Novel Omics Technologies In Nutrition Research Paper

Introduction

Sugar beet (Beta vulgaris. L), a species of Chenopodiaceae family, is an important sugar crop that supplies approximately 35% of the sugar in the world (Liu et al., 2008). In the United States, sugar beet has provided about 55 percent of the total sugar produced domestically since the mid-1990s (Benoit et al., 2015). Sugar beet was introduced to China from Arabia about 1500 years ago and it is a dicotyledonous plant with high economic value in many countries. Therefore, how to grow the crop efficiently has been a priority and extensively investigated (Draycott, 2006). Sugar beet is a biennial crop which grows a sugar-rich tap root in the first year (the vegetative stage) and a flowering seed stalk in the second year (the reproductive stage; Chen et al., 2016). The types of sugar beet can be distinguished according to various internal and external features, such as economic characters, trait diversity, and chromosome ploidy. Beta corolliflora Zoss. (2n = 36) is a wild species of the beet Corollinae section that has many characteristics including tolerance to drought, cold, salt and against disease. Sugar beets (Beta vulgaris) are classified as salt-tolerant crops (Dunajska-Ordak et al., 2014). Scientists have studied the interspecific crossing of cultivated sugar beet (Beta vulgaris L. 2n = 19) and B. corolliflora Zoss. for decades (Dalke et al., 1971; Filutowicz and Dalke, 1976). In our lab, Guo et al. obtained the sugar beet monosomic addition line M14 (Figure 1), which contains the Beta vulgaris L. genome with the addition of No. 9 chromosome of B. corolliflora Zoss (Guo et al., 1994). It has several interesting characteristics including apomixes and tolerance to drought, cold and salt stress (Guo et al., 1994). Apomixis is a mode of asexual reproduction characterized by the production of clonal seeds via the parthenogenesis development of an unreduced egg. The apomictic process bypasses meiosis and egg cell fertilization, producing offspring that are exact copies of the mother plant (Nogler, 1984; Ozias-Akins, 2006). Sugar beet M14 therefore can function as a unique germplasm for studying the characteristics of apomixes and tolerance to abiotic stresses.

Figure 1. Generation of sugar beet M14 apomict (B. vulgaris genome plus chromosome No. 9 from B. corolliflora).

During evolution, plants have developed complex strategies that regulate biochemical and physiological acclimation in order to respond to biotic stress (viral, bacterial, fungal, and oomycete infections; Baum et al., 2007) and abiotic stress (salinity, drought, and low temperature; Barnabas et al., 2008; Yolcu et al., 2016). Biotic and abiotic stresses severely reduce agricultural productivity worldwide (Munns and Tester, 2008; Pinhero et al., 2011; Mishra et al., 2012). Therefore, understanding how plants respond and tolerate biotic and abiotic stresses is important for boosting plant (e.g., sugar beet) productivity under these challenging conditions. In order to minimize the negative impact of these stresses, studying how the sugar beet has evolved stress coping mechanisms will provide new insights and lead to novel strategies for improving the breeding of stress-resistant sugar beet and other crops.

In recent years, genomics knowledge based on Next Generation Sequencing (NGS), gene editing systems, gene silencing, and over-expression methods have provided a large amount of genetic information to help reveal the mechanisms of biotic and abiotic stress responses in plants (Saad et al., 2013; Shan et al., 2013; Yin et al., 2014; Luan et al., 2015). At the transcriptome level, technological innovations have made it possible to overview the changes that occur at the transcriptomic level under different environmental stress conditions. Microarrays and RNA sequencing techniques are employed to elucidate the differential expression of genes involved in biotic and abiotic stress responses in a variety of plant species (Kreps et al., 2002; Shinozaki and Yamaguchi, 2007; Ergen and Budak, 2009; Mitchell et al., 2014; Akpinar et al., 2015; Budak and Akpinar, 2015; Wang et al., 2016). Proteomics and metabolomics are two emerging “-omic” techniques in the post-genomic era (Fernandez-Garcia et al., 2011). Proteomics technologies allow the simultaneous identification and quantification of thousands of proteins that are an essential tool for understanding the biological systems and their regulations (Silva-Sanchez et al., 2015). Proteomics can be used to compare proteomes under varying stress conditions (Draycott, 2006; Liu et al., 2008; Benoit et al., 2015). Metabolomics focuses on the global profile of the low molecular weight (< 1000 Da) metabolites which are the end products of metabolisms in biofluids, tissues and even whole organism (Brosché et al., 2005). Metabolomics has recently been utilized in an increasing number of applications to investigate plant metabolite responses to abiotic stresses, particularly drought, flooding, salinity, and extreme temperatures (heat and cold; Jorge et al., 2015; Jia et al., 2016). Obviously, a combination of OMICS techniques including genomics, transcriptomics, proteomics and metabolomics could could serve to validate and complement one another in order to provide an efficient way capable of improving stress tolerance in plants.

Sugar beet is a good plant resource to explore and identify genes and proteins involved in stress resistance. Sugar beet is widely used in sugar industry (Liu et al., 2008). It is a source of the clean energy via hydrogen gas and bioethanol (Dhar et al., 2015). It contains abundant betaine and betalain metabolites. Betaine is used to improve the plant stress tolerance (Catusse et al., 2008). Betalains are natural pigments which have potential health benefits (anticarcinogenic and antioxidative) and have attracted both scientific and economic interest (Stintzing and Carle, 2007; Moreno et al., 2008). A rich and cheap source of betalains in red beet root (Beta vulgaris L.) is very attractive to the pharmaceutical and food industries (Wybraniec, 2005; Wybraniec et al., 2011, 2013). In this review, we have summarized OMICS applications and covered the recent discoveries in sugar beet research including the M14 for identification of novel genes and proteins related to biotic and abiotic stresses, apomixes, and metabolites related to energy and food production. The knowledge gained is valuable for improving sugar beet and other crops tolerance to biotic and abiotic stresses as well as for enhancing the yield of sugar beet for energy and food production.

OMICS Overview for Discovering Novel Genes, Proteins and Metabolites in Sugar Beet

In recent years, the use of OMICS tools has considerably increased for studying biotic and abiotic stresses in plants. The existing methods include genomics, transcriptomics, proteomics, metabolomics, and several others capable of discovering and characterizing the expression of genes or proteins during biotic and abiotic stresses with high efficiency shown in Figure 2. These highly sensitive tools can analyze plant tissues and help to improve our understanding of the tolerance mechanisms utilized by sugar beet.

Figure 2. Schematic diagram showing information flow for OMICS studies in sugar beet.

Genomics Studies in Sugar Beet

The Whole Genome Sequence of Sugar Beet

The whole genome sequence of sugar beet has been reported by Dohm et al. (2014). A total of 27,421 protein-coding genes were predicted based on transcription data and annotated on the basis of sequence homology (Dohm et al., 2014). Compared to other flowering plants with the genome information, the sugar beet has a small number of genes encoding transcription factors. It has been suggested that the sugar beet may contain unknown genes associated with transcriptional control, and that the genetic interaction network of sugar beet may have evolved in unique ways compared with other species. Using the sugar beet genome sequence and related resources, we expected to find the molecular mechanisms underlying gene regulation and gene environment interaction. In addition, this information can help to develop crops with improved sugar and natural substance production and have an important role in future plant genomic research.

SMRT of the Sugar Beet Chloroplast Genome

SMRT (Single Molecule Real-Time) is a third generation sequencing method, which offers much longer read length compared to NGS methods. It is well suited for de novo- or re-sequencing projects. It not only contains reads originating from the nuclear genome, but also lots of reads from the organelles of the target organism (Sanger et al., 1977; Liu et al., 2012). Stadermann et al. described a workflow for de novo assembly of the sugar beet chloroplast genome based on data originating from a SMRT sequencing dataset targeted on nuclear genome (Stadermann et al., 2015). They identified a total of 114 individual genes. Of these, 79 genes encode mRNA (i.e., proteins), 7 encode rRNA and 28 are tRNAs. Nine genes are located within the inverted repeat (IR) regions which encode 5 mRNAs, 1 rRNA, and 3 tRNAs. In comparison to the Illumina assembly, the annotation showed some differences due to changes in the underlying sequences.

miRNAs Involved in Tolerance to Abiotic Stresses

miRNAs are small 19–23 nucleotides short non-coding RNAs, which play regulatory roles in many processes (Budak et al., 2015). miRNAs can act both at the transcriptional or post-transcriptional levels. miRNA mediated gene-silencing mechanism regulates the expression of transcription factors, phytohormones, and other developmental signaling pathways (Llave et al., 2002; Dalmay, 2006; Sunkar et al., 2007).

Earlier studies have shown that miRNAs mainly target transcription factors, but recent studies have revealed that miRNAs also target other development/stress signaling pathways, which are involved in various physiological processes, including root growth and development, response to stress, signal transduction, leaf morphogenesis, plant defenses, and biogenesis of sRNA (Curaba et al., 2014). Li J. L. et al. (2015) reported 13 mature miRNAs from 12 families using an in silico approach based on 29,857 expressed sequence tags and 279,223 genome survey sequences in B. vulgaris. The psRNA target server predicted 25 target genes for the 13 miRNAs. The target genes shown in Table 1 appeared to encode transcription factors or were involved in metabolism, signal transduction, stress response, growth, and development. However, there were no targets predicted from the current database of sugar beet for Bvu-miR4, Bvu-miR9, Bvu-miR10, Bvu-miR11, and Bvu-miR12.

Table 1. Predicted functions of sugar beet miRNAs in plant development, signaling and stress responses.

Several miRNAs identified have been shown to have critical roles in plants. For example, the expression of Bvu-miR1 (Protein ARABIDILLO) in A. thaliana regulates multicellular root development (Moody et al., 2012). Bvu-miR2 regulates the expression of ATPase during plant development and coordinates its induction in response to high salinity (Lehr et al., 1999). Through transcriptional regulation, it also affects the ATPase activity of magnesium chelatase subunit I in Barley (Lake et al., 2004) and abscisic acid insensitive 5 required to delay growth of germinated seedlings under environmental stress (Liu and Stone, 2013). Bvu-miR7 targets the respiratory burst oxidase gene family, which encodes the key enzymatic subunit of the plant NADPH oxidase (Torres and Dangl, 2005). Bvu-miR8 activates the transcription of a histone acetyltransferase GCN5 in A. thalianaina (Benhamed et al., 2008). Another target protein MYB6 acts as an immediate and positive activation signaling component of the active state of MLA immune receptors during transcriptional reprogramming for defense responses (Chang et al., 2013). Dehydration-responsive element-binding proteins form a major AP2/ethylene- responsive element-binding protein family and play crucial roles in the regulation of abiotic stress responses (Sun et al., 2008). Bvu-miR13 targets WD-repeat proteins in this diverse family of regulatory proteins. To date, genome-wide characterization of this family has only been conducted in Arabidopsis and little is known about WD-repeat protein-coding genes in other species. Recently, it has become known that the WD-repeat protein plays an important role in cucumber stress resistance (Li et al., 2014). Other targets, e.g., leucine-rich repeat proteins, plays critical roles in both animal and plant signaling pathways regulating growth, development, differentiation, cell death, and pathogenic defense responses (Gou et al., 2010). These studies have provided insights into the molecular mechanisms of the miRNAs and may have great potential for sugar beet improvement. The functions of these interesting miRNAs in sugar beet need to be investigated in the future.

QTL Mapping for Disease Resistance in Sugar Beet

Leaf spot is one of the most serious and widespread foliar diseases of sugar beet. It causes necrotic lesions and progressive destruction of the plant's foliar structure and function (Holtschulte, 2000). The disease has greatly impacted on the yield and sugar contents of the crop. Doubled haploids (DHs), F2 populations of recombinant inbred lines (RILS), and near isogenic lines (NILS) are suitable populations for quantitative trait loci (QTL) mapping (Ibrahim et al., 2012). In order to deal with the complex inheritance of resistance to Cercospora leaf spot (CLS), Taguchi et al. (2011) used RILs of sugar beet, which were generated by a cross between a resistant line (“NK-310mm-O”) and a susceptible (“NK-184mm-O”) line. These RILs were then tested for their resistance to the CLS pathogen in the field (Taguchi et al., 2011). Composite interval mapping (CIM) showed four QTLs involved in CLS resistance that were consistently detected. There were two resistant QTLs (qcr1 on chromosome III, qcr4 on chromosome IX) that promoted resistance in the cross between lines (“NK-310mm-O”). There were two further QTLs (qcr2 on chromosome IV, qcr3 on chromosome VI) which promoted resistance in the susceptible line. In addition, a number of important resistance gene cluster have been mapped on the chromosome III in the sugar beet genome, for example: a CLS resistance QTL (Setiawan et al., 2000), the genes Rz1 toRz5 (Grimmer et al., 2007), gene Acr1 (Taguchi et al., 2010), gene RGAs (Lein et al., 2007), and gene X, a restorer of fertility for Owen CMS (Hagihara et al., 2005). These resistance-gene clusters on the chromosome III are mainly responsible for the disease resistance in sugar beet.

BAC Library from the Genomic DNA of the No. 9 Chromosome in Sugar Beet M14

A plant-transformation-competent binary BAC library was constructed from the genomic DNA of the No. 9 chromosome in sugar beet M14 (Fang et al., 2004). A total of 2365 positive clones were obtained and arrayed into a sublibrary specific for the B. corolliflora chromosome 9 (designated bcBAC-IX). The bcBAC-IX sublibrary was further screened with a subtractive cDNA pool generated from the ovules of M14 and the floral buds of B. vulgaris by the suppression subtractive hybridization (SSH) method. One hundred and three positive binary BACs were obtained, which may potentially contain the genes of the alien No. 9 chromosome that is specifically expressed during the ovule and embryo development of M14, and which may be associated with apomictic reproduction. The binary BAC clones are useful for the identification of the genes responsible for apomixes by genetic transformation.

Transcriptomics Studies in Sugar Beet

Suppression Subtractive Hybridization (SSH) Applications in Sugar Beet

SSH is a technique used to identify differentially expressed genes in cells important for growth and differentiation (Lukyanov et al., 2007). This method has often been used to study molecular mechanisms of plants in biotic and abiotic stresses (Sahebi et al., 2015). The response to insect pests in the root of sugar beet is an interesting area of plant defense research.

Puthoff et al have identified more than 150 sugar beet root ESTs enriched for genes that respond to feeding by the sugar beet root maggot in both the moderately resistant genotype F1016 and the susceptible F1010 using SSH [49]. The differential expression of the root ESTs was confirmed via RT-PCR. The ESTs were further characterized using microarray-generated expression profiles from the F1016 sugar beet roots following mechanical wounding and treatment with the signaling molecules methyl jasmonate, salicylic acid and ethylene. Of the examined ESTs, 20% were regulated by methyl jasmonate, 17% by salicylic acid and 11% by ethylene, suggesting that these signaling pathways are involved in sugar beet root defense response. Identification of these sugar beet root ESTs provides knowledge concerning plant root defense and will likely lead to the development of novel strategies for the control of the sugar beet root maggot (Puthoff and Smigocki, 2007).

SSH was applied to isolating taproot expressed genes from sugar beet as well (Kloos et al., 2002). The taproot of sugar beet (Beta vulgaris L.) undergoes a specific developmental transition in order for it to function as a storage organ. SSH was utilized to isolate cDNA fragments of genes expressed in the taproot. Molecular analysis of six cDNAs that encoded complete gene products revealed that these genes comprise homologs of a drought-inducible linker histone, a major latex-like protein, a phosphoenolpyruvate carboxylase kinase, a putative vacuolar processing enzyme, a thaumatin-like protein and an alanine- and glutamic acid-rich protein. All of these genes are transcribed in taproots, while the expression in leaves is low or undetectable. SSH had also been used in the sugar beet M14 to identify differentially expressed genes. A subtractive cDNA library was prepared by SSH between the flower organ of M14 and that of B. vulgaris (Ma et al., 2011). A total of 190 unique sequences were identified in the library and their putative functions were analyzed using Gene Ontology (GO). All of the ESTs provide information about candidate genes useful for studying M14 reproductive development. One of the genes, designated as BvM14-MADS box, encodes a MADS box transcription factor. It was cloned from M14 and over-expressed in transgenic tobacco plants. Overexpression of BvM14-MADS box led to significant phenotypic changes in tobacco (Ma et al., 2011).

Li et al. (2009) reported a comparative proteomic and transcriptomic study of the sexual and apomictic processes in sugar beet. The cDNA libraries were constructed using SSH with the apomictic monosomic addition line M14 as the tester and B. vulgaris as the driver. Comparative analyses of proteomic data and transcriptomic data showed that eight proteins had significant agreement between protein and mRNA expression levels. Most of the matched proteins were associated with metabolism. Interestingly, two of the matched proteins, cystatin, and thioredoxin peroxidase, were found to be associated with disease and defense response, indicating that defense-related proteins may participate in the apomictic reproductive process. Yang et al. (2012) reported transcriptomic analysis of sugar beet M14 leaves and roots that were treated with 500 mM NaCl for 7 days. The SSH technology was used to produce a high quality subtractive cDNA library. A total of 600 positive clones were randomly selected and subjected to DNA sequencing, and 499 non-redundant ESTs were obtained. After assembly, 58 unigenes including 14 singletons and 44 contigs were obtained. Some salt-responsive genes were identified as important in metabolism (e.g., sadenosylmethionine synthase 2 (SAMS2) and nitrite reductase), photosynthesis (e.g., chloroplastic chlorophyll a–b binding protein 8), energy (e.g., phosphoglycerate kinase), protein synthesis (e.g., 60S ribosomal protein L19-3), and degradation (e.g., cysteine protease and carboxyl-terminal-processing protease), and stress and defense [e.g., glutathione S-transferase (GST)]. This study has revealed candidate genes for detailed functional characterization, and has set the stage for further investigation of salt tolerance mechanisms in sugar beet.

Transcriptomics of Sugar Beet in Response to Low Temperature Stress

Comparative transcriptomics is used to identify differences in transcript abundance between different cultivars, organs, development stages and/or treatment conditions (Mardis, 2008; Schuster, 2008; Bräutigam and Gowik, 2010). Low-temperature stress is a significant factor effecting of crop quality and causing production losses in agriculture. The survival of young sugar beet seedlings and the subsequent sugar yield of mature plants are often seriously limited by low temperature, especially when the plants are exposed to freezing temperatures at early developmental stages. Moliterni et al. (2015) determined the transcriptomic changes using high-throughput sequencing of the leaves and root RNAs (RNA-Seq) from sugar beets which had been exposed to cold stress which mimicked the conditions of spring nights sometimes experienced by young seedlings (Moliterni et al., 2015). In the root tissue, CBF3 is up-regulated within a few minutes of cold stress. The authors suggested that CBF3 transcription in the stressed plants is either maintained for a longer period or begins earlier in roots compared to leaves. The AP2/ERF family genes were also found to be either activated or up-regulated in all the organs by cold stress. This is an expected result, as it is known that these TFs are rapidly induced upon exposure to low temperature in Arabidopsis (Lee et al., 2005). AP2/ERF TFs are involved in the regulation of primary and specialized metabolism and in a number of JA responses (Licausi et al., 2013). It has been reported that the lack of ADA2b TFs leads to an increase of freezing tolerance by affecting nucleosome occupancy in Arabidopsis (Vlachonasios et al., 2003). In addition, a putative histone acetylase and a lysine-specific demethylase are strongly up-regulated in the leaves under cold stress, implicating chromatin remodeling, and modification in the response.

These studies suggested that the metabolic pathway most affected by low temperature was carbohydrate metabolism. In addition, the authors found 13 differentially expressed sequences related to phospholipid secondary metabolism, none of which were common to leaves and roots, implicating this pathways as another important component in early cold signaling in sugar beet roots. The high degree of organ specificity is probably due to the repertoire of compounds synthesized by the two organs upon stress. This data has illuminated the transcriptome of young sugar beet during cold stress at night, and has detailed both organ-specificity and shared pathways in the physiological response to low temperatures. These RNA-Seq based transcriptomics techniques are an effective and powerful tool, with the analyses identifying novel genes for future studies.

Proteomics Studies in Sugar Beet

Proteomic analysis has been carried out to address several important questions in many processes, such as: signaling, regulatory processes, and transport in plants (Zhang et al., 2008). Important knowledge of the proteomic response to stress has been mainly derived from studies of the model plants A. thaliana and rice (Janmohammadi et al., 2015; Liu et al., 2015; Xu et al., 2015). Proteomic analysis provides an important way to test the changes in protein levels to help identify novel proteins.

Proteins in Different Sugar Beets without Stress Treatment

Zhu et al. (2009) compared the proteomes of the monosomic addition line M14 and B. vulgaris using 2-DE (two-dimensional gel electrophoresis). They have identified 27 protein spots using MALDI-TOF MS. Among them, only two protein spots were found in B. vulgaris and five protein spots were unique to M14. These proteins were involved in many biological pathways. The results may be useful for us to better understand how genotype differences relate to proteome and phenotype differences.

Li et al. (2009) reported a comparative proteomic and transcriptomic analysis of the sexual and apomictic processes in sugar beet. A total of 71 differentially expressed protein spots from the floral organs of the M14 were identified in the course of apomictic reproductive development using 2-DE and MS analysis. The differentially expressed proteins were involved in several processes which may work cooperatively to promote apomictic reproduction, generating new potential protein markers important for apomictic development.

Proteome Changes in Response to Salt Stress of Sugar Beet

To date, some proteomic studies concerning the response of sugar beet to salt stress have been reported. Wakeel et al. identified six proteins from sugar beet shoots and three proteins from roots that significantly changed under 125 mM salt treatment (Wakeel et al., 2011). Our group has performed proteomic analysis of the monosomic addition line M14 under 500 mM salt stress for 7 days. A total of 71 differentially expressed protein 2D spots were identified using LC-MS/MS. The largest functional group is represented by metabolism (28%), followed by energy (21%), protein synthesis (10%), stress and defense (10%), destination proteins (8%), unknown proteins (8%), secondary metabolism (5%), signal transduction (4%), transporters (1%), and cell division (1%). Of the identified proteins, only eight had corresponding transcriptomic data. This highlights the importance of expression profiling at the protein level (Li et al., 2009). On this basis, we focused on the functions of cystatin (Wang et al., 2012), glyoxalase I (Wu et al., 2013), CCoAOMT, and thioredoxin peroxidase. All of these proteins showed increased protein levels under salt stress. Transgenic plants exhibited enhanced tolerance to salt stress. This research has directly improved our understanding of mechanisms underlying the M14's high salt tolerance.

Another proteomics study aims to identify salt-responsive proteins in the M14 plants under 0, 200, and 400 mM NaCl mild salt stress conditions using 2D-DIGE to separate the proteins from control and salt-treated M14 leaves and roots (Yang et al., 2013). The differentially expressed proteins were identified using nanoflow liquid chromatography (LC)−MS/MS and Mascot database searching. As a complementary approach, iTRAQ LC−MS/MS was employed to identify and quantify differentially expressed proteins during salinity response in M14. We have identified 86 protein spots representing 67 unique proteins in leaves, and 22 protein spots representing 22 unique proteins in roots. In addition, 75 differentially expressed proteins were identified in leaves and 43 differentially expressed proteins were identified in roots, respectively. The proteins were mainly involved in photosynthesis, energy, metabolism, protein folding and degradation, and stress and defense. Compared to the transcriptomic data, 13 proteins in leaves and 12 proteins in roots showed significant correlation in gene expression and protein levels. These results suggest that there are several processes underlying the M14 tolerance to salt stress.

Our group also reported the changes in membrane proteome of the M14 plants in response to salt stress (0, 200, 400 mM NaCl; Li H. et al., 2015). We have used an iTRAQ two-dimensional LC–MS/MS technology for quantitative proteomic analysis. In total, 274 proteins were identified and mostly of them were membrane proteins. A total of 50 differential proteins were identified, with 40 proteins showing increased expression and 10 with decreased expression. The proteins were mainly involved in transport (17%), metabolism (16%), protein synthesis (15%), photosynthesis (13%), protein folding and degradation (9%), signal transduction (6%), stress and defense (6%), energy (6%), and cell structure (2%). These results have revealed that membrane proteins contribute to the salt stress tolerance observed in M14.

Proteome Changes in Response to Drought Stress of Sugar Beet

Hajheidari et al. (2005) studied the proteome changes of sugar beet in response to drought stress. Leaves from well-watered and drought treated plants at 157 days after sowing were collected. The changes of proteins were analyzed using 2D-DIGE followed by image analysis. More than 500 protein spots were detected, and 79 spots had significant changes under drought stress. Twenty protein spots were digested and subjected to LC-MS/MS, and 11 proteins involved in oxidative stress, signal transduction and redox regulation were identified. These proteins may be important targets for improving plant abiotic stress tolerance via breeding.

Metabolomic Studies in Sugar Beet

Metabolomics is an exciting technology, which was used to identify secondary metabolites important for physiological processes and different stress responses (Capuano et al., 2013). During plant development and interaction with the environment, the dynamic metabolome reflects the plant's physiological and biochemical processes, and can determines the phenotypes and traits (Fernie et al., 2004; Oksman-Caldentay and Saito, 2005). Now there are gas chromatography (GC)–MS, LC–MS, capillary electrophoresis (CE)–MS, and nuclear magnetic resonance (NMR) as the major analytical tools in metabolomics. Kazimierczak et al. (2014) determined the levels of metabolites in both raw beet root and naturally fermented beet root juices from organic (ORG) vs. conventional (CONV) products. The aim of the paper was to find out the value of the fermented beetroot juices in terms of anticancer properties. The results showed that ORG fresh beetroots contained more useful compounds than CONV beetroots, such as dry matter and vitamin C, more than CONV beetroots. Compared to the CONV juice, it was found that the ORG fermented juices have stronger anticancer activity. Metabolomics is still in its infancy with these analyses of sugar beet being rare, but future research can be expected to implement this powerful technology.

Important Applications of Sugar Beet to be Enhanced by OMICS

Many plants accumulate glycine betaine (betaine) to regulate biochemical and physiological processes under abiotic stresses (Takabe et al., 2006). For example, glycine betaine serves as a methyl donor in several biochemical pathways (Pummer et al., 2000). Sugar beet is a betaine-accumulating dicotyledonous plant with high economic value (Catusse et al., 2008). It has been reported that betaine is synthesized by the two-step oxidation of choline in which choline monooxygenase (CMO) catalyzes the first step, and betaine aldehyde dehydrogenase (BADH) performs the second step (Yamada et al., 2009). CMO is therefore a key enzyme to protect plants against abiotic stresses. It has been found in Chenopodiaceae and Amaranthaceae, but not in some betaine-accumulating plants such as mangrove (Bhuiyan et al., 2007). Unlike sugar beet, many plants do not have the betaine biosysthesis pathway. Therefore, genetic engineering of the betaine biosynthesis pathways represents a potential way to improve the plant stress tolerance (Hibino et al., 2001; Fitzgerald et al., 2009).

In addition to betaine, betalains are rich in red beets and exist only in 10 families of plants of the Caryophyllale. Red beetroot (B. vulgaris) is widely used as a food ingredient because of its beetroot red color. Therefore, most studies on red beetroot constituents have focused on the betalains. Currently, the yield of betalains extracted and purified from the beetroot red is only about 10%. Phenolics (such as betalains) have been shown to have nutritional value and there has been an increasing interest utilizing these plant constituents to improve food ingredients and as antioxidants. Betalains are important plant phenolics with many attractive properties, such as: stability, antioxidant activity, antitumor properties, and reduction of blood lipid and sugar levels. In addition, betalains are effective free-radical scavengers, which help to maintain health and protect from diseases such as cancer and coronary heart disease (Kujala et al., 2000; Han et al., 2015; Mikołajczyk-Bator et al., 2016).

Additonally, sugar beet provides approximately 30% of the world's annual sugar production and is a source of both bioethanol and animal feed. Dhar et al. (2015) have developed two highly efficient methods to produce hydrogen gas from sugar beet juice as a clean energy source (Dhar et al., 2015). Sugar beet byproducts (SBB) generated during industrial sugar extraction are mainly composed of pulp and molasses and the use of SBB as a renewable energy resource could add additional economic and environmental benefits (Aboudi et al., 2015).

OMICS research can greatly enhance potential applications of sugar beet in at least three ways. One is to improve our knowledge of molecular networks involving key metabolite synthesis, e.g., glyceine betaine and betalains. The knowledge will enable modeling and rationale engineering of the important metabolites. Additionally, we can utilize OMICS to investigate the global molecular changes that occur in response to stress and the tolerance of sugar beet to stress conditions. This information can help to improve stress tolerance and thereby yields of sugar beet, even under non-ideal conditions. Finally, research on unique sugar beet germplasms (e.g., M14 under salt stress) may be useful for enhancing yield, and food and bioenergy production in other crops.

Conclusion

In this review, we have summarized OMICS technologies and applications in sugar beet including: M14 for identification of novel genes, proteins related to biotic and abiotic stresses, apomixes, and metabolites related to energy, food and human health. Genomics is a powerful technology to provide the whole genome blueprint of sugar beet. Mechanisms underlying apomixes and stress tolerance have mainly been studied using transcriptomics and proteomics technologies, while metabolomics studies in sugar beet are still rare. To date, a lot of genes and proteins related to apomixes and salt stress were identified to reveal apomixes and salt tolerance mechanisms in a special germplasm sugar beet M14. The results have enhanced our understanding of the molecular mechanisms of sugar beet in response to tolerance to biotic and abiotic stresses and apomixes, which may be applied to improving stress tolerance of sugar beet and other crops to improve food production, energy output (e.g., hydrogen gas and bioethanol), and accumulation of health promoting chemicals (such as betalains). Despite the use of sugar beet to produce the clean energy of hydrogen gas and bioethanol and to isolate betalains used for natural food colorants, dietary supplements and medicines were not widely applied in market, sugar beet as a high economic value crop will have a prosperous perspective of application in the food, bioenergy and pharmacy industries.

Author Contributions

YZ collected and analyzed references for this paper, and wrote the first draft, JN drew the figure and assisted in the reference organization. BY played a supervision role, and led the writing and organization. All three authors have edited the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This research was supported by the National Science Foundation of China (Project 31471552: The response of antioxidant enzymes to salt stress in sugar beet M14, and Project 31501359: Study on phosphorylation sites of a Ser/Thr protein kinase from sugar beet M14 line in response to salt stress) and 100 Academic Backbone Support Program for Young Teachers of Heilongjiang University in 2016 and the Common College Science and Technology Innovation Team of Heilongjiang Province (2014TD004). The paper represents serial 016 from our innovation team at the Heilongjiang University (Hdtd2010-05). We thank Joe Collins from Plant Molecular and Cellular Biology, University of Florida for critical reading of the manuscript.

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Transcriptomics, that is, the simultaneous measurement of nearly all genes expressed in a given cell, tissue, or organism, has been the most successful technology applied to nutritional systems biology in the recent decades (Capozzi and Bordoni 2013). Transcriptomics covers the step of passing information from DNA to RNA. DNA microarrays and, more recently, high-throughput RNA sequencing (RNA-Seq) technologies are the most commonly used transcriptomics tools in nutritional studies. Microarrays are capable of measuring the expression levels of thousands of genes simultaneously by hybridizing total mRNAs from biological samples to predesigned gene-specific probes. More recently, sequencing-based RNA-Seq has become a revolutionary approach to transcriptome profiling because it is more sensitive and has a broader dynamic range than microarray tools (Ozsolak and Milos 2011; Wang et al. 2009). More importantly, RNA-Seq can detect gene transcript signals from previously unannotated genes and also allow analysis of transcripts from either the forward or the reverse strand, offering higher discovery potential compared to microarrays which focus on previously known genes and transcripts with prespecified directionality. To date, the most commonly used RNA-seq technologies such as the Illumina platform typically generate short-reads in the range of a few hundred base pairs and impose challenges in the precise reconstruction of transcript structures. Long-read RNA-seq technologies such as Pacific Biosciences’ single-molecule real-time sequencing are capable of generating reads of more than 20,000 base pairs and can facilitate de novo assembly (Eid et al. 2009; Tilgner et al. 2014). Here we summarize key applications of transcriptomics in nutritional studies primarily in the last 4 years that are relevant to T2D research. The findings from nutritranscriptomic studies of high-fat diet (HFD) and high-sugar diets, which have been under more intense investigations, are summarized in Table 1.
Table 1

Differentially expressed biological processes and key genes in response to diet imbalance

The transition from a “lean, healthy” diet low in fat and carbohydrates to HFD has been robustly linked to many common, complex diseases or pathological conditions and thus has been the focus of nutritranscriptomic studies in the context of obesity, insulin resistance (IR), and diabetes (Wen et al. 2011). Numerous studies have investigated the HFD-induced gene expression changes in various T2D-related tissues including liver, adipose, muscle, islet, and hypothalamus. Due to differences in the length of intervention, the amount of fat in the diets used, the individual tissues examined, and the animal models involved, there is variability in the top differentially expressed genes detected between studies. However, certain consistent genes have been observed in at least two studies, including PCK1 (phosphoenolpyruvate carboxykinase 1), COL1A1 (collagen, type I, alpha 1), and PPARG. In addition to these individual genes, multiple perturbed biological pathways including lipid metabolism, inflammatory processes, and cell cycle regulation have been robustly detected by multiple studies. Moreover, tissue-specific patterns have emerged. For example, inflammatory and immune processes are captured more frequently in the adipose tissue, while lipid metabolism, oxidative phosphorylation, peroxisome proliferator-activating receptor (PPAR) signaling, and insulin signaling appear to be more affected in liver. Regarding the potential mechanisms underlying the effect of HFD on inflammation in adipose tissue, Sun et al. (2012) thoroughly reviewed this topic and summarized that HFD-induced lipid overload may initiate inflammation via its diverse effects on inflammasomes, innate receptors, nuclear receptors, cell death, ER stress, and gut microbiota. Furthermore, transcriptomic response to HFD exhibits genetic background- or strain-specific patterns. For instance, the liver transcriptome of C57BL/6J (sensitive to HFD-induced IR) and that of BALB/c (resistant to HFD-induced IR) showed opposite expression patterns in genes involved in proteasome and ubiquitin-mediated proteolysis pathways (Waller-Evans et al. 2013a, b). A study of islet transcriptome also revealed striking differences between C57BL/6J (IR sensitive) and BLKS (resistant to HFD-induced IR) in pathways related to cell cycle, growth, proliferation, inflammation, and insulin secretion (Sims et al. 2013). These differential pathways affected by HFD between strains with differential susceptibility to IR and T2D are more likely to be relevant to T2D. Lastly, transgenerational effects of HFD have been examined and the findings from the transcriptomic studies corroborate with the increased susceptibility of offspring to metabolic diseases. For instance, male offspring from mothers fed HFD developed IR and their muscle tissues demonstrated dysregulation in cytokine signaling, starch and sucrose metabolism, inflammatory response, oxidative phosphorylation, mitochondrial matrix, and electron transport/uncoupling (Latouche et al. 2014). In a study of paternal HFD, female offspring were found to have perturbations in olfactory transduction and cell cycle processes in both adipose tissue and islet, along with additional tissue-specific processes such as ubiquitin-mediated proteolysis and mitochondria in adipose, and lipid metabolism, cell–cell signaling, and nervous systems development in islet (Ng et al. 2014).

In addition to HFD, high-sugar diets such as high fructose and sucrose have been more recently recognized as a potential risk of metabolic syndrome and diabetes independent of energy intake (Abdelmalek et al. 2012; Goran et al. 2013). A recent global study revealed that the consumption of high-fructose corn syrup explains a remarkable 20 % increase in T2D incidents [12]. Unlike glucose from which lipid synthesis is under cellular control through the energy-sensitive enzyme phosphofructokinase, fructose is metabolized by fructokinase to fructose-1-phosphate, which can bypass the enzymatic control by phosphofructokinase and be converted into fat [8, 9]. In addition, there is no negative feedback mechanism that regulates the phosphorylation of fructose to prevent hepatic ATP depletion (van den Berghe et al. 1977). Cellular ATP depletion can cause an arrest in protein synthesis and induce inflammatory and pro-oxidative changes (Cirillo et al. 2009). Consistent with these findings, transcriptomics studies (Table 1) indicate that high-fructose consumption promotes fatty acid biosynthesis, endoplasmic reticulum stress and stress-related kinase, apoptotic activity, and mitochondrial dysfunction in the liver (Chang et al. 2014). In a study of high-sucrose diet, Nojima et al. (2013) investigated liver transcriptome changes in a diabetes mouse model and found alterations in many pathways such as lipid/amino acid/steroid metabolism, cell cycle, transcription, apoptosis, and immune response. Although systematic interrogation of high-sugar diets is still lacking at present, comparison of the results with those from the HFD studies reveals similar pathway level perturbations in lipid metabolic pathways.

Another major macronutrient being studied in T2D is protein, mainly in the context of protein restriction in pregnancy which has been associated with hypertension, endothelial dysfunction, and blood glucose levels in the offspring (Gluckman et al. 2009; Sahajpal and Ashton 2003). Transcriptomic analysis of liver from a porcine model has revealed that maternal protein restriction diets alter a diverse set of pathways including cell cycle regulation, Wnt signaling, fatty acid elongation, steroid biosynthesis, glucocorticoid receptor signaling, mTOR signaling, VEGF signaling, and the complement system (Oster et al. 2012a, b).

Compared to macronutrients described above, micronutrients such as vitamins and minerals are required for growth in lower amounts and serve mainly as constituents of enzymes and metalloproteins. In particular, several micronutrients such as vitamin D and iron have been associated with glucose metabolism, insulin signaling, and β cell function, and high rates of micronutrient deficiencies have been observed in obese and T2D populations (Kaidar-Person et al. 2008a, b). Of the micronutrients examined in nutritranscriptomic studies, vitamin D is particularly interesting. The prevalence of vitamin D deficiency in obese individuals is over 80 % (Kaidar-Person et al. 2008a), and the presence of vitamin D receptors and the responsiveness of insulin gene expression to vitamin D in human pancreatic β cells suggest a role of vitamin D in β cell function and diabetes. Both animal model and human epidemiology studies also support a tight inverse relationship between vitamin D levels and β cell function or T2D prevalence (Via 2012). A recent transcriptomic study of islets responding to vitamin D implicated pathways including lipid metabolism, cell cycle, cellular assembly and organization, cellular function and maintenance, vitamin and mineral metabolism, and molecular transport (Wolden-Kirk et al. 2013). Moreover, under inflammatory conditions, vitamin D was found to prevent islet apoptosis and restored insulin secretion, accompanied by significant modulation of islet genes involved in immune response, chemotaxis, chemokine production, cell death, and pancreatic β cell function (Wolden-Kirk et al. 2014). Although clinical trials have failed to substantiate the beneficial role of vitamin D supplement in improving T2D (Via 2012), these islet transcriptional signatures nevertheless support an important role of vitamin D in T2D pathogenesis based on the relevance of the molecular pathways to T2D.

Another micronutrient of relevance to T2D is iron, because both iron overload and deficiency have been linked to impaired pancreatic function and glucose homeostasis. In a microarray analysis of rat pancreas with either iron deficiency or iron overload, genes involved in lipid transport and encoding pancreatitis-associated proteins were found to be significantly affected in both conditions, thus supporting their involvement in pancreatic functions and T2D (Coffey et al. 2014). These micronutrient studies, therefore, also point to a role of lipid metabolism/transport as a shared mechanism through which nutrients influence T2D.

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