Integrated Transcriptomic and Metabolomic Analyses Reveal Trade-Off Mechanisms Underlying Phosphorus Acquisition Strategies in Soybean Roots

Integrated Transcriptomic and Metabolomic Analyses Reveal Trade-Off Mechanisms Underlying Phosphorus Acquisition Strategies in Soybean Roots | 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 Integrated Transcriptomic and Metabolomic Analyses Reveal Trade-Off Mechanisms Underlying Phosphorus Acquisition Strategies in Soybean Roots Guangyao Zhao, Tongli Yang, Zhu Chen, Wanping Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7738053/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Dec, 2025 Read the published version in BMC Plant Biology → Version 1 posted 16 You are reading this latest preprint version Abstract Under phosphorus (P) deficiency, soybean ( Glycine max ) adapts by modifying root architecture, increasing the release of organic exudates, enhancing arbuscular mycorrhizal (AM) colonization, and reshaping rhizosphere microbial communities; however, how these strategies trade off across a phosphorus gradient remains unclear. In this study, we integrated transcriptomic and metabolomic analyses to examine five soybean cultivars under soil P supplies of 0 mg P kg⁻¹ (severe deficiency, P0), 30 mg P kg⁻¹ (moderate deficiency, P30), 60 mg P kg⁻¹ (mild deficiency, P60), 90 mg P kg⁻¹ (adequate), and 120 mg P kg⁻¹ (excess). Our results indicate that the gradient of plant-available P drives dynamic switching among soybean P-acquisition strategies. Under moderately low P, soybean upregulated PPDK , accC , and FabI , which is consistent with a shift in carbon use that could support arbuscular mycorrhizal fungi, and AMF colonization increased by 30–50%. Under severe deficiency P, soybean primarily relied on root-driven strategies: pckA , MDH , aceB , and CS (genes associated with the PEPC shunt) were upregulated, the concentration of low-molecular-weight organic acids increased by 17– to 24–fold, and fine-root length increased by approximately 35%, thereby optimizing root system architecture. Cultivars differed in their adaptive preferences: AM-dependent types were better suited to temperate soils with moderate P limitation, whereas fine-rooted cultivars were advantageous in tropical and subtropical soils with severe P depletion. Overall, our findings reveal the regulatory networks underlying soybean P-acquisition strategies and highlight their breeding and management significance. This study provides a foundation for developing P-efficient soybean cultivars and for precision P management in sustainable agriculture. Soybean phosphorus acquisition strategy trade-off mechanism transcriptomics metabolomics carbon allocation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Phosphorus (P) is an essential macronutrient required for plant nucleic acid synthesis, energy metabolism, and signal transduction that directly influences crop productivity [ 1 ]. However, inorganic phosphate (Pi) is readily immobilized by cations such as iron and aluminum, resulting in low bioavailability of applied P fertilizers. Consequently, the in-season utilization rate of phosphorus fertilizers is only approximately 30%, representing a major limitation to sustainable agricultural production [ 2 , 3 ]. Thus, enhancing the inherent ability of crops to activate and utilize soil phosphorus pools is a crucial strategy for improving phosphorus use efficiency [ 4 ]. To adapt to persistent low-P environments, plants have evolved three primary P-acquisition strategies [ 5 ]: (1) Root architectural modification: Increasing specific root length (SRL) and reducing the root diameter to expand soil exploration [ 6 , 7 ]; (2) Arbuscular mycorrhizal fungal (AMF) symbiosis: Leveraging extensive fungal hyphal networks to access distant P sources[ 8 ]; and (3) Root exudate release: Enhancing secretion of organic acids and phosphatases to solubilize fixed P[ 9 ]. Critically, all three strategies consume substantial carbon (C) resources derived from photosynthesis [ 10 ]. This C cost forces plants to make trade-offs in resource allocation among these strategies [ 11 ]. For example, fine-rooted cultivars constrain carbon investment in AMF symbiosis strategy, which ultimately requires higher phosphorus fertilizer inputs to sustain yield [ 12 ]. Understanding how plants allocate carbon among competing strategies is thus critical for crop P efficiency [ 13 ]. Building on the preceding framework, carbon allocation among competing P-acquisition strategies is dynamic rather than fixed. Under phosphorus stress, plants divert photosynthates to roots, increasing the root-to-shoot ratio (R/S) and triggering divergent phenotypic adaptations, whereby some species develop longer, thinner roots to enhance soil exploration, whereas coarse-rooted species invest more in maintaining AMF symbiosis [ 14 ]. The relationship between root architecture and exudation is context-dependent, with exudation–SRL correlations shifting across phosphorus levels [ 2 , 15 , 16 ]. AMF colonization often suppresses organic acid release [ 17 ], but this association weakens under severe phosphorus limitation as fungal efficiency declines [ 18 ]. Consequently, root exudation becomes the dominant strategy, reflecting a shift from mutualism toward parasitism [ 19 ]. These contrasting P-acquisition strategies align with ecological niche differentiation along global P gradients. AMF-dependent species are more common in moderately P-limited soils. Cluster-rooted species dominate in ancient, severely P-depleted soils (e.g., Australia and South Africa) [ 20 , 21 ]. Soybean (Glycine max L.), while important in sustainable agroecosystems, is highly sensitive to phosphorus deficiency. [ 22 ]. As soybean cultivation expands into low-phosphorus regions such as southern China and Brazil, inadequate phosphorus availability increasingly constrains yield potential. Although soybean is known to respond to low phosphorus levels by increasing root length and reducing diameter [ 23 ], the way these morphological adjustments are coordinated with AMF colonization and root exudate secretion remains unclear. Given the carbon allocation trade-offs outlined above, clarifying this coordination in soybean across phosphorus gradients remains a key knowledge gap. To address this gap, we investigated physiological, morphological, transcriptomic, and metabolomic data for five soybean cultivars grown across a phosphorus gradient (0–120 mg P kg⁻¹). We hypothesized that: (1) soil phosphorus availability drives both the trade-offs and transitions among acquisition strategies; and (2) genotypic differences in root architecture underlie distinct preferences, with coarse-rooted cultivars primarily relying on AMF symbiosis, and fine-rooted cultivars exhibiting greater morphological plasticity to support root-driven acquisition under severe stress. 1 Materials and methods 1.1 Experimental Design On the basis of previous studies [ 23 ], five soybean cultivars were selected: Qiandou 11 (Qd11), Aixuan (Ax), and Zhonghuang 13 (Zh13) as phosphorus-efficient genotypes; Niumao (Nm) as a phosphorus-sensitive genotype; and Williams 82 (Wm82) as the reference genotype. The experiment was conducted in a greenhouse at Guizhou University, Guiyang, Guizhou Province, China. Soil was collected from Qiannan Prefecture, Guizhou (26°17′N, 106°45′E) and represents typical phosphorus-deficient leached soil. The total and available phosphorus contents were 0.32 g kg⁻¹ and 3 mg kg⁻¹, respectively. Additional soil properties are listed in Additional file 1: Table S1 . Plastic pots (30 cm top diameter, 20 cm height, and 20 cm base diameter) were filled with 7.5 kg of air-dried soil. Five phosphorus concentrations were applied: 0 mg P kg⁻¹ (severe deficiency, P0), 30 mg P kg⁻¹ (moderate deficiency, P30), 60 mg P kg⁻¹ (mild deficiency, P60), 90 mg P kg⁻¹ (optimal, P90), and 120 mg P kg⁻¹ (excess, P120). Phosphorus was supplied as KH₂PO₄, and potassium levels were equalized via KCl. A complete nutrient mixture was used to supply other elements and prevent nutrient deficiencies (Liao and Li, 2018; Additional file 1: Table S2 ). A completely randomized two-factor design was adopted with three replicates per treatment, resulting in 75 pots. 1.2 Plant Growth and Sample Collection The seedlings were thinned to four plants per pot at the first trifoliate leaf stage. The phosphorus treatments commenced once two true leaves had developed. The plants were irrigated every four days (500 mL per pot) for 30 days. At harvest, the roots and shoots were rinsed with distilled water and blotted dry. The samples for physiological analysis were stored at 4°C, whereas those for transcriptomic and metabolomic analyses were flash-frozen in liquid nitrogen and stored at − 80°C. 1.3 Plant biomass and phosphorus contents The samples were initially inactivated at 105°C for 30 minutes, followed by drying at 75°C to a constant weight. The dry biomass was measured using an analytical balance. The samples were ground and sieved through a 0.5 mm mesh. The phosphorus concentration was determined following H₂SO₄ and H₂O₂ digestion via the vanadium molybdenum yellow colorimetric method [ 24 ]. Phosphorus accumulation (mg plant⁻¹ DW) was calculated as the product of biomass and phosphorus concentration. 1.4 Root morphology The root systems were scanned via an Epson PV850 Pro scanner (Epson, Long Beach, CA, USA) and analyzed via WinRHIZO 2019 Pro software (Regent Instruments, Quebec, Canada). 1.5 Root acid phosphatase activity and organic acid exudation Acid phosphatase activity was measured as described by Shen [ 25 ]. The roots were incubated in a solution of p-nitrophenyl phosphate (pNPP) disodium salt at 25°C for 60 minutes in the dark. The reaction was terminated by adding 1 mL of 1 mol/L NaOH, and the absorbance was recorded at 405 nm. Organic acid exudation was quantified following Liu [ 26 ]. The roots were incubated in 100 mL of 0.5 mol/L CaCl₂ solution for 4 hours in the dark. The solution was filtered through a 0.22 µm membrane and stored at − 20°C [ 27 ]. Organic acids were identified and quantified via an Agilent 1260 Infinity II HPLC system (Agilent Technologies, Santa Clara, CA, USA) equipped with a ZORBAX SB-C18 column (4.6 × 250 mm, 5 µm). 1.6 Root Mycorrhizal Colonization Approximately 1-cm-long root segments were cleared in 10% KOH at 90°C, stained with 0.05% trypan blue, and decolorized in a lactoglycerol solution (lactic acid:glycerol:water = 1:1:1). Thirty stained root segments per replicate were randomly selected and mounted in 30% glycerol for microscopic observation via an Olympus BX51 microscope (Olympus, Tokyo, Japan). Mycorrhizal colonization was assessed via the gridline intersect method [ 28 ]. 1.7 Metabolomic analysis Root metabolite extraction was performed as described by Li [ 29 ], with a quality control sample inserted every nine test samples. Metabolomic profiling was conducted via ultrahigh-performance liquid chromatography coupled with Fourier transform mass spectrometry (UHPLC-Q Exactive HF-X, Thermo Fisher Scientific). The chromatographic and MS conditions followed those of Zhou [ 30 ]. Metabolites with a relative standard deviation > 30% were excluded. Compound identification was based on matches to HMDB ( http://www.hmdb.ca/ ), METLIN ( https://metlin.scripps.edu/ ), and an in-house database (Majorbio). A total of 1,072 and 1,117 metabolites were identified in positive and negative ion modes, respectively (Additional file 1: Table S3). Differentially accumulated metabolites (DAMs) were defined by a variable importance in projection > 1 and p < 0.05. 1.8 Transcriptomic analysis Total RNA was extracted from roots via the MJZol Total RNA Extraction Kit (Majorbio, Shanghai, China) and purified via the RNA Purification Kit (Majorbio). The RNA purity and concentration were measured via a NanoDrop 2000. Samples with OD260/280 ≥ 1.8 and OD260/230 ≥ 1.0 were retained. RNA integrity was confirmed by agarose gel electrophoresis. Only samples with ≥ 1 µg total RNA and concentrations ≥ 35 ng µL⁻¹ were used for sequencing. cDNA libraries were prepared via the Illumina TruSeq RNA Sample Prep Kit and sequenced on the NovaSeq X Plus platform (Illumina, Hayward, CA, USA). Each sample yielded > 6.15 Gb of clean data with a Q30 ≥ 93.46% (Additional file 1: Table S4). Clean reads were mapped to the soybean reference genome (Gmax 508 Wm82.a4.v1) via HISAT2 (v2.1.0), with mapping rates exceeding 88.7% (Additional file 1: Table S5). Transcript assembly was performed via Cufflinks (v2.2.1) [ 31 ]. Differentially expressed genes (DEGs) were identified via DESeq2 with significance criteria of adjusted p < 0.05 and |log₂FC| ≥ 1. Gene Ontology (GO) and KEGG pathway annotations were performed for the DEG sets. GO enrichment was conducted via GOATOOLS (v0.6.5) [ 32 ], and KEGG enrichment was conducted via gseapy [ 33 ] in conjunction with scipy[ 34 ]. Weighted gene coexpression network analysis (WGCNA) and visualization were conducted via the Majorbio Cloud Platform ( https://report.majorbio.com ). 1.9 Statistical analysis Statistical analysis was conducted in R (v4.3.0) via the agricolae package. When significant differences were detected, Duncan’s multiple range test was applied via the duncan.test function. Figures were generated via Origin 2021 and Adobe Illustrator CS6. Pathway analysis was conducted via SPSSPRO ( https://www.spsspro.com ), and network visualizations were performed via Cytoscape (v3.9.1). The data are presented as the means ± standard errors (SEs). Prior to regression, outliers were identified based on pre-specified biological/diagnostic criteria and excluded; excluded points are displayed as open circles in Fig. 3 . 2 Results and Analysis 2.1 Effects of Phosphorus Levels on Biomass and Phosphorus Accumulation With increasing phosphorus application, soybean biomass and phosphorus accumulation exhibited a trend of first increasing and then decreasing (Fig. 1 A). Biomass peaked at P90 (90 mg P kg⁻¹), but except for the Zh13 variety, biomass of the other varieties significantly decreased, indicating that P120 (120 mg P kg⁻¹) had induced phosphorus toxicity (Figure S1 ). The phosphorus accumulation of all soybean varieties peaked at P120, except for Qd11 and Wm82 (Fig. 1 B). Under severe deficiency (P0) and moderate deficiency (P30), the biomass and phosphorus accumulation of the Nm variety were the lowest, suggesting that it is a phosphorus-sensitive variety under low-phosphorus stress. Phosphorus application significantly influenced root morphology (P < 0.01, Fig. 2 A, Table S6). The varieties Qd11 and Wm82 exhibited higher sensitivity to phosphorus application changes: when the phosphorus application was ≤ 60 mg P kg⁻¹, their root diameter significantly decreased, whereas in the other varieties, the significant decrease in root diameter occurred under extremely low phosphorus conditions (P0). In contrast, root specific surface area (SRA; Fig. 2 B), specific root length (SRL; Fig. 2 C), root–shoot ratio (Table S6), and acid phosphatase activity (Fig. 2 E) all increased with increasing phosphorus supply and reached their lowest values under P0 conditions. By contrast, organic acid exudation showed an opposite trend, being strongly induced by P deficiency, with secretion under P0 17– to 24–fold higher than under P90 (Fig. 2 D; Table S7). The changes in mycorrhizal colonization rate were different from those of root morphology and exudates (Fig. 2 F): within the phosphorus application range from P120 to P30, the mycorrhizal colonization rate significantly increased as phosphorus application decreased, reaching a peak at P30 (30–50%). However, when phosphorus application dropped to P0, the colonization rate significantly decreased to 10–20%. Correlation analysis (Fig. 3 A) indicated that root diameter was significantly negatively correlated with organic acid exudation (R² = 0.50, P < 0.01), while its relationship with mycorrhizal colonization was weak and not significant (R² = 0.073, P = 0.293). Specific root length was positively correlated with organic acid exudation (R² = 0.25, P < 0.05), acid phosphatase content (R² = 0.21, P < 0.05), and mycorrhizal colonization (R² = 0.21, P < 0.01). PCoA analysis results (Fig. 3 B) showed that PCoA1 explained 90.1% of the variation in phosphorus acquisition strategies. Organic acid exudation was the dominant factor of PCoA1, with acid phosphatase activity, specific root length, and root diameter also highly correlated. Mycorrhizal colonization was the dominant factor for PCoA2 (accounting for 5.7% of variation). Samples from P0 and P30 clustered on the left axis of PCoA1, while samples from other phosphorus treatments were mainly distributed on the right axis (Fig. 3 B). PERMANOVA results were consistent with those of the PCoA analysis, showing that the differences between P0/P30 and other phosphorus levels were highly significant (P < 0.01, Table 1 ), indicating that the phosphorus availability gradient triggered a shift in soybean phosphorus acquisition strategies. Table 1 PERMANOVA results of phosphorus effects on soybean root traits and exudates Group P0/P30 P0/P60 P0/P90 P0/P120 P30/P60 P30/P90 P30/P120 P60/P90 P60/P120 P90/P120 Variation 0.47 0.78 0.80 0.67 0.37 0.42 0.31 0.05 0.11 0.04 P value 0.001 0.001 0.001 0.001 0.001 0.001 0.003 0.226 0.058 0.282 Under P0 conditions, soybean primarily relies on a root-based activation strategy, as indicated by the significant increase in root exudates and morphological plasticity (e.g., high SRL) (Fig. 3 B). Pathway analysis showed that the secretion of organic acids made the greatest positive contribution to phosphorus accumulation (path coefficient = 0.93), which was stronger than that of AMF (Fig. 4 ). Under P30 (30 mg P kg⁻¹, moderate low-phosphorus) conditions, soybean shifted to rely more on the symbiotic strategy (Fig. 4 ), showing the highest mycorrhizal colonization rate (Fig. 2 F), with only the mycorrhizal colonization rate being significantly correlated with phosphorus accumulation (path coefficient = 2.191; Fig. 4 ). Under P60-P120 (≥ 60 mg P kg⁻¹) conditions, the dependence on root exudates and mycorrhizal symbiosis gradually decreased, and root phosphorus acquisition became more reliant on direct ion absorption. 2.2 Metabolomic Basis of Strategy Shifts: Targeted Carbon Allocation To elucidate the metabolic mechanisms underlying phosphorus acquisition strategy shifts, particularly the “root-autonomous activation” observed under P0 and the “symbiosis-dependent” strategy under P30, we conducted metabolomic profiling of root samples collected from three phosphorus levels: P0 (representing rhizosphere activation and morphological plasticity), P30 (representing AMF symbiosis), and P90 (adequate phosphorus control). The analysis included three soybean genotypes: Ax (a phosphorus-efficient cultivar), Nm (a phosphorus-inefficient cultivar), and Wm82 (the reference genotype). The differentially accumulated metabolites (DAMs) were mainly classified into lipids and lipid-like molecules (24%), phenylpropanoids and polyketides (17%), and organic acids and their derivatives (16%) (Fig. 5 A). Principal component analysis (PCA) revealed 45.3% of the variance in the root metabolite profiles across all the treatments (Fig. 5 B), revealing a clear phosphorus gradient effect: the P0 samples clustered along the negative axis of PC1 (explaining 28.4% of the variation), the P90 samples clustered on the positive side, and the P30 samples were distributed in between. Under P0, 431 DAMs were identified, including organic acids such as malate and citrate, as well as lipid species such as phospholipids and eicosanoid-like compounds organic acids such as malate and citrate, as well as lipid species such as phospholipids and eicosanoid-like compounds. The accumulation of these metabolites aligns with increased organic acid exudation and membrane remodeling under extreme phosphorus limitation. Under P30, 588 DAMs were identified (430 upregulated and 158 downregulated, Fig. 5 C). Compared with P0, P30 featured more prominent changes in lipid species, especially fatty acids and glycerophospholipids, which are essential for supplying carbon to AMF. Additionally, the DAMs detected under P30 were enriched in pathways such as ABC transporters, flavonoid biosynthesis, and glycerophospholipid metabolism, collectively supporting the operation of the symbiosis-dependent strategy. KEGG pathway enrichment analysis further confirmed the reprogramming of metabolic fluxes associated with phosphorus acquisition. Under P0, the tricarboxylic acid (TCA) cycle was significantly enriched, which was consistent with the increased accumulation of organic acids. In contrast, linoleic acid metabolism was markedly enriched under P30 (Additional file 1: Figure S2 ), likely reflecting its role in fatty acid biosynthesis and carbon provisioning for AMF symbiosis. Notably, flavonoid biosynthesis was enriched under both the P0 and P30 conditions. 2.3 Transcriptomic responses to varying soil phosphorus availability The distinct metabolic profiles between P0 and P30 suggested fundamentally different carbon flux patterns. To elucidate the transcriptional basis underlying these differences, we performed transcriptomic analyses under the same treatments. DESeq2 analysis revealed a greater number of differentially expressed genes (DEGs) under P0 than under P30 across all the genotypes (Additional file 2: Figure S3a). Wm82 presented the greatest transcriptomic response under P0, with 11,506 DEGs (4,351 upregulated and 7,155 downregulated). PCA revealed that the P0-treated samples clustered along the negative axis of PC1, whereas the P90 samples were on the positive axis. The P30 samples were scattered in between (Additional file 2: Figure S3b). These results were corroborated by PERMANOVA (P < 0.01), indicating that increasing P deficiency triggers a broader transcriptional response. KEGG enrichment analysis revealed the top 30 significantly enriched pathways (Additional file 2: Figure S3c). In the P0 vs. P90 comparison, all genotypes showed enrichment in pathways such as starch and sucrose metabolism, glycolysis/gluconeogenesis, and alanine, aspartate, and glutamate metabolism. In contrast, P30 vs. P90 comparisons were enriched in carotenoid biosynthesis, glycerophospholipid metabolism, ABC transporters, and plant hormone signal transduction. Compared with the P-inefficient Nm, the P-efficient genotypes (Ax and Wm82) showed greater enrichment in isoflavonoid biosynthesis, plant-pathogen interaction, and MAPK signaling pathways, findings that may explain their higher AM colonization rates (Fig. 2 F). To identify regulatory modules and hub genes associated with P acquisition strategies, we performed weighted gene co-expression network analysis (WGCNA). Modules positively correlated with SRL and root exudation (e.g., blue, green and turquoise) were enriched in carboxylic acid metabolism. Genes such as pckA and MDH in these modules were highly expressed under P0. In contrast, AM-associated modules (e.g., dark turquoise, grey) showed upregulation of fatty acid biosynthesis genes including accA/B/C, FabF, FabI and FATB under P30 (Fig. 6 G), indicating carbon flux redirection toward symbiosis. 2.4 Integrated Transcriptomic and Metabolomic Analysis Analysis of the glycolytic pathway (Fig. 6 ) revealed that PEPC mediates a bypass route: phosphoenolpyruvate (PEP) is converted to oxaloacetate and malate via PEPC, pckA, and MDH, with corresponding genes exhibiting an expression gradient of P0 > P30 > P90. In contrast, the PK branch converts PEP to pyruvate, feeding acetyl-CoA/malonyl-CoA for fatty acid biosynthesis ( accA/B/C, FabF/I, FATB ). Under Pi stress, PPi-dependent bypass enzymes such as PFP and PPDK help conserve ATP/Pi and rebalance glycolytic flux. Genes such as accA/B and FabI showed a reversed trend (P30 > P90 > P0). As illustrated in Fig. 6 , soil phosphorus availability hierarchically regulates glycolytic flux partitioning by favoring organic acid biosynthesis under severe deficiency, while redirecting carbon toward fatty acid biosynthesis to support symbiotic engagement under moderate deficiency. Discussion Under phosphorus excess conditions (P120), root-surface high-affinity phosphate transporters (e.g., PHT1 family) fully satisfy soybean phosphorus demand. At this stage, further carbon allocation to maintain arbuscular mycorrhizal (AM) symbiosis or enhance root growth/exudation becomes metabolically inefficient [ 9 , 35 ]. Consequently, plants downregulate PSI genes (e.g., SPX1, PHO2), reduce carbon investment in root biomass and rhizosphere exudates, and strongly suppress AM fungal colonization [ 9 , 36 ]. This metabolic shift results in a phosphorus acquisition strategy dominated by transporter-mediated uptake. As phosphorus supply declines, soybean gradually activates a series of adaptive responses. The initial response often involves the activation of AMF symbiosis. Under P90 conditions, AM colonization rates in all genotypes were already significantly higher than those under P120, and colonization continued to increase as phosphorus availability declined, peaking at P30 (Fig. 2 F). Compared with fine-rooted species such as maize and wheat, soybean possesses thicker roots and exhibits lower morphological plasticity [ 13 , 14 , 27 ]. As a result, soybean tends to adopt an "outsourcing" strategy, relying on AM hyphal networks to expand its phosphorus acquisition zone. This compensates for the limitations associated with slower root elongation and reduced plasticity [ 37 ]. At the molecular level, phosphorus deficiency triggers a cascade of regulatory events that reshape root–fungal symbiosis. A decline in intracellular phosphorus inhibits the biosynthesis of the signaling molecule 1,5-InsP8 (diphosphoinositol pentakisphosphate). This reduction promotes ubiquitin-mediated degradation of SPX proteins [ 38 ]. This releases SPX-mediated repression on PHR transcription factors, which subsequently activate two critical pathways: (1) upregulation of high-affinity phosphate transporters (PHTs) (Additional file 1: Table S8) on root surfaces to enhance direct phosphorus uptake, and (2) induction of AM-specific regulators, including RAM1, WRI5A, and ERF (Fig. 6 A, D), that facilitate fungal colonization [ 39 ]. Consistent with this regulatory framework, transcriptomic analysis under P30 conditions revealed significant upregulation of fatty acid biosynthesis genes ( accA/B/C, FabF, FabI, FATB ) (Fig. 7 ). These genes mediate the conversion of photosynthates into lipids, which serve as essential carbon sources for AM fungi, thereby establishing a mutualistic exchange system. Structural equation modeling (SEM) further demonstrated that under moderate phosphorus deficiency (P30), AMF symbiosis exerts the strongest positive effect on plant phosphorus accumulation compared to direct uptake pathways (Fig. 4 ). This carbon-efficient phosphorus acquisition strategy becomes particularly advantageous when soil phosphorus availability is limited but not severely depleted. Under severe phosphorus deficiency (P0), the arbuscular mycorrhizal (AM) symbiosis strategy becomes severely constrained. Compared to the P30 treatment, AM colonization rates declined by 50–80% across all soybean genotypes at P0 (Fig. 2 F), a trend consistent with observations in maize and wheat [ 40 – 42 ]. In response, soybeans adopted an alternative P acquisition pathway. While fungal colonization diminished, root traits associated with a “mining strategy”, such as exudation of LMWOAs (Fig. 2 D), (SRL Fig. 2 C), and specific root surface area (SRA; Fig. 2 B), peaked under P0 conditions. Notably, organic acid concentrations in the rhizosphere were 17– to 24–fold higher at P0 than at P90 (Fig. 2 D). SEM confirmed that organic acid exudation exerted the strongest influence on P accumulation at P0 (path coefficient = 0.93, P < 0.01; Fig. 4 ), establishing it as the dominant response mechanism (Fig. 3 ). In this mining-based strategy, soybeans upregulated genes involved in carboxylic acid metabolism, such as PckA, MDH , and ME (Fig. 6 B), to synthesize large quantities of malate and other organic acids (Fig. 5 F). These compounds were secreted into the rhizosphere to mobilize insoluble phosphorus (Fig. 2 D). Concurrently, energy-conserving glycolytic bypasses, including diphosphate-dependent phosphofructokinase (PFP) and PPDK, were activated to support ATP production and internal phosphate recycling under energy-limited conditions [ 43 , 44 ]. In summary, soil phosphorus availability acts as a critical threshold that governs the selection and transition of phosphorus acquisition strategies in soybean. Rather than a binary switch, this transition reflects a dynamic trade-off based on carbon investment efficiency. Under mild to moderate phosphorus deficiency (e.g., P30 and P60), AM fungi, with their extensive hyphal networks, can efficiently acquire spatially dispersed phosphorus at a lower carbon cost than can root-based strategies. As a result, AMF symbiosis emerges as a more economical option under these conditions [ 20 , 45 ]. Under extreme phosphorus deficiency, however, AM fungal activity becomes limited. The increasing carbon investment required to sustain symbiosis leads to a reduced return on investment [ 10 , 46 ]. In the P0 treatment, all five soybean genotypes exhibited decreased root diameter (Fig. 2 A) and lower fatty acid levels (Fig. 7 ), potentially triggering the downregulation or termination of AMF symbiosis. At this stage, direct carbon allocation toward enhancing root morphological plasticity, such as increased SRL and SRA, and promoting organic acid biosynthesis becomes a more efficient strategy. This mining-based strategy, which reallocates carbon to root plasticity and organic acid biosynthesis, is consistent with the principles of carbon economy theory [ 47 ]. Notably, soybean genotypes exhibit considerable variation in their phosphorus sensitivity thresholds and in the trade-offs between strategies. Genotypes such as Wm82, Qd11, and Zh13 are more dependent on AMF symbiosis and can maintain effective fungal colonization even under phosphorus-sufficient conditions (Fig. 2 F). This trait allows them to adapt well to agricultural soils with high total but low available phosphorus, enhancing phosphorus use efficiency while minimizing fertilizer accumulation in the environment. In contrast, genotype Ax secretes greater quantities of organic acids and acid phosphatases under phosphorus-deficient conditions (Fig. 2 D, E), making it more suitable for soils that are both low in total phosphorus and prone to phosphorus fixation, such as acidic or alkaline soils [ 4 ]. These differences in phosphorus acquisition strategies reflect the underlying genetic variation in phosphorus sensing, carbon allocation regulation, and plant–fungus signaling pathways across soybean genotypes. Root system architecture also plays a critical role in shaping genotype-specific phosphorus strategies (Fig. 3 A). Considering the wide global variation in available soil phosphorus, ranging from 0.01 to 99.2 mg kg⁻¹ [ 48 ], matching soybean genotypes with soil phosphorus profiles becomes essential. Future research should focus on developing a comprehensive genotype–phosphorus–management decision framework. This dual optimization can be achieved by combining targeted breeding, such as genome editing of genes involved in pyruvate metabolic branching, with demand-based phosphorus fertilizer management. Conclusion This study demonstrates that soil phosphorus availability functions as a critical threshold factor governing the strategic shift in phosphorus acquisition in soybean. Under moderate phosphorus deficiency, soybean plants upregulate genes involved in fatty acid biosynthesis, including accA/B and FabI , thereby directing photosynthetically derived carbon toward arbuscular mycorrhizal (AM) fungi. This process underlies a symbiosis-dependent outsourcing strategy, which is particularly prominent in thick-rooted genotypes that exhibit inherently low root morphological plasticity and thus depend on AM fungal hyphal networks to access spatially dispersed phosphorus. In contrast, under severe phosphorus deficiency, soybean plants shift to a root-driven strategy. This involves the upregulation of genes such as pckA and MDH to enhance organic acid secretion, along with reduced root diameter and increased SRL, which collectively improve root morphological plasticity and soil exploration capacity. This strategy is especially advantageous for fine-rooted genotypes in phosphorus-fixing acidic soils. The transition between symbiosis-based and autonomous strategies is mediated by the redirection of carbon flux at the pyruvate metabolic branching point. However, the findings of this study were derived from experiments conducted in a single soil type, limiting the generalizability of the results. Future research should aim to establish a comprehensive genotype–soil phosphorus matching database across diverse agroecological zones. Such a resource would support the development of actionable, low-carbon phosphorus management strategies tailored to specific crop demands and environmental conditions. Our findings provide a foundation for precision P management and genotype–soil matching in sustainable soybean production systems. Abbreviations AMF Arbuscular mycorrhizal fungi DAMs Differentially accumulated metabolites DEGs Differentially expressed genes GO Gene Ontology HMDB Human Metabolome Database KEGG Kyoto Encyclopedia of Genes and Genomes MDH Malate dehydrogenase NCBI National Center for Biotechnology Information P Phosphorus PCA Principal component analysis PCoA Principal coordinate analysis PEPC Phosphoenolpyruvate carboxylase PPDK Pyruvate phosphate dikinase Pi Inorganic phosphate SRA specific root area SE Standard error SRL Specific root length WGCNA Weighted gene co-expression network analysis PERMANOVA Permutational Multivariate Analysis of Variance SEM Structural Equation Model Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The raw transcriptomic sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive under BioProject accession number PRJNA1129285 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1129285). The metabolomic datasets and other materials are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding National Natural Science Foundation of China (No. 32260804); Guizhou Key Technologies for Mountainous Agriculture Research Project (GZNYJHJX-2025001); Guizhou Highland Specialty Vegetable Production Science and Technology Innovation Talent Team (Qiannan Platform Talent-CXTD [2022]03) Construction of High Quality and Efficient Mechanized Stereoscopic Greenhouse Vegetable Production Base (qjzkhp2024009); Research and integrated application of key technologies of green and high yield in characteristic mountain agriculture (qjzdggzhz[2023]07); Cultivation Project of Guizhou University (202018). Authors' contributions ZGY participated in manuscript writing and figure preparation. CZ designed the research, performed data analysis, reviewed the literature, and contributed to manuscript writing.YTL conducted the soybean soil pot cultivation experiments and contributed to data analysis. Wanping Zhang: Funding acquisition All authors read and approved the final manuscript. Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 32260804), Guizhou Key Technologies for Mountainous Agriculture Research Project (GZNYJHJX-2025001), Guizhou Highland Specialty Vegetable Production Science and Technology Innovation Talent Team (Qiannan Platform Talent-CXTD [2022]03), Construction of High Quality and Efficient Mechanized Stereoscopic Greenhouse Vegetable Production Base (qjzkhp2024009), Research and integrated application of key technologies of green and high yield in characteristic mountain agriculture (qjzdggzhz[2023]07), the Cultivation Project of Guizhou University (202018). References Mishra S, Levengood H, Fan J, Zhang C. Plants under stress: exploring physiological and molecular responses to nitrogen and phosphorus deficiency. Plants. 2024;13(22):3144. Honvault N, Houben D, Nobile C, Firmin S, Lambers H, Faucon M. 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Enrichment of beneficial cucumber rhizosphere microbes mediated by organic acid secretion. Hortic Res. 2020;7:154. Luo X, Guo C, He X, Helfenstein J, Lambers H, Ren Q, et al. Global distribution and influencing factors of plant-available phosphorus in (semi-)natural soils. Global Biogeochem Cycles. 2025;39(6):e43. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1TablesS1S8.docx Additional file 1: Table S1. Soil physicochemical properties. Soil organic matter (SOM), total nitrogen (TN), alkali-soluble nitrogen (AN), total phosphorus (TP), available phosphorus (AP), total potassium (TK), and available potassium (AK) measured under experimental conditions (mean ± SE, n = 3). Additional file 1: Table S2. Nutrient solution composition. Concentrations of macronutrients and micronutrients used in the hydroponic solution for soybean cultivation. Additional file 1: Table S3. Metabolome preconditioning results. Numbers of detected peaks, identified metabolites, and annotations in KEGG and HMDB under positive and negative ion modes. Additional file 1: Table S4. Quality control of transcriptome sequencing. Raw and clean reads, error rate, Q20, Q30, and GC content across all samples. Additional file 1: Table S5. Summary of transcriptome sequence alignment. Mapping statistics including total mapped, uniquely mapped, and multiply mapped reads for each sample. Additional file 1: Table S6. Effects of phosphorus supply on root morphology. Average root diameter, root tissue density, and root/shoot ratio of soybean cultivars under five phosphorus treatments (P0–P120). Different lowercase letters denote significant differences at P < 0.05. Additional file 1: Table S7. Root exudation of organic acids under phosphorus treatments. Secretion rates of oxalic, acetic, citric, succinic, malic, and tartaric acids across cultivars. Additional file 1: Table S8. Expression of phosphorus starvation-responsive genes. Differential expression (Padj values, regulation) of PHT, PHO, PAP, PHR, and ALMT family genes in three soybean cultivars under P30 vs P90. Additionalfile2FigureS1S3.docx Additional file 2: Figure S1. Effects of phosphorus availability on soybean growth. Plant height and stem diameter under different phosphorus treatments. Lowercase letters indicate significant differences among treatments within the same cultivar (P < 0.05). Asterisks denote significance at *P < 0.05 and **P < 0.01. Additional file 2: Figure S2. KEGG enrichment of differential metabolites. KEGG pathway enrichment of differentially accumulated metabolites (DAMs) in soybean roots under phosphorus treatments. Additional file 2: Figure S3. KEGG enrichment of differentially expressed genes. (A) Expression profiles of root DEGs under different phosphorus treatments. (B) PCA of gene expression in three soybean cultivars. (C) KEGG pathway enrichment of DEGs in soybean roots. 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06:22:43","extension":"xml","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131331,"visible":true,"origin":"","legend":"","description":"","filename":"fdf6f9d812934239a35a6286659a16561structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/187b5d27a0c93fd15f3a22b0.xml"},{"id":93903786,"identity":"6bd56574-4bdf-46ae-88bd-c3d7b20e369e","added_by":"auto","created_at":"2025-10-20 06:30:43","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141758,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/16e9012563ff6ac52f1d0ac9.html"},{"id":93903107,"identity":"8d3b27e0-b4ec-4053-bb19-ac02ddd64318","added_by":"auto","created_at":"2025-10-20 06:22:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":554893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of soil phosphorus availability on soybean growth and P accumulation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoybean shoot and root biomass, as well as tissue phosphorus concentrations, were measured under different soil phosphorus levels. Bars represent mean ± SE (n = 3). Asterisks denote significant differences between treatments (*P \u0026lt; 0.05; **P \u0026lt; 0.01). Different lowercase letters indicate significant differences among cultivars within the same treatment (P \u0026lt; 0.05). Cultivar abbreviations: Qd11 (Qiandou11), Zh13 (Zhonghuang13), Ax (Aixuan), Nm (Niumao), and Wm82 (Williams 82).\u003c/p\u003e","description":"","filename":"Fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/38e0b29764e117f5341bca88.jpg"},{"id":93903108,"identity":"4e917c68-ccad-4085-88d3-5374a5acf8fc","added_by":"auto","created_at":"2025-10-20 06:22:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4200529,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of phosphorus supply on root morphology, exudates, and mycorrhizal colonization.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRoot length, organic acid exudation, and mycorrhizal colonization rates were quantified under different phosphorus conditions. Data are shown as mean ± SE (n = 3). Asterisks denote significance levels (*P \u0026lt; 0.05; **P \u0026lt; 0.01), and different letters indicate significant differences among cultivars within the same treatment (P \u0026lt; 0.05). Cultivar abbreviations are as in Fig. 1.\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/d4f0ccf0edc9b29e25a61872.jpg"},{"id":93903112,"identity":"043db765-07e8-45cb-aebc-239d142765ca","added_by":"auto","created_at":"2025-10-20 06:22:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":331680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation and ordination analyses of phosphorus uptake strategies.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Correlation analysis among uptake-related traits. (B) Principal coordinate analysis (PCoA) showing divergence of uptake strategies across phosphorus treatments. Open circles indicate data points excluded from regression analysis. Specifically, these correspond to the relationship between root diameter and AMF colonization under P120, and between SRL and AMF colonization under P0.\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/498cce3c093e517d1e0b836a.jpg"},{"id":93903111,"identity":"1b16552e-5e2e-495b-ac93-fd8c794c3c5a","added_by":"auto","created_at":"2025-10-20 06:22:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":363301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural equation model (SEM) of phosphorus uptake strategies in soybean roots.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePink arrows indicate significant positive paths (P \u0026lt; 0.05), red arrows indicate highly significant positive paths (P \u0026lt; 0.01), and dotted lines indicate non-significant paths (P \u0026gt; 0.05). Asterisks denote significance: * P \u0026lt; 0.05; ** P \u0026lt; 0.01. Model fit: RMSEA \u0026lt; 0.001, GFI = 0.902, NFI = 0.902.\u003c/p\u003e","description":"","filename":"Fig4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/c2b44cd84ddba5edda156d20.jpg"},{"id":93903115,"identity":"ae83d150-d1c9-422b-8d29-144ef017fc5d","added_by":"auto","created_at":"2025-10-20 06:22:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3015543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhosphorus-induced changes in soybean root metabolites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Classification of differential metabolites based on the Human Metabolome Database (HMDB). (B) Principal component analysis (PCA) of metabolite profiles. (C–D) Volcano plots comparing P30 vs P90 (C) and P0 vs P90 (D). (E–F) Classification statistics of differential metabolites in P0 vs P30 (E) and P30 vs P90 (F).\u003c/p\u003e","description":"","filename":"Fig5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/5669fd752b6e41cd02b11e98.jpg"},{"id":93903128,"identity":"8bde1ac1-3ec1-4865-b498-8bacf40f728b","added_by":"auto","created_at":"2025-10-20 06:22:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":869669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted gene co-expression network analysis (WGCNA) of root DEGs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Correlations between gene co-expression modules and root traits. Abbreviations: RD, root diameter; SRL, specific root length; SRA, specific root area; RTD, root tissue density; MC, mycorrhizal colonization; EXC, organic acid exudation; ACP, acid phosphatase activity. Only modules significantly correlated with uptake traits are shown. (B–G) GO enrichment and network analyses of representative modules (blue, green, light yellow, turquoise, yellow, grey60, salmon, dark turquoise, and grey).\u003c/p\u003e","description":"","filename":"Fig6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/155f0c48f1430d9bdb312ff6.jpg"},{"id":93904327,"identity":"c9a8b9ef-334a-49c6-9a29-112e186d17b0","added_by":"auto","created_at":"2025-10-20 06:38:42","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4891926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCarbon metabolism pathways in soybean roots under different P levels.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKey enzymes and pathways involved in sucrose degradation, glycolysis, the tricarboxylic acid cycle, and fatty acid metabolism are highlighted. Abbreviations: INV, invertase; HK, hexokinase; SUS, sucrose synthase; Scrk, fructokinase; UGPase, UDP-glucose pyrophosphorylase; PGM, phosphoglucomutase; GPI, glucose-6-phosphate isomerase; PFK, phosphofructokinase; PFP, diphosphate-dependent phosphofructokinase; ALDO, fructose-bisphosphate aldolase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; PGK, phosphoglycerate kinase; GapN, non-phosphorylating GAPDH; PGAM, phosphoglycerate mutase; ENO, enolase; PK, pyruvate kinase; PPDK, pyruvate phosphate dikinase; PckA, phosphoenolpyruvate carboxykinase; PEPC, phosphoenolpyruvate carboxylase; OAA, oxaloacetate; MDH, malate dehydrogenase; ME, malic enzyme; AceB, malate synthase; CS, citrate synthase; ACLY, ATP citrate lyase; AceA, isocitrate lyase; SDHA, succinate dehydrogenase; AAE3, oxalyl-CoA ligase; accA/B/C, acetyl-CoA carboxylase subunits; FabF/I/G, fatty acid biosynthesis enzymes; FATB, acyl-ACP thioesterase; ACSL, acyl-CoA synthetase long-chain family. Black arrows indicate standard pathways; red, alternative pathways; green, TCA cycle; blue, fatty acid cycle; solid lines, direct pathways; dotted lines, indirect pathways.\u003c/p\u003e","description":"","filename":"Fig7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/6696e9547d26d1e505c060d6.jpg"},{"id":98813827,"identity":"e5279084-49c9-4e11-8bfd-11a36de98070","added_by":"auto","created_at":"2025-12-22 16:03:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15332816,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/e7383665-8a20-41be-a0db-a2c8e8c1cafe.pdf"},{"id":93903771,"identity":"0f5ed3f3-cf77-4880-9e51-b86aa15c4b4f","added_by":"auto","created_at":"2025-10-20 06:30:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S1. Soil physicochemical properties.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil organic matter (SOM), total nitrogen (TN), alkali-soluble nitrogen (AN), total phosphorus (TP), available phosphorus (AP), total potassium (TK), and available potassium (AK) measured under experimental conditions (mean ± SE, n = 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S2. Nutrient solution composition.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcentrations of macronutrients and micronutrients used in the hydroponic solution for soybean cultivation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S3. Metabolome preconditioning results.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNumbers of detected peaks, identified metabolites, and annotations in KEGG and HMDB under positive and negative ion modes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S4. Quality control of transcriptome sequencing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw and clean reads, error rate, Q20, Q30, and GC content across all samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S5. Summary of transcriptome sequence alignment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMapping statistics including total mapped, uniquely mapped, and multiply mapped reads for each sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S6. Effects of phosphorus supply on root morphology.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAverage root diameter, root tissue density, and root/shoot ratio of soybean cultivars under five phosphorus treatments (P0–P120). Different lowercase letters denote significant differences at P \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S7. Root exudation of organic acids under phosphorus treatments.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSecretion rates of oxalic, acetic, citric, succinic, malic, and tartaric acids across cultivars.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S8. Expression of phosphorus starvation-responsive genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression (Padj values, regulation) of PHT, PHO, PAP, PHR, and ALMT family genes in three soybean cultivars under P30 vs P90.\u003c/p\u003e","description":"","filename":"Additionalfile1TablesS1S8.docx","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/3587a6a328a1afc757b075b5.docx"},{"id":93903120,"identity":"e404ffe7-c28e-4a92-be22-554bf0108e79","added_by":"auto","created_at":"2025-10-20 06:22:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7422444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2: Figure S1. Effects of phosphorus availability on soybean growth.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlant height and stem diameter under different phosphorus treatments. Lowercase letters indicate significant differences among treatments within the same cultivar (P \u0026lt; 0.05). Asterisks denote significance at *P \u0026lt; 0.05 and **P \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 2: Figure S2. KEGG enrichment of differential metabolites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment of differentially accumulated metabolites (DAMs) in soybean roots under phosphorus treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 2: Figure S3. KEGG enrichment of differentially expressed genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Expression profiles of root DEGs under different phosphorus treatments. (B) PCA of gene expression in three soybean cultivars. (C) KEGG pathway enrichment of DEGs in soybean roots.\u003c/p\u003e","description":"","filename":"Additionalfile2FigureS1S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7738053/v1/0284d43352e84c66b20a6d27.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Transcriptomic and Metabolomic Analyses Reveal Trade-Off Mechanisms Underlying Phosphorus Acquisition Strategies in Soybean Roots","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhosphorus (P) is an essential macronutrient required for plant nucleic acid synthesis, energy metabolism, and signal transduction that directly influences crop productivity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, inorganic phosphate (Pi) is readily immobilized by cations such as iron and aluminum, resulting in low bioavailability of applied P fertilizers. Consequently, the in-season utilization rate of phosphorus fertilizers is only approximately 30%, representing a major limitation to sustainable agricultural production [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, enhancing the inherent ability of crops to activate and utilize soil phosphorus pools is a crucial strategy for improving phosphorus use efficiency [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo adapt to persistent low-P environments, plants have evolved three primary P-acquisition strategies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]: (1) Root architectural modification: Increasing specific root length (SRL) and reducing the root diameter to expand soil exploration [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; (2) Arbuscular mycorrhizal fungal (AMF) symbiosis: Leveraging extensive fungal hyphal networks to access distant P sources[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; and (3) Root exudate release: Enhancing secretion of organic acids and phosphatases to solubilize fixed P[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Critically, all three strategies consume substantial carbon (C) resources derived from photosynthesis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This C cost forces plants to make trade-offs in resource allocation among these strategies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor example, fine-rooted cultivars constrain carbon investment in AMF symbiosis strategy, which ultimately requires higher phosphorus fertilizer inputs to sustain yield [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Understanding how plants allocate carbon among competing strategies is thus critical for crop P efficiency [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBuilding on the preceding framework, carbon allocation among competing P-acquisition strategies is dynamic rather than fixed. Under phosphorus stress, plants divert photosynthates to roots, increasing the root-to-shoot ratio (R/S) and triggering divergent phenotypic adaptations, whereby some species develop longer, thinner roots to enhance soil exploration, whereas coarse-rooted species invest more in maintaining AMF symbiosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The relationship between root architecture and exudation is context-dependent, with exudation\u0026ndash;SRL correlations shifting across phosphorus levels [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. AMF colonization often suppresses organic acid release [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], but this association weakens under severe phosphorus limitation as fungal efficiency declines [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Consequently, root exudation becomes the dominant strategy, reflecting a shift from mutualism toward parasitism [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese contrasting P-acquisition strategies align with ecological niche differentiation along global P gradients. AMF-dependent species are more common in moderately P-limited soils. Cluster-rooted species dominate in ancient, severely P-depleted soils (e.g., Australia and South Africa) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSoybean (Glycine max L.), while important in sustainable agroecosystems, is highly sensitive to phosphorus deficiency. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As soybean cultivation expands into low-phosphorus regions such as southern China and Brazil, inadequate phosphorus availability increasingly constrains yield potential. Although soybean is known to respond to low phosphorus levels by increasing root length and reducing diameter [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the way these morphological adjustments are coordinated with AMF colonization and root exudate secretion remains unclear. Given the carbon allocation trade-offs outlined above, clarifying this coordination in soybean across phosphorus gradients remains a key knowledge gap.\u003c/p\u003e\u003cp\u003eTo address this gap, we investigated physiological, morphological, transcriptomic, and metabolomic data for five soybean cultivars grown across a phosphorus gradient (0\u0026ndash;120 mg P kg⁻\u0026sup1;). We hypothesized that: (1) soil phosphorus availability drives both the trade-offs and transitions among acquisition strategies; and (2) genotypic differences in root architecture underlie distinct preferences, with coarse-rooted cultivars primarily relying on AMF symbiosis, and fine-rooted cultivars exhibiting greater morphological plasticity to support root-driven acquisition under severe stress.\u003c/p\u003e"},{"header":"1 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Experimental Design\u003c/h2\u003e\u003cp\u003eOn the basis of previous studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], five soybean cultivars were selected: Qiandou 11 (Qd11), Aixuan (Ax), and Zhonghuang 13 (Zh13) as phosphorus-efficient genotypes; Niumao (Nm) as a phosphorus-sensitive genotype; and Williams 82 (Wm82) as the reference genotype. The experiment was conducted in a greenhouse at Guizhou University, Guiyang, Guizhou Province, China. Soil was collected from Qiannan Prefecture, Guizhou (26\u0026deg;17\u0026prime;N, 106\u0026deg;45\u0026prime;E) and represents typical phosphorus-deficient leached soil. The total and available phosphorus contents were 0.32 g kg⁻\u0026sup1; and 3 mg kg⁻\u0026sup1;, respectively. Additional soil properties are listed in Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Plastic pots (30 cm top diameter, 20 cm height, and 20 cm base diameter) were filled with 7.5 kg of air-dried soil.\u003c/p\u003e\u003cp\u003eFive phosphorus concentrations were applied: 0 mg P kg⁻\u0026sup1; (severe deficiency, P0), 30 mg P kg⁻\u0026sup1; (moderate deficiency, P30), 60 mg P kg⁻\u0026sup1; (mild deficiency, P60), 90 mg P kg⁻\u0026sup1; (optimal, P90), and 120 mg P kg⁻\u0026sup1; (excess, P120). Phosphorus was supplied as KH₂PO₄, and potassium levels were equalized via KCl. A complete nutrient mixture was used to supply other elements and prevent nutrient deficiencies (Liao and Li, 2018; Additional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). A completely randomized two-factor design was adopted with three replicates per treatment, resulting in 75 pots.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Plant Growth and Sample Collection\u003c/h2\u003e\u003cp\u003eThe seedlings were thinned to four plants per pot at the first trifoliate leaf stage. The phosphorus treatments commenced once two true leaves had developed. The plants were irrigated every four days (500 mL per pot) for 30 days. At harvest, the roots and shoots were rinsed with distilled water and blotted dry. The samples for physiological analysis were stored at 4\u0026deg;C, whereas those for transcriptomic and metabolomic analyses were flash-frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Plant biomass and phosphorus contents\u003c/h2\u003e\u003cp\u003eThe samples were initially inactivated at 105\u0026deg;C for 30 minutes, followed by drying at 75\u0026deg;C to a constant weight. The dry biomass was measured using an analytical balance. The samples were ground and sieved through a 0.5 mm mesh. The phosphorus concentration was determined following H₂SO₄ and H₂O₂ digestion via the vanadium molybdenum yellow colorimetric method [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Phosphorus accumulation (mg plant⁻\u0026sup1; DW) was calculated as the product of biomass and phosphorus concentration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Root morphology\u003c/h2\u003e\u003cp\u003eThe root systems were scanned via an Epson PV850 Pro scanner (Epson, Long Beach, CA, USA) and analyzed via WinRHIZO 2019 Pro software (Regent Instruments, Quebec, Canada).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.5 Root acid phosphatase activity and organic acid exudation\u003c/h2\u003e\u003cp\u003eAcid phosphatase activity was measured as described by Shen [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The roots were incubated in a solution of p-nitrophenyl phosphate (pNPP) disodium salt at 25\u0026deg;C for 60 minutes in the dark. The reaction was terminated by adding 1 mL of 1 mol/L NaOH, and the absorbance was recorded at 405 nm.\u003c/p\u003e\u003cp\u003eOrganic acid exudation was quantified following Liu [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The roots were incubated in 100 mL of 0.5 mol/L CaCl₂ solution for 4 hours in the dark. The solution was filtered through a 0.22 \u0026micro;m membrane and stored at \u0026minus;\u0026thinsp;20\u0026deg;C [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Organic acids were identified and quantified via an Agilent 1260 Infinity II HPLC system (Agilent Technologies, Santa Clara, CA, USA) equipped with a ZORBAX SB-C18 column (4.6 \u0026times; 250 mm, 5 \u0026micro;m).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e1.6 Root Mycorrhizal Colonization\u003c/h2\u003e\u003cp\u003eApproximately 1-cm-long root segments were cleared in 10% KOH at 90\u0026deg;C, stained with 0.05% trypan blue, and decolorized in a lactoglycerol solution (lactic acid:glycerol:water\u0026thinsp;=\u0026thinsp;1:1:1). Thirty stained root segments per replicate were randomly selected and mounted in 30% glycerol for microscopic observation via an Olympus BX51 microscope (Olympus, Tokyo, Japan). Mycorrhizal colonization was assessed via the gridline intersect method [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e1.7 Metabolomic analysis\u003c/h2\u003e\u003cp\u003eRoot metabolite extraction was performed as described by Li [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], with a quality control sample inserted every nine test samples. Metabolomic profiling was conducted via ultrahigh-performance liquid chromatography coupled with Fourier transform mass spectrometry (UHPLC-Q Exactive HF-X, Thermo Fisher Scientific). The chromatographic and MS conditions followed those of Zhou [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Metabolites with a relative standard deviation\u0026thinsp;\u0026gt;\u0026thinsp;30% were excluded. Compound identification was based on matches to HMDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hmdb.ca/\u003c/span\u003e\u003cspan address=\"http://www.hmdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), METLIN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metlin.scripps.edu/\u003c/span\u003e\u003cspan address=\"https://metlin.scripps.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and an in-house database (Majorbio). A total of 1,072 and 1,117 metabolites were identified in positive and negative ion modes, respectively (Additional file 1: Table S3). Differentially accumulated metabolites (DAMs) were defined by a variable importance in projection\u0026thinsp;\u0026gt;\u0026thinsp;1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e1.8 Transcriptomic analysis\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from roots via the MJZol Total RNA Extraction Kit (Majorbio, Shanghai, China) and purified via the RNA Purification Kit (Majorbio). The RNA purity and concentration were measured via a NanoDrop 2000. Samples with OD260/280\u0026thinsp;\u0026ge;\u0026thinsp;1.8 and OD260/230\u0026thinsp;\u0026ge;\u0026thinsp;1.0 were retained. RNA integrity was confirmed by agarose gel electrophoresis. Only samples with \u0026ge;\u0026thinsp;1 \u0026micro;g total RNA and concentrations\u0026thinsp;\u0026ge;\u0026thinsp;35 ng \u0026micro;L⁻\u0026sup1; were used for sequencing.\u003c/p\u003e\u003cp\u003ecDNA libraries were prepared via the Illumina TruSeq RNA Sample Prep Kit and sequenced on the NovaSeq X Plus platform (Illumina, Hayward, CA, USA). Each sample yielded\u0026thinsp;\u0026gt;\u0026thinsp;6.15 Gb of clean data with a Q30\u0026thinsp;\u0026ge;\u0026thinsp;93.46% (Additional file 1: Table S4). Clean reads were mapped to the soybean reference genome (Gmax 508 Wm82.a4.v1) via HISAT2 (v2.1.0), with mapping rates exceeding 88.7% (Additional file 1: Table S5). Transcript assembly was performed via Cufflinks (v2.2.1) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Differentially expressed genes (DEGs) were identified via DESeq2 with significance criteria of adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log₂FC| \u0026ge; 1. Gene Ontology (GO) and KEGG pathway annotations were performed for the DEG sets. GO enrichment was conducted via GOATOOLS (v0.6.5) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and KEGG enrichment was conducted via gseapy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] in conjunction with scipy[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Weighted gene coexpression network analysis (WGCNA) and visualization were conducted via the Majorbio Cloud Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://report.majorbio.com\u003c/span\u003e\u003cspan address=\"https://report.majorbio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e1.9 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was conducted in R (v4.3.0) via the agricolae package. When significant differences were detected, Duncan\u0026rsquo;s multiple range test was applied via the duncan.test function. Figures were generated via Origin 2021 and Adobe Illustrator CS6. Pathway analysis was conducted via SPSSPRO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.spsspro.com\u003c/span\u003e\u003cspan address=\"https://www.spsspro.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and network visualizations were performed via Cytoscape (v3.9.1). The data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard errors (SEs). Prior to regression, outliers were identified based on pre-specified biological/diagnostic criteria and excluded; excluded points are displayed as open circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"2 Results and Analysis","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Effects of Phosphorus Levels on Biomass and Phosphorus Accumulation\u003c/h2\u003e\u003cp\u003eWith increasing phosphorus application, soybean biomass and phosphorus accumulation exhibited a trend of first increasing and then decreasing (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Biomass peaked at P90 (90 mg P kg⁻\u0026sup1;), but except for the Zh13 variety, biomass of the other varieties significantly decreased, indicating that P120 (120 mg P kg⁻\u0026sup1;) had induced phosphorus toxicity (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The phosphorus accumulation of all soybean varieties peaked at P120, except for Qd11 and Wm82 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Under severe deficiency (P0) and moderate deficiency (P30), the biomass and phosphorus accumulation of the Nm variety were the lowest, suggesting that it is a phosphorus-sensitive variety under low-phosphorus stress.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePhosphorus application significantly influenced root morphology (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Table S6). The varieties Qd11 and Wm82 exhibited higher sensitivity to phosphorus application changes: when the phosphorus application was \u0026le;\u0026thinsp;60 mg P kg⁻\u0026sup1;, their root diameter significantly decreased, whereas in the other varieties, the significant decrease in root diameter occurred under extremely low phosphorus conditions (P0). In contrast, root specific surface area (SRA; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), specific root length (SRL; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), root\u0026ndash;shoot ratio (Table S6), and acid phosphatase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) all increased with increasing phosphorus supply and reached their lowest values under P0 conditions. By contrast, organic acid exudation showed an opposite trend, being strongly induced by P deficiency, with secretion under P0 17\u0026ndash; to 24\u0026ndash;fold higher than under P90 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD; Table S7).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe changes in mycorrhizal colonization rate were different from those of root morphology and exudates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF): within the phosphorus application range from P120 to P30, the mycorrhizal colonization rate significantly increased as phosphorus application decreased, reaching a peak at P30 (30\u0026ndash;50%). However, when phosphorus application dropped to P0, the colonization rate significantly decreased to 10\u0026ndash;20%. Correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) indicated that root diameter was significantly negatively correlated with organic acid exudation (R\u0026sup2; = 0.50, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while its relationship with mycorrhizal colonization was weak and not significant (R\u0026sup2; = 0.073, P\u0026thinsp;=\u0026thinsp;0.293). Specific root length was positively correlated with organic acid exudation (R\u0026sup2; = 0.25, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), acid phosphatase content (R\u0026sup2; = 0.21, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and mycorrhizal colonization (R\u0026sup2; = 0.21, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). PCoA analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) showed that PCoA1 explained 90.1% of the variation in phosphorus acquisition strategies. Organic acid exudation was the dominant factor of PCoA1, with acid phosphatase activity, specific root length, and root diameter also highly correlated. Mycorrhizal colonization was the dominant factor for PCoA2 (accounting for 5.7% of variation). Samples from P0 and P30 clustered on the left axis of PCoA1, while samples from other phosphorus treatments were mainly distributed on the right axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). PERMANOVA results were consistent with those of the PCoA analysis, showing that the differences between P0/P30 and other phosphorus levels were highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating that the phosphorus availability gradient triggered a shift in soybean phosphorus acquisition strategies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003ePERMANOVA results of phosphorus effects on soybean root traits and exudates\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP0/P30\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP0/P60\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP0/P90\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP0/P120\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP30/P60\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP30/P90\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP30/P120\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP60/P90\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP60/P120\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eP90/P120\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUnder P0 conditions, soybean primarily relies on a root-based activation strategy, as indicated by the significant increase in root exudates and morphological plasticity (e.g., high SRL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Pathway analysis showed that the secretion of organic acids made the greatest positive contribution to phosphorus accumulation (path coefficient\u0026thinsp;=\u0026thinsp;0.93), which was stronger than that of AMF (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Under P30 (30 mg P kg⁻\u0026sup1;, moderate low-phosphorus) conditions, soybean shifted to rely more on the symbiotic strategy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), showing the highest mycorrhizal colonization rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), with only the mycorrhizal colonization rate being significantly correlated with phosphorus accumulation (path coefficient\u0026thinsp;=\u0026thinsp;2.191; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Under P60-P120 (\u0026ge;\u0026thinsp;60 mg P kg⁻\u0026sup1;) conditions, the dependence on root exudates and mycorrhizal symbiosis gradually decreased, and root phosphorus acquisition became more reliant on direct ion absorption.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Metabolomic Basis of Strategy Shifts: Targeted Carbon Allocation\u003c/h2\u003e\u003cp\u003eTo elucidate the metabolic mechanisms underlying phosphorus acquisition strategy shifts, particularly the \u0026ldquo;root-autonomous activation\u0026rdquo; observed under P0 and the \u0026ldquo;symbiosis-dependent\u0026rdquo; strategy under P30, we conducted metabolomic profiling of root samples collected from three phosphorus levels: P0 (representing rhizosphere activation and morphological plasticity), P30 (representing AMF symbiosis), and P90 (adequate phosphorus control). The analysis included three soybean genotypes: Ax (a phosphorus-efficient cultivar), Nm (a phosphorus-inefficient cultivar), and Wm82 (the reference genotype).\u003c/p\u003e\u003cp\u003eThe differentially accumulated metabolites (DAMs) were mainly classified into lipids and lipid-like molecules (24%), phenylpropanoids and polyketides (17%), and organic acids and their derivatives (16%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Principal component analysis (PCA) revealed 45.3% of the variance in the root metabolite profiles across all the treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), revealing a clear phosphorus gradient effect: the P0 samples clustered along the negative axis of PC1 (explaining 28.4% of the variation), the P90 samples clustered on the positive side, and the P30 samples were distributed in between.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUnder P0, 431 DAMs were identified, including organic acids such as malate and citrate, as well as lipid species such as phospholipids and eicosanoid-like compounds organic acids such as malate and citrate, as well as lipid species such as phospholipids and eicosanoid-like compounds. The accumulation of these metabolites aligns with increased organic acid exudation and membrane remodeling under extreme phosphorus limitation.\u003c/p\u003e\u003cp\u003eUnder P30, 588 DAMs were identified (430 upregulated and 158 downregulated, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Compared with P0, P30 featured more prominent changes in lipid species, especially fatty acids and glycerophospholipids, which are essential for supplying carbon to AMF. Additionally, the DAMs detected under P30 were enriched in pathways such as ABC transporters, flavonoid biosynthesis, and glycerophospholipid metabolism, collectively supporting the operation of the symbiosis-dependent strategy.\u003c/p\u003e\u003cp\u003eKEGG pathway enrichment analysis further confirmed the reprogramming of metabolic fluxes associated with phosphorus acquisition. Under P0, the tricarboxylic acid (TCA) cycle was significantly enriched, which was consistent with the increased accumulation of organic acids. In contrast, linoleic acid metabolism was markedly enriched under P30 (Additional file 1: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), likely reflecting its role in fatty acid biosynthesis and carbon provisioning for AMF symbiosis. Notably, flavonoid biosynthesis was enriched under both the P0 and P30 conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Transcriptomic responses to varying soil phosphorus availability\u003c/h2\u003e\u003cp\u003eThe distinct metabolic profiles between P0 and P30 suggested fundamentally different carbon flux patterns. To elucidate the transcriptional basis underlying these differences, we performed transcriptomic analyses under the same treatments.\u003c/p\u003e\u003cp\u003eDESeq2 analysis revealed a greater number of differentially expressed genes (DEGs) under P0 than under P30 across all the genotypes (Additional file 2: Figure S3a). Wm82 presented the greatest transcriptomic response under P0, with 11,506 DEGs (4,351 upregulated and 7,155 downregulated). PCA revealed that the P0-treated samples clustered along the negative axis of PC1, whereas the P90 samples were on the positive axis. The P30 samples were scattered in between (Additional file 2: Figure S3b). These results were corroborated by PERMANOVA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that increasing P deficiency triggers a broader transcriptional response.\u003c/p\u003e\u003cp\u003eKEGG enrichment analysis revealed the top 30 significantly enriched pathways (Additional file 2: Figure S3c). In the P0 vs. P90 comparison, all genotypes showed enrichment in pathways such as starch and sucrose metabolism, glycolysis/gluconeogenesis, and alanine, aspartate, and glutamate metabolism. In contrast, P30 vs. P90 comparisons were enriched in carotenoid biosynthesis, glycerophospholipid metabolism, ABC transporters, and plant hormone signal transduction. Compared with the P-inefficient Nm, the P-efficient genotypes (Ax and Wm82) showed greater enrichment in isoflavonoid biosynthesis, plant-pathogen interaction, and MAPK signaling pathways, findings that may explain their higher AM colonization rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eTo identify regulatory modules and hub genes associated with P acquisition strategies, we performed weighted gene co-expression network analysis (WGCNA). Modules positively correlated with SRL and root exudation (e.g., blue, green and turquoise) were enriched in carboxylic acid metabolism. Genes such as \u003cem\u003epckA\u003c/em\u003e and \u003cem\u003eMDH\u003c/em\u003e in these modules were highly expressed under P0. In contrast, AM-associated modules (e.g., dark turquoise, grey) showed upregulation of fatty acid biosynthesis genes including \u003cem\u003eaccA/B/C, FabF, FabI\u003c/em\u003e and \u003cem\u003eFATB\u003c/em\u003e under P30 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), indicating carbon flux redirection toward symbiosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Integrated Transcriptomic and Metabolomic Analysis\u003c/h2\u003e\u003cp\u003eAnalysis of the glycolytic pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed that PEPC mediates a bypass route: phosphoenolpyruvate (PEP) is converted to oxaloacetate and malate via PEPC, pckA, and MDH, with corresponding genes exhibiting an expression gradient of P0\u0026thinsp;\u0026gt;\u0026thinsp;P30\u0026thinsp;\u0026gt;\u0026thinsp;P90. In contrast, the PK branch converts PEP to pyruvate, feeding acetyl-CoA/malonyl-CoA for fatty acid biosynthesis (\u003cem\u003eaccA/B/C, FabF/I, FATB\u003c/em\u003e). Under Pi stress, PPi-dependent bypass enzymes such as \u003cem\u003ePFP\u003c/em\u003e and \u003cem\u003ePPDK\u003c/em\u003e help conserve ATP/Pi and rebalance glycolytic flux. Genes such as \u003cem\u003eaccA/B\u003c/em\u003e and \u003cem\u003eFabI\u003c/em\u003e showed a reversed trend (P30\u0026thinsp;\u0026gt;\u0026thinsp;P90\u0026thinsp;\u0026gt;\u0026thinsp;P0). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, soil phosphorus availability hierarchically regulates glycolytic flux partitioning by favoring organic acid biosynthesis under severe deficiency, while redirecting carbon toward fatty acid biosynthesis to support symbiotic engagement under moderate deficiency.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnder phosphorus excess conditions (P120), root-surface high-affinity phosphate transporters (e.g., \u003cem\u003ePHT1\u003c/em\u003e family) fully satisfy soybean phosphorus demand. At this stage, further carbon allocation to maintain arbuscular mycorrhizal (AM) symbiosis or enhance root growth/exudation becomes metabolically inefficient [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Consequently, plants downregulate PSI genes (e.g., SPX1, PHO2), reduce carbon investment in root biomass and rhizosphere exudates, and strongly suppress AM fungal colonization [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This metabolic shift results in a phosphorus acquisition strategy dominated by transporter-mediated uptake.\u003c/p\u003e\u003cp\u003eAs phosphorus supply declines, soybean gradually activates a series of adaptive responses. The initial response often involves the activation of AMF symbiosis. Under P90 conditions, AM colonization rates in all genotypes were already significantly higher than those under P120, and colonization continued to increase as phosphorus availability declined, peaking at P30 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Compared with fine-rooted species such as maize and wheat, soybean possesses thicker roots and exhibits lower morphological plasticity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. As a result, soybean tends to adopt an \"outsourcing\" strategy, relying on AM hyphal networks to expand its phosphorus acquisition zone. This compensates for the limitations associated with slower root elongation and reduced plasticity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt the molecular level, phosphorus deficiency triggers a cascade of regulatory events that reshape root\u0026ndash;fungal symbiosis. A decline in intracellular phosphorus inhibits the biosynthesis of the signaling molecule 1,5-InsP8 (diphosphoinositol pentakisphosphate). This reduction promotes ubiquitin-mediated degradation of SPX proteins [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This releases SPX-mediated repression on PHR transcription factors, which subsequently activate two critical pathways: (1) upregulation of high-affinity phosphate transporters (PHTs) (Additional file 1: Table S8) on root surfaces to enhance direct phosphorus uptake, and (2) induction of AM-specific regulators, including RAM1, WRI5A, and ERF (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, D), that facilitate fungal colonization [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConsistent with this regulatory framework, transcriptomic analysis under P30 conditions revealed significant upregulation of fatty acid biosynthesis genes (\u003cem\u003eaccA/B/C, FabF, FabI, FATB\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These genes mediate the conversion of photosynthates into lipids, which serve as essential carbon sources for AM fungi, thereby establishing a mutualistic exchange system. Structural equation modeling (SEM) further demonstrated that under moderate phosphorus deficiency (P30), AMF symbiosis exerts the strongest positive effect on plant phosphorus accumulation compared to direct uptake pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This carbon-efficient phosphorus acquisition strategy becomes particularly advantageous when soil phosphorus availability is limited but not severely depleted.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUnder severe phosphorus deficiency (P0), the arbuscular mycorrhizal (AM) symbiosis strategy becomes severely constrained. Compared to the P30 treatment, AM colonization rates declined by 50\u0026ndash;80% across all soybean genotypes at P0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), a trend consistent with observations in maize and wheat [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In response, soybeans adopted an alternative P acquisition pathway. While fungal colonization diminished, root traits associated with a \u0026ldquo;mining strategy\u0026rdquo;, such as exudation of LMWOAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), (SRL Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and specific root surface area (SRA; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), peaked under P0 conditions. Notably, organic acid concentrations in the rhizosphere were 17\u0026ndash; to 24\u0026ndash;fold higher at P0 than at P90 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). SEM confirmed that organic acid exudation exerted the strongest influence on P accumulation at P0 (path coefficient\u0026thinsp;=\u0026thinsp;0.93, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), establishing it as the dominant response mechanism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this mining-based strategy, soybeans upregulated genes involved in carboxylic acid metabolism, such as \u003cem\u003ePckA, MDH\u003c/em\u003e, and \u003cem\u003eME\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), to synthesize large quantities of malate and other organic acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These compounds were secreted into the rhizosphere to mobilize insoluble phosphorus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Concurrently, energy-conserving glycolytic bypasses, including diphosphate-dependent phosphofructokinase (PFP) and PPDK, were activated to support ATP production and internal phosphate recycling under energy-limited conditions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, soil phosphorus availability acts as a critical threshold that governs the selection and transition of phosphorus acquisition strategies in soybean. Rather than a binary switch, this transition reflects a dynamic trade-off based on carbon investment efficiency. Under mild to moderate phosphorus deficiency (e.g., P30 and P60), AM fungi, with their extensive hyphal networks, can efficiently acquire spatially dispersed phosphorus at a lower carbon cost than can root-based strategies. As a result, AMF symbiosis emerges as a more economical option under these conditions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnder extreme phosphorus deficiency, however, AM fungal activity becomes limited. The increasing carbon investment required to sustain symbiosis leads to a reduced return on investment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In the P0 treatment, all five soybean genotypes exhibited decreased root diameter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and lower fatty acid levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), potentially triggering the downregulation or termination of AMF symbiosis. At this stage, direct carbon allocation toward enhancing root morphological plasticity, such as increased SRL and SRA, and promoting organic acid biosynthesis becomes a more efficient strategy. This mining-based strategy, which reallocates carbon to root plasticity and organic acid biosynthesis, is consistent with the principles of carbon economy theory [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, soybean genotypes exhibit considerable variation in their phosphorus sensitivity thresholds and in the trade-offs between strategies. Genotypes such as Wm82, Qd11, and Zh13 are more dependent on AMF symbiosis and can maintain effective fungal colonization even under phosphorus-sufficient conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). This trait allows them to adapt well to agricultural soils with high total but low available phosphorus, enhancing phosphorus use efficiency while minimizing fertilizer accumulation in the environment. In contrast, genotype Ax secretes greater quantities of organic acids and acid phosphatases under phosphorus-deficient conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E), making it more suitable for soils that are both low in total phosphorus and prone to phosphorus fixation, such as acidic or alkaline soils [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese differences in phosphorus acquisition strategies reflect the underlying genetic variation in phosphorus sensing, carbon allocation regulation, and plant\u0026ndash;fungus signaling pathways across soybean genotypes. Root system architecture also plays a critical role in shaping genotype-specific phosphorus strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Considering the wide global variation in available soil phosphorus, ranging from 0.01 to 99.2 mg kg⁻\u0026sup1; [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], matching soybean genotypes with soil phosphorus profiles becomes essential. Future research should focus on developing a comprehensive genotype\u0026ndash;phosphorus\u0026ndash;management decision framework. This dual optimization can be achieved by combining targeted breeding, such as genome editing of genes involved in pyruvate metabolic branching, with demand-based phosphorus fertilizer management.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that soil phosphorus availability functions as a critical threshold factor governing the strategic shift in phosphorus acquisition in soybean. Under moderate phosphorus deficiency, soybean plants upregulate genes involved in fatty acid biosynthesis, including \u003cem\u003eaccA/B and FabI\u003c/em\u003e, thereby directing photosynthetically derived carbon toward arbuscular mycorrhizal (AM) fungi. This process underlies a symbiosis-dependent outsourcing strategy, which is particularly prominent in thick-rooted genotypes that exhibit inherently low root morphological plasticity and thus depend on AM fungal hyphal networks to access spatially dispersed phosphorus. In contrast, under severe phosphorus deficiency, soybean plants shift to a root-driven strategy. This involves the upregulation of genes such as \u003cem\u003epckA\u003c/em\u003e and \u003cem\u003eMDH\u003c/em\u003e to enhance organic acid secretion, along with reduced root diameter and increased SRL, which collectively improve root morphological plasticity and soil exploration capacity. This strategy is especially advantageous for fine-rooted genotypes in phosphorus-fixing acidic soils. The transition between symbiosis-based and autonomous strategies is mediated by the redirection of carbon flux at the pyruvate metabolic branching point. However, the findings of this study were derived from experiments conducted in a single soil type, limiting the generalizability of the results. Future research should aim to establish a comprehensive genotype\u0026ndash;soil phosphorus matching database across diverse agroecological zones. Such a resource would support the development of actionable, low-carbon phosphorus management strategies tailored to specific crop demands and environmental conditions. Our findings provide a foundation for precision P management and genotype\u0026ndash;soil matching in sustainable soybean production systems.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAMF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArbuscular mycorrhizal fungi\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDAMs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially accumulated metabolites\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially expressed genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Ontology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHMDB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Metabolome Database\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMDH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMalate dehydrogenase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNCBI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Center for Biotechnology Information\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphorus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal component analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCoA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal coordinate analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePEPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphoenolpyruvate carboxylase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPDK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePyruvate phosphate dikinase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePi\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInorganic phosphate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSRA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003especific root area\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSRL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSpecific root length\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWeighted gene co-expression network analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePERMANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePermutational Multivariate Analysis of Variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStructural Equation Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe raw transcriptomic sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive under BioProject accession number PRJNA1129285 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1129285). The metabolomic datasets and other materials are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNational Natural Science Foundation of China (No. 32260804);\u003c/p\u003e\n\u003cp\u003eGuizhou Key Technologies for Mountainous Agriculture Research Project (GZNYJHJX-2025001);\u003c/p\u003e\n\u003cp\u003eGuizhou Highland Specialty Vegetable Production Science and Technology Innovation Talent Team (Qiannan Platform Talent-CXTD [2022]03)\u003c/p\u003e\n\u003cp\u003eConstruction of High Quality and Efficient Mechanized Stereoscopic Greenhouse Vegetable Production Base (qjzkhp2024009);\u003c/p\u003e\n\u003cp\u003eResearch and integrated application of key technologies of green and high yield in characteristic mountain agriculture (qjzdggzhz[2023]07);\u003c/p\u003e\n\u003cp\u003eCultivation Project of Guizhou University (202018).\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eZGY participated in manuscript writing and figure preparation.\u003c/p\u003e\n\u003cp\u003eCZ designed the research, performed data analysis, reviewed the literature, and contributed to manuscript writing.YTL conducted the soybean soil pot cultivation experiments and contributed to data analysis. Wanping Zhang: Funding acquisition\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 32260804), Guizhou Key Technologies for Mountainous Agriculture Research Project (GZNYJHJX-2025001), Guizhou Highland Specialty Vegetable Production Science and Technology Innovation Talent Team (Qiannan Platform Talent-CXTD [2022]03), Construction of High Quality and Efficient Mechanized Stereoscopic Greenhouse Vegetable Production Base (qjzkhp2024009), Research and integrated application of key technologies of green and high yield in characteristic mountain agriculture (qjzdggzhz[2023]07), the Cultivation Project of Guizhou University (202018).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMishra S, Levengood H, Fan J, Zhang C. 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New Phytol. 1990;115(3):495-501.\u003c/li\u003e\n\u003cli\u003eLi P, Oyang X, Xie X, Li Z, Yang H, Xi J, et al. Phytotoxicity induced by perfluorooctanoic acid and perfluorooctane sulfonate via metabolomics. J Hazard Mater. 2020;389:121852.\u003c/li\u003e\n\u003cli\u003eZhou H, Li Y, Feng S, Wu Z, Zheng D, Feng N. Joint analysis of transcriptome and metabolome revealed the difference in carbohydrate metabolism between super hybrid rice and conventional rice under salt stress. Plant Stress. 2023;10:100251.\u003c/li\u003e\n\u003cli\u003eTrapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28(5):511-515.\u003c/li\u003e\n\u003cli\u003eKlopfenstein DV, Zhang L, Pedersen BS, Ramirez F, Vesztrocy AW, Naldi A, et al. GOATOOLS: a Python library for gene ontology analyses. 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J Plant Growth Regul. 2019;38(4):1243-1254.\u003c/li\u003e\n\u003cli\u003eWang P, Wang T, Wu S, Wen M, Lu L, Ke F, et al. Effect of arbuscular mycorrhizal fungi on rhizosphere organic acid content and microbial activity of trifoliate orange under different low P conditions. Arch Agron Soil Sci. 2019;65(14):2029-2042.\u003c/li\u003e\n\u003cli\u003eYang S, Lin W, Hsiao Y, Chiou T. Milestones in understanding transport, sensing, and signaling of the plant nutrient phosphorus. Plant Cell. 2024;36(5):1504-1523.\u003c/li\u003e\n\u003cli\u003eWang Q, Du W, Zhang S, Yu W, Wang J, Zhang C, et al. Functional study and elite haplotype identification of soybean phosphate starvation response transcription factors GmPHR14 and GmPHR32. Mol Breed. 2022;42(5):29.\u003c/li\u003e\n\u003cli\u003eChu Q, Zhang L, Zhou J, Yuan L, Chen F, Zhang F, et al. Soil plant-available phosphorus levels and maize genotypes determine the phosphorus acquisition efficiency and contribution of mycorrhizal pathway. Plant Soil. 2020;449(1):357-371.\u003c/li\u003e\n\u003cli\u003eLi J, Liu R, Zhang C, Yang J, Lyu L, Shi Z, et al. Selenium uptake and accumulation in winter wheat as affected by level of phosphate application and arbuscular mycorrhizal fungi. J Hazard Mater. 2022;433:128762.\u003c/li\u003e\n\u003cli\u003eZhang L, Chu Q, Zhou J, Rengel Z, Feng G. Soil phosphorus availability determines the preference for direct or mycorrhizal phosphorus uptake pathway in maize. Geoderma. 2021;403:115261.\u003c/li\u003e\n\u003cli\u003eNasr Esfahani M, Kusano M, Nguyen KH, Watanabe Y, Ha CV, Saito K, et al. Adaptation of the symbiotic Mesorhizobium\u0026ndash;chickpea relationship to phosphate deficiency relies on reprogramming of whole-plant metabolism. Proc Natl Acad Sci U S A. 2016;113(32):E4610-9.\u003c/li\u003e\n\u003cli\u003ePlaxton WC, Tran HT. Metabolic adaptations of phosphate-starved plants. Plant Physiol. 2011;156(3):1006-1015.\u003c/li\u003e\n\u003cli\u003eVen A, Verlinden MS, Verbruggen E, Vicca S. Experimental evidence that phosphorus fertilization and arbuscular mycorrhizal symbiosis can reduce the carbon cost of phosphorus uptake. Funct Ecol. 2019;33(11):2215-2225.\u003c/li\u003e\n\u003cli\u003ePe\u0026ntilde;a Venegas RA, Lee S, Thuita M, Mlay DP, Masso C, Vanlauwe B, et al. The phosphate inhibition paradigm: host and fungal genotypes determine arbuscular mycorrhizal fungal colonization and responsiveness to inoculation in cassava with increasing phosphorus supply. Front Plant Sci. 2021;12:693037.\u003c/li\u003e\n\u003cli\u003eWen T, Yuan J, He X, Lin Y, Huang Q, Shen Q. Enrichment of beneficial cucumber rhizosphere microbes mediated by organic acid secretion. Hortic Res. 2020;7:154.\u003c/li\u003e\n\u003cli\u003eLuo X, Guo C, He X, Helfenstein J, Lambers H, Ren Q, et al. Global distribution and influencing factors of plant-available phosphorus in (semi-)natural soils. Global Biogeochem Cycles. 2025;39(6):e43.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Soybean, phosphorus acquisition strategy, trade-off mechanism, transcriptomics, metabolomics, carbon allocation","lastPublishedDoi":"10.21203/rs.3.rs-7738053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7738053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnder phosphorus (P) deficiency, soybean (\u003cem\u003eGlycine max\u003c/em\u003e) adapts by modifying root architecture, increasing the release of organic exudates, enhancing arbuscular mycorrhizal (AM) colonization, and reshaping rhizosphere microbial communities; however, how these strategies trade off across a phosphorus gradient remains unclear. In this study, we integrated transcriptomic and metabolomic analyses to examine five soybean cultivars under soil P supplies of 0 mg P kg⁻\u0026sup1; (severe deficiency, P0), 30 mg P kg⁻\u0026sup1; (moderate deficiency, P30), 60 mg P kg⁻\u0026sup1; (mild deficiency, P60), 90 mg P kg⁻\u0026sup1; (adequate), and 120 mg P kg⁻\u0026sup1; (excess). Our results indicate that the gradient of plant-available P drives dynamic switching among soybean P-acquisition strategies. Under moderately low P, soybean upregulated \u003cem\u003ePPDK\u003c/em\u003e, \u003cem\u003eaccC\u003c/em\u003e, and \u003cem\u003eFabI\u003c/em\u003e, which is consistent with a shift in carbon use that could support arbuscular mycorrhizal fungi, and AMF colonization increased by 30\u0026ndash;50%. Under severe deficiency P, soybean primarily relied on root-driven strategies: \u003cem\u003epckA\u003c/em\u003e, \u003cem\u003eMDH\u003c/em\u003e, \u003cem\u003eaceB\u003c/em\u003e, and \u003cem\u003eCS\u003c/em\u003e (genes associated with the \u003cem\u003ePEPC\u003c/em\u003e shunt) were upregulated, the concentration of low-molecular-weight organic acids increased by 17\u0026ndash; to 24\u0026ndash;fold, and fine-root length increased by approximately 35%, thereby optimizing root system architecture. Cultivars differed in their adaptive preferences: AM-dependent types were better suited to temperate soils with moderate P limitation, whereas fine-rooted cultivars were advantageous in tropical and subtropical soils with severe P depletion. Overall, our findings reveal the regulatory networks underlying soybean P-acquisition strategies and highlight their breeding and management significance. This study provides a foundation for developing P-efficient soybean cultivars and for precision P management in sustainable agriculture.\u003c/p\u003e","manuscriptTitle":"Integrated Transcriptomic and Metabolomic Analyses Reveal Trade-Off Mechanisms Underlying Phosphorus Acquisition Strategies in Soybean Roots","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 06:22:37","doi":"10.21203/rs.3.rs-7738053/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-22T13:55:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T12:50:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T12:54:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T03:19:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T02:28:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336868149337083134943025418395178588348","date":"2025-10-09T12:36:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169011986004960224760940964211481830301","date":"2025-10-08T08:20:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18963295695220947397032568049115831248","date":"2025-10-07T23:44:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14519663131289432757295004918351519207","date":"2025-10-07T22:57:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T19:33:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278210535228413058006211295009748207502","date":"2025-10-07T12:29:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-07T12:13:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-06T11:33:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T07:55:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-06T07:55:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-09-29T04:58:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33070228-bc3b-4753-99c4-970621bc5829","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T15:59:11+00:00","versionOfRecord":{"articleIdentity":"rs-7738053","link":"https://doi.org/10.1186/s12870-025-07957-x","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-12-17 15:57:02","publishedOnDateReadable":"December 17th, 2025"},"versionCreatedAt":"2025-10-20 06:22:37","video":"","vorDoi":"10.1186/s12870-025-07957-x","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07957-x","workflowStages":[]},"version":"v1","identity":"rs-7738053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7738053","identity":"rs-7738053","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}