Next Article in Journal
Comprehensive Vitamer Profiling of Folate Mono- and Polyglutamates in Baker’s Yeast (Saccharomyces cerevisiae) as a Function of Different Sample Preparation Procedures
Previous Article in Journal
Lipidomic Profiling of the Epidermis in a Mouse Model of Dermatitis Reveals Sexual Dimorphism and Changes in Lipid Composition before the Onset of Clinical Disease
Previous Article in Special Issue
Identification of Metabolite and Lipid Profiles in a Segregating Peach Population Associated with Mealiness in Prunus persica (L.) Batsch
Open AccessReview

The Application of Metabolomics for the Study of Cereal Corn (Zea mays L.)

Laboratory of Research in Food Science, Universidad Catolica de Santa Maria, Urb. San Jose s/n, 04013 Arequipa, Peru
Metabolites 2020, 10(8), 300; https://doi.org/10.3390/metabo10080300
Received: 16 May 2020 / Revised: 17 July 2020 / Accepted: 20 July 2020 / Published: 23 July 2020

Abstract

Corn (Zea mays L.) is an important cereal crop indigenous to the Americas, where its genetic biodiversity is still preserved, especially among native populations from Mesoamerica and South America. The use of metabolomics in corn has mainly focused on understanding the potential differences of corn metabolomes under different biotic and abiotic stresses or to evaluate the influence of genetic and environmental factors. The increase of diet-linked non-communicable diseases has increased the interest to optimize the content of bioactive secondary metabolites in current corn breeding programs to produce novel functional foods. This review provides perspectives on the role of metabolomics in the characterization of health-relevant metabolites in corn biodiversity and emphasizes the integration of metabolomics in breeding strategies targeting the enrichment of phenolic bioactive metabolites such as anthocyanins in corn kernels.
Keywords: Zea mays L.; metabolomics; genetic diversity; secondary metabolites; crop improvement Zea mays L.; metabolomics; genetic diversity; secondary metabolites; crop improvement

1. Introduction

Corn or maize (Zea may L. ssp. mays) is indigenous to the Americas and is one of the most important cereal crops worldwide [1]. Corn is a staple food in many countries from Mesoamerica, South America, and Africa and also has a deep ethnic and cultural relevance among indigenous populations in these regions. In addition, this cereal is also used to produce feed, fuel, and as a raw material for diverse products at industrial level. Global corn production was about 1147 million tons in 2018, and the major producing countries were the United States, China, and Brazil [2].
Corn was domesticated from the wild annual teosinte (Zea mays spp. parviglumis) around 9000 years ago in Mexico in a single event [3]. The domestication process involved the adaptation of corn from its tropical origin to temperate and other diverse environmental conditions, undergoing a significant morphological transformation in the plant and the inflorescence architecture [4]. The primitive and early improved corn was spread across North and South America and adapted to new environments and uses, leading to the origin of “landraces or races” [5]. Landraces are highly heterozygous and heterogeneous open-pollinated populations [5]. Anderson and Cutler [6] first proposed the definition of “race” as “a group of related individuals with enough characteristics in common to permit their recognition as a group.” Salazar et al. [7] defined landraces or local races as the germplasm that has been cultivated for hundreds of years under traditional agriculture systems and which represent a high genetic diversity. This material has been generally selected by native farmers for better adaptation to specific environments, yield, nutritive value, cultural use, and resistance to biotic and abiotic stresses [8]. Landraces are dynamic populations that lack formal crop improvement and are genetically diverse [9]. With the advent of Europeans to America, corn was introduced to different continents including Europe, Africa, and Asia, following variable patterns of adaptation and breeding [10,11]. The advance of more modern breeding techniques has allowed the production of high-yielding inbred corn lines (made by self-pollination) from landraces, and then hybrids or commercial varieties have been obtained based on crossed inbred lines. The current corn germplasm is often referred to in terms of teosinte, landraces, and inbred lines and are important genetically diverse resources for crop improvement and food security [5].
The current challenge for feeding a growing population that would almost reach 10 billion over next 30 years is creating the need to improve existing crops and develop new crops with even higher yielding, more nutritious, and with climate change resilient characteristics [12]. On the other hand, the rise of diet-linked chronic diseases worldwide, such as type 2 diabetes, and other derived complications including cancer and certain degenerative diseases has increased the interest in plant-derived foods from global biodiversity [13,14,15]. Fruits and whole grains have been found to be sources of bioactive compounds with potential to decrease the risk of chronic diseases when consumed on a regular basis. The intake of whole grains rich in different health-relevant functional metabolites has been related to the reduction of risks factors of major chronic diseases according to several epidemiological and intervention studies [16,17,18]. The research and sustainable use of corn genetic diversity may help to counter such challenges.
Metabolomics has emerged as a valuable technology for the comprehensive profiling and comparison of metabolites in biological systems, and a diversity of applications in plant sciences have been reported [19,20]. Depending on the purpose of the study and the type of scientific information required, metabolomics may be focused into different approaches such as targeted analysis, metabolomic profiling and metabolomic fingerprinting [21,22]. In the case of corn, several metabolomic studies have focused on understanding the complex biochemical mechanisms involved in corn plant response to environmental directed biotic [23,24,25,26,27] and abiotic stress factors [28,29,30,31,32]. Other metabolomic studies have been aimed at comparing genetically modified and non-genetically modified corn lines [33] or at evaluating the influence of genetic factors and the growing location on corn kernel and plant function [34,35,36,37].
The current review summarizes the application of metabolomics for the characterization of health promoting metabolites in corn kernels and emphasizes the importance of corn genetic diversity, especially native landraces, as potential sources of bioactive metabolites. The role of metabolomics integrated to novel phenotype-genotype studies targeting the improvement of health-relevant metabolites in corn with a focus on anthocyanins is also presented. Future perspectives on the application of metabolomics for the valorization and understanding of corn genetic diversity and its impact on food security and health are also highlighted.

2. The Diversity of Corn Germplasm and the Importance of Native Landraces

The preservation of genetic resources including crop landraces and wild relatives is fundamental and crucial for future crop improvement [38]. Landraces have been found to possess important allelic diversity contributing, for example, with useful genes for a more efficient plant nutrient uptake and utilization, resistance or tolerance to biotic and abiotic stresses, and superior nutritional and health-linked bioactive compounds [39].
Ex situ and in situ strategies have been applied to conserve corn genetic diversity worldwide. The first international initiative for the ex situ conservation of corn germplasm was directed by the Latin American Maize Program (LAMP) (1987–1996). A group of 11 countries from Latin America and the United States evaluated the status of local corn germplasm, compiling information about this germplasm, and promoted the access to this information for research purposes and for the creation of superior varieties and hybrids by breeders [40]. Table 1 shows the genetic diversity of corn germplasm in terms of the number of native landraces reported by country in the Americas and in some countries from different continents. Corn genetic diversity is very high in Latin America, and there are around 300 landraces that comprise 90 percent of world’s corn diversity [40,41]. Mexico and Peru have around 30 percent of Latin American corn landrace diversity. The phenotypic and molecular variability of corn landraces from Mexico, the center of corn origin, has been reported by Prasana [8] and Prasana [42]. The Andean region has wide ecological diversity and corn landraces that are currently grown from areas at sea level to different highland regions. These adaptations have been developed by native farmers since ancient times. This may explain the fact that the Andean region is possibly the geographical area with the highest corn phenotypic diversity in the world (Figure 1) [43]. However, Andean corn landraces have been poorly investigated at phenotypic and molecular levels compared to the research focused on Mexican landraces and teosinte varieties.
In several places in Mesoamerica and Latin America, local farmers maintain the cultivation of corn landraces for subsistence under traditional agriculture practices (in situ conservation). However, the increasing introduction of modern corn hybrids is leading to a steady genetic erosion of native germplasm. This situation will compromise food security, potentially affecting indigenous food systems in the medium term and potentially long term.
Germplasm banks from the International Maize and Wheat Improvement Center (CIMMYT—Mexico City, Mexico) and the United States Department of Agriculture-National Plant Germplasm System (USDA-NPGS—Washington, DC, United States) currently preserves the world’s major corn collections [61,62]. However, modern breeding programs are mainly based on corn germplasm with narrow genetic base as was reported by Prasanna [8]. Considering the current demand of healthier plant-based foods due to the increase of metabolic chronic diseases worldwide, the integrated research of native corn landraces may increase the genetic base for crop improvement and targeting functional bioactive metabolites beyond yield and common agronomic traits.

3. The Application of Metabolomics for the Characterization of Health-Relevant Metabolites in Corn Genetic Diversity

Plant secondary metabolites are small molecules that are considered non-essential for the general survival of the organism but are key components for overall plant adaptation and protection to biotic and abiotic stress conditions [63]. Several epidemiological and intervention studies have shown an inverse correlation in the prevalence of major chronic diseases including cardiovascular disease, type 2 diabetes, and cancer and the consumption of whole grains [16,17,64]. This is due to the presence of a wide range of phytochemical compounds, mainly secondary metabolites, that also play a significant role for human health [64].
The composition of corn in terms of primary metabolites with major nutritional importance (proteins, carbohydrates, fat) along with the mineral composition has been shown to be comparable or even better than common cereal crops such as rice and wheat [65]. Some comprehensive reviews have also shown that the corn kernel is a good source of important health-relevant phytochemicals such as phenolic compounds, carotenoids, tocopherols, and phytosterols [65,66]. However, research on the phenolic and carotenoid composition in corn kernel has received more attention due to the wide chemical diversity and remarkable health-promoting properties associated to both metabolite groups [67,68].

3.1. Metabolomic Analysis of Phenolic Compounds in Corn Genetic Diversity

The characterization of phenolic bioactives in corn kernel diversity was first evaluated by applying spectrophotometric methods on Mexican landraces of variable kernel pigmentation [69,70,71,72]. This has allowed the rapid selection of promising corn accessions and genotypes for further breeding strategies. The specific chemical structure of phenolic compounds has been associated with different functional properties based on their antioxidant capacity [73]. In addition, phenolic compounds in cereals such as corn can be classified into free, esterified (covalently bound to other molecules), and insoluble-bound forms. Insoluble-bound phenolics form covalent bonds with cell wall macromolecules including pectin, cellulose, arabinoxylan, and structural proteins and generally represent the major phenolic fraction in cereals [74]. Bound phenolics are not significantly affected by the gastrointestinal digestion but are released through colonic fermentation exerting a myriad of positive biological effects at colon level [75].
The study of phenolic profiles (free and bound phenolic fractions) in corn (kernel) germplasm from different origins and types (landraces, inbred lines, hybrids, teosinte varieties) has been done using mainly targeted metabolomic platforms (Table 2 and Table 3). Corn landraces and hybrids with different phenotypes (kernel color) from American germplasm banks (Mexico and the United States) have been extensively screened as important sources of phenolic compounds through the use of a liquid chromatography diode array detector/ultraviolet-visible detector (LC-DAD/UV-Vis) and liquid chromatography coupled to mass spectrometry (LC-MS). The interest in the characterization of high-anthocyanin corn landraces and hybrids is increasing due to the growing demand for alternative sources of natural pigments for nutraceutical and food industry applications. Hong et al. [76] and Hong et al. [77] have recently combined the use of different mass separation techniques such as quadrupole-Orbitrap and triple quadrupole for the detection of at least 18–20 different types of anthocyanins in purple corn germplasm from Australia. These authors optimized the method for the extraction and detection of anthocyanins, revealing that some previously identified anthocyanins in purple corn such as succinyl and ethyl-malonyl derivatives are likely not to be endogenous but are esterification products formed during the extraction process [76,77].
The most comprehensive survey for the identification of high-anthocyanin corn germplasm was done by Paulsmeyer et al. [78]. Around 398 genetically diverse pigmented corn accessions from different origins were analyzed, and 167 accessions were selected due to their capability to produce anthocyanins. Selected germplasm was classified as blue aleurone, pink aleurone, purple pericarp with condensed anthocyanin forms, purple pericarp without condensed forms, and reduced acylation accessions. Accessions with purple pigmented pericarp with the presence of condensed forms of anthocyanins showed the highest anthocyanin contents (a Peruvian Andean purple corn had the highest levels). The same study also revealed that the anthocyanin biosynthesis has a broad potential of heritability, and this trait can be optimized through breeding strategies [78].
Peniche-Pavia [79] recently showed the potential of direct injection electrospray ionization mass spectrometry (DIESI-MSQD) as a cost-effective, more economical, and statistically robust method for high-throughput phenotypic characterization and targeting of high anthocyanin corn germplasm. Furthermore, Fourier-transformed near-infrared reflectance spectroscopy (FT-NIRS) has been used for the quantitative measure of anthocyanin contents in a wide array of corn germplasm [80]. Vibrational spectroscopy (infrared and NIR) includes non-destructive methods that can provide high-throughput analysis of a large number of samples with minimum sample preparation. However, major drawbacks are their poor resolution and limited information about the chemical structure of targeted metabolites [81].

3.2. Metabolomic Analysis of Carotenoid Compounds in Corn Genetic Diversity

Carotenoids are natural pigments found in most fruits, vegetables, and grains. These metabolites have shown a range of beneficial functional properties for human health. Besides their antioxidant effects, some individual carotenoids such as β-carotene, β-cryptoxanthin, and other derivatives have a pro-vitamin-A function [68]. Lutein and zeaxanthin are essential for macular pigments in the eye and have been linked to the reduction of the macular eye disease reducing the risk of cataracts [97]. LC coupled to detectors such as ultraviolet-visible (UV-Vis) and diode array detectors (DADs) have been widely used for the identification of pro and non-provitamin A carotenoids in different corn germplasm (Table 4). Liquid chromatography tandem mass spectrometry (LC-MS/MS) has allowed the generation of more information about the variability of carotenoid isomers present in corn kernels [98].
In contrast to the observations on corn phenolic compounds, several investigations from different continents have focused their attention on the characterization of carotenoids in worldwide corn biodiversity. Corn has been targeted as one of the major crops for provitamin A enrichment, and important global initiatives such as the HarvestPlus and CYMMIT programs have focused on obtaining high-provitamin-A corn cultivars through conventional and molecular breeding strategies [99,100]. The main objective of the HarvestPlus program (2003–2016) was to develop high-yielding provitamin-A-enriched corn cultivars with proven efficiency in reducing vitamin A deficiency that is profitable to farmers and acceptable to the consumers [100,101]. Targeted metabolomics have revealed that carotenoids are mostly concentrated in the endosperm and that overall orange-pigmented varieties show the highest carotenoids levels (Table 4). This germplasm has potential for applications in biofortification-linked breeding programs.

3.3. Use of Non-Targeted Metabolomic Platforms for the Research of Corn Kernel Metabolome

Untargeted metabolomics alone or integrated to other high-throughput platforms such as transcriptomics and proteomics have been recently used to characterize the composition of corn kernels and to understand the molecular regulation of metabolic pathways underlying the biosynthesis of phenolic antioxidants. Rao et al. [112] identified 210 metabolites (199 primary metabolites, nine secondary metabolites, and two phytohormones) in mature kernels of 14 corn lines from China using LC-MS/MS and gas chromatography mass spectrometry (GC-MS). A total of 32 metabolites were identified corresponding to basic and essential macronutrients (17 amino acids, five carbohydrates, seven lipids, and three cofactors, prosthetic groups, electro carriers). Other molecules such as vitamin E, stigmasterol, campesterol, β-sitosterol, phospholipids, antioxidants (dihydro-kaempferol and the lactone costunolide), and the antinutrient inositol hexaphosphate were also detected. An integrated metabolic map based on transcriptomic, proteomic and metabolomic data was built and included seven important pathways and 23 sub-pathways that represented the regulatory mechanisms of corn kernel metabolism [112].
Hu et al. [113] studied the metabolome of purple pigmented and non-pigmented kernels from China at different maturity stages (11, 16, 21 days after pollination (DAP)) using a combination of LC-MS/MS and GC-MS. A total of 247 metabolites were identified and metabolomes differed according to the maturity stage. The integration of transcriptomic data revealed that kinetic trends in the transcriptome and metabolome were similar across kernel development. An important metabolic shift from the primary to the secondary metabolism occurred at 16 DAP only in the purple pigmented kernel targeting the biosynthesis of anthocyanins and precursors of phlobaphenes [113]. The biochemical information generated in above studies would be valuable in molecular breeding strategies aimed at increasing health promoting metabolites such as anthocyanins in corn kernel.
Unlike the high-throughput analytical platforms such as LC/GC-MS for metabolomic studies, mass spectrometry imaging (MSI) is a relatively recent non-targeted method used for the simultaneous analysis of both the composition and spatial distribution of many compounds [114]. MSI is a two-dimensional analysis method that can detect intact molecules within tissue sections without requiring extraction, purification, separation, or labeling while allowing the detection of a wide range of molecules [114,115]. MSI has the capability to image thousands of metabolites such as lipids, peptides, proteins, and secondary metabolites (including phenolic compounds) in a single experiment [116,117,118]. Most common MSI configurations are matrix-assisted laser desorption ionization mass spectrometry (MALDI-MSI), secondary ion mass spectrometry (SIMS), and desorption electrospray ionization mass spectrometry (DESI), depending on the type of ionization [114]. In case of corn, MSI has been applied for the study of kernel tissue sections, the distribution of lysophospholipids within starch granule in corn endosperm, and the distribution of cellulose and hemicellulose structures in corn stems [119,120,121]. MSI can provide valuable information about the localization of important bioactive compounds such as anthocyanins for breeding purposes. The integration of these untargeted platforms with targeted methods might help to increase the analytical power for the comprehensive characterization of corn kernel diversity.

4. Role of Metabolomics in Corn Molecular Breeding Strategies Targeting Health-Relevant Phenolic Metabolites

4.1. Common Methods Used for the Study of Corn Genetic Diversity

Genetic diversity has been broadly defined as any variation in the nucleotides, genes, chromosomes, or genomes of a species at a level of the individual, population, species, or region for a given time [122]. This diversity is highly desirable in crop breeding programs since it offers a wide array of critical gene pools for further development of improved corn seeds relevant for nutrition and health. As mentioned above, corn genetic diversity (native landraces) may represent an important source of candidate genes for breeding applications.
The study of genetic diversity in corn germplasm has been assessed using different types of markers (Table 5). Morphological markers such as those related with plant traits (genetically determined characteristics) [5] with agronomic interest have been traditionally used in several studies for the elucidation of potential clustering patterns among different corn landraces. Ortiz et al. [123] and Ortiz et al. [124], using plant and internal ear traits, respectively, determined that several Andean corn accessions can be classified in different groups corresponding mainly to the original corn race classification. Likewise, Salazar et al. [125] highlighted the wide range of phenotypic variability based on different plant qualitative and quantitative traits at an intra-racial level in the Chilean Choclero landrace. The genetic diversity of Turkish corn genotypes can be classified into flint, pop, and dent races using ear and tassel traits [126].
Other authors have used molecular markers such as microsatellites (SSR) to assess the genetic diversity in germplasm from Brazil and Argentina [127,128]. Porta et al. [129] confirmed the genetic diversity and representativeness of the core collection (90 accessions) with respect to the whole Uruguayan corn landrace collection applying SSR markers in combination with morphological characteristics. The advance in the development of high-throughput sequencing techniques have allowed the genotyping-by-sequencing (GBS) of 349 commercial corn inbred lines based on single-nucleotide polymorphism (SNP) markers [130]. Same study revealed the presence of three different heterotic groups corresponding to the Stiff Stalk, Non-Stiff Stalk, and Iodent corn types among North American corn germplasm [130].

4.2. Metabolomic-Assisted Molecular Breeding Strategies for the Increase of Phenolic Antioxidants in Corn Kernels

Metabolomics enables the comprehensive high-throughput quantification of a wide array of metabolites and is becoming an important tool for corn phenotyping. Since the metabolic phenotype provides a link between gene sequence and visible phenotypes, metabolites can be used as markers for trait prediction relevant for crop genetic improvement [132,133]. Strategies known as association mapping studies including genome-wide association (GWAS) and linking studies have been developed for the identification of genes underlying quantitative trait loci (QTL) (a region of the genome associated with the control of a quantitative trait) [5,134,135]. GBS profiling combined with different omics technologies are currently applied to understand the genetic and biochemical regulation of metabolism linked to relevant agronomic and nutritional traits in corn.
Secondary metabolites such as anthocyanins are receiving more attention for breeding purposes due to their well-known health-relevant bioactivity and potential for use as natural pigments at the industrial level. Metabolite-based genome-wide association studies (mGWAS) are used to decipher the genetic basis of plant metabolite biosynthesis and regulation. This strategy is allowing the development of metabolic markers and genetic loci for metabolome-assisted biofortification [136]. The anthocyanin biofortification through mGWAS techniques has already been obtained in tomato and rice [137,138]. Rhodes et al. [139] evaluated a wide sorghum diversity panel (381) using NIRS and identified novel QTL for sorghum polyphenols through a GWAS with 404,628 SNP markers.
Conventional marker-assisted breeding has been used for the production of a colored polenta corn kernel and a purple popcorn rich in anthocyanins in Italy [140,141]. GWAS strategies have been more extensively used for the study of complex traits, such as yield and stress resistance in corn plants [142]. Novel metabolites and genes involved in the biosynthesis of flavonoids have been identified in mature kernels from a diverse corn inbred panel (368) through mGWAS using different LC-MS/MS platforms (electro spray ionization-triple quadrupole-linear ion trap and electro spray ionization-high-resolution quadrupole time-of-flight mass spectrometry, respectively) [133]. However, the application of mGWAS targeting the biofortification of corn kernel in relation to anthocyanin metabolites have not been well studied. This technique along with other new GWAS methods and new population designs would accelerate the discovery and use of allelic diversity in corn landraces to produce novel corn-based functional foods [12,142].

5. Future Perspectives

Metabolomics is an important technique for the characterization of relevant bioactive phytochemicals such as phenolic and carotenoids compounds in corn biodiversity. The integration of metabolomic information with data from other high-throughput omics technologies is critical for better understanding the underlying molecular mechanisms involved in the biosynthesis of functional metabolites. The application of novel phenotype-genotype molecular strategies for corn breeding such as mGWAS would speed up the production of innovative functional corn-derived foods with improved health-relevant compounds such as anthocyanins and carotenoids.
Corn landrace genetic diversity is a key source of candidate genes with potential applications in modern breeding programs aimed at producing healthier foods in resilient and sustainable ecosystems. A primary major challenge is the comprehensive molecular characterization of worldwide corn landrace diversity using an integrated multi-omics approach including metabolomics. A high-quality phenotypic data is essential to identify and use relevant corn germplasm for different breeding applications.
Extensive research has focused on corn diversity from North and Mesoamerica as highlighted in this review. The Andean region also represents an important hotspot of corn diversity, but the scientific information about germplasm from this area is limited. In fact, some breeding initiatives based on Peruvian corn with high anthocyanin contents have been carried out in the United States and Australia [77,78]. The effective in situ and ex situ preservation of corn diversity at local and international levels is critical to ensure adequate research on corn germplasm, especially in areas with high corn genetic diversity. This requires clear rules and policies for its accession and exchange involving not only researchers but also critical stakeholders such as farmers, government, and industry.
The next challenge that requires a multidisciplinary and intersectoral approach is the transition from the laboratory or greenhouse to the field. All strategies should consider the specific environmental conditions of the target region for corn cultivation. Applications in geographical areas where corn diversity is high may be strategic for improving corn in situ conservation while allowing the development of local agriculture within indigenous food systems.
The holistic integration of metabolomics with complementary omic-technologies may open the opportunity for the application of omic-wide association studies targeting the valorization, understanding, and sustainable use of worldwide corn genetic diversity.

Funding

This research was funded by the vice-presidency of research of Universidad Catolica de Santa Maria (Peru) grant number 26572-R-2019, and the APC was funded by the same grant.

Acknowledgments

The author would like to thank Kalidas Shetty from North Dakota State University for the review of the article.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Luo, Y.; Wang, Q. Bioactive compounds in corn. In Cereals and Pulses: Nutraceutical Properties and Health Benefits; Yu, L., Tsao, R., Shahidi, F., Eds.; John Wiley & Sons, Inc.: Oxford, UK, 2012; pp. 85–103. [Google Scholar]
  2. FAOSTAT. Available online: http://www.fao.org/faostat/es/#data/QC (accessed on 12 June 2020).
  3. Matsuoka, Y.; Vigouroux, Y.; Goodman, M.M.; Sanchez, J.; Buckler, E.; Doebley, J. A single domestication for maize shown by multilocus microsatellite genotyping. Proc. Natl. Acad. Sci. USA 2002, 99, 6080–6084. [Google Scholar] [CrossRef] [PubMed]
  4. Doebley, J. The genetics of maize evolution. Annu. Rev. Genet. 2004, 38, 37–59. [Google Scholar] [CrossRef] [PubMed]
  5. Flint-Garcia, S.A. Genetics and consequences of crop domestication. J. Agric. Food Chem. 2013, 61, 8267–8276. [Google Scholar] [CrossRef] [PubMed]
  6. Anderson, E.; Cutler, H.C. Races of Zea mays: I. Their recognition and classification. Ann. Mo. Bot. Gard. 1942, 29, 69–88. [Google Scholar] [CrossRef]
  7. Salazar-Suazo, E.R.; León-Lobos, P.; Rosas-Cerda, M.; Schick, C.M. Estado De La Conservación Ex Situ De Los Recursos Fitogenéticos Cultivados Y Silvestres En Chile, 1st ed.; Instituto de Investigaciones Agropecuarias-INIA: Santiago, Chile, 2006; pp. 17–18. [Google Scholar]
  8. Prasanna, B.M. Diversity in global maize germplasm: Characterization and utilization. J. Biosci. 2012, 37, 843–855. [Google Scholar] [CrossRef]
  9. Villa, T.C.C.; Maxted, N.; Scholten, M.; Ford-Lloyd, B. Defining and identifying crop landraces. Plant Gen. Res. 2005, 3, 373–384. [Google Scholar] [CrossRef]
  10. Rebourg, C.; Chastanet, M.; Gouesnard, B.; Welcker, C.; Dubreuil, P.; Charcosset, A. Maize introduction into Europe: The history reviewed in the light of molecular data. Theor. Appl. Gen. 2003, 106, 895–903. [Google Scholar] [CrossRef]
  11. Dubreuil, P.; Warburton, M.L.; Chastanet, M.; Hoisington, D.; Charcosset, A. More on the introduction of temperate maize into Europe: Large-scale bulk SSR genotyping and new historical elements. Maydica 2006, 51, 281–291. [Google Scholar]
  12. Hickey, L.T.; Hafeez, A.N.; Robinson, H.; Jackson, S.A.; Leal-Bertioli, S.C.; Tester, M.; Wulff, B.B. Breeding crops to feed 10 billion. Nat. Biotech. 2019, 37, 744–754. [Google Scholar] [CrossRef]
  13. Hayden, M.R. Type 2 Diabetes mellitus increases the risk of late-onset Alzheimer’s disease: Ultrastructural remodeling of the neurovascular unit and diabetic gliopathy. Brain Sci. 2019, 9, 262. [Google Scholar] [CrossRef]
  14. López-Suárez, A. Burden of cancer attributable to obesity, type 2 diabetes and associated risk factors. Metabolism 2019, 92, 136–146. [Google Scholar] [CrossRef] [PubMed]
  15. Wang, P.Y.; Fang, J.C.; Gao, Z.H.; Zhang, C.; Xie, S.Y. Higher intake of fruits, vegetables or their fiber reduces the risk of type 2 diabetes: A meta-analysis. J. Diabetes Invest. 2016, 7, 56–69. [Google Scholar] [CrossRef] [PubMed]
  16. Kyrø, C.; Tjønneland, A.; Overvad, K.; Olsen, A.; Landberg, R. Higher whole-grain intake is associated with lower risk of type 2 diabetes among middle-aged men and women: The Danish diet, cancer, and health cohort. J. Nutr. 2018, 148, 1434–1444. [Google Scholar] [CrossRef] [PubMed]
  17. Xie, M.; Liu, J.; Tsao, R.; Wang, Z.; Sun, B.; Wang, J. Whole Grain Consumption for the Prevention and Treatment of Breast Cancer. Nutrients 2019, 11, 1769. [Google Scholar] [CrossRef]
  18. Zhu, Y.; Sang, S. Phytochemicals in whole grain wheat and their health-promoting effects. Mol. Nutr. Food Res. 2017, 61, 1600852. [Google Scholar] [CrossRef]
  19. Dixon, R.A.; Gang, D.R.; Charlton, A.J.; Fiehn, O.; Kuiper, H.A.; Reynolds, T.L.; Tjeerdema, R.S.; Jeffery, E.H.; German, J.B.; Seiber, J.N. Applications of metabolomics in agriculture. J. Agric. Food Chem. 2006, 54, 8984–8994. [Google Scholar] [CrossRef]
  20. De Vos, R.C.; Moco, S.; Lommen, A.; Keurentjes, J.J.; Bino, R.J.; Hall, R.D. Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat. Protoc. 2007, 2, 778. [Google Scholar] [CrossRef]
  21. Dunn, W.B. Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes. Phys. Biol. 2008, 5, 011001. [Google Scholar] [CrossRef] [PubMed]
  22. Fiehn, O. Metabolomics—The link between genotypes and phenotypes. In Functional Genomics; Springer: Dordrecht, The Netherlands, 2002; pp. 155–171. [Google Scholar]
  23. Guo, J.; Qi, J.; He, K.; Wu, J.; Bai, S.; Zhang, T.; Zhao, J.; Wang, Z. The Asian corn borer Ostrinia furnacalis feeding increases the direct and indirect defence of mid-whorl stage commercial maize in the field. Plant Biotechnol. J. 2019, 17, 88–102. [Google Scholar] [CrossRef]
  24. Mafu, S.; Ding, Y.; Murphy, K.M.; Yaacoobi, O.; Addison, J.B.; Wang, Q.; Shen, Z.; Briggs, S.P.; Bohlmann, J.; Castro-Falcon, G.; et al. Discovery, biosynthesis and stress-related accumulation of dolabradiene-derived defenses in maize. Plant Physiol. 2018, 176, 2677–2690. [Google Scholar] [CrossRef]
  25. Vasmatkar, P.; Kaur, K.; Pannu, P.P.S.; Kaur, G.; Kaur, H. Unraveling the metabolite signatures of maize genotypes showing differential response towards southern corn leaf blight by 1H-NMR and FTIR spectroscopy. Physiol. Mol. Plant Pathol. 2019, 108, 101441. [Google Scholar] [CrossRef]
  26. Bernardi, J.; Stagnati, L.; Lucini, L.; Rocchetti, G.; Lanubile, A.; Cortellini, C.; De Poli, G.; Busconi, M.; Marocco, A. Phenolic profile and susceptibility to Fusarium infection of pigmented maize cultivars. Front. Plant Sci. 2018, 9, 1189. [Google Scholar] [CrossRef] [PubMed]
  27. Falade, T.; Chrysanthopoulos, P.K.; Hodson, M.P.; Sultanbawa, Y.; Fletcher, M.; Darnell, R.; Korie, S.; Fox, G. Metabolites identified during varied doses of Aspergillus species in Zea mays grains, and their correlation with aflatoxin levels. Toxins 2018, 10, 187. [Google Scholar] [CrossRef] [PubMed]
  28. Lamari, N.; Zhendre, V.; Urrutia, M.; Bernillon, S.; Maucourt, M.; Deborde, C.; Prodhomme, D.; Jacob, D.; Ballias, P.; Rolin, D.; et al. Metabotyping of 30 maize hybrids under early-sowing conditions reveals potential marker-metabolites for breeding. Metabolomics 2018, 14, 132. [Google Scholar] [CrossRef]
  29. Sun, C.; Gao, X.; Chen, X.; Fu, J.; Zhang, Y. Metabolic and growth responses of maize to successive drought and re-watering cycles. Agric. Water Manag. 2016, 172, 62–73. [Google Scholar] [CrossRef]
  30. Richter, J.A.; Erban, A.; Kopka, J.; Zörb, C. Metabolic contribution to salt stress in two maize hybrids with contrasting resistance. Plant Sci. 2015, 233, 107–115. [Google Scholar] [CrossRef]
  31. Boehlein, S.K.; Liu, P.; Webster, A.; Ribeiro, C.; Suzuki, M.; Wu, S.; Guan, J.-C.; Stewart, J.D.; Tracy, W.F.; Settles, A.M.; et al. Effects of long-term exposure to elevated temperature on Zea mays endosperm development during grain fill. Plant J. 2019, 99, 23–40. [Google Scholar] [CrossRef]
  32. McLoughlin, F.; Augustine, R.C.; Marshall, R.S.; Li, F.; Kirkpatrick, L.D.; Otegui, M.S.; Vierstra, R.D. Maize multi-omics reveal roles for autophagic recycling in proteome remodelling and lipid turnover. Nat. Plants 2018, 4, 1056–1070. [Google Scholar] [CrossRef]
  33. Harrigan, G.G.; Venkatesh, T.V.; Leibman, M.; Blankenship, J.; Perez, T.; Halls, S.; Goodacre, R. Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait. Metabolomics 2016, 12, 82. [Google Scholar] [CrossRef]
  34. Röhlig, R.M.; Eder, J.; Engel, K.H. Metabolite profiling of maize grain: Differentiation due to genetics and environment. Metabolomics 2009, 5, 459–477. [Google Scholar] [CrossRef]
  35. Skogerson, K.; Harrigan, G.G.; Reynolds, T.L.; Halls, S.C.; Ruebelt, M.; Iandolino, A.; Pandravada, A.; Glenn, K.C.; Fiehn, O. Impact of genetics and environment on the metabolite composition of maize grain. J. Agric. Food Chem. 2010, 58, 3600–3610. [Google Scholar] [CrossRef] [PubMed]
  36. Chen, M.; Rao, R.S.P.; Zhang, Y.; Zhong, C.; Thelen, J.J. Metabolite variation in hybrid corn grain from a large-scale multisite study. Crop J. 2016, 4, 177–187. [Google Scholar] [CrossRef]
  37. Tang, W.; Hazebroek, J.; Zhong, C.; Harp, T.; Vlahakis, C.; Baumhover, B.; Asiago, V. Effect of genetics, environment, and phenotype on the metabolome of maize hybrids using GC/MS and LC/MS. J. Agric. Food Chem. 2017, 65, 5215–5225. [Google Scholar] [CrossRef] [PubMed]
  38. Ford-Lloyd, B.V.; Schmidt, M.; Armstrong, S.J.; Barazani, O.; Engels, J.; Hadas, R.; Li, Y. Crop wild relatives—Undervalued, underutilized and under threat? Bioscience 2011, 61, 559–565. [Google Scholar] [CrossRef]
  39. Newton, A.C.; Akar, T.; Baresel, J.P.; Bebeli, P.J.; Bettencourt, E.; Bladenopoulos, K.V.; Koutsika-Sotiriou, M. Cereal landraces for sustainable agriculture. A review. Agron. Sust. Dev. 2010, 30, 237–269. [Google Scholar] [CrossRef]
  40. Latin American Maize Project (LAMP). Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Reference_Documents/LAMP-Final-Report-1997.pdf (accessed on 15 June 2020).
  41. Goodman, M.M.; Brown, W.L. Races of Corn. In Corn and Corn Improvement-Agronomy Monograph; Sprague, G.F., Dudley, J.W., Eds.; Agronomy Society of America: Wisconsin, WI, USA, 1988; Volume 18, pp. 33–79. [Google Scholar]
  42. Prasanna, B.M. Phenotypic and molecular diversity of maize landraces: Characterization and utilization. Indian J. Genet. 2010, 70, 315–327. [Google Scholar]
  43. Peruvian Ministry of Environment. Available online: http://bioseguridad.minam.gob.pe/wp-content/uploads/2019/01/Linea-de-base-ma%C3%ADz-LowRes.pdf (accessed on 15 June 2020).
  44. Catálogo de Germoplasma de Maíz de Argentina. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/Catalogo_Argentina_0_Book.pdf (accessed on 15 June 2020).
  45. Realini, M.F.; Poggio, L.; Cámara Hernández, J.; González, G.E. Exploring karyotype diversity of Argentinian guaraní maize landraces: Relationship among South American maize. PLoS ONE 2018, 13, e0198398. [Google Scholar] [CrossRef]
  46. CIMMYT (International Maize and Wheat Improvement Center). Latin American Maize Germplasm: Core Subset Development and Regeneration—Proceedings of a Workshop Held at CIMMYT June 1–5, 1998; CIMMYT: Mexico City, Mexico, 1999; pp. 1–67. [Google Scholar]
  47. Catálogos de Recursos Genéticos de Maíces Bolivianos. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/Catalogo_Bolivianos_0_Book.pdf (accessed on 15 June 2020).
  48. Races of Maize in Brazil and Adjacent Areas. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/Raza_Brazil_0_Book.pdf (accessed on 15 June 2020).
  49. Catálogo de Recursos Genéticos de Maíz de Chile. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/Catalogo_Chile_0_Book.pdf (accessed on 15 June 2020).
  50. Races of Maize in Central America. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/Races_Central-America_0_Book.pdf (accessed on 15 June 2020).
  51. Races of Maize in Ecuador. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/RoM_Ecuador_0_Book.pdf (accessed on 15 June 2020).
  52. Races of Maize in Peru. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/RoM_Peru_0_Book.pdf (accessed on 15 June 2020).
  53. Diversidad y Descripción de las Razas de Maíz del Perú. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/Diversidad%20y%20razas%20de%20maiz%20en%20Peru.pdf (accessed on 15 June 2020).
  54. Races of Maize in Paraguay. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/RoM_Paraguay_0_Book.pdf (accessed on 15 June 2020).
  55. Races of Corn. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/Corn_Improvement_0_Book.pdf (accessed on 15 June 2020).
  56. Races of Maize in Venezuela. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/RoM_Venezuela_0_Book.pdf (accessed on 15 June 2020).
  57. Brandolini, A.; Brandolini, A. Maize introduction, evolution and diffusion in Italy. Maydica 2009, 54, 233. [Google Scholar]
  58. Races of Maize in Portugal. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/RoM_Portugal_0_Book.pdf (accessed on 15 June 2020).
  59. Global Strategy for the Ex Situ Conservation and Utilization of Maize Germplasm. Available online: https://www.croptrust.org/wp/wp-content/uploads/2014/12/Maize-Strategy-FINAL-18Sept07.pdf (accessed on 15 June 2020).
  60. Races of Maize in India. Available online: https://www.ars.usda.gov/ARSUserFiles/50301000/Races_of_Maize/RoM_India_0_Book.pdf (accessed on 15 June 2020).
  61. Ortiz, R.; Taba, S.; Tovar, V.H.C.; Mezzalama, M.; Xu, Y.; Yan, J.; Crouch, J.H. Conserving and enhancing maize genetic resources as global public goods—A perspective from CIMMYT. Crop Sci. 2010, 50, 13–28. [Google Scholar] [CrossRef]
  62. Kurtz, B.; Gardner, C.A.; Millard, M.J.; Nickson, T.; Smith, J.S.C. Global access to maize germplasm provided by the US National Plant Germplasm System and by US plant breeders. Crop Sci. 2016, 56, 931–941. [Google Scholar] [CrossRef]
  63. Yang, L.; Wen, K.S.; Ruan, X.; Zhao, Y.X.; Wei, F.; Wang, Q. Response of plant secondary metabolites to environmental factors. Molecules 2018, 23, 762. [Google Scholar] [CrossRef] [PubMed]
  64. Okarter, N.; Liu, R.H. Health benefits of whole grain phytochemicals. Crit. Rev. Food Sci. Nutr. 2010, 50, 193–208. [Google Scholar] [CrossRef] [PubMed]
  65. Ranilla, L.G. Bioactive ingredients from corn and lactic acid bacterial biotransformation. In Functional Foods and Biotechnology, 1st ed.; Sarkar, D., Shetty, K., Eds.; CRC Press: Boca Raton, FL, USA, 2019; Volume 3, pp. 19–45. [Google Scholar]
  66. Siyuan, S.; Tong, L.; Liu, R. Corn phytochemicals and their health benefits. Food Sci. Hum. Wellness 2018, 7, 185–195. [Google Scholar] [CrossRef]
  67. Gutiérrez-Grijalva, E.P.; Ambriz-Pérez, D.L.; Leyva-López, N.; Castillo-López, R.I.; Heredia, J.B. Dietary phenolic compounds, health benefits and bioaccessibility. Archiv. Lat. Nutr. 2016, 66, 2. [Google Scholar]
  68. Eggersdorfer, M.; Wyss, A. Carotenoids in human nutrition and health. Arch. Biochem. Biophys. 2018, 652, 18–26. [Google Scholar] [CrossRef]
  69. Terán, J.C.; Orona, C.A.L.; Gómez, S.R.; Campos, A.M. Caracterización física, contenido de fenoles y capacidad antioxidante de maíces nativos (Zea mays L.) del Estado de México. ITEA Inf. Tec. Econ. Agr. 2017, 113, 5–19. [Google Scholar]
  70. Urias-Lugo, D.A.; Heredia, J.B.; Serna-Saldivar, S.O.; Muy-Rangel, M.D.; Valdez-Torres, J.B. Total phenolics, total anthocyanins and antioxidant capacity of native and elite blue maize hybrids (Zea mays L.). CyTA J. Food 2015, 13, 336–339. [Google Scholar] [CrossRef]
  71. Mendoza-Mendoza, C.G.; del Carmen Mendoza-Castillo, M.; Delgado-Alvarado, A.; Castillo-González, F.; Kato-Yamakake, T.Á.; Cruz-Izquierdo, S. Antocianinas totales y parámetros de color en líneas de maíz morado. Rev. Fit. Mex. 2017, 40, 471–479. [Google Scholar] [CrossRef]
  72. Martínez-Martínez, R.; Vera-Guzmán, A.M.; Chávez-Servia, J.L.; Nora, E.; Aquino-Bolaños, J.C.C.R.; Pérez-Herrera, A. Bioactive compounds and antioxidant activities in pigmentes maize landraces. Interciencia 2019, 44, 549–556. [Google Scholar]
  73. Farhoosh, R.; Johnny, S.; Asnaashari, M.; Molaahmadibahraseman, N.; Sharif, A. Structure—Antioxidant activity relationships of o-hydroxyl, o-methoxy, and alkyl ester derivatives of p-hydroxybenzoic acid. Food Chem. 2016, 194, 128–134. [Google Scholar] [CrossRef]
  74. Shahidi, F.; Yeo, J. Insoluble-bound phenolics in food. Molecules 2016, 21, 1216. [Google Scholar] [CrossRef] [PubMed]
  75. Acosta-Estrada, B.A.; Gutiérrez-Uribe, J.A.; Serna-Saldívar, S.O. Bound phenolics in foods, a review. Food Chem. 2014, 152, 46–55. [Google Scholar] [CrossRef] [PubMed]
  76. Hong, H.T.; Netzel, M.E.; O’Hare, T.J. Anthocyanin composition and changes during kernel development in purple-pericarp supersweet sweetcorn. Food Chem. 2020, 315, 126284. [Google Scholar] [CrossRef] [PubMed]
  77. Hong, H.T.; Netzel, M.E.; O’Hare, T.J. Optimisation of extraction procedure and development of LC–DAD–MS methodology for anthocyanin analysis in anthocyanin-pigmented corn kernels. Food Chem. 2020, 126515. [Google Scholar] [CrossRef]
  78. Paulsmeyer, M.; Chatham, L.; Becker, T.; West, M.; West, L.; Juvik, J. Survey of anthocyanin composition and concentration in diverse maize germplasms. J. Agric. Food Chem. 2017, 65, 4341–4350. [Google Scholar] [CrossRef]
  79. Peniche-Pavía, H.A.; Tiessen, A. Anthocyanin profiling of maize grains using DIESI-MSQD reveals that cyanidin-based derivatives predominate in purple corn, whereas pelargonidin-based molecules occur in red-pink varieties from Mexico. J. Agric. Food Chem. 2020, 68, 5980–5994. [Google Scholar] [CrossRef]
  80. Meng, Q.; Murray, S.C.; Mahan, A.; Collison, A.; Yang, L.; Awika, J. Rapid estimation of phenolic content in colored Maize by near-infrared reflectance spectroscopy and its use in breeding. Crop Sci. 2015, 55, 2234–2243. [Google Scholar] [CrossRef]
  81. Khakimov, B.; Bak, S.; Engelsen, S.B. High-throughput cereal metabolomics: Current analytical technologies, challenges and perspectives. J. Cereal Sci. 2014, 59, 393–418. [Google Scholar] [CrossRef]
  82. Cuevas Montilla, E.; Hillebrand, S.; Antezana, A.; Winterhalter, P. Soluble and bound phenolic compounds in different Bolivian purple corn (Zea mays L.) cultivars. J. Agric. Food Chem. 2011, 59, 7068–7074. [Google Scholar] [CrossRef]
  83. Velásquez-Ladino, Y.; Quiñones, R.; Coy-Barrera, E. Chemical profiling combined with multivariate analysis of unfractionated kernel-derived extracts of maize (Zea mays L.) landraces from central Colombia. Emir. J. Food Agric. 2016, 28, 713–724. [Google Scholar] [CrossRef]
  84. Nankar, A.N.; Dungan, B.; Paz, N.; Sudasinghe, N.; Schaub, T.; Holguin, F.O.; Pratt, R.C. Quantitative and qualitative evaluation of kernel anthocyanins from southwestern United States blue corn. J. Sci. Food Agric. 2016, 96, 4542–4552. [Google Scholar] [CrossRef] [PubMed]
  85. Nankar, A.; Holguin, F.O.; Scott, M.P.; Pratt, R.C. Grain and nutritional quality traits of southwestern US blue maize landraces. Cereal Chem. 2017, 94, 950–955. [Google Scholar]
  86. Harakotr, B.; Suriharn, B.; Tangwongchai, R.; Scott, M.P.; Lertrat, K. Anthocyanins and antioxidant activity in coloured waxy corn at different maturation stages. J. Funct. Foods 2014, 9, 109–118. [Google Scholar] [CrossRef]
  87. Zhang, Z.; Liang, Z.; Yin, L.; Li, Q.X.; Wu, Z. Distribution of four bioactive flavonoids in maize tissues of five varieties and correlation with expression of the biosynthetic genes. J. Agric. Food Chem. 2018, 66, 10431–10437. [Google Scholar] [CrossRef] [PubMed]
  88. Das, A.K.; Singh, V. Antioxidative free and bound phenolic constituents in botanical fractions of Indian specialty maize (Zea mays L.) genotypes. Food Chem. 2016, 201, 298–306. [Google Scholar] [CrossRef]
  89. Méndez-Lagunas, L.L.; Cruz-Gracida, M.; Barriada-Bernal, L.G.; Rodrıguez-Méndez, L.I. Profile of phenolic acids, antioxidant activity and total phenolic compounds during blue corn tortilla processing and its bioaccessibility. J. Food Sci. Tech. Mys. 2020. [Google Scholar] [CrossRef]
  90. Zavala-López, M.; López-Tavera, E.; de Dios Figueroa-Cárdenas, J.; Serna-Saldívar, S.O.; García-Lara, S. Screening of major phenolics and antioxidant activities in teosinte populations and modern maize types. J. Cereal Sci. 2018, 79, 276–285. [Google Scholar] [CrossRef]
  91. Gaxiola-Cuevas, N.; Mora-Rochín, S.; Cuevas-Rodriguez, E.O.; León-López, L.; Reyes-Moreno, C.; Montoya-Rodríguez, A.; Milán-Carrillo, J. Phenolic acids profiles and cellular antioxidant activity in tortillas produced from Mexican maize landrace processed by nixtamalization and lime extrusion cooking. Plant Foods Hum. Nutr. 2017, 72, 314–320. [Google Scholar] [CrossRef]
  92. Urias-Peraldí, M.; Gutiérrez-Uribe, J.A.; Preciado-Ortiz, R.E.; Cruz-Morales, A.S.; Serna-Saldívar, S.O.; García-Lara, S. Nutraceutical profiles of improved blue maize (Zea mays) hybrids for subtropical regions. Field Crops Res. 2013, 141, 69–76. [Google Scholar] [CrossRef]
  93. Lopez-Martinez, L.X.; Oliart-Ros, R.M.; Valerio-Alfaro, G.; Lee, C.H.; Parkin, K.L.; Garcia, H.S. Antioxidant activity, phenolic compounds and anthocyanins content of eighteen strains of Mexican maize. LWT Food Sci. Tech. 2009, 42, 1187–1192. [Google Scholar] [CrossRef]
  94. Ranilla, L.G.; Huamán-Alvino, C.; Flores-Báez, O.; Aquino-Méndez, E.M.; Chirinos, R.; Campos, D.; Sevilla, R.; Fuentealba, C.; Pedreschi, R.; Sarkar, D.; et al. Evaluation of phenolic antioxidant-linked in vitro bioactivity of Peruvian corn (Zea mays L.) diversity targeting for potential management of hyperglycemia and obesity. J. Food Sci. Technol. 2019, 56, 2909–2924. [Google Scholar] [CrossRef] [PubMed]
  95. González-Muñoz, A.; Quesille-Villalobos, A.M.; Fuentealba, C.; Shetty, K.; Ranilla, L.G. Potential of Chilean native corn (Zea mays L.) accessions as natural sources of phenolic antioxidants and in vitro bioactivity for hyperglycemia and hypertension management. J. Agric. Food Chem. 2013, 61, 10995–11007. [Google Scholar] [CrossRef] [PubMed]
  96. Lago, C.; Landoni, M.; Cassani, E.; Cantaluppi, E.; Doria, E.; Nielsen, E.; Pilu, R. Study and characterization of an ancient European flint white maize rich in anthocyanins: Millo Corvo from Galicia. PLoS ONE 2015, 10, e0126521. [Google Scholar] [CrossRef]
  97. Krinsky, N.I.; Landrum, J.T.; Bone, R.A. Biologic mechanisms of the protective role of lutein and zeaxanthin in the eye. Ann. Rev. Nutr. 2003, 23, 171–201. [Google Scholar] [CrossRef] [PubMed]
  98. Song, J.; Li, D.; He, M.; Chen, J.; Liu, C. Comparison of carotenoid composition in immature and mature grains of corn (Zea mays L.) varieties. Int. J. Food Prop. 2016, 19, 351–358. [Google Scholar] [CrossRef]
  99. Bouis, H.E.; Saltzman, A. Improving nutrition through biofortification: A review of evidence from HarvestPlus, 2003 through 2016. Glob. Food Secur. 2017, 12, 49–58. [Google Scholar] [CrossRef]
  100. Prasanna, B.M.; Palacios-Rojas, N.; Hossain, F.; Muthusamy, V.; Menkir, A.; Dhliwayo, T.; Zhang, X. Molecular breeding for nutritionally enriched maize: Status and prospects. Front. Genet. 2019, 10, 1–18. [Google Scholar] [CrossRef]
  101. Bouis, H.E.; Welch, R.M. Biofortification—A sustainable agricultural strategy for reducing micronutrient malnutrition in the global south. Crop Sci. 2010, 50, S-20. [Google Scholar] [CrossRef]
  102. Uarrota, V.G.; Severino, R.B.; Malinowsky, C.; De Oliveira, S.K.; Kuhnen, S.; Yunes, R.A.; Maraschin, M. Biochemical profile of leaf, silk and grain samples of eight maize landraces (Zea mays L.) cultivated in two low-input agricultural systems. J. Food Biochem. 2014, 38, 551–562. [Google Scholar] [CrossRef]
  103. Kuhnen, S.; Menel Lemos, P.M.; Campestrini, L.H.; Ogliari, J.B.; Dias, P.F.; Maraschin, M. Carotenoid and anthocyanin contents of grains of Brazilian maize landraces. J. Sci. Food Agric. 2011, 91, 1548–1553. [Google Scholar] [CrossRef]
  104. Da Silva Messias, R.; Galli, V.; Dos Anjos e Silva, S.D.; Rombaldi, C.V. Carotenoid biosynthetic and catabolic pathways: Gene expression and carotenoid content in grains of maize landraces. Nutrients 2014, 6, 546–563. [Google Scholar] [CrossRef] [PubMed]
  105. Corrales-Bañuelos, A.B.; Cuevas-Rodríguez, E.O.; Gutiérrez-Uribe, J.A.; Milán-Noris, E.M.; Reyes-Moreno, C.; Milán-Carrillo, J.; Mora-Rochín, S. Carotenoid composition and antioxidant activity of tortillas elaborated from pigmented maize landrace by traditional nixtamalization or lime cooking extrusion process. J. Cereal Sci. 2016, 69, 64–70. [Google Scholar] [CrossRef]
  106. Hwang, T.; Ndolo, V.U.; Katundu, M.; Nyirenda, B.; Bezner-Kerr, R.; Arntfield, S.; Beta, T. Provitamin A potential of landrace orange maize variety (Zea mays L.) grown in different geographical locations of central Malawi. Food Chem. 2016, 196, 1315–1324. [Google Scholar] [CrossRef] [PubMed]
  107. Menkir, A.; Liu, W.; White, W.S.; Maziya-Dixon, B.; Rocheford, T. Carotenoid diversity in tropical-adapted yellow maize inbred lines. Food Chem. 2008, 109, 521–529. [Google Scholar] [CrossRef]
  108. Song, J.; Li, D.; Liu, N.; Liu, C.; He, M.; Zhang, Y. Carotenoid composition and changes in sweet and field corn (Zea mays) during kernel development. Cereal Chem. 2016, 93, 409–413. [Google Scholar] [CrossRef]
  109. Capocchi, A.; Bottega, S.; Spanò, C.; Fontanini, D. Phytochemicals and antioxidant capacity in four Italian traditional maize (Zea mays L.) varieties. Int. J. Food Sci. Nutr. 2017, 68, 515–524. [Google Scholar] [CrossRef]
  110. Berardo, N.; Mazzinelli, G.; Valoti, P.; Lagana, P.; Redaelli, R. Characterization of maize germplasm for the chemical composition of the grain. J. Agric. Food Chem. 2009, 57, 2378–2384. [Google Scholar] [CrossRef]
  111. Žilić, S.; Serpen, A.; Akıllıoğlu, G.; Gökmen, V.; Vančetović, J. Phenolic compounds, carotenoids, anthocyanins, and antioxidant capacity of colored maize (Zea mays L.) kernels. J. Agric. Food Chem. 2012, 60, 1224–1231. [Google Scholar]
  112. Rao, J.; Cheng, F.; Hu, C.; Quan, S.; Lin, H.; Wang, J.; Chen, G.; Zhao, X.; Alexander, D.; Guo, L.; et al. Metabolic map of mature maize kernels. Metabolomics 2014, 10, 775–787. [Google Scholar] [CrossRef]
  113. Hu, C.; Li, Q.; Shen, X.; Quan, S.; Lin, H.; Duan, L.; Wang, Y.; Luo, Q.; Qu, G.; Han, Q.; et al. Characterization of factors underlying the metabolic shifts in developing kernels of colored maize. Sci. Rep. 2016, 6, 35479. [Google Scholar] [CrossRef]
  114. Morisasa, M.; Sato, T.; Kimura, K.; Mori, T.; Goto-Inoue, N. Application of matrix-assisted laser desorption/ionization mass spectrometry imaging for food analysis. Foods 2019, 8, 633. [Google Scholar] [CrossRef] [PubMed]
  115. Buchberger, A.R.; DeLaney, K.; Johnson, J.; Li, L. Mass spectrometry imaging: A review of emerging advancements and future insights. Anal. Chem. 2018, 90, 240–265. [Google Scholar] [CrossRef] [PubMed]
  116. Burrell, M.M.; Earnshaw, C.J.; Clench, M.R. Imaging matrix assisted laser desorption ionization mass spectrometry: A technique to map plant metabolites within tissues at high spatial resolution. J. Exp. Bot. 2007, 58, 757–763. [Google Scholar] [CrossRef]
  117. Miyamoto, S.; Hsu, C.C.; Hamm, G.; Darshi, M.; Diamond-Stanic, M.; Declèves, A.E.; Sharma, K. Mass spectrometry imaging reveals elevated glomerular ATP/AMP in diabetes/obesity and identifies sphingomyelin as a possible mediator. EBioMedicine 2016, 7, 121–134. [Google Scholar] [CrossRef]
  118. Enomoto, H.; Sato, K.; Miyamoto, K.; Ohtsuka, A.; Yamane, H. Distribution analysis of anthocyanins, sugars, and organic acids in strawberry fruits using matrix-assisted laser desorption/ionization-imaging mass spectrometry. J. Agric. Food Chem. 2018, 66, 4958–4965. [Google Scholar] [CrossRef] [PubMed]
  119. Kim, S.H.; Kim, J.; Lee, Y.J.; Lee, T.G.; Yoon, S. Sample preparation of corn seed tissue to prevent analyte relocations for mass spectrometry imaging. J. Am. Soc. Mass Spectrom. 2017, 28, 1729–1732. [Google Scholar] [CrossRef] [PubMed]
  120. Gayral, M.; Fanuel, M.; Rogniaux, H.; Dalgalarrondo, M.; Elmorjani, K.; Bakan, B.; Marion, D. The spatiotemporal deposition of lysophosphatidylcholine within starch granules of maize endosperm and its relationships to the expression of genes involved in endoplasmic reticulum—Amyloplast lipid trafficking and galactolipid synthesis. Plant Cell Physiol. 2019, 60, 139–151. [Google Scholar] [CrossRef] [PubMed]
  121. Arnaud, B.; Durand, S.; Fanuel, M.; Guillon, F.; Méchin, V.; Rogniaux, H. Imaging Study by Mass Spectrometry of the Spatial Variation of Cellulose and Hemicellulose Structures in Corn Stalks. J. Agric. Food Chem. 2020, 68, 4042–4050. [Google Scholar] [CrossRef]
  122. Fu, Y.B. Understanding crop genetic diversity under modern plant breeding. Theor. Appl. Genet. 2015, 128, 2131–2142. [Google Scholar] [CrossRef]
  123. Ortiz, R.; Crossa, J.; Franco, J.; Sevilla, R.; Burgueño, J. Classification of Peruvian highland maize races using plant traits. Genet. Resour. Crop Evol. 2008, 55, 151–162. [Google Scholar] [CrossRef]
  124. Ortiz, R.; Sevilla, R.; Alvarado, G.; Crossa, J. Numerical classification of related Peruvian highland maize races using internal ear traits. Genet. Resour. Crop Evol. 2008, 55, 1055–1064. [Google Scholar] [CrossRef]
  125. Salazar, E.; Correa, J.; Araya, M.J.; Méndez, M.A.; Carrasco, B. Phenotypic diversity and relationships among Chilean Choclero maize (Zea mays L. mays) landraces. Plant Genet. Resour. 2017, 15, 461–473. [Google Scholar] [CrossRef]
  126. Öner, F. Assessment of genetic variation in Turkish local maize genotypes using multivariate discriminate analysis. Appl. Ecol. Environ. Res. 2018, 16, 1369–1380. [Google Scholar] [CrossRef]
  127. Kulka, V.P.; Silva, T.A.D.; Contreras-Soto, R.I.; Maldonado, C.; Mora, F.; Scapim, C.A. Diallel analysis and genetic differentiation of tropical and temperate maize inbred lines. Crop Breed. Appl. Biotechnol. 2018, 18, 31–38. [Google Scholar] [CrossRef]
  128. Lia, V.V.; Poggio, L.; Confalonieri, V.A. Microsatellite variation in maize landraces from Northwestern Argentina: Genetic diversity, population structure and racial affiliations. Theor. Appl. Genet. 2009, 119, 1053–1067. [Google Scholar] [CrossRef] [PubMed]
  129. Porta, B.; Condón, F.; Franco, J.; Iriarte, W.; Bonnecarrère, V.; Guimaraens-Moreira, M.; Vidal, R.; Galván, G.A. Genetic structure, core collection, and regeneration quality in white dent corn landraces. Crop Sci. 2018, 58, 1644–1658. [Google Scholar] [CrossRef]
  130. Beckett, T.J.; Morales, A.J.; Koehler, K.L.; Rocheford, T.R. Genetic relatedness of previously plant-variety-protected commercial maize inbreds. PLoS ONE 2017, 12, e0189277. [Google Scholar] [CrossRef]
  131. Alvarado-Beltrán, G.; López-Sánchez, H.; Santacruz-Varela, A.; Muñoz-Orozco, A.; Valadez-Moctezuma, E.; Gutiérrez-Espinosa, M.A.; López, P.A.; Gil-Muñoz, A.; Guerrero-Rodríguez, J.D.; Taboada-Gaytán, O.R. Morphological variability of native maize (Zea mays L.) of the west highland of Puebla and east highland of Tlaxcala, Mexico. Rev. Fac. Cienc. Agrar. UNCUYO 2019, 51, 217–234. [Google Scholar]
  132. Riedelsheimer, C.; Lisec, J.; Czedik-Eysenberg, A.; Sulpice, R.; Flis, A.; Grieder, C.; Altman, T.; Stitt, M.; Willmitzer, L.; Melchinger, A.E. Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proc. Natl. Acad. Sci. USA 2012, 109, 8872–8877. [Google Scholar] [CrossRef]
  133. Wen, W.; Li, D.; Li, X.; Gao, Y.; Li, W.; Li, H.; Liu, J.; Liu, H.; Chen, W.; Lou, J.; et al. Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat. Commun. 2014, 5, 1–10. [Google Scholar] [CrossRef]
  134. Xu, X.; Wang, M.; Li, L.; Che, R.; Li, P.; Pei, L.; Li, H. Genome-wide trait-trait dynamics correlation study dissects the gene regulation pattern in maize kernels. BMC Plant Biol. 2017, 17, 163. [Google Scholar] [CrossRef] [PubMed]
  135. Li, K.; Wen, W.; Alseekh, S.; Yang, X.; Guo, H.; Li, W.; Wang, L.; Pan, Q.; Zhan, W.; Liu, J.; et al. Large-scale metabolite quantitative trait locus analysis provides new insights for high-quality maize improvement. Plant J. 2019, 99, 216–230. [Google Scholar] [CrossRef] [PubMed]
  136. Fang, C.; Luo, J.; Wang, S. The diversity of nutritional metabolites: Origin, dissection, and application in crop breeding. Front. Plant Sci. 2019, 10, 1028. [Google Scholar] [CrossRef] [PubMed]
  137. Butelli, E.; Titta, L.; Giorgio, M.; Mock, H.P.; Matros, A.; Peterek, S.; Martin, C. Enrichment of tomato fruit with health-promoting anthocyanins by expression of select transcription factors. Nat. Biotechnol. 2008, 26, 1301–1308. [Google Scholar] [CrossRef]
  138. Zhu, Q.; Yu, S.; Zeng, D.; Liu, H.; Wang, H.; Yang, Z.; Zhao, X. Development of “purple endosperm rice” by engineering anthocyanin biosynthesis in the endosperm with a high-efficiency transgene stacking system. Mol. Plant 2017, 10, 918–929. [Google Scholar] [CrossRef]
  139. Rhodes, D.H.; Hoffmann, L.J.; Rooney, W.L.; Ramu, P.; Morris, G.P.; Kresovich, S. Genome-wide association study of grain polyphenol concentrations in global sorghum [Sorghum bicolor (L.) Moench] germplasm. J. Agric. Food Chem. 2014, 62, 10916–10927. [Google Scholar] [CrossRef]
  140. Lago, C.; Cassani, E.; Zanzi, C.; Landoni, M.; Trovato, R.; Pilu, R. Development and study of a maize cultivar rich in anthocyanins: Coloured polenta, a new functional food. Plant Breed. 2014, 133, 210–217. [Google Scholar] [CrossRef]
  141. Lago, C.; Landoni, M.; Cassani, E.; Doria, E.; Nielsen, E.; Pilu, R. Study and characterization of a novel functional food: Purple popcorn. Mol. Breed. 2013, 31, 575–585. [Google Scholar] [CrossRef]
  142. Xiao, Y.; Liu, H.; Wu, L.; Warburton, M.; Yan, J. Genome-wide association studies in maize: Praise and stargaze. Mol. Plant 2017, 10, 359–374. [Google Scholar] [CrossRef]
Figure 1. Phenotypic variability of some corn landraces from Southern Andean region of Peru (from author’s personal file).
Figure 1. Phenotypic variability of some corn landraces from Southern Andean region of Peru (from author’s personal file).
Metabolites 10 00300 g001
Table 1. Number of native corn landraces per country (ex situ conservation).
Table 1. Number of native corn landraces per country (ex situ conservation).
Geographical AreaCountryNº LandracesNº Accessions in Local Germplasm Bank
AmericaArgentina43 [44]/51 [45]1927 [44]/2035 [46]
Bolivia45 [47]>1000 [47]/1080 [46]
Brazil27 [48]1743 [46]
Chile23 [49]929/945 [46]
Central America20 [50]1231 [50]
Colombia23 [46]2050 [46]
Ecuador29 [51]532 [46]
Mexico59 [46]6738 [46]
Peru49 [52]/52 [53]3023 [46]
Paraguay10 [54]478 [46]
United States9 [55]900 [46]
Venezuela19 [56]724 [46]
EuropeItaly34 [57]562 [57]
Portugal10 [58]
Yugoslavia18 [59]˜2000 [59]
AsiaIndia15 [60]1300 [60]
Table 2. Targeted metabolomics using liquid chromatography mass spectrometry (LC-MS) platforms for the characterization of phenolic bioactive compounds in worldwide corn biodiversity.
Table 2. Targeted metabolomics using liquid chromatography mass spectrometry (LC-MS) platforms for the characterization of phenolic bioactive compounds in worldwide corn biodiversity.
Geographical AreaType of SampleGermplasm Origin (Place/Bank)Analytical ConfigurationPhenolic Metabolite in Mature Corn KernelsRelevant GermplasmReference
Free FractionBound Fraction
America Ten landraces: (purple)
Kulli, Ayzumo, Paru, Tuimuru, Oke, Huaca, Songo, Colorado, Huillcaparu, Checchi
Bank: Centro de Investigaciones Fitoecogenéticas de Pairumani (Cochabamba, Bolivia)HPLC-ESI- MS/MS (IT) 1Phenolic acids: p-coumaric acid, ferulic acid
Anthocyanins (10):
Cy3G 12, Cy3MG 13, Cy3DMG 14
Pg3G 15, Pg3MG 16, Pg3DMG 17
Pn3G 18, Pn3MG 19, Pn3DMG 20
Epicatechin-Cy3,5DG 21
Phenolic acids: p-coumaric acid, ferulic acid, ferulic acid dehydrodimers (8-5’diferulic acid), ferulic acid dehydrotrimers.Kulli (high anthocyanin contents)[82]
Twenty-three landraces: (colored and uncolored) with a Peruvian purple corn as a control Departments: Santander (4), Cundinamarca (7), Boyacá (12)
(Colombia)
HPLC-UV-DAD-ESI-MS 2Phenolic acids: caffeic acid, chlorogenic acid, caffeic acid derivative, ferulic acid derivative, sinapic acid derivative.
Flavonols: isoquercetin, astragalin, quercetin-rutinoside, isorhamnetin-glucoside, quercetin-diglucoside.
Anthocyanins (9):
Cy3G 12, Cy3MG 13, Cy3SG 22, Cy3EMG 23
Pg3G 15, Pg3MG 16, Pg3DG 24
Pn3G 18, Pn3MG 19.
Purple colored samples
(high phenolic contents)
[83]
Eight accessions (landraces and open pollinated varieties): Navajo Blue, Santa Clara Blue, Flor del Rio, Yoeme Blue, Hopi Blue, Taos Blue, Ohio Blue (Corn Belt dent), Los Lunas High Bank: Native Seeds/SEARCH (AZ, United States)
Bank: Plants of the Southwest (NM, United States)
HPLC-DAD-MS 3
HPLC-MS (FTICR) 4
Anthocyanins (5):
Cy3G 12, Cy3SG 22, Cy3DSG 25
Pg3G 15
Pn3G 18
Navajo Blue and Ohio Blue (high anthocyanin contents)[84,85]
OceaniaSix varieties: purple-pericarp sweet corn, reddish-purple-pericarp sweet corn, purple-pericarp, corn, purple-pericarp-blue-aleurone corn, blue aleurone, cherry-aleurone cornGatton Research Facility
(QLD, Australia)
UHPLC-DAD 5
UHPLC-MS/MS (Q-Orbitrap) 6
UHPLC-ESI-MS/MS (QQQ) 7
Anthocyanins (18):
Cy3G 12, Cy3MG 13 (4 isomers), Cy3DMG 14 (3 isomers).
Pg3G 15, Pg3MG 16 (2 isomers), Pg3DMG 17 (2 isomers).
Pn3G 18, Pn3MG 19 (3 isomers), Pn3DMG 20
Purple pericarp-corn (high anthocyanin contents)[76]
Two varieties (derived from PB12.5-2-1 line and a purple Peruvian corn 9PW1):
Purple-pericarp and reddish-purple pericarp “super sweet”
Anthocyanins (20):
Cy3G 12, Cy3MG 13 (4 isomers), Cy3DMG 14 (3 isomers).
Pg3G 15, Pg3MG 16 (4 isomers), Pg3DMG 17 (3 isomers).
Pn3G 18, Pn3MG 19 (2 isomers), Pn3DMG 20
Purple-pericarp corn[77]
AsiaTwelve waxy colored corn genotypes (commercial and landraces)Bank: Plant Breeding Center for Sustainable Agriculture (Khon University, Thailand).
Genotypes from various countries of Asia: Thailand, Korea, China, Laos, Taiwan.
HPLC-DAD-MS 3Anthocyanins (10):
Cy3G 12, Cy3MG 13, Cy3DMG 14, Cy3SG 22, Cy3MSG 26
Pg3G 15, Pg3MG 16.
Pn3G 18, Pn3MG 19, Pn3SG 27
Purplish black genotype KKU-WX111031 (high anthocyanin contents)[86]
Five corn hybrids:
Jingke 968, Zhengdan 958, Xianyu 335, Jingkenuo 2000, Jingketion 183
Bank: Beijing Academy of Agriculture and Forestry Sciences (Beijing, China)UPLC-MS/MS
(QTOF) 8
Flavanones: eriodyctiol, luteolin Jingke 968 hybrid (high phenolic contents)[87]
Four corn hybrids:
HQPM-7 (quality protein maize), HM-4 (baby corn), VL Amber (popcorn), Madhuri (sweet corn)
Bank: Directorate of Maize Research
(New Delhi, India)
HPLC-DAD 9
LC-ESI-MS/MS
(QTOF) 10
Phenolic acids: p-hydroxybenzoic acid, vanillic acid, syringic acid, caffeic acid, p-coumaric acid, ferulic acid, iso-ferulic acid.
Anthocyanins: Cy3G 12
Flavonols: kaempferol, quercetin.
Phenolic acids: p-hydroxybenzoic acid, vanillic acid, syringic acid, caffeic acid, p-coumaric acid, ferulic acid, iso-ferulic acid.
Anthocyanins: Cy3G 12
Flavonols: kaempferol, quercetin.
Madhuri (sweet corn) (high phenolic contents)[88]
Mixed398 genetically diverse pigmented corn accessions from different originsBanks:
NCRPIS (North Central Regional Plant Introduction Station, Ames, United States).
CIMMYT (International Maize and Wheat Improvement Center, Mexico).
Native Seeds/SEARCH (AZ, United States).
MGCSC (Maize Genetics Cooperation Stock Center, IL, United States).
Commercial sources.
HPLC-UV-VIS 11
LC-MS
Anthocyanins (9-10):
Cy3G 12, Cy3MG 13
Pg3G 15, Pg3MG 16, Pg3DMG 17
Pn3G 18, Pn3DMG 20
Cy3DMG 14/Pn3MG 19 (coeluted)
Flavanol-anthocyanin dimers (condensed anthocyanin forms)
167 accessions (with anthocyanin production)
Peruvian landrace: Arequipa 204 (PI571427) (high anthocyanin contents)
[78]
1 High-performance liquid chromatography electro spray ionization ion trap tandem mass spectrometry. 2 High-performance liquid chromatography ultraviolet-diode array detector electro spray ionization-mass spectrometry. 3 High-performance liquid chromatography diode array detector mass spectrometry. 4 High performance liquid chromatography Fourier-transformed ion cyclotron mass spectrometry. 5 Ultra-high-performance liquid chromatography diode array detector. 6 Ultra-high-performance liquid chromatography quadrupole orbitrap tandem mass spectrometry. 7 Ultra-high-performance liquid chromatography electro spray ionization triple quadrupole tandem mass spectrometry. 8 Ultra-performance liquid chromatography quadrupole time of flight tandem mass spectrometry. 9 High-performance liquid chromatography diode array detector. 10 Liquid chromatography electro spray ionization-quadrupole time of flight tandem mass spectrometry. 11 High-performance liquid chromatography ultraviolet-visible detector. 12 Cyanidin-3-glucoside. 13 Cyanidin-3-malonylglucoside. 14 Cyanidin-3-dimalonylglucoside. 15 Pelargonidin-3-glucoside. 16 Pelargonidin-3-malonylglucoside. 17 Pelargonidin-3-dimalonylglucoside. 18 Peonidin-3-glucoside. 19 Peonidin-3-malonylglucoside. 20 Peonidin-3-dimalonylglucoside. 21 Epicatechin-cyanidin-3,5-diglucoside. 22 Cyadinin-3-succinyl glucoside. 23 Cyadinin-3-ethyl malonyl glucoside. 24 Pelargonidin-3-diglucoside. 25 Cyanidin-3-disuccinyl glucoside. 26 Cyanidin-3-malonyl-succinyl glucoside. 27 Peonidin-3-succinyl glucoside.
Table 3. Other targeted metabolomic platforms used for the characterization of phenolic bioactive compounds in worldwide corn biodiversity.
Table 3. Other targeted metabolomic platforms used for the characterization of phenolic bioactive compounds in worldwide corn biodiversity.
Geographical AreaType of SampleGermplasm Origin (Place/Bank)Analytical ConfigurationPhenolic Metabolite in Mature Corn KernelsRelevant GermplasmReference
Free FractionBound Fraction
America Landrace:
Blue Bolita
Oaxaxa (Mexico)HPLC-UV 1Phenolic acids: syringic acid, chlorogenic acid, caffeic acid, vanillic acid, 4-hydroxybenzoic acid.Phenolic acids: Ferulic acid, syringic acid, p-coumaric acid, chlorogenic acid. [89]
Twenty-two teosinte varieties: (Zea perennis, Zea diploperennis, Zea nicagaraguensis, Zea luxurians, Zea mays spp. huehuetenangensis, Zea mays ssp. mexicana, Zea mays ssp. parviglumis,
Six modern corn varieties (Zea mays ssp. mays)
Banks:
CIMMYT (International Maize and Wheat Improvement Center, Mexico).
CINVESTAN (Centro de Investigación y de Estudios avanzados del Instituto Politécnico Nacional, Mexico)
Corn seeds from Mexico, Nicaragua and Guatemala.
HPLC-DAD 2 Phenolic acids: p-coumaric acid, trans-ferulic acid.Phenolic acids: p-coumaric acid, ferulic acid, diferulic acids (8-5’diferulic acid, 5-5’ diferulic acid).Teosinte varieties (higher phenolic contents than commercial corn)[90]
Landraces:
Elotero (blue), Chapalote (white)
Concordia (Sinaloa, Mexico)HPLC-DAD 2Phenolic acids: p-hydroxybenzoic acid, p-coumaric acid, sinapic acid, ferulic acid.Phenolic acids: ferulic acid, p-coumaric acid, sinapic acid, vanillic acid, syringic acid, p-hydroxybenzoic acid.White corn (high phenolic acid contents)[91]
Twenty-five blue hybrids derived from Criollo negro and Criollo Colorado landracesBank: INIFAP (National Forestry Agricultural and Husbandry Research Institute, Guanajuato, Mexico)HPLC-DAD 2 Phenolic acid: ferulic acidHybrids with similar phenolic contents than landraces[92]
Eighteen corn phenotypes (commercial varieties and accessions)Banks:
CIMMYT (Mexico).
Campo Cotaxtla (Mexico)
Colegio de Postgraduados (Mexico)
HPLC-UV 1Phenolic acids: ferulic acid
Flavonoid: Anthocyanins
Phenolic acids: ferulic acidAREQ516540TL and Veracruz 42 accessions high anthocyanin levels[93]
Twenty-two accessions from five landraces (Arequipeño, Kculli, Cabanita, Coruca, Granada.Bank:
Maize Research Program (Agraria University of La Molina, Peru)
UPLC-DAD 3Phenolic acids: p-coumaric acid, p-coumaric acid derivatives, ferulic acid, ferulic acid derivatives, caffeic acid derivatives.
Flavonols: quercetin derivatives.
Anthocyanins.
p-coumaric acid, ferulic acid, ferulic acid derivativesKculli landrace (high phenolic contents)[94]
America Thirty-three accession from 14 local landracesBank:
INIA (National Institute of Agronomic Research, La Platina, Chile)
HPLC-UV 1Phenolic acids: vanillic acid, protocatecuic acid, p-coumaric acid, ferulic acid.Phenolic acids: Ferulic acid, p-coumaric acid.Pisankalla red landrace (high phenolic content)[95]
Landraces: Elote Occidental (red), Criollo Pozolero purpura (black), Cónico negro (black), Criollo amarillo.
Vitamaize cultivars (blue/purple): E3E4, VMATF, VM366
Banks:
Landraces (Michoacan, Mexico)
Cultivars (CINVESTAV, Mexico)
Screening:
DIESI-MSQD 4
Confirmation with: LC-MS/MS (QQQ) 5
Anthocyanins (18):
Cy3G 7, Cy3MG 8, Cy3DMG 9, Cy3SG 10, Cy3DSG 11, Cy3MSG 12
Pg3G 13, Pg3MG 14, Pg3DMG 15, Pg3SG 16, Pg3DSG 17, Pg3MSG 18
Pn3G 19, Pn3MG 20, Pn3DMG 21, Pn3SG 22, Pn3DSG 23, Pn3MSG 24
Red (high in pelargonidin-based anthocyanins). Black (high in cyanidin derivatives) [79]
479 hybrids and 81 lines (White, yellow, red, blue, purple phenotypes)Bank:
Quantitative Genetics and Maize Breeding Program (Texas A&M University, United States)
FT-NIRS 6Free soluble phenolic compounds Well correlated with visual color selection for high anthocyanin corn [80]
EuropeLandraces: Millo Corvo (blue), Scagliolo (yellow), Ottofile (yellow)Blue corn (Galicia, Spain)
Yellow corn (Careno and Zinasco, Italy)
HPLC-DAD 2Anthocyanins: Cy3G, Pg3G, Pn3G Millo Corvo (high anthocyanins)[96]
1 High-performance liquid chromatography ultraviolet detector. 2 High-performance liquid chromatography diode array detector. 3 High-performance liquid chromatography diode array detector. 4 Direct injection electrospray ionization mass spectrometry. 5 Liquid chromatography triple quadrupole tandem mass spectrometry. 6 Fourier-transformed near-infrared reflectance spectroscopy. 7 Cyanidin-3-glucoside. 8 Cyanidin-3-malonyl glucoside. 9 Cyadinin-3-dimalonyl glucoside. 10 Cyanidin-3-succinyl glucoside. 11 Cyanidin-3-disuccinyl glucoside. 12 Cyanidin-3-malonyl succinyl glucoside. 13 Pelargonidin-3-glucoside. 14 Pelargonidin-3-malonyl glucoside. 15 Pelargonidin-3-dimalonyl glucoside. 16 Pelargonidin-3-succinyl glucoside. 17 Pelargonidin-3-disuccinyl glucoside. 18 Pelargonidin-3-malonyl succinyl glucoside. 19 Peonidin-3-glucoside. 20 Peonidin-3-malonyl glucoside. 21 Peonidin-3-dimalonyl glucoside. 22 Peonidin-3-succinyl glucoside. 23 Peonidin-3-disuccinyl glucoside. 24 Peonidin-3-malonyl succinyl glucoside.
Table 4. Targeted metabolomics for the characterization of carotenoid compounds in worldwide corn biodiversity.
Table 4. Targeted metabolomics for the characterization of carotenoid compounds in worldwide corn biodiversity.
Geographical AreaType of SampleGermplasm Origin (Place/Bank)Analytical ConfigurationCarotenoid Metabolite in Mature Corn KernelsRelevant GermplasmReference
Provitamin ANon-provitamin A
AmericaLandraces:
Roxo, Palha Roxa, Mato Grosso, Palha Roxa, Rajado, Rajado 8 Carreiras, Roxo do Emilio, MPA, Língua de Papagaio
Santa Catarina (Brazil)HPLC-UV-VIS 1β-cryptoxanthin, α-carotene, cis-β-carotene, trans-β-caroteneLutein, zeaxanthinPalha Roxa, MPA, Roxo (high contents)[102]
Twenty-six landraces (yellow, white, orange, variegated, purple)SINTRA-Small Farmer Association (Santa Catarina, Brazil)HPLC-UV-VIS 1β-cryptoxanthin, α-carotene, β-carotene.Lutein, zeaxanthinRoxo 41 and MPA1 (high contents)[103]
Twenty-two landraces (white, yellow, orange)Brazil
(Bank not informed)
HPLC-DAD 2β-cryptoxanthin, α-cryptoxanthin, α-carotene, β-carotene.Lutein, zeaxanthinMC3, MC14 (high contents)[104]
Eight landraces: Tuxpeño (yellow), Tablocillo (red), Chapalote (red)Sinaloa (Mexico)HPLC-DAD 2β-cryptoxanthin, β-caroteneLutein, zeaxanthinTuxpeño (high contents)[105]
AfricaLandraces: 26 white and 35 orange (Mthikinya)Central MalawiHPLC-DAD 2β-cryptoxanthin, β-caroteneLutein, zeaxanthinOrange group with high contents[106]
421 tropical adapted yellow endosperm inbred linesIbadan (Nigeria)HPLC-DAD 2β-cryptoxanthin, α-carotene, β-carotene (cis+trans isomers)Lutein, zeaxanthinVariable [107]
AsiaSweet corn varieties: Jingtian 3, Jingtian 5.
Waxy corn varieties: Suyunuo 11, Jignuo 8, Jingtianzihuanuo 2
China
(Bank not informed)
HPLC-DAD-APCI-MS/MS 3 All-trans-α-carotene, 9-cis-α-carotene, 9′-cis-α-carotene, all-trans-β-carotene, 9-cis-β-carotene, 13-cis-β-carotene, all-trans-β-cryptoxanthin, 9-cis-β-cryptoxanthin, 9′-cis- β-cryptoxanthin, 13 or 13’-cis- β-cryptoxanthin, 15-cis- β-cryptoxanthin, all-trans-α-cryptoxanthin, 9-cis-α-cryptoxanthinAll-trans-lutein, 9 or 9′-cis-lutein, 13-cis-lutein-5,6-epoxide, all-trans-zeaxanthin, violaxanthin, neochrome, neoxanthin, 13-cis-neoxanthinSweet varieties (high contents)[98]
Sweet corn varieties: Jingtian 5, Suyu 29Luhe Experimental Station of Jiangsu Academy of Agricultural Sciences
(China)
HPLC-DAD-APCI-MS/MS 3All-trans-α-carotene, all-trans-β-carotene, all-trans-β-cryptoxanthin, all-trans-α-cryptoxanthinNeoxanthin, violaxanthin, all-trans-lutein, all-trans-zeaxanthin.Suyu 29 with orange kernel (high contents)[108]
EuropeFour landrances: Formentone ottofile rosso (red), Formentone ottofile giallo (yellow), Nostrato del Palazzaccio (yellow), Nano di Verni (yellow), yellow comercial variety.Regional Germplasm Bank Network (Italy).
Comunitá Montana della Mediavalle del Serchio (Italy)
HPLC-DAD 2β-cryptoxanthin, β-carotene.Lutein, zeaxanthinNano di Verni (high contents)[109]
93 LandracesEuropean Union Maize Landraces Core Collection (EUMLCC)HPLC-DAD 2 Lutein, zeaxanthinOverall landraces from Italy and France (high contents)[110]
MixedTen landraces and inbred linesCYMMIT (International Maize and Wheat Improvement Center, Mexico).
BSSS (Iowa Stiff Stalk Syntetic, United States).
MRI (Maize Research Institute, Serbia).
Samples from Mexico, United States, France, Serbia, Netherland.
HPLC-DAD 2β-caroteneLutein Orange and red colored corn kernels (high contents)[111]
1 High-performance liquid chromatography ultraviolet-visible detector. 2 High-performance liquid chromatography diode array detector. 3 High-performance liquid chromatography diode array detector atmospheric pressure chemical ionization tandem mass spectrometry.
Table 5. Investigations about the corn genetic diversity using different types of markers.
Table 5. Investigations about the corn genetic diversity using different types of markers.
GermplasmOriginEvaluated TraitType of MarkerMethod for Diversity ClassificationReference
Eight Peruvian highland corn landraces: Confite Morocho (5) 1, Chullpi (6), Uchuquilla (5), Cusco Gigante (9), Huayleño (9), Paro (4), San Gerónimo-Huancavelicano (6), Shajatu (6)PeruPlant traits: height, ear height, leaf number, leaf number above ear, leaf length, leaf widthMorphologicalWard-Modified Location Model (MLM)[123]
Four Peruvian highland corn landraces: Confite Morocho (5) 1, Confite Punteagudo (9), Cusco Gigante (5), Uchuquilla (5)PeruInternal ear traits: cob and pith diameters, glume length and texture, cupule length and widthMorphologicalWard-MLM[124]
134 corn populations from 34 highland localities and 10 witnessesMexico32 vegetative, reproductive, and yield traitsMorphologicalModified Location Model (MLM)[131]
Thirty-four accessions (Choclero landrace)Chile2 phenological, 16 vegetative 12 reproductive and 9 qualitative traitsMorphologicalWard and Manhattan distance method[125]
Seventy-nine corn accessions: flint (59) 1, pop (16) and dent (4) racesTurkey16 traits: 6 ear and 10 tassel traitsMorphologicalUnweighted pair group method of arithmetic average (UPMGA)[126]
Six corn landraces: Altiplano (32) 1, Blanco (13), Amarillo Grande (25), Amarillo Chico (34), Pisingallo (16) and Orgullo Cuarentón (24) Argentina---Molecular: 18 SSR (microsatellites or simple sequence length polymorphisms)Bayesian method and Nei’s genetic distances[128]
Corn inbred lines: group I: 7 tropical and subtropical lines; group II: 7 temperate linesGroup I: Brazil
Group II: United States
---Molecular: 22 SSRMarkov Chain Monte Carlo algorithm for the Bayesian clustering method and the Self-Organizing Tree Algorithm (SOTA)[127]
Ninety white dent race accessionsUruguay17 traits: vegetative, reproductive and yieldMorphological
Molecular: 26 SSR
Molecular results: Ward, canonical and Bayesian methods[129]
349 inbred lines: 283 ex-PVP (Plant Variety Protected) inbreds and 66 public inbred lines.United States---Sequence-based markers:
Genotyping-by-sequencing (GBS), 77,314 SNP (single-nucleotide polymorphism) markers
Ward and Nei’s distance methods[130]
1. Number of accessions.
Back to TopTop