Cadmium (Cd) is a ubiquitous heavy metal with a widely distributed and known toxicity , and contamination of agricultural land by Cd is primarily caused by human activities, such as industrial wastewater discharge, disposal of large quantities of metal wastes and sewage sludge, and pesticide misuse . Cd is more mobile than other heavy metals in plants, leading to its increased accumulation and subsequent induction of severe toxicity , . This is caused by cation deficiency, inhibition of the biosynthesis of chlorophyll or other essential substances, and increased oxidative damage, such as structural and functional cellular degeneration and destruction of biomolecules, which are closely linked to plant growth and development . The composition and functional activities of the plant rhizosphere microbiome are dynamically regulated under various environmental stresses, providing a critical foundation for plant adaptation and health – . Interestingly, some microorganisms can reduce the toxicity of Cd to plants and themselves by adsorbing, complexing, enzymatically converting, and redox-converting Cd ions , . For instance, Klebsiella mobilis CIAM 880 has the ability to form complexes with unbound Cd ions, which reduces their bioavailability of Cd to barley plants and mitigates the toxic effects . Enterobacter bugandensis can also reduce Cd accumulation in wheat grains by bioprecipitation and extracellular adsorption , . Plant functional genes, including those involved in biotic and abiotic stress response and nutrient uptake and transport, could modulate the secretion of root exudates and some ionic signals, which significantly influence the rhizosphere microorganisms and are essential for enhancing plant stress tolerance and nutrient use efficiency – . Conversely, the activity of rhizosphere microorganisms can induce systemic tolerance in plants by releasing metabolites, which can affect host genes expression, and alter phytohormone secretion , . For example, Pseudomonas aeruginosa and Burkholderia gladioli are able to reduce Cd uptake and expression of metal transporter genes in tomatoes plants, leading to improved growth and photosynthetic pigmentation . NRT1.1B , encoding a nitrate transporter in rice, is involved in the attraction of bacteria that are enriched in O. sativa subsp. indica rice . These bacteria can improve rice growth under organic nitrogen (N) conditions using the synthetic community . Our recent work has shown that loss of sst gene function in rice can affect rhizosphere microbes by altering plant metabolites, such as salicin and arbutin, which in turn can help the host to resist salt stress . Ammonium transporters (AMTs), involved in ammonium (NH 4 + -N) uptake and transport in most plants, promote the uptake of N sources by plants , . Previous studies have investigated the role of AMTs in promoting NH 4 + -N transport to alleviate salt stress . A growing body of evidence suggests that AMTs-targeted NH 4 + -N is more sensitive to Cd concentration than NO 3 - -N. For example, NH 4 + -N can inhibit Cd translocation from roots to shoots, thereby protecting Arabidopsis thaliana from Cdtoxicity , . Recent research has also found in Solanum nigrum L . that the transcription level of Cd transport-related genes is regulated by NH 4 + -N signaling to prevent the Cd accumulation and flux, compared to NO 3 - -N. The researchers also suggest that NH 4 + -N can fix Cd in the cell wall component, thereby improving Cd tolerance. However, the potential of GmAMT2.1/2.2 genes to alleviate external Cd toxicity by regulating metabolites and microbiota in the soybean rhizosphere remains to be determined. Synthetic communities (SynComs) have been increasingly used to study the interactions between microbes and their hosts in recent years , . The development of high-throughput microbial isolation techniques has enabled researchers to efficiently isolate more microbial strains . The isolated strains were then compared with the sequencing results to select suitable candidates for SynComs construction. Despite the fact that research on SynComs has often focused on bacteria, fungi also play a very important role in helping plants resist heavy metal stress . In addition, fungi can establish symbiotic networks with bacteria , which may be more effective in helping plants resist stresses. Therefore, it is necessary to evaluate the role of different synthetic communities in helping soybeans resist Cd toxicity. In this study, we hypothesized that the GmAMT2.1/2.2 genes enhance NH 4 + -N uptake in soybeans, and help them to resist Cd toxicity by influencing metabolites to recruit beneficial rhizosphere microorganisms. To test these hypotheses, high-throughput sequencing, and liquid chromatography-mass spectrometry (LC-MS) assays were used to identify taxonomic and metabolic differences and their correlations in the rhizosphere. In addition, we isolated and identified bacteria and fungi in the rhizosphere of different genotypes and constructed bacterial, fungal, and bacterial-fungal cross-kingdom SynComs. Finally, we verified that these SynComs influence the expression of heavy metal tolerance-related genes in soybean roots. The objectives of this study were to: (1) investigate how the GmAMT2.1/2.2 genes influence rhizosphere microbes by regulating metabolites and N patterns, which in turn help soybean to resist Cd toxicity; and (2) determine which SynCom consisting of recruited microorganisms is most effective in alleviating Cd toxicity in soybean, and to elucidate the corresponding physiological and molecular mechanisms. 

GmAMT2.1/2.2 are responsible for the Cd toxicity alleviation through affecting N patterns in soybean To identify potential candidate AMTs in response to the Cd toxicity alleviation, we examined the transcriptome data of AMT homologous genes in the CK and Cd treatment. Among these, two homologous genes GmAMT2.1/2.2 , which are predominantly expressed in roots (Supplementary Fig. ), are significantly up regulated by Cd treatment (Fig. ). To further explore the transcriptional response of GmAMT2.1/2 .2 to Cd treatment, we analyzed the expression levels of GmAMT2.1/2 .2 using the soybean plants treated with Cd at different concentrations. The expression levels of GmAMT2.1/2 .2 exhibited an upward trend with increasing Cd concentration and time (except for a decrease in GmAMT2.1 expression after 6 h of treatment and a decrease in GmAMT2.2 expression after 12 h of treatment) (Fig. ). In addition, we generated four lines to confirm the identification and function of GmAMT2.1/2.2 , including two stable double knockout lines (GmAMT2.1/2.2) and two overexpression lines (OXAMT2.2). Mu1 deleted 5 bp (AGCAT) in sgRNA1 of GmAMT2.1 , and deleted 7 bp (CAATGGG) in sgRNA2 of GmAMT2.2 . Mu2 inserted 1 bp (T) in sgRNA1 of both GmAMT2.1 and GmAMT2.2 , which ultimately lead to Mu1 and Mu2 are loss-of-function mutants. In the two overexpressed lines (OX1 and OX2), the expression of GmAMT2.2 was up-regulated 1.87- and 2.07-fold, respectively. (Fig. ; Supplementary Figs. , ). When exposed to identical Cd concentrations, the knockout lines consistently displayed a more pronounced Cd sensitivity compared to the wild-type (WT) lines in terms of plant growth. In contrast, the overexpressed lines did not show significant differences compared to the WT (Fig. ). Cd treatment resulted in a significant decrease in the fresh weight of all four lines. Furthermore, an increase in N content, especially NH 4 + -N, was observed in response to Cd toxicity. This upward trend in N content progression was also observed upon overexpression of GmAMT2.2 as well, whereas an opposite relationship was observed in the mutants (Fig. ). Taken together, these findings suggest that NH 4 + -N may play a role in alleviating Cd toxicity and that the GmAMT2.1/2.2 genes may inhibit Cd transport and accumulation by increasing NH 4 + -N levels, thereby mitigating the detrimental effects of Cd toxicity on soybean plants. Modification of rhizosphere microbiota by GmAMT2.1/2.2 under Cd toxicity From 2,236,825 high-quality bacterial 16 S rRNA reads and 2,078,640 fungal ITS reads, we identified a total of 11,980 bacterial OTUs and 1,702 fungal OTUs. Cd toxicity had no significant effect ( P > 0.05) on the alpha diversity of rhizobacterial communities, but increased the alpha diversity of fungal communities in both WT and OX soybean genotypes. Under no-Cd toxicity conditions, the Mu genotype had a higher Shannon index than the other two genotypes, while Cd toxicity did not result in a significant difference between the three genotypes (Fig. ). In terms of beta diversity, Cd toxicity and the interaction of Cd toxicity and genotype significantly altered the structure of microbial communities (Fig. ; Supplementary Table ). Furthermore, WT, OX, and Mu genotypes generally harbored significantly different microbial communities under both Cd and no-Cd toxicity conditions (PERMANOVA, pairwise comparison, n = 6, P < 0.05) (Fig. ; Supplementary Table ), suggesting that the GmAMT2.1/2.2 gene activity affects the assembly of the soybean rhizosphere microbiome. The rhizosphere soil of the three genotypes exhibited comparable community composition under Cd and no-Cd toxicity conditions (Fig. ). Briefly, the most prevalent bacterial phyla were Acidobacteria, Proteobacteria, Actinobacteria and Chloroflexi with the relative abundances ranging from 21.56% to 22.65%, 7.42% to 7.98%, 5.09% to 5.55% and 4.98% to 5.40%, respectively. The fungal communities were mainly Ascomycota, Basidiomycota and Chytridiomycota, with the relative abundances varying from 62.08% to 70.27%, 12.71% to 25.92% and 0.73% to 4.26%, respectively. In general, five bacterial phyla (i.e., Proteobacteria, Actinobacteria, Myxococcota, Firmicutes and Gemmatimonadetes) and five fungal phyla (i.e., Ascomycota, Basidiomycota, Chytridiomycota, Mortierellomycota and Kickxellomycota) were significantly affected by Cd toxicity. One bacterial phylum (i.e., Verrucomicrobiota) and one fungal phylum (i.e., Basidiomycot) were influenced by genotypes. Moreover, three bacterial (i.e., Acidobacteriota, Actinobacteriota and Verrucomicrobiota) and three fungal phyla (i.e., Ascomycota, Basidiomycot and Glomeromycota) were altered by the interactions between genotype and Cd toxicity (Supplementary Table ). All genotypes were observed to be significantly affected by Cd toxicity in terms of the co-occurrence network structure, but the responses varied (Fig. ; Supplementary Table ). Specifically, Cd toxicity increased network complexity, as evidenced by an increase in both the clustering coefficient and the number of edges. Mu genotype showed the highest number of edges and clustering coefficient under Cd toxicity, followed by WT and OX genotypes. These results underline the impact of Cd toxicity on microbial community dynamics and the genotype-specific mechanisms that shape community composition under Cd toxicity. Enriched potential beneficial microorganisms by GmAMT2.1/2.2 under Cd toxicity We investigated the effect of Cd toxicity on the relative abundance of microbes in three soybean genotypes (Figs. , ). Cd toxicity increased the number of microbial OTUs in Mu, WT and OX by 306, 256, and 280 (bacteria), and 77, 71, and 94 (fungi), respectively (Figs. b, ). The enriched bacterial OTUs were mainly belonged to Proteobacteria, Firmicutes, Chloroflexi, and Verrucomicrobiota, while the enriched fungal OTUs were mainly associated with Ascomycota and Rozellomycota. Venn analysis revealed that WT and OX genotypes carrying the AMT genes showed a specific enrichment of 274 bacterial and 96 fungal OTUs under Cd toxicity. Based on the information of the isolation and identification of culturable microbes, six bacterial OTUs including OTU759 ( Tumebacillus ), OTU215 ( Ralstonia ), OTU1135 ( Alicyclobacillus ), OTU57 ( Burkholderia ), OTU419 ( Paenibacillus ), OTU5695 ( Methylophilus ), and six fungal OTUs including OTU76 ( Aspergillus ), OTU99 ( Talaromyces ), OTU144 ( Penicillium ) and OTU168 ( Cladosporium ) were identified and selected to show their relative abundance in different treatments (Figs. c, ). Response of soil metabolites to GmAMT2.1/2.2 under Cd toxicity To explore the complex metabolic alterations in the rhizosphere soil under different treatments and genotypes, we performed LC-MS analysis on soil metabolites of Mu, WT, and OX with and without Cd exposure. A total of 798 distinct peaks with defined identities were detected across the three genotypes. Orthogonal partial least squares discriminant analysis (OPLS-DA) illustrated a clear separation between the treatments (Supplementary Fig. ; Supplementary Table ). Using a combination of filtering procedures and a threshold of variable importance in projection (VIP) > 1.0 and P < 0.05, we identified 35 Cd-induced metabolites that increased and 30 that decreased in OX and/or WT, respectively (Supplementary Table ). KEGG pathway enrichment analysis revealed differential enrichment and depletion of metabolites in secondary metabolism biosynthesis, including purine metabolism, arginine biosynthesis, galactose metabolism, stilbenoid, flavonoid biosynthesis, tryptophan metabolism, isoflavonoid biosynthesis, and phenylpropanoid biosynthesis (Fig. ). Notably, the levels of genistein, chrysin, piceatannol, glycitein, daidzein, daidzin, and coumestrol increased, while xanthurenic acid, sinapoyl alcohol, L-glutamic acid, guanine, 2-deoxyguanosine and sucrose decreased in WT and/or OX plants under Cd exposure (Fig. ). Correlation analysis between metabolites and microbial species revealed a positive association between sinapyl Alcohol, daidzein, and glycitein with Alicyclobacillus , Tumebacillus , Ralstonia , and Methylophilus in Mu and WT genotypes. However, a significant increase in the levels of these metabolites in the OX genotypewas positively correlated with the abundance of different microbial species (Fig. ). These findings suggest that GmAMT2.1/2.2 may play a role in regulating metabolic pathways associated with microbial community assembly in the rhizosphere. Effect of different SynComs on Cd tolerance in soybean Using a high-throughput cultivation method, we sequenced and identified 403 bacterial and 268 fungal colonies from the rhizosphere compartment across different treatments. Among them, six bacterial and eight fungal strains, identical to the microorganisms enriched in the soybean genotypes carrying the AMT genes based on the sequencing results, were used to construct three SynComs (bacterial, fungal, and cross-kingdom SynComs) (Supplementary Table ). The efficacy of these SynComs in enhancing soybean Cd toxicity tolerance was evaluated (Fig. ). The results showed that SynComs-treated plants had significantly higher shoot and root fresh weight, NH 4 + -N content, and translocation factor, with reduced Cd content in shoot and root compared to heat-killed control plants (Fig. ). Additionally, cross-kingdom and fungal SynComs had more pronounced impact on the indicators mentioned above compared to bacterial SynCom. RNA-Seq analysis was conducted to elucidate the molecular mechanisms underlying the SynCom-mediated enhancement of soybean growth under Cd toxicity. PCA showed distinct separation of the four treatments ( P < 0.05) (Fig. ). SynComs application induced differential expression of numerous genes in soybean root tissues (Fig. ). Notably, all three SynComs significantly upregulated the expression of genes involved in cytochrome P450 ( Glyma.08G350800 ), endopeptidase inhibitor activity ( Glyma.16G211700 ), metabolic process related ( Glyma.07G00700 0, Glyma.01G205900 and Glyma.02G104600 ), zinc finger protein ( Glyma.04G044900 ), messenger RNA biogenesis ( Glyma.12G093100 ), ethylene response factor (Glyma.12G117000) and BURP domain family member ( Glyma.12G217300 ). Bacterial SynCom specifically upregulated genes related to cell redox homeostasis and casparian strip membrane domain proteins (CASP) family member, while fungal SynCom exclusively upregulated genes involved in protein phosphorylation. KEGG enrichment analysis was further performed to explore the distinct metabolic pathways modulated by the three SynComs in alleviating Cd toxicity (Fig. ). The results showed that the three SynComs mainly influenced metabolic processes associated with amino acid metabolism, biosynthesis of other secondary metabolites, carbohydrate metabolism and signal transduction. Among these, phenylpropanoid biosynthesis exhibited the highest enrichment in both bacterial and fungal SynComs, while plant hormone signal transduction showed the highest enrichment in cross-kingdom SynCom. However, certain pathways demonstrated specific enrichment under different SynCom treatments. For instance, the toll-like receptor signaling pathway was specially enriched in bacterial SynCom treatment and linked to plant disease resistance and immune response. Flavonoid biosynthesis, ascorbate and aldarate metabolism and glucosinolate biosynthesis were exclusively associated with fungal SynCom, while plant-pathogen interaction and mineral absorption were only associated with cross-kingdom SynCom. 

are responsible for the Cd toxicity alleviation through affecting N patterns in soybean To identify potential candidate AMTs in response to the Cd toxicity alleviation, we examined the transcriptome data of AMT homologous genes in the CK and Cd treatment. Among these, two homologous genes GmAMT2.1/2.2 , which are predominantly expressed in roots (Supplementary Fig. ), are significantly up regulated by Cd treatment (Fig. ). To further explore the transcriptional response of GmAMT2.1/2 .2 to Cd treatment, we analyzed the expression levels of GmAMT2.1/2 .2 using the soybean plants treated with Cd at different concentrations. The expression levels of GmAMT2.1/2 .2 exhibited an upward trend with increasing Cd concentration and time (except for a decrease in GmAMT2.1 expression after 6 h of treatment and a decrease in GmAMT2.2 expression after 12 h of treatment) (Fig. ). In addition, we generated four lines to confirm the identification and function of GmAMT2.1/2.2 , including two stable double knockout lines (GmAMT2.1/2.2) and two overexpression lines (OXAMT2.2). Mu1 deleted 5 bp (AGCAT) in sgRNA1 of GmAMT2.1 , and deleted 7 bp (CAATGGG) in sgRNA2 of GmAMT2.2 . Mu2 inserted 1 bp (T) in sgRNA1 of both GmAMT2.1 and GmAMT2.2 , which ultimately lead to Mu1 and Mu2 are loss-of-function mutants. In the two overexpressed lines (OX1 and OX2), the expression of GmAMT2.2 was up-regulated 1.87- and 2.07-fold, respectively. (Fig. ; Supplementary Figs. , ). When exposed to identical Cd concentrations, the knockout lines consistently displayed a more pronounced Cd sensitivity compared to the wild-type (WT) lines in terms of plant growth. In contrast, the overexpressed lines did not show significant differences compared to the WT (Fig. ). Cd treatment resulted in a significant decrease in the fresh weight of all four lines. Furthermore, an increase in N content, especially NH 4 + -N, was observed in response to Cd toxicity. This upward trend in N content progression was also observed upon overexpression of GmAMT2.2 as well, whereas an opposite relationship was observed in the mutants (Fig. ). Taken together, these findings suggest that NH 4 + -N may play a role in alleviating Cd toxicity and that the GmAMT2.1/2.2 genes may inhibit Cd transport and accumulation by increasing NH 4 + -N levels, thereby mitigating the detrimental effects of Cd toxicity on soybean plants. 

GmAMT2.1/2.2 under Cd toxicity From 2,236,825 high-quality bacterial 16 S rRNA reads and 2,078,640 fungal ITS reads, we identified a total of 11,980 bacterial OTUs and 1,702 fungal OTUs. Cd toxicity had no significant effect ( P > 0.05) on the alpha diversity of rhizobacterial communities, but increased the alpha diversity of fungal communities in both WT and OX soybean genotypes. Under no-Cd toxicity conditions, the Mu genotype had a higher Shannon index than the other two genotypes, while Cd toxicity did not result in a significant difference between the three genotypes (Fig. ). In terms of beta diversity, Cd toxicity and the interaction of Cd toxicity and genotype significantly altered the structure of microbial communities (Fig. ; Supplementary Table ). Furthermore, WT, OX, and Mu genotypes generally harbored significantly different microbial communities under both Cd and no-Cd toxicity conditions (PERMANOVA, pairwise comparison, n = 6, P < 0.05) (Fig. ; Supplementary Table ), suggesting that the GmAMT2.1/2.2 gene activity affects the assembly of the soybean rhizosphere microbiome. The rhizosphere soil of the three genotypes exhibited comparable community composition under Cd and no-Cd toxicity conditions (Fig. ). Briefly, the most prevalent bacterial phyla were Acidobacteria, Proteobacteria, Actinobacteria and Chloroflexi with the relative abundances ranging from 21.56% to 22.65%, 7.42% to 7.98%, 5.09% to 5.55% and 4.98% to 5.40%, respectively. The fungal communities were mainly Ascomycota, Basidiomycota and Chytridiomycota, with the relative abundances varying from 62.08% to 70.27%, 12.71% to 25.92% and 0.73% to 4.26%, respectively. In general, five bacterial phyla (i.e., Proteobacteria, Actinobacteria, Myxococcota, Firmicutes and Gemmatimonadetes) and five fungal phyla (i.e., Ascomycota, Basidiomycota, Chytridiomycota, Mortierellomycota and Kickxellomycota) were significantly affected by Cd toxicity. One bacterial phylum (i.e., Verrucomicrobiota) and one fungal phylum (i.e., Basidiomycot) were influenced by genotypes. Moreover, three bacterial (i.e., Acidobacteriota, Actinobacteriota and Verrucomicrobiota) and three fungal phyla (i.e., Ascomycota, Basidiomycot and Glomeromycota) were altered by the interactions between genotype and Cd toxicity (Supplementary Table ). All genotypes were observed to be significantly affected by Cd toxicity in terms of the co-occurrence network structure, but the responses varied (Fig. ; Supplementary Table ). Specifically, Cd toxicity increased network complexity, as evidenced by an increase in both the clustering coefficient and the number of edges. Mu genotype showed the highest number of edges and clustering coefficient under Cd toxicity, followed by WT and OX genotypes. These results underline the impact of Cd toxicity on microbial community dynamics and the genotype-specific mechanisms that shape community composition under Cd toxicity. 

GmAMT2.1/2.2 under Cd toxicity We investigated the effect of Cd toxicity on the relative abundance of microbes in three soybean genotypes (Figs. , ). Cd toxicity increased the number of microbial OTUs in Mu, WT and OX by 306, 256, and 280 (bacteria), and 77, 71, and 94 (fungi), respectively (Figs. b, ). The enriched bacterial OTUs were mainly belonged to Proteobacteria, Firmicutes, Chloroflexi, and Verrucomicrobiota, while the enriched fungal OTUs were mainly associated with Ascomycota and Rozellomycota. Venn analysis revealed that WT and OX genotypes carrying the AMT genes showed a specific enrichment of 274 bacterial and 96 fungal OTUs under Cd toxicity. Based on the information of the isolation and identification of culturable microbes, six bacterial OTUs including OTU759 ( Tumebacillus ), OTU215 ( Ralstonia ), OTU1135 ( Alicyclobacillus ), OTU57 ( Burkholderia ), OTU419 ( Paenibacillus ), OTU5695 ( Methylophilus ), and six fungal OTUs including OTU76 ( Aspergillus ), OTU99 ( Talaromyces ), OTU144 ( Penicillium ) and OTU168 ( Cladosporium ) were identified and selected to show their relative abundance in different treatments (Figs. c, ). 

GmAMT2.1/2.2 under Cd toxicity To explore the complex metabolic alterations in the rhizosphere soil under different treatments and genotypes, we performed LC-MS analysis on soil metabolites of Mu, WT, and OX with and without Cd exposure. A total of 798 distinct peaks with defined identities were detected across the three genotypes. Orthogonal partial least squares discriminant analysis (OPLS-DA) illustrated a clear separation between the treatments (Supplementary Fig. ; Supplementary Table ). Using a combination of filtering procedures and a threshold of variable importance in projection (VIP) > 1.0 and P < 0.05, we identified 35 Cd-induced metabolites that increased and 30 that decreased in OX and/or WT, respectively (Supplementary Table ). KEGG pathway enrichment analysis revealed differential enrichment and depletion of metabolites in secondary metabolism biosynthesis, including purine metabolism, arginine biosynthesis, galactose metabolism, stilbenoid, flavonoid biosynthesis, tryptophan metabolism, isoflavonoid biosynthesis, and phenylpropanoid biosynthesis (Fig. ). Notably, the levels of genistein, chrysin, piceatannol, glycitein, daidzein, daidzin, and coumestrol increased, while xanthurenic acid, sinapoyl alcohol, L-glutamic acid, guanine, 2-deoxyguanosine and sucrose decreased in WT and/or OX plants under Cd exposure (Fig. ). Correlation analysis between metabolites and microbial species revealed a positive association between sinapyl Alcohol, daidzein, and glycitein with Alicyclobacillus , Tumebacillus , Ralstonia , and Methylophilus in Mu and WT genotypes. However, a significant increase in the levels of these metabolites in the OX genotypewas positively correlated with the abundance of different microbial species (Fig. ). These findings suggest that GmAMT2.1/2.2 may play a role in regulating metabolic pathways associated with microbial community assembly in the rhizosphere. 

Using a high-throughput cultivation method, we sequenced and identified 403 bacterial and 268 fungal colonies from the rhizosphere compartment across different treatments. Among them, six bacterial and eight fungal strains, identical to the microorganisms enriched in the soybean genotypes carrying the AMT genes based on the sequencing results, were used to construct three SynComs (bacterial, fungal, and cross-kingdom SynComs) (Supplementary Table ). The efficacy of these SynComs in enhancing soybean Cd toxicity tolerance was evaluated (Fig. ). The results showed that SynComs-treated plants had significantly higher shoot and root fresh weight, NH 4 + -N content, and translocation factor, with reduced Cd content in shoot and root compared to heat-killed control plants (Fig. ). Additionally, cross-kingdom and fungal SynComs had more pronounced impact on the indicators mentioned above compared to bacterial SynCom. RNA-Seq analysis was conducted to elucidate the molecular mechanisms underlying the SynCom-mediated enhancement of soybean growth under Cd toxicity. PCA showed distinct separation of the four treatments ( P < 0.05) (Fig. ). SynComs application induced differential expression of numerous genes in soybean root tissues (Fig. ). Notably, all three SynComs significantly upregulated the expression of genes involved in cytochrome P450 ( Glyma.08G350800 ), endopeptidase inhibitor activity ( Glyma.16G211700 ), metabolic process related ( Glyma.07G00700 0, Glyma.01G205900 and Glyma.02G104600 ), zinc finger protein ( Glyma.04G044900 ), messenger RNA biogenesis ( Glyma.12G093100 ), ethylene response factor (Glyma.12G117000) and BURP domain family member ( Glyma.12G217300 ). Bacterial SynCom specifically upregulated genes related to cell redox homeostasis and casparian strip membrane domain proteins (CASP) family member, while fungal SynCom exclusively upregulated genes involved in protein phosphorylation. KEGG enrichment analysis was further performed to explore the distinct metabolic pathways modulated by the three SynComs in alleviating Cd toxicity (Fig. ). The results showed that the three SynComs mainly influenced metabolic processes associated with amino acid metabolism, biosynthesis of other secondary metabolites, carbohydrate metabolism and signal transduction. Among these, phenylpropanoid biosynthesis exhibited the highest enrichment in both bacterial and fungal SynComs, while plant hormone signal transduction showed the highest enrichment in cross-kingdom SynCom. However, certain pathways demonstrated specific enrichment under different SynCom treatments. For instance, the toll-like receptor signaling pathway was specially enriched in bacterial SynCom treatment and linked to plant disease resistance and immune response. Flavonoid biosynthesis, ascorbate and aldarate metabolism and glucosinolate biosynthesis were exclusively associated with fungal SynCom, while plant-pathogen interaction and mineral absorption were only associated with cross-kingdom SynCom. 

Plant functional genes can actively shape the microbial community structure of the rhizosphere by regulating root metabolites, which improving the nutrient uptake efficiency and environmental stress resistance of crops , , . AMTs regulate the uptake and transport of NH 4 + -N, thereby promoting plant uptake of N sources . NH 4 + -N produces various N-containing macromolecules, which regulate Cd uptake and translocation by plants, and alter the soybean root exudate and rhizosphere environment , . In this study, we used three soybean genotypes with normal expression (WT), overexpression (OX) and knockout (Mu) of the GmAMT2.1/2.2 genes, respectively, to demonstrate the key role of the GmAMT2.1/2.2 genes in influencing NH 4 + -N uptake, Cd levels, and soil metabolites, resulting in significant shifts in rhizosphere microbial community structure (Fig. ). Plant materials carrying the GmAMT2.1/2.2 genes have been found to attract a diverse array of potentially beneficial microorganisms, including bacterial species such as Tumebacillus , Alicyclobacillus , Paenibacillus , and Methylphilus , and fungal species such as Aspergillus , Penicillium , Cladosporium , and Talaromyces (Figs. , ). The enriched microbial species with relevant function may play an essential role in enhancing plant resilience to Cd toxicity. For instance, Tumebacillus and Alicyclobacillus are soil organic matter (SOM) decomposers that can mobilize scarce nutrients, thereby improving plant adaptation to Cd toxicity – . Paenibacillus and Methylphilus have been demonstrated to promote plant N uptake , . Talaromyces sp., a plant growth-promoting fungus, releases terpenoid-like volatiles that stimulate plant growth . Certain Ralstonia species, such as Ralstonia eutropha Q2-8 and Ralstonia Bcul-1 , are considered heavy metal-resistant strains and can enhance plant Cd tolerance through different mechanisms, including modulating heavy metal resistance gene expression, strengthening cell wall components, or sequestering Cd into root cell vacuoles , . Aspergillus sp. activated genes encoding glutathione (GSH) under Cd toxicity, alleviating the detrimental effects of Cd . Moreover, Paenibacillus , Penicillium and Cladosporium are known to directly absorb and immobilize soil Cd by exopolysaccharide production, formation of stable phosphate precipitates and oxidation of Mn (II) to biogenic manganese oxides (BMOs), respectively – . Thus, these rhizosphere microorganisms with diverse functionalities can synergistically enhance plant Cd tolerance through various mechanisms, including nutrient provision, gene expression regulation, chemical transformation, and direct Cd absorption/immobilization. They formed a complex functional network of rhizosphere microorganisms that promote healthy plant growth under Cd toxicity. Microbial community composition and metabolite profiles exhibited significant variations among soybean varieties. In particular, the abundances of Paenibacillus , Tumebacillus , Methylophilus , Aspergillus , Penicillium and Talaromyces were positively correlated with sinapyl alcohol and coumestrol levels. Sinapyl alcohol serves as a precursor of coumestrol, a type of coumarin. Coumarin exudation, regulated by MYB72 , have been shown to shape the root microbiome, promoting the health of Arabidopsis thaliana . Therefore, our findings suggest that GmAMT2.1/2.2 may enhance soybean adaptation to Cd toxicity by modulating the root microbiome through coumarin synthesis. GmAMT2.1/2.2 could influence soil N content and alter the synthesis of flavonoids, including genistein, daidzin and daidzein (Fig. ). These flavonoids may influence the symbiotic relationship between rhizobia and legumes, enriching beneficial microorganisms such as Cladosporium and Aspergillus , and further modulate plant defense responses , . In addition, low concentrations of the flavonoid chrysin can promote spore germination and root colonization by arbuscular mycorrhizal fungi, enhancing the rhizobia-plant symbiosis and potentially increasing soybean tolerance to Cd . Moreover, Paenibacillus , Ralstonia and Penicillium were significantly correlated with piceatannol, a compound known to enhance plant resistance to biotic and abiotic stresses. This indicates that piceatannol may also selectively recruit beneficial microbes. Although a direct link between piceatannol and specific microbes has not yet been established, this could be a direction for future research. All three SynComs alleviated Cd toxicity, with fungal SynCom and cross-kindom SynCom exhibiting greater efficacy than the bacterial SynCom (Fig. ). This suggests that GmAMT2.1/2.2 genes-regulated microbiome plays a crucial role in enhancing soybean resistance Cd toxicity, and fungi may hold greater potential than bacteria in assisting soybeans to adapt to Cd toxicity. Fungi mitigation of Cd toxicity involves diverse mechanisms, including metal biosorption onto the cell wall, intracellular accumulation and sequestration, and deposition of metal compounds within and around the mycelium . Additionally, unlike bacteria, fungi can form extensive mycelial networks, facilitating bidirectional nutrient exchange with plants . Additionally, the fungal SynCom may promote soybean adaptation Cd toxicity through the induction of genes involved in flavonoid and glucosinolate biosynthesis and ascorbic acid metabolism in the soybean root, which was not observed with the bacterial SynCom. These substances contribute to Cd toxicity mitigation by enriching certain beneficial microorganisms, increasing sulfur availability and scavenging excess H 2 O 2 from plant cells – . Of note, a recent study by Xie et al. showed that microorganisms might regulate the immune system responsible for Cd resistance in host plants . This aligns with our finding that the bacterial SynCom specifically enriched for toll-like receptor signalling pathway, which served as a sensor for pathogens and enhances the immune response of plants , . Furthermore, DEGs significantly upregulated by SynComs were involved in plant defense signalling, abiotic stress resistance and plant hormone signalling. It has been reported that lipoxygenases 1 (LOX1) and ZAT10 were directly related to Cd toxicity response. When Arabidopsis thaliana was exposed to Cd toxicity, LOX1 was involved in Cd-induced signalling transduction and increased jasmonic acid (JA) biosynthesis , . Similarly, ZAT10, a member of the C 2 H 2 zinc finger gene family, negatively regulated Cd uptake in Arabidopsis thaliana and enhanced Cd detoxification, and positively regulating heavy metal detoxification-related genes such as NAS1, IRT2 and MTP3 . RD22, containing the plant-specific BURP domain, was also highly up-regulated under SynCom application and was involved in several abiotic resistances, including drought, salt and osmotic stresses , . CYP93D1, belonging to the cytochrome P450 monooxygenases family, is involved in JA metabolism and can be activated by ZmCLA4 to affect lignin biosynthesis . Cd toxicity stimulates lignin biosynthesis, and the upregulation of CYP93D1 increases lignin content, which enhances Cd adsorption and promotes plant growth , . Two other significantly up-regulated DEGs under SynComs application are related to plant defense mechanisms. UGT73B3 is involved in regulating redox status and ROS reactivity, while ERF9 serves as a negative regulator of DREB AND EAR MOTIF PROTEIN 1 (DEAR1)-dependent ethylene/JA-mediated plant defense mechanisms , . Furthermore, phenylpropanoid biosynthesis was the key enriched pathway under SynComs treatments, indicating that SynComs activate the phenylpropanoid biosynthetic pathway, synthesizing lignin from the accumulated phenolic compounds to overcome heavy metal-induced stress . In summary, SymComs application enables the coordinated regulation of LOX1, ZAT10, RD22, CYP93D1, UGT73B3 and ERF9 expression, triggering an interconnected network of defense responses, plant hormone signalling pathways, lignin biosynthesis and heavy metal detoxification mechanisms to enhance soybean’s ability to mitigate Cd toxicity. It is noteworthy that the soil metabolites in this study are not the same as those from root exudates. Although the study used a single soil type, theoretically, the majority of the differences in soil metabolites are likely due to soybean genotypes. However, we cannot ignore the potential for soil microorganisms to be influenced by root exudates under GmAMT2.1/2.2 genetic regulation, which could lead to the up-regulation or down-regulation of extracellular metabolites. Several metabolites from root exudates have been shown to alter soil microbial community composition , suggesting that changes in the soil metabolite profile may partly due to extracellular compounds readily released by microorganisms. Thus, future studies should dedicate greater effort to distinguishing the relative importance of specific root- or soil-derived metabolites in shaping rhizosphere microbial structures , . Moreover, advanced techniques such as microfluidic-based cultivation and diversification, should be applied to isolate additional uncultivated microbes, including fungi and bacteria . The efficacy of SynComs in promoting Cd tolerance in other crops and under field agricultural conditions requires further validation. In summary, our study reveals that Cd toxicity induces the GmAMT2.1/2.2 , leading to the recruitment of beneficial microorganisms, such as Tumebacillus , Alicyclobacillus , Methylophilus , Aspergillus , Talaromyces , and Penicillium , by altering metabolites such as sinapyl alcohol, genistein, coumestrol, and piceatannol. SynComs composed of these microorganisms collectively affecting various molecular mechanisms, thereby enhancing plant resistance to Cd toxicity. These mechanisms include ascorbate and aldarate metabolism and glucosinolate biosynthesis pathways associated with fungal SynCom, and plant-pathogen interaction and mineral absorption pathways associated with cross-kingdom SynCom. Notably, cross-kingdom SynCom and fungal SynCom showed greater potential for synergistic effects. Overall, our study results draw attention to the crucial role of microorganisms, especially fungi within SynComs, in the molecular mechanism underlying Cd toxicity resistance in soybean. The advanced insights provided by our metabarcoding data, and subsequent validation by microbial culture profiling, substantially enhance our understanding of plant functional gene regulation and synergistic resistance mechanisms within the root microbiome. This study provides critical data and guidance for the identification of agriculturally important microbial communities as potential breeding targets, as well as the development of microbial formulations to enhance resistance to Cd toxicity in sustainable agriculture. 

Soil and soybean materials Experimental soils were collected at the surface layer at a depth of 10 cm from acidic soils in China in the summer of 2020. The sampling site was Guangzhou (113°35′N, 23°15′E), Guangdong Province, which is classified as Udic Agrosol, according to USDA soil taxonomy. The characteristics of the soil were: pH 5.3, porosity 40.2, Cd 0.2 mg/kg, organic matter 5.2 g/kg, NH 4 + -N 5.3 mg/kg, available phosphorus (P) 15.5 mg/kg, NO 3 - -N 24 mg/kg, and available potassium (K) 85.2 mg/kg. To construct the CaMV35S-drived GmAMT2.2 , the full-length CDS of GmAMT2.2 was cloned into the pTF101-eGFP vector, and the resultant construct was introduced into Agrobacterium tumefaciens EHA101. To produce the GmAMT2.1 and GmAMT2.2 loss-of-function mutants, the gene-editing tool CRISPR-Cas9 was used to knockout GmAMT2.1 and GmAMT2.2 . CRISPR/Cas9-mediated gene editing was performed as previously reported . Briefly, the two sgRNAs targeting the first exon of GmAMT2.1 and GmAMT2.2 were designed on CRISPR-P server ( http://crispr.hzau.edu.cn/CRISPR/ ) . The sgRNAs was sub-cloned into pGES201 plasmid, and the resultant construct was further introduced into Agrobacterium tumefaciens EHA105. Agrobacterium -mediated transformation as previously reported , , and the soybean variety Young was used as the transgene receptor. RNA extraction and quantitative real-time PCR (qRT-PCR) Total RNA was extracted from soybean or Arabidopsis thaliana with a TRNzol Universal Kit (DP424, TIANGEN, Beijing, China). The cDNA was synthesized from total RNA using a PrimeScript RT Reagent Kit with gDNA Eraser (RR047A, Takara Bio, Japan) according to the manufacturer’s instructions. DNA fragment amplification was performed using KOD FX neo (TOYOBO (SHANGHAI) BIOTECH CO., Shanghai, China). qRT-PCR was conducted using TB Green Premix Ex Taq II (RR820, Takara Bio, Japan) with a CFX96 Real-Time System (Bio-Rad, Hercules, CA, USA). Data were normalized to the reference genes GmActin3 . All analyses were performed with three biological replicates and three technical replicates. The results were analyzed using the 2 –ΔΔCt method. Student’s t -test implemented in Excel software (Excel 2016) was used to evaluate the statistical significance of the data. The primers for the markers were listed in Supplementary Table . Experimental design A pot experiment was conducted at the College of Agriculture, South China Agricultural University, located in Guangzhou, China. A randomized complete block design was used for the experiment, with three soybean genotypes (with or without Cd), for a total of six treatments (3 soybean genotypes × 2 Cd treatments = 6 treatments). Soybean seeds of the three genotypes were sterilized with alcohol and then planted in separate pots. The pots were of the same size and contained approximately 2.5 kg of soil. In each pot, eight seeds of uniform size were initially sown and subsequently thinned to two on the 10th day. The soil moisture content was maintained at 80% of the field capacity throughout the experiment. For each treatment, there were six replicates (plots). The soybeans were grown under controlled greenhouse conditions (day temperature 28 ~ 32 °C, night temperature 16 ~ 20 °C). Sample collection and soil chemical analysis Twenty days after soybean sowing, 36 samples were collected and subjected to amplicon sequencing. Briley, the roots were shaken gently to remove the soil adhering to the roots. The roots and attached soil, which consider the rhizosphere soil, were then transferred to 1x phosphate-buffered saline. Ten grams of rhizosphere soil were obtained, of which two grams were stored at -80°C for the microbial experiment and LC-MS analysis. The remaining samples were stored at 4 °C for subsequent microbial isolation and soil chemical characterization measurements. Amplicon sequencing and data analysis Utilizing the Fast DNA SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA), microbial DNA was extracted, and the bacterial 16 S rRNA gene was specifically targeted by amplifying the V4 region using primers 515 F/806R . Meanwhile, amplification of the fungal ITS region was achieved by targeting the ITS1 region using primers ITS5/1737 F. Briefly, each PCR reaction contained 4 μL buffer, 2 μL dNTPs (2 mM), 1 μL of forward/reverse primer (10 μM), 10 ng of DNA and 10 μL ddH 2 O. The PCR was then performed on an ABI 7900 system according to the following program 94 °C for 45 s; 35 cycles of 95 °C for 15 s, 55 °C for 10 s, 72 °C for 10 s; and 50 for 15 min. The amplicons that were pooled in equimolar amounts were subjected to paired-end sequencing on the Illumina MiSeq platform (Shanghai Majorbio Bio-pharm Technology Co., Ltd). The bacterial 16 S rRNA gene and fungal ITS sequences were processed with QIIME (v1.9.1) and VSEARCH, starting from the raw FASTQ files. After quality filtering, the primers and low-quality sequences with scores < 20 and lengths < 200 bp were removed. The paired-end reads that correspond to bacterial 16 S rRNA genes and fungal ITS regions were merged into single files, respectively. The operational taxonomic units (OTUs) were classified at a 97% sequence similarity cutoff using CD-HIT . Taxonomic assignment was performed using the SILVA (v138) and UNITE (v8.0) databases for bacteria and fungi, respectively. Mothur and QIIME (v1.91) were used, respectively, to calculate the alpha and beta diversity of microbial communities. Rarefaction of bacterial and fungal OTU tables resulted in 60,024 and 60,049 reads, respectively, for alpha diversity estimation. Metabolite measurement Fifty mg of the rhizosphere soil sample were added to a solvent of acetonitrile, methanol, and water (volume ratio 2:2:1). The sample was then vortexed and sonicated three times in an ice-water bath. The supernatant was centrifuged and subjected to UHPLC-Q Exactive (QE) Orbitrap MS analysis. Quality control samples were generated by pooling the supernatant from all samples and detecting soil metabolites via Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) methodology (Shanghai Majorbio Bio-pharm Technology Co., Ltd). The OPLS model was used to identify metabolites with the greatest group-specific differences based on their variable importance prediction (VIP) scores, with a threshold of 1 (OPLS, VIP > 1). High-throughput cultivation of microbes and construction of synthetic communities The rhizosphere bacteria and fungi were isolated using high-throughput culture according to Zhang et al., (2021) and Zhou et al., (2022) with minor modifications. Briefly, the rhizosphere suspensions were diluted to an optimal dilution such that 30% of the wells showed bacterial growth. Then, 100 μL of the diluted suspension was plated on the 96-well plates containing different culture media. For the bacteria, the media included LB medium, beef extract peptone and Trytone yeast extract glucose medium. For the fungi, the media included 1/4 strength RBM, 1/4 strength RBM, 1/10 strength PDA and 1/4 strength MEA . In addition, 0.01 M CdCl 2 was added to the media to obtain bacteria and fungi more resistant to Cd. After 7 days of inoculation, microbial identification was used to identify each well. Bacterial 16 S rDNA and fungal 18 S rDNA were amplified and sequenced at Sangon Biotech Co., Ltd (Shanghai, China). Sequence alignment was performed on the NCBI website, and 1 mL of 30% glycerol (v/v) at -80°C was used to preserve each microbial species. We identified bacteria and fungi that were significantly enriched in the rhizosphere of the soybean carrying GmAMT2.1/2.2 genes by analyzing the composition of the rhizosphere microbial community in different treatments. These strains were then considered as potential candidates for building SynComs. The OD 600 of each bacterial fermentation broth was adjusted to 0.02 (approximately 10 7 cells/ml). Fungal strains were propagated using shake flask fermentation in 1/10th strength PDB medium, and diluted to 10 6 conidia/ml . To identify how root-derived microbes enhance plant adaptation to Cd toxicity, we conducted an experiment consisting of three synthetic SynComs, including bacterial, fungal, and bacterial and fungal cross-kingdom SynComs. Each SynCom consisted of an equal proportion of the respective bacterial or fungal strains. Different SynComs were added to the soil 10 days after soybean sowing. Plant fresh weight, root Cd 2+ and NH 4 + -N content and translocation factor were determined 15 days after the addition of the SynComs. RNA‐seq for soybean treat with SynComs RNA-seq was used to investigate the effect of SynComs on the expression of related genes in soybean. Total RNA was extracted from soybean root samples using RNAiso Plus reagent (Takara Bio). The constructed libraries underwent a thorough quality assessment before sequencing on the Illumina platform (PE 150). Then, the raw data files were converted into raw reads via base calling analysis. RSEM and STAR were used to perform the sequence the alignment and quantification of the detected genes . Using the “stats” package in R 4.1.2, PCA (Principal components analysis) was conducted to show the effect of three SynComs on the gene expression of the soybean root. DESeq2 with the FDR < 0.05 was used to identify the differentially expressed genes (DEGs) . A hierarchical clustering heatmap was employed to identify DEGs, which were subsequently annotated and analyzed for enriched pathways using the “stats” package’s hclust function . The identified genes were annotated according to SoyBase ( https://www.soybase.org ) and then KEGG ( http://www.genome.jp/kegg/ ) analysis was performed using the software package “clusterProfiler” to find significantly enriched metabolic or signalling pathways with a threshold of FDR < 0.05 , . Statistical analysis Principal coordinate analysis (PCoA) was conducted in R 4.1.2 using the “vegan” package, based on Bray-Curtis dissimilarities. The PERMANOVA (Permutation Multivariate Analysis of Variance) and the Mantel test were also used to evaluate the significance of the PCoA results. A generalized linear model was run in R using the “edgeR” package to analyze the microbial and metabolite differences between each two treatments. The results of the generalized linear model were visualized in volcano plots . Using the “vcd” package, the Kruskal-Wallis test was performed in R to calculate the enrichment of microbes, and was then visualized in ternary plots using “ggplot2” package . Differences in chemical properties of soil and microbial relative abundance on the phylum level were evaluated using Genstat (version 13.0) with the two-way analysis of variance (ANOVA). Analysis of the microbiome and metabolome data integration was performed using M 2 IA . Microbial co-occurrence networks with an average OTU abundance greater than 0.1% across samples were constructed and analyzed to determine network connectivity in the genotypes. With the ’Hmisc’ and ’igraph’ packages in the R environment, spearman coefficients between OTUs were determined, and correlations with r > 0.8 and P < 0.05 were included in the network . The networks were explored and visualized using Gephi (v 0.8.2). The codes used for the study are accessible on GitHub ( https://github.com/liantengxiang1988/GmAMT2.1-2.2-shape-rhizosphere-microbiome-mitigate-cadmium-toxicity ). Reporting summary Further information on research design is available in the linked to this article. 

Experimental soils were collected at the surface layer at a depth of 10 cm from acidic soils in China in the summer of 2020. The sampling site was Guangzhou (113°35′N, 23°15′E), Guangdong Province, which is classified as Udic Agrosol, according to USDA soil taxonomy. The characteristics of the soil were: pH 5.3, porosity 40.2, Cd 0.2 mg/kg, organic matter 5.2 g/kg, NH 4 + -N 5.3 mg/kg, available phosphorus (P) 15.5 mg/kg, NO 3 - -N 24 mg/kg, and available potassium (K) 85.2 mg/kg. To construct the CaMV35S-drived GmAMT2.2 , the full-length CDS of GmAMT2.2 was cloned into the pTF101-eGFP vector, and the resultant construct was introduced into Agrobacterium tumefaciens EHA101. To produce the GmAMT2.1 and GmAMT2.2 loss-of-function mutants, the gene-editing tool CRISPR-Cas9 was used to knockout GmAMT2.1 and GmAMT2.2 . CRISPR/Cas9-mediated gene editing was performed as previously reported . Briefly, the two sgRNAs targeting the first exon of GmAMT2.1 and GmAMT2.2 were designed on CRISPR-P server ( http://crispr.hzau.edu.cn/CRISPR/ ) . The sgRNAs was sub-cloned into pGES201 plasmid, and the resultant construct was further introduced into Agrobacterium tumefaciens EHA105. Agrobacterium -mediated transformation as previously reported , , and the soybean variety Young was used as the transgene receptor. 

Total RNA was extracted from soybean or Arabidopsis thaliana with a TRNzol Universal Kit (DP424, TIANGEN, Beijing, China). The cDNA was synthesized from total RNA using a PrimeScript RT Reagent Kit with gDNA Eraser (RR047A, Takara Bio, Japan) according to the manufacturer’s instructions. DNA fragment amplification was performed using KOD FX neo (TOYOBO (SHANGHAI) BIOTECH CO., Shanghai, China). qRT-PCR was conducted using TB Green Premix Ex Taq II (RR820, Takara Bio, Japan) with a CFX96 Real-Time System (Bio-Rad, Hercules, CA, USA). Data were normalized to the reference genes GmActin3 . All analyses were performed with three biological replicates and three technical replicates. The results were analyzed using the 2 –ΔΔCt method. Student’s t -test implemented in Excel software (Excel 2016) was used to evaluate the statistical significance of the data. The primers for the markers were listed in Supplementary Table . 

A pot experiment was conducted at the College of Agriculture, South China Agricultural University, located in Guangzhou, China. A randomized complete block design was used for the experiment, with three soybean genotypes (with or without Cd), for a total of six treatments (3 soybean genotypes × 2 Cd treatments = 6 treatments). Soybean seeds of the three genotypes were sterilized with alcohol and then planted in separate pots. The pots were of the same size and contained approximately 2.5 kg of soil. In each pot, eight seeds of uniform size were initially sown and subsequently thinned to two on the 10th day. The soil moisture content was maintained at 80% of the field capacity throughout the experiment. For each treatment, there were six replicates (plots). The soybeans were grown under controlled greenhouse conditions (day temperature 28 ~ 32 °C, night temperature 16 ~ 20 °C). 

Twenty days after soybean sowing, 36 samples were collected and subjected to amplicon sequencing. Briley, the roots were shaken gently to remove the soil adhering to the roots. The roots and attached soil, which consider the rhizosphere soil, were then transferred to 1x phosphate-buffered saline. Ten grams of rhizosphere soil were obtained, of which two grams were stored at -80°C for the microbial experiment and LC-MS analysis. The remaining samples were stored at 4 °C for subsequent microbial isolation and soil chemical characterization measurements. 

Utilizing the Fast DNA SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA), microbial DNA was extracted, and the bacterial 16 S rRNA gene was specifically targeted by amplifying the V4 region using primers 515 F/806R . Meanwhile, amplification of the fungal ITS region was achieved by targeting the ITS1 region using primers ITS5/1737 F. Briefly, each PCR reaction contained 4 μL buffer, 2 μL dNTPs (2 mM), 1 μL of forward/reverse primer (10 μM), 10 ng of DNA and 10 μL ddH 2 O. The PCR was then performed on an ABI 7900 system according to the following program 94 °C for 45 s; 35 cycles of 95 °C for 15 s, 55 °C for 10 s, 72 °C for 10 s; and 50 for 15 min. The amplicons that were pooled in equimolar amounts were subjected to paired-end sequencing on the Illumina MiSeq platform (Shanghai Majorbio Bio-pharm Technology Co., Ltd). The bacterial 16 S rRNA gene and fungal ITS sequences were processed with QIIME (v1.9.1) and VSEARCH, starting from the raw FASTQ files. After quality filtering, the primers and low-quality sequences with scores < 20 and lengths < 200 bp were removed. The paired-end reads that correspond to bacterial 16 S rRNA genes and fungal ITS regions were merged into single files, respectively. The operational taxonomic units (OTUs) were classified at a 97% sequence similarity cutoff using CD-HIT . Taxonomic assignment was performed using the SILVA (v138) and UNITE (v8.0) databases for bacteria and fungi, respectively. Mothur and QIIME (v1.91) were used, respectively, to calculate the alpha and beta diversity of microbial communities. Rarefaction of bacterial and fungal OTU tables resulted in 60,024 and 60,049 reads, respectively, for alpha diversity estimation. 

Fifty mg of the rhizosphere soil sample were added to a solvent of acetonitrile, methanol, and water (volume ratio 2:2:1). The sample was then vortexed and sonicated three times in an ice-water bath. The supernatant was centrifuged and subjected to UHPLC-Q Exactive (QE) Orbitrap MS analysis. Quality control samples were generated by pooling the supernatant from all samples and detecting soil metabolites via Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) methodology (Shanghai Majorbio Bio-pharm Technology Co., Ltd). The OPLS model was used to identify metabolites with the greatest group-specific differences based on their variable importance prediction (VIP) scores, with a threshold of 1 (OPLS, VIP > 1). 

The rhizosphere bacteria and fungi were isolated using high-throughput culture according to Zhang et al., (2021) and Zhou et al., (2022) with minor modifications. Briefly, the rhizosphere suspensions were diluted to an optimal dilution such that 30% of the wells showed bacterial growth. Then, 100 μL of the diluted suspension was plated on the 96-well plates containing different culture media. For the bacteria, the media included LB medium, beef extract peptone and Trytone yeast extract glucose medium. For the fungi, the media included 1/4 strength RBM, 1/4 strength RBM, 1/10 strength PDA and 1/4 strength MEA . In addition, 0.01 M CdCl 2 was added to the media to obtain bacteria and fungi more resistant to Cd. After 7 days of inoculation, microbial identification was used to identify each well. Bacterial 16 S rDNA and fungal 18 S rDNA were amplified and sequenced at Sangon Biotech Co., Ltd (Shanghai, China). Sequence alignment was performed on the NCBI website, and 1 mL of 30% glycerol (v/v) at -80°C was used to preserve each microbial species. We identified bacteria and fungi that were significantly enriched in the rhizosphere of the soybean carrying GmAMT2.1/2.2 genes by analyzing the composition of the rhizosphere microbial community in different treatments. These strains were then considered as potential candidates for building SynComs. The OD 600 of each bacterial fermentation broth was adjusted to 0.02 (approximately 10 7 cells/ml). Fungal strains were propagated using shake flask fermentation in 1/10th strength PDB medium, and diluted to 10 6 conidia/ml . To identify how root-derived microbes enhance plant adaptation to Cd toxicity, we conducted an experiment consisting of three synthetic SynComs, including bacterial, fungal, and bacterial and fungal cross-kingdom SynComs. Each SynCom consisted of an equal proportion of the respective bacterial or fungal strains. Different SynComs were added to the soil 10 days after soybean sowing. Plant fresh weight, root Cd 2+ and NH 4 + -N content and translocation factor were determined 15 days after the addition of the SynComs. 

RNA-seq was used to investigate the effect of SynComs on the expression of related genes in soybean. Total RNA was extracted from soybean root samples using RNAiso Plus reagent (Takara Bio). The constructed libraries underwent a thorough quality assessment before sequencing on the Illumina platform (PE 150). Then, the raw data files were converted into raw reads via base calling analysis. RSEM and STAR were used to perform the sequence the alignment and quantification of the detected genes . Using the “stats” package in R 4.1.2, PCA (Principal components analysis) was conducted to show the effect of three SynComs on the gene expression of the soybean root. DESeq2 with the FDR < 0.05 was used to identify the differentially expressed genes (DEGs) . A hierarchical clustering heatmap was employed to identify DEGs, which were subsequently annotated and analyzed for enriched pathways using the “stats” package’s hclust function . The identified genes were annotated according to SoyBase ( https://www.soybase.org ) and then KEGG ( http://www.genome.jp/kegg/ ) analysis was performed using the software package “clusterProfiler” to find significantly enriched metabolic or signalling pathways with a threshold of FDR < 0.05 , . 

Principal coordinate analysis (PCoA) was conducted in R 4.1.2 using the “vegan” package, based on Bray-Curtis dissimilarities. The PERMANOVA (Permutation Multivariate Analysis of Variance) and the Mantel test were also used to evaluate the significance of the PCoA results. A generalized linear model was run in R using the “edgeR” package to analyze the microbial and metabolite differences between each two treatments. The results of the generalized linear model were visualized in volcano plots . Using the “vcd” package, the Kruskal-Wallis test was performed in R to calculate the enrichment of microbes, and was then visualized in ternary plots using “ggplot2” package . Differences in chemical properties of soil and microbial relative abundance on the phylum level were evaluated using Genstat (version 13.0) with the two-way analysis of variance (ANOVA). Analysis of the microbiome and metabolome data integration was performed using M 2 IA . Microbial co-occurrence networks with an average OTU abundance greater than 0.1% across samples were constructed and analyzed to determine network connectivity in the genotypes. With the ’Hmisc’ and ’igraph’ packages in the R environment, spearman coefficients between OTUs were determined, and correlations with r > 0.8 and P < 0.05 were included in the network . The networks were explored and visualized using Gephi (v 0.8.2). The codes used for the study are accessible on GitHub ( https://github.com/liantengxiang1988/GmAMT2.1-2.2-shape-rhizosphere-microbiome-mitigate-cadmium-toxicity ). 

Further information on research design is available in the linked to this article. 

Supplementary Information Reporting summary 

