Sugar beet (Beta vulgaris L. subsp. vulgaris) is an important crop that satisfies 25% of world sugar demand with a total production of 269 Mt (FAO, 2013). In the Mediterranean area, global climate change has led to a decrease of about 1 t ha^-1 in sugar production due to water shortage and low nutrient availability (Barbanti et al., 2007). Particularly, in modern day agriculture, sulfur deficiency has become a major constraint, since its availability in the soil is gradually decreasing (Lewandowska and Sirko, 2008).

In sugar beet, sulfur is essential for protein synthesis and to keep the presence of amino and sulfur-containing compounds balanced (Hoffmann et al., 2004). S-demand and S-removal of sugar beet is 30 and 5 kg ha^-1, respectively (Haneklaus et al., 1998). Critical leaf concentration for S-deficiency, which results in yield depression of 5%, was estimated as 3 mg g^-1 S (d.w.) by Haneklaus et al. (2007). An unbalanced proportion between N and S, due to low S concentration, results in alfa-amino N accumulation leading to a lower sugar beet technical quality and decreasing root storage capacity (Burba, 1996). Thomas et al. (2000) reported a N/S threshold value of 20:1 in the shoot for yield reduction, whereas Haneklaus and Schnug (1996) found a lower N/S ratio of 14 as limiting value in sugar beet.

Genes related to sulfate response, able to transmit external signals and trigger adaptive changes, are well known and studied. Plants can adapt to the environment in a highly coordinated and dynamic manner (Giehl et al., 2014). This consists of multiple organization levels and links genes through different molecular pathways.

An approach to investigate the effect of changes in sulfur availability on plant composition concerns the application and integration of advanced omics technologies (Huang and Salt, 2016). The omics technologies are specifically utilized to describe the global profiling of biological matrices. Combinations of these techniques have been applied and served as high-throughput screening to allow the identification of potential specific biomarkers (Yuan et al., 2008).

Many studies attribute a fundamental role to the plant root system in the competition for survival in natural environments and the greatest selection pressure is for the acquisition of elements of soil fertility (water and nutrients), which is strictly dependent on root morpho-physiology (Gruber et al., 2013). The characterization of root phenomics is therefore essential to understand how trait variations are attributable to genotype and environmental factors (Cichy et al., 2009).

Changing metabolic homeostasis due to environmental stresses triggers the production of different proteins that could restore a new homeostasis. An integration of ionomic and metabolomic approaches could give a comprehensive assessment to understand which metabolites are involved in responses to a specific environment (Atkinson and Urwin, 2012). A specific stress response can be identified by specific metabolic fingerprinting (Shulaev et al., 2008). Moreover, target metabolite analysis combined with a dynamic gene expression profile is used to elucidate gene-to-gene and metabolite-to-gene networks through which plants coordinately modulate their responses to nutritional stresses.

In this study, a method based on the combination of different omics technologies was set-up for sugar beet (Beta vulgaris L. subsp. vulgaris) evaluation to achieve a holistic view of plant response to changes in sulfate availability. Root morphology, plant ionome and metabolome, together with gene expression profiling, were analyzed in leaves and roots of sulfate-deprived and supplied plants. In particular, we evaluated the capacity of the proposed omics techniques to depict complex plant–sulfate interactions.

The accumulation dynamics of sulfate was evaluated for a period of 36 h in deprived control plants (-S) and supplied plants (+S) (Figure 1). Sulfate content increased strongly in leaves of treated plants and, as expected, remained mostly stable in deprived plants. After 36 h of treatment with 100 μM of MgSO₄, the sulfate contents of leaves were significantly higher (P < 0.01) than those of the deprived plants.

Leaves were analyzed with ICP-OES in order to reveal the effect of changes in sulfate availability on the ionome profile (Table 1). No significant increases were detected in the elements concentration in response to S nutrition, except for S and Mg. S concentration significantly increased (P < 0.01) in leaves (+162%) in treated compared to untreated plants. A significant difference (P < 0.05) in Mg concentration was observed in leaves of treated plants, which showed a 4.5% increase. Samples within treatments showed a clear separation as detected by the principal component analysis (PCA) (Figure 2). Factors 1 and 2 explained 38.27% and 24.61% of the total variation, respectively. Factor 1 is related to the variation of S and Mg in leaves ionome.

To evaluate the sulfate treatment effects on root apparatus, we studied three different parameters: total root length, surface area and number of tips. As represented in Figure 3, plants grown in 100 μM of sulfate solution show significantly higher values (P < 0.01) for all the parameters analyzed than deprived plants. In addition, the data obtained reveal that the mechanism actuated by deprived plants, in response to an additional 36 h of sulfate deprivation, is expressed in a significant increase (P < 0.01) in the number of root tips.

A total of 2,400 metabolites were identified via UHPLC/Q-TOF. The unsupervised cluster analysis was performed on the dataset to better focus on the effect of the sulfate treatment (Figure 4). The results showed that the two treatments were properly grouped in both roots and leaves, thus indicating the presence of a chemical signature from the treatment in the metabolomics dataset. The output of PLS-DA, shown in Figure 5, consistently indicated a good discrimination between treatments on the basis of their metabolic profile. Indeed, the class prediction model gave good accuracies for both tissues and treatments (overall accuracy of 100%). The compounds selected from the Volcano analysis of plants treated with 100 μM of MgSO₄ for 36 h (using a fold change cut off >5 and a p-value of 0.001) are reported as Supplementary Material S2. Most of the differential compounds identified were free amino acids such as tryptophan, proline, lysine, glutamate, glutamine and cysteine. The Volcano analysis also revealed high levels of O-acetyl-L-serine, Jasmonic acid and 12-hydroxy-jasmonoyl-L-isoleucine in leaves.

The expression level of the 53 sulfate-related genes was evaluated in deprived and supplied leaves and roots. The ANOVA showed a significant effect of treatments (P < 0.01) and tissues (P < 0.01), as well as genes (P < 0.01) (Table 2). Relative expression of 53 genes in leaves (Figure 6) was significantly higher compared to roots (Figure 7) of treated and untreated plants. Moreover, gene expression analysis revealed that the highest relative quantity is shown in supplied roots by Flavonol sulfotransferase-like (AIY90PI, Bv6_137840_uaap), 28 kDa ribonucleoprotein, chloroplastic (AIT970M, Bv7u_180460_dcmt) and Glutamate/leucine/phenylalanine/valine dehydrogenases (AIWR4C1, Bv3_057000_nenr). A sixfold increase of the gene auxin efflux carrier component 1 (AI1RW1X, Bv3_065290_srwc) was detected in roots compared to leaves. Supplied leaves have a high relative quantity of the same three genes highly expressed in roots. In particular, Flavonol sulfotransferase-like showed a onefold increase compared to roots. However, supplied leaves are subjected to a significant expression of other genes: Aspartic proteinase-like protein 1 (AIVI56U, Bv_24910_jato), 3-ketoacyl-CoA synthase 17 (AIMSIZA, Bv7_156890_eowm) and cysteine synthase, chloro plastic/chromoplastic, O-acetylserine sulfhydrylase (AIGJR36, Bv1_004580_xnrs). Gene coding for thioredoxin reductase (NADPH) (AIKAMMV, Bv3_063630_mpup) and 2-Cys peroxiredoxin BAS1, chloroplastic (AII1OGN, Bv7_157460_rcod) were twofold and onefold more expressed in leaves than roots, respectively.

Many studies have focused their attention on explaining plant nutritional stress using an integrated omics approach (Saito and Matsuda, 2010). In particular, sulfur starvation has been extensively studied for the model plant Arabidopsis (Nikiforova et al., 2005, 2006). In this work, sulfur shortage, one of the main sugar beet nutritional deficiencies that causes significant sugar yield losses, is dissected by means of omics technologies to set up a method able to detect and describe this nutritional limitation. The 53 genes used in this study were selected on the basis of a previous experiment of RNA-seq analysis and validated for their involvement in plant responses to nutritional changes (Barone et al., 2017). The product of these genes plays important roles in many biological processes, such as biosynthesis, response to stress, cellular amino acid metabolism, transport, and sulfur compound metabolism. Changes in root morphology, plant ionome, metabolome and gene expression profile, in leaves and roots of sulfate-deprived and supplied sugar beet plants, highlighted the presence of potential biomarkers involved in sulfate nutrition. These biomarkers are mainly related to auxin synthesis, plant protection, amino acid and cysteine biosynthesis.

Root morphology is closely related to plant S uptake efficiency, especially the root length, surface area and number of tips (Stevanato et al., 2015). These parameters show a significant difference in plants treated with 100 μM of MgSO₄ for 36 h compared to sulfur deprived plants. This has also been observed in the soil, since sulfur is mobile and present in the deeper soil profile and stimulates plants to rapidly elongate the primary root (Zhao et al., 2014). From a transcriptomics and metabolomics point of view, this is reflected in a high expression of auxin efflux carrier component 1 (PIN) and in the up regulation of 5-phosphoribosyl-anthranilate, precursor of tryptophan and auxin synthesis. The root morphological analysis also revealed a significant increase in the number of root tips in plants maintained at sulfur deficiency for 36 h. Root tips have a fundamental role in nutrients acquisition and the perception of nutritional stress (López-Bucio et al., 2003). A previous study on Arabidopsis reported that sulfate-deprived plants have a larger number of root tips and fine roots, increasing the root/shoot ratio (Gläser et al., 2014). In addition, Zhao et al. (2014) observed that sulfate deprivation stimulated cell division activity and root tip expansion.

Plants grown without sulfur show different mineral composition from plants grown with 100 μM of MgSO₄ as highlighted by ICP-OES analysis. The leaf sulfur content of the treated plants was much lower than the threshold value below which significant production losses were observed by Haneklaus et al. (2007). PCA analysis of mineral elements revealed that leaves and root samples belonged to separate clusters corresponding to their nutritional regime (S-deficiency and S-sufficiency). Plants treated with sulfur show higher Mg levels than S-deprived ones, demonstrating that these nutrients are correlated with each other (Dietz, 1989). Sulfur is essential for chlorophyll formation and magnesium is the central core of the chlorophyll molecule. In sugar beet, a decrease of chlorophyll under sulfur deficiency has already been described by Thomas et al. (2000).

Many genes highly expressed in the leaves supplied with 100 μM of MgSO₄ are involved in stress response, in particular 2-Cys peroxiredoxin BAS1, chloroplastic and thioredoxin reductase (NADPH). The first one is an antioxidant enzyme that has an important role in cell protection against oxidative stress and is involved in protecting photosynthesis (Dietz et al., 2002). The second is involved in regulation of chlorophyll biosynthetic process and in the removal of superoxide radicals. Furthermore, the enhanced defense activity was also observed at metabolic levels with an up regulation of Jasmonic acid and 12-hydroxy-jasmonoyl-L-isoleucine in leaves, which have a crucial role in mediating plant stress response.

The presence of sulfur in the form of cysteine residues is essential for the conformation and stability of proteins. Transcriptomics analysis shows an over expression of cysteine synthase, chloroplastic/chromoplastic, O-acetylserine sulfhydrylase in supplied leaves that turn into an up regulation of O-acetyl-L-serine, glutamate and glutamine. O-acetyl-L-serine is a direct precursor of cysteine biosynthesis and is hence crucial for sulfur assimilation, while glutamate and glutamine are involved in the biosynthetic pathway of glutathione (Hirai et al., 2003). Change in the oxidation state of Cys residues and its thiol group promotes the response to change in redox environments and enable an organism to adapt to stress conditions (Montrichard et al., 2009). Plants grown with 100 μM of MgSO₄ started to up regulate the synthesis of cysteine and several free amino acids, as detected by gene expression (high level Glutamate/leucine/phenylalanine/valine dehydrogenases and Flavonol sulfotransferase-like) and Volcano analysis, and to activate the protein synthesis pathway (Durenkamp et al., 2007). Furthermore, the accumulation of both glutamate and glutamine suggests an increase in activity of the GS-GOGAT cycle, likely to support nitrogen assimilation and protein synthesis. Similar results were also found studying the response to sulfur stress in Brassica napus (Zhang et al., 2015).

Our approach was able to identify and highlight the main determinants of sugar beet response to changes in sulfate availability. The combination of ionomics, morphological, metabolomics and molecular approaches appeared to be a particularly valuable system for the evaluation of sugar beet nutritional status under different nutrient availability conditions.

PS, CB, and GB wrote the main manuscript and performed the OA analysis. VM and GM developed the panel of TaqMan assays for the OA analysis. MC and MB conducted the ionomic analysis. LL performed the metabolomic analysis. VB, DP, and AB conducted the morphological analysis. GC, AS, and SN coordinated data collection and reviewed the manuscript.

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.