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Multiple “omics” approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat ( Triticum aestivum L.), soybean ( Glycine max ), tomato ( Solanum lycopersicum ), barley ( Hordeum vulgare L.), maize ( Zea mays L.), millet ( Setaria italica L.), cotton ( Gossypium hirsutum L.), Medicago truncatula , and rice ( Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the “phenotype to genotype” and “genotype to phenotype” concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Heavy metal contamination of soil and water causing toxicity/stress has become one important constraint to crop productivity and quality. This situation has further worsened by the increasing population growth and inherent food demand. It has been reported in several studies that counterbalancing toxicity due to heavy metal requires complex mechanisms at molecular, biochemical, physiological, cellular, tissue, and whole plant level, which might manifest in terms of improved crop productivity. Recent advances in various disciplines of biological sciences such as metabolomics, transcriptomics, proteomics, etc., have assisted in the characterization of metabolites, transcription factors, and stress-inducible proteins involved in heavy metal tolerance, which in turn can be utilized for generating heavy metal-tolerant crops. This review summarizes various tolerance strategies of plants under heavy metal toxicity covering the role of metabolites (metabolomics), trace elements (ionomics), transcription factors (transcriptomics), various stress-inducible proteins (proteomics) as well as the role of plant hormones. We also provide a glance of some strategies adopted by metal-accumulating plants, also known as \"metallophytes.\"

While cadmium (Cd) tolerance is a constitutive trait in the Arabidopsis halleri species, Cd accumulation is highly variable. Recent adaptation to anthropogenic metal stress has occurred independently within the genetic units of A. halleri and the evolution of different mechanisms involved in Cd tolerance and accumulation has been suggested. To gain a better understanding of the mechanisms underlying Cd tolerance and accumulation in A. halleri, ionomic inductively coupled plasma mass spectrometry (ICP-MS), transcriptomic (RNA sequencing) and metabolomic (high-performance liquid chromatography–mass spectrometry) profiles were analysed in two A. halleri metallicolous populations from different genetic units (PL22 from Poland and I16 from Italy). The PL22 and I16 populations were both hypertolerant to Cd, but PL22 hyperaccumulated Cd while I16 behaved as an excluder both in situ and when grown hydroponically. The observed hyperaccumulator vs excluder behaviours were paralleled by large differences in the expression profiles of transporter genes. Flavonoid-related transcripts and metabolites were strikingly more abundant in PL22 than in I16 shoots. The role of novel A. halleri candidate genes possibly involved in Cd hyperaccumulation or exclusion was supported by the study of corresponding A. thaliana knockout mutants. Taken together, our results are suggestive of the evolution of divergent strategies for Cd uptake, transport and detoxification in different genetic units of A. halleri.

The characterization of nutrient and biostimulant effects in crops is complex and needs rigorous evaluations. In this study, we evaluated morphological and molecular responses induced by microalgae (Chlorella vulgaris and Scenedesmus quadricauda) extracts in Beta vulgaris L. The two microalgae extracts were firstly characterized by CNS, Fourier transform infrared spectroscopic analysis (FT-IR), and carbon-13 nuclear magnetic resonance (13C NMR). Seedlings were grown in Hoagland’s solution under controlled conditions. After 5 days of growth, 2 mL L−1 (1 mg Corg L−1) and 4 mL L−1 (2 mg Corg L−1) of the two microalgae extracts were added to the Hoagland solution. Roots were sampled 36 h after treatments. Inductively coupled plasma spectrometry (ICP-OES) and nanofluidic real-time PCR (OpenArray system) were used for sample profiling. Fifty-three sugar beet genes putatively involved in sulfate starvation were tested in treated and untreated samples. Root morphological traits were measured by means of a scanner-based image analysis system. Multivariate statistical analysis revealed no significant changes in the ionomic profile of Hoagland’s solutions treated with the two microalgae extracts with respect to that of the untreated solution. At the molecular level, microalgae extract supplies upregulated many of the evaluated genes. Functional categorization revealed these genes to be related to various biological pathways and processes including primary and secondary metabolism and intracellular transport. At the morphological level, the treated seedlings showed significantly higher values for root traits related to soil exploration and nutrient uptake, such as total root length, fine root length (diameter < 0.5 mm), and number of root tips, than the untreated plants. These data indicate that microalgae extracts have biostimulant effects on the expression of root traits and genes related to nutrient acquisition in sugar beet.

The present day’s ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant’s innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the ‘omics’ toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant’s genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence ‘multi-omics’) integrated-omics approaches can decipher the plant’s abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop’s variable abiotic stress tolerance to ensure food security under changing environmental circumstances.

Mineral nutrition is one of the key factors determining plant productivity. In plants, metal homeostasis is achieved through the functioning of a complex system governing metal uptake, translocation, distribution, and sequestration, leading to the maintenance of a regulated delivery of micronutrients to metal-requiring processes as well as detoxification of excess or non-essential metals. Low-molecular-weight ligands, such as nicotianamine, histidine, phytochelatins, phytosiderophores, and organic acids, play an important role in metal transport and detoxification in plants. Nicotianamine and histidine are also involved in metal hyperaccumulation, which determines the ability of some plant species to accumulate a large amount of metals in their shoots. In this review we extensively summarize and discuss the current knowledge of the main pathways for the biosynthesis of these ligands, their involvement in metal uptake, radial and long-distance transport, as well as metal influx, isolation and sequestration in plant tissues and cell compartments. It is analyzed how diverse endogenous ligand levels in plants can determine their different tolerance to metal toxic effects. This review focuses on recent advances in understanding the physiological role of these compounds in metal homeostasis, which is an essential task of modern ionomics and plant physiology. It is of key importance in studying the influence of metal deficiency or excess on various physiological processes, which is a prerequisite to the improvement of micronutrient uptake efficiency and crop productivity and to the development of a variety of applications in phytoremediation, phytomining, biofortification, and nutritional crop safety.

With the aim of identifying rice (Oryza spp.) germplasm having enhanced grain nutritional value, the mineral nutrient and trace element concentrations (or ionome) of whole (unmilled) grains from a set of 1763 rice accessions of diverse geographic and genetic origin were evaluated. Seed for analysis of P, Mg, K, S, Ca, As, Cd, Co, Cu, Fe, Mn, Mo, Ni, Rb, Sr, and Zn concentrations by inductively coupled plasma mass spectrometry was produced over 2 yr in Beaumont, TX, under both flooded and unflooded watering regimes. The distributions of all element concentrations analyzed were skewed toward higher concentration. A significant portion of this ionomic variation has a genetic basis (broad sense heritabilities 0.14–0.75), indicating an ability to breed for improved grain concentration of all elements except possibly Ni. Variation in grain elemental concentrations was not strongly associated with plant height, heading time, or grain shape, suggesting these physiological factors are not of primary importance in controlling ionomic variation in rice grain. Accessions high in specific elements were sometimes found to have similar genetic or geographic origins, suggesting they share a heritable mechanism underlying their enhanced ionomes. For example, accessions with high Ca, Mg, or K were more common in the indica than in the japonica subgroup; low As was most common among temperate japonica accessions; and several lines high in Mo originated in Malaysia or adjacent Brunei.

It has been more than 10 years since the concept of the ionome, all of the mineral nutrients in a cell tissue or organism, was introduced. In the intervening years, ionomics, high throughput elemental profiling, has been used to analyse over 400 000 samples from at least 10 different organisms. There are now multiple published examples where an ionomics approach has been used to find genes of novel function, find lines or environments that produce foods with altered nutritional profiles, or define gene by environmental effects on elemental accumulation. In almost all of these studies, the ionome has been treated as a collection of independent elements, with the analysis repeated on each measured element. However, many elements share chemical properties, are known to interact with each other, or have been shown to have similar interactions with biological molecules. Accordingly, there is strong evidence from ionomic studies that the elements of the ionome do not behave independently and that combinations of elements should be treated as the phenotypes of interest. In this review, I will consider the evidence that we have for the interdependence of the ionome, some of its causes, methods for incorporating this interdependence into analyses and the benefits, drawbacks, and challenges of taking these approaches.

One of the main limiting factors of plant yield is drought, and while the physiological responses to this environmental stress have been broadly described, research addressing its impact on mineral nutrition is scarce. Brassica napus and Triticum aestivum were subjected to moderate or severe water deficit, and their responses to drought were assessed by functional ionomic analysis, and derived calculation of the net uptake of 20 nutrients. While the uptake of most mineral nutrients decreased, Fe, Zn, Mn, and Mo uptake were impacted earlier and at a larger scale than most physiological parameters assessed (growth, ABA concentration, gas exchanges and photosynthetic activity). Additionally, in B. napus, the patterns of 183 differentially expressed genes in leaves related to the ionome (known ionomic genes, KIGs) or assumed to be involved in transport of a given nutrient were analyzed. This revealed three patterns of gene expression under drought consisting of up (transport of Cl and Co), down (transport of N, P, B, Mo, and Ni), or mixed levels (transport of S, Mg, K, Zn, Fe, Cu, or Mn) of regulation. The three patterns of gene regulations are discussed in relation to specific gene functions, changes of leaf ionomic composition and with consideration of the crosstalks that have been established between elements. It is suggested that the observed reduction in Fe uptake occurred via a specific response to drought, leading indirectly to reduced uptake of Zn and Mn, and these may be taken up by common transporters encoded by genes that were downregulated. Highlight In wheat and rapeseed, drought downregulates the uptake of Fe, Mo, Zn, and Mn before reduction of growth, and this was associated with differential expressions of transport‐related genes.

Background Rice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers. Results To identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p -value < 1.53 × 10 − 8 , which was determined by the Bonferroni correction for p -value of 0.05. While 49 (~ 20%) of the 250 QTLs were coinciding with previously reported QTLs/genes, about 201 (~ 80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods. Conclusions While 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.