments, which depends on yield trials and takes decades. Breeding could be accelerated by marker-assisted selection (MAS). As an alternative to genomic markers, transcript and metabolite markers have been suggested for important crops but also for orphan corps. For potato, we suggested a random-forest-based model that predicts tolerance from leaf metabolite and transcript levels with a precision of more than 90% independent of the agro-environment. To find out how the model based selection compares to yield-based selection in arid environments, we applied this approach to a population of 200 tetraploid Solanum tuberosum ssp. tuberosum lines segregating for drought tolerance. Twenty-four lines were selected into a phenotypic subpopulation (PP_t) for superior tolerance based on relative tuber starch yield data from three drought stress trials. Two subpopulations with superior (MP_t) and inferior (MP_s) tolerance were selected based on drought tolerance predictions based on leaf metabolite and transcript levels from two sites. The 60 selected lines were phenotyped for yield and drought tolerance in 10 multi-environment drought stress trials representing typical Central European drought scenarios. Neither selection affected development or yield potential. Lines with superior drought tolerance and high yields under stress were over-represented in both populations selected for superior tolerance, with a higher number in PP_t compared to MP_t. However, selection based on leaf metabolites may still be an alternative to yield-based selection in arid environments as it works on leaves sampled in breeder's fields independent of drought trials. As the selection against low tolerance was ineffective, the method is best used in combination with tools that select against sensitive genotypes. Thus, metabolic and transcript marker-based selection for drought tolerance is a viable alternative to the selection on yield in arid environments.

Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice

Metabolite profiles obtained from parental seedlings can predict yield heterosis of rice hybrids with correctly dissected population structure across different growth conditions.

Improvement of the breeding efficiencies of heterotic crops adaptive to different conditions can mitigate the food shortage crisis due to overpopulation and climate change. To date, diverse molecular markers have been used to guide field phenotypic selection, whereas accurate predictions of complex heterotic traits are rarely reported. Here, we present a practical metabolome-based strategy for predicting yield heterosis in rice. The dissection of population structure based on untargeted metabolite profiles as the initial critical step in multivariate modeling performed better than the screening of predictive variables. Then the assessment of each predictive variable’s contribution to predictive models according to all latent factors was more precise than the conventional first one. Metabolites belonging to specific pathways were closely associated with yield heterosis, and the up-regulation of galactose metabolism promoted robust yield heterosis in hybrids under different growth conditions. Our study demonstrates that metabolome-based predictive models with correctly dissected population structure and screened predictive variables can facilitate accurate predictions of yield heterosis and have great potential for establishing molecular marker–based precision breeding programs.

Climatic changes and potatoes: How can we cope with the abiotic stresses?

Climate change triggers increases in temperature, drought, and/or salinity that threaten potato production, because they necessitate specific amounts and quality of water, meanwhile lower temperatures generally support stable crop yields. Various cultivation techniques have been developed to reduce the negative effects of drought, heat and/or salinity stresses on potato. Developing innovative varieties with relevant tolerance to abiotic stress is absolutely necessary to guarantee competitive production under sub-optimal environments. Commercial varieties are sensitive to abiotic stresses, and substantial changes to their higher tolerance levels are not easily achieved because their genetic base is narrow. Nonetheless, there are several other possibilities for genetic enhancement using landraces and wild relatives. The complexity of polysomic genetics and heterozygosity in potato hamper the phenotype evaluation over abiotic stresses and consequent conventional introgression of tolerance traits, which are more challenging than previous successes shown over diseases and insects resistances. Today, potatoes face more challenges with severe abiotic stresses. Potato wild relatives can be explored further using innovative genomic, transcriptomic, proteomic, and metabolomic approaches. At the field level, appropriate cultivation techniques must be applied along with precision farming technology and tolerant varieties developed from various breeding techniques, in order to realize high yield under multiple stresses.

Photorespiratory serine hydroxymethyltransferase 1 activity impacts abiotic stress tolerance and stomatal closure

The photorespiratory cycle is a crucial pathway in photosynthetic organisms because it removes toxic 2-phosphoglycolate made by the oxygenase activity of ribulose-1,5-bisphosphate carboxylase/oxygenase and retrieves its carbon as 3-phosphoglycerate. Mitochondrial serine hydroxymethyltransferase 1 (SHMT1) is an essential photorespiratory enzyme converting glycine to serine. SHMT1 regulation remains poorly understood although it could involve the phosphorylation of serine 31. Here, we report the complementation of Arabidopsis thaliana shm1-1 by SHMT1 wild-type, phosphorylation-mimetic (S31D) or nonphophorylatable (S31A) forms. All SHMT1 forms could almost fully complement the photorespiratory growth phenotype of shm1-1; however, each transgenic line had only 50% of normal SHMT activity. In response to either a salt or drought stress, Compl-S31D lines showed a more severe growth deficiency compared with the other transgenic lines. This sensitivity to salt appeared to reflect reduced SHMT1-S31D protein amounts and a lower activity that impacted leaf metabolism leading to proline underaccumulation and overaccumulation of polyamines. The S31D mutation in SHMT1 also led to a reduction in salt-induced and ABA-induced stomatal closure. Taken together, our results highlight the importance of maintaining photorespiratory SHMT1 activity in salt and drought stress conditions and indicate that SHMT1 S31 phosphorylation could be involved in modulating SHMT1 protein stability.

Discovery of food identity markers by metabolomics and machine learning technology

Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chemicals are unique identifiers of food components. Non-targeted metabolomics is ideally suited to screen food markers when coupled to efficient data analysis. This study explored feasibility of random forest (RF) machine learning, specifically its inherent feature extraction for non-targeted metabolic marker discovery. The distinction of chia, linseed, and sesame that have gained attention as “superfoods” served as test case. Chemical fractions of non-processed seeds and of wheat cookies with seed ingredients were profiled. RF technology classified original seeds unambiguously but appeared overdesigned for material with unique secondary metabolites, like sesamol or rosmarinic acid in the Lamiaceae, chia. Most unique metabolites were diluted or lost during cookie production but RF technology classified the presence of the seed ingredients in cookies with 6.7% overall error and revealed food processing markers, like 4-hydroxybenzaldehyde for chia and succinic acid monomethylester for linseed additions. RF based feature extraction was adequate for difficult classifications but marker selection should not be without human supervision. Combination with alternative data analysis technologies is advised and further testing of a wide range of seeds and food processing methods.

A metabolome-based core hybridisation strategy for the prediction of rice grain weight across environments

Marker-based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F1 hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F1 hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate-adaptive varieties.

Metabolic responses of rice cultivars with different tolerance to combined drought and heat stress under field conditions

BACKGROUND

Rice is susceptible to both drought and heat stress, in particular during flowering and grain filling, when both grain yield and quality may be severely compromised. However, under field conditions, these 2 stresses rarely occur separately. Under well-watered conditions, plants avoid heat stress by transpirational cooling, while this is not possible under drought conditions. Although investigating combined drought and heat stress is clearly more agronomically relevant than analyzing the effects of the single stresses, only a few studies of this stress combination, in particular under field conditions, have been published.

RESULTS

Three rice cultivars differing in drought and heat tolerance were grown in the field under control and drought conditions in 3 consecutive years. Drought was applied either during flowering or during early grain filling and resulted in simultaneous heat stress, leading to reduced grain yield and quality. Analysis by gas chromatography-mass spectrometry showed distinct metabolic profiles for the 3 investigated organs (flag leaves, flowering spikelets, developing seeds). The metabolic stress responses of the plants also strongly differed between cultivars and organs. Correlation analysis identified potential metabolic markers for grain yield and quality under combined drought and heat stress from both stress-regulated metabolites and from metabolites with constitutive differences between the cultivars.

CONCLUSIONS

Gas chromatography-mass spectrometry resolved metabolic responses to combined drought and heat stress in different organs of field-grown rice. The metabolite profiles can be used to identify potential marker metabolites for yield stability and grain quality that are expected to improve breeding efforts towards developing rice cultivars that are resilient to climate change.

Molecular mechanisms of combined heat and drought stress resilience in cereals

Global climate change leads to increased temperatures and decreased precipitation in many parts of the world. The simultaneous occurrence of high temperature and water deficit results in heat stress damage in plants. Cereals provide the majority of calories for human consumption, making this stress scenario particularly threatening for global food security. Several studies in both dicots and cereals indicate that the molecular reactions of plants to combined stresses cannot be predicted from reactions to single stresses. Recent results indicate that the regulation of heat shock proteins and of sugar transport and accumulation in flowers are crucial factors determining resilience of tolerant genotypes to combined heat and drought stress.

Metabolite Profiles of Sugarcane Culm Reveal the Relationship Among Metabolism and Axillary Bud Outgrowth in Genetically Related Sugarcane Commercial Cultivars

Metabolic composition is known to exert influence on several important agronomic traits, and metabolomics, which represents the chemical composition in a cell, has long been recognized as a powerful tool for bridging phenotype-genotype interactions. In this work, sixteen truly representative sugarcane Brazilian varieties were selected to explore the metabolic networks in buds and culms, the tissues involved in the vegetative propagation of this species. Due to the fact that bud sprouting is a key trait determining crop establishment in the field, the sprouting potential among the genotypes was evaluated. The use of partial least square discriminant analysis indicated only mild differences on bud outgrowth potential under controlled environmental conditions. However, primary metabolite profiling provided information on the variability of metabolic features even under a narrow genetic background, typical for modern sugarcane cultivars. Metabolite-metabolite correlations within and between tissues revealed more complex patterns for culms in relation to buds, and enabled the recognition of key metabolites (e.g., sucrose, putrescine, glutamate, serine, and myo-inositol) affecting sprouting ability. Finally, those results were associated with the genetic background of each cultivar, showing that metabolites can be potentially used as indicators for the genetic background.