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Article

Expression Profiling of Candidate Genes in Sugar Beet Leaves Treated with Leonardite-Based Biostimulant

by
Hanifeh Seyed Hajizadeh
1,
Bahram Heidari
2,
Giovanni Bertoldo
3,
Maria Cristina Della Lucia
3,
Francesco Magro
4,
Chiara Broccanello
3,
Andrea Baglieri
5,
Ivana Puglisi
5,
Andrea Squartini
3,
Giovanni Campagna
6,
Giuseppe Concheri
3,
Serenella Nardi
3 and
Piergiorgio Stevanato
3,*
1
Department of Horticulture, Faculty of Agriculture, University of Maragheh, Maragheh 55136-553, Iran
2
Department of Crop Production and Plant Breeding, School of Agriculture, Shiraz University, Shiraz 7144165186, Iran
3
Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
4
SIPCAM S.p.A., 20016 Pero (MI), Italy
5
Department of Agriculture, Food and Environment, University of Catania, 95131 Catania, Italy
6
COPROB, 40061 Minerbio (BO), Italy
*
Author to whom correspondence should be addressed.
High-Throughput 2019, 8(4), 18; https://doi.org/10.3390/ht8040018
Submission received: 30 July 2019 / Revised: 4 October 2019 / Accepted: 5 October 2019 / Published: 11 October 2019
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Abstract

:
Leonardite-based biostimulants are a large class of compounds, including humic acid substances. Foliar application of biostimulants at field level improves plant growth, yield and quality through metabolic changes and stimulation of plant proton pumps. The present study aimed at identifying optimum dosage of BLACKJAK, a humic acid-based substance, which is able to modify genes involved in sugar beet growth. Thirty-three genes belonging to various biochemical pathway categories were tested in leaves of treated sugar beet (Beta vulgaris L.) samples to assess gene expression profiling in response to BLACKJAK. Seedlings of a diploid and multigerm variety were grown in plastic pots and sprayed with two dilutions of BLACKJAK (dilution 1:500–1.0 mg C L−1 and dilution 1:1000–0.5 mg C L−1). Leaf samples were collected after 24, 48, and 72 h treatment with BLACKJAK for each dilution. RNA was extracted and the quantification of gene expression was performed while using an OpenArray platform. Results of analysis of variance demonstrated that, 15 genes out of a total of 33 genes tested with OpenArray qPCR were significantly affected by treatment and exposure time. Analysis for annotation of gene products and pathways revealed that genes belonging to the mitochondrial respiratory pathways, nitrogen and hormone metabolisms, and nutrient uptake were up-regulated in the BLACKJAK treated samples. Among the up-regulated genes, Bv_PHT2;1 and Bv_GLN1 expression exerted a 2-fold change in 1:1000 and 1:500 BLACKJAK concentrations. Overall, the gene expression data in the BLACKJAK treated leaves demonstrated the induction of plant growth–related genes that were contributed almost to amino acid and nitrogen metabolism, plant defense system, and plant growth.

1. Introduction

Biostimulants affect plant responses to biotic and abiotic stresses by enhancing the nutrient uptake and by influencing the growth of roots and plants [1]. Different biostimulants proved to elicit morphological and physiological changes, improving the plant growth [2,3]. The positive effects of biostimulants on the plant growth encourage farmers to use them to reduce the reliance on fertilizers and pesticides. The European biostimulants industry council (EBIC) documented that over six million hectares of Europe agricultural farms have been treated with biostimulants since 2012 [4]. As stated by du Jardin, plant biostimulant is any substance or microrganism applied to plants with the aim to enhance nutrition efficiency, abiotic stress tolerance, and/or crop quality traits, regardless of its nutrients content [5].
Humic acid substances (HS) are abundant in the soil being the end products of microbial decompositions [1]. Proteins, carbohydrates, aliphatic biopolymers, and lignin represent the main compound classes in HS as well as the principal components in plants [6]. HS derivatives proved to enhance lateral roots and improve seedling root growth in over 16 plant species, including tomato [7], wheat [8], and maize [9]. Bean plants that were treated with HS were resistant to salt stress (120 mM NaCl) with respect to the control plants [10]. The effects of HS on salt and drought tolerances have also been demonstrated in other plant species [11,12]. Foliar spray of HS preparations increased photosynthetic pigment contents in eggplant [13] and the final yield in peanut [14], enhanced tolerance against abiotic stresses [15], improved phytohormone contents in snap bean [16], as well as the fresh and dry weight per plant in common bean [17].
Leonardite, which is an oxidized form of lignite enriched in HS, modifies physiological reactions in plants through transcriptional mechanism of the regulation of hormones and modifications in nitrogen and carbon metabolisms [8]. Although the application of leonardite-based biostimulants in the early stages of sugar beet growth demonstrated that these substances accelerate root growth and total sugar yield [18], their effects on the alteration of gene expression in sugar beet were investigated to a lesser extent. In tomato, the effects of a new biostimulant, EXPANDO®, containing different bioactive compounds, were tested at gene expression level, showing that the expression of about 4.000 genes, involved in several biological processes, such as transcription, stress responses, signal transduction, carbohydrate, and protein metabolism [19].
Specific functions and mechanisms of actions have not yet been determined for many biostimulant products [20]. The acquisition of a detailed knowledge of the molecular mechanisms underlying biostimulants helps to understand the biological functions of such substances. The aim of this study was to perform a high-throughput expression profiling of candidate genes in sugar beet leaves that were treated with leonardite-based biostimulant. The outcome of this study might assist sugar beet research community for a better understanding of the influence of biostimulants on metabolic changes for wide application of BLACKJAK at the field level.

2. Materials and Methods

2.1. Plant Materials and Growing Conditions

The sugar beet hybrid that was used in this study was the “Variety_1”, provided by the Department of Agronomy, Food, Natural Resources, Animals, and Environment—DAFNAE (University of Padova, Legnaro (PD), Italy). Variety_1 is a diploid and multigerm genotype showing homozygosity for resistance to rhizomania (Rz1 gene) disease. This hybrid was a fingerprinted genotype selected from a preliminary genotypic screening in a previous study [21].
Seeds of 4 mm diameter size were soaked in 76% ethanol for 5 min, rinsed with sterilized water, and allocated on distilled water-moistened filter paper to germinate. Germination was carried out in a growth chamber at 25 °C in the dark for five days. Plants were grown on peat-based potting mix inside containers with depth-filtration system allowing gas exchange with limited dehydration and avoiding the dispersal of BLACKJAK treatments (Figure 1). Seedlings were cultivated in plastic pots kept in a climatic chamber at 25/18 °C and 70/90% relative humidity with a 14/10 h light/dark cycle (PPFD above shoot: 300 µE m−2 s−1). Plants were grown for 30 days and they were then sprayed with two dilutions of BLACKJAK (dilution 1:1000–0.5 mg C L−1 and dilution 1:500–1.0 mg C L−1). Leaf samples were collected 24, 48, and 72 h after the BLACKJAK treatment for each dilution. Three replicates were used for each treatment combinations. The new leonardite-formulate BLACKJAK used in the present study was a liquid commercial product provided by Sofbey (Puplinge, CH). Chemical, physical, and biological characteristics of leonardite-formulate BLACKJAK were described by Barone et al. [22]. The adopted dilution at farm level is 1:1000–0.5 mg C L−1. Sugar yield (t ha−1), sugar content, sodium, and potassium were determined for BLACKJAK untreated plants and those that were treated with 1:1000–0.5 mg C L−1 dilution.

2.2. Leaf RNA Isolation and cDNA Synthesis

Total RNA was extracted from 0.2 g of leaf tissues using a EurogoldTriFast kit (EuroClone, 360 Milan, Italy) and DNase-digested with TURBO DNA-free kit (Thermo Fisher Scientific, Waltham, MA, USA), according to manufacturer recommendations. The RNA was quantified by absorbance at 260 nm and Agilent 2100 Bioanalyzer checked RNA quality (Agilent Technologies, Palo Alto, CA, USA). Reverse transcription of RNA (500 ng) was achieved while using the SuperScript III reverse transcriptase kit (Thermo Fisher Scientific, Waltham, MA, USA), following the manufacturer’s instructions.

2.3. Real Time PCR analysis

The transcript levels of 33 sugar beet genes were analyzed (Table 1). Primers and probes were designed based on sequences that were selected from the reference genome RefBeet_1.2 by Thermo Fisher Scientific. Reactions for real-time PCR experiments consisted of a final volume of 5 µL containing 2.5 µL of 2× TaqMan Open Array master mix (Thermo Fisher Scientific, Waltham, MA, USA), and 2.5 µL of cDNA. Real-time PCR was analyzed on the QuantStudio 12K Flex Real-Time PCR System (Thermo Fisher Scientific) with respect to a thermocycler program as follow: 10 min pre-incubation at 95 °C, followed by 50 cycles of 15 s at 95 °C and 1 min at 60 °C. A negative and a positive control were routinely included, for each set of experiments. The melting curves post-PCR analyses were performed in order to estimate the specificity of the amplified products. Analysis of the genes relative expression was performed while using the comparative threshold Ct method [23,24]. The data for gene expression were normalized with respect to the arithmetic Ct mean of three housekeeping genes (Tubulin, Bv2_037220_rayf; GAPDH, Bv5_107870_ygnn; Histone H3, Bv6_127000_pera). The average Ct values from each sample of three housekeeping genes were converted into relative quantities while using the formula 2−ΔCt, where ΔCt was obtained after subtracting the lowest Ct value from the Ct value of the reference gene from the corresponding sample (ΔCt = each sample Ct value − the lowest Ct value) [25]. The relative expression (RE) for the expressed genes in response to BLACKJAK treatment was calculated while using the formula 2−ΔΔCt where ΔΔCt was estimated by the difference between (Ct in target gene—average Ct in reference genes) for treatment and (Ct of the target gene—average Ct of the reference genes) in control.

2.4. Statistical Analysis

The mean for transcript abundance of the three housekeeping genes (Tubulin, GAPDH, and Histone H3) was used to normalize the data obtained. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database [26] was used to search for the enzyme commission (EC) number of all assigned function related to a specific enzyme. Analysis of variance for factorial experimental design was performed in SAS software (SAS V 9.2 Institute, Cary, NC, USA). The data for gene expression heatmap were analyzed while using heatmapper software [27].

3. Results

Previous field trials at two locations in Italy were carried out to assess the consequences of BLACKJAK treatments on sugar content, yield, and quality (G. Campagna, pers. comm.). The results for agronomic traits demonstrated that BLACKJAK treated plants had higher sugar content and yield when compared with untreated plants whilst presented lower sodium and potassium (Table 2). Based on this evidence, the aim of this study was to perform an expression profiling of candidate genes in sugar beet leaves that were treated with BLACKJAK biostimulant for better understanding of its influence on sugar beet metabolic changes. The effects of different treatment dilutions were investigated for three exposure times (24 h, 48 h, and 72 h). Analysis of variance (ANOVA) showed that, among the 33 genes assayed with OpenArray qPCR, 15 had significant differences due to treatment, exposure time, and their interactions (Table 3). For eight genes, the main and interaction effects were significant. For the remainders, no significant effects for BLACKJAK treatment and exposure time were identified.
Figure 2 shows representative amplification curves from 12 selected genes. A large variation in their expression profiles is revealed from the different levels of the amplification curves. The plot for ΔRn versus PCR cycle associated with the expression of genes demonstrated the efficiency of the qPCR method used for differential response to treatment with BLACKJACK biostimulant (Figure 3). For each gene analyzed, a mean RE value was calculated by averaging all treatments and sampling times. Among the considered genes, the highest RE (1.4838) belonged to Bv_GLN1, whereas the lowest (0.00619) was identified for the EXO gene (Table 3). The data for RE demonstrated that the relative expression of most of the genes tested rose up as the exposure time with BLACKJACK increased. The relative expression of the genes assayed demonstrated that the number of up-regulated genes was increased as the concentration of BLACKJAK increased in the treated samples (Figure S1).
The expression of the Bv_UQCRH and Bv_AOAT1 genes increased as BLACKJAK exposure time increased in plant. Relative expression of these two genes in plants treated with 1:500 BLACKJAK after 72 h was higher than RE in other treatments (Figure 4). The gene Bv_AHG3 with the highest RE at BLACKJAK 1:1000 concentration after 72 h represented no direct relation with BLACKJAK concentration at the three time levels, whilst Bv_ATTIR1 was more expressed at a higher BLACKJAK concentration (Figure 5). The Bv_GLT1 and Bv_GLN1 genes were both more expressed as exposure time with BLACKJAK was elevated (Figure 6). Bv_PHT2;1 and Bv_PI4K showed higher expression in 1:1000 BLACKJAK concentration than in 1:500 BLACKJAK (Figure 7). In 1:1000 BLACKJAK treated samples, several genes were up-regulated at the end of experiment, of which Bv_PHT2;1 showed the highest RE.
The heat map for expression profiling in samples that were treated with 1:500 BLACKJAK showed that, except Bv_HAB1, the expression level of the tested genes was elevated as the exposure time was increased (Figure 8). The majority of genes were up-regulated 72 h after treatment with 1:500 BLACKJAK.
The pattern of expression profiling revealed that genes with differential expression are distributed between three exposure times in samples that were treated with 1:1000 BLACKJAK (Figure 9). The heat map revealed that the number of up-regulated genes increased as the BLACKJAK concentration increased. The highest number of up-regulated genes was identified in 72 h exposure time. Genes that were up-regulated in 24 h exposure time showed less expression in the remainder exposure time levels. The expression pattern of genes tested demonstrated that few genes showed up-regulation in two BLACKJAK treatments and exposure times.

4. Discussion

A better understanding of the effect of biostimulants in Beta vulgaris L. growth might be achieved through the knowledge of molecular mechanisms underpinning changes in gene expression in plant tissues. In the present study, the effect of various concentrations of BLACKJAK biostimulant on sugar yield, sugar content, and concentration of potassium and sodium ions was assessed in a sugar beet variety at different sampling times. Furthermore, several genes that belong to various categories (i.e., mitochondrial respiratory and signal transduction pathway, lipid, and hormone metabolisms) were selected for gene expression analysis in response to the treatment with BLACKJAK [28,29,30,31,32]. Treating sugar beet leaves with 1:1000 (0.5 mg C L−1) BLACKJAK resulted in higher sugar content (1.08%) and yield (6.78%) as compared with untreated plants. It has been demonstrated that biostimulants enhance processes such as photosynthesis, senescence, modulation of phytohormones, uptake of nutrients, resulting in higher performance and yield in plants [20]. In a study with sugar beet, the application of BLACKJAK increased sugar content (17.38%) and root yield than control demonstrating the positive effect of this biostimulant on sugar and plant growth [33]. The results of the current study demonstrated that the use of BLACKJAK as foliar spray reduced potassium (3.38%) and sodium (8.04%) components. Potassium and sodium ions are main components that make the sugar extraction difficult. Sodium and potassium both influence the pH of the raw sugar extract where alkalinity must be minimized for the efficient extraction of sugar.
The results of analysis of variance demonstrated that some of the evaluated genes significantly responded to treatment with BLACKJAK. Bv_UQCRH was up-regulated in leaves that were treated with 1:500 BLACKJAK, in only 72 h samples. Mechanism of action of humic acid-based biostimulants demonstrated that they enhance respiration and invertase activities making C substrates available [5]. The Bv_UQCRH gene encodes for an enzyme (cytochrome c reductase) belonging to the mitochondrial respiratory pathway and it is involved in mitochondrial electron transport, ATP synthesis, and cytochrome c reductase. Plant mitochondria act as “signaling organelles”, able to influence processes, such as nuclear gene expression. Respiration provides energy and carbon skeletons that are required for maintenance of existing dry matter [31]. Cytochrome c reductase plays a central role in the electron transport chain, because it collects reducing equivalents from NADH- and FADH2-linked dehydrogenases and passes them on to the terminal cytochrome system to form H2O. The increase in Bv_UQCRH expression level under the highest concentration of BLACKJAK tested could be effective for modulating the respiration to produce ATP instead of reactive oxygen species (ROS).
The results demonstrated that Bv_AOAT1 after 72 h exposure to 1:500 BLACKJAK concentration and Bv_GLT1and Bv_GLN1 in both concentrations were up-regulated. These three genes belong to the nitrogen metabolism category. The functions of these genes are associated with amino acid metabolism and glutamine synthetase (GS, EC 6.3.1.2). Glutamine synthetase is a central enzyme in nitrogen metabolism in leaves and a key component of nitrogen use efficiency and grain yield [33]. The modulation of GS expression and activity during a diurnal cycle, linked to carbon and nitrogen assimilation [34], is important for plant growth and development. In a study, Bao et al. [35] demonstrated that the GS-suppressed plants exhibited poor growth and were nitrogen transport deficient.
Three genes (Bv_AHG3, Bv_ATTIR1, Bv_PI4K) showing up-regulation in response to BLACKJAK treatment were associated with hormone metabolism and signal transduction pathways. Bv_AHG3 and Bv_ATTIR1, up-regulated after 48 and 72 h time exposure with BLACKJAK, are abscisic acid and auxin-related genes, respectively. Biostimulants that are developed from humic substances affect plant hormonal status [20]. It is possible that de novo synthesis of hormones might be induced in response to treating plants with humic based biostimulants. Amino acids, glycosides, polysaccharides, and organic acids are contained in many biostimulants and they may act as precursors or activators of endogenous plant hormones [20]. Abscisic acid (ABA) is involved in embryogenesis, seed physiology reactions, plant growth, and biotic and abiotic stress tolerance [36]. Young tissues have high ABA levels, although ABA has historically been considered a growth inhibitor [36,37]. It has been shown that ABA-deficient mutant plants are severely stunted and conversely exogenous ABA treatment of mutants restores normal cell expansion and growth [37]. The higher expression of Bv_AHG3 could be due to the activation of the abscisic acid hormone signal transduction pathway (Table 1). Annotation of Bv_ATTIR1, another up-regulated gene, was linked to auxin signal transduction. Auxins, a class of essential plant hormones, contribute to several plant growth and development processes, including cell elongation and cell division [38,39]. It has been shown that the growth regulator auxin contributed to both the initiation and elaboration of final morphology of leaf tissue and vascular networks [40]. Furthermore, in the model plants, Arabidopsis, rice and sorghum, the auxin-response genes (auxin/indole-3-acetic acid (Aux/IAA), auxin-response factor (ARF), and small auxin-up RNAs) are involved in growth/development and stress/defense responses [41]. Bv_PI4K was up-regulated in one of BLACKJAK levels assayed. This gene belongs to a signal transduction pathway category and it is involved in phosphoinositides (Pis) signaling. Studies of expression patterns of genes encoding key enzymes in PIs signalling have demonstrated that the expression of several enzymes is differentially regulated by various hormones and abiotic stressors, especially in the family of PIs phosphate kinase [42,43]. Besides their contribution to membrane building blocks, PIs exert regulatory effects on membrane polarity and plant growth [44]. Plants produce particular species of phosphoinositides under abiotic stress conditions through an increase in the degree of unsaturation in fatty acids [45,46]. The PIs associated pathways contribute to DNA replication, chromatin remodeling, response to environmental stresses, RNA export and cell cycle progression, hormone signaling, and plant defense network [47,48,49].
Bv_PHT2;1 expression exerted a three-fold change in 1:1000 BLACKJAK concentration whereas this gene was down-regulated in the other BLACKJAK treatment. This gene belongs to nutrient uptake category and it is involved in phosphate transport in plants. Plant growth is limited by the availability of P in most natural ecosystems. Phosphate transporters contribute to phosphate absorption and distribution, being important for physiologic reactions in plants [50]. In a study, cis elements were identified in the upstream regions of the phosphate transporter genes, many of which were related to growth or hormones [51].
The results of gene expression analysis showed that four genes (Bv_PEI, Bv_LAX1, Bv_LAX2, and Bv_MY10) were down-regulated as the exposure time with BLACKJAK increased in both 1:500 and 1:1000 treatments. The Bv_PEI belongs to lipid metabolism/degradation category. Several genes showed down-regulation as the exposure time was increased under one of BLACKJAK treatment only.

5. Conclusions

The results of the present study suggested that the genes belonging to various functional categories were differentially expressed in response to BLACKJAK and not all of the genes showed similar response to this leonardite-based substance. Few genes were up-regulated, demonstrating that BLACKJAK can stimulate specific pathways. Exposure time was an important factor for inducing higher gene expression in both BLACKJAK treatments. Increased expression level of mitochondria respiratory genes, glutamine synthase genes and the auxin and abscisic acid genes involved in signal transduction pathway might be an important mechanism explaining the enhanced growth. The possible contribution of Bv_GLN1 and Bv_PHT2;1, with higher expression in BLACKJAK treatments, to growth, stress tolerance, and disease resistance could be assessed in next studies. In conclusion, the results of our study provided information about genes and metabolic changes in response to BLACKJAK treatment in sugar beet that might assist sugar beet research community for better understanding the effects of biostimulants and, consequently, assure their wide use at the field level.

Supplementary Materials

The following are available online at https://www.mdpi.com/2571-5135/8/4/18/s1, Figure S1: Relative expression of genes in BLACKJAK untreated and treated sugar beet in three time exposures. BJK: BLACKJAK, 500 and 1000 stand for 1.0 mg C L−1 and 0.5 mg C L−1 BLACKJAK, respectively. Gene codes refer to the name of genes in Table 1.

Author Contributions

Conceptualization, P.S., B.H., A.B., F.M. and S.N.; methodology, P.S., S.N., G.C. (Giovanni Campagna), A.B., F.M., C.B. and A.S.; formal analysis, I.P. and B.H.; investigation, H.S.H., G.B., M.C.D.L., C.B. and I.P.; data curation, H.S.H., C.B. and B.H.; project administration, P.S.; funding acquisition, P.S., writing—original draft preparation, P.S. and B.H.; supervision, P.S., F.M., A.S., G.C. (Giuseppe Concheri) and S.N.

Funding

This project was funded by Veneto Region in the frame of the PSR 2014-2020 (Project: “Implementation and validation of innovative plant protection methods to increase environmental sustainability of organic and sugar beet production”).

Acknowledgments

We are grateful to Enrico Biancardi for critical reading of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Calvo, P.; Nelson, L.; Kloepper, J.W. Agricultural uses of plant biostimulants. Plant Soil 2014, 383, 3–41. [Google Scholar] [CrossRef] [Green Version]
  2. Clapp, C.E.; Chen, Y.; Hayes, M.H.B.; Cheng, H.H. Plant growth promoting activity of humic substances. In Understanding and Managing Organic Matter in Soils, Sediments and Waters; Swift, R.S., Sparks, K.M., Eds.; Int Humic Sci Society: Madison, WI, USA, 2001. [Google Scholar]
  3. Nardi, S.; Carletti, P.; Pizzeghello, D.; Muscolo, A. Biological activities of humic substances. In Biophysico-Chemical Processes Involving Natural Nonliving Organic Matter in Environmental Systems; Senesi, N., Xing, B., Huang, P.M., Eds.; Wiley: Hoboken, NJ, USA, 2009; pp. 305–339. [Google Scholar]
  4. EBIC. 2013. Available online: http://www.biostimulants.eu/ (accessed on 18 April 2013).
  5. Du Jardin, P. Plant biostimulants: Definition, concept, main categories and regulation. Sci. Hort. 2015, 196, 3–14. [Google Scholar] [CrossRef] [Green Version]
  6. Kelleher, B.P.; Simpson, A.J. Humic substances in soils: Are they really chemically distinct? Environ. Sci. Technol. 2006, 40, 4605–4611. [Google Scholar] [CrossRef]
  7. Canellas, L.P.; Dantas, D.J.; Aguiar, N.O. Probing the hormonal activity of fractionated molecular humic components in tomato auxin mutants. Ann. Appl. Biol. 2011, 159, 202–211. [Google Scholar] [CrossRef] [Green Version]
  8. Tahir, M.M.; Khurshid, M.; Khan, M.Z.; Abbasi, M.K.; Hazmi, M.H. Lignite-derived humic acid effect on growth of wheat plants in different soils. Pedosphere 2011, 2, 124–131. [Google Scholar] [CrossRef]
  9. Jindo, K.; Martim, S.A.; Navarro, E.C. Root growth promotion by humic acids from composted and noncomposted urban organic wastes. Plant Soil 2012, 353, 209–220. [Google Scholar] [CrossRef]
  10. Aydin, A.; Kant, C.; Turan, M. Humic acid application alleviate salinity stress of bean (Phaseolus vulgaris L.) plants decreasing membrane leakage. Afr. J. Agric. Res. 2012, 7, 1073–1086. [Google Scholar] [CrossRef]
  11. Khaleda, L.; Park, H.J.; Yun, D.J.; Jeon, J.R.; Kim, M.G.; Kim, W.Y. Humic acid confers high-affinity K+ transporter 1-mediated salinity stress tolerance in Arabidopsis. Mol. Cells 2017, 40, 966–975. [Google Scholar] [CrossRef]
  12. Cimrin, K.M.; Önder, T.; Turan, M.; Burcu, T. Phosphorus and humic acid application alleviate salinity stress of pepper seedling. Afr. J. Biotechnol. 2010, 9, 5845–5851. [Google Scholar]
  13. García, A.C.; Santos, L.A.; Izquierdo, F.G.; Rumjanek, V.M.; Castro, R.N.; dos Santos, F.S.; de Souza, L.G.A.; Berbara, R.L.L. Potentialities of vermicompost humic acids to alleviate water stress in rice plants (Oryza sativa L.). J. Geochem. Explor. 2013, 136, 48–54. [Google Scholar] [CrossRef]
  14. Fathy, E.S.L.E.S.; Khedr, Z.M.A.; Moghazy, A.M. Improves metabolical and agronomical performances of eggplant under high temperature stressful condition (late summer) by using some antioxidants and mineral nutrients. Conference of untraditional tools in production and improvement of agriculture crops. Agri. Res. Centre Hortic. Inst. Cairo 2003, 1–3. [Google Scholar]
  15. Salwa, A.; Eisa, A.I. Effect of amendments, humic and amino acids on increases soils fertility, yields and seeds quality of peanut and sesame on sandy soils. Res. J. Agric. Biol. Sci. 2011, 7, 115–125. [Google Scholar] [CrossRef]
  16. Van Oosten, M.J.; Pepe, P.; De Pascale, S.; Silletti, S.; Maggio, A. The role of biostimulants and bioeffectors as alleviators of abiotic stress in crop plants. Chem. Biol. Technol. Agric. 2017, 4, 5. [Google Scholar] [CrossRef] [Green Version]
  17. El-Bassiony, A.M.; Fawzy, Z.F.; AbdEi-Baky, M.M.H.; Mahmood, A.R. Response of snap bean plants to mineral fertilizers and humic acid application. J. Plant Biol. Soil Health 2010, 4, 1–7. [Google Scholar]
  18. Kaya, M.; Atak, M.; Khawar, K.M.; Cemalettin, Y.C.; Özcan, S. Effect of pre-sowing seed treatment with zinc and foliar spray of humic acids on yield of common bean (Phaseolus vulgaris L.). Int. J. Agric. Biol. 2005, 7, 875–878. [Google Scholar]
  19. Dina, S.S.; Ibrahim, A.H.; Nour El-Deen, A.E.K.; Fatma, A.M.M. Induction of systemic resistance in sugar-beet infected with meloidogyne incognita by humic acid, hydrogen peroxide, thiamine and two amino acids. Egypt J. Agronematol. 2011, 12, 22–41. [Google Scholar]
  20. Contartese, V.; Garabello, G.; Ochipinti, A.; Barbero, F.; Bertea, C.M. Effects of a new biostimulant on gene expression and metabolic responses of tomato plants. Acta Hortic. 2016, 1148, 35–42. [Google Scholar] [CrossRef]
  21. Yakhin, O.I.; Lubyanov, A.A.; Yakhin, I.A.; Brown, P.H. Biostimulants in plant science: A global perspective. Front. Plant Sci. 2017, 7, 2048. [Google Scholar] [CrossRef]
  22. Stevanato, P.; Broccanello, C.; Biscarini, F.; Del Corvo, M.; Sablok, G.; Panella, L.; Stella, A.; Concheri, G. High-throughput RAD-SNP genotyping for characterization of sugar beet genotypes. Plant Mol. Biol. Rep. 2014, 32, 691–696. [Google Scholar] [CrossRef]
  23. Barone, V.; Bertoldo, G.; Magro, F.; Broccanello, C.; Puglisi, I.; Baglieri, A.; Cagnin, M.; Concheri, G.; Squartini, A.; Pizzeghello, D.; et al. Molecular and morphological changes induced by leonardite-based biostimulant in Beta vulgaris L. Plants 2019, 8, 181. [Google Scholar] [CrossRef]
  24. Heid, C.A.; Stevens, J.; Livak, K.J.; Williams, P.M. Real time quantitative PCR. Genome Res. 1996, 6, 986–994. [Google Scholar] [CrossRef] [PubMed]
  25. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef] [PubMed]
  26. Schmittgen, T.D.; Livak, K.J. Analyzing real-time PCR data by the comparative C(T) method. Nat. Protoc. 2008, 3, 1101–1108. [Google Scholar] [CrossRef] [PubMed]
  27. KEGG. 2019. Available online: http://www.kegg.jp (accessed on 1 September 2019).
  28. Babicki, S.; Arndt, D.; Marcu, A.; Liang, Y.; Grant, J.R.; Maciejewski, A.; Wishart, D.S. Heatmapper: Web-enabled heat mapping for all. Nucleic Acids Res. 2016, 14, 148–153. [Google Scholar] [CrossRef] [PubMed]
  29. Beevers, H. Metabolic production of sucrose from fat. Nature 1961, 191, 433–436. [Google Scholar] [CrossRef] [PubMed]
  30. Cornah, J.E.; Germain, V.; Ward, J.L.; Beale, M.H.; Smith, S.M. Lipid utilization, gluconeogenesis, and seedling growth in arabidopsis mutants lacking the glyoxylate cycle enzyme malate synthase. J. Biol. Chem. 2004, 279, 42916–42923. [Google Scholar] [CrossRef] [PubMed]
  31. Kovács, G.; Sorvari, S.; Scott, P.; Toldi, O. Pyrophosphate: Fructose 6-phosphate 1-phosphotransferase operates in net gluconeogenic direction in taproots of cold and drought stressed carrot plants. Acta Biol. Szeged. 2006, 50, 25–30. [Google Scholar] [CrossRef]
  32. Shaar-Moshe, L.; Hübner, S.; Peleg, Z. Identification of conserved drought-adaptive genes using a cross-species meta-analysis approach. BMC Plant Biol. 2015, 15, 111. [Google Scholar] [CrossRef]
  33. Amthor, J.S. Respiration and Crop Productivity; Springer: New York, NY, USA, 1989. [Google Scholar]
  34. Seabra, A.R.; Silva, L.S.; Carvalho, H.G. Novel aspects of glutamine synthetase (GS) regulation revealed by a detailed expression analysis of the entire GS gene family of Medicago truncatula under different physiological conditions. BMC Plant Biol. 2013, 13, 137. [Google Scholar] [CrossRef]
  35. Černý, I.; Pačuta, V.; Ernst, D.; Gažo, J. Formation of sugar beet yield and sugar content depending on year and foliar application of biologically active substances and fertilizers. LCaR 2018, 134, 141–145. [Google Scholar]
  36. Bao, A.; Zhao, Z.; Ding, G.; Shi, L.; Xu, F.; Cai, H. The Stable Level of Glutamine synthetase 2 plays an important role in rice growth and in carbon-nitrogen metabolic balance. Int. J. Mol. Sci. 2015, 16, 12713–12736. [Google Scholar] [CrossRef] [PubMed]
  37. Xiong, L.; Lee, B.; Ishitani, M.; Lee, H.; Zhang, C.; Zhu, J.K. FIERY1 encoding an inositol polyphosphate 1-phosphatase is a negative regulator of abscisic acid and stress signaling in Arabidopsis. Genes Dev. 2001, 15, 1971–1984. [Google Scholar] [CrossRef] [PubMed]
  38. Sharp, R.E.; Lenoble, M.E. ABA, ethylene and the control of shoot and root growth under water stress. J. Exp. Bot. 2002, 53, 33–37. [Google Scholar] [CrossRef] [PubMed]
  39. Woodward, A.W.; Bartel, B. Auxin: Regulation, Action, and Interaction. Annal. Bot. 2005, 95, 707–735. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Vanneste, S.; Frim, J. Auxin: A trigger for change in plant development. Cell 2009, 136, 1005–1016. [Google Scholar] [CrossRef] [PubMed]
  41. Overvoorde, P.; Fukaki, H.; Beeckman, T. Auxin control of root development. Cold Spring Harb Perspect. Biol. 2010, 2, a001537. [Google Scholar] [CrossRef] [PubMed]
  42. Wang, A.; Garcia, D.; Zhang, H.; Feng, K.; Chaudhury, A.; Berger, F.; Peacock, W.J.; Dennis, E.S.; Luo, M. The VQ motif protein IKU1 regulates endosperm growth and seed size in Arabidopsis. Plant Journal 2010, 63, 670–679. [Google Scholar] [CrossRef]
  43. Xue, H.W.; Xu, C.; Mei, Y. Function and regulation of phospholipid signalling in plants. Biochem. J. 2009, 421, 145–156. [Google Scholar] [CrossRef]
  44. Lin, W.H.; Ye, R.; Ma, H.; Xu, Z.H.; Xue, H.W. DNA chip-based expression profile analysis indicates involvement of the phosphatidyl inositol signaling pathway in multiple plant responses to hormone and abiotic treatments. Cell Res. 2004, 14, 34–45. [Google Scholar] [CrossRef]
  45. Helimann, I. Phosphoinositide signaling in plant development. Development 2016, 143, 2044–2055. [Google Scholar] [CrossRef] [Green Version]
  46. König, S.; Mosblech, A.; Heilmann, I. Stress-inducible and constitutive phosphoinositide pools have distinctive fatty acid patterns in Arabidopsis thaliana. FASEB J. 2007, 21, 1958–1967. [Google Scholar] [CrossRef] [PubMed]
  47. König, S.; Ischebeck, T.; Lerche, J.; Stenzel, I.; Heilmann, I. Salt stress-induced association of phosphatidylinositol 4,5-bisphosphate with clathrin-coated vesicles in plants. Biochem. J. 2008, 415, 387–399. [Google Scholar] [CrossRef] [PubMed]
  48. Dieck, C.B.; Boss, W.F.; Imara, Y.; Perera, I.Y. A role for phosphoinositides in regulating plant nuclear functions. Front Plant Sci. 2012, 3, 1–12. [Google Scholar] [CrossRef] [PubMed]
  49. Hung, C.Y.; Aspesi, P.; Hunter, M.R.; Lomax, A.W.; Perera, I.Y. Phosphoinositide-signaling is one component of a robust plant defense response. Front Plant Sci. 2014, 5, 1–15. [Google Scholar] [CrossRef] [PubMed]
  50. Pan, L.; Zhang, X.; Wang, J.; Ma, X.; Zhou, M.; Huang, L.; Nie, G.l.; Wang, P.; Yang, Z.; Li, J. Transcriptional profiles of drought-related genes in modulating metabolic processes and antioxidant defenses in Loliumm ultiflorum. Front. Plant Sci. 2016, 7, 519. [Google Scholar] [CrossRef] [PubMed]
  51. Zhang, C.; Meng, S.; Li, M.; Zhao, Z. Genomic identification and expression analysis of the phosphate T\transporter gene family in Poplar. Front. Plant Sci. 2016, 7, 1398. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Plants grown on soil inside containers with depth-infiltration allowing for gas exchange with limited dehydration.
Figure 1. Plants grown on soil inside containers with depth-infiltration allowing for gas exchange with limited dehydration.
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Figure 2. A representative amplification curve for the expression of several genes tested with OpenArray qPCR in sugar beet in response to treatment with BLACKJAK biostimulant.
Figure 2. A representative amplification curve for the expression of several genes tested with OpenArray qPCR in sugar beet in response to treatment with BLACKJAK biostimulant.
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Figure 3. An amplification plot showing variations in ΔRn versus PCR cycles for the expression of genes tested in response to treatment with BLACKJACK in sugar beet leaves. Rn is the reporter signal normalized to the fluorescence signal dye.
Figure 3. An amplification plot showing variations in ΔRn versus PCR cycles for the expression of genes tested in response to treatment with BLACKJACK in sugar beet leaves. Rn is the reporter signal normalized to the fluorescence signal dye.
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Figure 4. Relative expression of Bv_UQCRH and Bv_AOAT1 genes in BLACKJAK untreated and treated sugar beet in three exposure times. BJK: BLACKJAK, 500 and 1000 stand for 1.0 mg C L−1 and 0.5 mg C L−1 BLACKJAK, respectively.
Figure 4. Relative expression of Bv_UQCRH and Bv_AOAT1 genes in BLACKJAK untreated and treated sugar beet in three exposure times. BJK: BLACKJAK, 500 and 1000 stand for 1.0 mg C L−1 and 0.5 mg C L−1 BLACKJAK, respectively.
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Figure 5. Relative expression of Bv_AHG3 and Bv_ATTIR1 genes in BLACKJAK untreated and treated sugar beet in three exposure times. BJK: BLACKJAK, 500 and 1000 stand for 1.0 mg C L−1 and 0.5 mg C L−1 BLACKJAK, respectively.
Figure 5. Relative expression of Bv_AHG3 and Bv_ATTIR1 genes in BLACKJAK untreated and treated sugar beet in three exposure times. BJK: BLACKJAK, 500 and 1000 stand for 1.0 mg C L−1 and 0.5 mg C L−1 BLACKJAK, respectively.
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Figure 6. Relative expression of Bv_GLT1 and Bv_GLN1 genes in BLACKJAK untreated and treated sugar beet in three exposure times. BJK: BLACKJAK, 500 and 1000 stand for 1.0 mg C L−1 and 0.5 mg C L−1 BLACKJAK, respectively.
Figure 6. Relative expression of Bv_GLT1 and Bv_GLN1 genes in BLACKJAK untreated and treated sugar beet in three exposure times. BJK: BLACKJAK, 500 and 1000 stand for 1.0 mg C L−1 and 0.5 mg C L−1 BLACKJAK, respectively.
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Figure 7. Relative expression of Bv_PIK4 and Bv_PHT2;1 genes in BLACKJAK untreated and treated sugar beet in three exposure times. BJK: BLACKJAK, 500 and 1000 stand for 1.0 mg C L−1 and 0.5 mg C L−1 BLACKJAK, respectively.
Figure 7. Relative expression of Bv_PIK4 and Bv_PHT2;1 genes in BLACKJAK untreated and treated sugar beet in three exposure times. BJK: BLACKJAK, 500 and 1000 stand for 1.0 mg C L−1 and 0.5 mg C L−1 BLACKJAK, respectively.
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Figure 8. Heat map showing the expression profiling of genes in 1:500 BLACKJAK treated samples at three exposure times (h) in sugar beet leaf. Gene codes are referred to gene ID in Table 1.
Figure 8. Heat map showing the expression profiling of genes in 1:500 BLACKJAK treated samples at three exposure times (h) in sugar beet leaf. Gene codes are referred to gene ID in Table 1.
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Figure 9. Heat map showing the expression profiling of genes in 1:1000 BLACKJAK treated samples at three exposure time (h) in sugar beet leaf. Gene codes are referred to gene ID in Table 1.
Figure 9. Heat map showing the expression profiling of genes in 1:1000 BLACKJAK treated samples at three exposure time (h) in sugar beet leaf. Gene codes are referred to gene ID in Table 1.
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Table
Table 1. List of genes tested for expression profiling in sugar beet plants.
Table 1. List of genes tested for expression profiling in sugar beet plants.
Gene CodeGene IDGene Function CategoryEC Number
G2Bv_6PGDMitocondrial respiratory pathwayOPP oxidative PP.6-phosphogluconate dehydrogenase1.1.1.44
G3Bv_R5PIOPPnon-reductive PP.ribose 5-phosphate isomerase5.3.1.6
G4Bv_UQCRHmitochondrial electron transport/ATP synthesis, cytochrome c reductase1.10.2.2
G5Bv_UQCR7mitochondrial electron transport/ATP synthesis, cytochrome c reductase1.10.2.2
G6Bv_QCO mitochondrial electron transport/ATP synthesis, cytochrome c 1.10.2.2
G7Bv_PEILipid metabolismlipid metabolism, lipid degradation, lipases3.1.1.3
G8Bv_SAC2lipid metabolism, lipid degradation, lysophospholipases3.1.1.5
G9Bv_AOAT1N-metabolismamino acid metabolism, central amino acid metabolism, alanine, alanine aminotransferase2.6.1.2
G10ATP-PRT2amino acid metabolism, synthesis histidine, ATP phosphoribosyl transferase2.4.2.17
G11Bv_GLT1N-metabolism, ammonia metabolism, glutamate synthase NADH dependent1.4.1.14
G12Bv_GLN1N-metabolism, ammonia metabolism, glutamine synthetase6.3.1.2
G13Bv_AREB1 Hormone metabolismhormone metabolism, abscisic acid, induced-regulated-responsive-activated244.319.5
G14Bv_HAB1 hormone metabolism, abscisic acid signal transduction
G15Bv_AHG3 hormone metabolism, abscisic acid signal transduction
G16Bv_LAX1 hormone metabolism, auxin signal transduction
G17Bv_ATTIR1 hormone metabolism, auxin signal transduction
G18Bv_PIN1hormone metabolism, auxin signal transduction
G19Bv_LAX2 hormone metabolism, auxin signal transduction
G20Bv_PIN3 hormone metabolism, auxin signal transduction
G21Bv_SAUR hormone metabolism, auxin induced-regulated-responsive-activated
G22Bv_CKX3hormone metabolism, cytokinin synthesis-degradation1.5.99.12
G23Bv_AHK2 hormone metabolism, cytokinin signal transduction
G24Bv_CKDHase hormone metabolism, cytokinin synthesis-degradation1.5.99.12
G25Bv_2OGDhormone metabolism, gibberelin synthesis-degradation
G26Bv_GID1Chormone metabolism, gibberelin signal transduction
G27Bv_GRP hormone metabolism, gibberelin induced-regulated-responsive-activated
G28Bv_GAST1hormone metabolism, gibberelin induced-regulated-responsive-activated
G29Bv_GASA14hormone metabolism, gibberelin induced-regulated-responsive-activated
G30Bv_SAMT1hormone metabolism, salicylic acid synthesis-degradation
G31Bv_SAMT2hormone metabolism.salicylicacid.synthesis-degradation.synthesis.methyl-SA methyl esterase
G32Bv_HSP Abiotic stressheat stress
G33Bv_sah heat stress
G34Bv_HPO3cold stress
G35Bv_PMT21drought/salt stresses
G36Bv_PMTI4drought/salt stresses
G37Bv_TXN Oxidative stressredox thioredoxin
G38Bv_APX redox ascorbate and glutathione ascorbate______________
G39Bv_GR redox ascorbate and glutathione glutathione______________
G40Bv_FSD redox dismutases and catalases1.15.1.1/1.11.1.6
G41Bv_CATredox dismutases and catalases1.15.1.1/1.11.1.6
G42Bv_CZSODredox dismutases and catalases1.15.1.1/1.11.1.6
G43Bv_CBR redox misc
G44Bv_TIM DNA synthesis/repairprotein targeting mitochondria
G45Bv_MY10 DNA synthesis/chromatin structure
G46Bv_DRT111 DNA repair
G47CRK27Signal transduction pathwaysignalling receptor kinases., leucine rich repeat I
G48IKU2signalling receptorkinases, leucine rich repeat XI
G51SFH3signallingin sugar and nutrient physiology
G52Bv_PI4Ksignalling phosphinositides
G49ABCI6Protein assemblyprotein targeting secretory pathway
G50EXO protein assembly and cofactor ligation
G53GABAt1Nutrient uptaketransport amino acids
G54PHT1-3transport phosphatase
G55Bv_PHT2;1transport phosphatase
G56Bv_VHP1transport H+ transporting pyrophosphatase3.6.1.1
Note: EC: enzyme commission number.
Table
Table 2. Mean of sugar content, yield, and processing quality related traits in BLACKJAK treated and untreated sugar beet.
Table 2. Mean of sugar content, yield, and processing quality related traits in BLACKJAK treated and untreated sugar beet.
BLACKJAKStatisticTrait
Sugar Content (%)Sugar Yield
(t ha−1)		Sodium
(mmol 100g−1)		Potassium
(mmol 100g−1)
UntreatedMean12.7412.632.263.25
STDEV0.40.920.120.49
TreatedMean12.8813.552.073.14
STDEV0.491.120.10.46
Note: STDEV: standard deviation.
Table
Table 3. Results of analysis of variance (ANOVA) and relative expression analyses for genes tested with OpenArray qPCR in BLACKJAK treated sugar beet plants.
Table 3. Results of analysis of variance (ANOVA) and relative expression analyses for genes tested with OpenArray qPCR in BLACKJAK treated sugar beet plants.
CodeGene IDMean Squares
Treatment
(T)		Exposure
(E)		T × EErrorRelative Expression
(RE)
G2Bv_6PGD0.00430.0223 *0.0020.00350.1416
G3Bv_R5PI0.00520.02150.00450.0070.1562
G4Bv_UQCRH0.02650.4461 **0.1120.05460.447
G5Bv_UQCR70.0810.03770.10930.03250.4909
G6Bv_QCO 0.0008 **0.0003 **0.0002 **0.000010.0316
G7Bv_PEI0.00050.00560.00060.00130.0873
G8Bv_SAC20.0006 **0.0003 **0.0005 **0.000010.0281
G9Bv_AOAT10.3806 **0.5031 **0.2094 **0.0040.458
G10ATP-PRT20.0036 **0.0066 **0.0032 **0.000080.0995
G11Bv_GLT10.00670.3501 **0.8100 *0.01740.7757
G12Bv_GLN10.5487 **1.9005 **0.05610.02671.4838
G13Bv_AREB1 0.000040.0002 *0.000010.000030.0158
G14Bv_HAB1 0.0000010.00004 *0.000010.0000070.02499
G15Bv_AHG3 0.05850.04270.3608 **0.03390.9499
G16Bv_LAX1 0.000050.000170.000060.000160.03032
G17Bv_ATTIR1 0.00470.10160.00060.03970.7697
G18Bv_PIN10.00230.00050.02580.00250.4049
G19Bv_LAX2 0.0000030.0000050.0000060.000070.0451
G20Bv_PIN3 0.000000080.00000030.00000010.0000010.0037
G33Bv_sah 0.000000080.000000030.000000010.0000010.0037
G42Bv_CZSOD 0.0009 **0.0057 **0.0108 **0.000030.0819
G43Bv_CBR 0.0004 *0.0016 **0.0004 **0.000060.0205
G44Bv_TIM 0.0300 **0.0655 **0.0139 **0.00070.139
G45Bv_MY100.0031 **0.0088 **0.0042 **0.000090.0691
G46Bv_DRT1110.0335 **0.0241 **0.0103 **0.00120.1162
G48IKU20.0091 **0.0150 **0.0058 **0.00020.0854
G51SFH30.0464 **0.767 **0.0233 **0.00060.1764
G52Bv_PI4K0.3502 **0.7668 **0.1543 **0.00330.436
G50EXO0.00001 **0.00002 **0.00004 **0.0000010.00619
G54PHT1-30.0857 *0.1624 **0.04610.01660.1811
G55Bv_PHT2;12.5789 **8.8989 **1.8073 **0.07221.0261
G56Bv_VHP10.0658 *0.1954 **0.0538 *0.00980.1529
Note: * and ** stand for significant differences at 5 and 1% probability level.

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Hajizadeh, H.S.; Heidari, B.; Bertoldo, G.; Della Lucia, M.C.; Magro, F.; Broccanello, C.; Baglieri, A.; Puglisi, I.; Squartini, A.; Campagna, G.; et al. Expression Profiling of Candidate Genes in Sugar Beet Leaves Treated with Leonardite-Based Biostimulant. High-Throughput 2019, 8, 18. https://doi.org/10.3390/ht8040018

AMA Style

Hajizadeh HS, Heidari B, Bertoldo G, Della Lucia MC, Magro F, Broccanello C, Baglieri A, Puglisi I, Squartini A, Campagna G, et al. Expression Profiling of Candidate Genes in Sugar Beet Leaves Treated with Leonardite-Based Biostimulant. High-Throughput. 2019; 8(4):18. https://doi.org/10.3390/ht8040018

Chicago/Turabian Style

Hajizadeh, Hanifeh Seyed, Bahram Heidari, Giovanni Bertoldo, Maria Cristina Della Lucia, Francesco Magro, Chiara Broccanello, Andrea Baglieri, Ivana Puglisi, Andrea Squartini, Giovanni Campagna, and et al. 2019. "Expression Profiling of Candidate Genes in Sugar Beet Leaves Treated with Leonardite-Based Biostimulant" High-Throughput 8, no. 4: 18. https://doi.org/10.3390/ht8040018

APA Style

Hajizadeh, H. S., Heidari, B., Bertoldo, G., Della Lucia, M. C., Magro, F., Broccanello, C., Baglieri, A., Puglisi, I., Squartini, A., Campagna, G., Concheri, G., Nardi, S., & Stevanato, P. (2019). Expression Profiling of Candidate Genes in Sugar Beet Leaves Treated with Leonardite-Based Biostimulant. High-Throughput, 8(4), 18. https://doi.org/10.3390/ht8040018

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