Metabolomics of sorghum roots during nitrogen stress reveals compromised metabolic capacity for salicylic acid biosynthesis

Abstract Sorghum (Sorghum bicolor [L.] Moench) is the fifth most productive cereal crop worldwide with some hybrids having high biomass yield traits making it promising for sustainable, economical biofuel production. To maximize biofuel feedstock yields, a more complete understanding of metabolic responses to low nitrogen (N) will be useful for incorporation in crop improvement efforts. In this study, 10 diverse sorghum entries (including inbreds and hybrids) were field‐grown under low and full N conditions and roots were sampled at two time points for metabolomics and 16S amplicon sequencing. Roots of plants grown under low N showed altered metabolic profiles at both sampling dates including metabolites important in N storage and synthesis of aromatic amino acids. Complementary investigation of the rhizosphere microbiome revealed dominance by a single operational taxonomic unit (OTU) in an early sampling that was taxonomically assigned to the genus Pseudomonas. Abundance of this Pseudomonas OTU was significantly greater under low N in July and was decreased dramatically in September. Correlation of Pseudomonas abundance with root metabolites revealed a strong negative association with the defense hormone salicylic acid (SA) under full N but not under low N, suggesting reduced defense response. Roots from plants with N stress also contained reduced phenylalanine, a precursor for SA, providing further evidence for compromised metabolic capacity for defense response under low N conditions. Our findings suggest that interactions between biotic and abiotic stresses may affect metabolic capacity for plant defense and need to be concurrently prioritized as breeding programs become established for biofuels production on marginal soils.

tions and roots were sampled at two time points for metabolomics and 16S amplicon sequencing. Roots of plants grown under low N showed altered metabolic profiles at both sampling dates including metabolites important in N storage and synthesis of aromatic amino acids. Complementary investigation of the rhizosphere microbiome revealed dominance by a single operational taxonomic unit (OTU) in an early sampling that was taxonomically assigned to the genus Pseudomonas. Abundance of this Pseudomonas OTU was significantly greater under low N in July and was decreased dramatically in September. Correlation of Pseudomonas abundance with root metabolites revealed a strong negative association with the defense hormone salicylic acid (SA) under full N but not under low N, suggesting reduced defense response. Roots from plants with N stress also contained reduced phenylalanine, a precursor for SA, providing further evidence for compromised metabolic capacity for defense response under low N conditions. Our findings suggest that interactions between biotic and abiotic stresses may affect metabolic capacity for plant defense and need to be concurrently prioritized as breeding programs become established for biofuels production on marginal soils.

K E Y W O R D S
metabolism, metabolomics, microbiome, nitrogen, rhizosphere, roots, salicylic acid, sorghum, stress 1 | INTRODUC TI ON Sorghum (Sorghum bicolor [L.] Moench) is the fifth most productive cereal crop worldwide with a variety of uses including animal forage, sugar production, and more recently, lignocellulosic biomass for bioenergy (Rooney, Blumenthal, Bean, & Mullet, 2007). Sorghum's high biomass yield and drought tolerance potential make it a promising feedstock for sustainable, economical biofuel production.
Production strategies that utilize agricultural lands not suitable for food production and improve economic viability are a top priority for biofuel feedstocks (Langholtz, Stokes, & Eaton, 2016). Improving biomass production will require maximizing yield while reducing costly inputs, including nitrogen (N), and water (Fazio & Monti, 2011, National Research Council, 2012. Fortunately, sorghum possesses significant genetic diversity for N use efficiency (Gelli et al., 2014(Gelli et al., , 2016, yield potential and drought tolerance (Mace et al., 2013).
Furthermore, the availability of a reference genome (Paterson et al., 2009), an expression atlas (Shakoor et al., 2014), and over 400,000 markers (Morris et al., 2013) identified for marker-assisted breeding makes sorghum well-suited for significant crop improvement.
Modern "omics" techniques function as valuable molecular discovery tools for assessing plant stress responses on a molecular level (Urano, Kurihara, Seki, & Shinozaki, 2010). The "metabolome" is described by surveying all low molecular weight metabolites generated as a result of gene-regulated and environmentally induced responses (Brunetti, George, Tattini, Field, & Davey, 2013;Ganie et al., 2015).
Since metabolites are one measurable endpoint of all cellular regulatory activities, they have been described as "the ultimate response of biological systems to genetic or environmental changes" (Fiehn, 2002). Thus, metabolomics is a robust approach for determining the molecular phenotype, especially in assessing response during both abiotic (Ganie et al., 2015;Sánchez-Martín et al., 2015) and biotic (Scandiani et al., 2014) stress and in identifying potential targets for metabolic engineering (Tsogtbaatar, Cocuron, Sonera, & Alonso, 2015). For example, previous research has associated biomass accumulation with specific sorghum leaf metabolites that vary across different sorghum genetic lines (Turner et al., 2016). However, relationships between root metabolites and biomass yields in fieldgrown sorghum have been largely ignored.
As a synergistic approach to plant breeding, beneficial microbes offer an underutilized opportunity to improve plant performance, especially on marginal lands with minimal inputs (Coleman-Derr & Tringe, 2014). Plant growth and health may be improved through interactions with beneficial soil microorganisms via a number of mechanisms including increased nutrient availability, production of protective or growth-promoting enzymes and compounds, and competitive exclusion of pathogens (Chaparro, Sheflin, Manter, & Vivanco, 2012;Farrar, Bryant, & Cope-Selby, 2014). Under marginal soil conditions microbes may play an important role in providing nitrogen to cereals and have been shown to potentially assist sorghum with the acquisition of N in nutrient depleted environments (Carvalho, Balsemão-Pires, Saraiva, Ferreira, & Hemerly, 2014;Kochar & Singh, 2016;Rupaedah, Anas, Santosa, Sumaryono, & Budi, 2016). Next-generation sequencing provides an opportunity to identify potential microbes that may provide these benefits, even those not amenable to rapid laboratory cultivation (Knief, 2014). Sorghum-associated bacterial communities have been recently reviewed (Kochar & Singh, 2016) and one study identified N application as a primary factor in determining N-fixing community structure in the sorghum rhizosphere (Rodrigues Coelho et al., 2007). Conversely, detrimental microorganisms present additional challenges to maximizing yield in marginal environments that lack the optimal amounts of water or nitrogen (van der Heijden, Bardgett, & van Straalen, 2008). A better understanding of these interactions among soil nutrients, bacteria, and sorghum yields will be an important part of the development of sustainable and economical biofuel production on marginal land.
The primary goal of this study is to improve our understanding of the root metabolic response to low N in field grown sorghum.
Utilizing an integrative approach, we incorporated data from agronomic traits, the root metabolome and the rhizosphere microbiome of 10 sorghum lines (including inbreds and hybrids) grown under conditions of both low and full N and sampled on two dates.
Taken together, our findings highlight an array of metabolic disadvantages due to N stress that may reduce capacity for plant defense. Therefore, secondary biotic stresses should be considered as a potential consequence of abiotic stresses, such as low N availability.

| Field sample collection
Soil, rhizosphere, and root samples were collected from each field two times during the growing season. The sampling dates were July 22 and September 15, 2015. Two plants per genotype were excavated from the top 30 cm of soil using a shovel at two different locations in each plot as described previously (McPherson, Wang, Marsh, Mitchell, & Schachtman, 2018). Soil was removed from the roots using a tiller and collected. A variety of roots including crown, seminal, and primary roots were excised from two plants and placed in a 50 ml Falcon tube containing 35 ml of phosphate buffer (6.33 g/L NaH 2 PO 4 , 8.5 g/L Na 2 HPO 4 anhydrous, 200 μl/L Silwet L-77).
Roots were then shaken for 1-2 min to remove some of the rhizosphere soil. Roots were then separated for downstream analyses: DNA extraction and microbiome analysis, and metabolite analysis.
Rhizosphere soil and roots for DNA extraction were stored in 50 ml Falcon tubes on ice for transport to the laboratory.
Samples were collected for biomass analysis on October 8, 2015.
An above ground portion of plants from 1 square meter for each plot was weighed for fresh biomass measurements. The same samples were oven dried in paper sacks until reaching a stable water content before weighing for dry biomass measurements.

| Laboratory preparation of roots, soil, and rhizosphere
Roots that were brought back to the laboratory on regular ice were surface sterilized by rinsing for 30 s in 5.25% sodium hypochlorite + 0.01% Tween 20, followed by a 30-s rinse in 70% ethanol, followed by three rinses in sterile ultrapure water. Roots were blotted dry on a clean paper towel, placed in a 15 ml tube, frozen at −80°C, and then ground in liquid N prior to DNA extraction.
The rhizosphere samples were filtered through a sterile 100 μm mesh filter (Fisher Scientific, USA), into a clean 50 ml tube. The rhizosphere was pelleted at 3,000 × g for 10 min at room temperature.
The pellet was resuspended in 1.5 ml phosphate buffer (6.33 g/L NaH 2 PO 4 , 8.5 g/L Na 2 HPO 4 anhydrous), and transferred to a sterile 2 ml microfuge tube. The rhizosphere was re-pelleted by spinning tubes for 5 min at full speed. The supernatant was drained off and the rhizosphere soil pellet was stored at −20°C until DNA extraction.
A small 2 ml tube of soil was removed carefully to avoid any root pieces and stored for DNA extraction at −20°C. Soil was then sieved through US Standard Sieve #4, 4750 micron, followed by Sieve #8, 2360 micron to remove debris and roots. Approximately 100 g of sieved soil was sent for soil analysis (Ward Labs, Kearney, NE) to determine the organic carbon and nutrient concentrations of the soil.

| Amplification and Illumina sequencing of 16S tag sequences
DNA was quantified and amplified in 96 well plates with single indexed primers targeting the V4 region of the bacterial 16S rRNA gene (Walters et al., 2015). Chloroplast and mitochondrial peptide nucleic acid (PNA) blockers were used to prevent chloroplast and mitochondrial amplification in all samples (Lundberg, Yourstone, Mieczkowski, Jones, & Dangl, 2013). Amplified samples were multiplexed at 184 samples per PE 2 × 300 Illumina MiSeq sequencing run. Data from the sequencer was demultiplexed and processed through bbduk for end trimming, quality filtering, and masking (https://jgi.doe.gov/dataand-tools/bbtools/bb-tools-user-guide/bbduk-guide/). High quality reads were processed by iTagger version 2.2 (Tremblay et al., 2015).

| Sample preparation and extraction for metabolomics analysis
Immediately upon arrival, sorghum tissue samples were stored at −80°C with subsequent lyophilization for further analysis.
Mobile phase A consisted of LC-MS grade water with 0.1% formic acid and mobile phase B was 100% acetonitrile. The elution gradient was initially 0.1% B for 1 min, which was increased to 55.0% B at 12 min and further increased to 97.0% B at 15 min, then decreased to 0.1% B at 15.5 min. The column was re-equilibrated for 4.5 min for a total run time of 20 min. The flow rate was set to 120 μl/min and the column temperature was maintained at 45°C. Mass spectrometry was performed on a Xevo TQ-S triple quadrupole MS (Waters Corporation) operated in selected reaction monitoring (SRM) mode (Supporting Information Table S5).
Skyline bioinformatics software (MacLean, Bioinformatics 2010) was used to detect and integrate peak areas and to calculate linear regression of analytical standards used for quantification. Prior to quantification, each analyte peak area was normalized to the internal standard (Supporting Information Table S6).
All raw data files were imported into the Skyline open source software package (MacLean et al., 2010). Each target analyte was visually inspected for retention time and peak area integration. Peak areas were exported to Excel and absolute quantitation was determined by using the linear regression equation generated for each compound from the calibration curve. To make the calibration curve, analytical standards were diluted in pure methanol serially from 400 ng/ml to 0.54 ng/ml before adding an equal amount of every internal standard to each vial. The linear regression equation of the analytical standard curve was used to convert the normalized peak area to quantity (ng/ml) for each analyte. The values were then adjusted for precise weight of root tissue for each sample and reported as ng/g root tissue.

| Non-targeted reverse phase UPLC-MS/MS analysis
A 200 μl aliquot of the organic layer was dried and resuspended in 100 μl of methanol and toluene (1:4, v/v). Single injections of 3 μl of extract were made on an Acquity UPLC system (Waters Corporation) in discrete, randomized blocks. The pooled QC was injected after every 10 sample injections.
Separation was performed with an Acquity UPLC CSH Phenyl

| Non-targeted UPLC-MS/MS HILIC analysis
Single injections of 3 μl of the aqueous extract were made on a Waters Acquity UPLC system in discrete, randomized blocks. The pooled QC was injected every after every 10 injections. Separation was performed using a ZIC-pHilic (5 μM, 2.0 × 150 mm; EMD Millipore), using a gradient from solvent A (acetonitrile) to solvent B (water, 10 mM Ammomium Bicarbonate, pH 9.6). Flow rate was 0.27 ml/ min unless noted otherwise, and the column was held at 50°C. The gradient is as follows: time (t) = 0 min, 10% A; t = 1.5 min, 10% A;

| Metabolomics data analysis
GC-MS and LC-MS data sets were processed independently using the R statistics software (R Core Team, 2013) as described previously (Yao, Sheflin, Broeckling, & Prenni, 2019

| Statistical analysis
Differences between N treatments for agronomic traits were tested using a two-tailed, unpaired Mann-Whitney (nonparametric) test (Mann & Whitney, 1947 (Beals, 1984). The adonis2 function has been described as a "permutational MANOVA" (Anderson, 2001;McArdle & Anderson, 2001) and offers an alternative to parametric MANOVA. A two-way ANOVA univariate test was used to determine differential relative abundance of a single OTU in rhizosphere soil by date, treatment, or the interaction in Prism 7 (GraphPad, La Jolla, California, US). Correlation analyses utilized the cor function in R and with the nonparametric Spearman's rank option. The corrplot package (Wei et al., 2017) for R was used to visualize correlation analyses as heatmaps.

| RE SULTS
The aboveground biomass and heights (Table 2 and Supporting Information Data S1) were significantly reduced in the low N treatment group. The concentration of N in each field was determined via soil composition analysis (Table 3) and determined to be 7.8 ± 0.7 ppm in the low N field and 8.5 ± 0.5 ppm in the full N field.
Total aboveground dry biomass (kg/ha) was reduced by 68% and total aboveground fresh biomass was reduced by 63% (kg/ha) under low N conditions. These data show that the N treatment had a strong effect on sorghum growth and that plants were experiencing N stress.
The number of plants per square meter did not significantly vary by N treatment. Since crop hybridization is known to result in superior vigor and yield (Packer & Rooney, 2014;Quinby, 1963), fold change for agronomic traits in hybrids grown with low N was compared to inbreds and was calculated (Supporting Information Figure S1). This comparison did not reveal any significant differences in biomass accumulation for hybrids versus inbreds grown under low N (p = 0.05), therefore hybrid status was not included as a factor for other analyses.

| Significant metabolite variation in low N roots
A nontargeted metabolomics approach was used to evaluate biochemical variation in roots receiving low N compared to plants grown in the full N field. PCA analysis was used to visualize this variation and shows that higher trehalose, quinic, and shikimic acids drove the variation in metabolite profiles under low N conditions (Figure 1).
Roots from the full N field were higher in asparagine, other amino acids, allantoin, 2-imidazolidone -4-carboxylic acid (2-I-4-C), and carbodiimide in both July and September. Permutational MANOVA (PERMANOVA) analysis revealed that the global metabolite profile was significantly different between low versus full N in both July and September (p < 0.01; Figure 1a,b).
The altered metabolite profile with N stress allows for the discovery of associations with agronomic traits, for example, biomass.
Spearman rank correlations (Sheskin, 2003;Weatherburn, 1961)  N treatment, revealed that metabolite associations with biomass differed with N treatment and also changed over the growing season ( Figure 2). Under low N conditions, the July sampling revealed many root metabolites that were correlated with reduced biomass including several proteinogenic amino acids: serine, threonine, asparagine, valine, and phenylalanine. Oleamide, an amide derivative of oleic acid, was associated with higher biomass under full N for both sampling dates and under high N in September. However, Oleamide was negatively correlated with biomass in September under low N conditions. Higher biomass in the low N field was associated with root lactic acid, an end product of anaerobic respiration (Rivoal & Hanson, 1994), but only for the September sampling.
TA B L E 3 Mean soil nutrient concentrations ± standard deviation of the mean Treatment

| Rhizosphere microbiota significantly vary with N treatment and collection date
Rhizosphere bacterial community profiles significantly differed according to collection date (PERMANOVA: R 2 = 0.83, p < 0.01) and were clearly separated in a principal coordinate analysis (PCoA; Supporting Information Figure S2). While date accounted for the majority of the variation in rhizosphere bacterial composition, significant variation due to N treatment was also seen along PCo2 explaining 6% of the variation. PERMANOVA analysis revealed that the rhizosphere bacterial community profile was significantly different between low versus full N in both July and September (p = 0.001).
All of these genera were significantly different by interaction of date and treatment and all except for the Burkholderia genus significantly differed by date (Supporting Information Table S2). The bacterial composition of the rhizosphere was largely dominated by a single operational taxonomic unit (OTU), OTU 0, in July that mapped taxonomically to the Pseudomonas genus. In July, this single OTU dominated the rhizosphere, comprising 47% of the bacterial community under full N conditions and significantly more under low N at 66% of the bacterial community (ANOVA, p < 0.05; Figure 3).

| Metabolic pathway analysis suggests altered flux through shikimate pathway
The complete annotated list of metabolites detected in roots from the July sampling was used to generate the metabolic pathways associated with these molecules using the pathway analysis tool in MetaboAnalyst (http://www.metaboanalyst.ca/). The list of pathways matching with the highest number of metabolites in the F I G U R E 2 Heatmap showing spearman rank correlations of agronomic traits (rows) and root metabolites (columns). Color scale for correlation value is dark blue: R 2 = 1; dark red (strong positive association): R 2 = −1 (strong negative association). Squares are also sized according to R 2 values with larger squares indicating values close to 1 (blue) or −1 (red). Rows are grouped by collection date (July or September) and treatment (low or full N) with a colored key along the left edge as shown in the legend. Agronomic traits are abbreviated as: wet = total plant (includes stems, leaves, and panicle) fresh weight, total dry = total plant (includes stems, leaves, and panicle) dry matter weight, veg dry = vegetative portion of plant (stems and leaves) dry weight measured in kilograms per hectare   Table S1 and was used to prioritize KEGG reference pathways of interest. Metabolites that differentiated between low and full N treatments were identified through the PCA biplot analysis (Figure 1) to narrow pathways of interest. Two of these KEGG pathways, "Alanine, aspartate, and glutamate metabolism" and "Phenylalanine, tyrosine, and tryptophan biosynthesis", contained metabolites shown to discriminate between low and full N treatment groups and were selected for further analysis. Generally, metabolites in the "Alanine, aspartate, and glutamate" pathway were less abundant in roots under N stress, consistent with decreased N availability (Figure 4a, Supporting Information Table S3). Biosynthesis of the aromatic amino acids phenylalanine, tyrosine, and tryptophan occurs via the shikimate pathway. In roots sampled in July, three intermediary metabolites in this pathway, quinic acid, 3-dehydroshikimic acid, and shikimic acid, were more abundant under N stress, while two end products of this pathway, phenylalanine and tyrosine, were less abundant with N stress (Figure 4b, Supporting Information Table S3).

| N stress and the sorghum defense response
Analysis of phytohormones in root tissue was performed and included quantitative measurement of 12-oxo-phytodienoic acid, trans-zeatin riboside, jasmonic acid, salicylic acid, abscisic acid, phaseic acid, indole-3-carboxylic acid, and dihydrophaseic acid. When treating genotype as a random factor, 12-oxo-phytodienoic acid, jasmonic acid, and trans-zeatin riboside significantly varied in the September root sampling as compared to roots sampled in July, but did not vary significantly by treatment or by interaction of date and treatment (Supporting Information Table S4 and Data S1). Salicylic acid was significantly reduced in low N conditions (Figure 5a), but did not vary significantly by date or by date x treatment interaction (Supporting Information Table S4). No other root phytohormones that were analyzed were significantly altered by N treatment.
Furthermore, for roots sampled in July, less SA content was correlated with greater abundance of the rhizosphere-dominating OTU 0 (Pseudomonas) under full N (Figure 5b) but not under low N conditions (Figure 5c). To further investigate root defense response, orthologs of genes previously described as having altered expression during pathogenesis (van Loon, Rep, & Pieterse, 2006) were investigated using RNA-seq data generated from roots sampled in parallel from the same field and dates as the root metabolite collections (Supporting Information Figure S4). The RNA-seq data were generated from only a subset of genotypes (PI 297130, PI 655972,

| D ISCUSS I ON
Root and rhizosphere soil samples from N stressed sorghum revealed differences in root metabolite profiles, rhizosphere microbial community composition, and SA production. Phytohormones and bacterial community composition also varied over the growing season. Metabolites that accumulated in N stressed roots are consistent with previous studies on the effects of nutrient deprivation on plant metabolism and go beyond what is currently known. In the July sampling, for example, we observed increased trehalose and sucrose content in roots with low N relative to full N. Similarly, an increased emphasis on carbohydrate storage in roots of soybean plants has been previously associated with a limited ability to synthesize sucrose in leaves with N stress (Rufty, Huber, & Volk, 1988). Enhanced storage of carbohydrates in roots versus leaves could explain the observed increase in trehalose and sucrose in roots with low N relative to full N. However, trehalose accumulation has been associated with both biotic and abiotic plant stress response (Fernandez, Béthencourt, Quero, Sangwan, & Clément, 2010). Accumulation of trehalose in roots was observed in Arabidopsis thaliana infected with Plasmodiophora brassicae (Brodmann et al., 2002) and in Pinus sylvestris (pine) infected with Armillaria ostoyae (Isidorov, Lech, Żółciak, Rusak, & Szczepaniak, 2008). Therefore, it is possible that trehalose accumulation in roots of N stressed sorghum resulted from the F I G U R E 3 OTU 0 (Pseudomonas) dominated the rhizosphere under both high and low N conditions, but was significantly more abundant under low N conditions (p < 0.05, ANOVA). Boxplot of OTU 0 (Pseudomonas) shown as percent abundance of total normalized reads in rhizosphere soil from the July sampling and demonstrates the dominance of the rhizosphere community by OTU 0 In addition, metabolites identified in previous research as important to N cycling and mobility in plants were more abundant in roots grown with full N relative to low N. For example, both aspartate and asparagine function were reduced with low N and act as carriers when mobilizing N to sink tissues with asparagine being particularly important as it is efficiently transported (Cañas, Quilleré, Lea, & Hirel, 2010;Gaufichon, Rothstein, & Suzuki, 2015;Masclaux-Daubresse et al., 2010). In other studies looking at N stress, reduced amino acids were observed in both root exudates (Carvalhais et al., 2011) and tomato root tissue (Sung et al., 2015). Thus, increased mobilization of N can reflect higher availability of soil N. However, it is also possible that this increased mobilization of N is related to competition with soil microbes because access to N is important not only to plants, but also to pathogenic and beneficial microorganisms living in association with plants. While N application to crops likely benefits plant defense, it may also allow rhizosphere microorganisms to gain access to N coming from root cellular pools (Hoffland, Jeger, & van Beusichem, 2000;Jensen & Munk, 1997). Gene expression patterns consistent with remobilization of N as a strategy to sequester N stores away from bacteria have been reported previously in Phaseolus vulgaris (common bean) (Tavernier et al., 2007), tomato (Olea et al., 2004), Nicotiana tabacum L. (tobacco) (Pageau, Reisdorf-Cren, Morot-Gaudry, & Masclaux-Daubresse, 2005), and Arabidopsis thaliana (AbuQamar et al., 2006). Glutamine is typically the preferred carrier during N mobilization, but asparagine, aspartate, or alanine may be utilized when glutamine is limited (Pellier, Laugé, Veneault-Fourrey, & Langin, 2003). A decrease in N-carrying amino acids in response to N stress likely reflects lower N stores already present in plant cells. Pathogens, as well as other soil microbes, may have experienced reduced opportunity for N exploitation in roots of N stressed sorghum.
Nitrogen stressed sorghum showed unique metabolic associations with agronomic traits that changed over the growing season.
Previous research with non-stressed, greenhouse-grown sorghum revealed that higher levels of intermediaries of the shikimate pathway, quinic, and shikimic acids, in 4-week-old leaves was associated with higher biomass (Turner et al., 2016). However, in the current work, lower content of quinic and shikimic acids was associated with higher biomass in roots under full N conditions. Shikimic acid was not strongly correlated with biomass under N stress but reduced 3-dehydroshikimic acid, a related intermediary in the shikimate pathway, was negatively correlated with higher biomass. Lower quinic acid content was also correlated with higher biomass under low N conditions, but only in roots from the September sampling. Whether this discrepancy is due to greenhouse versus field conditions or reflects tissue-specific effects is not clear (Turner et al., 2016).  Table S3). The end products of the shikimate pathway are aromatic amino acids, including phenylalanine, which is used in the biosynthesis of plant defense hormone SA (Chen, Zheng, Huang, Lai, & Fan, 2009;Maeda, Yoo, & F I G U R E 4 Pathway analysis (a) alanine, aspartate, and glutamate metabolism and (b) phenylalanine, tyrosine, and tryptophan biosynthesis (shikimate pathway). Metabolites detected during metabolomics analysis have peak intensities presented as bar graphs overlaid on the pathway map. Peak intensity reflects the semiquantitative nature of the nontargeted approach used for this global metabolite analysis. Statistical significance when using a nonparametric factorial ANOVA test (Supporting Information Table S2) is denoted as follows: *significant by date, **significant by treatment and date but not the interaction, ***significant by date treatment interaction (p < 0.05). Dark green = July high N; Dark purple = September high N; Light green = July low N; Light purple = September low N F I G U R E 5 Salicylic acid and OTU 0 abundance. The main effect of nitrogen treatment showed significantly reduced root salicylic acid content under low N compared to high N when averaged over the two sampling dates and treating genotype as a random effect. Panel (a) shows the effects plot with 95% confidence interval for the linear mixed model, (b) OTU 0 is negatively correlated with salicylic acid with full N and (c) not with low N 0 5 ,000 10,000 15,000 8,000 1 0,000 12,000 14,000 16,000 Dudareva, 2011). Our results show that SA concentration was significantly lower in roots experiencing N stress and this effect was not influenced by collection date when treating genotype as a random effect in a mixed linear model analysis (Figure 5a, Supporting Information Table S4). Since SA plays an important role in plant immunity and defense, reduced metabolic capacity to produce SA may alter the overall plant defense response.
RNA-seq analysis of a subset of root samples for sorghum genes involved in the pathogenesis response provided some evidence of altered expression with three of the 12 genes being downregulated (t test, p = 0.01) in samples from multiple genotypes (Supporting Information Figure S2). Homologs in Arabidopsis thaliana for two of these genes, PR1b, were previously found to be induced in response to SA (Thomma et al., 1998). In sorghum, PR1b and other PR genes were also induced when SA was added to growth solution for hydroponically grown plants (Salzman et al., 2005). Furthermore, sorghum grown under full N conditions was able to accumulate more root SA and also had lower abundance of the rhizosphere-dominating OTU 0 (Pseudomonas) (Figure 5b) consistent with successful plant defense of the rhizosphere. However, sorghum experiencing N stress had less SA accumulation in root tissue and did not show any reduction in rhizosphere abundance of OTU 0 (Pseudomonas) (Figure 5c). These results suggest that the reduced abundance of SA in roots experiencing N stress is insufficient to induce some important defense genes, which is also supported by the RNA-seq analysis (Supporting Information Figure   S4). However, since bacterial composition in the rhizosphere also varied significantly under low N conditions (Supporting Information Table S2), we cannot determine conclusively if the reduced abundance of SA was due to metabolic effects of N stress or soil bacterial interactions. When interpreting both metabolic and microbial differences due to treatment effect, it is important to note other differences between fields. The low N field utilized in this study, which had not had N applied for more than 20 years, also had other differences relative to the full N field including crop rotation, soil composition and likely others. However, 78.8% of variation in the rhizosphere bacterial composition was explained by sampling date and only 5.7% variation was due to different treatments/fields (Supporting Information Figure S3). Similarly, when metabolite data were plotted together in PCA analysis, separation by date along PC1 explained 49.3% of variation and PC2 explained only 13.4%. These results suggest that the influence of non-field specific factors such as plant growth and developmental stage on the soil rhizosphere community composition are exerting a stronger effect on sample variation than any potential effects due to bulk soil composition, which has also been demonstrated in other research (Shi et al., 2015).
Identifying production strategies that incorporate marginal soils with reduced inputs is a high priority for biofuels research and our findings revealed important implications for improving biomass yield under N stress and in the context of microbial interactions.
As breeding programs are established for biofuels production on marginal lands, priorities should be established for both abiotic and biotic stress tolerance since plant response to N deficiency is likely to affect critical metabolic pathways for plant defense.

| ACCE SS I ON N UMB ER S
Sequence data from this article can be found in the NCBI SRA submission library under the following accession numbers: 1. 16S amplicons: Sequencing project IDs #1095844, #1095845, #1095846; SRA Identifier #SRP165130.

We thank Rebecca Bart of the Donald Danforth Plant Science
Center for her guidance in locating pathogenesis-related genes for our investigation.

CO N FLI C T O F I NTE R E S T
The authors have no conflict of interest to declare for this research.