Rhizosphere microbiome promotes wheat salt tolerance through root lignin biosynthesis

Rhizosphere microbiome promotes wheat salt tolerance through root lignin biosynthesis | 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 Article Rhizosphere microbiome promotes wheat salt tolerance through root lignin biosynthesis Guangzhou Wang, Gang Ni, Shiqian Meng, Jiyu Jia, Dapu Zhou, Martijn Bezemer, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8835674/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Salinity imposes strong selective pressure on plant roots, yet how rhizosphere microbiomes contribute to root structural adaptation under salt stress remains unclear. Using a plant–soil feedback framework across 100 wheat accessions, we show that salt-tolerant genotypes condition distinct rhizosphere microbiomes that enhance salt tolerance across host backgrounds. These microbiome effects are linked to activation of host phenylpropanoid biosynthesis and enhanced lignin deposition in roots. Metagenomic analyses reveal enrichment of microbial functions related to stress tolerance and carbon metabolism, while root transcriptomics identify coordinated induction of lignin biosynthetic genes. Isolation and reconstitution of core taxa identified a synthetic microbial community that promoted endodermal lignin deposition, enhanced Na⁺ efflux, restricted Na⁺ entry into the stele, and increased wheat yield in salinized field sites. Analyses using Arabidopsis lignin-deficient mutants further indicate that lignification is a key, though not exclusive, component of this response. Together, our results uncover an unappreciated microbiome-driven mechanism of root anatomical remodeling that contributes to plant salt tolerance. Biological sciences/Microbiology Biological sciences/Plant sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Main text As one of the most pervasive abiotic constraints on global crop production, salinity affects about 33% of the irrigated land 1 . Despite significant advances in breeding and molecular improvement, the genetic basis of salt tolerance remains complex, polygenic, and environmentally dependent 2,3 . Increasing evidence indicates that the stress resilience of plants to abiotic stress is strongly shaped by interactions with root-associated microbiomes. Hence, reinforcing the interactions between plants and the surrounding microorganisms can be pivotal in enhancing plant health and stress resilience 4,5 . However, intensive breeding for high-yield traits has inadvertently weakened relationships between crops and beneficial microbes 6,7 . Understanding how plants and their microbiomes co-evolve and coordinate adaptive responses under saline conditions is therefore essential for developing sustainable agroecosystems in a world with increasing salinity stress. Numerous beneficial microbes have been shown to mitigate salt-induced stress in plants 8,9 . However, the performance of these introduced strains is often inconsistent, due to competition with native taxa and limited adaptability to local conditions 10 . The rhizosphere microbiomes, shaped by plant genotype and soil environment, has emerged as a key determinant of plant performance under stress. Salt-tolerant plants have been shown to recruit microbial communities that enhance nutrient solubilization 11,12 , mitigate osmotic and ionic stress 13 , and synthesize hormones and secondary metabolites 14 . However, most evidence remains correlative, leaving the causal links between plant genotype, microbiome composition, and physiological performance under salinity unresolved. Recent developments in root phenotyping have emphasized the critical role of root architecture in determining salt tolerance 15 . Key traits including primary root elongation 16 , lateral root density 17 , root hair development 18 , and the integrity of the endodermal barrier 19 , govern water and ion transport, root-shoot signaling, and overall plant vigor under saline conditions 20 . While the genetic and hormonal regulation of root architecture is well documented, the contribution of the rhizosphere microbiome to root architectural remodeling during salt stress remains poorly understood. We hypothesize that genotypes with differing salt tolerance assemble distinct microbial communities that subsequently influence root plasticity. This knowledge gap obscures the mechanistic links between microbial function, root anatomical modification, and whole-plant salt adaptation. To fill these gaps, we conducted a multi-dimensional investigation of wheat accessions with contrasting salt tolerance and their associated rhizosphere microbiomes. We used a plant-soil feedback approach combined with multi-omics, microbial isolation, and field validation to test the hypotheses that (i) salt-tolerant genotypes condition functionally distinct microbial communities, (ii) these microbiomes enhance plant performance under salinity independently of host genotype, and (iii) microbial effects are mediated, in part, through the induction of root barrier function that restrict Na⁺ entry and promote ionic homeostasis. By integrating microbial community analyses with host transcriptomics, root anatomy, and field performance, this study aims to resolve how microbiomes translate into durable host defenses under saline stress. Results Responses of wheat rhizosphere microbiomes to salt stress We firstly assessed the salt tolerance of all 100 wheat accessions. Based on the highest and lowest plant salt-tolerance index (PSTI) values, we selected two salt-tolerant varieties (T1, T2) and two salt-sensitive varieties (S1, S2) for subsequent plant-soil feedback experiments (Fig. 1a-c; Extended Data Fig. 1). As expected, salt-tolerant varieties maintained higher dry weight than salt-sensitive varieties after two rounds of soil conditioning (Fig. 1d). For microbial community diversity, neither alpha diversity nor the relative abundances of the 15 dominant bacterial and fungal phyla differed significantly among varieties (Supplementary Fig. 1), whereas clear shifts were observed in community composition (adonis: R 2 = 0.247, p = 0.022; R 2 = 0.249, p = 0.006) (Fig. 1e, f). Differential abundance analysis revealed significant enrichment of 491 bacterial and 103 fungal ASVs in the rhizosphere soils of T1 and T2 relative to S1 and S2 varieties (Fig. 1g, h). At the phylum level, rhizosphere communities associated with salt-tolerant varieties were dominated by Proteobacteria, Bacteroidetes and Actinobacteria for bacteria, Ascomycota and Chytridiomycota for fungi (Supplementary Figs 2 and 3). At the genus level, salt-tolerant varieties were enriched in bacterial genera such as Algoriphagus , Allorhizobium , and Devosia , and fungal genera including Myrothecium , Plectosphaerella , and Cephaliophora (Supplementary Figs 4 and 5). Soil microbial feedback effects on wheat To assess the influence of rhizosphere microorganisms on crop performance, conditioned soils were used as inocula in a feedback experiment (Fig. 2a). In the feedback phase, soils conditioned by salt-tolerant varieties significantly enhanced wheat growth performace compared with soils conditioned by salt-sensitive varieties (Fig. 2b). The significant effects of both crop variety and conditioned soil on plant growth were revealed by a two-way ANOVA (Plant: F = 9.057, p < 0.001; Soil: F = 3.363, p = 0.024) (Fig. 2c). Notably, the magnitude of the soil inoculation effect varied among varieties and was particularly strong for T1 and S2, indicating that these varieties were more responsive to differences of microbial communities between conditioned soils. When all conditioned soils were sterilized, no significant effect of soil inoculum was observed (Soil: F = 2.447, p = 0.072), confirming that the observed feedback effects were mediated by soil microorganisms (Fig. 2d). Functional potential of salt-tolerant wheat rhizosphere microbiome We further analyzed functional differences between rhizosphere soils conditioned by salt-tolerant and salt-sensitive varieties using metagenomic sequencing. Analysis of clusters of orthologous groups (COG) revealed that genes associated with 10 functional processes were significantly more abundant in salt-tolerant conditioned soils compared to salt-sensitive soils (Extended Data Fig. 2a). Notably, genes associated with K + uptake proteins, Na + /H + -translocating membrane pyrophosphatases, Mg 2+ /serine and Na + /threonine symporters, and catalase production were significantly more abundant in soils conditioned by salt-tolerant varieties (Wilcox, p < 0.05) (Extended Data Fig. 2b). Consistent with these results, KEGG pathway analysis indicated higher abundances of stress-responsiveness and carbon-acquisition genes in salt-tolerant soils, reflecting the microbial growth potentials under stress (Fig. 3a, b). Specifically, genes associated with stress responsiveness included functions related to H + transport, K + uptake, Na + extrusion, antioxidant activity, and osmotic adjustment (Fig. 3c). Genes involved in carbon acquisition encompassed pathways including six carbohydrate cycles (Fig. 3d). Among these functional pathways, H⁺ transport (Fig. 3e), osmotic adjustment (Fig. 3f), and the reductive citrate cycle (Arnon-Buchanan cycle) (Fig. 3g) were significantly enriched in the T1 and T2 treamtents compared with S1 and S2, whereas the remaining ones showed no significant differences. The rhizosphere microbiome enhances wheat lignin biosynthesis To investigate host transcriptional responses associated with microbiome-mediated salt responses, we performed transcriptome analysis of what roots grown in soils conditioned by T1, T2 andS1, S2. We selected T1 and S2 as test plants because they showed the strongest growth responses to soil microbial effects (Fig. 2c), and RNA-seq analysis revealed distinct transcriptional profiles that were significantly influenced by both wheat variety and conditioned soil (Supplementary Fig. 6). In T1 plants, 7635 genes were differentially expressed in response to T1- and S2-conditioned soils, with 3400 genes enriched under T1-conditioned soil (Fig. 4a). Similarly, in S2 plants, 9320 DEGs were detected between T1- and S2-conditioned soils, with 3114 DEGs enriched under T1-conditioned soil (|log2FC| ≥1, adjusted p < 0.05) (Fig. 4b). Among pathways enriched in both T1 and S2, six were shared between varieties (Fig. 4c, d), three of which were associated with secondary metabolic pathways linked to salt tolerance, including phenylpropanoid biosynthesis, flavonoid biosynthesis, and zeatin biosynthesis. Notably, genes associated with phenylpropanoid biosynthesis were more strongly enriched in T1-conditioned soils than in S2-conditioned soils in both T1 (Fig. 4e) and S2 plants (Fig. 4f). These genes included F5H , C4H , E2.3.1.133 , E2.1.1.104 , CYP98A , COMT , CAD , CCR , and E1.11.1.7 , which are core components of the lignin biosynthetic pathway. Consistent with this pattern, T1 plants exhibited significant enrichment of all 9 lignin-related genes in T1-conditioned compared to S2-conditioned soils (Fig. 4g), whereas S2 plants showed significant enrichment of 5 of these genes (Fig. 4h). The RT-qPCR analysis confirmed these trends with higher expression level of lignin biosynthesis genes in T1-conditioned soils (Wilcoxon, p < 0.05) (Extended Data Fig. 3). Consistent with gene expression level, root lignin content was generally higher in T1-conditioned soils than S2-conditioned soils (Fig. 4i). Statistical analysis indicated that root lignin content was significantly affected by the inoculated soil but not by wheat variety (Plant: F = 2.793, p = 0.103; Soil: F = 4.926, p = 0.033). Wheat adaptation to salt stress is bolstered by core taxa-mediated lignin accumulation within the root endodermis To validate the role of rhizosphere bacteria in wheat growth, 86 strains were isolated from T1 and T2 rhizosphere soils after the second conditioning phase (Additional Supplementary File 1). Using integrated analysis-including abundance-occupancy curves, random forest models, and upset plots (see Methods), we identified 186 core ASVs associated with improved salt tolerance (Extended Data Fig. 4). Among these, ASV102, ASV114, and ASV59 phylogenetically related to three isolated strains of Agrobacterium tumefaciens ( Agr ), Devosia riboflavina ( Dev ),and Achromobacter pulmonis ( Ach ), respectively (Supplementary Figs 7-9). The effects of individual strains and the synthetic community (SynCom; Agr _ Ach _ Dev ) on wheat growth and lignin accumulation were tested in two repeated experiments. Compared with individual strain inoculation and non-inoculated controls, SynCom inoculation on average increased the dry weight of T1 by 17.97% and 41.49% (Fig. 5a, b) and S2 by 45.69% and 48.57% (Fig. 5c, d), respectively. SynCom-inoculated plants also exhibited higher root lignin content compared with non-inoculated controls (Fig. 5e). In addition, SynCom inoculation significantly enhanced net Na + efflux from roots of both T1 and S2, as measured by non-invasive micro-test technology (NMT). Notably, T1 displayed a stronger Na + secretion capacity than S2 (Fig. 5f). Visualization of lignin spatial distribution showed that SynCom inoculation enhanced lignin deposition (phloroglucinol staining) in endodermal cells across the differentiation zone (DZ), elongation zone (EZ), and meristematic zone (MZ) in both T1 and S2 roots (Fig. 5g). Consistent with enhanced endodermal lignification, SynCom inoculation altered root permeability to the fluorescent tracer propidium iodide (PI), indicating strengthened barrier function. In inoculated plants, PI fluorescence was largely restricted to the endodermis in the MZ and did not penetrate into the phloem, whereas in non-inoculated plants, PI penetrated more deeply into the xylem vessels (Extended Data Fig.5). Scanning electron microscopy, coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) further illustrated that SynCom inoculation was associated with increased Na + signal intensity in tissues exterior to the endodermis and reduced Na + signal interior to the endodermis in both T1 and S2 roots (Fig. 5h-i). In contrast, Na + distribution in non-inoculated roots was more uniform across the root cross-section. Together, these observations indicate that SynCom inoculation is associated with enhance endodermal lignification and altered Na + distribution within wheat roots. To further assess the contribution of lignin biosynthesis to microbiome-mediated salt responses, we evaluated the effects of microbial inoculation on Arabidopsis ecotype Col-0 and lignin biosynthesis-deficient mutants CCoAOMT1 , fah1-2 , CAD (Extended Data Fig.6a, d, g, j). Compared with the control and S2-conditioned soil suspension, inoculation with either the SynCom (Agr_Ach_Dev) and T1-conditioned soil suspension significantly increased fresh weight of Col-0, CCoAOMT1 , and fah1-2 (Extended Data Fig.6b, e, h, k). In addition, SynCom and T1-conditioned soil inoculation increased root lignin content in CCoAOMT1 and CAD mutants, whereas S2-conditioned soil did not differ from the control treatment for any of the mutants (Extended Data Fig.6c, f, i, l). These findings suggest that lignin biosynthesis contributes substantially, but not exclusively, to microbiome-mediated enhancement of plant performance under salt stress. To examine potential mechanisms underlying preferential enrichment of core taxa, we performed comparative metabolomic analyses of rhizosphere soils between T1 and S2 plants in the second conditioning stage. Across all samples, 591 metabolites were detected. Principal coordinate analysis (PCoA) indicated a clear separation between the rhizosphere metabolomes of T1 and S2 (R 2 = 0.471, p = 0.007) (Extended Data Fig.7a). Seven metabolites were significantly enriched in T1 compared to S2 (FC > 1.2, p < 0.05), including berberine, histamine, and dimethyl caffeic acid, which were available as pure compounds (Extended Data Fig.7b). In vitro assays showed that berberine, histamine, and dimethyl caffeic acid idividually promoted the growth of Agrobacterium tumefaciens , Achromobacter pulmonis , and Devosia riboflavina , respectively. Notably, a mixture of the three metabolites stimulated significantly greater biomass production of the SynCom than any individual metabolite alone (Extended Data Fig.7c-f). SynCom increases wheat yield in salinized farmland To evaluate the effects of SynCom ( Agr _ Ach _ Dev ) under field conditions, we conducted trials at two salinized farmland sites. SynCom inoculation significantly increased wheat grain yield by 26.80% at Quzhou and 18.13% at Dongying (Fig. 6a, b). Statistical analysis indicated that microbial inoculation, rather than site location, was the main factor influencing yield. In addition, SynCom inoculation led to higher grain numbers per spike at Dongying and showed a trend of increased shoot weight at both sites (Fig. 6c, d). Discussion Understanding how crops convert microbial associations into effective defenses against abiotic stress is a central challenge for both plant ecology and sustainable agriculture. While lignin deposition and endodermal barrier function are well-established components of plant salt tolerance, the extent to which these traits can be induced by the rhizosphere microbiome has remained largely unexplored. Our results demonstrate that wheat does not rely solely on genetically programmed root defenses under salinity; instead, salt-tolerant genotypes condition rhizosphere microbiomes that in turn induce root lignification and barrier reinforcement (Fig.5e, g-j). This microbiome-mediated activation of root anatomical defenses provides a mechanistic link between plant genotype, microbial community function, and ionic regulation under salt stress. By combining plant-soil feedback experiments with microbial reconstitution and field trials, our study moves beyond correlative associations to show that specific microbial consortia can trigger host transcriptional programs associated with phenylpropanoid metabolism, leading to increased endodermal lignin deposition and reduced Na⁺ penetration into the stele. These findings position the rhizosphere microbiome as an active inducer of root barrier function, rather than a passive contributor to plant stress tolerance, and suggest that microbial control of host anatomical defenses represents an important, and underappreciated, dimension of plant–microbe cooperation under abiotic stress. Complex hormonal and signaling networks regulate lignin biosynthesis. Abscisic acid (ABA)-mediated pathways enhance lignification under osmotic stress 21 , supporting water retention and mineral homeostasis 22 , while salicylic acid and MAPK-MYB modules modulate lignin synthesis during pathogen attack 23,24 . It is plausible that specific rhizobacterial elicitors activate these hormonal cascades, converging on phenylpropanoid metabolism to reinforce apoplastic barriers. The resultant lignin deposition strengthens Casparian strips and exodermal lamellae, effectively impeding Na⁺ diffusion into the stele. Beyond reinforcing the physical barrier, beneficial soil microbes also enhanced dynamic ionic detoxification by promoting Na⁺ efflux from root cells (Figs 5f and 7). This microbe-induced Na⁺ extrusion may correlate with upregulation of plasma membrane Na⁺/H⁺ antiporters such as SOS1, which exchanges Na⁺ for H⁺ to maintain cytosolic ion homeostasis 25 . Previous work showed that halotolerant Dietzia natronolimnaea STR1 inoculation increased wheat root SOS1 transcript levels more than twofold under salt stress 26 , while Bacillus amyloliquefaciens SQR9 activated Na⁺ efflux transporters (e.g., NHX and HKT families) in maize 27,28 . Together with our findings, these results suggest that wheat rhizosphere microbes not only fortify the root’s physical barrier via lignification but also stimulate active Na⁺ extrusion, thereby limiting Na⁺ entry and accelerating its removal from the root symplast. Plants under stress often modify their root exudation profiles to attract beneficial microbes. In our study, salt-stressed tolerant wheat released specific metabolites, notably dimethyl caffeic acid and berberine, which served as chemical cues for microbial recruitment (Extended Data Fig.7). Dimethyl caffeic acid, a derivative of the lignin precursor caffeic acid 29 , highlights the biochemical dialogue between plant and microbiome. Phenolic acids, such as caffeic acid, can scavenge reactive oxygen species, fortify cell walls, and influence microbial behavior 30 . The accumulation of this compound in tolerant plant rhizospheres suggests that the microbiome both triggers lignin synthesis and is sustained by its byproducts, analogous to the regulation of Pseudomonadales in the phyllosphere by 4-hydroxycinnamic acid 31 . The SynCom bacteria we identified likely induce phenylpropanoid metabolism, producing lignin and phenolic exudates that serve as carbon sources or signals favoring the same microbes. This feedback may explain the stable and persistent beneficial association under salinity. In addition, berberine is a benzylisoquinoline alkaloid with antimicrobial activity 32 , which likely suppresses pathogenic fungi and bacteria that proliferate under saline stress 33 , creating a niche that favors microbes capable of tolerating or metabolizing such compounds. Several of the bacterial genera we identified, including Agrobacterium , Devosia , and Achromobacter , are known to boost crop growth under saline conditions 34-37 . In conclusion, our work uncovers a hidden alliance between wheat and its rhizosphere microbiome, in which microbial partners reinforce root anatomical structures and activate ionic homeostasis to confer salt resilience. This insight broadens our understanding of plant–microbe cooperation under abiotic stress and reveals practical avenues for enhancing crop tolerance. By manipulating root exudation patterns, selecting microbiome-responsive genotypes, or designing synthetic microbial consortia that promote lignification and Na⁺ efflux, it may be possible to engineer salt-tolerant cropping systems with minimal environmental cost. As soil salinization intensifies globally, such biologically based solutions will be integral to sustaining crop productivity and food security. Methods Experiment 1: Screening salt-tolerant and salt-sensitive wheat varieties We screened 100 wheat accessions to identify genotypes with contrasting salt tolerance. These accessions originated from 11 countries (Additional Supplementary File 1), and seeds were provided by the Institute of Crop Science, Chinese Academy of Agricultural Sciences (Beijing, China). Soil was collected from saline farmland in Dongying City, Shandong Province (37°42’N, 118°48’E; EC 1:5 261 μS/cm; total N 0.09%; total C 1.32%). The collected soil was air-dried at room temperature for four days, sieved (<2 mm), mixed with sand at a 3:1 (w/w) ratio, and sterilized using 25 kGy gamma irradiation for use as the growth substrate. Seeds were surface-sterilized with 10% H 2 O 2 for 30 min, rinsed five times with ultrapure water, and then allowed to germinate for 48 h in a climate-controlled incubator. Uniform seedlings were transplanted into plastic vessels packed with 200 g of sterilized soil, with two seedlings per pot. After the emergence of two true leaves, each variety was subjected to either a salt treatment (NaCl) or a non-salt control (deionized water), with four replicates per treatment, resulting in a total of 800 pots. The NaCl concentration was gradually increased from 50 mM to 250 mM to prevent salt shock 11 . Plants were grown for four weeks to assess physiological responses to salinity. At harvest, we measured shoot/root biomass (both fresh and dry weight), plant height, root length, and stress-related markers such as plant water content and salt injury index. Briefly, salt injury was scored on a 1-4 scale based on chlorosis of the second leaf above the seedling base, corresponding to none, slight, moderate, and severe, respectively 38 . Indices representing phenotypic changes under salt versus non-salt conditions were calculated, including the relative decreases in plant height (RDPH), fresh weight (RDFW), dry weight (RDDW), shoot biomass (RDSB), root biomass (RDRB), plant water content (RDPWC), and salt injury index (SI). The relative decrease for each trait was calculated as (Control – Salt) / Control. All indices were integrated using principal component analysis. Principal components (PCs) with eigenvalues > 1.0 and variance contrinution > 5% were retained. PCs with cumulative variance < 85% were examined to identify key indices and remove redundant variables. Because RDDW, RDFW, RDPWC, and RDRB were negatively correlated with salt tolerance, these indices were selected to calculate the plant salt-tolerance index (PSTI) following 11,39 : Y = 1 - ( x - s )/ ( t - s ) where x is the index value for the sample, t is the maximum index value, and s is the minimum index value. The final PSTI was calculated as: where Yi denotes score for index i and Wi represents its associated PC weight factor. PSTI was significantly correlated with wheat growth traits (Supplementary Fig. 1), confirming its reliability for evaluating salt tolerance. Based on PSTI rankings (Additional Supplementary File 1), we selected representative salt-tolerant (T1: 12738; T2: 13130) and salt-sensitive varieties (S1: 12783; S2: 13434) for subsequent experiments. Experiment 2: Plant-soil feedback experiment To discern the microbially-mediated mechanisms behind wheat salt tolerance, a plant-soil feedback (PSF) assay was carried out to quantify the impact of the rhizosphere microbiome on host fitness (Fig. 2a). The PSF experiment consisted of two phases: a conditioning phase and a feedback phase. Four selected wheat varieties were cultivated in saline soil to establish host-specific microbiomes during the conditioning phase. Two rounds of conditioning were performed to strengthen host imprinting. For each variety, five replicates were established, with two seedlings grown in 1 kg of saline soil per replicate. Once seedlings reached the two-true-leaf stage, NaCl was applied gradually from 50 mM to 250 mM. After four weeks of salt treatment, the top 0-2 cm of soil was removed to minimize contamination from dust deposition. The remaining soil was stored at 4℃ and used for the second conditioning cycle, which followed the same procedures. At harvest, shoots were cut, washed, and dried. Rhizosphere soil was brushed from roots after removing loosely attached particles. The collected soil was either stored at -80°C (for DNA extraction) or 4°C (for subsequent feedback phases), followed by root washing and drying. In the feedback phase, we performed reciprocal inoculations using all combinations of conditioned soils and wheat varieties. The fully factorial design included four conditioned soils and four plant varieties, yielding 16 treatments. Each treatment had five biological replicates, with two seedlings per pot. Conditioned soils were used as inocula and mixed with sterilized substrate at a 1:9 ratio to minimize nutrients-driven effects 40 . Each pot was prepared with 600 g of sterilized substrate at the base, a 100 g inoculum layer, and a 300 g sterilized capping layer. A parallel control experiment using sterilized conditioned soils was included to confirm whether observed feedback effects were microbially mediated. Seedlings were again subjected to salt treatment at the two-true-leaf stage, and plants were harvested after four weeks. Shoots and rhizosphere soils were collected as described in the conditioning phase. Portions of the roots were stored at -80℃, followed by RNA extraction, transcriptome sequencing, and RT-qPCR analysis. Remaining root material was dried for lignin quantification. Rhizosphere soils were immediately flash-frozen and maintained at -80℃ for DNA extraction and amplicon sequencing. 16S & ITS amplicon sequencing and analyses Rhizosphere soil (0.5 g) from the conditioning and feedback phases was processed for total DNA extraction using the FastDNA® SPIN Kit for Soil (MP Biomedicals). The purity and quality of the resulting DNA were evaluated using 1% agarose gel electrophoresis and a NanoDrop2000 spectrophotometer. For microbial profiling, the V3-V4 region of the 16S rRNA gene was targeted through PCR with universal primers 515F (5’-GTGYCAGCMGCCGCGGTAA-3’) and 806R (5’-GGACTACNVGGGTWTCTAAT-3’) 41 , and ITS2 region was amplified with ITS3F (5’-GCATCGATGAAGAACGCAGC-3’) and ITS4R (5’-TCCTCCGCTTATTGATATGC-3’) 42 . PCR reactions (20 μL) consisted of 10 μL 2× Pro Taq, 0.8 μL (5 μM) of each primer, 10 ng template DNA, and ddH 2 O. The amplification of the 16S rRNA gene was executed under the following conditions: 95 °C for 3 min, followed by 27 cycles of 95°C (30 s), annealing at 55°C (30 s), and extension at 72°C (45 s); with a final extension at 72 °C for 10 min. The reactions were subsequently maintained at 10°C. ITS2 amplification used 35 cycles under the same cycling parameters. Following amplification, PCR products were purified using the AxyPrep DNA Gel Extraction Kit (Axygen, USA), and the concentration was determined via the QuantiFluor® dsDNA System (Promega, USA). After being normalized to equimolar concentrations, the samples were pooled and subjected to paired-end sequencing on the Illumina PE300 platform (Illumina, USA) in accordance with the established protocols. Quality filtering of the raw sequencing data was executed via fastp (v0.19.6) 43 , followed by the assembly of overlapping paired-end reads using FLASH (v1.2.7) 44 to generate ∼350 bp sequences. ASVs were denoised and clustered via DADA2 45 plugin in QIIME2 46 . Taxonomic assignment of prokaryotic and eukaryotic ASVs was performed using SILVA 138 16S rRNA database 47 and UNITE 8.0 ITS database 48 , respectively. To identify potential core taxa contributing to wheat salt tolerance, we combined abundance–occupancy analysis 49 , random forest modeling 50 , and upset plots. The abundance–occupancy curve was applied to define the “persistent microbiome”, comprising ASVs present in ³50% of samples and increasing Bray-Curtis similarity by ≥2% 49,51 . Random forest modeling, optimized with five repeated tenfold cross-validations, identified “RFM biomarkers” with 53.75% predictive accuracy. ASVs present only in salt-sensitive treatments were excluded, and shared ASVs across TT, ST, and TS treatments were defined as “salt-tolerant shared ASVs”. Core taxa were defined as ASVs overlapping among the persistent microbiome, RFM biomarkers, and salt-tolerant shared ASVs, yielding 186 ASVs in total. Metagenomic sequencing and data mining Metagenomic sequencing was conducted to explore the functional potential of rhizosphere microbiomes recruited by different wheat varieties after the conditioning phase. Genomic DNA, derived from the samples described above, was processed to a mean length of ~400 bp using Covaris M220 (Covaris, USA) to construct sequencing libraries. Paired-end libraries were prepared with the NEXTFLEX ® Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA), and the resulting libraries were then sequenced on an Illumina NovaSeq X Plus platform (Illumina Inc., San Diego, CA, USA). Raw metagenomic reads were quality-filtered and trimmed for adapters via fastp (v0.20.0) 43 . Clean reads were assembled using MEGAHIT (v1.1.2) 52 , and contigs ≥300 bp 14,53 were selected for further processing. Open reading frames were identified with Prodigal (v2.6.3) 54 and clustered using CD-HIT (v4.6.1) 55 with parameters -i 0.9 -c 0.9 . Cleaned data from individual sample were mapped against the non-redundant gene set (95% identity) via SOAPaligner 56 to quantify gene abundance. Non-redundant genes were converted into amino acid sequences and compared against NCBI-eggNOG database (v4.5.1) and KEGG database (v94.2) using Diamond (v0.8.35) 57 . Hits with an e-value <10 –5 were considered significant, and corresponding taxonomic and functional information was used to represent the non-redundant genes. Gene abundances were normalized as RPKM values (reads per kilobase of non-redundant genes per million mapped reads). RNA extraction and transcriptome analysis of wheat roots in the feedback phase To elucidate the molecular mechanisms by which stress-adaptive microorganisms enhance wheat salt tolerance, we selected the two most responsive wheat varieties (T1 and S2) (Fig. 2) for transcriptome sequencing. Their transcriptional expression differences in T1 and S2 conditioned soils were compared. Root RNA was extracted using MJZol extraction kit. DNA contamination was removed via RNA Purification Kit, and prepared libraries were processed for high-throughput sequencing on Illumina NovaSeq X Plus platform. Raw transcriptomic reads was processed with fastp (v0.19.5) 43 and the filtered sequences were then aligned against the IWGSC RefSeq (v2.1) reference genome of Triticum aestivum (cv. Chinese Spring) using HiSAT2 58 , and transcripts were assembled with StringTie (v2.1.2). The abundance of genetic transcripts was estimated using RSEM and standardized to FPKM units (fragments per kilobases of transcript per million mapped reads). DEGs between T1- and S2-conditioned soil treatments were identified using DESeq2 in R. Statistical significance for differential expression was defined by |log2fold-change (FC)| ≥1 and Benjamini–Hochberg (BH) adjusted p < 0.05 via Wald test. RT-qPCR of core functional genes Consistent with the transcriptome analysis, root samples from the four treatments (T1 and S2 wheat plants in T1 and S2 conditioned soils) were collected to assess the expression levels of 9 core functional genes related to wheat stress tolerance (Supplementary Table 1). Total RNA was extracted using the extraction kit (Beijing Solarbio Science & Technology Co., Ltd., China). Then, the reverse transcription of RNA into complementary DNA (cDNA) was performed with the Hifair ® Ⅱ 1st Strand cDNA Synthesis Kit (Yeasen Biotechnology, China). RT-qPCR reactions (20 µL) contained 10 µL Hieff® qPCR SYBR Green Master Mix, 0.4 µL of each primer, 2 µL cDNA template, and RNase-free H 2 O. Reactions were performed in three biological replicates on an ABI 7300 Real-Time PCR System (Thermo Fisher, Singapore) under the following cycling conditions: 95 °C for 80 s, followed by 40 cycles at 60 °C for 20 s and 72 °C for 30 s. To normalize the data, the GAPDH gene was utilized as an internal reference. Relative gene expression levels were determined using the 2 –∆∆Ct method: 2 – ∆∆Ct = 2 –((Gene Ct – targeting gene Ct) – average of Control group Ct) Metabolite profiling Rhizosphere soils from T1 and S2 varieties, collected from the second conditioning phase, were rapidly frozen at –80°C for metabolite analysis. Approximately 100 mg of each sample was extracted with 80% methanol containing internal standards. The extraction protocol included cryogenic grinding, sonication, and centrifugation. The resulting supernatant was analyzed by LC-MS using a Triple TOF 6600 system (SCIEX, USA). Progenesis QI (Waters Corporation, Milford, USA) was employed to process raw LC-MS data for peak alignment, retention time correction, baseline filtering, and peak detection and integration, resulting in a data matrix comprising mass-to-charge ratio, peak intensity, and retention time. Metabolites were annotated by matching spectral data against a plant-specific metabolite database curated by Majorbio Biotechnology Co. Ltd. (Shanghai, China). Lignin quantification Dried root samples were ground, then sieved through a 50-mesh sieve. Samples (10 mg) were extracted twice in 80% ethanol (1 mL), vortexed thoroughly, and maintained at 50°C for 20 minutes. After a 10-min centrifugation (12,000 rpm, 25°C), the precipitate was collected. The pellet was then treated with 0.2 mL of 25% acetyl bromide-acetic acid solution at 70°C for 30 minutes. To terminate the reaction, 2 M NaOH (0.2 mL), glacial acetic acid (0.4 mL), and 7.5 M hydroxylamine hydrochloride (0.02 mL) were sequentially added. The mixture was centrifuged, and 50 μL of the supernatant was diluted to 1 mL with glacial acetic acid for absorbance measurement at 280 nm. Blank controls without plant material were prepared in parallel. Experiment 3: Functional verification of core taxa We isolated 86 bacterial strains from the rhizosphere soils of T1 and T2 varieties in the conditioning phase (Additional Supplementary File 1). Briefly, rhizosphere soil (approx. 10 g) was suspended in 90 mL of sterile water and shaken at 30°C (30 min, 180 rpm). The suspension was serially diluted, 200 µL from each dilution was plated in triplicate on LB, TSA, and R2A agar. After 2 days of incubation, individual colonies were picked, streaked onto fresh LB plates, and purified through three successive rounds. Single colonies were then selected for species identification. Among the 186 core ASVs, sequences ASV114, ASV102, and ASV59 phylogenetically corresponded to Devosia riboflavina , Agrobacterium tumefaciens , and Achromobacter pulmonis , respectively (Supplementary Figs 7-9). Then, we conducted a pot experiment to test the effects of individual strains and a three-strain synthetic community (SynCom) on wheat growth. Two identical experimental trials ensured reproducibility. Five inoculation treatments were applied: (1-3) single inoculations of Agrobacterium tumefaciens , Achromobacter pulmonis , and Devosia riboflavina ; (4) a three-strain SynCom ( Agr _ Ach _ Dev ); and (5) a sterile water control. T1 and S2 varieties were selected as test plants due to their contrasting salt tolerance. Seed sterilization, planting procedures, and the sterilized substrate were identical to previous experiments. Strains were cultured in LB liquid medium (26℃, 48 h), centrifuged, and resuspended to an OD 600 of 0.5. For each treatment, 15 mL of inoculum was applied, with the SynCom prepared by mixing equal volumes (5 mL each) of the three strains. After four weeks of salt exposure, plants were harvested, washed with tap and distilled water, and fresh weight was measured. Samples from the first trial were used for Na⁺ flux measurement, followed by dry weight determination, while samples from the second trial were used solely for dry weight determination. The difference of root Na⁺ flux between the SynCom and control treatments was measured using the NMT method. Brielfy, roots were fixed in a Petri dish containing 0.5 mM NaCl solution, and the Na + flux microsensor was positioned 500 μm from the root elongation zone, and then was recorded for 5 minutes. Lignin staining and microscopy observation To examine lignin deposition patterns in root cross-sections, a replicate of Experiment 3 was conducted as previously described. Roots were randomly excised from each pot for histochemical staining and microscopy. Approximately 1-cm segments were collected from the meristematic zone (MZ), elongation zone (EZ), and differentiation zone (DZ), and then immediately fixed in 50% formalin-aceto-alcohol solution. After dehydration in a graded ethanol series, the fixed tissues were paraffin-embedded and subsequently cut into 10-µm-thick slices via a Leica RM2016 microtome (Leica, Germany). Sections were sequentially dewaxed in environmentally friendly transparent liquids I and II (G1128, Servicebio, China), absolute ethanol, and 75% ethanol, then rinsed with distilled water. Lignin was visualized by staining with phloroglucinol solution (G1060, Servicebio, China), which produces a purple color. Excess stain was removed, sections were mounted with coverslips, and observations were performed with a Nikon Eclipse E100 microscope (Nikon, Japan). Propidium iodide test and microscopy observation Propidium iodide (PI) penetration assays were performed 59 . Seedlings were incubated in PI solution (15 µg·mL -1 ) in a dark environment (30°C, 60 minutes). After rinsing with PBS, roots were embedded in low-melting agarose (5% (w/v)), transversely sectioned at 100 μm from the DZ, EZ, and MZ using a vibrating microtome (VT1000 S, Leica, Germany). Sections were mounted in glycerol (50% (v/v)), imaged with a Zeiss LSM 900 microscope (excitation/emission: 488/600-650 mm, Germany). Scanning electron microscopy and energy dispersive X-ray spectroscopy Fresh root segments (~5 mm) were fixed in 2.5% glutaraldehyde at 4°C. Then, fixed roots were triple-washed with PBS and subsequently processed through a graded series of ethanol washes (30-100%), and then subjected to critical point drying. Dried samples were gold-coated using a sputter coater and examined using the scanning electron microscope (Zeiss Sigma 300, Germany). Energy-dispersive X-ray spectroscopy was performed to map the spatial distribution of Na + across the root cross-section, from the stele to the epidermis. Experiment 4 Effect of root metabolites on core taxa growth Metabolomic analysis of T1 and S2 rhizosphere soils from the conditioning phase revealed that T1 was significantly enriched in seven metabolites, including berberine, histamine, and dimethyl caffeic acid (FC >1.2, p < 0.05). Commercially standard products of these three metabolites were obtained for further validation. To assess their effects on microbial growth, Agrobacterium tumefaciens , Achromobacter pulmonis , and Devosia riboflavina were cultured in LB medium (26℃, 48 h), centrifuged, and resuspended to an OD 600 of 0.5. A 300 μL aliquot of each bacterial suspension was inoculated into R2A medium supplemented with 5 μM of berberine, histamine, and dimethyl caffeic acid, or sterile water (control) and incubated (26℃, 24 h). In total, 16 treatments were tested (four inocula: the three individual strains and the three-strain SynCom; four media conditions), with five replicates per treatment. After incubation, bacterial cells were centrifuged (7,000 rpm, 10 minutes), and the pellet was weighed to determine biomass by analytical balance. Experiment 5 Microbial effects on lignin-deficient Arabidopsis To verify the microbial-driven promotion of lignin synthesis, Arabidopsis mutants were inoculated with T1- and S2-conditioned soil suspensions, as well as with the Agr _ Ach _ Dev SynCom. Arabidopsis seeds were surface-sterilized (5 minutes) with sodium hypochlorite (NaClO) (5% (v/v)), placed in 1/2 MS medium, and stratified at 4℃ for 72 h. Seedlings were initially grown in a climate-controlled incubator (14/10 h, light/dark, 22℃), and 10-day-old seedlings were transplanted into sterilized growth substrate (salinized farmland soil: peat: vermiculite = 1:2:2, w/w/w). Microbial inoculation and NaCl treatment were applied one and three weeks after the seedlings established stable growth. For soil inocula, 150 g of T1- and S2-conditioned soils were mixed with sterile water (1:3, w/w), allowed to settle for 15 minutes, filtered through a 1-mm sieve, and the filtrate was applied to the substrate at 10 mL per pot. For the SynCom treatment, Agrobacterium tumefaciens , Achromobacter pulmonis , and Devosia riboflavina were cultured in LB medium (26℃, 48 h), centrifuged, washed three times, and resuspended in sterile water to OD 600 of 0.5. Equal volumes of the three strains (100 mL each) were combined and applied at 10 mL per pot. Sterile water aerved as the control. Arabidopsis ecotype Col-0 and lignin synthesis-deficient mutants CCoAOMT1 (SALK_151507C), fah1-2 , and CAD (SALK_201575C) were used. Col-0 and fah1-2 were provided by Dr. Xuebin Zhang, while CCoAOMT1 and CAD were obtained from AraShare. Experiment 6 Field evaluation of the Syncom inoculation on wheat growth The widely cultivated wheat variety Jimai 22 was used to validate the effects of microbial inoculation under field conditions. Two long-term saline-alkali field sites were selected: Quzhou County, Hebei Province (115°1’N, 36°51’E) and Dongying City, Shandong Province (37°42’N, 118°48’E). At each site, SynCom ( Agr _ Ach _ Dev ) and control (sterile water) treatments were applied to 1 m × 1 m plots, with six replicates per treatment, totaling 12 plots per site. Starting at the regreening stage (March 17), 1 L of SynCom suspension (OD 600 = 0.7) or sterile water was applied via furrow to the corresponding plots, repeated three times at two-week intervals. All other field management practices followed local standards. At maturity, plants from each plot were harvested, grain yield, grain number per spike and shoot dry weight were measured. Statistical analyses Differences in PSTI, plant dry weight, and microbial relative abundance between salt-tolerant and salt-sensitive varieties, as well as the effects of different inocula on Arabidopsis lignin content, were evaluated using one-way ANOVA. And, the impacts of inocula and wheat variety on plant dry weight, lignin content, gene expression level, and root Na⁺ flux were analyzed via two-way ANOVA. Similarly, the effects of location and microbial inocula on the yield parameters of wheat under field conditions was assessed using two-way ANOVA. Microbial beta diversity (16S rRNA and ITS) was estimated using the “capscale” function in the “vegan” package 60 and visualized with “ggplot2” 61 . PERMANOVA (Bray-Curtis distances; “adonis” in “vegan”) with permutation tests assessed the effects of soil inocula and wheat varieties on microbial community structure. Kruskal-Wallis tests were performed to evaluate the significance of differences in microbial composition and functional potential among the treatments. Random forest model was implemented using “randomForest” package 62 in R (4.3.1) to identify representative ASVs. The optimal number of ASVs was determined using the “rfcv ()” function with 10-fold cross-validation, repeated five times, to ensure model stability. Shared ASVs were visualized using “VennDetail” upset plots. Linear regression between ASV relative abundance and plant biomass was implemented via the “lm ()” function in R. The PCoA plot was generated using “vegan” package 60 . Differential gene expression between T1- and S2-conditioned soil treatments was analyzed using “DESeq2” 63 , with significance thresholds were established at |log 2 FC| ≥ 1 and FDR < 0.05. Wilcoxon rank-sum tests were employed to evaluate differences in metagenomic gene abundance (T vs. S) and the relative expression of selected genes between treatments. Differential metabolites were determined through orthogonal projections to latent structures-discriminant analysis (OPLS-DA), implemented using “opls()” function in “ropls” 64 . Significance was defined by three criteria: variable importance in projection (VIP) > 1, FC threshold > 1.2, and p -value < 0.05 from Student’s t-test (“t.test()” function in R). Declarations Data availability The 16S rRNA and ITS amplicon sequencing data generated in this study have been deposited in the Beijing Institute of Genomics Data Center, Chinese Academy of Sciences, under project accession CRA036366 and CRA036372. The transcriptome sequencing data are accessible under accession number CRA036380, and metagenomic sequencing data are available under accession number CRA036367. 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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-8835674","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":589078249,"identity":"09c2f434-d509-4b5b-96ca-ac3c4b4bd41a","order_by":0,"name":"Guangzhou Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYPACGwjFQ4KWNNK1HCZBi8Hxs4dffvlzXp5/RgLjg7dtDPLmBLWcyUuzluG5bTjjRgKz4dw2BsOdDQS0mB3IMTOWkLidwHAjgU2at40hweAAIS3n3wC1GJxLkL+RwP6bOC03cowffkg4kGAAtIWZKC32N96YMTMcSDbceOZhs+SccxKGGwhpkezPMf7444+dvNzx5IMf3pTZyBO0BQjYpCHRwdgAJCQIqwcC5o8/iFI3CkbBKBgFIxYAALJZQLdCkxntAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7244-882X","institution":"China Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Guangzhou","middleName":"","lastName":"Wang","suffix":""},{"id":589078250,"identity":"90bc3641-a1d3-4a6a-9898-74974543075a","order_by":1,"name":"Gang Ni","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Ni","suffix":""},{"id":589078251,"identity":"8d14864a-42a5-4194-bc2c-703ee350542c","order_by":2,"name":"Shiqian Meng","email":"","orcid":"","institution":"State Key Laboratory of Nutrient Use and Management (SKL-NUM), College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shiqian","middleName":"","lastName":"Meng","suffix":""},{"id":589078252,"identity":"d37d8ef1-4076-4c33-a9eb-40f55bfa5475","order_by":3,"name":"Jiyu Jia","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jiyu","middleName":"","lastName":"Jia","suffix":""},{"id":589078253,"identity":"8ce883d8-dd54-410b-be72-bf559865e347","order_by":4,"name":"Dapu Zhou","email":"","orcid":"","institution":"State Key Laboratory of Nutrient Use and Management (SKL-NUM), College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dapu","middleName":"","lastName":"Zhou","suffix":""},{"id":589078254,"identity":"ccf21615-608f-48e9-b1c7-36e4bb508889","order_by":5,"name":"Martijn Bezemer","email":"","orcid":"https://orcid.org/0000-0002-2878-3479","institution":"leiden university institute of biology","correspondingAuthor":false,"prefix":"","firstName":"Martijn","middleName":"","lastName":"Bezemer","suffix":""},{"id":589078255,"identity":"8974f369-bc24-4818-80b8-d28d3b9a1669","order_by":6,"name":"John Klironomos","email":"","orcid":"","institution":"American University of Sharjah","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Klironomos","suffix":""},{"id":589078256,"identity":"b9983b5c-286a-4d3c-bced-b18f4ddf9679","order_by":7,"name":"Fusuo Zhang","email":"","orcid":"https://orcid.org/0000-0001-8971-0129","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Fusuo","middleName":"","lastName":"Zhang","suffix":""},{"id":589078257,"identity":"7a021527-1732-4e11-ab6d-a1fe429558bb","order_by":8,"name":"Kadambot Siddique","email":"","orcid":"https://orcid.org/0000-0001-6097-4235","institution":"University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Kadambot","middleName":"","lastName":"Siddique","suffix":""},{"id":589078258,"identity":"820f5eef-75c5-43ab-9f56-43a1c9189c82","order_by":9,"name":"Junling Zhang","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Junling","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-10 03:00:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8835674/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8835674/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102746116,"identity":"6d6ec673-cc12-4284-94bf-21386362e7df","added_by":"auto","created_at":"2026-02-16 08:55:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3225731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrowth performance and rhizosphere microbial differences between wheat varieties. (a)\u003c/strong\u003e Plant salt-tolerance index (PSTI) of all randomly selected varieties. \u003cstrong\u003e(b) \u003c/strong\u003eGrowth performance of salt-tolerant (T1, T2) and salt-sensitive (S1, S2) varieties under control and salt treatment. \u003cstrong\u003e(c)\u003c/strong\u003e PSTI of T1, T2, S1, and S2. Results represent mean ± s.e.m. of four replicates. Different letters indicate significant differences between varieties (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, one-way ANOVA with LSD test). \u003cstrong\u003e(d)\u003c/strong\u003e Plant dry weight in the second\u003cstrong\u003e \u003c/strong\u003econditioning phase. Boxplots indicate median, 25th (box bottom line), 75th (box top line) percentiles, and 5th (the lower whisker) and 95th (the upper whisker) percentiles (n = 5). Constrained principal coordinate analysis (CPCoA) of rhizosphere \u003cstrong\u003e(e)\u003c/strong\u003e bacterial and \u003cstrong\u003e(f)\u003c/strong\u003e fungal communities in T1, T2, S1, and S2. Differentially enriched \u003cstrong\u003e(g)\u003c/strong\u003e bacterial and \u003cstrong\u003e(h) \u003c/strong\u003efungal ASVs in the rhizosphere of salt-tolerant versus salt-sensitive plants. Dark blue dots represent ASVs meeting the cut-off of |log2FC| \u0026gt; 1 and \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01. Source data are included in the Source Data file.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/448f5a977ef92d54b8dfeedf.png"},{"id":102746731,"identity":"b39cf191-d696-4430-a869-e0d510c144c2","added_by":"auto","created_at":"2026-02-16 09:00:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1346009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlant-soil feedback experiments and wheat growth responses. (a) \u003c/strong\u003eSchematic of the plant-soil feedback experiment. T1, T2, S1, and S2 were grown in sterilized soil for 4 weeks to condition it. The conditioned soil was then mixed with fresh sterilized soil (1:9 w/w) for a second conditioning phase. In the feedback phase, conditioned soils were used as inocula to assess microbial effects on their own host and other varieties, either sterilized or left non-sterilized. \u003cstrong\u003e(b–d)\u003c/strong\u003e Shoot dry weight of T1, T2, S1, and S2 plants in soils conditioned by \u003cstrong\u003e(b)\u003c/strong\u003e themselves and the other variety on average, \u003cstrong\u003e(c)\u003c/strong\u003e all 16 unsterilized conditioned soils, and \u003cstrong\u003e(d)\u003c/strong\u003e 16 sterilized conditioned soils. Boxplots indicate median, 25th (box bottom line), 75th (box top line) percentiles, and 5th (the lower whisker) and 95th (the upper whisker) percentiles (n = 5). Different letters in panel \u003cstrong\u003eb\u003c/strong\u003eindicate significant differences between conditioned soil treatments (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, one-way ANOVA with LSD test). *, **denote \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, respectively (Wilcoxon rank-sum exact test). Source data are included in the Source Data file.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/aa57d1a627110a99274b8ed0.png"},{"id":102474417,"identity":"3e4200b3-46d3-48e3-b6b3-98d20b6f6ffd","added_by":"auto","created_at":"2026-02-12 05:06:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2194147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional differences of rhizosphere microorganisms (KEGG analysis).\u003c/strong\u003e \u003cstrong\u003e(a, b)\u003c/strong\u003e Total abundance of \u003cstrong\u003e(a)\u003c/strong\u003e stress-responsive KOs and \u003cstrong\u003e(b) \u003c/strong\u003ecarbon acquisition KOs. \u003cstrong\u003e(c, d)\u003c/strong\u003e Heatmap of all genes in the \u003cstrong\u003e(c) \u003c/strong\u003estress-responsive group and\u003cstrong\u003e (d) \u003c/strong\u003ecarbon acquisition group.\u003cstrong\u003e (e-g)\u003c/strong\u003eTotal abundance of KOs in the \u003cstrong\u003e(e) \u003c/strong\u003eH⁺ transport, \u003cstrong\u003e(f) \u003c/strong\u003eosmotic adjustment, and \u003cstrong\u003e(g) \u003c/strong\u003ereductive citrate cycle (Arnon-Buchanan cycle) modules. Boxplots indicate median, 25th (box bottom line), 75th (box top line) percentiles, and 5th (the lower whisker) and 95th (the upper whisker) percentiles (n = 5). Different letters in panels \u003cstrong\u003ea,\u003c/strong\u003e \u003cstrong\u003eb, e,\u003c/strong\u003e and \u003cstrong\u003eg\u003c/strong\u003e denote significant differences between conditioned soil treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, Kruskal-Wallis sum-rank test). Source data are included in the Source Data file.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/0c2a35d65780d38e9bd6f1a9.png"},{"id":102745880,"identity":"e2be124f-6a7f-4a94-902c-8b2cdfdcabd4","added_by":"auto","created_at":"2026-02-16 08:54:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2756241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRoot gene expression changes in T1 and S2 varieties under different conditioned soils. (a, b) \u003c/strong\u003eVolcano plot of DEGs between \u003cstrong\u003e(a) \u003c/strong\u003eT1T1 (T1 in T1-conditioned soil) and S2T1 (T1 in S2-conditioned soil), and \u003cstrong\u003e(b)\u003c/strong\u003e T1S2 (S2 in T1-conditioned soil) and S2S2 (S2 in S2-conditioned soil). Colored regions indicate |log2FC| \u0026gt; 1 and adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt;0.05 (Benjamini-Hochberg). \u003cstrong\u003e(c, d)\u003c/strong\u003eKEGG pathway enrichment analysis in \u003cstrong\u003e(c)\u003c/strong\u003e T1T1 and \u003cstrong\u003e(d) \u003c/strong\u003eT1S2 treaments.\u003cstrong\u003e(e, f)\u003c/strong\u003e Heatmap of DEGs in phenylpropanoid, flavonoid, and zeatin biosynthesis pathways in \u003cstrong\u003e(e) \u003c/strong\u003eT1 and \u003cstrong\u003e(f) \u003c/strong\u003eS2 plants. \u003cstrong\u003e(g, h) \u003c/strong\u003eGenes enriched in the phenylpropanoid pathway in \u003cstrong\u003e(g) \u003c/strong\u003eT1T1 and \u003cstrong\u003e(h)\u003c/strong\u003e T1S2 treatments. Dark blue background indicates significant enrichment in T1 compared to S2. \u003cstrong\u003e(i) \u003c/strong\u003eRoot lignin content under different feedback treatments. Boxplots indicate median, 25th (box bottom line), 75th (box top line) percentiles, and 5th (the lower whisker) and 95th (the upper whisker) percentiles (n = 5). Different letters in panel \u003cstrong\u003ei\u003c/strong\u003e indicate significant differences between feedback treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, one-way ANOVA with LSD test). Source data are included in the Source Data file.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/6f99e5d365598bfe12746409.png"},{"id":102474423,"identity":"83fac608-fee5-441b-aa7b-c3e010afc0fa","added_by":"auto","created_at":"2026-02-12 05:06:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":10534720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of single strains and SynCom on T1 and S2 growth, lignin content, and Na\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e flux. (a–d) \u003c/strong\u003eDry weight of T1 and S2 plants in the \u003cstrong\u003e(a, b)\u003c/strong\u003e first and \u003cstrong\u003e(c, d)\u003c/strong\u003e second experiments. \u003cstrong\u003e(e)\u003c/strong\u003e Root lignin content and \u003cstrong\u003e(f) \u003c/strong\u003enet Na\u003csup\u003e+\u003c/sup\u003e flux measured every 6 s for 5 min. Boxplots indicate median, 25th (box bottom line), 75th (box top line) percentiles, and 5th (the lower whisker) and 95th (the upper whisker) percentiles. Different letters in panels \u003cstrong\u003ea-f\u003c/strong\u003e indicate significant differences between treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, one-way ANOVA with LSD test). \u003cstrong\u003e(g) \u003c/strong\u003eLignin deposition (purple) in root zones: differentiation zone (DZ), elongation zone (EZ), and meristematic zone (MZ). \u003cstrong\u003e(h) \u003c/strong\u003eScanning electron microscopy and \u003cstrong\u003e(i, j)\u003c/strong\u003e energy dispersive X-ray spectroscopy (SEM-EDS) images showing Na\u003csup\u003e+\u003c/sup\u003e distributions in root cross-sections; s, stele; en, endodermis; e, epidermis. Yellow in panel \u003cstrong\u003ej\u003c/strong\u003e indicates Na\u003csup\u003e+\u003c/sup\u003e signal intensity. Source data are included in the Source Data file.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/ebdc5f3823abb1176bd3ddf7.png"},{"id":102474425,"identity":"0b160577-b52a-4b1a-a845-80a8250da994","added_by":"auto","created_at":"2026-02-12 05:06:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3871284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSynCom increases wheat yield in salinized fields. (a) \u003c/strong\u003eField trials and wheat seeds (100 seeds) and spikes of Jimai 22 in Quzhou and Dongying sites. \u003cstrong\u003e(b)\u003c/strong\u003e Grain yield, \u003cstrong\u003e(c) \u003c/strong\u003egrain number per spike, and \u003cstrong\u003e(d)\u003c/strong\u003e shoot dry weight in the fields. Boxplots indicate median, 25th (box bottom line), 75th (box top line) percentiles, and 5th (the lower whisker) and 95th (the upper whisker) percentiles (n = 6).Different letters in panels \u003cstrong\u003eb, c, d\u003c/strong\u003e denote significant differences between treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, one-way ANOVA with LSD test). Source data are included in the Source Data file.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/b23edf4cce932ff1c46d5db9.png"},{"id":102474424,"identity":"27f5705d-b3e9-47eb-9f41-9fd8bb4f8cec","added_by":"auto","created_at":"2026-02-12 05:06:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2379570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual model of microbiome-mediated enhancement of wheat salt tolerance via root barrier reinforcement. \u003c/strong\u003eUnder saline conditions, salt-tolerant wheat genotypes condition rhizosphere microbial communities that differ in taxonomic composition and functional potential from those associated with salt-sensitive genotypes. These microbiomes, including a core synthetic community (\u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e, \u003cem\u003eAchromobacter pulmonis\u003c/em\u003e, and \u003cem\u003eDevosia riboflavina\u003c/em\u003e), induce host transcriptional responses associated with phenylpropanoid and lignin biosynthetic pathways. Enhanced lignin deposition in the root endodermis strengthens root barrier function, which is associated with altered Na⁺ distribution, increased Na⁺ efflux, and reduced Na⁺ penetration into the stele. Collectively, these microbiome-induced host responses contribute to improved plant growth and yield under saline conditions. Dashed arrows indicate associations supported by multi-omics and physiological measurements, whereas solid arrows indicate experimentally validated functional effects observed in inoculation and field trials.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/e2f592add8da8edddda7bbf3.png"},{"id":102963846,"identity":"54f1aaaa-b736-471b-82cb-baf9d0b78340","added_by":"auto","created_at":"2026-02-19 04:20:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":26281365,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/f09ba207-e399-41f0-bb41-2eb022e0348d.pdf"},{"id":102474421,"identity":"3df66b4c-5700-4e1d-b08c-e7a4eb3a286a","added_by":"auto","created_at":"2026-02-12 05:06:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3817070,"visible":true,"origin":"","legend":"Supplementary Information of","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/bd4e95e20cca7c5f1f1232fa.docx"},{"id":102474422,"identity":"aeabc750-0e85-4af1-9c50-126f83c95ab7","added_by":"auto","created_at":"2026-02-12 05:06:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3515869,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-8835674/v1/b8935d8407297ac888b54425.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Rhizosphere microbiome promotes wheat salt tolerance through root lignin biosynthesis","fulltext":[{"header":"Main text","content":"\u003cp\u003eAs one of the most pervasive abiotic constraints on global crop production, salinity affects about 33% of the irrigated land\u003csup\u003e1\u003c/sup\u003e. Despite significant advances in breeding and molecular improvement, the genetic basis of salt tolerance remains complex, polygenic, and environmentally dependent\u003csup\u003e2,3\u003c/sup\u003e. Increasing evidence indicates that the stress resilience of plants to abiotic stress is strongly shaped by interactions with root-associated microbiomes. Hence, reinforcing the interactions between plants and the surrounding microorganisms can be pivotal in enhancing plant health and stress resilience\u003csup\u003e4,5\u003c/sup\u003e. However, intensive breeding for high-yield traits has inadvertently weakened relationships between crops and beneficial microbes\u003csup\u003e6,7\u003c/sup\u003e. Understanding how plants and their microbiomes co-evolve and coordinate adaptive responses under saline conditions is therefore essential for developing sustainable agroecosystems in a world with increasing salinity stress.\u003c/p\u003e\n\u003cp\u003eNumerous beneficial microbes have been shown to mitigate salt-induced stress in plants\u003csup\u003e8,9\u003c/sup\u003e. However, the performance of these introduced strains is often inconsistent, due to competition with native taxa and limited adaptability to local conditions\u003csup\u003e10\u003c/sup\u003e. The rhizosphere microbiomes, shaped by plant genotype and soil environment, has emerged as a key determinant of plant performance under stress. Salt-tolerant plants have been shown to recruit microbial communities that enhance nutrient solubilization\u003csup\u003e11,12\u003c/sup\u003e, mitigate osmotic and ionic stress\u003csup\u003e13\u003c/sup\u003e, and synthesize hormones and secondary metabolites\u003csup\u003e14\u003c/sup\u003e. However, most evidence remains correlative, leaving the causal links between plant genotype, microbiome composition, and physiological performance under salinity unresolved.\u003c/p\u003e\n\u003cp\u003eRecent developments in root phenotyping have emphasized the critical role of root architecture in determining salt tolerance\u003csup\u003e15\u003c/sup\u003e. Key traits\u0026nbsp;including primary root elongation\u003csup\u003e16\u003c/sup\u003e, lateral root density\u003csup\u003e17\u003c/sup\u003e, root hair development\u003csup\u003e18\u003c/sup\u003e, and the integrity of the endodermal barrier\u003csup\u003e19\u003c/sup\u003e, govern water and ion transport, root-shoot signaling, and overall plant vigor under saline conditions\u003csup\u003e20\u003c/sup\u003e. While the genetic and hormonal regulation of root architecture is well documented, the contribution of the rhizosphere microbiome to root architectural remodeling during salt stress remains poorly understood. We hypothesize that genotypes with differing salt tolerance assemble distinct microbial communities that subsequently influence root plasticity. This knowledge gap obscures the mechanistic links between microbial function, root anatomical modification, and whole-plant salt adaptation.\u003c/p\u003e\n\u003cp\u003eTo fill these gaps, we conducted a multi-dimensional investigation of wheat accessions with contrasting salt tolerance and their associated rhizosphere microbiomes. We used a plant-soil feedback approach combined with multi-omics, microbial isolation, and field validation to test the hypotheses that (i) salt-tolerant genotypes condition functionally distinct microbial communities, (ii) these microbiomes enhance plant performance under salinity independently of host genotype, and (iii) microbial effects are mediated, in part, through the induction of root barrier function that restrict Na⁺ entry and promote ionic homeostasis. By integrating microbial community analyses with host transcriptomics, root anatomy, and field performance, this study aims to resolve how microbiomes translate into durable host defenses under saline stress.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eResponses of wheat rhizosphere microbiomes to salt stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe firstly assessed the salt tolerance of all 100 wheat accessions. Based on the highest and lowest plant salt-tolerance index (PSTI) values, we selected two salt-tolerant varieties (T1, T2) and two salt-sensitive varieties (S1, S2) for subsequent plant-soil feedback experiments (Fig. 1a-c; Extended Data Fig. 1). As expected, salt-tolerant varieties maintained higher dry weight than salt-sensitive varieties after two rounds of soil conditioning\u0026nbsp;(Fig. 1d). For microbial community diversity, neither alpha diversity nor the relative abundances of the 15 dominant bacterial and fungal phyla differed significantly among varieties (Supplementary Fig. 1), whereas clear shifts were observed in community composition (adonis: R\u003csup\u003e2\u003c/sup\u003e = 0.247, \u003cem\u003ep\u003c/em\u003e = 0.022; R\u003csup\u003e2\u003c/sup\u003e = 0.249, \u003cem\u003ep\u003c/em\u003e = 0.006) (Fig. 1e, f). Differential abundance analysis revealed significant enrichment of 491 bacterial and 103 fungal ASVs in the rhizosphere soils of T1 and T2 relative to S1 and S2 varieties (Fig. 1g, h). At the phylum level, rhizosphere communities associated with salt-tolerant varieties were dominated by Proteobacteria, Bacteroidetes and Actinobacteria for bacteria, Ascomycota and Chytridiomycota for fungi (Supplementary Figs 2 and 3). At the genus level, salt-tolerant varieties were enriched in bacterial genera such as \u003cem\u003eAlgoriphagus\u003c/em\u003e, \u003cem\u003eAllorhizobium\u003c/em\u003e, and \u003cem\u003eDevosia\u003c/em\u003e, and fungal genera including \u003cem\u003eMyrothecium\u003c/em\u003e, \u003cem\u003ePlectosphaerella\u003c/em\u003e, and \u003cem\u003eCephaliophora\u0026nbsp;\u003c/em\u003e(Supplementary Figs 4 and 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoil microbial feedback effects on wheat\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the influence of rhizosphere microorganisms on crop performance, conditioned soils were used as inocula in a feedback experiment (Fig. 2a). In the feedback phase, soils conditioned by salt-tolerant varieties significantly enhanced wheat growth performace compared with soils conditioned by salt-sensitive varieties (Fig. 2b). The significant effects of both crop variety and conditioned soil on plant growth were revealed by a two-way ANOVA (Plant: F = 9.057, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Soil: F = 3.363, \u003cem\u003ep\u003c/em\u003e = 0.024) (Fig. 2c). Notably, the magnitude of the soil inoculation effect varied among varieties and was particularly strong for T1 and S2, indicating that these varieties were more responsive to differences of microbial communities between conditioned soils. When all conditioned soils were sterilized, no significant effect of soil inoculum was observed (Soil: F = 2.447, \u003cem\u003ep\u003c/em\u003e = 0.072), confirming that the observed feedback effects were mediated by soil microorganisms (Fig. 2d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional potential of salt-tolerant wheat rhizosphere microbiome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further analyzed functional differences between rhizosphere soils conditioned by salt-tolerant and salt-sensitive varieties using metagenomic sequencing. Analysis of clusters of orthologous groups (COG) revealed that genes associated with 10 functional processes were significantly more abundant in salt-tolerant conditioned soils compared to salt-sensitive soils (Extended Data Fig. 2a). Notably, genes associated with K\u003csup\u003e+\u003c/sup\u003e uptake proteins, Na\u003csup\u003e+\u003c/sup\u003e/H\u003csup\u003e+\u003c/sup\u003e-translocating membrane pyrophosphatases, Mg\u003csup\u003e2+\u003c/sup\u003e/serine and Na\u003csup\u003e+\u003c/sup\u003e/threonine symporters, and catalase production were significantly more abundant in soils conditioned by salt-tolerant varieties (Wilcox, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (Extended Data Fig. 2b). Consistent with these results, KEGG pathway analysis indicated higher abundances of stress-responsiveness and carbon-acquisition genes in salt-tolerant soils, reflecting the microbial growth potentials under stress (Fig. 3a, b). Specifically, genes associated with stress responsiveness included functions related to H\u003csup\u003e+\u003c/sup\u003e transport, K\u003csup\u003e+\u003c/sup\u003e uptake, Na\u003csup\u003e+\u003c/sup\u003e extrusion, antioxidant activity, and osmotic adjustment (Fig. 3c). Genes involved in carbon acquisition encompassed pathways including six carbohydrate cycles (Fig. 3d). Among these functional pathways, H⁺ transport (Fig. 3e), osmotic adjustment (Fig. 3f), and the reductive citrate cycle (Arnon-Buchanan cycle) (Fig. 3g) were significantly enriched in the T1 and T2 treamtents compared with S1 and S2, whereas the remaining ones showed no significant differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe rhizosphere microbiome enhances wheat lignin biosynthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate host transcriptional responses associated with microbiome-mediated salt responses, we performed transcriptome analysis of what roots grown in soils conditioned by T1, T2 andS1, S2. We selected T1 and S2 as test plants because they showed the strongest growth responses to soil microbial effects (Fig. 2c), and RNA-seq analysis revealed distinct transcriptional profiles that were significantly influenced by both wheat variety and conditioned soil (Supplementary Fig. 6). In T1 plants, 7635 genes were differentially expressed in response to T1- and S2-conditioned soils, with 3400 genes enriched under T1-conditioned soil (Fig. 4a). Similarly, in S2 plants, 9320 DEGs were detected between T1- and S2-conditioned soils, with 3114 DEGs enriched under T1-conditioned soil (|log2FC| ≥1, adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (Fig. 4b).\u003c/p\u003e\n\u003cp\u003eAmong pathways enriched in both T1 and S2, six were shared between varieties (Fig. 4c, d), three of which were associated with secondary metabolic pathways linked to salt tolerance, including phenylpropanoid biosynthesis, flavonoid biosynthesis, and zeatin biosynthesis. Notably, genes associated with phenylpropanoid biosynthesis were more strongly enriched in T1-conditioned soils than in S2-conditioned soils in both T1 (Fig. 4e) and S2 plants (Fig. 4f). These genes included \u003cem\u003eF5H\u003c/em\u003e, \u003cem\u003eC4H\u003c/em\u003e, \u003cem\u003eE2.3.1.133\u003c/em\u003e, \u003cem\u003eE2.1.1.104\u003c/em\u003e, \u003cem\u003eCYP98A\u003c/em\u003e, \u003cem\u003eCOMT\u003c/em\u003e, \u003cem\u003eCAD\u003c/em\u003e, \u003cem\u003eCCR\u003c/em\u003e, and \u003cem\u003eE1.11.1.7\u003c/em\u003e, which are core components of the lignin biosynthetic pathway. Consistent with this pattern, T1 plants exhibited significant enrichment of all 9 lignin-related genes in T1-conditioned compared to S2-conditioned soils (Fig. 4g), whereas S2 plants showed significant enrichment of 5 of these genes (Fig. 4h). The RT-qPCR analysis confirmed these trends with higher expression level of lignin biosynthesis genes in T1-conditioned soils (Wilcoxon, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (Extended Data Fig. 3). Consistent with gene expression level, root lignin content was generally higher in T1-conditioned soils than S2-conditioned soils (Fig. 4i). Statistical analysis indicated that root lignin content was significantly affected by the inoculated soil but not by wheat variety (Plant: F = 2.793, \u003cem\u003ep\u003c/em\u003e = 0.103; Soil: F = 4.926, \u003cem\u003ep\u003c/em\u003e = 0.033).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWheat adaptation to salt stress is bolstered by core taxa-mediated lignin accumulation within the root endodermis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the role of rhizosphere bacteria in wheat growth, 86 strains were isolated from T1 and T2 rhizosphere soils after the second conditioning phase (Additional Supplementary File 1).\u0026nbsp;Using integrated analysis-including abundance-occupancy curves, random forest models, and upset plots (see Methods), we identified 186 core ASVs associated with improved salt tolerance (Extended Data Fig. 4). Among these, ASV102, ASV114, and ASV59 phylogenetically related to three isolated strains of \u003cem\u003eAgrobacterium tumefaciens\u0026nbsp;\u003c/em\u003e(\u003cem\u003eAgr\u003c/em\u003e), \u003cem\u003eDevosia riboflavina\u003c/em\u003e (\u003cem\u003eDev\u003c/em\u003e),and\u003cem\u003e\u0026nbsp;Achromobacter pulmonis\u0026nbsp;\u003c/em\u003e(\u003cem\u003eAch\u003c/em\u003e), respectively (Supplementary Figs 7-9).\u003c/p\u003e\n\u003cp\u003eThe effects of individual strains and the synthetic community (SynCom; \u003cem\u003eAgr\u003c/em\u003e_\u003cem\u003eAch\u003c/em\u003e_\u003cem\u003eDev\u003c/em\u003e) on wheat growth and lignin accumulation were tested in two repeated experiments. Compared with individual strain inoculation and non-inoculated controls,\u0026nbsp;SynCom inoculation\u0026nbsp;on average increased the dry weight of T1 by 17.97% and 41.49%\u0026nbsp;(Fig. 5a, b)\u0026nbsp;and S2 by 45.69% and 48.57%\u0026nbsp;(Fig. 5c, d), respectively. SynCom-inoculated plants also exhibited higher root lignin content compared with non-inoculated controls\u0026nbsp;(Fig. 5e).\u0026nbsp;In addition, SynCom inoculation significantly enhanced net Na\u003csup\u003e+\u003c/sup\u003e efflux from roots of both T1 and S2, as measured by non-invasive micro-test technology (NMT). Notably, T1 displayed a stronger Na\u003csup\u003e+\u003c/sup\u003e secretion capacity than S2 (Fig. 5f).\u003c/p\u003e\n\u003cp\u003eVisualization of lignin spatial distribution showed that SynCom inoculation enhanced lignin deposition (phloroglucinol staining) in endodermal cells across the differentiation zone (DZ), elongation zone (EZ), and meristematic zone (MZ) in both T1 and S2 roots (Fig. 5g). Consistent with enhanced endodermal lignification, SynCom inoculation altered root permeability to the fluorescent tracer propidium iodide (PI), indicating strengthened barrier function. In inoculated plants, PI fluorescence was largely restricted to the endodermis in the MZ and did not penetrate into the phloem, whereas in non-inoculated plants, PI penetrated more deeply into the xylem vessels (Extended Data Fig.5).\u003c/p\u003e\n\u003cp\u003eScanning electron microscopy, coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) further illustrated that SynCom inoculation was associated with increased Na\u003csup\u003e+\u003c/sup\u003e signal intensity in tissues exterior to the endodermis and reduced Na\u003csup\u003e+\u0026nbsp;\u003c/sup\u003esignal interior to the endodermis in both T1 and S2 roots (Fig. 5h-i). In contrast, Na\u003csup\u003e+\u003c/sup\u003e distribution in non-inoculated roots was more uniform across the root cross-section. Together, these observations indicate that SynCom inoculation is associated with enhance endodermal lignification and altered Na\u003csup\u003e+\u003c/sup\u003e distribution within wheat roots.\u003c/p\u003e\n\u003cp\u003eTo further assess the contribution of lignin biosynthesis to microbiome-mediated salt responses, we evaluated the effects of microbial inoculation on \u003cem\u003eArabidopsis\u0026nbsp;\u003c/em\u003eecotype Col-0 and lignin biosynthesis-deficient mutants \u003cem\u003eCCoAOMT1\u003c/em\u003e, \u003cem\u003efah1-2\u003c/em\u003e, \u003cem\u003eCAD\u0026nbsp;\u003c/em\u003e(Extended Data Fig.6a, d, g, j). Compared with the control and S2-conditioned soil suspension, inoculation with either the SynCom (Agr_Ach_Dev) and T1-conditioned soil suspension significantly increased fresh weight of Col-0, \u003cem\u003eCCoAOMT1\u003c/em\u003e, and \u003cem\u003efah1-2\u0026nbsp;\u003c/em\u003e(Extended Data Fig.6b, e, h, k). In addition, SynCom and T1-conditioned soil inoculation increased root lignin content in \u003cem\u003eCCoAOMT1\u003c/em\u003e and \u003cem\u003eCAD\u003c/em\u003e mutants, whereas S2-conditioned soil did not differ from the control treatment for any of the mutants (Extended Data Fig.6c, f, i, l). These findings suggest that lignin biosynthesis contributes substantially, but not exclusively, to microbiome-mediated enhancement of plant performance under salt stress.\u003c/p\u003e\n\u003cp\u003eTo examine potential mechanisms underlying preferential enrichment of core taxa, we performed comparative metabolomic analyses of rhizosphere soils between T1 and S2 plants in the second conditioning stage. Across all samples, 591 metabolites were detected. Principal coordinate analysis (PCoA) indicated a clear separation between the rhizosphere metabolomes of T1 and S2\u0026nbsp;(R\u003csup\u003e2\u003c/sup\u003e = 0.471, \u003cem\u003ep\u003c/em\u003e = 0.007) (Extended Data Fig.7a). Seven metabolites were significantly enriched in T1 compared to S2 (FC \u0026gt; 1.2, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), including berberine, histamine, and dimethyl caffeic acid, which were available as pure compounds (Extended Data Fig.7b).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e assays showed that berberine, histamine, and dimethyl caffeic acid idividually promoted the growth of \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e, \u003cem\u003eAchromobacter pulmonis\u003c/em\u003e, and \u003cem\u003eDevosia riboflavina\u003c/em\u003e, respectively. Notably, a mixture of the three metabolites stimulated significantly greater biomass production of the SynCom than any individual metabolite alone (Extended Data Fig.7c-f).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynCom increases wheat yield in salinized farmland\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the effects of SynCom (\u003cem\u003eAgr\u003c/em\u003e_\u003cem\u003eAch\u003c/em\u003e_\u003cem\u003eDev\u003c/em\u003e) under field conditions, we conducted trials at two salinized farmland sites. SynCom inoculation significantly increased wheat grain yield by 26.80% at Quzhou and 18.13% at Dongying (Fig. 6a, b). Statistical analysis indicated that microbial inoculation, rather than site location, was the main factor influencing yield. In addition, SynCom inoculation led to higher grain numbers per spike at Dongying and showed a trend of increased shoot weight at both sites (Fig. 6c, d).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding how crops convert microbial associations into effective defenses against abiotic stress is a central challenge for both plant ecology and sustainable agriculture. While lignin deposition and endodermal barrier function are well-established components of plant salt tolerance, the extent to which these traits can be induced by the rhizosphere microbiome has remained largely unexplored. Our results demonstrate that wheat does not rely solely on genetically programmed root defenses under salinity; instead, salt-tolerant genotypes condition rhizosphere microbiomes that in turn induce root lignification and barrier reinforcement (Fig.5e, g-j). This microbiome-mediated activation of root anatomical defenses provides a mechanistic link between plant genotype, microbial community function, and ionic regulation under salt stress.\u003c/p\u003e\n\u003cp\u003eBy combining plant-soil feedback experiments with microbial reconstitution and field trials, our study moves beyond correlative associations to show that specific microbial consortia can trigger host transcriptional programs associated with phenylpropanoid metabolism, leading to increased endodermal lignin deposition and reduced Na⁺ penetration into the stele. These findings position the rhizosphere microbiome as an active inducer of root barrier function, rather than a passive contributor to plant stress tolerance, and suggest that microbial control of host anatomical defenses represents an important, and underappreciated, dimension of plant–microbe cooperation under abiotic stress.\u003c/p\u003e\n\u003cp\u003eComplex hormonal and signaling networks regulate lignin biosynthesis. Abscisic acid (ABA)-mediated pathways enhance lignification under osmotic stress\u003csup\u003e21\u003c/sup\u003e, supporting water retention and mineral homeostasis\u003csup\u003e22\u003c/sup\u003e, while salicylic acid and MAPK-MYB modules modulate lignin synthesis during pathogen attack\u003csup\u003e23,24\u003c/sup\u003e. It is plausible that specific rhizobacterial elicitors activate these hormonal cascades, converging on phenylpropanoid metabolism to reinforce apoplastic barriers. The resultant lignin deposition strengthens Casparian strips and exodermal lamellae, effectively impeding Na⁺ diffusion into the stele.\u003c/p\u003e\n\u003cp\u003eBeyond reinforcing the physical barrier, beneficial soil microbes also enhanced dynamic ionic detoxification by promoting Na⁺ efflux from root cells (Figs 5f and 7). This microbe-induced Na⁺ extrusion may correlate with upregulation of plasma membrane Na⁺/H⁺ antiporters such as SOS1, which exchanges Na⁺ for H⁺ to maintain cytosolic ion homeostasis\u003csup\u003e25\u003c/sup\u003e. Previous work showed that halotolerant \u003cem\u003eDietzia natronolimnaea\u003c/em\u003e STR1 inoculation increased wheat root SOS1 transcript levels more than twofold under salt stress\u003csup\u003e26\u003c/sup\u003e, while \u003cem\u003eBacillus amyloliquefaciens\u003c/em\u003e SQR9 activated Na⁺ efflux transporters (e.g., NHX and HKT families) in maize\u003csup\u003e27,28\u003c/sup\u003e. Together with our findings, these results suggest that wheat rhizosphere microbes not only fortify the root’s physical barrier via lignification but also stimulate active Na⁺ extrusion, thereby limiting Na⁺ entry and accelerating its removal from the root symplast.\u003c/p\u003e\n\u003cp\u003ePlants under stress often modify their root exudation profiles to attract beneficial microbes. In our study, salt-stressed tolerant wheat released specific metabolites, notably dimethyl caffeic acid and berberine, which served as chemical cues for microbial recruitment (Extended Data Fig.7). Dimethyl caffeic acid, a derivative of the lignin precursor caffeic acid\u003csup\u003e29\u003c/sup\u003e, highlights the biochemical dialogue between plant and microbiome. Phenolic acids, such as caffeic acid, can scavenge reactive oxygen species, fortify cell walls, and influence microbial behavior\u003csup\u003e30\u003c/sup\u003e. The accumulation of this compound in tolerant plant rhizospheres suggests that the microbiome both triggers lignin synthesis and is sustained by its byproducts, analogous to the regulation of \u003cem\u003ePseudomonadales\u003c/em\u003e in the phyllosphere by 4-hydroxycinnamic acid\u003csup\u003e31\u003c/sup\u003e. The SynCom bacteria we identified likely induce phenylpropanoid metabolism, producing lignin and phenolic exudates that serve as carbon sources or signals favoring the same microbes. This feedback may explain the stable and persistent beneficial association under salinity. In addition, berberine is a benzylisoquinoline alkaloid with antimicrobial activity\u003csup\u003e32\u003c/sup\u003e, which likely suppresses pathogenic fungi and bacteria that proliferate under saline stress\u003csup\u003e33\u003c/sup\u003e, creating a niche that favors microbes capable of tolerating or metabolizing such compounds. Several of the bacterial genera we identified, including \u003cem\u003eAgrobacterium\u003c/em\u003e,\u0026nbsp;\u003cem\u003eDevosia\u003c/em\u003e, and \u003cem\u003eAchromobacter\u003c/em\u003e, are known to boost crop growth under saline conditions\u003csup\u003e34-37\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our work uncovers a hidden alliance between wheat and its rhizosphere microbiome, in which microbial partners reinforce root anatomical structures and activate ionic homeostasis to confer salt resilience. This insight broadens our understanding of plant–microbe cooperation under abiotic stress and reveals practical avenues for enhancing crop tolerance. By manipulating root exudation patterns, selecting microbiome-responsive genotypes, or designing synthetic microbial consortia that promote lignification and Na⁺ efflux, it may be possible to engineer salt-tolerant cropping systems with minimal environmental cost. As soil salinization intensifies globally, such biologically based solutions will be integral to sustaining crop productivity and food security.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eExperiment 1: Screening salt-tolerant and salt-sensitive wheat varieties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe screened 100 wheat accessions to identify genotypes with contrasting salt tolerance. These accessions originated from 11 countries (Additional Supplementary File 1), and seeds were provided by the Institute of Crop Science, Chinese Academy of Agricultural Sciences (Beijing, China). Soil was collected from saline farmland in Dongying City, Shandong Province (37\u0026deg;42\u0026rsquo;N, 118\u0026deg;48\u0026rsquo;E; EC\u003csub\u003e1:5\u003c/sub\u003e 261 \u0026mu;S/cm; total N 0.09%; total C 1.32%). The collected soil was air-dried at room temperature for four days, sieved (\u0026lt;2 mm), mixed with sand at a 3:1 (w/w) ratio, and sterilized using 25 kGy gamma irradiation for use as the growth substrate.\u003c/p\u003e\n\u003cp\u003eSeeds were surface-sterilized with 10% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for 30 min, rinsed five times with ultrapure water, and then allowed to germinate for 48 h in a climate-controlled incubator. Uniform seedlings were transplanted into plastic vessels packed with 200 g of sterilized soil, with two seedlings per pot. After the emergence of two true leaves, each variety was subjected to either a salt treatment (NaCl) or a non-salt control (deionized water), with four replicates per treatment, resulting in a total of 800 pots. The NaCl concentration was gradually increased from 50 mM to 250 mM to prevent salt shock\u003csup\u003e11\u003c/sup\u003e. Plants were grown for four weeks to assess physiological responses to salinity.\u003c/p\u003e\n\u003cp\u003eAt harvest, we measured shoot/root biomass (both fresh and dry weight), plant height, root length, and stress-related markers such as plant water content and\u0026nbsp;salt injury index. Briefly,\u0026nbsp;salt injury was scored on a 1-4 scale based on chlorosis of the second leaf above the seedling base, corresponding to none, slight, moderate, and severe, respectively\u003csup\u003e38\u003c/sup\u003e.\u0026nbsp;Indices representing phenotypic changes under salt versus non-salt conditions were calculated, including\u0026nbsp;the relative decreases in plant height (RDPH), fresh weight (RDFW), dry weight (RDDW), shoot biomass (RDSB), root biomass (RDRB), plant water content (RDPWC), and salt injury index (SI). The relative decrease for each trait was calculated as (Control \u0026ndash; Salt) / Control. All indices were integrated using principal component analysis. Principal components (PCs) with eigenvalues \u0026gt; 1.0 and variance contrinution \u0026gt; 5% were retained. PCs with cumulative variance \u0026lt; 85% were examined to identify key indices and remove redundant variables. Because RDDW, RDFW, RDPWC,\u0026nbsp;and\u0026nbsp;RDRB were negatively correlated with salt tolerance, these indices were selected to calculate the plant salt-tolerance index (PSTI) following\u003csup\u003e11,39\u003c/sup\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eY\u003c/em\u003e = 1 - (\u003cem\u003ex\u003c/em\u003e -\u003cem\u003es\u003c/em\u003e)/ (\u003cem\u003et\u003c/em\u003e -\u003cem\u003es\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003ex\u003c/em\u003e is the index value for the sample, \u003cem\u003et\u003c/em\u003e is the maximum index value, and \u003cem\u003es\u003c/em\u003e is the minimum index value.\u003c/p\u003e\n\u003cp\u003eThe final PSTI was calculated as:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"117\" height=\"19\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eYi\u003c/em\u003e denotes score for index \u003cem\u003ei\u0026nbsp;\u003c/em\u003eand \u003cem\u003eWi\u003c/em\u003e represents its associated PC weight factor. PSTI was significantly correlated with wheat growth traits (Supplementary Fig. 1), confirming its reliability for evaluating salt tolerance.\u0026nbsp;Based on PSTI rankings (Additional Supplementary File 1), we selected representative salt-tolerant (T1: 12738; T2: 13130) and salt-sensitive varieties (S1: 12783; S2: 13434) for subsequent experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment 2: Plant-soil feedback experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo discern the microbially-mediated mechanisms behind wheat salt tolerance, a plant-soil feedback (PSF) assay was carried out to quantify the impact of the rhizosphere microbiome on host fitness (Fig. 2a). The PSF experiment consisted of two phases: a conditioning phase and a feedback phase. Four selected wheat varieties were cultivated in saline soil to establish host-specific microbiomes during the conditioning phase. Two rounds of conditioning were performed to strengthen host imprinting. For each variety, five replicates were established, with two seedlings grown in 1 kg of saline soil per replicate. Once seedlings reached the two-true-leaf stage, NaCl was applied gradually from 50 mM to 250 mM. After four weeks of salt treatment, the top 0-2 cm of soil was removed to minimize contamination from dust deposition. The remaining soil was stored at 4℃ and used for the second conditioning cycle, which followed the same procedures. At harvest, shoots were cut, washed, and dried. Rhizosphere soil was brushed from roots after removing loosely attached particles. The collected soil was either stored at -80\u0026deg;C (for DNA extraction) or 4\u0026deg;C (for subsequent feedback phases), followed by root washing and drying.\u003c/p\u003e\n\u003cp\u003eIn the feedback phase, we performed reciprocal inoculations using all combinations of conditioned soils and wheat varieties. The fully factorial design included four conditioned soils and four plant varieties, yielding 16 treatments. Each treatment had five biological replicates, with two seedlings per pot. Conditioned soils were used as inocula and mixed with sterilized substrate at a 1:9 ratio to minimize nutrients-driven effects\u003csup\u003e40\u003c/sup\u003e. Each pot was prepared with 600 g of sterilized substrate at the base, a 100 g inoculum layer, and a 300 g sterilized capping layer. A parallel control experiment using sterilized conditioned soils was included to confirm whether observed feedback effects were microbially mediated. Seedlings were again subjected to salt treatment at the two-true-leaf stage, and plants were harvested after four weeks. Shoots and rhizosphere soils were collected as described in the conditioning phase. Portions of the roots were stored at -80℃, followed by RNA extraction, transcriptome sequencing, and RT-qPCR analysis. Remaining root material was dried for lignin quantification. Rhizosphere soils were\u0026nbsp;immediately flash-frozen and maintained at -80℃ for DNA extraction and amplicon sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e16S \u0026amp; ITS amplicon sequencing and analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRhizosphere soil (0.5 g) from the conditioning and feedback phases was processed for total DNA extraction using the FastDNA\u0026reg; SPIN Kit for Soil (MP Biomedicals). The purity and quality of the resulting DNA were evaluated using 1% agarose gel electrophoresis and a NanoDrop2000 spectrophotometer. For microbial profiling, the V3-V4 region of the 16S rRNA gene was targeted through PCR with universal primers 515F (5\u0026rsquo;-GTGYCAGCMGCCGCGGTAA-3\u0026rsquo;) and 806R (5\u0026rsquo;-GGACTACNVGGGTWTCTAAT-3\u0026rsquo;)\u003csup\u003e\u0026nbsp;41\u003c/sup\u003e, and ITS2 region was amplified with ITS3F (5\u0026rsquo;-GCATCGATGAAGAACGCAGC-3\u0026rsquo;) and ITS4R (5\u0026rsquo;-TCCTCCGCTTATTGATATGC-3\u0026rsquo;)\u003csup\u003e\u0026nbsp;42\u003c/sup\u003e. PCR reactions (20 \u0026mu;L) consisted of 10 \u0026mu;L 2\u0026times; Pro Taq, 0.8 \u0026mu;L (5 \u0026mu;M) of each primer, 10 ng template DNA, and ddH\u003csub\u003e2\u003c/sub\u003eO. The amplification of the 16S rRNA gene was executed under the following conditions: 95 \u0026deg;C for 3 min, followed by 27 cycles of 95\u0026deg;C (30 s), annealing at 55\u0026deg;C (30 s), and extension at 72\u0026deg;C (45 s); with a final extension at 72 \u0026deg;C for 10 min. The reactions were subsequently maintained at 10\u0026deg;C. ITS2 amplification used 35 cycles under the same cycling parameters. Following amplification, PCR products were purified\u0026nbsp;using the AxyPrep DNA Gel Extraction Kit (Axygen, USA), and the concentration was determined via the QuantiFluor\u0026reg; dsDNA System (Promega, USA).\u0026nbsp;After being normalized to equimolar concentrations, the samples were pooled and subjected to paired-end sequencing on the Illumina PE300 platform (Illumina, USA) in accordance with the established protocols.\u003c/p\u003e\n\u003cp\u003eQuality filtering of the raw sequencing data was executed via fastp (v0.19.6)\u003csup\u003e\u0026nbsp;43\u003c/sup\u003e, followed by the assembly of overlapping paired-end reads using FLASH (v1.2.7)\u003csup\u003e\u0026nbsp;44\u003c/sup\u003e to generate\u0026nbsp;\u0026sim;350 bp\u0026nbsp;sequences. ASVs were denoised and clustered via DADA2\u003csup\u003e45\u003c/sup\u003e plugin in QIIME2\u003csup\u003e46\u003c/sup\u003e. Taxonomic assignment of prokaryotic and eukaryotic ASVs was performed using SILVA 138 16S rRNA database\u003csup\u003e47\u003c/sup\u003e and UNITE 8.0 ITS database\u003csup\u003e48\u003c/sup\u003e, respectively.\u003c/p\u003e\n\u003cp\u003eTo identify potential core taxa contributing to wheat salt tolerance, we combined abundance\u0026ndash;occupancy analysis\u003csup\u003e49\u003c/sup\u003e, random forest modeling\u003csup\u003e50\u003c/sup\u003e, and upset plots. The abundance\u0026ndash;occupancy curve was applied to define the \u0026ldquo;persistent microbiome\u0026rdquo;, comprising ASVs present in \u0026sup3;50% of samples and increasing Bray-Curtis similarity by \u0026ge;2%\u003csup\u003e49,51\u003c/sup\u003e. Random forest modeling, optimized with five repeated tenfold cross-validations, identified \u0026ldquo;RFM biomarkers\u0026rdquo; with 53.75% predictive accuracy. ASVs present only in salt-sensitive treatments were excluded, and shared ASVs across TT, ST, and TS treatments were defined as \u0026ldquo;salt-tolerant shared ASVs\u0026rdquo;. Core taxa were defined as ASVs overlapping among the persistent microbiome, RFM biomarkers, and salt-tolerant shared ASVs, yielding 186 ASVs in total.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetagenomic sequencing and data mining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetagenomic sequencing was conducted to explore the functional potential of rhizosphere microbiomes recruited by different wheat varieties after the conditioning phase. Genomic DNA, derived from the samples described above, was processed to a mean length of ~400 bp using Covaris M220 (Covaris, USA) to construct sequencing libraries. Paired-end libraries were prepared with the NEXTFLEX\u003csup\u003e\u0026reg;\u003c/sup\u003e Rapid DNA-Seq\u0026nbsp;(Bioo Scientific, Austin, TX, USA), and the resulting libraries were then sequenced on an Illumina NovaSeq X Plus platform (Illumina Inc., San Diego, CA, USA).\u003c/p\u003e\n\u003cp\u003eRaw metagenomic reads were quality-filtered and trimmed for adapters via fastp (v0.20.0)\u003csup\u003e\u0026nbsp;43\u003c/sup\u003e. Clean reads were assembled using MEGAHIT (v1.1.2)\u003csup\u003e\u0026nbsp;52\u003c/sup\u003e, and contigs \u0026ge;300 bp\u003csup\u003e14,53\u003c/sup\u003e were selected for further processing. Open reading frames were identified with Prodigal (v2.6.3)\u003csup\u003e\u0026nbsp;54\u003c/sup\u003e and clustered using CD-HIT (v4.6.1)\u003csup\u003e\u0026nbsp;55\u003c/sup\u003e with parameters \u003cem\u003e-i 0.9 -c 0.9\u003c/em\u003e. Cleaned data from individual sample were mapped against the non-redundant gene set (95% identity) via SOAPaligner\u003csup\u003e56\u003c/sup\u003e to quantify gene abundance. Non-redundant genes were converted into amino acid sequences and compared against NCBI-eggNOG database (v4.5.1) and KEGG database (v94.2) using Diamond (v0.8.35)\u003csup\u003e\u0026nbsp;57\u003c/sup\u003e. Hits with an e-value \u0026lt;10\u003csup\u003e\u0026ndash;5\u003c/sup\u003e were considered significant, and corresponding taxonomic and functional information was used to represent the non-redundant genes. Gene abundances were normalized as RPKM values (reads per kilobase of non-redundant genes per million mapped reads).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA extraction and transcriptome analysis of wheat roots in the feedback phase\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the molecular mechanisms by which stress-adaptive microorganisms enhance wheat salt tolerance, we selected the two most responsive wheat varieties (T1 and S2) (Fig. 2) for transcriptome sequencing. Their transcriptional expression differences in T1 and S2 conditioned soils were compared. Root RNA was extracted using MJZol extraction kit. DNA contamination was removed via RNA Purification Kit, and prepared libraries were\u0026nbsp;processed for high-throughput sequencing on Illumina NovaSeq X Plus platform.\u003c/p\u003e\n\u003cp\u003eRaw transcriptomic reads was processed with fastp (v0.19.5)\u003csup\u003e43\u003c/sup\u003e and the filtered sequences were then aligned against the IWGSC RefSeq (v2.1) reference genome\u003cem\u003e\u0026nbsp;\u003c/em\u003eof \u003cem\u003eTriticum aestivum\u003c/em\u003e (cv. Chinese Spring)\u0026nbsp;using HiSAT2\u003csup\u003e58\u003c/sup\u003e, and transcripts were assembled with StringTie (v2.1.2).\u0026nbsp;The abundance of genetic transcripts was estimated using RSEM and standardized to FPKM units (fragments per kilobases of transcript per million mapped reads).\u0026nbsp;DEGs between T1- and S2-conditioned soil treatments were identified using DESeq2 in R. Statistical significance for differential expression was defined by\u0026nbsp;|log2fold-change (FC)| \u0026ge;1 and Benjamini\u0026ndash;Hochberg (BH) adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 via Wald test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRT-qPCR of core functional genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with the transcriptome analysis, root samples from the four treatments (T1 and S2 wheat plants in T1 and S2 conditioned soils) were collected to assess the expression levels of 9 core functional genes related to wheat stress tolerance (Supplementary\u0026nbsp;Table 1). Total RNA was extracted\u0026nbsp;using the extraction kit (Beijing Solarbio Science \u0026amp; Technology Co., Ltd., China). Then, the reverse transcription of RNA into complementary DNA (cDNA) was performed with the Hifair\u003csup\u003e\u0026reg;\u003c/sup\u003e Ⅱ 1st Strand cDNA Synthesis Kit (Yeasen Biotechnology, China). RT-qPCR reactions (20 \u0026micro;L) contained 10 \u0026micro;L\u0026nbsp;Hieff\u0026reg; qPCR SYBR Green Master Mix, 0.4 \u0026micro;L of each primer, 2 \u0026micro;L cDNA template, and RNase-free H\u003csub\u003e2\u003c/sub\u003eO. Reactions were performed in three biological replicates on an ABI 7300 Real-Time PCR System (Thermo Fisher, Singapore) under the following cycling conditions: 95 \u0026deg;C for 80 s, followed by 40 cycles at 60 \u0026deg;C for 20 s and 72 \u0026deg;C for 30 s. To normalize the data, the GAPDH gene was utilized as an internal reference.\u0026nbsp;Relative gene expression levels were\u0026nbsp;determined\u0026nbsp;using the 2\u003csup\u003e\u0026ndash;∆∆Ct\u003c/sup\u003e method:\u003c/p\u003e\n\u003cp\u003e2\u003csup\u003e\u0026ndash;\u003c/sup\u003e\u003csup\u003e∆∆Ct\u003c/sup\u003e = 2\u003csup\u003e\u0026ndash;((Gene Ct \u0026ndash; targeting gene Ct) \u0026ndash; average of Control group Ct)\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolite profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRhizosphere soils from T1 and S2 varieties, collected from the second conditioning phase, were rapidly frozen at \u0026ndash;80\u0026deg;C for metabolite analysis. Approximately 100 mg of each sample was extracted with 80% methanol containing internal standards. The extraction protocol included cryogenic grinding, sonication, and centrifugation. The resulting supernatant was analyzed by LC-MS using a Triple TOF 6600 system (SCIEX, USA).\u003c/p\u003e\n\u003cp\u003eProgenesis QI (Waters Corporation, Milford, USA) was employed to process raw LC-MS data for peak alignment, retention time correction, baseline filtering, and peak detection and integration, resulting in a data matrix comprising mass-to-charge ratio, peak intensity, and retention time. Metabolites were annotated by matching spectral data against a plant-specific metabolite database curated by Majorbio Biotechnology Co. Ltd. (Shanghai, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLignin quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDried root samples were ground, then sieved through a 50-mesh sieve. Samples (10 mg) were extracted twice in 80% ethanol (1 mL), vortexed thoroughly, and maintained at 50\u0026deg;C for 20 minutes. After a 10-min centrifugation (12,000 rpm, 25\u0026deg;C), the precipitate was collected. The pellet was then treated with 0.2 mL of 25% acetyl bromide-acetic acid solution at 70\u0026deg;C for 30 minutes. To terminate the reaction, 2 M NaOH (0.2 mL), glacial acetic acid (0.4 mL), and 7.5 M hydroxylamine hydrochloride (0.02 mL) were sequentially added. The mixture was centrifuged, and 50 \u0026mu;L of the supernatant was diluted to 1 mL with glacial acetic acid for absorbance measurement at 280 nm. Blank controls without plant material were prepared in parallel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment 3: Functional verification of core taxa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe isolated 86 bacterial strains from the rhizosphere soils of T1 and T2 varieties in the conditioning phase (Additional Supplementary File 1). Briefly, rhizosphere soil (approx. 10 g) was suspended in 90 mL of sterile water and shaken at 30\u0026deg;C (30 min, 180 rpm). The suspension was serially diluted, 200 \u0026micro;L from each dilution was plated in triplicate on LB, TSA, and R2A agar. After 2 days of incubation, individual colonies were picked, streaked onto fresh LB plates, and purified through three successive rounds. Single colonies were then selected for species identification.\u003c/p\u003e\n\u003cp\u003eAmong the 186 core ASVs, sequences ASV114, ASV102, and ASV59 phylogenetically corresponded to \u003cem\u003eDevosia riboflavina\u003c/em\u003e, \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e, and \u003cem\u003eAchromobacter pulmonis\u003c/em\u003e, respectively (Supplementary Figs 7-9). Then, we conducted a pot experiment to test the effects of individual strains and a three-strain synthetic community (SynCom) on wheat growth. Two identical experimental trials ensured reproducibility. Five inoculation treatments were applied: (1-3) single inoculations of \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e, \u003cem\u003eAchromobacter pulmonis\u003c/em\u003e, and \u003cem\u003eDevosia riboflavina\u003c/em\u003e; (4) a three-strain SynCom\u003cem\u003e\u0026nbsp;\u003c/em\u003e(\u003cem\u003eAgr\u003c/em\u003e_\u003cem\u003eAch\u003c/em\u003e_\u003cem\u003eDev\u003c/em\u003e); and (5) a sterile water control. T1 and S2 varieties were selected as test plants due to their contrasting salt tolerance.\u003c/p\u003e\n\u003cp\u003eSeed sterilization, planting procedures, and the sterilized substrate were identical to previous experiments. Strains were cultured in LB liquid medium (26℃, 48 h), centrifuged, and resuspended to an OD\u003csub\u003e600\u003c/sub\u003e of 0.5. For each treatment, 15 mL of inoculum was applied, with the SynCom prepared by mixing equal volumes (5 mL each) of the three strains. After four weeks of salt exposure, plants were harvested, washed with tap and distilled water, and fresh weight was measured. Samples from the first trial were used for Na⁺ flux measurement, followed by dry weight determination, while samples from the second trial were used solely for dry weight determination. The difference of root Na⁺ flux between the SynCom\u003cem\u003e\u0026nbsp;\u003c/em\u003eand control treatments was measured using the NMT method. Brielfy, roots were fixed in a Petri dish containing 0.5 mM NaCl solution, and the Na\u003csup\u003e+\u003c/sup\u003e flux microsensor was positioned 500 \u0026mu;m from the root elongation zone, and then was recorded for 5 minutes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLignin staining and microscopy observation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine lignin deposition patterns in root cross-sections, a replicate of Experiment 3 was conducted as previously described. Roots were randomly excised from each pot for histochemical staining and microscopy. Approximately 1-cm segments were collected from the meristematic zone (MZ), elongation zone (EZ), and differentiation zone (DZ), and then immediately fixed in 50% formalin-aceto-alcohol solution. After dehydration in a graded ethanol series, the fixed tissues were paraffin-embedded and subsequently cut into 10-\u0026micro;m-thick slices via a Leica RM2016 microtome (Leica, Germany). Sections were sequentially dewaxed in environmentally friendly transparent liquids I and II (G1128, Servicebio, China), absolute ethanol, and 75% ethanol, then rinsed with distilled water. Lignin was visualized by staining with phloroglucinol solution (G1060, Servicebio, China), which produces a purple color. Excess stain was removed, sections were mounted with coverslips, and observations were performed with a Nikon Eclipse E100 microscope (Nikon, Japan).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePropidium iodide test and microscopy observation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePropidium iodide (PI) penetration assays were performed\u003csup\u003e59\u003c/sup\u003e. Seedlings were incubated in PI solution (15 \u0026micro;g\u0026middot;mL\u003csup\u003e-1\u003c/sup\u003e) in a dark environment (30\u0026deg;C, 60 minutes). After rinsing with PBS, roots were embedded in low-melting agarose (5% (w/v)), transversely sectioned at 100 \u0026mu;m from the DZ, EZ, and MZ using a vibrating microtome (VT1000 S, Leica, Germany). Sections were mounted in glycerol (50% (v/v)), imaged with a Zeiss LSM 900 microscope (excitation/emission: 488/600-650 mm, Germany).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScanning electron microscopy and energy dispersive X-ray spectroscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFresh root segments (~5 mm) were fixed in 2.5% glutaraldehyde at 4\u0026deg;C. Then, fixed roots were triple-washed with PBS\u0026nbsp;and subsequently processed\u0026nbsp;through a graded series of ethanol washes\u0026nbsp;(30-100%),\u0026nbsp;and then subjected to critical point drying. Dried samples were\u0026nbsp;gold-coated\u0026nbsp;using a sputter coater\u0026nbsp;and examined using the scanning electron microscope (Zeiss Sigma 300, Germany). Energy-dispersive X-ray spectroscopy was performed to map the spatial distribution of Na\u003csup\u003e+\u003c/sup\u003e across the root cross-section, from the stele to the epidermis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment 4 Effect of root metabolites on core taxa growth\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolomic analysis of T1 and S2 rhizosphere soils from the conditioning phase revealed that T1 was significantly enriched in seven metabolites, including berberine, histamine, and dimethyl caffeic acid (FC \u0026gt;1.2, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Commercially standard products of these three metabolites were obtained for further validation. To assess their effects on microbial growth, \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e, \u003cem\u003eAchromobacter pulmonis\u003c/em\u003e, and \u003cem\u003eDevosia riboflavina\u003c/em\u003e were cultured in LB medium (26℃, 48 h), centrifuged, and resuspended to an OD\u003csub\u003e600\u003c/sub\u003e of 0.5. A 300 \u0026mu;L aliquot of each bacterial suspension was inoculated into R2A medium supplemented with 5 \u0026mu;M of berberine, histamine, and dimethyl caffeic acid, or sterile water (control) and incubated (26℃, 24 h). In total, 16 treatments were tested (four inocula: the three individual strains and the three-strain SynCom; four media conditions), with five replicates per treatment. After incubation, bacterial cells were centrifuged (7,000 rpm, 10 minutes), and the pellet was weighed to determine biomass by analytical balance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment 5 Microbial effects on lignin-deficient \u003cem\u003eArabidopsis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo verify the microbial-driven promotion of lignin synthesis,\u0026nbsp;\u003cem\u003eArabidopsis\u003c/em\u003e mutants were inoculated with T1- and S2-conditioned soil suspensions, as well as with the\u0026nbsp;\u003cem\u003eAgr\u003c/em\u003e_\u003cem\u003eAch\u003c/em\u003e_\u003cem\u003eDev\u003c/em\u003e SynCom.\u0026nbsp;\u003cem\u003eArabidopsis\u003c/em\u003e seeds were surface-sterilized (5 minutes) with sodium hypochlorite (NaClO) (5% (v/v)), placed in 1/2 MS medium, and stratified at 4℃ for 72 h. Seedlings were initially grown in a\u0026nbsp;climate-controlled incubator\u0026nbsp;(14/10 h, light/dark, 22℃), and 10-day-old seedlings were transplanted into sterilized growth substrate (salinized farmland soil: peat: vermiculite = 1:2:2, w/w/w). Microbial inoculation and NaCl treatment were applied one and three weeks after the seedlings established stable growth.\u003c/p\u003e\n\u003cp\u003eFor soil inocula, 150 g of T1- and S2-conditioned soils were mixed with sterile water (1:3, w/w), allowed to settle for 15 minutes, filtered through a 1-mm sieve, and the filtrate was applied to the substrate at 10 mL per pot. For the SynCom treatment, \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e, \u003cem\u003eAchromobacter pulmonis\u003c/em\u003e, and \u003cem\u003eDevosia riboflavina\u003c/em\u003e were cultured in LB medium (26℃, 48 h), centrifuged, washed three times, and resuspended in sterile water to OD\u003csub\u003e600\u003c/sub\u003e of 0.5. Equal volumes of the three strains (100 mL each) were combined and applied at 10 mL per pot. Sterile water aerved as the control.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eArabidopsis\u003c/em\u003e ecotype Col-0 and lignin synthesis-deficient mutants \u003cem\u003eCCoAOMT1\u0026nbsp;\u003c/em\u003e(SALK_151507C), \u003cem\u003efah1-2\u003c/em\u003e, and \u003cem\u003eCAD\u003c/em\u003e (SALK_201575C) were used. Col-0 and \u003cem\u003efah1-2\u0026nbsp;\u003c/em\u003ewere provided by Dr. Xuebin Zhang, while \u003cem\u003eCCoAOMT1\u003c/em\u003e and \u003cem\u003eCAD\u003c/em\u003e were obtained from AraShare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperiment 6 Field evaluation of the Syncom inoculation on wheat growth\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe widely cultivated wheat variety Jimai 22 was used to validate the effects of microbial inoculation under field conditions. Two long-term saline-alkali field sites were selected: Quzhou County, Hebei Province\u0026nbsp;(115\u0026deg;1\u0026rsquo;N, 36\u0026deg;51\u0026rsquo;E) and Dongying City, Shandong Province (37\u0026deg;42\u0026rsquo;N, 118\u0026deg;48\u0026rsquo;E). At each site, SynCom (\u003cem\u003eAgr\u003c/em\u003e_\u003cem\u003eAch\u003c/em\u003e_\u003cem\u003eDev\u003c/em\u003e) and control (sterile water) treatments were applied to 1 m \u0026times; 1 m plots, with six replicates per treatment, totaling 12 plots per site. Starting at the regreening stage (March 17), 1 L of SynCom suspension (OD\u003csub\u003e600\u003c/sub\u003e = 0.7) or\u0026nbsp;sterile\u0026nbsp;water was applied via furrow to the corresponding plots, repeated three times at two-week intervals. All other field management practices followed local standards. At maturity, plants from each plot were harvested, grain yield, grain number per spike and shoot dry weight were measured.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferences in PSTI, plant dry weight, and microbial relative abundance between salt-tolerant and salt-sensitive varieties, as well as the effects of different inocula on \u003cem\u003eArabidopsis\u003c/em\u003e lignin content, were evaluated using one-way ANOVA. And, the impacts of inocula and wheat variety on plant dry weight, lignin content, gene expression level, and root Na⁺ flux were\u0026nbsp;analyzed via two-way ANOVA. Similarly, the effects of location and microbial inocula on the yield parameters of wheat under field conditions was assessed using two-way ANOVA.\u0026nbsp;Microbial beta diversity (16S rRNA and ITS) was estimated using the \u0026ldquo;capscale\u0026rdquo; function in the \u0026ldquo;vegan\u0026rdquo; package\u003csup\u003e60\u003c/sup\u003e and\u0026nbsp;visualized with \u0026ldquo;ggplot2\u0026rdquo;\u003csup\u003e\u0026nbsp;61\u003c/sup\u003e.\u0026nbsp;PERMANOVA (Bray-Curtis distances; \u0026ldquo;adonis\u0026rdquo; in \u0026ldquo;vegan\u0026rdquo;) with permutation tests assessed the effects of soil inocula and wheat varieties on microbial community structure.\u0026nbsp;Kruskal-Wallis tests were performed to evaluate the significance of differences in microbial composition and functional potential among the treatments.\u003c/p\u003e\n\u003cp\u003eRandom forest model was implemented using \u0026ldquo;randomForest\u0026rdquo; package\u003csup\u003e62\u003c/sup\u003e in R (4.3.1) to identify representative ASVs. The optimal number of ASVs was determined using the \u0026ldquo;rfcv ()\u0026rdquo; function with 10-fold cross-validation, repeated five times, to ensure model stability. Shared ASVs were visualized using \u0026ldquo;VennDetail\u0026rdquo; upset plots. Linear regression between ASV relative abundance and plant biomass was implemented via the \u0026ldquo;lm ()\u0026rdquo; function in R.\u003c/p\u003e\n\u003cp\u003eThe PCoA plot was generated using \u0026ldquo;vegan\u0026rdquo; package\u003csup\u003e60\u003c/sup\u003e. Differential gene expression between T1- and S2-conditioned soil treatments was analyzed using \u0026ldquo;DESeq2\u0026rdquo;\u003csup\u003e\u0026nbsp;63\u003c/sup\u003e, with significance thresholds were established at |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1 and FDR \u0026lt; 0.05. Wilcoxon rank-sum tests were employed to evaluate differences in metagenomic gene abundance (T vs. S) and the relative expression of selected genes between treatments.\u003c/p\u003e\n\u003cp\u003eDifferential metabolites were determined through orthogonal projections to latent structures-discriminant analysis (OPLS-DA), implemented using \u0026nbsp;\u0026ldquo;opls()\u0026rdquo; function in \u0026ldquo;ropls\u0026rdquo;\u003csup\u003e\u0026nbsp;64\u003c/sup\u003e. Significance was defined by three criteria: variable importance in projection (VIP) \u0026gt; 1, FC threshold \u0026gt; 1.2, and \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 from Student\u0026rsquo;s t-test (\u0026ldquo;t.test()\u0026rdquo; function in R).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 16S rRNA and ITS amplicon sequencing data generated in this study have been deposited in the Beijing Institute of Genomics Data Center, Chinese Academy of Sciences, under project accession CRA036366 and CRA036372. The transcriptome sequencing data are accessible under accession number CRA036380, and metagenomic sequencing data are available under accession number CRA036367.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll codes used for data processing and analyses in this study are publicly available on GitHub (https://github.com/ Researchworker /PSF-experiment).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMachado, R. M. A., \u0026amp; Serralheiro, R. P. Soil Salinity: Effect on Vegetable Crop Growth. Management Practices to Prevent and Mitigate Soil Salinization. \u003cem\u003eHorticulturae 3\u003c/em\u003e(2), 30 (2017).\u003c/li\u003e\n\u003cli\u003eKotula, L., Zahra, N., Farooq, M., Shabala, S., \u0026amp; Siddique, K. H. M. 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Proteome Res. 14\u003c/em\u003e(8), 3322-3335 (2015).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8835674/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8835674/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Salinity imposes strong selective pressure on plant roots, yet how rhizosphere microbiomes contribute to root structural adaptation under salt stress remains unclear. Using a plant–soil feedback framework across 100 wheat accessions, we show that salt-tolerant genotypes condition distinct rhizosphere microbiomes that enhance salt tolerance across host backgrounds. These microbiome effects are linked to activation of host phenylpropanoid biosynthesis and enhanced lignin deposition in roots. Metagenomic analyses reveal enrichment of microbial functions related to stress tolerance and carbon metabolism, while root transcriptomics identify coordinated induction of lignin biosynthetic genes. Isolation and reconstitution of core taxa identified a synthetic microbial community that promoted endodermal lignin deposition, enhanced Na⁺ efflux, restricted Na⁺ entry into the stele, and increased wheat yield in salinized field sites. Analyses using Arabidopsis lignin-deficient mutants further indicate that lignification is a key, though not exclusive, component of this response. Together, our results uncover an unappreciated microbiome-driven mechanism of root anatomical remodeling that contributes to plant salt tolerance.","manuscriptTitle":"Rhizosphere microbiome promotes wheat salt tolerance through root lignin biosynthesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 05:05:55","doi":"10.21203/rs.3.rs-8835674/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be1aecf7-b0a1-45f6-afbd-b4621607bc7c","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62678223,"name":"Biological sciences/Microbiology"},{"id":62678224,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-02-18T10:41:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 05:05:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8835674","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8835674","identity":"rs-8835674","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}