Host Genotype Shapes Rhizosphere Microbiome Assembly and Function to Modulate Cadmium Translocation in Rice

Host Genotype Shapes Rhizosphere Microbiome Assembly and Function to Modulate Cadmium Translocation in Rice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Host Genotype Shapes Rhizosphere Microbiome Assembly and Function to Modulate Cadmium Translocation in Rice Zhengliang Luo, Shangdu Zhang, Lv Yanmei, Di Guan, Liu Qingyun, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9471108/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Aims Cadmium (Cd) contamination in rice ( Oryza sativa L. ) threatens food safety and human health. Developing low-Cd-accumulating cultivars and understanding their interaction mechanisms with the environmental microbiome has become a key task for ensuring food security. This study explores the role of host genotype (plant variety) in shaping the rhizosphere microbiome and its functional implications for modulating Cd translocation. Methods We compared a high-Cd-accumulating control (CK, Tianyou) with two low-Cd cultivars, SX (Shaoxiang) and QL (Qinglian). Both SX and QL significantly reduced grain Cd content, primarily through restricting Cd translocation from roots to aerial tissues. Results Integrated metagenomic and correlation analyses revealed that the host genotype shaped the assembly of functionally distinct rhizosphere microbiomes. The SX cultivar assembled a sulfur-cycling anaerobic microbiota, enriched with methanogenic archaea (e.g., Methanothrix ) and the sulfate-reducing bacterium Desulfovibrio -forming a consortium implicated in reducing Cd bioavailability. In contrast, the QL cultivar enriched a heterotrophic, carbon-metabolizing microbiota, characterized by organic matter-degrading bacteria (e.g., Labilithrix ), suggesting a role in Cd complexation. Beta-diversity analysis confirmed that varietal differences were a key factor shaping microbial community structure. Co-occurrence network analysis linked these community shifts to Cd distribution, identifying specific taxa (e.g., Betaproteobacteria and Chloroflexota ) with opposing correlations to aerial tissue Cd content. Conclusions Together, these results demonstrate that host genotype shapes rhizosphere Microbiome assembly to modulate cadmium translocation in rice. This establishes a genotype-microbiota-function link, where host genotype shapes microbiome assembly, and the recruited microbial consortia, in turn, modulate Cd dynamics and translocation in rice. Cadmium accumulation Rhizosphere microbiome Host genotype Low-Cd rice cultivars Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Rice ( Oryza sativa L. ) is the primary staple food for approximately half of the global population [ 1 , 2 ]. Recently, an increasing number of rice crops have been cultivated on contaminated soils due to multiple factors, including anthropogenic activities (e.g., chemical fertilizer and agrochemical overuse, solid and liquid waste discharge, smelting, and mining), the demands of a growing global population, and limited land resources. These factors have led to the accumulation of various potentially toxic elements, including heavy metals in rice crops [ 3 ]. Cadmium (Cd) is a common heavy metal contaminant in rice cultivation [ 4 , 5 ]. Excessive Cd uptake and accumulation decrease rice plant growth and reduce grain quality [ 6 ]. Moreover, consumption of Cd-contaminated rice trigger multiple health issues in humans, including cancer and cardiovascular, reproductive, and nervous system diseases [ 3 , 6 ]. Cd accumulation in rice plants and seeds has been closely linked to multiple factors, including rice plant genotypes, soil physicochemical properties, and rhizosphere microorganisms [ 7 , 8 ]. Conventional remediation approaches for Cd-contaminated paddy fields include chemical remediation, organic amendments (e.g., biochar, composts, and manures), fertilization, soil removal and replacement, and phytoremediation using plants with Cd absorptive capacity [ 9 , 10 ]. Above all, soil microbial remediation has emerged as a highly acceptable technique for restoring heavy metal-polluted soils due to its advantages, including high efficiency, low cost, and environmental friendliness [ 11 , 12 ]. Evidence has implicated microorganisms in the bioremediation of Cd-contaminated farmland and the reduction of Cd accumulation in rice [ 13 – 15 ]. For example, microorganisms can reduce Cd toxicity and availability through multiple mechanisms, including biomineralization, biosorption, biotransformation, and bioaccumulation [ 16 , 17 ]. Global research on the plant microbiome enhanced the complex interactions mechanisms between plants and microorganisms. Evidence emphasize that rhizosphere microbiome plays a significant role in plant breeding, as modulating the structure of microbial communities can enhance crop stress tolerance, growth performance, and yield [ 18 ]. Specific studies have shown that rice cultivars can shape their rhizosphere microbiomes through root exudates and selective recruitment [ 19 , 20 ]. For instance, some low-Cd-accumulating rice cultivars recruit specific microbial taxa associated with sulfur cycling and Cd immobilization, or those involved in siderophore secretion and iron activation, thereby inhibiting Cd uptake [ 20 , 21 ]. Despite these advances, the role of the rhizosphere microbiome in Cd accumulation and translocation in low-Cd-accumulating rice cultivars remains to be fully elucidated. We hypothesize that low-Cd-accumulating cultivars distinctly shape the rhizosphere microbiome, which contribute to reduced Cd uptake and translocation. Accordingly, in this study, we aim to explore the effects of rhizosphere microbiome biodiversity on Cd accumulation in two newly cultivated low-Cd-accumulating rice varieties (SX (Shaoxiang) and QL (Qinglian)) at maturity and to elucidate how different rice varieties influence the rhizosphere microecological environment. Materials and methods Experimental design and cultivation This study was conducted at the experimental field station of the Hunan Hybrid Rice Research Center (113°26'57.2"E, 28°29'36.9"N). Three rice cultivars with divergent cadmium (Cd) accumulation capacities were investigated: a high-grain-Cd accumulator (Tianyou, CK) and two low-grain-Cd accumulators (Shaoxiang, SX; Qinglian, QL). The experiment employed three independent, fully enclosed cultivation systems within a steel-framed greenhouse. Each system comprised an irrigation and drainage tank regulated by a remotely controlled water management system to ensure environmental uniformity. The soil contained native cadmium at a background concentration of 0.31 ± 0.1 mg/kg (Table 1). A completely randomized block design with three replicates was implemented. Seeds were sown on 23 June 2023, and seedlings were transplanted on 18 July 2023. For each cultivar, plants were arranged in a two-row plot with ten hills per row and two seedlings per hill, using a hill spacing configuration of 20 cm × 20 cm. No base fertilizer was applied. Top-dressing with urea (60 kg/ha) and potassium chloride (30 kg/ha) was uniformly administered across all tanks. A regime of intermittent irrigation was maintained, incorporating three scheduled soil-drying phases: (i) at the peak tillering stage (3 days), (ii) post-heading (3 days), and (iii) pre-harvest (3 days). Continuous flooding was sustained during intervening periods. Irrigation was recommenced upon the visual observation of initial soil surface cracking to impose a standardized moisture stress. Prior to rhizosphere soil collection, foreign material such as debris, stones, and plant litter was removed. Rhizosphere soil (defined as the soil adhering tightly to the root surface) was subsequently collected using a sterile spatula. For each cultivar, samples were collected from three randomly selected hills (constituting biological replicates) within each of the three experimental units (serving as technical replicates). We acknowledge that the comparative scope of this study is limited to three specific cultivars; consequently, the generalizability of the identified microbiota-Cd relationships requires future validation with larger and more diverse genetic panels. Determination of Cd content in plants Plant samples were ground individually using a grinder. To avoid cross-contamination between samples, the grinder was cleaned thoroughly before each use. The ground material was passed through a sieve and stored in a desiccator. Dried samples (0.5000 g) were weighed and placed into microwave digestion vessels. Nitric acid (5 mL) and hydrogen peroxide (2 mL) were added. After digestion, the acid was evaporated until nearly dry. The digestion vessel was rinsed three times with nitric acid solution, and the rinsate was transferred to a 25 mL volumetric flask. The solution was diluted to the mark with nitric acid solution and mixed thoroughly. Reagent blank tests were performed concurrently. Cadmium content was determined by atomic absorption spectrophotometry in accordance with the National Food Safety Standard for the Determination of Cadmium in Foods (GB 5009.15-2014). Soil physicochemical property determination Soil properties and cadmium concentrations in roots, stem sheaths, leaves, and seeds at rice maturity were determined using the microwave digestion method. For soil analysis, after removing impurities, stones, and plant residues, ten soil samples from each treatment condition were pooled, air-dried, pulverized, and passed through a 10-mesh (2 mm) standard sieve. The following physicochemical properties were then determined: Total soil cadmium was measured by the complete digestion method using graphite furnace atomic absorption spectrophotometry (GB/T 17141-1997). Available soil cadmium was determined by the leaching method using graphite furnace atomic absorption spectrophotometry (GB/T 23739-2009). Total nitrogen and alkaline-hydrolyzable nitrogen were determined by the Kjeldahl method and the alkaline hydrolysis diffusion method, respectively (LY/T 1228-2015). Total phosphorus was determined using the perchloric acid-sulfuric acid method (GB 9837-88). Available phosphorus was determined using a spectrophotometer (NY/T 1121.7-2014). Total potassium was measured using the sodium hydroxide fusion method (GB 9837-88). Available potassium was measured by flame atomic absorption spectrophotometry (NY/T 889-2004). Soil pH was determined potentiometrically at a water-to-soil ratio of 1:2.5 (NY/T 1121.2-2006). Soil organic matter content was measured by redox calorimetry (NY/T 1121.6-2006). Plant tissue samples (roots, stem sheaths, leaves, and seeds) were digested using the microwave digestion method described above, and cadmium content was determined by atomic absorption spectrophotometry. Heavy metal translocation assessment The capacity for heavy metal translocation within the plant was evaluated by calculating the biological transfer factor (TF) and the bioconcentration factor (BCF). The BCF was calculated using the following formulas: (1) (2) The translocation factor (TF) from roots to aboveground parts was calculated as: (3) Where is the bioconcentration factor of the aboveground parts, is the bioconcentration factor of the roots, is the heavy metal content in the aboveground parts (mg/kg), is the heavy metal content in the roots (mg/kg), is the heavy metal content in the soil (mg/kg). Statistical data analysis was performed using GraphPad Prism version 7 (La Jolla, CA, USA). The results are displayed as the means ± standard deviations. Soil DNA Library Preparation, and Metagenomic Sequencing A total of 1 μg of DNA per sample was used for sequencing. Briefly, DNA fragments of approximately 350 bp were generated by sonication. The fragments were end-polished, A-tailed, and ligated with full-length adaptors for Illumina sequencing, followed by PCR amplification. The PCR products were purified using the AMPure XP system, and libraries were prepared and assessed for size distribution using the Agilent 2100 Bioanalyzer. Three biological replicates were analyzed per sample. Raw sequencing data were obtained using the Illumina NovaSeq 6000 platform. Clean data were acquired using Readfq V8 and subsequently subjected to host sequence removal by alignment against a host database using the Basic Local Alignment Search Tool (BLAST), with Bowtie 2.2.4 employed as the default software to filter host-origin reads. All remaining reads were combined, and SOAP denovo was used to generate mixed assemblies. Scaftigs (≥ 500 bp) obtained from both single and mixed assemblies were used for open reading frame (ORF) prediction with MetaGeneMark software. Sequences shorter than 100 nt were filtered from the prediction results. To reduce sequence redundancy, ORFs were clustered using CD-HIT software to generate a unique initial gene catalog. Clean reads from each sample were mapped to the initial gene catalog using Bowtie 2.2.4. Genes with two or fewer reads in any sample were filtered out, and the resulting gene catalog (UniGene database) was used for subsequent analyses. Gene abundance in each sample was calculated based on the number of mapped reads and gene length. Basic statistical analysis, core- and pan-genome analyses, sample correlation analysis, and Venn diagram analysis of gene numbers were all performed based on gene abundance in the gene catalog. DIAMOND software was used to blast the UniGene database against sequences of Bacteria, Fungi, Archaea, and Viruses extracted from the NCBI nr database (https://www.ncbi.nlm.nih.gov/). The clean reads were deposited in the NCBI Sequence Read Archive (SRA) database under the accession number: SUB16093140. Functional annotation and resistance gene analysis Functional annotation of resistance genes was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups (eggNOG), and the Carbohydrate-Active Enzymes (CAZy) databases. Unigenes were aligned to the Comprehensive Antibiotic Resistance Database (CARD) using Resistance Gene Identifier (RGI) software with default parameter settings. Based on the alignment results, the relative abundance of antibiotic resistance ontology (ARO) clusters was visualized using abundance bar charts and abundance cluster heatmaps. Differences in the number of resistance genes among soil groups were also displayed. Additionally, the distribution of resistance genes across samples, the species attribution of these genes, and their associated resistance mechanisms were investigated. Statistical significance of differences in resistance gene abundance among soil groups was assessed using Wilcoxon rank-sum test or ANOVA, with considered statistically significant. Results Varietal Differences in Cadmium Accumulation and Root Physiology Significant differences in root system architecture and grain morphology were observed among the three studied rice cultivars (QL, SX, and CK) at the mature stage (Fig. 1). Cultivar SX exhibited a dense root system characterized by extensive lateral root development and a high density of root tips, a morphology typically associated with enhanced nutrient acquisition potential. In contrast, cultivar QL displayed a comparatively sparse root system, with pronounced primary root elongation but reduced lateral root proliferation. Cultivar CK demonstrated an intermediate root phenotype (Fig. 1A). Corresponding varietal differences were evident in grain morphology, with SX producing significantly longer grains than both QL and CK (Fig. 1B), a trait potentially linked to superior nutrient partitioning and yield capacity. Despite a consistent rhizosphere soil pH across all cultivars (approximately 5.8 throughout the growth cycle; Fig. 2A), significant varietal differences were observed in Cd bioavailability. At the mature stage, the concentration of bioavailable Cd (DTPA-extractable fraction) in the rhizosphere soil of the two low-grain-Cd cultivars, QL (0.30 mg/kg) and SX (0.20 mg/kg), was higher than that of the high-grain-Cd cultivar CK (0.16 mg/kg) (Fig. 2C). This indicates that the low-accumulating cultivars did not reduce Cd uptake by decreasing its general soil bioavailability. Instead, it suggests a cultivar-specific rhizosphere modification that alters Cd speciation or ligand complexation, potentially increasing the pool of soluble Cd ions while simultaneously deploying a more efficient root-level exclusion or sequestration strategy. BCF and TF of Cd in different rice cultivars The subsequent fate of bioavailable Cd is determined at the root-soil interface (rhizoplane and root apoplast). Analysis of Cd distribution in plant tissues revealed that roots were the primary sink, accumulating Cd at concentrations (0.50-2.80 mg/kg) an order of magnitude higher than stems (0.05-1.2 mg/kg) and leaves (0.02-0.25 mg/kg) (Fig. 2D, 2E). This implicates the root system as the critical filter. The Biological Concentration Factor (BCF) and Translocation Factor (TF) provide a quantitative measure of this interface control. BCF analysis confirmed that cultivar CK had the strongest capacity to concentrate Cd from the rhizosphere soil into root tissues (Fig. 3B, 3C). Crucially, TF analysis revealed the decisive mechanism for low grain Cd: while CK efficiently transferred Cd from roots to shoots, cultivars QL and SX exhibited a significantly reduced capacity to translocate Cd from roots to aerial tissues (stems and leaves; Fig. 3D, 3E). The lack of a significant difference in the seed-stem TF between QL and SX (Fig. 3F) further emphasizes that the major regulatory checkpoint is at the root-to-shoot barrier, not during remobilization within the shoot. Collectively, these results demonstrate that the low-Cd-accumulating cultivars QL and SX achieve a marked reduction in grain Cd content primarily by restricting the root-to-shoot translocation of Cd, rather than by limiting its initial uptake from the soil. This underscores the role of varietal genetic factors in determining differential Cd accumulation in rice. Host Genotype Shapes Rhizosphere Microbial Community Structure The distinct root system architectures and divergent cadmium (Cd) accumulation phenotypes observed in the QL and SX cultivars prompted an investigation into whether these varietal differences are associated with concomitant shifts in rhizosphere microbial community structure. Alpha diversity metrics, reflecting species richness and evenness, exhibited no significant differences among cultivars (Fig. S1B, C). In contrast, beta-diversity analysis revealed pronounced, cultivar-dependent clustering of microbial communities (Fig. S1D, E), indicating that plant genotype is a principal determinant of rhizosphere microbiome composition, irrespective of similar taxonomic richness. Although dominant bacterial phyla (e.g., Pseudomonadota, Acidobacteriota, Chloroflexota) were conserved across cultivars (Fig. 4A-C), their relative abundances varied significantly. Subsequent differential abundance analysis at finer taxonomic resolutions elucidated distinct, cultivar-specific enrichment patterns (Fig. 5A-C). At the phylum level, the relative abundance of Spirochaetota and Candidatus Cloacimonadota was significantly elevated in the SX cultivar, whereas Candidatus Parcubacteria demonstrated a significant reduction compared to the other two cultivars (p< 0.05; Fig. 5A). These findings suggest that host genotype exerts a selective influence on the recruitment of specific, low-abundance microbial lineages within the rhizosphere. Genus-level differential abundance analysis further delineated distinct enrichment profiles corresponding to each cultivar (Fig. 5B, C). Notably, key taxa including the class Betaproteobacteria, the class Planctomycetia, the genus Methylocystis , the phylum Armatimonadota, and the genus Methanocella exhibited significant differential abundance across all three cultivars, underscoring systematic reorganization of the community. Relative to the high-Cd accumulator CK, the SX cultivar was primarily characterized by the enrichment of a saprotrophic functional guild implicated in organic matter decomposition, encompassing genera such as Labilithrix , Tardiphaga , and the acid-tolerant Candidatus Acidiferum , alongside plant-associated genera like Bradyrhizobium , Sphingomonas , and Ideonella . This enrichment profile implies the SX rhizosphere fosters an environment conducive to enhanced carbon turnover and nutrient mobilization, potentially linked to its unique root exudation chemistry. Conversely, the QL cultivar, when compared to CK, exhibited a pronounced enrichment of a functionally distinct microbiome dominated by strictly anaerobic and redox-cycling taxa. This consortium included methanogenic archaea ( Methanothrix , Methanocella , Methanoregula , Methanospirillum ), the sulfate-reducing bacterium Desulfovibrio, and the plant growth-promoting rhizobacterium (PGPR) Burkholderia. The co-enrichment of methanogens and sulfate reducers signifies the establishment of anaerobic microniches and active sulfur cycling in the QL rhizosphere, a biogeochemical process that may directly attenuate cadmium bioavailability through the precipitation of metal sulfides. Direct comparison between the two low-Cd cultivars highlighted their contrasting ecological strategies: QL significantly enriched Desulfovibrio , Burkholderia , Sphingomonas , and methanogenic archaea relative to SX, reinforcing the predominance of an anaerobic, sulfur-transforming metabolic network. Notably, both SX and QL recruited known PGPR (e.g., Burkholderia , Sphingomonas ), suggesting a convergent strategy to bolster plant fitness under cadmium stress, albeit enacted within distinct overarching microbial functional frameworks. Functional Diversification of Microbiomes Linked to Cadmium Dynamics Functional profiling of the rhizosphere microbiomes, based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, revealed pronounced metabolic divergence that aligned with the observed taxonomic differentiation among cultivars (Fig. 6A, B, D). Comparative analysis of metabolic pathways demonstrated distinct functional specializations. Relative to the high-cadmium accumulator (CK), the low-accumulating cultivar QL exhibited significant enrichment in pathways integral to anaerobic energy metabolism and biosynthesis. This included methane metabolism, starch and sucrose metabolism, glycolysis/gluconeogenesis, oxidative phosphorylation, and amino acid biosynthesis. This metabolic profile, coupled with the taxonomic co-enrichment of methanogenic archaea and sulfate-reducing bacteria (e.g., Desulfovibrio ), indicates that the QL rhizosphere sustains a microbiome proficient in anaerobic redox processes and sulfur cycling, a key mechanism for cadmium immobilization via metal sulfide precipitation. In contrast, the low-accumulating cultivar SX displayed a divergent functional signature, characterized by the enrichment of pathways governing the biosynthesis of specialized metabolites and cellular components. These included pathways for secondary metabolite biosynthesis, amino sugar and nucleotide sugar metabolism, riboflavin metabolism, porphyrin metabolism, peptidoglycan biosynthesis, and glycerophospholipid metabolism. This ensemble suggests the SX rhizosphere microbiome possesses an enhanced genetic potential for the production of stress-related compounds, cofactors, and specific membrane constituents, which may facilitate metal chelation and environmental adaptation. The high-accumulator CK was distinguished by the enrichment of pathways associated with central intermediary metabolism, including glyoxylate and dicarboxylate metabolism and fatty acid metabolism, reflecting a metabolic network oriented toward core carbon and lipid transformation. Analysis of antibiotic resistance genes (ARGs) further elucidated varietal differences in microbial adaptive traits. The SX rhizosphere harbored a significantly broader and more abundant suite of ARGs, including optrA (pleuromutilin resistance), tetA(46) (tetracycline resistance), the vancomycin resistance regulatory genes vanRN and vanRM , and numerous genes encoding multidrug efflux systems (e.g., bcrA , imrD , rosB ). The co-enrichment of this diverse array of resistance determinants suggests the SX rhizosphere may function as a reservoir for multidrug resistance genes, with a notable potential for horizontal gene transfer. Conversely, the QL rhizosphere demonstrated enrichment of a different ARG repertoire, featuring genes such as cpxA (envelope stress response), vgaE and vanL (streptogramin and vancomycin resistance), and efflux genes including mdtB and adeF . This result indicates that, while both low-Cd cultivars enrich for antibiotic resistance, they foster microbiomes employing distinct resistance strategies—with SX favoring a broad-spectrum, multidrug efflux and target protection approach, and QL exhibiting a profile more focused on specific antibiotic modifications and stress sensing. Microbial-phenotype Correlations: Associating the Microbiome with Cd Translocation Correlation analyses were employed to identify statistical associations between specific microbial taxa, functional gene profiles, and the spatial distribution of cadmium (Cd) within the rice-soil system. Spearman rank correlation (Fig. 7A) revealed significant relationships between microbial community features and Cd accumulation metrics. Notably, the relative abundance of specific taxa, including the phyla Acidobacteriota and Nitrospirota and the class Deltaproteobacteria, showed positive correlations with Cd bioconcentration factors in root tissues. In contrast, leaf Cd content was more strongly correlated with the abundance of phyla such as Verrucomicrobiota and Planctomycetota. Hierarchical cluster analysis of microbial community profiles yielded a dendrogram with distinct clustering by sample type (root, soil, leaf), providing further evidence for the compartment-specific assembly of microbiomes along the plant axis. Heatmap visualization of correlation matrices (Fig. 7B-D) delineated specific associations between microbial functional traits and host Cd phenotypes. Among antibiotic resistance determinants, the regulatory gene mtrA (associated with macrolide resistance) exhibited a significant negative correlation with leaf samples. Functional gene categories related to complex carbon degradation—specifically, carbohydrate-active enzyme (CAZyme) families such as Glycoside Hydrolases, Carbohydrate-Binding Modules, and cellulosomal components—were positively correlated with root and rhizosphere soil samples. This underscores the rhizosphere as a primary site for polysaccharide depolymerization, a process integral to nutrient cycling. Similarly, broad functional categories encompassing microbial signaling (e.g., Two-component systems, Quorum sensing), transport (ABC transporters), and central carbon metabolism (Pyruvate metabolism) were predominantly associated with the root-soil interface, reflecting a microenvironment characterized by high metabolic activity and microbe-microbe/plant-microbe communication. Key correlative results integrating taxonomy and phenotype as following. Taxa within the class Betaproteobacteria, including the order Burkholderiales, displayed positive correlations with Cd content in aboveground (shoot) tissues. The phylum Chloroflexota, which was consistently enriched in both low-Cd cultivars (QL and SX), demonstrated a significant negative correlation with shoot Cd content. This suggests a potential functional role for this phylum in the mitigation of Cd translocation to aerial plant parts. Functional gene profiling indicated that the SX rhizosphere, distinguished by its enrichment of sulfur-cycling and anaerobic taxa, concurrently harbored a greater abundance of specific ARGs. This co-occurrence implies a potential linkage between adaptation to metal stress and the maintenance of other resistance determinants in the rhizosphere. Co-occurrence network analysis (Fig. 7E) elucidated the structural relationships between microbial communities across different plant compartments. The analysis revealed robust connectivity, evidenced by a high density of edges, between microbial nodes derived from root and soil communities across all cultivars. This finding reinforces the conceptual model of a tightly coupled rhizosphere microbiome. Notably, the network associated with the low-Cd cultivar QL exhibited stronger root-soil connectivity than that of the high-Cd cultivar CK. In contrast, microbial communities inhabiting stem and leaf tissues demonstrated weaker topological associations with the soil network, indicating that the assembly of aerial tissue microbiomes is governed by more independent or distinct ecological processes. Discussion Host genotype modulates rhizosphere microbiome assembly, influencing Cd dynamics Compelling evidence demonstrates that plant genotype, primarily through root exudation, acts as a selective filter to shape the composition and function of rhizosphere microbial communities, thereby modulating critical processes such as nutrient cycling and metal(loid) bioavailability [ 24 , 25 ]. However, the precise mechanisms linking specific rice genotypes—particularly those with divergent cadmium (Cd) accumulation capacities—to the assembly of functionally specialized microbiomes remain inadequately characterized. Elucidating this genotype-microbiome linkage is essential for a mechanistic understanding of how host genetic factors interact with the soil microbiome to determine metal accumulation phenotypes. To address this, we employed an integrated multi-omics framework, analyzing community diversity, taxonomic composition, differential abundance, and functional potential to compare the rhizosphere microbiomes of a high-Cd-accumulating cultivar (CK) and two low-Cd-accumulating cultivars (QL and SX). Consistent with prior studies where host effects are more pronounced in community structure than in richness [ 3 , 11 , 24 , 25 ], we found that alpha-diversity indices were comparable across cultivars, whereas beta-diversity analysis revealed statistically distinct microbial community clusters. This confirms that host genotype is a significant determinant of microbiome assembly. Importantly, these structural shifts were correlated with plant Cd phenotypes. For example, the relative abundance of the class Betaproteobacteria showed a significant positive correlation with Cd content in aerial tissues. Furthermore, the differential enrichment of candidate phyla associated with anaerobic niches (e.g., Candidatus Parcubacteria [ 26 ], Candidatus Cloacimonadota [ 27 ]) suggests genotype-specific recruitment of low-abundance, potentially functionally important taxa. A key finding supporting our hypothesis is the apparent paradox in the low-Cd cultivars: QL and SX achieved a ~ 75% reduction in grain Cd, yet maintained or even slightly elevated the concentration of bioavailable Cd in their rhizosphere soil at maturity compared to CK. This suggests that the primary mechanism limiting grain Cd is not a generalized reduction of Cd solubility in the bulk rhizosphere, but rather a spatially focused interception and sequestration of the bioavailable Cd pool at or near the root interface. The subsequent analyses of the differentially enriched microbial consortia provide testable mechanistic hypotheses for how this root-level control may be facilitated. Functional consortia hypothesized to modulate Cd speciation and bioavailability Our data indicate that the two low-Cd cultivars assemble functionally divergent microbiomes that influence Cd dynamics in the root zone. Anaerobic Sulfur-Cycling Consortium.​ Compared to CK, SX enriched a microbiome dominated by strictly anaerobic taxa, including methanogenic archaea (e.g., Methanothrix [ 30 ], Methanocella [ 31 ]) and the sulfate-reducing bacterium (SRB) Desulfovibrio. The co-enrichment of these taxa is indicative of active sulfur cycling in anaerobic microniches, a process that can profoundly influence metal bioavailability. Sulfate reduction by SRBs generates sulfide (S²⁻), which can immobilize Cd through the precipitation of highly insoluble cadmium sulfide (CdS) [ 34 ]. The significant negative correlation we observed between Desulfovibrio abundance and bioavailable Cd in the SX rhizosphere is consistent with this mechanism, as previously demonstrated in studies where microbial inoculants increasing SRB abundance reduced grain Cd [ 13 , 35 ]. Concurrent enrichment of PGPR like Burkholderia [ 32 ] and Sphingomonas [ 33 ] may further bolster plant fitness under metal stress, potentially influencing root physiology and Cd handling [ 14 ]. Heterotrophic Carbon-Metabolizing Consortium.​ In contrast, QL enriched a saprotrophic microbiome characterized by taxa involved in complex organic matter decomposition (e.g., Labilithrix [ 28 ], Tardiphaga [ 29 ]) and acid-tolerant organisms. This suggests enhanced heterotrophic carbon turnover, likely driven by cultivar-specific root exudates. Such communities can influence metal speciation through (i) biosorption​ to microbial cell walls and extracellular polymeric substances (EPS) and (ii) chelation​ by microbially produced organic acids, which can alter Cd solubility and its phytoavailability [ 36 , 37 ]. The observed negative correlation between the abundance of these carbon-metabolizing taxa and grain Cd content supports a potential role in limiting Cd uptake, potentially by increasing Cd retention in the rhizosphere soil or root apoplast. A propsed integrated model: from rhizospherespeciation to root-level control Synthesizing these findings, we propose a microbiota-mediated mual-megulation model​ for Cd accumulation in rice. This model posits that the net Cd translocation to grains is governed by the dynamic balance between two broad categories of root/rhizosphere processes. Cadmium Mobilization:​ microbial processes (e.g., acidification) or specific taxa may increase Cd solubility or facilitate its plant uptake. Cadmium Immobilization:​ this includes direct sequestration in the rhizosphere (e.g., via biotic sulfide precipitation) or stabilization at the root interface (e.g., via biosorption/chelation or apoplastic trapping). In this experiment, the high-Cd cultivar CK may maintain a balance that permits net mobilization and translocation. In contrast, the distinct root phenotypes of SX (dense root system) and QL (elongated primary root) appear to recruit specialized microbiomes that potentiate specific immobilization pathways, thereby shifting the balance. For SX, the recruited anaerobic consortium is hypothesized to drive in situ CdS precipitation. For QL, the active saprotrophic community is posited to enhance Cd complexation and biosorption. In both scenarios, a key outcome is the interception and stabilization of bioavailable Cd in the root zone, effectively depleting the pool of Cd available for xylem loading. The results align with emerging concepts of the root-soil interface as a critical filter for metal contaminants [ 38 , 39 ]. The root-soil microbial network connectivity observed, particularly in QL, underscores the tight functional coupling at this decisive interface. The concurrent enrichment of antibiotic resistance genes (ARGs) alongside metal-tolerant taxa highlights the potential for co-selection of resistance determinants in contaminated agroecosystems, an important consideration for environmental risk assessment [ 40 ]. Conclusion and Future Perspectives This study establishes a correlative link between rice genotype, rhizosphere microbiome assembly, and Cd accumulation phenotype. We demonstrate that low-Cd cultivars recruit distinct microbiomes associated with putative Cd-immobilizing functions, providing a plausible extrinsic mechanism complementing intrinsic root physiology for reduced metal translocation. While our multi-omics approach identifies these associations, causal validation is required.​ Future manipulative experiments, such as gnotobiotic studies with synthetic microbial communities (SynComs) [41] or targeted inhibition of specific pathways (e.g., sulfate reduction), are essential to definitively test the roles of key taxa like Desulfovibrio and Chloroflexota . Nonetheless, this work provides a foundational framework for developing novel, microbiome-informed strategies—such as breeding rice cultivars optimized to recruit beneficial microbial consortia or applying tailored bio-inoculants to mitigate Cd accumulation in grains and enhance the safety of rice production. Declarations Acknowledgements This study was supported by grants from the National Key R&D Program of China (Grant No. 2024YFD2301401) to Z.L., S.Z. and K.Z. and Agricultural Science and Technology Innovation Fund Project of Hunan Province, China (Grant No. 2024CX09) to K.Z. Funding This study was supported by the National Key R&D Program of China (2024YFD2301401) and the Agricultural Science and Technology Innovation Fund Project of Hunan Province, China (2024CX09). Conflict of interest The authors have no conflicts of interest to declare. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Data availability The clean reads were deposited into the NCBI Sequence Read Archive (SRA) database under the accession number SUB11813545. Materials availability Not applicable. Code availability Not applicable. Author contributions Conceptualization, Z.L.; methodology, Z.L., S.Z., Y.L., D.G., Q.L., F.Z. and K.Z.; investigation, Z.L., S.Z., D.G., and K.Z.; writing-original draft preparation, Z.L., S.Z., D.G., F.Z. and K.Z.; writing-review and editing, Z.L. and F.Z.; supervision, K.Z.; funding acquisition, Z.L., S.Z. and K.Z. All authors have read and agreed to the published version of the manuscript. References FAO. 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A novel anoxygenic phototrophic Chloroflexota lineage uses a heliorhodopsin-based proton pump to drive anaerobic photoheterotrophy. Science. 2023;380: 1210-1215. https://doi.org/10.1126/science.add5853 Li J, Wang H, Zhang K, et al. A plant microbiome-based strategy for phytoremediation: Cupriavidus (Betaproteobacteria) degrades trichloroethylene and promotes poplar growth via auxin secretion. Nat Biotechnol. 2024;42: 456-465. https://doi.org/10.1038/s41587-023-01952-z Zhang Y, Liu H, Wang X, et al. A Paraburkholderia (Burkholderiales) rhizobacterium orchestrates plant systemic resistance against leaf pathogens via a novel root-to-shoot signaling metabolite. Nat Commun. 2023;14: 5678. https://doi.org/10.1038/s41467-023-41445-w Chi, J.; Liu, K.; Huang, M.; Wu, S.; Meng, X.; Zhang, W.; Zhang, X.; Putnis, C. V.; Fang, L.; Li, F., Oxygen vacancy-augmented extracellular ROS generation and surface affinity for arsenic sequestration in rice iron plaques. Environ. Sci. Technol. 2025, 59, 25889−25899. https://doi.org/10.1021/acs.est.5c11257 Chi, Y., Ma, X., Chu, S. et al. Nitrogen cycle induced by plant growth-promoting rhizobacteria drives “microbial partners” to enhance cadmium phytoremediation. Microbiome 13, 113 (2025). https://doi.org/10.1186/s40168-025-02113-x Xue, R., Zhang, Y., Li, H. et al. Persistent antimicrobial resistance during soil remediation driven by residual heavy metal co-selection. ISME J. 2026 Jan 14;20(1):wrag058. https://doi.org/ 10.1093/ismejo/wrag058 Table Table 1 Initial soil physicochemical properties of the experimental field. Total N (g/kg) Total P (g/kg) Total K (g/kg) SOM (g/kg) N (mg/kg) Available P (mg/kg) Available K (mg/kg) PH Cd content in soil (mg/kg) 2.43 1.111 13.42 42.1 231 12.6 74 5.5 0.31 Supplementary Files S1.jpeg Comparison of rhizosphere microbial community structure and functional differences among different varieties at maturity stage (A) Data information, (B) Alpha diversity analysis of the coverage curves, (C) Alpha diversity analysis of the Simpson index, (D) Distances box plot of beta diversity, (E) PCA at the OTU level. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 22 Apr, 2026 Editor assigned by journal 22 Apr, 2026 First submitted to journal 21 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-9471108","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628127962,"identity":"e7e5b489-7698-4c75-ac2b-4ff4cb3274b7","order_by":0,"name":"Zhengliang Luo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhengliang","middleName":"","lastName":"Luo","suffix":""},{"id":628127963,"identity":"480443b6-71e1-4e39-95ae-5f138139e52c","order_by":1,"name":"Shangdu 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11:01:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9471108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9471108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108493693,"identity":"eb970f6a-8a74-4ea9-8be2-18f75f26ee94","added_by":"auto","created_at":"2026-05-05 10:01:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":421447,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic comparison of three rice cultivars and soil properties\u003c/p\u003e\n\u003cp\u003e(A) Plant architecture, (B) Grain characteristics.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9471108/v1/3f1c137760b5475acf5a1621.jpeg"},{"id":108493676,"identity":"a8a09e14-e418-4f8f-b9af-b4ac8ff0d6e3","added_by":"auto","created_at":"2026-05-05 10:01:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":363380,"visible":true,"origin":"","legend":"\u003cp\u003eSoil physicochemical properties and cadmium content in different rice cultivars across growth stages\u003c/p\u003e\n\u003cp\u003e(A) Bar plot of significantly different rhizosphere soil PH, (B) Bar plot of significantly different rhizosphere soil total Cd, (C) Bar plot of significantly different rhizosphere soil available Cd, (D) Bar plot of significantly different Cd content in roots, (E) Bar plot of significantly different Cd content in leaves, (F) Bar plot of significantly different Cd content in stems.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9471108/v1/6494d87cd1bee885bdf3a966.jpeg"},{"id":108493644,"identity":"30fb9695-ac96-4a0c-be2c-04369c3ce2d2","added_by":"auto","created_at":"2026-05-05 10:01:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":275301,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of cadmium enrichment capacity in three different rice cultivars at maturity\u003c/p\u003e\n\u003cp\u003e(A) Bar plot of significantly different Cd content in seeds, (B) Bar plot of significantly different rhizosphere soil available Cd, (C) Bar plot of significantly different bioconcentration factor of Cd, (D) Bar plot of significantly different transfer coefficients of stem-root, (E) Bar plot of significantly different transfer coefficients of leaf-stem, (F) Bar plot of significantly different transfer coefficients of seed-stem. Note: **P\u0026lt;0.01, ***P\u0026lt;0.001, ****P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9471108/v1/5f20e7cc3f87a7495c4a8b33.jpeg"},{"id":108492829,"identity":"018c1a83-e5ac-4fc4-a2f3-bdc94734e0ad","added_by":"auto","created_at":"2026-05-05 09:58:44","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":532338,"visible":true,"origin":"","legend":"\u003cp\u003eRhizosphere microbiome community composition analysis\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram of bacterial community composition between different groups, (B) Bacterial community bar plot, (C) Circos plot of samples and Phylum.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9471108/v1/5fe77326394792f69550a964.jpeg"},{"id":108436391,"identity":"cca18f33-778a-40ec-864d-29315843c1c6","added_by":"auto","created_at":"2026-05-04 15:53:05","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":496910,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical differences of rhizosphere Community composition\u003c/p\u003e\n\u003cp\u003e(A) Kruskal-Wallis H test bar plot on phylum level (B) Kruskal-Wallis H test bar plot on genus level, (C) Bar plot of multi-species differential significance test. Note: *P\u0026lt;0.05, **P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9471108/v1/90842be84dce3a914d151cd8.jpeg"},{"id":108492805,"identity":"8cc61f16-af76-4e91-92c5-81a6cd134ca7","added_by":"auto","created_at":"2026-05-05 09:58:40","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":545094,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of functional genome differences among species\u003c/p\u003e\n\u003cp\u003e(A) LEfSe-based discriminant bar plot of differential features on KEGG_Level3, (B) LEfSe-based discriminant bar plot of differential features on KEGG_KO, (C) Bar plot of multi-species differential significance test based on CAZY_Class, (D) Bar plot of multi-species differential significance test based on CARD_ARO_name. Note: *P\u0026lt;0.05, **P\u0026lt;0.001, ***P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9471108/v1/eef2267f2536cc4fe6a95c54.jpeg"},{"id":108493339,"identity":"5d6d58d4-a335-4cb9-88dd-4e2a312f45d0","added_by":"auto","created_at":"2026-05-05 09:59:59","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":402540,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation heatmap of environmental factors and microbiome communities and functional genes\u003c/p\u003e\n\u003cp\u003e(A) Spearman correlation heatmap on phylum level, (B) Spearman correlation heatmap on CAZY Classification, (C) Spearman correlation heatmap on CARD_ARO_name, (D) Spearman correlation heatmap on KEGG_Level3, (E) Mantel test network heatmap on KEGG level3.\u003c/p\u003e\n\u003cp\u003eNote: Correlation coefficient R was presented by different colors. *p-value \u0026lt; 0.05, **p-value \u0026lt; 0.01, ***p-value \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9471108/v1/e3a5fdd5a858145b4e94af12.jpeg"},{"id":109070132,"identity":"0ef0efde-d40b-40ba-901d-8f605b018f39","added_by":"auto","created_at":"2026-05-12 10:29:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3362255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9471108/v1/ef6eff8a-6ebd-44e8-9890-a95301582b70.pdf"},{"id":109067701,"identity":"afce30fd-8bd0-4b17-bfe5-4ad4e0dd4a18","added_by":"auto","created_at":"2026-05-12 10:00:04","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":468679,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of rhizosphere microbial community structure and functional differences among different varieties at maturity stage\u003c/p\u003e\n\u003cp\u003e(A) Data information, (B) Alpha diversity analysis of the coverage curves, (C) Alpha diversity analysis of the Simpson index, (D) Distances box plot of beta diversity, (E) PCA at the OTU level.\u003c/p\u003e","description":"","filename":"S1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9471108/v1/66aa691f243bcb87a445ce17.jpeg"}],"financialInterests":"","formattedTitle":"Host Genotype Shapes Rhizosphere Microbiome Assembly and Function to Modulate Cadmium Translocation in Rice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa L.\u003c/em\u003e) is the primary staple food for approximately half of the global population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recently, an increasing number of rice crops have been cultivated on contaminated soils due to multiple factors, including anthropogenic activities (e.g., chemical fertilizer and agrochemical overuse, solid and liquid waste discharge, smelting, and mining), the demands of a growing global population, and limited land resources. These factors have led to the accumulation of various potentially toxic elements, including heavy metals in rice crops [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Cadmium (Cd) is a common heavy metal contaminant in rice cultivation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Excessive Cd uptake and accumulation decrease rice plant growth and reduce grain quality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, consumption of Cd-contaminated rice trigger multiple health issues in humans, including cancer and cardiovascular, reproductive, and nervous system diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCd accumulation in rice plants and seeds has been closely linked to multiple factors, including rice plant genotypes, soil physicochemical properties, and rhizosphere microorganisms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Conventional remediation approaches for Cd-contaminated paddy fields include chemical remediation, organic amendments (e.g., biochar, composts, and manures), fertilization, soil removal and replacement, and phytoremediation using plants with Cd absorptive capacity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Above all, soil microbial remediation has emerged as a highly acceptable technique for restoring heavy metal-polluted soils due to its advantages, including high efficiency, low cost, and environmental friendliness [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Evidence has implicated microorganisms in the bioremediation of Cd-contaminated farmland and the reduction of Cd accumulation in rice [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, microorganisms can reduce Cd toxicity and availability through multiple mechanisms, including biomineralization, biosorption, biotransformation, and bioaccumulation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobal research on the plant microbiome enhanced the complex interactions mechanisms between plants and microorganisms. Evidence emphasize that rhizosphere microbiome plays a significant role in plant breeding, as modulating the structure of microbial communities can enhance crop stress tolerance, growth performance, and yield [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Specific studies have shown that rice cultivars can shape their rhizosphere microbiomes through root exudates and selective recruitment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For instance, some low-Cd-accumulating rice cultivars recruit specific microbial taxa associated with sulfur cycling and Cd immobilization, or those involved in siderophore secretion and iron activation, thereby inhibiting Cd uptake [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these advances, the role of the rhizosphere microbiome in Cd accumulation and translocation in low-Cd-accumulating rice cultivars remains to be fully elucidated. We hypothesize that low-Cd-accumulating cultivars distinctly shape the rhizosphere microbiome, which contribute to reduced Cd uptake and translocation. Accordingly, in this study, we aim to explore the effects of rhizosphere microbiome biodiversity on Cd accumulation in two newly cultivated low-Cd-accumulating rice varieties (SX (Shaoxiang) and QL (Qinglian)) at maturity and to elucidate how different rice varieties influence the rhizosphere microecological environment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003ch2\u003eExperimental design\u0026nbsp;and cultivation\u003c/h2\u003e\n\u003cp\u003eThis study was conducted at the experimental field station of the Hunan Hybrid Rice Research Center (113\u0026deg;26\u0026apos;57.2\u0026quot;E, 28\u0026deg;29\u0026apos;36.9\u0026quot;N). Three rice cultivars with divergent cadmium (Cd) accumulation capacities were investigated: a high-grain-Cd accumulator (Tianyou, CK) and two low-grain-Cd accumulators (Shaoxiang, SX; Qinglian, QL).\u003c/p\u003e\n\u003cp\u003eThe experiment employed three independent, fully enclosed cultivation systems within a steel-framed greenhouse. Each system comprised an irrigation and drainage tank regulated by a remotely controlled water management system to ensure environmental uniformity. The soil contained native cadmium at a background concentration of 0.31 \u0026plusmn; 0.1 mg/kg (Table 1). A completely randomized block design with three replicates was implemented. Seeds were sown on 23 June 2023, and seedlings were transplanted on 18 July 2023. For each cultivar, plants were arranged in a two-row plot with ten hills per row and two seedlings per hill, using a hill spacing configuration of 20 cm \u0026times; 20 cm.\u0026nbsp;No base fertilizer was applied. Top-dressing with urea (60 kg/ha) and potassium chloride (30 kg/ha) was uniformly administered across all tanks. A regime of intermittent irrigation was maintained, incorporating three scheduled soil-drying phases: (i) at the peak tillering stage (3 days), (ii) post-heading (3 days), and (iii) pre-harvest (3 days). Continuous flooding was sustained during intervening periods. Irrigation was recommenced upon the visual observation of initial soil surface cracking to impose a standardized moisture stress.\u003c/p\u003e\n\u003cp\u003ePrior to rhizosphere soil collection, foreign material such as debris, stones, and plant litter was removed. Rhizosphere soil (defined as the soil adhering tightly to the root surface) was subsequently collected using a sterile spatula. For each cultivar, samples were collected from three randomly selected hills (constituting biological replicates) within each of the three experimental units (serving as technical replicates). We acknowledge that the comparative scope of this study is limited to three specific cultivars; consequently, the generalizability of the identified microbiota-Cd relationships requires future validation with larger and more diverse genetic panels.\u003c/p\u003e\n\u003ch2\u003eDetermination of\u0026nbsp;Cd content in plants\u003c/h2\u003e\n\u003cp\u003ePlant samples were ground individually using a grinder. To avoid cross-contamination between samples, the grinder was cleaned thoroughly before each use. The ground material was passed through a sieve and stored in a desiccator. Dried samples (0.5000 g) were weighed and placed into microwave digestion vessels. Nitric acid (5 mL) and hydrogen peroxide (2 mL) were added. After digestion, the acid was evaporated until nearly dry. The digestion vessel was rinsed three times with nitric acid solution, and the rinsate was transferred to a 25 mL volumetric flask. The solution was diluted to the mark with nitric acid solution and mixed thoroughly. Reagent blank tests were performed concurrently. Cadmium content was determined by atomic absorption spectrophotometry in accordance with the National Food Safety Standard for the Determination of Cadmium in Foods (GB 5009.15-2014).\u003c/p\u003e\n\u003ch2\u003eSoil physicochemical property determination\u003c/h2\u003e\n\u003cp\u003eSoil properties and cadmium concentrations in roots, stem sheaths, leaves, and seeds at rice maturity were determined using the microwave digestion method. For soil analysis, after removing impurities, stones, and plant residues, ten soil samples from each treatment condition were pooled, air-dried, pulverized, and passed through a 10-mesh (2 mm) standard sieve. The following physicochemical properties were then determined: Total soil cadmium was measured by the complete digestion method using graphite furnace atomic absorption spectrophotometry (GB/T 17141-1997). Available soil cadmium was determined by the leaching method using graphite furnace atomic absorption spectrophotometry (GB/T 23739-2009). Total nitrogen and alkaline-hydrolyzable nitrogen were determined by the Kjeldahl method and the alkaline hydrolysis diffusion method, respectively (LY/T 1228-2015). Total phosphorus was determined using the perchloric acid-sulfuric acid method (GB 9837-88). Available phosphorus was determined using a spectrophotometer (NY/T 1121.7-2014). Total potassium was measured using the sodium hydroxide fusion method (GB 9837-88). Available potassium was measured by flame atomic absorption spectrophotometry (NY/T 889-2004). Soil pH was determined potentiometrically at a water-to-soil ratio of 1:2.5 (NY/T 1121.2-2006). Soil organic matter content was measured by redox calorimetry (NY/T 1121.6-2006). Plant tissue samples (roots, stem sheaths, leaves, and seeds) were digested using the microwave digestion method described above, and cadmium content was determined by atomic absorption spectrophotometry.\u003c/p\u003e\n\u003ch2\u003eHeavy metal translocation assessment\u003c/h2\u003e\n\u003cp\u003eThe capacity for heavy metal translocation within the plant was evaluated by calculating the biological transfer factor (TF) and the bioconcentration factor (BCF). The BCF was calculated using the following formulas:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"136\" height=\"37\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e(1)\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"94\" height=\"37\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(2)\u003c/p\u003e\n\u003cp\u003eThe translocation factor (TF) from roots to aboveground parts was calculated as:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"67\" height=\"37\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;(3)\u003c/p\u003e\n\u003cp\u003eWhere\u0026nbsp;\u003cimg width=\"53\" height=\"27\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eis the bioconcentration factor of the aboveground parts,\u0026nbsp;\u003cimg width=\"47\" height=\"27\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u0026nbsp;is the bioconcentration factor of the roots,\u0026nbsp;\u003cimg width=\"37\" height=\"27\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u0026nbsp;is the heavy metal content in the aboveground parts (mg/kg),\u003cimg width=\"35\" height=\"27\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADUAAAAoCAMAAABpcEIHAAAAAXNSR0IArs4c6QAAAF1QTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGa2OgAAOjoAOjo6OmZmOpDbZgAAZpC2Zrb/kDoAkLbbkNvbkNv/tmYAtmY6tpBmtv//25A627Zm2////7Zm/9uQ//+2///bRT/adAAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAv0lEQVRIS+1TyRLCIAwl1gWXaq24tKX8/2cKJNA4o6PRiwfS0rDkZXmhShUpDBQGCgOBAXdZA8Bm30vo6PSiVc7AUoIa0NzqnSCU1bNTTLM5CFAGJDHIsdXVVRCDTG+wkoNcA5JycoLIhUwG+KYsatYHoYDZ/IYa63ek8FhjjXUZr/wNVvjJiq/pJCbq+9Urd8a7CP4Jbx7TJM4m6cJfMt8eMQLuoyIPtHxEcQ/p5P9Rr9h4VptnJdGRGsBaELfuHMcFiafrCzMAAAAASUVORK5CYII=\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u0026nbsp;is the heavy metal content in the roots (mg/kg),\u0026nbsp;\u003cimg width=\"32\" height=\"27\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u0026nbsp;is the heavy metal content in the soil (mg/kg).\u003c/p\u003e\n\u003cp\u003eStatistical\u0026nbsp;data analysis was performed using GraphPad Prism version 7 (La Jolla, CA, USA). The results are displayed as the means \u0026plusmn; standard deviations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoil DNA Library Preparation, and Metagenomic Sequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1 \u0026mu;g of DNA per sample was used for sequencing. Briefly, DNA fragments of approximately 350 bp were generated by sonication. The fragments were end-polished, A-tailed, and ligated with full-length adaptors for Illumina sequencing, followed by PCR amplification. The PCR products were purified using the AMPure XP system, and libraries were prepared and assessed for size distribution using the Agilent 2100 Bioanalyzer. Three biological replicates were analyzed per sample. Raw sequencing data were obtained using the Illumina NovaSeq 6000 platform.\u0026nbsp;Clean data were acquired using Readfq V8 and subsequently subjected to host sequence removal by alignment against a host database using the Basic Local Alignment Search Tool (BLAST), with Bowtie 2.2.4 employed as the default software to filter host-origin reads. All remaining reads were combined, and SOAP denovo was used to generate mixed assemblies. Scaftigs (\u0026ge; 500 bp) obtained from both single and mixed assemblies were used for open reading frame (ORF) prediction with MetaGeneMark software. Sequences shorter than 100 nt were filtered from the prediction results.\u003c/p\u003e\n\u003cp\u003eTo reduce sequence redundancy, ORFs were clustered using CD-HIT software to generate a unique initial gene catalog. Clean reads from each sample were mapped to the initial gene catalog using Bowtie 2.2.4. Genes with two or fewer reads in any sample were filtered out, and the resulting gene catalog (UniGene database) was used for subsequent analyses. Gene abundance in each sample was calculated based on the number of mapped reads and gene length. Basic statistical analysis, core- and pan-genome analyses, sample correlation analysis, and Venn diagram analysis of gene numbers were all performed based on gene abundance in the gene catalog. DIAMOND software was used to blast the UniGene database against sequences of Bacteria, Fungi, Archaea, and Viruses extracted from the NCBI nr database (https://www.ncbi.nlm.nih.gov/). The clean reads were deposited in the NCBI Sequence Read Archive (SRA) database under the accession number: SUB16093140.\u003c/p\u003e\n\u003ch2\u003eFunctional annotation and resistance gene analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFunctional annotation of resistance genes was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups (eggNOG), and the Carbohydrate-Active Enzymes (CAZy) databases. Unigenes were aligned to the Comprehensive Antibiotic Resistance Database (CARD) using Resistance Gene Identifier (RGI) software with default parameter settings.\u0026nbsp;Based on the alignment results, the relative abundance of antibiotic resistance ontology (ARO) clusters was visualized using abundance bar charts and abundance cluster heatmaps. Differences in the number of resistance genes among soil groups were also displayed. Additionally, the distribution of resistance genes across samples, the species attribution of these genes, and their associated resistance mechanisms were investigated.\u0026nbsp;Statistical significance of differences in resistance gene abundance among soil groups was assessed using Wilcoxon rank-sum test or ANOVA, with\u0026nbsp;\u003cimg width=\"60\" height=\"27\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u0026nbsp;considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eVarietal Differences in Cadmium Accumulation and Root Physiology\u003c/h2\u003e\n\u003cp\u003eSignificant differences in root system architecture and grain morphology were observed among the three studied rice cultivars (QL, SX, and CK) at the mature stage (Fig. 1). Cultivar SX exhibited a dense root system characterized by extensive lateral root development and a high density of root tips, a morphology typically associated with enhanced nutrient acquisition potential. In contrast, cultivar QL displayed a comparatively sparse root system, with pronounced primary root elongation but reduced lateral root proliferation. Cultivar CK demonstrated an intermediate root phenotype (Fig. 1A). Corresponding varietal differences were evident in grain morphology, with SX producing significantly longer grains than both QL and CK (Fig. 1B), a trait potentially linked to superior nutrient partitioning and yield capacity.\u003c/p\u003e\n\u003cp\u003eDespite a consistent rhizosphere soil pH across all cultivars (approximately 5.8 throughout the growth cycle; Fig. 2A), significant varietal differences were observed in Cd bioavailability. At the mature stage, the concentration of bioavailable Cd (DTPA-extractable fraction) in the rhizosphere soil of the two low-grain-Cd cultivars, QL (0.30 mg/kg) and SX (0.20 mg/kg), was higher than that of the high-grain-Cd cultivar CK (0.16 mg/kg) (Fig. 2C). This indicates that the low-accumulating cultivars did not reduce Cd uptake by decreasing its general soil bioavailability. Instead, it suggests a cultivar-specific rhizosphere modification that alters Cd speciation or ligand complexation, potentially increasing the pool of soluble Cd ions while simultaneously deploying a more efficient root-level exclusion or sequestration strategy.\u003c/p\u003e\n\u003ch2\u003eBCF and TF of Cd in different rice cultivars\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe subsequent fate of bioavailable Cd is determined at the root-soil interface (rhizoplane and root apoplast). Analysis of Cd distribution in plant tissues revealed that roots were the primary sink, accumulating Cd at concentrations (0.50-2.80 mg/kg) an order of magnitude higher than stems (0.05-1.2 mg/kg) and leaves (0.02-0.25 mg/kg) (Fig. 2D, 2E). This implicates the root system as the critical filter.\u003c/p\u003e\n\u003cp\u003eThe Biological Concentration Factor (BCF) and Translocation Factor (TF) provide a quantitative measure of this interface control. BCF analysis confirmed that cultivar CK had the strongest capacity to concentrate Cd from the rhizosphere soil into root tissues (Fig. 3B, 3C). Crucially, TF analysis revealed the decisive mechanism for low grain Cd: while CK efficiently transferred Cd from roots to shoots, cultivars QL and SX exhibited a significantly reduced capacity to translocate Cd from roots to aerial tissues (stems and leaves; Fig. 3D, 3E). The lack of a significant difference in the seed-stem TF between QL and SX (Fig. 3F) further emphasizes that the major regulatory checkpoint is at the root-to-shoot barrier, not during remobilization within the shoot.\u003c/p\u003e\n\u003cp\u003eCollectively, these results demonstrate that the low-Cd-accumulating cultivars QL and SX achieve a marked reduction in grain Cd content primarily by restricting the root-to-shoot translocation of Cd, rather than by limiting its initial uptake from the soil. This underscores the role of varietal genetic factors in determining differential Cd accumulation in rice.\u003c/p\u003e\n\u003ch2\u003eHost Genotype Shapes Rhizosphere Microbial Community Structure\u003c/h2\u003e\n\u003cp\u003eThe distinct root system architectures and divergent cadmium (Cd) accumulation phenotypes observed in the QL and SX cultivars prompted an investigation into whether these varietal differences are associated with concomitant shifts in rhizosphere microbial community structure.\u003c/p\u003e\n\u003cp\u003eAlpha diversity metrics, reflecting species richness and evenness, exhibited no significant differences among cultivars (Fig. S1B, C). In contrast, beta-diversity analysis revealed pronounced, cultivar-dependent clustering of microbial communities (Fig. S1D, E), indicating that plant genotype is a principal determinant of rhizosphere microbiome composition, irrespective of similar taxonomic richness. Although dominant bacterial phyla (e.g., Pseudomonadota, Acidobacteriota, Chloroflexota) were conserved across cultivars (Fig. 4A-C), their relative abundances varied significantly. Subsequent differential abundance analysis at finer taxonomic resolutions elucidated distinct, cultivar-specific enrichment patterns (Fig. 5A-C).\u003c/p\u003e\n\u003cp\u003eAt the phylum level, the relative abundance of Spirochaetota and Candidatus Cloacimonadota was significantly elevated in the SX cultivar, whereas Candidatus Parcubacteria demonstrated a significant reduction compared to the other two cultivars (p\u0026lt; 0.05; Fig. 5A). These findings suggest that host genotype exerts a selective influence on the recruitment of specific, low-abundance microbial lineages within the rhizosphere.\u003c/p\u003e\n\u003cp\u003eGenus-level differential abundance analysis further delineated distinct enrichment profiles corresponding to each cultivar (Fig. 5B, C). Notably, key taxa including the class Betaproteobacteria, the class Planctomycetia, the genus \u003cem\u003eMethylocystis\u003c/em\u003e, the phylum Armatimonadota, and the genus \u003cem\u003eMethanocella\u003c/em\u003e exhibited significant differential abundance across all three cultivars, underscoring systematic reorganization of the community. Relative to the high-Cd accumulator CK, the SX cultivar was primarily characterized by the enrichment of a saprotrophic functional guild implicated in organic matter decomposition, encompassing genera such as \u003cem\u003eLabilithrix\u003c/em\u003e, \u003cem\u003eTardiphaga\u003c/em\u003e, and the acid-tolerant \u003cem\u003eCandidatus Acidiferum\u003c/em\u003e, alongside plant-associated genera like \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, and \u003cem\u003eIdeonella\u003c/em\u003e. This enrichment profile implies the SX rhizosphere fosters an environment conducive to enhanced carbon turnover and nutrient mobilization, potentially linked to its unique root exudation chemistry.\u003c/p\u003e\n\u003cp\u003eConversely, the QL cultivar, when compared to CK, exhibited a pronounced enrichment of a functionally distinct microbiome dominated by strictly anaerobic and redox-cycling taxa. This consortium included methanogenic archaea (\u003cem\u003eMethanothrix\u003c/em\u003e, \u003cem\u003eMethanocella\u003c/em\u003e, \u003cem\u003eMethanoregula\u003c/em\u003e, \u003cem\u003eMethanospirillum\u003c/em\u003e), the sulfate-reducing bacterium Desulfovibrio, and the plant growth-promoting rhizobacterium (PGPR) Burkholderia. The co-enrichment of methanogens and sulfate reducers signifies the establishment of anaerobic microniches and active sulfur cycling in the QL rhizosphere, a biogeochemical process that may directly attenuate cadmium bioavailability through the precipitation of metal sulfides. Direct comparison between the two low-Cd cultivars highlighted their contrasting ecological strategies: QL significantly enriched \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eBurkholderia\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, and methanogenic archaea relative to SX, reinforcing the predominance of an anaerobic, sulfur-transforming metabolic network.\u003c/p\u003e\n\u003cp\u003eNotably, both SX and QL recruited known PGPR (e.g., \u003cem\u003eBurkholderia\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e), suggesting a convergent strategy to bolster plant fitness under cadmium stress, albeit enacted within distinct overarching microbial functional frameworks.\u003c/p\u003e\n\u003ch2\u003eFunctional Diversification of Microbiomes Linked to Cadmium Dynamics\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFunctional profiling of the rhizosphere microbiomes, based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, revealed pronounced metabolic divergence that aligned with the observed taxonomic differentiation among cultivars (Fig. 6A, B, D). Comparative analysis of metabolic pathways demonstrated distinct functional specializations. Relative to the high-cadmium accumulator (CK), the low-accumulating cultivar QL exhibited significant enrichment in pathways integral to anaerobic energy metabolism and biosynthesis. This included methane metabolism, starch and sucrose metabolism, glycolysis/gluconeogenesis, oxidative phosphorylation, and amino acid biosynthesis. This metabolic profile, coupled with the taxonomic co-enrichment of methanogenic archaea and sulfate-reducing bacteria (e.g., \u003cem\u003eDesulfovibrio\u003c/em\u003e), indicates that the QL rhizosphere sustains a microbiome proficient in anaerobic redox processes and sulfur cycling, a key mechanism for cadmium immobilization via metal sulfide precipitation. In contrast, the low-accumulating cultivar SX displayed a divergent functional signature, characterized by the enrichment of pathways governing the biosynthesis of specialized metabolites and cellular components. These included pathways for secondary metabolite biosynthesis, amino sugar and nucleotide sugar metabolism, riboflavin metabolism, porphyrin metabolism, peptidoglycan biosynthesis, and glycerophospholipid metabolism. This ensemble suggests the SX rhizosphere microbiome possesses an enhanced genetic potential for the production of stress-related compounds, cofactors, and specific membrane constituents, which may facilitate metal chelation and environmental adaptation. The high-accumulator CK was distinguished by the enrichment of pathways associated with central intermediary metabolism, including glyoxylate and dicarboxylate metabolism and fatty acid metabolism, reflecting a metabolic network oriented toward core carbon and lipid transformation.\u003c/p\u003e\n\u003cp\u003eAnalysis of antibiotic resistance genes (ARGs) further elucidated varietal differences in microbial adaptive traits. The SX rhizosphere harbored a significantly broader and more abundant suite of ARGs, including \u003cem\u003eoptrA\u0026nbsp;\u003c/em\u003e(pleuromutilin resistance),\u003cem\u003e\u0026nbsp;tetA(46)\u0026nbsp;\u003c/em\u003e(tetracycline resistance), the vancomycin resistance regulatory genes \u003cem\u003evanRN\u0026nbsp;\u003c/em\u003eand \u003cem\u003evanRM\u003c/em\u003e, and numerous genes encoding multidrug efflux systems (e.g., \u003cem\u003ebcrA\u003c/em\u003e, \u003cem\u003eimrD\u003c/em\u003e, \u003cem\u003erosB\u003c/em\u003e). The co-enrichment of this diverse array of resistance determinants suggests the SX rhizosphere may function as a reservoir for multidrug resistance genes, with a notable potential for horizontal gene transfer. Conversely, the QL rhizosphere demonstrated enrichment of a different ARG repertoire, featuring genes such as \u003cem\u003ecpxA\u003c/em\u003e(envelope stress response), \u003cem\u003evgaE\u003c/em\u003e and \u003cem\u003evanL\u003c/em\u003e(streptogramin and vancomycin resistance), and efflux genes including \u003cem\u003emdtB\u003c/em\u003e and\u003cem\u003e\u0026nbsp;adeF\u003c/em\u003e. This result indicates that, while both low-Cd cultivars enrich for antibiotic resistance, they foster microbiomes employing distinct resistance strategies\u0026mdash;with SX favoring a broad-spectrum, multidrug efflux and target protection approach, and QL exhibiting a profile more focused on specific antibiotic modifications and stress sensing.\u003c/p\u003e\n\u003ch2\u003eMicrobial-phenotype Correlations: Associating the Microbiome with Cd Translocation\u003c/h2\u003e\n\u003cp\u003eCorrelation analyses were employed to identify statistical associations between specific microbial taxa, functional gene profiles, and the spatial distribution of cadmium (Cd) within the rice-soil system. Spearman rank correlation (Fig. 7A) revealed significant relationships between microbial community features and Cd accumulation metrics. Notably, the relative abundance of specific taxa, including the phyla Acidobacteriota and Nitrospirota and the class Deltaproteobacteria, showed positive correlations with Cd bioconcentration factors in root tissues. In contrast, leaf Cd content was more strongly correlated with the abundance of phyla such as Verrucomicrobiota and Planctomycetota. Hierarchical cluster analysis of microbial community profiles yielded a dendrogram with distinct clustering by sample type (root, soil, leaf), providing further evidence for the compartment-specific assembly of microbiomes along the plant axis.\u003c/p\u003e\n\u003cp\u003eHeatmap visualization of correlation matrices (Fig. 7B-D) delineated specific associations between microbial functional traits and host Cd phenotypes. Among antibiotic resistance determinants, the regulatory gene\u003cem\u003e\u0026nbsp;mtrA\u003c/em\u003e(associated with macrolide resistance) exhibited a significant negative correlation with leaf samples. Functional gene categories related to complex carbon degradation\u0026mdash;specifically, carbohydrate-active enzyme (CAZyme) families such as Glycoside Hydrolases, Carbohydrate-Binding Modules, and cellulosomal components\u0026mdash;were positively correlated with root and rhizosphere soil samples. This underscores the rhizosphere as a primary site for polysaccharide depolymerization, a process integral to nutrient cycling. Similarly, broad functional categories encompassing microbial signaling (e.g., Two-component systems, Quorum sensing), transport (ABC transporters), and central carbon metabolism (Pyruvate metabolism) were predominantly associated with the root-soil interface, reflecting a microenvironment characterized by high metabolic activity and microbe-microbe/plant-microbe communication.\u003c/p\u003e\n\u003cp\u003eKey correlative results integrating taxonomy and phenotype as following. Taxa within the class Betaproteobacteria, including the order Burkholderiales, displayed positive correlations with Cd content in aboveground (shoot) tissues. The phylum Chloroflexota, which was consistently enriched in both low-Cd cultivars (QL and SX), demonstrated a significant negative correlation with shoot Cd content. This suggests a potential functional role for this phylum in the mitigation of Cd translocation to aerial plant parts. Functional gene profiling indicated that the SX rhizosphere, distinguished by its enrichment of sulfur-cycling and anaerobic taxa, concurrently harbored a greater abundance of specific ARGs. This co-occurrence implies a potential linkage between adaptation to metal stress and the maintenance of other resistance determinants in the rhizosphere.\u003c/p\u003e\n\u003cp\u003eCo-occurrence network analysis (Fig. 7E) elucidated the structural relationships between microbial communities across different plant compartments. The analysis revealed robust connectivity, evidenced by a high density of edges, between microbial nodes derived from root and soil communities across all cultivars. This finding reinforces the conceptual model of a tightly coupled rhizosphere microbiome. Notably, the network associated with the low-Cd cultivar QL exhibited stronger root-soil connectivity than that of the high-Cd cultivar CK. In contrast, microbial communities inhabiting stem and leaf tissues demonstrated weaker topological associations with the soil network, indicating that the assembly of aerial tissue microbiomes is governed by more independent or distinct ecological processes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHost genotype modulates rhizosphere microbiome assembly, influencing Cd dynamics\u003c/h2\u003e \u003cp\u003eCompelling evidence demonstrates that plant genotype, primarily through root exudation, acts as a selective filter to shape the composition and function of rhizosphere microbial communities, thereby modulating critical processes such as nutrient cycling and metal(loid) bioavailability [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the precise mechanisms linking specific rice genotypes\u0026mdash;particularly those with divergent cadmium (Cd) accumulation capacities\u0026mdash;to the assembly of functionally specialized microbiomes remain inadequately characterized. Elucidating this genotype-microbiome linkage is essential for a mechanistic understanding of how host genetic factors interact with the soil microbiome to determine metal accumulation phenotypes. To address this, we employed an integrated multi-omics framework, analyzing community diversity, taxonomic composition, differential abundance, and functional potential to compare the rhizosphere microbiomes of a high-Cd-accumulating cultivar (CK) and two low-Cd-accumulating cultivars (QL and SX).\u003c/p\u003e \u003cp\u003eConsistent with prior studies where host effects are more pronounced in community structure than in richness [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], we found that alpha-diversity indices were comparable across cultivars, whereas beta-diversity analysis revealed statistically distinct microbial community clusters. This confirms that host genotype is a significant determinant of microbiome assembly. Importantly, these structural shifts were correlated with plant Cd phenotypes. For example, the relative abundance of the class Betaproteobacteria showed a significant positive correlation with Cd content in aerial tissues. Furthermore, the differential enrichment of candidate phyla associated with anaerobic niches (e.g., Candidatus Parcubacteria [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Candidatus Cloacimonadota [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]) suggests genotype-specific recruitment of low-abundance, potentially functionally important taxa.\u003c/p\u003e \u003cp\u003eA key finding supporting our hypothesis is the apparent paradox in the low-Cd cultivars: QL and SX achieved a\u0026thinsp;~\u0026thinsp;75% reduction in grain Cd, yet maintained or even slightly elevated the concentration of bioavailable Cd in their rhizosphere soil at maturity compared to CK. This suggests that the primary mechanism limiting grain Cd is not a generalized reduction of Cd solubility in the bulk rhizosphere, but rather a spatially focused interception and sequestration of the bioavailable Cd pool at or near the root interface. The subsequent analyses of the differentially enriched microbial consortia provide testable mechanistic hypotheses for how this root-level control may be facilitated.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional consortia hypothesized to modulate Cd speciation and bioavailability\u003c/h3\u003e\n\u003cp\u003eOur data indicate that the two low-Cd cultivars assemble functionally divergent microbiomes that influence Cd dynamics in the root zone.\u003c/p\u003e \u003cp\u003eAnaerobic Sulfur-Cycling Consortium.​ Compared to CK, SX enriched a microbiome dominated by strictly anaerobic taxa, including methanogenic archaea (e.g., \u003cem\u003eMethanothrix\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], \u003cem\u003eMethanocella\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]) and the sulfate-reducing bacterium (SRB) Desulfovibrio. The co-enrichment of these taxa is indicative of active sulfur cycling in anaerobic microniches, a process that can profoundly influence metal bioavailability. Sulfate reduction by SRBs generates sulfide (S\u0026sup2;⁻), which can immobilize Cd through the precipitation of highly insoluble cadmium sulfide (CdS) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The significant negative correlation we observed between Desulfovibrio abundance and bioavailable Cd in the SX rhizosphere is consistent with this mechanism, as previously demonstrated in studies where microbial inoculants increasing SRB abundance reduced grain Cd [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Concurrent enrichment of PGPR like \u003cem\u003eBurkholderia\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and \u003cem\u003eSphingomonas\u003c/em\u003e [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] may further bolster plant fitness under metal stress, potentially influencing root physiology and Cd handling [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHeterotrophic Carbon-Metabolizing Consortium.​ In contrast, QL enriched a saprotrophic microbiome characterized by taxa involved in complex organic matter decomposition (e.g., \u003cem\u003eLabilithrix\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], \u003cem\u003eTardiphaga\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]) and acid-tolerant organisms. This suggests enhanced heterotrophic carbon turnover, likely driven by cultivar-specific root exudates. Such communities can influence metal speciation through (i) biosorption​ to microbial cell walls and extracellular polymeric substances (EPS) and (ii) chelation​ by microbially produced organic acids, which can alter Cd solubility and its phytoavailability [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The observed negative correlation between the abundance of these carbon-metabolizing taxa and grain Cd content supports a potential role in limiting Cd uptake, potentially by increasing Cd retention in the rhizosphere soil or root apoplast.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eA propsed integrated model: from rhizospherespeciation to root-level control\u003c/h2\u003e \u003cp\u003eSynthesizing these findings, we propose a microbiota-mediated mual-megulation model​ for Cd accumulation in rice. This model posits that the net Cd translocation to grains is governed by the dynamic balance between two broad categories of root/rhizosphere processes. Cadmium Mobilization:​ microbial processes (e.g., acidification) or specific taxa may increase Cd solubility or facilitate its plant uptake. Cadmium Immobilization:​ this includes direct sequestration in the rhizosphere (e.g., via biotic sulfide precipitation) or stabilization at the root interface (e.g., via biosorption/chelation or apoplastic trapping).\u003c/p\u003e \u003cp\u003eIn this experiment, the high-Cd cultivar CK may maintain a balance that permits net mobilization and translocation. In contrast, the distinct root phenotypes of SX (dense root system) and QL (elongated primary root) appear to recruit specialized microbiomes that potentiate specific immobilization pathways, thereby shifting the balance. For SX, the recruited anaerobic consortium is hypothesized to drive in situ CdS precipitation. For QL, the active saprotrophic community is posited to enhance Cd complexation and biosorption. In both scenarios, a key outcome is the interception and stabilization of bioavailable Cd in the root zone, effectively depleting the pool of Cd available for xylem loading. The results align with emerging concepts of the root-soil interface as a critical filter for metal contaminants [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe root-soil microbial network connectivity observed, particularly in QL, underscores the tight functional coupling at this decisive interface. The concurrent enrichment of antibiotic resistance genes (ARGs) alongside metal-tolerant taxa highlights the potential for co-selection of resistance determinants in contaminated agroecosystems, an important consideration for environmental risk assessment [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion and Future Perspectives","content":"\u003cp\u003eThis study establishes a correlative link between rice genotype, rhizosphere microbiome assembly, and Cd accumulation phenotype. We demonstrate that low-Cd cultivars recruit distinct microbiomes associated with putative Cd-immobilizing functions, providing a plausible extrinsic mechanism complementing intrinsic root physiology for reduced metal translocation. While our multi-omics approach identifies these associations, causal validation is required.​ Future manipulative experiments, such as gnotobiotic studies with synthetic microbial communities (SynComs) [41] or targeted inhibition of specific pathways (e.g., sulfate reduction), are essential to definitively test the roles of key taxa like \u003cem\u003eDesulfovibrio\u003c/em\u003e and \u003cem\u003eChloroflexota\u003c/em\u003e. Nonetheless, this work provides a foundational framework for developing novel, microbiome-informed strategies\u0026mdash;such as breeding rice cultivars optimized to recruit beneficial microbial consortia or applying tailored bio-inoculants to mitigate Cd accumulation in grains and enhance the safety of rice production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Key R\u0026amp;D Program of China (Grant No. 2024YFD2301401) to Z.L., S.Z. and K.Z. and Agricultural Science and Technology Innovation Fund Project of Hunan Province, China (Grant No. 2024CX09) to K.Z.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Key R\u0026amp;D Program of China (2024YFD2301401) and the Agricultural Science and Technology Innovation Fund Project of Hunan Province, China (2024CX09).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clean reads were deposited into the NCBI Sequence Read Archive (SRA) database under the accession number SUB11813545.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Z.L.; methodology, Z.L., S.Z., Y.L., D.G., Q.L., F.Z. and K.Z.; investigation, Z.L., S.Z., D.G., and K.Z.; writing-original draft preparation, Z.L., S.Z., D.G., F.Z. and K.Z.; writing-review and editing, Z.L. and F.Z.; supervision, K.Z.; funding acquisition, Z.L., S.Z. and K.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFAO. Rice market monitor. Food and Agriculture Organization of the United Nations; 2023.\u003c/li\u003e\n\u003cli\u003eShi J, Ye J, Fang Z, Xie S, Wang Y, Chen X, et al. Metagenomic and machine learning-aided identification of biomarkers driving distinctive Cd accumulation features in the root-associated microbiome of two rice cultivars. ISME Commun. 2023;3:14. https://doi.org/10.1038/s43705-023-00219-7 \u003c/li\u003e\n\u003cli\u003eWang M, Liu J, Wang Y, Zhang H, Li X, Chen S, et al. Deciphering the mechanism of rhizosphere microecosystem in modulating rice cadmium accumulation via integrating metabolomics and metagenomics. Sci Total Environ. 2025;958:178012. https://doi.org/10.1016/j.scitotenv.2024.178012 \u003c/li\u003e\n\u003cli\u003eLiu A, Wang W, Zheng X, Chen X, Fu W, Wang G, et al. 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Persistent antimicrobial resistance during soil remediation driven by residual heavy metal co-selection. \u003cem\u003eISME J.\u003c/em\u003e 2026 Jan 14;20(1):wrag058. https://doi.org/ 10.1093/ismejo/wrag058 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Initial soil physicochemical properties of the experimental field.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eTotal N (g/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eTotal P\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(g/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eTotal K\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(g/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eSOM\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(g/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003cp\u003e(mg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eAvailable P\u003c/p\u003e\n \u003cp\u003e(mg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eAvailable K\u003c/p\u003e\n \u003cp\u003e(mg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCd content in soil\u003c/p\u003e\n \u003cp\u003e(mg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.111\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e13.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e42.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e231\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e12.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e74\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e5.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cadmium accumulation, Rhizosphere microbiome, Host genotype, Low-Cd rice cultivars","lastPublishedDoi":"10.21203/rs.3.rs-9471108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9471108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eCadmium (Cd) contamination in rice (\u003cem\u003eOryza sativa L.\u003c/em\u003e) threatens food safety and human health. Developing low-Cd-accumulating cultivars and understanding their interaction mechanisms with the environmental microbiome has become a key task for ensuring food security. This study explores the role of host genotype (plant variety) in shaping the rhizosphere microbiome and its functional implications for modulating Cd translocation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe compared a high-Cd-accumulating control (CK, Tianyou) with two low-Cd cultivars, SX (Shaoxiang) and QL (Qinglian). Both SX and QL significantly reduced grain Cd content, primarily through restricting Cd translocation from roots to aerial tissues.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIntegrated metagenomic and correlation analyses revealed that the host genotype shaped the assembly of functionally distinct rhizosphere microbiomes. The SX cultivar assembled a sulfur-cycling anaerobic microbiota, enriched with methanogenic archaea (e.g., \u003cem\u003eMethanothrix\u003c/em\u003e) and the sulfate-reducing bacterium \u003cem\u003eDesulfovibrio\u003c/em\u003e-forming a consortium implicated in reducing Cd bioavailability. In contrast, the QL cultivar enriched a heterotrophic, carbon-metabolizing microbiota, characterized by organic matter-degrading bacteria (e.g., \u003cem\u003eLabilithrix\u003c/em\u003e), suggesting a role in Cd complexation. Beta-diversity analysis confirmed that varietal differences were a key factor shaping microbial community structure. Co-occurrence network analysis linked these community shifts to Cd distribution, identifying specific taxa (e.g., \u003cem\u003eBetaproteobacteria\u003c/em\u003e and \u003cem\u003eChloroflexota\u003c/em\u003e) with opposing correlations to aerial tissue Cd content.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTogether, these results demonstrate that host genotype shapes rhizosphere Microbiome assembly to modulate cadmium translocation in rice. This establishes a genotype-microbiota-function link, where host genotype shapes microbiome assembly, and the recruited microbial consortia, in turn, modulate Cd dynamics and translocation in rice.\u003c/p\u003e","manuscriptTitle":"Host Genotype Shapes Rhizosphere Microbiome Assembly and Function to Modulate Cadmium Translocation in Rice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 15:53:00","doi":"10.21203/rs.3.rs-9471108/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-23T05:15:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2026-04-22T22:57:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T07:14:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2026-04-21T06:06:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4aba8f08-fad1-4c6c-90cc-ccf3d028c9f4","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T15:53:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 15:53:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9471108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9471108","identity":"rs-9471108","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}