Cultivar-specific wheat-associated bacterial communities and metabolites in response to nitrogen deficiency

Cultivar-specific wheat-associated bacterial communities and metabolites in response to nitrogen deficiency | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cultivar-specific wheat-associated bacterial communities and metabolites in response to nitrogen deficiency Lok Hang Chan, Shu Kee Lam, Deli Chen, Caixian Tang, Qinglin Chen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4738104/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Oct, 2024 Read the published version in Plant and Soil → Version 1 posted 6 You are reading this latest preprint version Abstract Background and Aims Nitrogen (N) deficiency in soil constrains plant growth, which may potentially be alleviated by beneficial soil bacterial communities. However, there is limited knowledge of the plant-bacteria interactions of wheat cultivars with different N-use efficiency (NUE) under N deficiency. Methods We investigated the responses of soil and root endosphere bacterial communities as well as root metabolites of two wheat cultivars (cv. Mace and Gladius) with reported high and low NUE, respectively, using a glasshouse experiment and a hydroponic experiment with three N levels. Results The rhizosphere bacterial community of Mace shifted under N deficiency, but not in its root endosphere. Conversely, the rhizosphere bacterial community of Gladius remained unchanged under N deficiency but shifted in its root endosphere. The metagenomic analysis illustrated increased detection of genes related to bacterial growth and motility in the rhizosphere of Mace, but not of Gladius, under N deficiency. A 4-fold increase in octadecanoic acid in the root of Mace, but not Gladius, under N deficiency, suggesting the potential role of octadecanoic acid in shaping the rhizobacterial community in Mace with higher reported NUE. Conclusion Our study highlights the divergent responses of wheat-associated microorganisms and root metabolites to N deficiency in the two cultivars. We found that wheat cultivars with higher NUE increased octadecanoic acid secretion, which potentially shaped the rhizobacterial communities, thereby enhancing their growth under N-limited conditions. Nitrogen-use efficiency Wheat Plant-microbe interaction Metagenomics Metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Nitrogen (N) is essential for plant growth and development (Andrews et al. 2004 ); and its deficiency in soil has reduced yield of some major crops, including wheat (Guo et al. 2022 ; van Grinsven et al. 2023 ; Xu et al. 2022 ). Synthetic N fertilisers promote crop growth, but their mismanagement adversely affects soil microbial community structure (Ding et al. 2020 ; Enebe and Babalola 2020 ; Yang et al. 2022 ), and environmental quality (Canfield et al. 2010 ; Pahalvi et al. 2021 ). Alternative strategies that reduce dependency on chemical fertilisers without compromising crop yield should be explored. Microorganisms establish mutualistic associations with plants by obtaining essential carbon sources from plant metabolites and in turn enhancing plant nutrient acquisition (Bai et al. 2022 ) thus improving plant adaption to diverse environmental conditions (Ahlawat et al. 2022 ; Nerva et al. 2022 ; Pang et al. 2020 ). Although bio-fertilisers leverage the beneficial effects of soil microorganisms to improve plant growth (Bhardwaj et al. 2014 ) and soil health (Sun et al. 2020 ), the efficacy of bioinoculants is inconsistent (Fagorzi et al. 2023 ). Harnessing plant metabolites to modulate soil microbial interactions in the development of next-generation fertilisers potentially enhances bio-fertiliser efficiency (Lam et al. 2022 ). However, our understanding of the interaction between plant metabolites and soil microbiomes under N deficiency remains limited (Hong et al. 2022 ). Addressing this knowledge gap is crucial for advancing sustainable agricultural practices and optimising fertiliser strategies under N-deficient conditions. The "cry-for-help" theory posits that plants under stress alter their physiological processes and immune responses, actively recruiting microbes to adapt to challenging conditions (Castrillo et al. 2017 ; Liu et al. 2020 ; Song and Haney 2021 ). Substantial evidence supports the notion that soil microorganisms are recruited by plants to withstand extreme environmental conditions such as drought, flooding, heat, and salinity (Fadiji et al. 2023 ; Hong et al. 2022 ; Kumar and Verma 2018 ). Nitrogen deficiency poses stress on plant growth and influences the metabolite interaction with microbes (Chai and Schachtman 2022 ). In non-leguminous plants, the modulation of the N cycle by soil bacteria has been shown to benefit plant growth under low-N conditions (Chai and Schachtman 2022 ; Coskun et al. 2017 ). For example, maize grown in a N-limited soil enhanced the contribution of soil bacteria to the N cycle (Jiang et al. 2023 ). This finding has also been validated recently in Viola epidendroides and Baptisia macrantha , suggesting that plant experiencing N deficiency maintains close associations with diverse N-cycling microorganisms (Camargo et al. 2023 ). In addition to the N cycle regulation, a recent study provided evidence that maize enhanced N-use efficiency (NUE) under N deficiency, through the recruitment of Massilia sp. with flavones identified as the chemotaxis attractants for Massilia sp. (Yu et al. 2021 ). While the study has not detected plant growth hormone production, it confirmed that Massilia sp. stimulated lateral root growth and proposed that it altered the balance of proliferation and differentiation of root cells (Yu et al. 2021 ). This highlights the critical role of plant metabolites in the recruitment of soil bacteria to shape a beneficial microbiome, particularly in adapting to N deficiency (Haney et al. 2015 ). Advances in breeding and domestication have improved plant root growth, development (Zhang et al. 2019 ) and morphology (Cormier et al. 2016 ), consequently influencing plant NUE (Nerva et al. 2022 ; Sun et al. 2016 ) and the tolerance to low-N conditions (Chen et al. 2020 ). These improvements have enhanced the plant N acquisition (Chen et al. 2021 ) and have altered the plant-microbe interactions (Gholizadeh et al. 2022 ; Quiza et al. 2023 ; Yue et al. 2023 ). Notably, studies indicate that breeding lines of maize with varying NUE distinctly influence the rhizosphere microbiome, particularly in the context of N turnover (Pathan et al. 2015 ). Microbes can enhance plant NUE through various mechanisms, including increased microbial diversity (Zhang et al. 2019 ), enhanced N-cycling processes (Zhang et al. 2019 ), and alterations in root architecture (Yu et al. 2021 ). While rhizobacteria play a crucial role in plant NUE, their sensitivity to diverse soil fertility conditions must also be acknowledged (Chai et al. 2021 ). A growing body of evidence reveals variations in the metabolome among different plant species (McLaughlin et al. 2023 ) and cultivars/varieties (Kogel et al. 2010 ; Zheng et al. 2021 ). However, our understanding of the genotypic variation in plant-microbe interactions in response to N deficiency remains limited. Further investigations into these interactions will elucidate the intricate dynamics between plants and microbes under N deficiency, ultimately facilitating the development of strategies to enhance NUE in crops. In this study, we employed amplicon sequencing and metagenomic technology to compare the root-associated bacterial communities of two wheat cultivars (cv. Mace and Gladius) with high and low reported NUE, respectively (Alhabbar et al. 2018 ), under different N availability conditions. The GC-MS metabolomics approach was further conducted to determine the primary metabolic profile in the root of N-deficient wheat grown in a hydroponic system. We hypothesised that (1) wheat cultivars with contrasting NUE establish distinct soil bacterial communities subjected to N deficiency and (2) the wheat cultivars alter root metabolite composition in response to N deficiency. Materials and methods Soil collection and glasshouse experiment Soil samples used in this study were collected in March 2022 from a wheat experimental farm on the Dookie Campus at The University of Melbourne, Australia (36.382° S, 145.711° E). The experimental field has a history of wheat-pasture rotation. We collected approximately 400 kg of soil (0–10 cm) using a shovel after removing the root debris for the pot experiment. The soil is classified as a Dermosol (Isbell 2024 ) with the following physicochemical properties (measured by the Nutrient Advantage lab, Victoria, Australia): total C, 0.021 g kg − 1 soil, total N, 0.002 g kg − 1 soil, organic C, 0.018 g kg − 1 soil, nitrate-N, 56 mg kg − 1 soil, ammonium-N, 2.9 mg kg − 1 soil, and soil pH of 6.3. The collected soil samples were sieved to 1 cm, air-dried, and homogenised before the glasshouse experiment. Pots with an inner diameter of 16.5 cm were filled with 4.6 kg of soil. Two wheat cultivars, high-NUE Mace and low-NUE Gladius, provided by the Australian Grain Technology (AGT) company, were selected for the pot experiment. Five uniform-sized seeds were sown at a depth of 3 cm and seedlings were thinned to three per pot at the two-leaf stage. To establish varying N levels, ammonium sulphate was applied at three rates: (1) N0, without N addition, (2) N1, 45 kg N ha − 1 , and (3) N2, 90 kg N ha − 1 , with eight replicates for each N treatment. The soil moisture content was maintained at around 80% field capacity throughout the experiment. The plant growth conditions were maintained at 24°C /10°C for 12 h/12 h day/night, 65% humidity and ~ 225 µmol m − 2 s − 1 during the daytime throughout the study. The wheat rhizosphere and bulk soils, as well as plant roots, were destructively sampled at the stem-elongation and the mid-anthesis stages. The wheat root system was placed in a sampling bag and subjected to gentle shaking to obtain rhizosphere soil samples. Soils remained in the pots were randomly collected as the bulk soil. Wheat roots (5–20 cm) were separated from the plant and were gently rinsed with reverse osmosis (RO) water to remove large soil particles. Root samples were subsequently shaken with 30 ml of phosphate-buffer solution (PBS) in a falcon tube at 250 rpm to remove any adhered soil and briefly dried with filter paper. The soil and root samples were then stored at -20°C. Plant and soil physicochemical characterisation The fresh above ground plant materials were oven-dried at 60°C for 7 days for biomass measurement. The dried plant material was ground to a 0.2-mm fine powder using the ultra-centrifugal mill (ZM200 Ultra Centrifugal Mill, RETSCH). A set of 48 bulk soil samples (2 growth stages, 2 cultivars, 3 N levels and 4 replicates) were oven-dried at 40°C for 7 days, and then ball milled using a tissue grinder (TissueLyser II, QIAGEN). The resulting, finely ground plant and soil samples were subjected to the CN analyser (LECO TruMac Series) for total C and N measurements (Figure S1 ). Another set of 48 bulk soil samples were oven-dried at 105°C for 2 days for the estimation of soil moisture content. For soil pH and electrical conductivity (EC) measurements, an alternative set of fresh soil was suspended in Milli-Q water at a 1:5 ratio and shaken at 250 rpm for 60 min. Soil pH was measured with a professional benchtop pH meter (HANNA) and EC was measured with the conductivity meter (Multiparameter Laboratory Benchtop Conductivity meter, SmartCHEM-LAB). DNA extraction and amplicon sequencing Frozen root samples were ground into a fine powder with liquid N. The total DNA was extracted from 250 mg of the frozen rhizosphere soil and root samples using the DNeasy PowerSoil Pro Kits (QIAGEN), and quality was assessed using Nanodrop (NanoDrop One, Thermo Scientific). A total of 48 rhizosphere and 48 root endosphere DNA samples were subjected to polymerase chain reaction (PCR) amplification. The V3–V4 hypervariable region of the bacterial 16S rRNA gene was amplified with the 341F/806R primer pairs (Klindworth et al. 2013 ) and sequenced using the Illumina MiSeq System platform at the Australian Genome Research Facility, Melbourne, Australia. Amplicon sequencing was processed using QIIME 2 (v.2022.11) (Bolyen et al. 2019 ). PCR primer sequences were removed from the raw sequences using cutadapt (v.4.2) (Martin 2011 ) and were filtered with an average quality score cutoff > 20 using DADA2 (v1.26.0) (Callahan et al. 2016 ). Filtered reads were further subject to DADA2 for denoising (Callahan et al. 2016 ). Non-chimeric amplicon sequence variants (ASVs) were obtained by merging read pairs that overlapped at least 12 bp and removing PCR chimeras. Taxonomic assignment of ASVs was performed with QIIME 2 (v.2022.11) (Bolyen et al. 2019 ) using the SILVA database (v.138.1) (Quast et al. 2013 ) for the 16S rRNA gene of the rhizosphere (Supplementary data 1) and root endosphere samples (Supplementary data 2). A minor proportion of archaeal ASVs were detected and their alpha and beta diversity had no significant differences across samples (Figure S2 & S3). Hence, ASVs belonging to mitochondria, chloroplast, archaea and Triticum aestivum were removed before the downstream analysis. The count matrix was rarefied using microeco (v.1.1.0) (Liu et al. 2021 ) to ensure even read depth for sample comparisons. Metagenomic analysis To further investigate the functional changes of rhizosphere bacterial communities in response to N deficiency, shotgun metagenomic sequencing was performed on the extracted DNA of the rhizosphere samples of Mace and Gladius under the two contrasting treatments, N0 and N2. A total of 16 metagenomic libraries of the rhizosphere soil DNA were generated with the NovaSeq 6000 sequencing platform using the Nextera Flex primer (Bruinsma et al. 2018 ), yielding 2 × 150 bp reads at the Australian Genome Research Facility, Australia. The resultant metagenome sequences (24 Gb per sample) were subjected to quality checks using fastqc (v.0.11.9) (Andrews 2010 ) and adapter trimming using trimmomatic (v.0.39) (Bolger et al. 2014 ). Samples were decontaminated using the Triticum aestivum genome (IWGSC) retrieved from the EnsemblPlants (Yates et al. 2022 ) using bowtie2 (v.2.4.2) (Langmead and Salzberg 2012 ). Clean reads were processed using the Metaphor (v.1.7.9) pipeline (Salazar et al. 2022 ). Two co-assemblies were generated by pooling the reads from samples of the same cultivar and assembled with MEGAHIT (v.1.2.9) (Li et al. 2015 ). Genes were predicted over the assembled contigs using Prodigal (v.2.6.3) (Hyatt et al. 2010 ) and functionally annotated with DIAMOND (v.2.1.0) (Buchfink et al. 2015 ) against the Clusters of Orthologous Genes (COGs) (v.1.0) database (Tatusov et al. 2000 ). Metaphor outputs the relative abundance of contigs falling in different COGs functions (Supplementary data 3) and categories (Supplementary data 4). These files were used to compare the functional similarity between treatment samples. We integrated the contig count and COGs annotation output from Metaphor to explore the N metabolism functions in bacterial genera. Contig depth was estimated by the reads per kilobases (RPK) method (Supplementary data 5) and the contig annotation file contained COGs taxonomy and function information generated from DIAMOND (Supplementary data 6). We first filtered out contigs related to N-metabolism and then searched for the presence of the bacterial genera in Mace or Gladius under N deficiency, in their corresponding samples. Hydroponic experiment The seeds of two wheat cultivars were sterilised with 80% ethanol for 1 min, followed by 2.5% sodium hypochlorite (NaOCl) for 15 mins and rinsed with Milli-Q water for 5 times. Sterilised seeds were germinated on petri dishes with Milli-Q water inside a fume cupboard, covered in dark, at room temperature, until the two-leaf stage was reached (Lu et al. 2021 ). Two uniform seedlings of each cultivar were transferred into a 4.3-L hydroponic system. The hydroponic pots were filled with a modified Hoagland solution with the following composition: 1 mM KH 2 PO 4 ; 1 mM MgSO 4 ; 0.05 mM H 3 BO 3 ; 0.01 mM Fe-EDTA; 0.009 mM MnSO 4 ; 0.0007 mM ZnSO 4 ; 0.0003 mM CuSO 4 ; 0.0001 mM NaCl and 0.0001 mM H 2 MoO 4 . Three N levels, (1) N0, 2 mM, (2) N1, 4 mM and (3) N2, 8 mM were achieved by adding various amounts of Ca(NO 3 ) 2 ·4H 2 O, KNO 3 and (NH 4 ) 2 SO 4 , with a NH 4 + to NO 3 − molar ratio of 1:4 across all N treatments (Thomas and Paparozzi 2013 ). Additional K 2 SO 4 and CaCl 2 were introduced to balance the levels of K + and Ca 2+ in the solution. The hydroponic solution was renewed with a half-strength Hoagland solution once every two weeks before the stem elongation stage and with a full-strength solution weekly thereafter. The solution pH was maintained within the range of 5.5–6.5 by adding 0.1 M KOH, while the water level was maintained using RO water. An aeration system was implemented in the hydroponic pots at the onset of tillering, and the arrangement of hydroponic pots was randomised on a weekly basis. During the mid-anthesis stage, wheat root samples were collected and rinsed with 0.2 mM CaCl 2 , followed by immersion in 800 mL of 0.2 mM CaCl 2 for 3 h with bubbling to remove adhered salt and microbes from the root surface (Liu et al. 2019 ). Root was separated from the plant and immediately stored in a freeze room at 20°C. Frozen root samples were freeze-dried for 2 weeks, cut into ~ 3 cm pieces, and homogenised (Saiman et al. 2012 ). Freeze-dried root samples were ground to fine powder with liquid N and stored at -20°C for metabolite extraction. GC-MS analysis for polar metabolites Organic compounds in root were extracted from 60 ± 2 mg of the stored lyophilised and ground materials using 500 µl of 100% MeOH containing 4% (v/v) of [ 13 C 6 ] sorbitol/[ 13 C 5 15 N] valine (0.5 mg ml − 1 ). The supernatant was stored in a 2-ml reaction tube as the first extraction product. A second extraction was performed on the remaining pellet with 500 µl of Milli-Q water, vortexed and centrifuged. The two extracted supernatants were combined and 100 µl of the combined supernatant was dried using a speed vacuum dryer (John Morris Scientific Pty Ltd). Derivatization of the dried extracts was performed with 20 µl of methoxyamine hydrochloride in pyridine and bis-(trimethylsilyl)-trifluoroacetamide (BSTFA), as described by (Dias et al. 2015 ). Specifically, a solution of 30 mg ml − 1 methoxyamine hydrochloride in pyridine was added to all samples, followed by a derivatization process at 37°C for 120 minutes with agitation at 500 rpm (61.0 g). Subsequently, 20 µl of BSTFA was added to the samples and were further incubated on a thermomixer (500 rpm/61.0 g) at 37°C for an additional 30 minutes. Samples were settled for 60 minutes before the injection. The samples were analysed with a GC-MS system comprised of an autosampler (Gerstel 2.5.2), a gas chromatograph (7890A Agilent), and a quadrupole mass spectrometer (5975C Agilent) (Agilent, Santa Clara, United States). An injection volume of 1 µl was used for each derivatized sample. The mass spectrometer was tuned using tris-(perfluorobutyl)-amine (CF43) following the manufacturer’s recommendations. Chromatography was carried out on a 30 m column (J&W VF-5MS Agilent) with a film thickness of 0.25 µm and an internal diameter of 0.25 mm. A 10 m Integra guard column was additionally equipped. The inlet temperature was set at 250°C; the transfer line of MS was at 280°C; the ion source was adjusted to 230°C; and the quadrupole was maintained at 150°C. Helium was served as the carrier gas at a flow rate of 1 ml min − 1 . Metabolite data pre-processing Data were pre-processed in silico for mass spectral deconvolution, with peak picking and identification using the Automated Mass Spectral Deconvolution and Identification System (AMDIS) software (National Institute of Standards and Technology, Gaithersburg, MD, USA). The target component library was created with the PBQC file and peak identification was confirmed with the Agilent MassHunter Qualitative Analysis B.05.00 (Agilent Technologies, Inc., 2011). The peak area data was exported with the Agilent MassHunter Quantitative Analysis software version B.08.00/Build 7.0.457.0 (Agilent Technologies, Inc., 2008). The peak area data was normalised against the internal standard [ 13 C 6 ] sorbitol and the weight of root samples to generate response values, which were log-transformed for the downstream analysis (Gupta et al. 2022 ) (Supplementary data 7). Statistical analyses All statistical analyses were performed using R (v.4.2.1) and all figures were generated with ggplot2 (v.3.4.4). Wheat growth parameters were statistically compared using the analysis of variance (ANOVA). The number of samples used for statistical analysis is provided in Table S1 . Shannon index was used to estimate the bacterial diversity and the differences between treatments were compared using the nonparametric Kruskal-Wallis test. Principal coordinates analysis (PCoA) based on the Bray-Curtis distance matrix was performed to assess the dissimilarity of the bacterial community compositions between treatments. Analysis of similarity (ANOSIM) with the ‘vegan’ package (Oksanen et al. 2022 ) was performed to statistically compare the similarity of bacterial community between treatment samples. Bacterial biomarkers were determined between N0 and N2 samples at the mid-anthesis stage. The linear discriminant analysis effect size (LEfSe) was quantified at the ASV level using the count per million (CPM) transformation with microeco (v.1.1.0). ASVs were considered significantly enriched with the criteria of p 2. For the metagenomic data, the relative abundance of COG functional code from Metaphor was subjected to the PCoA and ANOSIM analyses (Supplementary data 3). The relative abundance of COG categories was further compared between the N0 and N2 treatments using the Kruskal-Wallis test (Supplementary data 4). For the GC-MS metabolomics analysis, unsupervised learning principal component analysis (PCA) was performed to assess the similarity of metabolite profiles between treatments. The metabolite composition between treatment samples was statistically compared with the Permutational Multivariate Analysis of Variance (PERMANOVA) using the ‘vegan’ package (Oksanen et al. 2022 ). The fold-change of non-redundant known metabolites was analysed on the response values through multiple Student’s t-tests with false discovery rate (FDR) adjustment (adj. p < 0.05). A significance threshold of p < 0.05 was applied to all statistical results. Results Changes in the diversity and structure of bacterial communities under N deficiency The 16S rRNA gene sequencing yielded a total of 9,566 and 4,558 bacterial ASV after filtering, in the rhizosphere and root endosphere samples, respectively. The Kruskal-Wallis test revealed no significant changes in the alpha diversity of bacterial communities across N treatments and cultivars at both the stem elongation and the mid-anthesis stages (Figure S4 ). The PCoA ordination plots revealed that the bacterial community structure of Mace rhizosphere soil significantly changed across N treatments at the mid-anthesis stage (ANOSIM p < 0.05) (Fig. 1 a), while N treatments had a less significant impact on the bacterial community structure of Gladius rhizosphere, with R 2 = 0.19 (ANOSIM p 0.05) (Fig. 1 c), but significantly shifted the bacterial community in Gladius, with N0 samples clustering away from N2 samples (ANOSIM p < 0.05) (Fig. 1 d). Functional changes in rhizosphere soil bacterial communities under N deficiency We further explored the functional changes in the rhizosphere soil bacterial communities through metagenomic analyses of the N0 and N2 samples of both cultivars at the mid-anthesis stage. Across all samples, a total of 4311 functions belonging to 25 categories were identified from the COGs database. The PCoA ordinations revealed that N treatments significantly shifted the functional composition of the Mace rhizobacteria (ANOSIM p 0.05) (Fig. 2 b). The COG functional categories were further analysed with the Kruskal-Wallis statistical test, which revealed enrichment of signal transduction mechanisms (T) in the rhizobacteria of both cultivars under N deficiency ( p < 0.05) (Fig. 2 c). Further comparisons between the cultivars revealed that the Mace rhizobacteria had enhanced mobility and development, evident by functional enrichment in the cell wall membrane, envelope and biogenesis (M), post-translational modification, protein turnover and chaperones (O), intracellular trafficking, secretion and vesicular transport (U), cell motility (N), extracellular structure (W) and cytoskeleton (Z) ( p < 0.05) (Fig. 2 c). Enrichment pattern of intraspecific rhizobacteria under N deficiency We conducted LEfSe analysis of the 16S rRNA gene sequences, revealing distinct responses of rhizobacteria to N deficiency between Mace and Gladius. Specifically, 21 ASVs were significantly enriched in Mace but not in Gladius ( p 2). In comparison, 20 ASVs were significantly enriched in Gladius but not in Mace ( p 2) (Fig. 3 a & 3 b). A comparison of ASVs at the genus level revealed 10 genera observed exclusively in N-deficient Mace and 7 in N-deficient Gladius (Fig. 3 c). The functional profile of bacterial genera Haliangium, Clostridium, Ilumatobacter and Lysinibacillus observed in N-deficient Mace, as well as Massilia, Blastocococcus, Gemmatimonas, Conexibacter and Bacillus observed in N-deficient Gladius, were also detected in the metagenomic data. The metagenomics analysis further revealed that rhizobacterial genera enriched in N-deficient Mace exhibited a distinct N metabolism compared to those enriched in N-deficient Gladius, particularly in categories related to N regulation, transport, nitrification, and denitrification (Fig. 3 d). Intraspecific metabolite production in wheat under N deficiency The GC-MS metabolomics analysis identified 78 compounds across all samples including 40 non-redundant known organic compounds. Among the identified metabolites, 20 were amino acids, 10 sugars, three organic acids, and two fatty acids. The PCA analysis revealed a significant N treatment effect on the metabolite profiles for both cultivars ( p < 0.05) (Fig. 4 a & 4 b). This result was further validated by a heatmap presenting the log-transformed response value, with a clear distinction between N treatments for both cultivars (Fig. 4 d). The PCA plot revealed a marked disparity in the metabolite composition between Mace and Gladius under the N0 treatment ( p 0.05) (Fig. 4 c). The fold-change of metabolites between the N0 and N2 treatments in both cultivars revealed a predominant effect of N supply on the accumulation of organic compounds, with glycine, tyrosine and glucose exhibiting a significantly greater response to the N2 treatment for both cultivars (adj. p 2) (Fig. 4 e). Furthermore, the diverse sensitivity of wheat metabolites to between N0 and N2 treatments was evident, with octadecanoic acid, gluconic acid, and maltose significantly changed in Mace; and alanine, leucine, thymine, cysteine, glycolic acid, trehalose, sorbose, glucopyranose, sucrose and glycerol-3-phosphate significantly changed in Gladius (Fig. 4 e). In particular, the levels of octadecanoic acid in N-deficient Mace were 4-fold higher in N0 than in N2 (Fig. 4 e). Discussion Understanding the intricacies of plant-microbe interactions in N-poor environments is crucial for better N management for sustainable agriculture (Lam et al. 2022 ). While a previous study has demonstrated that plant metabolites facilitate beneficial plant-microbe interaction (Yu et al. 2021 ), our understanding of plant-microbe interactions in wheat cultivars differing in reported NUE, in response to N deficiency, remains limited. We found that the bacterial community in the rhizosphere significantly differed across N treatments in Mace but not in Gladius, while that in the root endosphere significantly differed by N treatments in Gladius but not in Mace (Fig. 1 ). This finding suggested that Mace and Gladius with different reported NUE interact differently with bacterial communities under N deficiency. We further examined the bacterial enrichment in the rhizosphere under N deficiency and found a distinct set of bacterial genera being enriched in Mace and Gladius (Fig. 3 ). Although limited studies have compared the genotypic influences on the plant-associated bacterial community under N deficiency, different wheat genotypes exert a distinct influence on shaping the root-associated bacterial community to adapt to abiotic stresses (Azarbad et al. 2018 ; Corneo et al. 2016 ; Yin et al. 2019 ). These results suggest a primary role of the genetic variation of cultivars in bacterial recruitment. We have identified the Haliangium , Clostridium , Ilumatobacter and Lysinibacillus bacterial genera distinctly enriched in Mace and Massilia , Blastococcus , Gemmatimonas , Conexibacter and Bacillus distinctly enriched in Gladius (Fig. 3 ), suggesting their beneficial potential to wheat growth under N deficiency. Species from the genera of Haliangium , Massilia , Gemmatimonas and Bacillus have previously been reported as wheat-associated plant growth promotion rhizobacteria (PGPR) (Kavamura et al. 2021 ), suggesting that Mace and Gladius interacted with different PGPR under N deficiency. These findings based on our sequencing data suggest that wheat cultivars with distinct NUE have a differential interaction with PGPR under N deficiency. The rhizobacterial functional composition significantly differed between N treatments in Mace but not in Gladius, suggesting differential functional responses to N deficiency (Fig. 2 a). This is in line with a recent study that reported the variation of the relative abundance of the rhizosphere bacterial functional genes involved in multiple nutrient cycles between rice cultivars with different plant cadmium accumulation (Cheng et al. 2023 ), supporting that crop cultivars have a predominant effect on the rhizobacteria functions. Comparison of functional categories revealed a significant increase in the detection of genes related to signal transduction mechanism in both Mace and Gladius, under N deficiency (Fig. 2 c). It also revealed a significant increase in the detection of genes related to rhizobacterial growth and motility in Mace, under N deficiency, a phenomenon not observed in Gladius (Fig. 2 c). Genes in the categories of signal transduction mechanism and extracellular structure can encode important functions that reflect increased flagellar motility in bacteria, as recently reported (Ramoneda et al. 2024 ). The bacterial chemotaxis ability refers to bacteria motile towards a chemical compound and is critical in facilitating symbiotic relationships with the host plant (Raina et al. 2019 ). A recent study demonstrated a connection between rhizobacterial chemotaxis and enhanced N acquisition of maize growing with rhizobacteria carrying chemotaxis genes (Sun et al. 2023 ). Together, these findings suggest that the higher NUE cultivar Mace, might have enhanced rhizobacterial chemotaxis in response to N deficiency, compared to Gladius. Additionally, the N metabolism of the bacterial community differed between two rice cultivars with distinct NUE, which suggested that the N metabolism of bacteria directly contributes to the rice NUE (Zhang et al. 2019 ). We found a distinct pattern of N metabolism among bacterial genera. Specifically, functions related to N transport, nitrification and denitrification were absent in Mace but present in Gladius under N deficiency (Fig. 3 d). Nitrification and denitrification contribute to N losses that reduce plant NUE in agricultural systems (Saud et al. 2022 ), suggesting that Mace interacted with bacteria in the rhizosphere to adapt to N deficiency through strategies other than the N metabolism. Indeed, Lysinibacillus sp. is known for various plant-beneficial traits, including N fixation, phosphorus solubilisation, auxin production (Hernández-Santana et al. 2022 ; Pantoja-Guerra et al. 2023b ; Passera et al. 2021 ) and virus biocontrol (Passera et al. 2021 ). A recent study has also demonstrated that Lysinibacillus sp. can stimulate Arabidopsis root growth and development, suggesting its ability to enhance nutrient acquisition (Pantoja-Guerra et al. 2023a ). This implies that the enhancement of bacterial chemotaxis ability, such as Lysinibacillus sp., may be a critical mechanism to drive the cultivar difference in NUE under N deficiency. Primary metabolites secreted by plant root act extensively as energy sources (Zhalnina et al. 2018 ) and chemotaxis attractants for bacteria (Raina et al. 2019 ), which are crucial in shaping the rhizosphere microbiota. In our hydroponic experiment, we have unsuccessfully concentrated the primary metabolite in the wheat root exudate for GC-MS measurement. Primary metabolites inside the root, such as amino acids, organic acids and sugars are secreted in root exudates through diffusion between concentration gradient differences (Canarini et al. 2019 ). Hence, we analysed the primary metabolic profile of wheat roots, which reflects the secretion to some extent. Our metabolomic data revealed a significant shift in both Mace and Gladius under different N availability (Fig. 4 a & 4 b), suggesting that both Mace and Gladius are sensitive to the N treatments. In addition, the metabolite composition demonstrated the difference in organic compound syntheses between wheat cultivars, particularly under N-deficient conditions (Fig. 4 c & 4 d). We then performed the enrichment analysis between the N0- and N2-treated samples and further revealed a significant reduction in glucose, tyrosine and glycine synthesis under N deficiency in both cultivars (Fig. 4 e), which has also been demonstrated in multiple crops (Sung et al. 2015 ; Tawaraya et al. 2018 ; Zhen et al. 2016 ). Moreover, the enrichment analysis of metabolites showed a notable increase in octadecanoic acid (Fig. 4 e), known as stearic acid (C18:0), in Mace under N deficiency, whereas such accumulation was not observed in Gladius. These findings demonstrate genotype-specific metabolic processes in Mace and Gladius in response to N deficiency. Interestingly, a previous study has found no effect of N deficiency on stearic acid production in wheat (Nasiroleslami et al. 2021 ), which contrasts with our results for Mace but aligns with those for Gladius. We suspected that stearic acid plays an important role in the plant-microbe interaction of Mace under N deficiency. Indeed, stearic acid accumulation in the root nodules of Lotus japonicas cv. GIFU indicates its potential role in the symbiotic N fixation (Desbrosses et al. 2005 ). Stearic acid has also been identified as a chemotaxis attractant of the PGPR, Pseudomonas isolate RP2 (Ankati et al. 2019 ). Together, the GC-MS metabolomic data suggests that stearic acid might be secreted in Mace but not in Gladius, under N deficiency, which potentially shaped the intraspecific wheat-bacterial community. Our data unveiled a differential rhizobacterial community and root metabolite profile between wheat cultivars of contrasting NUE in response to N deficiency Notably, stearic acid accumulation in the root of the higher NUE cultivar, Mace, indicating a potential increase in root exudates, may shape the soil bacterial community under N deficiency. Our metagenomic analysis further suggests that Mace may have stimulate rhizobacteria motility and development as well as the enrichment of PGPR under N deficiency, presenting distinct differences from the responses observed in Gladius. A better understanding of the chemotaxis ability of PGPR towards stearic acid in wheat-grown soils could be a promising avenue for the development of next-generation bio-fertilisers to achieve sustainable agriculture. Abbreviations N – Nitrogen NUE – Nitrogen Use Efficiency ASVs – Amplicon Sequence Variants COGs - Clusters of Orthologous Genes PCoA – Principal Coordinate analysis PCA – Principal Components analysis ANOVA – Analysis Of Variance ANOSIM – Analysis Of Similarity PERMANOVA - Permutational Multivariate Analysis of Variance CPM - count per million PGPR- Plant Growth Promoting Rhizobacteria Declarations Acknowledgements This research was supported by the Australia Research Council’s Industrial Transformation Research Program funding scheme (IH200100023). The wheat seeds for this research were provided by the Australian Grain Technology (AGT). The computational resources were supported by The University of Melbourne’s Research Computing Services and the Petascale Campus Initiative. The authors wish to thank Pongsathorn Sukdanont for glasshouse assistance. Competing interests The authors have no relevant financial or non-financial interests to disclose. Author contributions LHC, HWH, SKL, DC, and CT designed the glasshouse experiments, performed by LHC and supervised by HWH, SKL, DC, and CT. CT & UR supported the development of the metabolite collection method. QC supported DNA extraction. SG and DAD performed primary metabolite extraction and the GC-MS metabolomic analysis. LHC performed amplicon sequencing, metagenomic and metabolomic data analysis. HWH and QC supported amplicon sequence data analysis. VWS supported metagenomic analysis. SG supported metabolomic data analysis. LHC prepared the manuscript draft. HWH, SKL and LHC edited and revised the manuscript. All authors read and approved the final version of the manuscript. Data availability Raw sequencing files can be publicly accessed from the NCBI database with the accession number PRJNA1126416. The QIIME2 output of the 16s amplicon sequencing is provided in supplementary data 1 and 2. The Metaphor outputs of the metagenome are provided in supplementary data 3 and 4. 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Global Change Biology 28: 6446-6461. doi: https://doi.org/10.1111/gcb.16361. Yates AD, Allen J, Amode RM, Azov AG, Barba M, Becerra A, Bhai J, Campbell LI, Martinez MC, Chakiachvili M, Chougule K, Christensen M, Contreras-Moreira B, Cuzick A, Fioretto LD, Davis P, De Silva NH, Diamantakis S, Dyer S, Elser J, Filippi CV, Gall A, Grigoriadis D, Guijarro-Clarke C, Gupta P, Hammond-Kosack KE, Howe KL, Jaiswal P, Kaikala V, Kumar V, Kumari S, Langridge N, Le T, Luypaert M, Maslen GL, Maurel T, Moore B, Muffato M, Mushtaq A, Naamati G, Naithani S, Olson A, Parker A, Paulini M, Pedro H, Perry E, Preece J, Quinton-Tulloch M, Rodgers F, Rosello M, Ruffier M, Seager J, Sitnik V, Szpak M, Tate J, Tello-Ruiz MK, Trevanion SJ, Urban M, Ware D, Wei S, Williams G, Winterbottom A, Zarowiecki M, Finn RD, Flicek P (2022) Ensembl Genomes 2022: an expanding genome resource for non-vertebrates. Nucleic Acids Research 50: D996-D1003. doi: https://doi.org/10.1093/nar/gkab1007. 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Zheng L, Karim MR, Hu YG, Shen RF, Lan P (2021) Greater morphological and primary metabolic adaptations in roots contribute to phosphate-deficiency tolerance in the bread wheat cultivar Kenong199. BMC Plant Biology 21: 381. doi: https://doi.org/10.1186/s12870-021-03164-6. Supplementary Files PSsupplemetarydoc.docx SupData1rawrhizosphere16Scounttable.csv SupData2rawroot16Scounttable.csv SupData3contigbyCOGfunction.tsv SupData4contigbyCOGcatergory.tsv SupData5contigdepth.txt SupData7GCMSrawdata.xlsx Cite Share Download PDF Status: Published Journal Publication published 31 Oct, 2024 Read the published version in Plant and Soil → Version 1 posted Editorial decision: Minor revisions 10 Oct, 2024 Reviewers agreed at journal 17 Jul, 2024 Reviewers invited by journal 16 Jul, 2024 Editor invited by journal 15 Jul, 2024 Editor assigned by journal 15 Jul, 2024 First submitted to journal 14 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4738104","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327976527,"identity":"b2ef5346-ff9b-43bd-9889-c44b68522e7b","order_by":0,"name":"Lok Hang Chan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lok","middleName":"Hang","lastName":"Chan","suffix":""},{"id":327976528,"identity":"0484d173-523e-4416-bcf9-45db83944aea","order_by":1,"name":"Shu Kee Lam","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"Kee","lastName":"Lam","suffix":""},{"id":327976529,"identity":"e8d332bc-624f-4ad5-aed7-454da0b332b4","order_by":2,"name":"Deli Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Deli","middleName":"","lastName":"Chen","suffix":""},{"id":327976530,"identity":"6a672a7f-1f0a-43b1-a567-9a8d0c5ae415","order_by":3,"name":"Caixian Tang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Caixian","middleName":"","lastName":"Tang","suffix":""},{"id":327976531,"identity":"b74d6b34-4ccd-470b-8a3c-837be998ccff","order_by":4,"name":"Qinglin Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qinglin","middleName":"","lastName":"Chen","suffix":""},{"id":327976532,"identity":"ee661338-695c-4ca5-b2a8-9fb1a00514fa","order_by":5,"name":"Ute Roessner","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ute","middleName":"","lastName":"Roessner","suffix":""},{"id":327976533,"identity":"a05a0275-b1e4-4149-ab37-b71c1bdab0ee","order_by":6,"name":"Vinícius Werneck Salazar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vinícius","middleName":"Werneck","lastName":"Salazar","suffix":""},{"id":327976534,"identity":"50279e65-87a2-47e9-b6c0-516d0a937ba6","order_by":7,"name":"Sneha Gupta","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sneha","middleName":"","lastName":"Gupta","suffix":""},{"id":327976535,"identity":"36f3bc49-d4eb-45fb-a958-a6ff49bf061b","order_by":8,"name":"Daniel Anthony Dias","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Anthony","lastName":"Dias","suffix":""},{"id":327976536,"identity":"0805d128-5593-4d98-8500-443cf9e8dade","order_by":9,"name":"Hang-Wei Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYBACAyjNwyDBwPiAAUgysPMA+WzEaWE2AGthJlILSDGbBJhBSIs5+9nDLxjbbGQYpHvMqnl3WETzM/MeYPhQdpiBf0YCVi2WPXlpFoxtaTwMMmfMbvOekcid2cyXwDjj3GEGiRvYtRgcyDEz/rvtMNAvOUAtbRK5Gw7zGDDzth1mYMCl5fwbMwPGbf/BWopBWvaDtPwFapHHpeVGjvEDxm0HwFqYwbYwA7UwArUY4NTyxoyB8V8yD5tEWrHkXKCWGYf5Eg72nEvnMTzzAIfDcow/MJyxs+eXSN744W1bXW5/e+/BBz/KrOXkjmO3BQgg0YESEQcYQJGLGzB/wCM5CkbBKBgFo4CBAQBWSlWTSENvNwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3294-102X","institution":"University of Melbourne","correspondingAuthor":true,"prefix":"","firstName":"Hang-Wei","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-07-14 11:05:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4738104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4738104/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11104-024-07048-0","type":"published","date":"2024-10-31T16:05:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62095682,"identity":"26d8244e-eb5c-4f1f-bebf-f29205cffcf8","added_by":"auto","created_at":"2024-08-09 08:37:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":257706,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinates analysis (PCoA) of the bacterial community composition in the rhizosphere soils of (a) Mace and (b) Gladius and in the root endosphere of (c) Mace and (d) Gladius at the mid-anthesis stage, under N treatments, as revealed by the amplicon sequencing analysis. The samples were coloured brown, blue and green, for N0, N1 and N2 treated samples, respectively. The ANOSIM statistics are indicated on the top-right of each PCoA plot.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/790a2cfa4ffbc37b348943d1.png"},{"id":62095675,"identity":"3d8ec811-6759-4e5e-99cb-e4ffc2ffee1f","added_by":"auto","created_at":"2024-08-09 08:37:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":325943,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinates analysis (PCoA) of the rhizobacterial COG functions detected in (a) Mace and (b) Gladius between the N0 and N2 treatments at the mid-anthesis stage, as revealed by the metagenomic analysis. The samples were coloured brown and green, for N0 and N2 treated samples, respectively. The ANOSIM statistics are indicated on the top-right of each PCoA plot. (c) The relative abundance of the grouped functional COG categories in N0- (brown) and N2- (green) treated Mace and Gladius with significant difference indicated on the bar (* = \u003cem\u003ep\u003c/em\u003e \u0026lt;0.05). The letter code description is indicated in the bracket.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/5e1fabe6f40835c06610ee28.png"},{"id":62095674,"identity":"334c2a1e-4437-4794-acdb-d3113e679370","added_by":"auto","created_at":"2024-08-09 08:37:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":592056,"visible":true,"origin":"","legend":"\u003cp\u003eLEfSe analysis of (a) Mace and (b) Gladius between the N0 and N2 treatments at the mid-anthesis stage, showing the bacterial ASVs with significant enrichment (P \u0026lt; 0.05, LDA score \u0026gt;2). (c) Overlap of bacterial genera enriched in the N0 and N2 treatments, between Mace and Gladius, based on the amplicon sequencing. (d) The presence of N metabolism functions in the bacterial genera enriched in Mace and Gladius in the N0 treatment, as revealed by the metagenomic analysis.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/e54a8186d0f2c3a90724b96d.png"},{"id":62095677,"identity":"f90a52a9-943e-4c41-a50f-828526c28f1c","added_by":"auto","created_at":"2024-08-09 08:37:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":367827,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) comparing polar metabolites detected in roots across N treatments of (a) Mace, (b) Gladius and comparing polar metabolites detected in (c) Mace and Gladius under N0 and N2 treatments at the mid-anthesis stage. The samples are coloured brown, blue and green, for N0, N1 and N2 treated samples, respectively. The PERMANOVA statistics are indicated on the top-right of each PCoA plot. (d) Heatmap of the log-transformed response values of metabolites in Mace and Gladius across N treatments. (e) Log scaled fold-changes of metabolites of Gladius and Mace between the N0 and N2 treatments with significant changes indicated on the bar (* adj. \u003cem\u003ep\u003c/em\u003e \u0026lt;0.05, ** adj. \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (FAs = fatty acids, AAs = amino acids, OAs = organic acids, and OCs = organic compounds).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/a5b3170542a69a4be5f0c93e.png"},{"id":68207099,"identity":"bfcad16f-d892-4407-b801-b3622f7aedea","added_by":"auto","created_at":"2024-11-04 16:34:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1867778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/c9694af5-cd89-4fdd-acc0-15e5f0e80d07.pdf"},{"id":62095684,"identity":"302d01da-1ad9-439c-90c1-1e38c8eb1f6b","added_by":"auto","created_at":"2024-08-09 08:37:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1011007,"visible":true,"origin":"","legend":"","description":"","filename":"PSsupplemetarydoc.docx","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/808ea9438a4fee3c889924f5.docx"},{"id":62096123,"identity":"d8b987cd-4dd1-44a5-90e5-8503e6b2a2a3","added_by":"auto","created_at":"2024-08-09 08:45:37","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2658040,"visible":true,"origin":"","legend":"","description":"","filename":"SupData1rawrhizosphere16Scounttable.csv","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/fdcc9837289b7a8f8a3dbc72.csv"},{"id":62096124,"identity":"befbc7c8-b20a-4e92-b58d-1081f8526624","added_by":"auto","created_at":"2024-08-09 08:45:37","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1294540,"visible":true,"origin":"","legend":"","description":"","filename":"SupData2rawroot16Scounttable.csv","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/0949fc84f39c90b975e08733.csv"},{"id":62095681,"identity":"f0bdfdd9-b9b3-41e3-828b-47620f964d29","added_by":"auto","created_at":"2024-08-09 08:37:38","extension":"tsv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1190353,"visible":true,"origin":"","legend":"","description":"","filename":"SupData3contigbyCOGfunction.tsv","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/a77a61c7964fb7acb808e937.tsv"},{"id":62095679,"identity":"b1edb4e3-6ef0-4e5d-8d3a-5e70592e83c0","added_by":"auto","created_at":"2024-08-09 08:37:38","extension":"tsv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":7852,"visible":true,"origin":"","legend":"","description":"","filename":"SupData4contigbyCOGcatergory.tsv","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/f9bc9b828817864152103a79.tsv"},{"id":62095683,"identity":"b8f915a6-24c8-4da8-ac26-81af67a8104e","added_by":"auto","created_at":"2024-08-09 08:37:40","extension":"txt","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":197279275,"visible":true,"origin":"","legend":"","description":"","filename":"SupData5contigdepth.txt","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/83bcd5c95dacee96cc899ce9.txt"},{"id":62096125,"identity":"199c045e-fd20-4064-8825-a82f4ddaad90","added_by":"auto","created_at":"2024-08-09 08:45:38","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":100385,"visible":true,"origin":"","legend":"","description":"","filename":"SupData7GCMSrawdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4738104/v1/967cccb3149bfbf6da88e835.xlsx"}],"financialInterests":"","formattedTitle":"Cultivar-specific wheat-associated bacterial communities and metabolites in response to nitrogen deficiency","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNitrogen (N) is essential for plant growth and development (Andrews et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e); and its deficiency in soil has reduced yield of some major crops, including wheat (Guo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; van Grinsven et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Synthetic N fertilisers promote crop growth, but their mismanagement adversely affects soil microbial community structure (Ding et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Enebe and Babalola \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and environmental quality (Canfield et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pahalvi et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Alternative strategies that reduce dependency on chemical fertilisers without compromising crop yield should be explored. Microorganisms establish mutualistic associations with plants by obtaining essential carbon sources from plant metabolites and in turn enhancing plant nutrient acquisition (Bai et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) thus improving plant adaption to diverse environmental conditions (Ahlawat et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nerva et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although bio-fertilisers leverage the beneficial effects of soil microorganisms to improve plant growth (Bhardwaj et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and soil health (Sun et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the efficacy of bioinoculants is inconsistent (Fagorzi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Harnessing plant metabolites to modulate soil microbial interactions in the development of next-generation fertilisers potentially enhances bio-fertiliser efficiency (Lam et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, our understanding of the interaction between plant metabolites and soil microbiomes under N deficiency remains limited (Hong et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Addressing this knowledge gap is crucial for advancing sustainable agricultural practices and optimising fertiliser strategies under N-deficient conditions.\u003c/p\u003e \u003cp\u003eThe \"cry-for-help\" theory posits that plants under stress alter their physiological processes and immune responses, actively recruiting microbes to adapt to challenging conditions (Castrillo et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Song and Haney \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Substantial evidence supports the notion that soil microorganisms are recruited by plants to withstand extreme environmental conditions such as drought, flooding, heat, and salinity (Fadiji et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hong et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kumar and Verma \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nitrogen deficiency poses stress on plant growth and influences the metabolite interaction with microbes (Chai and Schachtman \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In non-leguminous plants, the modulation of the N cycle by soil bacteria has been shown to benefit plant growth under low-N conditions (Chai and Schachtman \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Coskun et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, maize grown in a N-limited soil enhanced the contribution of soil bacteria to the N cycle (Jiang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This finding has also been validated recently in \u003cem\u003eViola epidendroides\u003c/em\u003e and \u003cem\u003eBaptisia macrantha\u003c/em\u003e, suggesting that plant experiencing N deficiency maintains close associations with diverse N-cycling microorganisms (Camargo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition to the N cycle regulation, a recent study provided evidence that maize enhanced N-use efficiency (NUE) under N deficiency, through the recruitment of \u003cem\u003eMassilia\u003c/em\u003e sp. with flavones identified as the chemotaxis attractants for \u003cem\u003eMassilia\u003c/em\u003e sp. (Yu et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While the study has not detected plant growth hormone production, it confirmed that \u003cem\u003eMassilia\u003c/em\u003e sp. stimulated lateral root growth and proposed that it altered the balance of proliferation and differentiation of root cells (Yu et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This highlights the critical role of plant metabolites in the recruitment of soil bacteria to shape a beneficial microbiome, particularly in adapting to N deficiency (Haney et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdvances in breeding and domestication have improved plant root growth, development (Zhang et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and morphology (Cormier et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), consequently influencing plant NUE (Nerva et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and the tolerance to low-N conditions (Chen et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These improvements have enhanced the plant N acquisition (Chen et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and have altered the plant-microbe interactions (Gholizadeh et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Quiza et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yue et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, studies indicate that breeding lines of maize with varying NUE distinctly influence the rhizosphere microbiome, particularly in the context of N turnover (Pathan et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Microbes can enhance plant NUE through various mechanisms, including increased microbial diversity (Zhang et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), enhanced N-cycling processes (Zhang et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and alterations in root architecture (Yu et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While rhizobacteria play a crucial role in plant NUE, their sensitivity to diverse soil fertility conditions must also be acknowledged (Chai et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A growing body of evidence reveals variations in the metabolome among different plant species (McLaughlin et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and cultivars/varieties (Kogel et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, our understanding of the genotypic variation in plant-microbe interactions in response to N deficiency remains limited. Further investigations into these interactions will elucidate the intricate dynamics between plants and microbes under N deficiency, ultimately facilitating the development of strategies to enhance NUE in crops.\u003c/p\u003e \u003cp\u003eIn this study, we employed amplicon sequencing and metagenomic technology to compare the root-associated bacterial communities of two wheat cultivars (cv. Mace and Gladius) with high and low reported NUE, respectively (Alhabbar et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), under different N availability conditions. The GC-MS metabolomics approach was further conducted to determine the primary metabolic profile in the root of N-deficient wheat grown in a hydroponic system. We hypothesised that (1) wheat cultivars with contrasting NUE establish distinct soil bacterial communities subjected to N deficiency and (2) the wheat cultivars alter root metabolite composition in response to N deficiency.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eSoil collection and glasshouse experiment\u003c/p\u003e \u003cp\u003eSoil samples used in this study were collected in March 2022 from a wheat experimental farm on the Dookie Campus at The University of Melbourne, Australia (36.382\u0026deg; S, 145.711\u0026deg; E). The experimental field has a history of wheat-pasture rotation. We collected approximately 400 kg of soil (0\u0026ndash;10 cm) using a shovel after removing the root debris for the pot experiment. The soil is classified as a Dermosol (Isbell \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) with the following physicochemical properties (measured by the Nutrient Advantage lab, Victoria, Australia): total C, 0.021 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil, total N, 0.002 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil, organic C, 0.018 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil, nitrate-N, 56 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil, ammonium-N, 2.9 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil, and soil pH of 6.3. The collected soil samples were sieved to 1 cm, air-dried, and homogenised before the glasshouse experiment. Pots with an inner diameter of 16.5 cm were filled with 4.6 kg of soil. Two wheat cultivars, high-NUE Mace and low-NUE Gladius, provided by the Australian Grain Technology (AGT) company, were selected for the pot experiment. Five uniform-sized seeds were sown at a depth of 3 cm and seedlings were thinned to three per pot at the two-leaf stage. To establish varying N levels, ammonium sulphate was applied at three rates: (1) N0, without N addition, (2) N1, 45 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and (3) N2, 90 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with eight replicates for each N treatment. The soil moisture content was maintained at around 80% field capacity throughout the experiment. The plant growth conditions were maintained at 24\u0026deg;C /10\u0026deg;C for 12 h/12 h day/night, 65% humidity and ~\u0026thinsp;225 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e during the daytime throughout the study.\u003c/p\u003e \u003cp\u003eThe wheat rhizosphere and bulk soils, as well as plant roots, were destructively sampled at the stem-elongation and the mid-anthesis stages. The wheat root system was placed in a sampling bag and subjected to gentle shaking to obtain rhizosphere soil samples. Soils remained in the pots were randomly collected as the bulk soil. Wheat roots (5\u0026ndash;20 cm) were separated from the plant and were gently rinsed with reverse osmosis (RO) water to remove large soil particles. Root samples were subsequently shaken with 30 ml of phosphate-buffer solution (PBS) in a falcon tube at 250 rpm to remove any adhered soil and briefly dried with filter paper. The soil and root samples were then stored at -20\u0026deg;C.\u003c/p\u003e \u003cp\u003ePlant and soil physicochemical characterisation\u003c/p\u003e \u003cp\u003eThe fresh above ground plant materials were oven-dried at 60\u0026deg;C for 7 days for biomass measurement. The dried plant material was ground to a 0.2-mm fine powder using the ultra-centrifugal mill (ZM200 Ultra Centrifugal Mill, RETSCH). A set of 48 bulk soil samples (2 growth stages, 2 cultivars, 3 N levels and 4 replicates) were oven-dried at 40\u0026deg;C for 7 days, and then ball milled using a tissue grinder (TissueLyser II, QIAGEN). The resulting, finely ground plant and soil samples were subjected to the CN analyser (LECO TruMac Series) for total C and N measurements (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Another set of 48 bulk soil samples were oven-dried at 105\u0026deg;C for 2 days for the estimation of soil moisture content. For soil pH and electrical conductivity (EC) measurements, an alternative set of fresh soil was suspended in Milli-Q water at a 1:5 ratio and shaken at 250 rpm for 60 min. Soil pH was measured with a professional benchtop pH meter (HANNA) and EC was measured with the conductivity meter (Multiparameter Laboratory Benchtop Conductivity meter, SmartCHEM-LAB).\u003c/p\u003e \u003cp\u003eDNA extraction and amplicon sequencing\u003c/p\u003e \u003cp\u003eFrozen root samples were ground into a fine powder with liquid N. The total DNA was extracted from 250 mg of the frozen rhizosphere soil and root samples using the DNeasy PowerSoil Pro Kits (QIAGEN), and quality was assessed using Nanodrop (NanoDrop One, Thermo Scientific). A total of 48 rhizosphere and 48 root endosphere DNA samples were subjected to polymerase chain reaction (PCR) amplification. The V3\u0026ndash;V4 hypervariable region of the bacterial 16S rRNA gene was amplified with the 341F/806R primer pairs (Klindworth et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and sequenced using the Illumina MiSeq System platform at the Australian Genome Research Facility, Melbourne, Australia.\u003c/p\u003e \u003cp\u003eAmplicon sequencing was processed using QIIME 2 (v.2022.11) (Bolyen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). PCR primer sequences were removed from the raw sequences using cutadapt (v.4.2) (Martin \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and were filtered with an average quality score cutoff\u0026thinsp;\u0026gt;\u0026thinsp;20 using DADA2 (v1.26.0) (Callahan et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Filtered reads were further subject to DADA2 for denoising (Callahan et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Non-chimeric amplicon sequence variants (ASVs) were obtained by merging read pairs that overlapped at least 12 bp and removing PCR chimeras. Taxonomic assignment of ASVs was performed with QIIME 2 (v.2022.11) (Bolyen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) using the SILVA database (v.138.1) (Quast et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) for the 16S rRNA gene of the rhizosphere (Supplementary data 1) and root endosphere samples (Supplementary data 2). A minor proportion of archaeal ASVs were detected and their alpha and beta diversity had no significant differences across samples (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e \u0026amp; S3). Hence, ASVs belonging to mitochondria, chloroplast, archaea and \u003cem\u003eTriticum aestivum\u003c/em\u003e were removed before the downstream analysis. The count matrix was rarefied using microeco (v.1.1.0) (Liu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to ensure even read depth for sample comparisons.\u003c/p\u003e \u003cp\u003eMetagenomic analysis\u003c/p\u003e \u003cp\u003eTo further investigate the functional changes of rhizosphere bacterial communities in response to N deficiency, shotgun metagenomic sequencing was performed on the extracted DNA of the rhizosphere samples of Mace and Gladius under the two contrasting treatments, N0 and N2. A total of 16 metagenomic libraries of the rhizosphere soil DNA were generated with the NovaSeq 6000 sequencing platform using the Nextera Flex primer (Bruinsma et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), yielding 2 \u0026times; 150 bp reads at the Australian Genome Research Facility, Australia. The resultant metagenome sequences (24 Gb per sample) were subjected to quality checks using fastqc (v.0.11.9) (Andrews \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and adapter trimming using trimmomatic (v.0.39) (Bolger et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Samples were decontaminated using the Triticum aestivum genome (IWGSC) retrieved from the EnsemblPlants (Yates et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) using bowtie2 (v.2.4.2) (Langmead and Salzberg \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Clean reads were processed using the Metaphor (v.1.7.9) pipeline (Salazar et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Two co-assemblies were generated by pooling the reads from samples of the same cultivar and assembled with MEGAHIT (v.1.2.9) (Li et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Genes were predicted over the assembled contigs using Prodigal (v.2.6.3) (Hyatt et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and functionally annotated with DIAMOND (v.2.1.0) (Buchfink et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) against the Clusters of Orthologous Genes (COGs) (v.1.0) database (Tatusov et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetaphor outputs the relative abundance of contigs falling in different COGs functions (Supplementary data 3) and categories (Supplementary data 4). These files were used to compare the functional similarity between treatment samples. We integrated the contig count and COGs annotation output from Metaphor to explore the N metabolism functions in bacterial genera. Contig depth was estimated by the reads per kilobases (RPK) method (Supplementary data 5) and the contig annotation file contained COGs taxonomy and function information generated from DIAMOND (Supplementary data 6). We first filtered out contigs related to N-metabolism and then searched for the presence of the bacterial genera in Mace or Gladius under N deficiency, in their corresponding samples.\u003c/p\u003e \u003cp\u003eHydroponic experiment\u003c/p\u003e \u003cp\u003eThe seeds of two wheat cultivars were sterilised with 80% ethanol for 1 min, followed by 2.5% sodium hypochlorite (NaOCl) for 15 mins and rinsed with Milli-Q water for 5 times. Sterilised seeds were germinated on petri dishes with Milli-Q water inside a fume cupboard, covered in dark, at room temperature, until the two-leaf stage was reached (Lu et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Two uniform seedlings of each cultivar were transferred into a 4.3-L hydroponic system. The hydroponic pots were filled with a modified Hoagland solution with the following composition: 1 mM KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e; 1 mM MgSO\u003csub\u003e4\u003c/sub\u003e; 0.05 mM H\u003csub\u003e3\u003c/sub\u003eBO\u003csub\u003e3\u003c/sub\u003e; 0.01 mM Fe-EDTA; 0.009 mM MnSO\u003csub\u003e4\u003c/sub\u003e; 0.0007 mM ZnSO\u003csub\u003e4\u003c/sub\u003e; 0.0003 mM CuSO\u003csub\u003e4\u003c/sub\u003e; 0.0001 mM NaCl and 0.0001 mM H\u003csub\u003e2\u003c/sub\u003eMoO\u003csub\u003e4\u003c/sub\u003e. Three N levels, (1) N0, 2 mM, (2) N1, 4 mM and (3) N2, 8 mM were achieved by adding various amounts of Ca(NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e\u0026middot;4H\u003csub\u003e2\u003c/sub\u003eO, KNO\u003csub\u003e3\u003c/sub\u003e and (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, with a NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e to NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e molar ratio of 1:4 across all N treatments (Thomas and Paparozzi \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additional K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e and CaCl\u003csub\u003e2\u003c/sub\u003e were introduced to balance the levels of K\u003csup\u003e+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e in the solution. The hydroponic solution was renewed with a half-strength Hoagland solution once every two weeks before the stem elongation stage and with a full-strength solution weekly thereafter. The solution pH was maintained within the range of 5.5\u0026ndash;6.5 by adding 0.1 M KOH, while the water level was maintained using RO water. An aeration system was implemented in the hydroponic pots at the onset of tillering, and the arrangement of hydroponic pots was randomised on a weekly basis.\u003c/p\u003e \u003cp\u003eDuring the mid-anthesis stage, wheat root samples were collected and rinsed with 0.2 mM CaCl\u003csub\u003e2\u003c/sub\u003e, followed by immersion in 800 mL of 0.2 mM CaCl\u003csub\u003e2\u003c/sub\u003e for 3 h with bubbling to remove adhered salt and microbes from the root surface (Liu et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Root was separated from the plant and immediately stored in a freeze room at 20\u0026deg;C. Frozen root samples were freeze-dried for 2 weeks, cut into ~\u0026thinsp;3 cm pieces, and homogenised (Saiman et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Freeze-dried root samples were ground to fine powder with liquid N and stored at -20\u0026deg;C for metabolite extraction.\u003c/p\u003e \u003cp\u003eGC-MS analysis for polar metabolites\u003c/p\u003e \u003cp\u003eOrganic compounds in root were extracted from 60\u0026thinsp;\u0026plusmn;\u0026thinsp;2 mg of the stored lyophilised and ground materials using 500 \u0026micro;l of 100% MeOH containing 4% (v/v) of [\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003e6\u003c/sub\u003e] sorbitol/[\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003e5\u003c/sub\u003e\u003csup\u003e15\u003c/sup\u003eN] valine (0.5 mg ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The supernatant was stored in a 2-ml reaction tube as the first extraction product. A second extraction was performed on the remaining pellet with 500 \u0026micro;l of Milli-Q water, vortexed and centrifuged. The two extracted supernatants were combined and 100 \u0026micro;l of the combined supernatant was dried using a speed vacuum dryer (John Morris Scientific Pty Ltd). Derivatization of the dried extracts was performed with 20 \u0026micro;l of methoxyamine hydrochloride in pyridine and bis-(trimethylsilyl)-trifluoroacetamide (BSTFA), as described by (Dias et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Specifically, a solution of 30 mg ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e methoxyamine hydrochloride in pyridine was added to all samples, followed by a derivatization process at 37\u0026deg;C for 120 minutes with agitation at 500 rpm (61.0 g). Subsequently, 20 \u0026micro;l of BSTFA was added to the samples and were further incubated on a thermomixer (500 rpm/61.0 g) at 37\u0026deg;C for an additional 30 minutes. Samples were settled for 60 minutes before the injection.\u003c/p\u003e \u003cp\u003eThe samples were analysed with a GC-MS system comprised of an autosampler (Gerstel 2.5.2), a gas chromatograph (7890A Agilent), and a quadrupole mass spectrometer (5975C Agilent) (Agilent, Santa Clara, United States). An injection volume of 1 \u0026micro;l was used for each derivatized sample. The mass spectrometer was tuned using tris-(perfluorobutyl)-amine (CF43) following the manufacturer\u0026rsquo;s recommendations. Chromatography was carried out on a 30 m column (J\u0026amp;W VF-5MS Agilent) with a film thickness of 0.25 \u0026micro;m and an internal diameter of 0.25 mm. A 10 m Integra guard column was additionally equipped. The inlet temperature was set at 250\u0026deg;C; the transfer line of MS was at 280\u0026deg;C; the ion source was adjusted to 230\u0026deg;C; and the quadrupole was maintained at 150\u0026deg;C. Helium was served as the carrier gas at a flow rate of 1 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMetabolite data pre-processing\u003c/p\u003e \u003cp\u003eData were pre-processed in silico for mass spectral deconvolution, with peak picking and identification using the Automated Mass Spectral Deconvolution and Identification System (AMDIS) software (National Institute of Standards and Technology, Gaithersburg, MD, USA). The target component library was created with the PBQC file and peak identification was confirmed with the Agilent MassHunter Qualitative Analysis B.05.00 (Agilent Technologies, Inc., 2011). The peak area data was exported with the Agilent MassHunter Quantitative Analysis software version B.08.00/Build 7.0.457.0 (Agilent Technologies, Inc., 2008). The peak area data was normalised against the internal standard [\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003e6\u003c/sub\u003e] sorbitol and the weight of root samples to generate response values, which were log-transformed for the downstream analysis (Gupta et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (Supplementary data 7).\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R (v.4.2.1) and all figures were generated with ggplot2 (v.3.4.4). Wheat growth parameters were statistically compared using the analysis of variance (ANOVA). The number of samples used for statistical analysis is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Shannon index was used to estimate the bacterial diversity and the differences between treatments were compared using the nonparametric Kruskal-Wallis test. Principal coordinates analysis (PCoA) based on the Bray-Curtis distance matrix was performed to assess the dissimilarity of the bacterial community compositions between treatments. Analysis of similarity (ANOSIM) with the \u0026lsquo;vegan\u0026rsquo; package (Oksanen et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was performed to statistically compare the similarity of bacterial community between treatment samples. Bacterial biomarkers were determined between N0 and N2 samples at the mid-anthesis stage. The linear discriminant analysis effect size (LEfSe) was quantified at the ASV level using the count per million (CPM) transformation with microeco (v.1.1.0). ASVs were considered significantly enriched with the criteria of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a linear discriminant analysis (LDA) effect score\u0026thinsp;\u0026gt;\u0026thinsp;2. For the metagenomic data, the relative abundance of COG functional code from Metaphor was subjected to the PCoA and ANOSIM analyses (Supplementary data 3). The relative abundance of COG categories was further compared between the N0 and N2 treatments using the Kruskal-Wallis test (Supplementary data 4). For the GC-MS metabolomics analysis, unsupervised learning principal component analysis (PCA) was performed to assess the similarity of metabolite profiles between treatments. The metabolite composition between treatment samples was statistically compared with the Permutational Multivariate Analysis of Variance (PERMANOVA) using the \u0026lsquo;vegan\u0026rsquo; package (Oksanen et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The fold-change of non-redundant known metabolites was analysed on the response values through multiple Student\u0026rsquo;s t-tests with false discovery rate (FDR) adjustment (adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A significance threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied to all statistical results.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eChanges in the diversity and structure of bacterial communities under N deficiency\u003c/p\u003e \u003cp\u003eThe 16S rRNA gene sequencing yielded a total of 9,566 and 4,558 bacterial ASV after filtering, in the rhizosphere and root endosphere samples, respectively. The Kruskal-Wallis test revealed no significant changes in the alpha diversity of bacterial communities across N treatments and cultivars at both the stem elongation and the mid-anthesis stages (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The PCoA ordination plots revealed that the bacterial community structure of Mace rhizosphere soil significantly changed across N treatments at the mid-anthesis stage (ANOSIM \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), while N treatments had a less significant impact on the bacterial community structure of Gladius rhizosphere, with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.19 (ANOSIM \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). For the root endosphere, N treatments did not significantly affect the bacterial community structure of Mace (ANOSIM \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), but significantly shifted the bacterial community in Gladius, with N0 samples clustering away from N2 samples (ANOSIM \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctional changes in rhizosphere soil bacterial communities under N deficiency\u003c/p\u003e \u003cp\u003eWe further explored the functional changes in the rhizosphere soil bacterial communities through metagenomic analyses of the N0 and N2 samples of both cultivars at the mid-anthesis stage. Across all samples, a total of 4311 functions belonging to 25 categories were identified from the COGs database. The PCoA ordinations revealed that N treatments significantly shifted the functional composition of the Mace rhizobacteria (ANOSIM \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), while that of Gladius rhizobacteria remained largely unchanged (ANOSIM \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The COG functional categories were further analysed with the Kruskal-Wallis statistical test, which revealed enrichment of signal transduction mechanisms (T) in the rhizobacteria of both cultivars under N deficiency (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Further comparisons between the cultivars revealed that the Mace rhizobacteria had enhanced mobility and development, evident by functional enrichment in the cell wall membrane, envelope and biogenesis (M), post-translational modification, protein turnover and chaperones (O), intracellular trafficking, secretion and vesicular transport (U), cell motility (N), extracellular structure (W) and cytoskeleton (Z) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEnrichment pattern of intraspecific rhizobacteria under N deficiency\u003c/p\u003e \u003cp\u003eWe conducted LEfSe analysis of the 16S rRNA gene sequences, revealing distinct responses of rhizobacteria to N deficiency between Mace and Gladius. Specifically, 21 ASVs were significantly enriched in Mace but not in Gladius (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2). In comparison, 20 ASVs were significantly enriched in Gladius but not in Mace (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). A comparison of ASVs at the genus level revealed 10 genera observed exclusively in N-deficient Mace and 7 in N-deficient Gladius (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The functional profile of bacterial genera Haliangium, Clostridium, Ilumatobacter and Lysinibacillus observed in N-deficient Mace, as well as Massilia, Blastocococcus, Gemmatimonas, Conexibacter and Bacillus observed in N-deficient Gladius, were also detected in the metagenomic data. The metagenomics analysis further revealed that rhizobacterial genera enriched in N-deficient Mace exhibited a distinct N metabolism compared to those enriched in N-deficient Gladius, particularly in categories related to N regulation, transport, nitrification, and denitrification (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIntraspecific metabolite production in wheat under N deficiency\u003c/p\u003e \u003cp\u003eThe GC-MS metabolomics analysis identified 78 compounds across all samples including 40 non-redundant known organic compounds. Among the identified metabolites, 20 were amino acids, 10 sugars, three organic acids, and two fatty acids. The PCA analysis revealed a significant N treatment effect on the metabolite profiles for both cultivars (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This result was further validated by a heatmap presenting the log-transformed response value, with a clear distinction between N treatments for both cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The PCA plot revealed a marked disparity in the metabolite composition between Mace and Gladius under the N0 treatment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but not under the N2 treatment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The fold-change of metabolites between the N0 and N2 treatments in both cultivars revealed a predominant effect of N supply on the accumulation of organic compounds, with glycine, tyrosine and glucose exhibiting a significantly greater response to the N2 treatment for both cultivars (adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, fold-change\u0026thinsp;\u0026gt;\u0026thinsp;2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Furthermore, the diverse sensitivity of wheat metabolites to between N0 and N2 treatments was evident, with octadecanoic acid, gluconic acid, and maltose significantly changed in Mace; and alanine, leucine, thymine, cysteine, glycolic acid, trehalose, sorbose, glucopyranose, sucrose and glycerol-3-phosphate significantly changed in Gladius (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). In particular, the levels of octadecanoic acid in N-deficient Mace were 4-fold higher in N0 than in N2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding the intricacies of plant-microbe interactions in N-poor environments is crucial for better N management for sustainable agriculture (Lam et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While a previous study has demonstrated that plant metabolites facilitate beneficial plant-microbe interaction (Yu et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our understanding of plant-microbe interactions in wheat cultivars differing in reported NUE, in response to N deficiency, remains limited. We found that the bacterial community in the rhizosphere significantly differed across N treatments in Mace but not in Gladius, while that in the root endosphere significantly differed by N treatments in Gladius but not in Mace (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This finding suggested that Mace and Gladius with different reported NUE interact differently with bacterial communities under N deficiency. We further examined the bacterial enrichment in the rhizosphere under N deficiency and found a distinct set of bacterial genera being enriched in Mace and Gladius (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although limited studies have compared the genotypic influences on the plant-associated bacterial community under N deficiency, different wheat genotypes exert a distinct influence on shaping the root-associated bacterial community to adapt to abiotic stresses (Azarbad et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Corneo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yin et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These results suggest a primary role of the genetic variation of cultivars in bacterial recruitment. We have identified the \u003cem\u003eHaliangium\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, \u003cem\u003eIlumatobacter\u003c/em\u003e and \u003cem\u003eLysinibacillus\u003c/em\u003e bacterial genera distinctly enriched in Mace and \u003cem\u003eMassilia\u003c/em\u003e, \u003cem\u003eBlastococcus\u003c/em\u003e, \u003cem\u003eGemmatimonas\u003c/em\u003e, \u003cem\u003eConexibacter\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e distinctly enriched in Gladius (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting their beneficial potential to wheat growth under N deficiency. Species from the genera of \u003cem\u003eHaliangium\u003c/em\u003e, \u003cem\u003eMassilia\u003c/em\u003e, \u003cem\u003eGemmatimonas\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e have previously been reported as wheat-associated plant growth promotion rhizobacteria (PGPR) (Kavamura et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), suggesting that Mace and Gladius interacted with different PGPR under N deficiency. These findings based on our sequencing data suggest that wheat cultivars with distinct NUE have a differential interaction with PGPR under N deficiency.\u003c/p\u003e \u003cp\u003eThe rhizobacterial functional composition significantly differed between N treatments in Mace but not in Gladius, suggesting differential functional responses to N deficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This is in line with a recent study that reported the variation of the relative abundance of the rhizosphere bacterial functional genes involved in multiple nutrient cycles between rice cultivars with different plant cadmium accumulation (Cheng et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), supporting that crop cultivars have a predominant effect on the rhizobacteria functions. Comparison of functional categories revealed a significant increase in the detection of genes related to signal transduction mechanism in both Mace and Gladius, under N deficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). It also revealed a significant increase in the detection of genes related to rhizobacterial growth and motility in Mace, under N deficiency, a phenomenon not observed in Gladius (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Genes in the categories of signal transduction mechanism and extracellular structure can encode important functions that reflect increased flagellar motility in bacteria, as recently reported (Ramoneda et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The bacterial chemotaxis ability refers to bacteria motile towards a chemical compound and is critical in facilitating symbiotic relationships with the host plant (Raina et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A recent study demonstrated a connection between rhizobacterial chemotaxis and enhanced N acquisition of maize growing with rhizobacteria carrying chemotaxis genes (Sun et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Together, these findings suggest that the higher NUE cultivar Mace, might have enhanced rhizobacterial chemotaxis in response to N deficiency, compared to Gladius. Additionally, the N metabolism of the bacterial community differed between two rice cultivars with distinct NUE, which suggested that the N metabolism of bacteria directly contributes to the rice NUE (Zhang et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We found a distinct pattern of N metabolism among bacterial genera. Specifically, functions related to N transport, nitrification and denitrification were absent in Mace but present in Gladius under N deficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Nitrification and denitrification contribute to N losses that reduce plant NUE in agricultural systems (Saud et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), suggesting that Mace interacted with bacteria in the rhizosphere to adapt to N deficiency through strategies other than the N metabolism. Indeed, Lysinibacillus sp. is known for various plant-beneficial traits, including N fixation, phosphorus solubilisation, auxin production (Hern\u0026aacute;ndez-Santana et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pantoja-Guerra et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Passera et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and virus biocontrol (Passera et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A recent study has also demonstrated that Lysinibacillus sp. can stimulate Arabidopsis root growth and development, suggesting its ability to enhance nutrient acquisition (Pantoja-Guerra et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). This implies that the enhancement of bacterial chemotaxis ability, such as Lysinibacillus sp., may be a critical mechanism to drive the cultivar difference in NUE under N deficiency.\u003c/p\u003e \u003cp\u003ePrimary metabolites secreted by plant root act extensively as energy sources (Zhalnina et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and chemotaxis attractants for bacteria (Raina et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which are crucial in shaping the rhizosphere microbiota. In our hydroponic experiment, we have unsuccessfully concentrated the primary metabolite in the wheat root exudate for GC-MS measurement. Primary metabolites inside the root, such as amino acids, organic acids and sugars are secreted in root exudates through diffusion between concentration gradient differences (Canarini et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, we analysed the primary metabolic profile of wheat roots, which reflects the secretion to some extent. Our metabolomic data revealed a significant shift in both Mace and Gladius under different N availability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), suggesting that both Mace and Gladius are sensitive to the N treatments. In addition, the metabolite composition demonstrated the difference in organic compound syntheses between wheat cultivars, particularly under N-deficient conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). We then performed the enrichment analysis between the N0- and N2-treated samples and further revealed a significant reduction in glucose, tyrosine and glycine synthesis under N deficiency in both cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), which has also been demonstrated in multiple crops (Sung et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tawaraya et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhen et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, the enrichment analysis of metabolites showed a notable increase in octadecanoic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), known as stearic acid (C18:0), in Mace under N deficiency, whereas such accumulation was not observed in Gladius. These findings demonstrate genotype-specific metabolic processes in Mace and Gladius in response to N deficiency. Interestingly, a previous study has found no effect of N deficiency on stearic acid production in wheat (Nasiroleslami et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which contrasts with our results for Mace but aligns with those for Gladius. We suspected that stearic acid plays an important role in the plant-microbe interaction of Mace under N deficiency. Indeed, stearic acid accumulation in the root nodules of Lotus japonicas cv. GIFU indicates its potential role in the symbiotic N fixation (Desbrosses et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Stearic acid has also been identified as a chemotaxis attractant of the PGPR, Pseudomonas isolate RP2 (Ankati et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Together, the GC-MS metabolomic data suggests that stearic acid might be secreted in Mace but not in Gladius, under N deficiency, which potentially shaped the intraspecific wheat-bacterial community.\u003c/p\u003e \u003cp\u003eOur data unveiled a differential rhizobacterial community and root metabolite profile between wheat cultivars of contrasting NUE in response to N deficiency Notably, stearic acid accumulation in the root of the higher NUE cultivar, Mace, indicating a potential increase in root exudates, may shape the soil bacterial community under N deficiency. Our metagenomic analysis further suggests that Mace may have stimulate rhizobacteria motility and development as well as the enrichment of PGPR under N deficiency, presenting distinct differences from the responses observed in Gladius. A better understanding of the chemotaxis ability of PGPR towards stearic acid in wheat-grown soils could be a promising avenue for the development of next-generation bio-fertilisers to achieve sustainable agriculture.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eN \u0026ndash; Nitrogen\u003c/p\u003e\n\u003cp\u003eNUE \u0026ndash; Nitrogen Use Efficiency\u003c/p\u003e\n\u003cp\u003eASVs \u0026ndash; Amplicon Sequence Variants\u003c/p\u003e\n\u003cp\u003eCOGs - Clusters of Orthologous Genes\u003c/p\u003e\n\u003cp\u003ePCoA \u0026ndash; Principal Coordinate analysis\u003c/p\u003e\n\u003cp\u003ePCA \u0026ndash; Principal Components analysis\u003c/p\u003e\n\u003cp\u003eANOVA \u0026ndash; Analysis Of Variance\u003c/p\u003e\n\u003cp\u003eANOSIM \u0026ndash; Analysis Of Similarity\u003c/p\u003e\n\u003cp\u003ePERMANOVA - Permutational Multivariate Analysis of Variance\u003c/p\u003e\n\u003cp\u003eCPM - count per million\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePGPR- Plant Growth Promoting Rhizobacteria\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Australia Research Council\u0026rsquo;s Industrial Transformation Research Program funding scheme (IH200100023). The wheat seeds for this research were provided by the Australian Grain Technology (AGT). The computational resources were supported by The University of Melbourne\u0026rsquo;s Research Computing Services and the Petascale Campus Initiative. The authors wish to thank Pongsathorn Sukdanont for glasshouse assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLHC, HWH, SKL, DC, and CT designed the glasshouse experiments, performed by LHC and supervised by HWH, SKL, DC, and CT. CT \u0026amp; UR supported the development of the metabolite collection method. QC supported DNA extraction. SG and DAD performed primary metabolite extraction and the GC-MS metabolomic analysis. LHC performed amplicon sequencing, metagenomic and metabolomic data analysis. HWH and QC supported amplicon sequence data analysis. VWS supported metagenomic analysis. SG supported metabolomic data analysis. LHC prepared the manuscript draft. HWH, SKL and LHC edited and revised the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequencing files can be publicly accessed from the NCBI database with the accession number PRJNA1126416. The QIIME2 output of the 16s amplicon sequencing is provided in supplementary data 1 and 2. The Metaphor outputs of the metagenome are provided in supplementary data 3 and 4. Supplementary data 5 and 6 are available on reasonable request. The processed metabolite data is provided in supplementary data 7. Other datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhlawat OP, Yadav D, Kashyap PL, Khippal A, Singh G (2022) Wheat endophytes and their potential role in managing abiotic stress under changing climate. Journal of Applied Microbiology 132: 2501-2520. doi: https://doi.org/10.1111/jam.15375.\u003c/li\u003e\n\u003cli\u003eAlhabbar Z, Yang RC, Juhasz A, Xin H, She MY, Anwar M, Sultana N, Diepeveen D, Ma WJ, Islam S (2018) NAM gene allelic composition and its relation to grain-filling duration and nitrogen utilisation efficiency of Australian wheat. PLOS ONE 13: e0205448. doi: https://doi.org/10.1371/journal.pone.0205448.\u003c/li\u003e\n\u003cli\u003eAndrews M, Lea PJ, Raven JA, Lindsey K (2004) Can genetic manipulation of plant nitrogen assimilation enzymes result in increased crop yield and greater N-use efficiency? 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BMC Plant Biology 21: 381. doi: https://doi.org/10.1186/s12870-021-03164-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Nitrogen-use efficiency, Wheat, Plant-microbe interaction, Metagenomics, Metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-4738104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4738104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground and Aims\u003c/p\u003e\n\u003cp\u003eNitrogen (N) deficiency in soil constrains plant growth, which may potentially be alleviated by beneficial soil bacterial communities. However, there is limited knowledge of the plant-bacteria interactions of wheat cultivars with different N-use efficiency (NUE) under N deficiency.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eWe investigated the responses of soil and root endosphere bacterial communities as well as root metabolites of two wheat cultivars (cv. Mace and Gladius) with reported high and low NUE, respectively, using a glasshouse experiment and a hydroponic experiment with three N levels.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eThe rhizosphere bacterial community of Mace shifted under N deficiency, but not in its root endosphere. Conversely, the rhizosphere bacterial community of Gladius remained unchanged under N deficiency but shifted in its root endosphere. The metagenomic analysis illustrated increased detection of genes related to bacterial growth and motility in the rhizosphere of Mace, but not of Gladius, under N deficiency. A 4-fold increase in octadecanoic acid in the root of Mace, but not Gladius, under N deficiency, suggesting the potential role of octadecanoic acid in shaping the rhizobacterial community in Mace with higher reported NUE.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eOur study highlights the divergent responses of wheat-associated microorganisms and root metabolites to N deficiency in the two cultivars. We found that wheat cultivars with higher NUE increased octadecanoic acid secretion, which potentially shaped the rhizobacterial communities, thereby enhancing their growth under N-limited conditions.\u003c/p\u003e","manuscriptTitle":"Cultivar-specific wheat-associated bacterial communities and metabolites in response to nitrogen deficiency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 08:37:32","doi":"10.21203/rs.3.rs-4738104/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2024-10-11T00:29:30+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-17T19:43:47+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-17T03:57:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2024-07-16T01:28:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-16T01:25:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2024-07-14T07:05:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"06fe9182-4767-43ea-90fd-da12c6bb6585","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:24:39+00:00","versionOfRecord":{"articleIdentity":"rs-4738104","link":"https://doi.org/10.1007/s11104-024-07048-0","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2024-10-31 16:05:16","publishedOnDateReadable":"October 31st, 2024"},"versionCreatedAt":"2024-08-09 08:37:32","video":"","vorDoi":"10.1007/s11104-024-07048-0","vorDoiUrl":"https://doi.org/10.1007/s11104-024-07048-0","workflowStages":[]},"version":"v1","identity":"rs-4738104","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4738104","identity":"rs-4738104","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}