Comparative Transcriptome Analysis of Two Ascophyllum nodosum

Article pubs.acs.org/JAFC

Comparative Transcriptome Analysis of Two Ascophyllum nodosum Extract Biostimulants: Same Seaweed but Different Oscar Goñi,† Antoine Fort,§ Patrick Quille,† Peter C. McKeown,§ Charles Spillane,§ and Shane O’Connell*,† †

Plant Biostimulant Group, Shannon Applied Biotechnology Centre, Institute of Technology, Tralee, Co. Kerry, Ireland Genetics and Biotechnology Laboratory, Plant and AgriBiosciences Research Centre (PABC), National University of Ireland Galway, University Road, Galway H91 REW4, Ireland

§

S Supporting Information *

ABSTRACT: Biostimulants for crop management are gaining increased attention with continued demand for increased crop yields. Seaweed extracts represent one category of biostimulant, with Ascophyllum nodosum extracts (ANE) widely used for yield and quality enhancement. This study investigated how the composition of two ANE biostimulants (ANE A and ANE B) affects plant mRNA transcriptomes, using the model plant Arabidopsis thaliana. Using Affymetrix Ath1 microarrays, significant heterogeneity was detected between the ANE biostimulants in terms of their impacts on the mRNA transcriptome of A. thaliana plants, which accumulated significantly more biomass than untreated controls. Genes dysregulated by the ANE biostimulants are associated with a wide array of predicted biological processes, molecular functions, and subcellular distributions. ANE A dysregulated 4.47% of the transcriptome, whereas ANE B dysregulated 0.87%. The compositions of both ANEs were significantly different, with a 4-fold difference in polyphenol levels, the largest observed. The standardization of the composition of ANE biostimulants represents a challenge for providing consistent effects on plant gene expression and biostimulation. KEYWORDS: biostimulant, seaweed extract, Ascophyllum nodosum extract, microarray, Arabidopsis thaliana

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INTRODUCTION Biostimulants are an emerging class of crop management products that target the modulation of crop stress to increase productivity. Current market forecasts for the biostimulant sector project 10−20% compound annual growth rate with the market expected to be valued at U.S. $2.5 billion by 2019.1 Due to the rapid growth in the use of biostimulants in agriculture, momentum for regulation of marketed biostimulants is growing. The interest in establishing a regulatory framework for biostimulants has led to the establishment of industry groups such as the European Biostimulant Industry Council (EBIC) and the Biostimulant Coalition in the United States of America to lobby for market regulation. EBIC has defined biostimulants as “containing substance(s) and/or microorganisms whose function when applied to plants or the rhizosphere is to stimulate natural processes to enhance/benefit nutrient uptake, nutrient efficiency, tolerance to abiotic stress, and crop quality”.2 Scientific data to support biostimulant claims will likely be required for future registration. The use of biostimulants in agriculture has been reviewed.3,4 The assignment of biostimulants into different categories varies between authors. Du Jardin4 assigned biostimulants into eight categories: (i) humic substances, (ii) complex organic materials, (iii) beneficial chemical elements, (iv) inorganic salts, (v) seaweed extracts, (vi) chitin and chitosan derivatives, (vii) antitranspirants, and (viii) free amino acids and other Ncontaining substances, with microorganisms a potential ninth category not included in that review. Calvo et al.3 assigned biostimulants into five categories: (i) microbial innoculants, (ii) humic substances, (iii) fulvic acids, (iv) protein hydrolysates © XXXX American Chemical Society

and amino acids, and (v) seaweed extracts, with some categories overlapping with those proposed by du Jardin.4 Seaweed extracts are prominent in the biostimulant market, representing the fastest growing biostimulant product category.5 The effects of seaweed extracts on plants have been reviewed6,7 with a range of biostimulant effects reported, including improved plant vigor and root development; enhanced chlorophyll synthesis; earlier flowering and fruit set; uniformity of fruit; delay of senescence; stress tolerance (drought, salinity, and frost); adjuvant action in pesticide mixtures. A number of studies have investigated potential modes of action for seaweed extract biostimulants in model plants and crops.2,8−10 However, it is important to recognize that seaweed extract biostimulants are not a homogeneous category of products. Seaweed extract biostimulants vary depending on the type of seaweed used for manufacture (e.g., which may be brown, green, or red),11 the spatiotemporal source and environment of the seaweed raw material (i.e., season harvested, geographical location, sheltered vs exposed shoreline, water temperature and salinity),12 and the process used for manufacture/extraction.13−15 These three variables significantly contribute to the differential chemical compositions and physicochemical properties of seaweed extracts. Brown seaweeds are the most widely used for the manufacture of biostimulant products with Ascophyllum Received: February 4, 2016 Revised: March 21, 2016 Accepted: March 24, 2016

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DOI: 10.1021/acs.jafc.6b00621 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Journal of Agricultural and Food Chemistry

3′IVT Express Kit and GeneChip Hybridization, Wash, and Stain Kit reagents were obtained from Affymetrix (Santa Clara, CA, USA). A. nodosum Extracts. Two commercially available liquid seaweed extracts of A. nodosum (ANE A and ANE B) manufactured using different methods were applied to plants as biostimulant treatments. ANE A was manufactured using a proprietary process at a temperature of >100 °C at neutral pH. ANE B was also manufactured using a proprietary process at a temperature of >100 °C at alkaline pH. Chemical Composition Analysis of ANEs. Solids were determined after drying in a convection oven at 105 °C for 18 h. These same samples were then used to determine ash content by placing in a furnace at 550 °C for 6 h.30 Fucoidan was determined according to the cysteine−sulfuric acid method using L-fucose as a standard.31 Samples were digested in a sulfuric acid/water mixture (6:1) at 100 °C and then allowed to cool in an ice bath before the color-forming agent (3% w/v L-cysteine in HCl) was added. Color was allowed to form at room temperature for an hour before absorbance was measured at 400 nm and corrected at 460 nm. Uronic acids were determined using metahydroxydiphenyl reagent, and alginic acid was used as a standard.30 Samples were digested in 0.0125 M sodium tetraborate in sulfuric acid at 100 °C, and absorbance was measured at 510 nm. Total polyphenol content was measured using Folin− Ciocalteu’s phenol reagent, and phloroglucinol was used as the standard. Laminaran was determined using β-glucosidase (β-Glucan Kit Assay Kit, Megazyme). GOPOD reagent was then used to measure the amount of glucose formed at 510 nm. Laminaran was used as a standard. Mannitol determination was performed by HPAE-PAD using a Carbopac PA-100 anion-exchange resin (4.6 × 250 mm) connected to a Carbopac PA-100 guard column (4.6 × 50 mm) (Thermo Scientific Dionex, Ireland) at room temperature. The system consisted of an LC-20AT gradient pump (Shimadzu, Japan) and Decade II electrochemical detector (Antec, Leyden, The Netherlands). The amperometry detector cell contained a gold electrode and a HyREF reference electrode. An isocratic gradient of 27 mM NaOH (degassed by bubbling with helium) at 1 mL/min was applied. D-Mannitol was identified by comparison of retention time to that of standard and quantified by integration of peak area with LCsolution software (Shimadzu). Plant Materials and Growth Conditions. Arabidopsis (Col-0) seeds were surface-sterilized prior to seeding in sterile MS medium. At the 4−5-leaf stage plants were transplanted to individual pots in a medium of compost/vermiculite/perlite (5:1:1). Following 1 day of growth, biostimulant and control treatments were applied by foliar spray at a dilution of 0.2% (v/v). Water was applied as a control. Plants were maintained for 1 week in growth room conditions of 21 °C with a night/day period of 16 h/8 h (light intensity during day hours of 120 μmol of photons/m2), after which plant height and rosette leaf number were measured. RNA Extraction and Purification from Arabidopsis Leaves. All plant rosettes including leaves and shoots from five biological replicates (one plant per replicate) were snap-frozen in liquid nitrogen. Sample collection was conducted 1 week after biostimulant treatment. RNA extraction was performed using the NucleoSpin RNA plant Plasmid kit (Macherey-Nagel) according to the manufacturer’s instructions. After RNA extraction, DNase treatment was applied (DNase1, Sigma-Aldrich), following the manufacturer’s instruction. RNA quality was confirmed by agarose gel electrophoresis, and the concentration and purity of the RNA samples were assessed using an Implen NanoPhotometer. cDNA Synthesis, Fluorescent Labeling, and Microarray Hybridization. Samples for microarray hybridization were prepared using a 3′IVT express kit from Affymetrix, following the manufacturer’s instruction. Briefly, 100 ng of purified RNA was reverse transcribed and biotin-labeled using 16 h of in vitro transcription. After amplified RNA (aRNA) purification and fragmentation, samples were hybridized on Affymetrix ATH1 microarrays using an Affymetrix GeneChip Hybridization, Wash, and Stain Kit and Affymetrix Fluidic Station 450, again following the manufacturer’s instructions.

nodosum Le Jol (AN) the dominant species due to its long history of positive results in enhancing crop productivity.7 Large beds of A. nodosum exist in the North Atlantic ocean, with sustainable harvesting practices contributing to the renewal of the resource in its naturally occurring habitat of the intertidal zone on rocky shores.16 A number of different seaweed extract biostimulant manufacturing processes are in use including micronization, cell burst, ultrasound, alkaline hydrolysis, water extraction, enzyme extraction, and fermentation.17 The role of biostimulants in the future of agricultural production is expected to grow significantly with the widely accepted drivers (growing population, changing climate, nonfood use of land, and deregistration of pesticides) for sustainable production of food. A more detailed understanding of the key attributes affecting the performance of seaweed extract biostimulants is required for this product category to fulfill its potential. Current impediments to the adoption of A. nodosum extracts (ANE) into mainstream crop management practices include variability in the consistency and magnitude of the crop response to their application,18 variability in the timing of application promoted for different product offerings, and lack of clarity for growers in the differences between seaweed extract biostimulants (i.e., how do they know which is the best seaweed extract biostimulant for their needs). The lack of seaweed extract compositional information and its relationship with biostimulant activity is a contributor to these issues. A number of reports have compared the performance of different seaweed extracts as biostimulants.19,20 However, there is very little data linking chemical composition with biostimulant activity and changes at the molecular level within the plant. The European Union REACH registration for seaweed extract has identified alginate, fucoidan, laminaran, mannitol and polyphenols as key components within seaweed extracts (as they make up the majority of the organic component).21 There are a number of reports demonstrating the biostimulant activity of these components.22−27 However, the relationship between the concentration and physicochemical characteristics of these seaweed components to biostimulant activity is poorly understood. Transcriptome analysis is a powerful tool that can identify differential gene expression in particular tissues or development stages or in response to an external stimulus. Transcriptome analyses of the effects of biostimulants on plants have to date highlighted global changes in gene transcription across multiple processes, pathways, and cell functions.9,28 Such transcriptome analysis has displayed both sensitivity and specificity to identify differences between biostimulant treatments eliciting responses in plants.29 Our objective was to determine whether a relationship exists between the composition of two A. nodosum extracts and their biostimulant activity as measured through changes in plant growth and their impacts on the transcriptome of the model plant Arabidopsis thaliana.

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MATERIALS AND METHODS

Chemicals. All chemicals were purchased from Sigma-Aldrich (Arklow, Ireland). Seeds of A. thaliana (L.) Heynh, Brassicaceae, of accession Columbia-0 (Col-0) (hereafter Arabidopsis) were obtained from the Eureopean Arabidopsis Stock Centre (NASC, University of Nottingham, U.K.) and bulked up by self-ing in-house. Compost, vermiculite, and perlite were purchased from NAD (Dublin, Ireland), and all plant containers were obtained from JF McKenna (Armagh, Northern Ireland). GeneChip ATH1 Affymetrix microarrays with B



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Scanning and Image Analysis. Microarray scanning was performed on an Affymetrix GeneChip Scanner 3000 7G to generate scanned .CEL files. Microarray Data Analysis. The probe-level intensities from the .CEL files were analyzed using Flex Array statistical software 1.6.3 (Genome Quebec, Montreal, Canada). Background correction, normalization, and summarization of probe-level gene chip data were performed using the Robust Multiarray Averaging (RMA) algorithm. The normalized expression values were then analyzed by using Wright and Simon’s implementation of the Empirical Bayes method.32 Only changes in gene expression with a raw p value ≤0.005 and a fold change (≥2 or ≤ −2) were considered significant. The gene list was imported from the annotation file ATH1-121501.na34.annot.csv (Affymetrix). Venn Diagram. Gene sets filtered as explained above were selected for drawing Venn diagrams using the Web-based tool Venn Diagram Generator (http://www.bioinformatics.lu/venn.php). Gene Classification into Functional Categories. Four transcript lists with p ≤ 0.005 and a fold change (≥2 or ≤ −2), total ANE A (1011), total ANE B (196), unique ANE A (849), and unique ANE B (34), were annotated in the Classification SuperViewer Tool w/ Bootstrap Web database based on MapMan ontology (http://bbc. botany.utoronto.ca/ntools/cgi-bin/ntools_classification_superviewer. cgi).33 The absolute values and normalized frequencies in the Arabidopsis genomic set of each functional category were then calculated automatically online. Normalized frequency was calculated as follows: Number_in_Classinput_set/Number_Classifiedinput_set/Number_in_Classreference_set/Number_Classifiedreference_set. The SuperViewer appended the annotation to the Gene IDs based on the MapMan data set shown on http://mapman.gabipd.org/web/guest/mapmanstore (file Ath_AGI_LOCUS_TAIR10_Aug2012.txt). The effects of ANE treatments on metabolic pathways were analyzed using MapMan software (version 3.5.1R2), which is a userdriven tool that visualizes gene expression data sets onto diagrams of metabolic pathways or other processes.34 Relative Quantitative Reverse-Transcription PCR (qRT-PCR) Microarray Validation. One microgram of total RNA was used for first-strand cDNA synthesis (Fermentas RevertAid First Strand cDNA Synthesis Kit), and qRT-PCR reactions were performed in technical triplicates using the Bioline SensiMix No-ROX One-Step kit (QT23505) and run in a CFX96 thermocyler (Bio-Rad). For each reaction, the melt-curve was analyzed to ensure correct amplification and primer specificity. qRT-PCR specific primers were designed using the online tool QuantPrime: http://www.quantprime.de/main.php?page=home. Three defense-related genes, PDF1.2 (at5g44420), PR1 (at2g14610), and PR5 (at1g75040), were synthesized for analysis. ACTIN2 (at3g18780) was used as a reference for normalization. The ΔΔCt method35 was used to quantify normalized gene expression. The primer sequences used were as follows: PDF1.2, forward 5′GCTAAGTTTGCTTCCATCATC-3′ and reverse 5′-GACGTAACAGATACACTTGTG-3′; PR1, forward 5′-ACACGTGCAATGGAGTTTGTGG-3′ and reverse 5′-TTGGCACATCCGAGTCTCACTG3′; PR5, forward 5′-ATCACCCACAGCACAGAGACAC-3′ and reverse 5′-AGCAATGCCGCTTGTGATGAAC-3′; ACTIN2, forward 5′-TCGGTGGTTCCATTCTTGCTTC-3′ and reverse 5′-CTGTGAACGATTCCTGGACCTG-3′. Statistical Analysis. ANE compositional data and rtPCR data were analyzed using the unpaired t test for differences in p values using the statistical program SigmaPlot 12.0 for Windows. The effect of ANE treatment on plant phenotype was evaluated using the one-way ANOVA function of SigmaPlot software with a significance level set at p ≤ 0.05 as determined by Tukey−HSD test. Principal component analysis (PCA) was performed to establish if a correlation existed between the differences in the composition of ANE A, ANE B, and fold change of selected gene transcripts common to both ANEs using the Microsoft Excel add in XLSTAT.

RESULTS Compositional Analysis. The typical compositional analysis provided with ANE biostimulants is based on the total solids and ash contents. The two extracts evaluated in this study differed significantly (p ≤ 0.001) in these two parameters. However, such a simple analysis does not adequately reflect the complexity and variability within these extracts. The results presented in Table 1 provide a more detailed compositional Table 1. Compositional Analysis of Two A. nodosum Biostimulants (ANE A and ANE B) Currently Used in Agricultural Practice treatmenta component % (w/w) % solids (w/v) ash polyphenol fucoidan uronics laminarin mannitol other

ANE A 32.03 35.94 2.54 24.83 8.89 1.99 8.02 18.64

± ± ± ± ± ± ±

0.54 0.1 0.14 0.66 0.54 0.03 0.18

ANE B 40.62 44.53 10.50 20.03 12.75 1.49 6.94 15.79

± ± ± ± ± ± ±

0.3 *** 1.05 *** 0.55 *** 0.26 ** 0.62 *** 0.02 *** 0.22 *

Data are the means ± SD (n = 3). *, **, ***: difference was significant with p ≤ 0.05, p ≤ 0.01, and p ≤ 0.001, respectively (t test). a

evaluation of the two ANE biostimulants by determining the levels of key components. This demonstrates that both ANE biostimulants had significant differences in all components (polyphenols, fucoidan, uronics, laminaran, and mannitol) and that the level of difference detected was not always associated with changes in total solids (fucoidan, laminarin, and mannitol contents were significantly higher in the lower total solids extract). Overall, the largest difference was observed in the levels of polyphenols, with ANE B being 4-fold higher. The uronics level (representing alginate) was also significantly different, with ANE B being 1.4-fold higher than ANE A. The concentrations of fucoidan, laminarin, and mannitol in ANE A were found to be 15−33% higher than ANE B (Table 1). Growth of Biostimulant-Treated A. thaliana. To determine the effect of the biostimulants on plant growth, 14-day-old Arabidopsis Col-0 plants were treated with ANE A or ANE B products (foliar spray), and control plants were sprayed with distilled water. After 1 week, earlier flowering time and longer stems were observed in the ANE A- and ANE Btreated plants. In contrast, the control plants remained in the rosette production stage (Figure 1). Plant height and rosette leaf number were chosen as growth parameters to test the effects of ANE treatments. We determined that plants treated with ANE A displayed an increase in plant height of 4.5-fold over untreated controls, whereas ANE B-treated plants showed a 6-fold increase over untreated controls. Treatment with ANE A and ANE B also resulted in an increase in the average number of leaves compared to control plants, that is, by 43 and 69%, respectively (Table 2). Global Transcriptome Changes after Plant Treatment with ANE Biostimulants. To investigate the transcriptomes of 21-day-old plants that had been treated with ANE biostimulants, Affymetrix 22K ATH1 Arabidopsis gene expression microarray slides were used. The treatment with ANE A had a significant effect on the plant transcriptome, by dysregulating expression of about 4.47% (1011 genes) of the C


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Figure 1. Morphological phenotypes of 21-day-old Arabidopsis thaliana Col-0 plants after treatment with water control, ANE A, and ANE: (left) control, water treated; (center) ANE A treated; (right) ANE B treated.

(0.15% microarray). Among the genes significantly upregulated by the treatments, 501 were unique to ANE Atreated plants, and 29 were unique to ANE B-treated plants, with 98 genes common to both treatments (Figure 3A). There was a similar distribution of down-regulated genes, with 348, 5, and 64 genes significantly lower in expression levels in response to ANE A, ANE B, and both treatments, respectively (Figure 3B). Validation of Microarray Data by qRT-PCR. As large differences in gene expression were found between both ANE treatments, we validated the expression values obtained from microarray data by relative quantification real-time PCR (qRTPCR) using three defense-related genes (PDF1.2, PR1, and PR5) differentially expressed by ANE A and ANE B. The RNA samples used in the DNA microarray analysis were used as templates in qRT-PCR (Table 3). For ANE A, the expression measured with qRT-PCR displayed similarities to microarray data. Even though the PDF1.2 and PR5 genes displayed a significantly higher relative expression in the qRT-PCR analysis, the overall response pattern was similar to that found with the ANE A microarrays. The microarray results of the ANE B samples indicated that these three genes did not show a differential gene expression at p ≤ 0.005, having raw p values

Table 2. Effect of Biostimulants ANE A and ANE B on Arabidopsis thaliana Col-0 Plant Growth Parameters plant growth parametera treatment

plant height (cm)

rosette leaf number

water control ANE A ANE B

3.7 ± 0.9 a 16.8 ± 1.6 b 23.3 ± 1.9 c

16.3 ± 2.9 a 23.3 ± 1.2 b 27.7 ± 2.0 b

a For each column mean values ± SD (n = 5) with significant differences, as calculated by the Tukey HSD test with p ≤ 0.05, indicated by different letters.

genes detected by the microarray. There were more upregulated genes (599) than down-regulated genes (412) in plants treated with ANE A (Figure 2A). A smaller proportion of the transcripts was differentially expressed in plants treated with ANE B, when compared to untreated controls. Less than 0.87% of the genes on the array displayed significant changes in transcript levels at p ≤ 0.005 and a fold change ≥2 or ≤ −2, with 127 genes showing an increased expression and 69 genes having a significantly decreased expression (Figure 2B). There were a total of 849 unique genes (3.75%) dysregulated by ANE A only, whereas ANE B had 34 unique genes dysregulated

Figure 2. Volcano plot of changes in gene expression for ANE A-treated (A) and ANE B-treated (left) Arabidopsis thaliana Col-0 plants relative to water-treated control. Plotted are the p values on the y-axes in log 10 scale against the ratio of gene expression on the x-axes in log 2 scale. The cutoff criteria (change, ≥2-fold; p ≤ 0.005) are indicated. D


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Figure 3. Venn diagrams showing the comparison in gene expression of Arabidopsis thaliana Col-0 plants treated with ANE A and ANE B (change, ≥2-fold; p ≤ 0.005). Non-overlapping numbers represent the number of genes unique to a particular treatment. Overlapping numbers represent the number of mutual genes between treatments. The left figure represents up-regulated genes, and the right figure represents down-regulated genes.

Table 3. Microarray Validation for Selected Plant Defence Related Genes in Arabidopsis thaliana Plants by Relative Quantification Real-Time PCR (qRT-PCR) method (fold change) gene ID

a

gene name

treatment

array dataa

qRT-PCR

At5g44420

PDF 1.2 (plant defensin)

ANE A ANE B

5.26 ± 1.59 1.03 ± 2.20 ns

54.00 ± 7.10 1.00 ± 0.32

At2g14610

PR1 (pathogenesis-related gene 1)

ANE A ANE B

27.6 ± 2.95 5.15 ± 8.51 ns

53.20 ± 5.05 0.20 ± 0.06

At1g75040

PR5 (pathogenesis-related gene 5)

ANE A ANE B

18.20 ± 2.51 3.60 ± 3.34 ns

103.10 ± 8.55 23.00 ± 7.80

ns indicates genes not significantly dysregulated at a cutoff (p ≤ 0.005).

that ranged between 0.086 and 0.948. Although fold change patterns correlated, discrepancies in magnitude between the two platforms are not uncommon and could reflect the differences in normalization methods used. A global normalization was applied to the microarray data, whereas the use of the endogenous control ACTIN2 was used for normalization of qRT-PCR data. Taken together, these results suggest that the differentially expressed gene sets obtained by applying a stringent cutoff p ≤ 0.005 and a fold-change of 2 provide a good representation of the changes in gene expression of Arabidopsis plants grown after application of the two different commercial ANE biostimulants. Functional Classification of Gene Expression Patterns after Treatment with ANE Biostimulants. To further investigate the transcriptomic changes arising from the application of ANE biostimulants, we analyzed the MapMan ontology of the differentially expressed genes using the Webbased tool Classification SuperViewer. The gene lists were assigned to 31 different MapMan bins with 2 different output modes: (1) the absolute number (Table 4) and (2) the normalized frequency of genes (Figure S1−S4). The functional categories containing the highest number of dysregulated genes after application of ANE A were in five supracategories: metabolism (transport, lipid metabolism, and secondary metabolism), development (cell wall, development), stress (redox, stress), hormone metabolism, and other (protein, RNA and signaling). Similar categories, with the addition of metal handling to the metabolism group, represented the major functional groups dysregulated by ANE B. Approximately 25% of genes dysregulated by both treatments encoded proteins

with insufficient similarity to any protein of known function. The total and unique ANE A gene data sets showed similar functional rankings based on the absolute number of genes, whereas transcripts assigned to secondary metabolism and stress were highly represented in the unique ANE B gene list (Table 4). To identify trends in gene expression patterns after the application of ANE treatments, we also analyzed the normalized frequency option, where bias caused by number of genes in each MapMan category on the ATH1 array was removed. This approach determined those categories significantly over- or under-represented at p ≤ 0.05. As shown in Figures S1 and S2, there were similarities between the effect of both ANE A and ANE B treatments. Four of the five supracategories indicated above included the most overrepresented MapMan bins: metabolism (gluconeogenesis/ glyoxylate cycle, metal handling, and secondary metabolism), development (cell wall, development), stress (redox, stress), or hormone metabolism. Transcriptome differences between the biostimulants were also detected; other categories of metabolism such as transport, signaling, tetrapyrrole synthesis, major carbohydrate metabolism or lipid metabolism were overrepresented after ANE A treatment but not after ANE B treatment (Figure S3). Our functional analysis also indicated that three categories related to specific metabolic pathways (Sassimilation, N-metabolism, and nucleotide metabolism) were significantly over-represented in the unique ANE B gene data set (Figure S4). Changes in Gene Expression Resulting from ANE Biostimulants. To identify possible gene functions associated E


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Table 4. Functional Categorization by MapMan Classification of Genes Dysregulated by Biostimulants ANE A and ANE B absolute number genesa

a

group

pathway

ANE A (total)

ANE B (total)

ANE A (unique)

ANE B (unique)

metabolism

amino acid metabolism C1-metabolism cofactor and vitamine metabolism fermentation gluconeogenesis/glyoxylate cycle glycolysis lipid metabolism major CHO metabolism metal handling minor CHO metabolism mitochondrial electron transport N-metabolism nucleotide metabolism oxidative pentose phosphate S-assimilation secondary metabolism TCA cycle/organic acid tetrapyrrole synthesis transport total annotations

14 2 2 2 2 2 30 8 14 8 4 1 7 3 0 26 2 8 51 186

3 1 1 1 2 0 5 1 4 2 1 1 2 0 1 11 1 0 8 45

13 1 2 1 1 2 25 7 10 6 3 1 7 3 0 22 1 8 40 153

2 0 0 0 0 0 2 0 0 0 0 1 2 0 1 5 0 0 0 13

development

cell cell wall development photosynthesis total annotations

30 59 36 26 151

5 16 9 8 38

25 42 27 19 113

1 2 0 0 3

stress

redox stress total annotations

21 57 78

4 14 18

17 47 64

0 4 4

hormones

hormone metabolism total annotations

35 35

5 5

29 29

0 0

other

DNA miscellaneous enzyme not assigned protein RNA signaling total annotations

3 95 261 101 73 55 588

1 22 45 17 11 4 100

3 76 198 85 64 52 478

1 3 10 1 2 1 18

Individual genes may be assigned to more than one functional class.

with the application of the ANE biostimulants, further metabolic pathway analysis were performed using MapMan software. The analysis of genes involved in stress-, redox-, signaling-, and secondary metabolite-related pathways indicated differences between ANE A and ANE B (Figure 4). ANE A was found to have 33 genes associated with hormone metabolism dysregulated, whereas ANE B had only 5. Five genes dysregulated by ANE B were common with ANE A (Figure 5). The data generated indicated that a large number of stressrelated genes were dysregulated after application of the ANE biostimulants. ANE A had 60 more genes associated with stress response dysregulated than ANE B. There were 15 stress- and redox-related genes that were common to both biostimulants (Figure 5). Metabolism-associated genes represented the largest group of genes dysregulated by the ANE biostimulants. Seventy percent

of metabolism-associated genes dysregulated by ANE B were common with ANE A, with pathways for transport, lipid metabolism, and secondary metabolism the most represented. There were 151 genes uniquely dysregulated by ANE A with most of these transcripts having a wider influence on those processes that were common to both ANE biostimulants. Genes involved in the transport of amino acids (LHT1 and AAP5), calcium (CAX3, CAX7, and ACA1) peptides (ATOPT3), nucleotide-sugar derivatives (UTR2 and UTR 3), copper (COPT2), nitrate (NRT1.5), nucleotides (ATPUP10), sugar (MSS1), and sulfate (SULTR1, SULTR3, and AST56) were all up-regulated, with CAX 3 and COPT2 the strongest changes (21- and 8-fold, respectively). There were 26 genes associated with lipid metabolism dysregulated by ANE A. Upregulated genes were mainly involved in lipid degradation, whereas down-regulated genes were associated with fatty acid F


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Figure 4. Mapman overview of dysregulated genes related to hormone, stress, and metabolic responses. Arabidopsis thaliana Col-0 plants were treated with biostimulants ANE A (A) and ANE B (B). Fold changes are indicated by the color scale (red and blue represent up- and down-regulated genes, respectively).

Figure 6. Relationship between fold change in the expression of selected genes common to ANE A and ANE B. ANE A exerts a larger fold change for all of the selected genes. TAIR database gene nomenclature is used for gene names.

Figure 5. Mapman overview of common dysregulated genes by ANE A and ANE B related to hormone, stress, and metabolic responses. The gene list was obtained using the Web-based tool Venn Diagram Generator. Average fold changes of both gene lists are indicated by the color scale (red and blue represent up- and down-regulated genes, respectively).

biostimulants. This analysis (Figure 7) indicated that the variation in the levels of mannitol, fucoidan, laminarin, and other organic components in the ANE biostimulants is more closely related to the dysregulation of five of the common genes than polyphenol, ash, and alginate. Three genes appear to increase in expression with increasing concentrations of mannitol, fucoidan, laminarin, and other organic components, whereas two genes are down-regulated.

and phospholipid synthesis. ANE A secondary metabolism upregulated genes included flavonoid and sulfur metabolism mediators. ANE B had five genes associated with sulfur metabolism uniquely dysregulated. Thirty-five of the genes classified with a role in plant development were common to both ANE biostimulants. The up-regulated genes were assigned to pathways involved in cell wall organization (ATCSLE1, UGE1 and PAE8), cell cycle/ organization (AtPP2-A11 and FIB), and plant development (LEA14, LEA3, NAM, TET 3). LEA 3 and LEA 14 were upregulated 5-fold by ANE A, which was the strongest change in this group of genes. There were 117 additional genes related to plant development dysregulated by ANE A only and 3 by ANE B. Principal Component Analysis of ANE Composition and Gene Expression. PCA of selected genes and the level of biomolecules in ANE A and ANE B was performed to establish if a relationship existed between the expression level of these genes (Figure 6) and the level of the components in the ANE

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DISCUSSION The results presented in this study reveal the phenotypic and transcriptome differences induced by two seaweed biostimulants (manufactured from the same A. nodosum raw material) when applied to the model plant Arabidopsis. The differences between the ANE biostimulants can be partly attributed to their chemical composition differences (Table 1). However, the significance of these differences is not well understood. Treatment with ANE A and ANE B led to enhanced vegetative (increased number of rossette leaves) and reproductive growth (early flowering) versus the control treatment. These observations of enhanced plant growth and development are G


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samples 3 and 30 days after treatment, with 724 and 612 genes dysregulated of the 31,561 genes represented on the microarray, respectively. The elemental analysis and hormone content of the ANE extract was reported, but no additional compositional information was provided.36 On reviewing the partial list of genes selected by the authors as having a role in photosynthesis and nitrogen and sulfur metabolism, a total of 23 of the selected 88 genes dysregulated in shoot tissue (26%) were in common with either ANE A or ANE B. Only 7 of the 88 genes were common with ANE B with 22 in common with ANE A. The influence of ANE biostimulants on plants has been linked to plant growth regulator (PGRs)-mediated effects for some time.11,13,37 The application of both ANEs enhanced the relative expression of SAUR59; however, SAUR1, SAUR33, SAUR50, and SAUR71 genes were also dysregulated by ANE A. It has been recently proposed that SAUR gene expression can be regulated by several hormones and environmental signals.38 Wally et al.39 reported a reduction in the length of seedling roots when an ANE biostimulant treatment was applied along with a corresponding reduction in IAA levels and the expression of the auxin sensitive reporter DR5::GUS. However, no auxin biosynthetic genes were found to be dysregulated by either ANE A or ANE B in our microarray data. ABA biosynthetic related gene NCED4 was found to be affected by biostimulant ANE A only. Others have shown significant accumulation of ABA content along with up-regulation of NCED3 gene in ANE-treated Arabidopsis plants from 24 to 144 h post treatment.39 The up-regulation of two putative 2oxoglutarate-dependent dioxygenases (at5g43450 and at2g25450) that catalyze the final step for production of ethylene40 was observed for both ANEs. Up-regulation of these genes by ANE was also reported in oilseed rape (OSR).36 Arabidopsis plants treated with ANE A also exhibited upregulation of the ethylene-responsive transcription factors ERF2 and ERF72. ERF72 has been found to be associated with activation of JA/ET-responsive genes such as the PDF1.2 marker gene.41 Our microarray and qRT-PCR results confirmed this observation (Table 3), indicating a possible switching on of transcription of ethylene signaling by ANE A. The application of ANE A significantly induced the expression of GA responsive gene GASA1, whereas GASA4 was downregulated. Wally et al.39 previously reported an increased GA content after ANE treatment in A. thaliana. ANE biostimulants are well recognized for their ability to enhance the tolerance of plants to stress. Redox stress genes common to both ANEs include at1g0320 and GRXC2, which are members of the glutaredoxin family of proteins.42 ANE A dysregulated four additional members of the glutaredoxin family. Previous ANE microarray data from OSR reported down-regulation of glutaredoxin C10 and GPX6, which is opposite to the results reported here.36 On the basis of the transcripts dysregulated in our data, ANE biostimulants likely have an impact on the enzymatic antioxidant system in plants. In the case of ANE A, the most prominent subsets of abiotic stress genes were associated with responses to cold, heat, drought, phosphate starvation, and wounding. The coldinduced gene COR47 has previously been shown to be induced by mannitol,43 which is one of the few genes dysregulated in this study found to have a direct relationship with a component in the ANEs. A hyperosmotic salinity response gene, AZF2, which was up-regulated 3-fold by ANE A, was also reported in the microarray analysis of Nair et al.9 Up-

Figure 7. Principal component analysis (PCA) of selected dysregulated genes in treated Arabidopsis thaliana Col-0 plants and the level of biomolecules in ANE A and ANE B: triangles, key components of ANE; diamonds, common up- and down-regulated genes. The variation in the concentrations of fucoidan, laminarin, mannitol, and other appears to be related to the changes in the expression of the selected genes.

similar to those experienced by growers of commercial crops using these biostimulants. In our study, we focused on mRNA levels (i.e., transcriptome) as an entry point for improved understanding of the role of ANE biostimulant composition on plant growth. The analysis of our transcriptome data highlighted differential gene expression between untreated plants and ANE Aand ANE B-treated plants. One hundred and sixty-two of the genes dysregulated by ANE B (83% of total genes dysregulated by ANE B) are common to ANE A, which suggests a common response by a specific subset of genes. A previous study reported 1113 genes (∼5% of transcriptome) were dysregulated by the application of a lipophilic fraction of an ANE biostimulant to Arabidopsis plants exposed to −2 °C for 24 h.9 Adjustment for those genes dysregulated due to the stress revealed the ANE fraction dysregulated 398 genes (1.65% of the transcriptome) using a cutoff of 1.5-fold change. On further review of the selection of dysregulated genes, it was found that none of the genes were in common with ANE B, whereas only six genes were common to those dysregulated by ANE A. The composition reported for this ANE fraction included 15.6% w/ w fatty acids and 50% w/w fucosterol, which is significantly enriched from the source ANE biostimulant.13 Fresh seaweed biomass has been reported to contain 0.1% w/w fucosterol.11,16 Transcription changes observed for ANE A and ANE B are not likely due to the fatty acids and fucosterol contents due to the small number of genes in common. These components can be concluded to have a very small role, if any, on the changes observed for ANE A and ANE B. A study by Jannin et al.,36 using an acid extract of A. nodosum applied through the roots of Brassica napus (oilseed rape), also reported significant dysregulation of the transcriptome in shoot H


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Journal of Agricultural and Food Chemistry Notes

regulation of six WRKY genes by ANE A along with induction of PR proteins suggests an activation of the SA pathway.44 ANE B had three unique genes associated with abiotic stress upregulated (COR15A, COR15B, and at3g26450). A previous study by Rayirath13 reported an ANE to induce a 2-fold increase in the expression of COR15A in response to freezing stress at −2 °C. The composition of the ANE was similar to the ANE B composition reported here. The number of stressrelated gene transcripts dysregulated by the ANEs could be suggestive of potential benefits to plants growing in stressful environments. The transport genes common to both ANEs were all upregulated and include amino acid transport, ammonium, calcium, and metal transport. Jannin et al.36 previously reported the impact of an ANE biostimulant on a selection of photosynthesis, nitrogen, and sulfur metabolism genes in the shoot of OSR. ANE A was found to have 13 genes dysregulated in the same direction (i.e., positive or negative), whereas ANE B had 5. Sulfur and nitrate transport, glutathione-S-transferase, and carbonic anhydrase genes were among those genes common among the three ANE biostimulants. ANE biostimulants have been reported to initiate flowering and enhance fruit set and fruit production.3 Genes associated with plant development dysregulated by the ANE biostimulants are associated with gamete development, embyrogenesis, cell wall development, and vascular development. ANE biostimulants can influence plant growth, crop quality, and response to stress, but the variation in crop response to different ANE biostimulant products has affected the robustness and credibility of these crop inputs. Our PCA revealed that a relationship exists between the level of some ANE biostimulant components and gene expression levels for selected genes. However, we recognize that the ANE composition−biostimulant activity relationship is complex, and progress in unraveling this relationship will require more comprehensive experiments assessing the effect of the major and minor components of ANE biostimulants singly and in combination. Our results serve to highlight the heterogeneity that exists within this class of biostimulants.

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We acknowledge Dr. Joan Cleary for assistance with statistical analysis and Dr. Srinivasan Krishnamoorthy for assistance with sample preparation. We thank Brandon Bioscience for the gift of the ANE biostimulants used in this study.

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ABBREVIATIONS USED ABA, abscisic acid; BR, brassinosteroids; GA, gibberellins; ANE, Ascophyllum nodosum extract

(1) MarketsandMarkets.com. Biostimulants Market by Active Ingredient (Acid-Based & Extract Based), by Application Type (Foliar, Soil, & Seed), by Crop Type (Row Crops, Fruits & Vegetables, and Turf & Ornamentals) & by Region − Global Trends & Forecasts to 2019; available at http://www.marketsandmarkets. com/search.asp?Search=biostimulants. (2) Khan, W.; Hiltz, D.; Critchley, A. T.; Prithiviraj, B. Bioassay to detect Ascophyllum nodosum extract-induced cytokinin-like activity in Arabidopsis thaliana. J. Appl. Phycol. 2011, 23, 409−414. (3) Calvo, P.; Nelson, L.; Kloepper, J. Agricultural uses of plant biostimulants. Plant Soil 2014, 383, 3−41. (4) du Jardin, P. Plant biostimulants: definition, concept, main categories and regulation. Sci. Hortic. 2015, 196, 3−14. (5) Watkins, S. Agrow Biostimulants, 2015 ed.; Informa Life Sciences, 2015. (6) Sangha, J. S.; Kelloway, S.; Critchley, A. T.; Prithiviraj, B. Seaweed (macroalgae) and their extracts as contributor of plant productivity and quality the current status of our understanding In Advances in Botanical Research Seaplants, 1st ed.; Bourgougnon, N., Ed.; Academic Press: London, UK, 2014; Vol. 71, pp 189−214. (7) Craigie, J. Seaweed extract stimuli in plant science and agriculture. J. Appl. Phycol. 2011, 23, 371−393. (8) Jithesh, M. N.; Wally, O. S.; Manfield, I.; Critchley, A. T.; Hiltz, D.; Prithiviraj, B. Analysis of seaweed extract-induced transcriptome leads to identification of a negative regulator of salt tolerance in Arabidopsis. HortScience 2012, 47, 704−709. (9) Nair, P.; Kandasamy, S.; Zhang, J.; Ji, X.; Kirby, C.; Benkel, B.; Hodges, M. D.; Critchley, A. T.; Hiltz, D.; Prithiviraj, B. Transcriptional and metabolomic analysis of Ascophyllum nodosum mediated freezing tolerance in Arabidopsis thaliana. BMC Genomics 2012, 13, 643. (10) Rayorath, P.; Khan, W.; Palanisamy, R.; MacKinnon, S.; Stefanova, R.; Hankins, S.; Critchley, A.; Prithiviraj, B. Extracts of the brown seaweed Ascophyllum nodosum induce gibberellic acid (GA3)independent amylase activity in barley. J. Plant Growth Regul. 2008, 27, 370−379. (11) Khan, W.; Rayirath, U.; Subramanian, S.; Jithesh, M.; Rayorath, P.; Hodges, D. M.; Critchley, A.; Craigie, J.; Norrie, J.; Prithiviraj, B. Seaweed extracts as biostimulants of plant growth and development. J. Plant Growth Regul. 2009, 28, 386−399. (12) Connan, S.; Goulard, F.; Stiger, V.; Deslandes, E.; Ar Gall, E. Interspecific and temporal variation in phlorotannin levels in an assemblage of brown algae. Bot. Mar. 2004, 47, 410. (13) Rayirath, P.; Benkel, B.; Mark Hodges, D.; Allan-Wojtas, P.; Mackinnon, S.; Critchley, A. T.; Prithiviraj, B. Lipophilic components of the brown seaweed, Ascophyllum nodosum, enhance freezing tolerance in Arabidopsis thaliana. Planta 2009, 230, 135−147. (14) Kadam, S. U.; Tiwari, B. K.; O’Connell, S.; O’Donnell, C. P. Effect of ultrasound pretreatment on the extraction kinetics of bioactives from brown seaweed (Ascophyllum nodosum). Sep. Sci. Technol. 2015, 50, 670−675. (15) Sharma, S. H. S.; Lyons, G.; McRoberts, C.; McCall, D.; Carmichael, E.; Andrews, F.; Swan, R.; McCormack, R.; Mellon, R.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.6b00621. Distribution of dysregulated genes in plants treated with biostimulants ANE A and ANE B in the functional categories present in the MapMan platform (Figures S1− S4) and a list of all the genes mentioned under Results and Discussion (Table S1) (PDF)

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*(S.O.’C.) Phone: +353667164188. E-mail: shane.oconnell@ staff.ittralee.ie. Funding

This research was supported by the Irish Research Council for Science Engineering and Technology (IRCSET) under the Enterprise Partnership Scheme and Enterprise Ireland under the Innovation Partnership Programme and the Applied Research Centre Plus program. C.S. acknowledges support from Science Foundation Ireland (SFI), Grants 08/IN.1/B1931 and 13/IA/1820. I


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Comparative Transcriptome Analysis of Two Ascophyllum nodosum

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