Molecular Mechanisms Underlying Sweet Potato (Ipomoea batatas L.) Responses to Phosphorus Deficiency

Abstract

Phosphorus deficiency poses a significant challenge to the growth and productivity of crops, particularly in nutrient-poor soils. This study investigates the effects of phosphorus deficiency on the growth, endogenous phytohormones, metabolome, and transcriptome of sweet potato (Ipomoea batatas L.) over a growth period from 30 to 120 days. We found that low phosphorus conditions significantly reduced both above- and below-ground biomass, while tuber number remained unchanged. Endogenous phytohormone analysis revealed altered levels of abscisic acid (ABA), indole-3-acetic acid (IAA), and cytokinins, indicating a complex hormonal response to phosphorus starvation. Transcriptomic analysis identified a total of 6324 differentially expressed genes (DEGs) at 60 days, with significant enrichment in pathways related to stress response and phosphorus utilization (PAPs and PHO1). Metabolomic profiling revealed notable shifts in key metabolites, with consistent downregulation of several phosphorous-related compounds. Our findings highlight the intricate interplay between growth, hormonal regulation, metabolic reprogramming, and gene expression in response to phosphorus deficiency in sweet potato. This research underscores the importance of understanding nutrient stress responses to enhance sweet potato resilience and inform sustainable agricultural practices. Future research should focus on exploring the potential for genetic and agronomic interventions to mitigate the effects of phosphorus deficiency and optimize sweet potato productivity in challenging environments.

1. Introduction

Phosphorus (P) is a critical macronutrient that significantly influences plant growth, development, and overall productivity [1,2,3]. In agricultural systems, phosphorus deficiency is a prevalent challenge that can severely limit crop yield and quality, particularly in regions with low soil fertility [4,5]. Sweet potato (Ipomoea batatas L.), a globally important root crop known for its nutritional benefits and adaptability to various environmental conditions, is particularly sensitive to phosphorus availability [6,7]. Understanding the effects of phosphorus deficiency on sweet potato is essential for optimizing its cultivation and enhancing food security.

The impact of phosphorus deficiency on plant growth has been well-documented, with studies indicating that inadequate phosphorus supply leads to reduced biomass [8,9], stunted growth [10], and altered root architecture [11,12]. These morphological changes are often accompanied by shifts in the levels of endogenous phytohormones, which play vital roles in regulating plant responses to nutrient stress [5,13]. For instance, hormones such as abscisic acid (ABA) and cytokinins (CKs) are crucial for mediating stress responses and developmental processes [3,12]. However, the specific hormonal dynamics in sweet potato under phosphorus-deficient conditions remain inadequately explored.

In addition to hormonal alterations, phosphorus deficiency triggers significant metabolic reprogramming within plants [1,3]. Metabolomics, the comprehensive analysis of metabolites, offers insights into the biochemical pathways affected by nutrient stress [1]. Previous research has highlighted the importance of various metabolites, including amino acids and secondary metabolites, in plant stress responses [8,14]. Yet, the relationship between metabolomic changes and phosphorus deficiency in sweet potato has not been thoroughly investigated. Moreover, advancements in transcriptomic technologies have enabled researchers to analyze gene expression patterns in response to nutrient deficiencies [15,16]. Differentially expressed genes (DEGs) can provide valuable information regarding the molecular mechanisms underlying plant responses to phosphorus stress [17,18,19]. Despite the growing interest in this area, the interplay between transcriptomic changes, hormonal responses, and metabolic shifts in sweet potato under phosphorus deficiency remains poorly understood.

This study aims to elucidate the effects of phosphorus deficiency on the growth, endogenous phytohormones, metabolome, and transcriptome of sweet potato over a growth period of 30 to 120 days. By integrating data from plant growth measurements, phytohormone analyses, metabolomic profiling, and transcriptomic sequencing, we seek to provide a comprehensive understanding of how phosphorus deficiency impacts sweet potato at multiple biological levels. We hypothesize that phosphorus deficiency will lead to significant alterations in plant growth, hormonal balance, metabolic profiles, and gene expression patterns, ultimately affecting the plant resilience and productivity. The findings from this research will contribute to the development of strategies for enhancing sweet potato cultivation in phosphorus-limited soils, supporting sustainable agricultural practices and improving food security.

2. Materials and Methods

2.1. Materials Prepareation

Prior to the experiment, sweet potato (Ipomoea batatas L.; Guangshu 87) seedlings were acclimatized. To enhance the adaptability of the tissue-cultured seedlings to external environmental conditions, they were initially cultivated in a room-temperature, low-light environment for 7 days. For the first 5 days, the bottle caps remained closed, and for the subsequent 2 days, the caps were opened. After 7 days, the virus-free tissue-cultured seedlings were rinsed with purified water, and healthy, robust seedlings were selected and transplanted to the hardening area in the net house. During the hardening process, the seedlings were cultivated in peat soil beds, shaded with an 85% sunshade net, and covered with insect-proof nets. Acclimatization was completed after 45 days. Following acclimatization, the sweet potato seedlings were propagated. Stem segments of approximately 15 cm in length were cut and transplanted into the field for cultivation. After 40 days, 15 cm-long stem segments were cut and planted in quartz sand for the formal sand culture experiment.

The phosphorus-deficient (NS10205-P, Coolaber, Beijing, China) and full-phosphorus (NSP1020, Coolaber) nutrient solutions were prepared from their respective stock solutions using purified water, following the manufacturer’s protocols [20,21]. During preparation, the pH of the nutrient solutions was adjusted to 5.8 ± 0.1. The nutrient composition of the phosphorus-deficient nutrient solution (PDNS) and full-phosphorus nutrient solution (FPNS) is presented in

Table 1. Content of nutrients in phosphorus-deficient nutrient solution (PDNS) and full-phosphorus nutrient solution (FPNS).

No.	Nutrient	PDNS (mg/L)	FPNS (mg/L)
1	KNO3	607.2	506
2	NH4NO3	40	80
3	KH2PO4	0	136
4	MgSO4	241	241
5	FeNaEDTA	36.7	36.7
6	KI	0.83	0.83
7	H3BO3	6.2	6.2
8	MnSO4·H2O	22.3	22.3
9	ZnSO4·7H2O	8.6	8.6
10	Na2MoO4·2H2O	0.25	0.25
11	CuSO4·5H2O	0.025	0.025
12	CoCl2·6H2O	0.025	0.025
13	Ca(NO3)2·4H2O	945	945

.

2.2. Experimental Design

The sand culture experiment was conducted in cultivation tanks (50 cm in length × 40 cm in width × 38 cm in height) filled with 2–4 mm quartz sand as the substrate, with a substrate height of 30 cm. Each tank was planted with one 15 cm-long sweet potato stem segment, totaling 24 tanks. The experiment included two treatments: low phosphorus (LP) and control (CK), with 12 tanks for each treatment. The LP and CK treatments were irrigated daily with PDNS and FPNS, respectively. The samples were collected at 30-day intervals (30, 60, 90, and 120 days), with three plants per treatment (serving as three biological replicates) destructively sampled for further analysis.

2.3. Plant Growth Characteristics and Endogenous Phytohormones

At 30, 60, 90, and 120 days, the plant height, fresh above- and below-ground biomass (AGB and BGB), tuber number, maximum tuber diameter, and primary root length of the sweet potato were measured. The experiments were conducted from August to December 2023 in Guangzhou, Guangdong Province, China.

Meanwhile, tuber samples were collected from the sweet potato plants. Approximately 1.5 g of tuber tissue per plant was placed in a 2 mL internal screw-cap cryotube and rapidly frozen in liquid nitrogen. The samples were then transferred to a −80 °C freezer for storage until the end of the experiment for endogenous phytohormone analysis. Endogenous phytohormones in the tuber samples were extracted using isopropanol-water-hydrochloric acid. The contents of endogenous phytohormones, including indole-3-acetic acid (IAA), abscisic acid (ABA), isopentenyl adenosine (IPA), dihydrozeatin riboside (DZR), cis-zeatin riboside (CZR), and trans-zeatin riboside (TZR), were determined using an Agilent 1290 high-performance liquid chromatography (HPLC) system coupled with an AB Sciex QTRAP 6500+ mass spectrometer. Internal standards were added to the extraction solution to correct the detection results. Standard solutions were prepared using methanol with 0.1% formic acid as the solvent, and a standard curve was constructed for the quantitative calculation of endogenous phytohormone concentrations.

2.4. Cellulose and Lignin

At 30, 60, 90, and 120 days, some tubers of harvested sweet potato were dried at 60 °C and then crushed. The dried tuber powder was used for subsequent determination of cellulose and lignin content.

The cellulose content of tuber was determined using the anthrone-sulfuric acid method [22,23]. Briefly, 0.5 g of dried tuber powder was hydrolyzed with 25 mL of concentrated sulfuric acid at room temperature for 30 min. The mixture was then diluted with 100 mL of distilled water and boiled for 1 h. After cooling, the hydrolysate was filtered, and 2 mL of the filtrate was mixed with 5 mL of anthrone reagent (0.2% anthrone in concentrated sulfuric acid). The mixture was heated in a boiling water bath for 10 min, cooled to room temperature, and the absorbance was measured at 620 nm using a spectrophotometer. A standard curve was prepared using glucose solutions, and the cellulose content was calculated.

The lignin content was determined using the Klason lignin method with titration [24,25]. Approximately 1.0 g of dried tuber powder was treated with 10 mL of 72% sulfuric acid at room temperature for 2 h. The mixture was then diluted with 150 mL of distilled water and boiled for 4 h. The insoluble lignin residue was collected by filtration, transferred to a flask, and oxidized with 25 mL of 0.1 N potassium dichromate (K₂Cr₂O₇) and 25 mL of concentrated sulfuric acid. The mixture was boiled for 1 h, cooled, and titrated with 0.1 N ferrous ammonium sulfate (FAS) using o-phenanthroline as an indicator. In addition, a blank titration was performed without the sample. Finally, the lignin content of tuber was calculated.

2.5. Transcriptome Analysis

At 30, 60, 90, and 120 days, tuber samples of approximately 1.5 g were collected from the sweet potato plants, flash-frozen in liquid nitrogen, and subsequently stored at −80 °C for subsequent transcriptomic and metabolomic analyses. Three biological replicates were set up, with no technical replicates.

Total RNA was extracted from the tuber tissue using TRIzol^® Reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol, and genomic DNA was removed using DNase I (TaKara, Otsu, Shiga, Japan). RNA quality was assessed using the 2100 Bioanalyser (Agilent, Santa Clara, CA, USA) and quantified with the ND-2000 (NanoDrop Technologies, Wilmington, DE, USA). High-quality RNA samples were used to construct sequencing libraries. RNA-seq transcriptome libraries were prepared using the TruSeqTM RNA Sample Preparation Kit (Illumina, San Diego, CA, USA) with 1 μg of total RNA. Briefly, mRNA was isolated via polyA selection using oligo (dT) beads and fragmented. cDNA synthesis, end repair, A-base addition, and Illumina-indexed adaptor ligation were performed according to the manufacturer’s instructions. Libraries were size-selected for cDNA fragments of 200–300 bp using 2% Low Range Ultra Agarose and amplified by PCR with Phusion DNA polymerase (NEB) for 15 cycles. After quantification with TBS380, paired-end libraries were sequenced on the Illumina NovaSeq 6000 platform (150 bp × 2, Shanghai BIOZERON Co., Ltd., Shanghai, China).

Raw paired-end reads were trimmed and quality-filtered using Trimmomatic (version 0.36, parameters: SLIDINGWINDOW:4:15 MINLEN:75) [26]. Clean reads were aligned to the reference genome in orientation mode using HISAT2 with default parameters [27]. Data quality was assessed using Qualimap v2.2.1 [28]. Gene read counts were generated using HTSeq [29].

To identify differentially expressed genes (DEGs) between samples, the gene expression levels were calculated using the fragments per kilobase of exon per million mapped reads (FPKM) method. Differential expression analysis was performed using the edgeR package [30] in R, with DEGs selected based on a log2 fold change > 2 and a false discovery rate (FDR) of <0.05. Gene Ontology (GO) functional enrichment analysis of DEGs was conducted using Goatools [31], with significant enrichment defined as a Bonferroni-corrected p-value < 0.05. The relationships of upregulated and downregulated DEGs at different time points were visualized using Venn diagrams. The shared DEGs across all time points were displayed in the form of heatmaps.

2.6. Metabolome Analysis

Tuber tissues (100 mg) were ground in liquid nitrogen and homogenized in 80% methanol with 0.1% formic acid. After vortexing and ice incubation, samples were centrifuged at 15,000× g (4 °C) for 5 min. The supernatant was diluted to 53% methanol, centrifuged again at 15,000× g (4 °C) for 10 min, and analyzed by LC-MS/MS [32]. The samples were analyzed using a Vanquish UHPLC system coupled with an Orbitrap Q Exactive™ HF mass spectrometer. Separation was performed on a Hypesil Gold column (100 × 2.1 mm, 1.9 μm) with a 17 min gradient at 0.2 mL/min. Eluents were 0.1% formic acid in water (positive mode) or 5 mM ammonium acetate (pH 9.0, negative mode), with methanol as eluent B. The gradient was: 2% B (1.5 min), 2–100% B (12 min), 100% B (14 min), 100–2% B (14.1 min), and 2% B (17 min). MS settings included a spray voltage of 3.2 kV, capillary temperature of 320 °C, and sheath/auxiliary gas flow rates of 40/10 arb.

Raw data were processed using Compound Discoverer 3.1 for peak alignment, picking, and quantification. Parameters included: retention time tolerance (0.2 min), mass tolerance (5 ppm), and signal-to-noise ratio (3). Peaks were normalized, matched against mzCloud/mzVault/MassList databases, and analyzed using R (4.4.3), Python (3.11.5), and CentOS (Stream 9). Non-normal data were normalized using area normalization.

Metabolites were annotated using the KEGG [33], HMDB [34], and LIPID Maps databases [35,36]. Data analysis included Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) performed with metaX software (1.6.2) [37]. Univariate analysis (t-test) was used to calculate statistical significance (p-value). Metabolites with VIP > 1, p-value < 0.05, and fold change ≥ 2 or ≤0.5 were identified as differentially abundant metabolites (DAMs). The relationships of upregulated and downregulated DAMs at different time points were visualized using Venn diagram. The shared DAMs across all time points were displayed in the form of heatmaps.

2.7. Association Analysis of Transcriptome and Metabolome

Spearman correlation analysis was performed between common DEGs and common differential metabolites across different time points using psych packages in R 4.4.1. Paths with a correlation coefficient (r) of >0.8 and a significance level (p) of <0.01 were selected to construct a correlation network between common DEGs and differential metabolites. The network was visualized using Cytoscape 3.10.0.

2.8. Statistical Analysis

Independent samples t-tests (Student’s t-test for homogeneous variances or Welch’s t-test for heterogeneous variances) were performed to evaluate the differences between different treatment data (plant growth characteristics and endogenous phytohormones), with significance set at p < 0.05.

3. Results

3.1. Plant Growth

During the entire growth period (30–120 days), the aboveground and underground (belowground) biomass of sweet potato under LP treatment were significantly lower than those under the CK treatment. However, from 30 to 90 days, no significant difference in tuber number was observed between the two treatments. At 120 days, the tuber number under LP treatment was significantly lower than that under CK treatment. From 60 to 120 days, the plant height of sweet potato under LP treatment was significantly lower than that under CK treatment. Between 90 and 120 days, the maximum tuber diameter of sweet potato under LP treatment was significantly smaller than that under CK treatment. At 90 days, the primary root length of sweet potato under LP treatment was significantly shorter than that under CK treatment (Figure 1A).

3.2. Endogenous Phytohormones

We measured the endogenous phytohormones in the tuber of sweet potatoes during their growth period (Figure 1B). From 30 to 120 days, the concentration of ABA and TZR hormones in the tuber under LP treatment were significantly lower than those in the CK group. At 30 and 120 days, the IAA hormone levels in the tuber under LP treatment were significantly higher than those in the CK group, while at 60 days, they were significantly lower. Between 30 and 90 days, the levels of IPA and ZR hormones in the tuber under LP treatment were significantly lower than those in the CK group. From 30 to 60 days, the DZR hormone levels in the tuber under LP treatment were significantly lower than those in the CK group; however, at 120 days, they were significantly higher.

3.3. Cellulose and Lignin

We measured the contents of cellulose and lignin in the tuber of sweet potatoes during their growth period (Figure 1C). From 30 to 60 days, no significant difference in cellulose content of tuber was observed between the two treatments. However, from 90 to 120 days, the content of cellulose under LP treatment was significantly lower than that under CK treatment. Similarly, at 30 and 120 days, the content of lignin under LP treatment was significantly higher than that under CK treatment. From 60 to 90 days, no significant difference in lignin content of tuber was observed between the two treatments.

3.4. Transcriptome

Significant differences in genes expression were observed between the two treatments during the 30–120 days of sweet potato growth (Figure 2A). At 30, 60, 90, and 120 days, the numbers of DEGs between the LP and CK treatments were 601 (116 downregulated and 485 upregulated), 6324 (3461 downregulated and 2863 upregulated), 2769 (1736 downregulated and 1033 upregulated), and 771 (291 upregulated and 480 downregulated), respectively (Figure 2B). Across different time points, 4 DEGs were consistently downregulated (Figure 2C), and 43 DEGs were consistently upregulated (Figure 2D,E), especially the PAPs involved in hydrolyzing organic phosphorus, and PHO1 related to transporting inorganic phosphorus.

We performed GO functional enrichment analysis on DEGs across different time points (Figure 3). At 30 days, the top five significantly enriched GO terms for DEGs were response to external stimulus, response to extracellular stimulus, lipid metabolic process, cellular response to stress, and cellular response to nutrient levels (Figure 4A). At 60 days, the top five significantly enriched GO terms were catalytic activity, response to organic substance, response to biotic stimulus, response to biotic stimulus, and response to biotic stimulus (Figure 4B). At 90 days, the top five significantly enriched GO terms were cell periphery, developmental process, anatomical structure development, external encapsulating structure, and cell wall (Figure 4C). At 120 days, the top five significantly enriched GO terms were response to stress, response to external stimulus, external encapsulating structure, cell wall, and response to nutrient levels (Figure 4D).

3.5. Metabolome

Significant differences in tuber metabolites were observed between the two treatments during the 30–90 days of sweet potato growth (Figure 4A). At 30, 60, 90, and 120 days, the numbers of differential metabolites between the LP and CK treatments were 407 (187 downregulated and 220 upregulated), 387 (151 downregulated and 236 upregulated), 197 (127 downregulated and 70 upregulated), and 139 (75 upregulated and 64 downregulated), respectively (Figure 4B). Across different time points, 14 metabolites were consistently downregulated (Figure 4C), and 17 metabolites were consistently upregulated (Figure 4D). The consistently downregulated differential metabolites included O-Phospho-L-serine, Phosphocholine, Guanosine-3′,5′-cyclic monophosphate, Aspartylphenylalanine, Trehalose 6-phosphate, 1,5,8-Trihydroxy-9-oxo-9H-xanthen-3-yl beta-D-glucopyranoside, 2-{1-[2-(1-benzothiophen-5-ylamino)-2-oxoethyl]cyclohexyl}acetic acid, Vindoline, HLK, Demethylzeylasteral, Glucose 1-phosphate, and Fructose 1,6-diphosphate, 3-methyl-5-oxo-5-(4-toluidino)pentanoic acid, N-[(4-hydroxy-3-methoxyphenyl)methyl]-8-methylnonanamide (Figure 4E). The consistently upregulated differential metabolites included Ornithine, 2,6-Diaminooimelic Acid, Sinapyl Alcohol, DL-Arginine, Vanillactic acid, RQH, Schisandrin C, Diaminopimelic acid, morpholine-4-carboximidamide hydrobromide, Histamine, trans-3,5-Dimethoxy-4-hydroxycinnamaldehyde, 4-Guanidinobutanoic acid, Rosavin, Argininosuccinic acid, Ethyl-.-D-glucuronide, 4-(2-((4-Cyanophenyl)amino)oxazol-5-yl)benzonitrile, and Hypoxanthine-9-beta-D-Arabinofuranoside (Figure 4F).

3.6. Association of Transcriptome and Metabolome

We analyzed the relationship between DEGs and differential metabolites across different time points. A total of 47 DEGs showed strong correlations with the differential metabolites. Among these, the metabolites 2,6-Diaminoheptanedioic Acid (38/47), Histamine (39/47), Diaminopimelic acid (37/47), and morpholine-4-carboximidamide hydrobromide (35/47) exhibited strong associations with the majority of the DEGs (Figure 5).

4. Discussion

This study provides a comprehensive analysis of the effects of phosphorus deficiency on the growth, endogenous phytohormones, metabolome, and transcriptome of sweet potato. Our integrated multi-omics analysis reveals a coordinated adaptation mechanism in sweet potato under P deficiency (Figure 6). At the transcriptional level, the sustained upregulation of PAPs (encoding acid phosphatases) and PHO1 (phosphate transporter) across all growth stages directly addresses P acquisition challenges by enhancing organic P hydrolysis and inorganic P translocation [38,39,40]. Under phosphorus deficiency, PAPs have been observed to be upregulated in potato and tomato [41,42]. Similarly, PHO1 has been observed to be upregulated in Arabidopsis and soybean [43,44]. Concurrently, metabolomic profiling showed consistent downregulation of phospho-compounds (e.g., O-Phospho-L-serine, Glucose-1-phosphate; Figure 4E), indicating resource reallocation from P-intensive metabolites. These changes triggered hormonal rebalancing: reduced ABA levels likely mitigate growth suppression under stress [45,46,47], while dynamic IAA fluctuations may modulate root architecture for P foraging [48,49,50]. Crucially, the correlation network (Figure 5) highlights that key DAMs like 2,6-Diaminoheptanedioic acid (a lysine precursor) and Histamine strongly co-expressed with PAPs, suggesting amino acid metabolism supports P-starvation responses. The distinct timing of metabolic (30-day peak) versus transcriptional (60-day peak) responses reflects a hierarchical adaptation strategy. The early metabolic shifts represent immediate biochemical adjustments to conserve inorganic phosphate (Pi), evidenced by rapid depletion of P-containing metabolites (e.g., Phosphocholine; Figure 4E). In contrast, the delayed transcriptional surge aligns with the storage root rapid expansion stage—a developmental checkpoint where sustained P deprivation activates systemic genetic reprogramming. This phased response mirrors P-stress kinetics in Arabidopsis and maize, where metabolic changes precede transcriptional reorganization by 2–4 weeks [51,52].

Our results demonstrate that LP conditions significantly impair both above- and below-ground biomass, highlighting the critical role of phosphorus in supporting plant growth. Notably, while tuber number remained unchanged, the reduction in tuberous root diameter under LP treatment indicates that phosphorus deficiency primarily affects tuberous root expansion rather than formation. The significant ABA reduction under P deficiency (Figure 1B) aligns with its role as a stress-signaling molecule [53,54]. Lower ABA may promote carbon flux toward secondary metabolites like Sinapyl Alcohol (lignin precursor; Figure 4F), explaining increased tuber lignin content at 30/120 days (Figure 1C). This metabolic shift enhances structural integrity under nutrient stress [55,56], while transcriptional upregulation of cell wall-related GO terms at 90 days (Figure 3C) further supports this adaptation. Conversely, cytokinin depletion (TZR, IPA) correlates with growth retardation [57,58,59], consistent with biomass reduction (Figure 1A).

The observed alterations in endogenous phytohormones, particularly the significant reductions in ABA and zeatin riboside (ZR) levels, suggest that phosphorus deficiency disrupts the hormonal balance essential for plant development and stress responses. The increase in IAA levels at certain growth stages further complicates this relationship, indicating a potential compensatory mechanism in response to nutrient stress. These findings align with previous research that emphasizes the importance of phytohormones in mediating plant responses to nutrient availability [5,13,60], and extend this understanding by demonstrating how hormonal shifts are mechanistically linked to metabolic reprogramming (e.g., lignin accumulation) and transcriptional changes in cell wall pathways. The inverse responses of cellulose and lignin reflect strategic resource reallocation under P deficiency. Reduced cellulose synthesis (Figure 1C) aligns with growth suppression, conserving carbon and energy for P-scavenging processes like organic acid exudation [61,62]. Conversely, elevated lignin accumulation serves dual functions: (1) Structural compensation. Enhanced lignification maintains tuber mechanical strength despite cellulose loss, critical for pathogen defense when P-deficiency compromises innate immunity [63,64]. (2) Metabolic sink. Lignin biosynthesis redirects phenylalanine flux from respiratory pathways, reducing ROS generation while consuming excess carbon [65]. Research on tuberous root development has revealed a competitive mechanism between lignification and starch synthesis [66]. Excessive lignification inhibits the expansion of sweet potato roots, leading to the formation of pencil roots or fibrous roots [66]. Therefore, the over-accumulation of lignin is a key factor inhibiting tuberous root expansion under phosphorus deficiency.

Our transcriptomic analysis revealed a substantial number of differentially expressed genes (DEGs) across the growth period, with a notable peak at 60 days. This striking peak coincides with two critical physiological events: (1) the storage root rapid expansion stage, revealing a stage-specific developmental vulnerability; and (2) significant fluctuations in cytokinin metabolites (IPA, TZR and ZR), suggesting a hormone–transcriptome interaction during this transitional phase. Enriched GO terms like “response to biotic stimulus” (Figure 3B) imply crosstalk between P-deficiency and pathogen defense–a trade-off potentially compromising stress resilience [67,68,69]. Notably, the downregulation of Trehalose-6-phosphate (a sugar signaling molecule; Figure 4E) during this phase may disrupt carbon partitioning [70,71], contributing to reduced tuber diameter at later stages (Figure 1A). The enrichment of DEGs in pathways related to stress response and nutrient signaling underscores the molecular mechanisms by which sweet potato responds to phosphorus deficiency. The consistent upregulation of specific DEGs across multiple time points indicates a robust transcriptional response aimed at mitigating the effects of nutrient stress [72,73]. This aligns with findings from other studies that have shown how plants activate stress-responsive genes to cope with nutrient limitations [74,75].

Metabolomic profiling further elucidated the biochemical changes associated with phosphorus deficiency. The consistent downregulation of several phosphorous-related metabolites suggests a reprogramming of metabolic pathways that may prioritize survival overgrowth under nutrient-limited conditions [76]. The upregulation of certain metabolites, such as ornithine and sinapyl alcohol, may indicate shifts towards alternative metabolic pathways that could enhance stress tolerance [77,78,79]. Specifically, ornithine accumulation (Figure 4F) may fuel polyamine synthesis for antioxidant protection [80,81,82], while sinapyl alcohol elevation directly supports lignin biosynthesis (Figure 1C). These metabolic adjustments are crucial for maintaining cellular functions and supporting growth under adverse conditions [77,83,84].

The interplay between transcriptomic alterations and metabolomic variations underscores the complexity of plant phosphorus deficiency responses. The observed associations with phosphate transporters and acid phosphatases suggest these metabolites may modulate phosphorus metabolism by interfering with uptake and remobilization pathways—a mechanism warranting further investigation. Furthermore, we synthesized all our results into a sequential adaptation cascade: (1) P deficiency rapidly induces PAPs/PHO1; (2) Phospho-metabolite depletion signals ABA reduction; (3) Lower ABA releases growth inhibition, allowing IAA-mediated root remodeling; (4) Amino acid accumulation (Ornithine, Diaminopimelic acid) fuels antioxidant synthesis; (5) Lignin/cellulose adjustments maintain tuber integrity. This cascade prioritizes survival overgrowth, explaining unchanged tuber number despite biomass loss. This multifaceted approach provides insights into the adaptive strategies employed by sweet potato, which could inform breeding programs aimed at enhancing phosphorus use efficiency. These findings emphasize the need for targeted strategies to improve phosphorus availability and utilization in sweet potato cultivation, particularly in phosphorus-limited soils. Future research should focus on exploring genetic and agronomic interventions that can enhance sweet potato resilience to nutrient stress, ultimately contributing to sustainable agricultural practices and food security in the face of global challenges. Although sand culture provides valuable insights into the direct effects of phosphorus on plant growth, the role of mycorrhizal fungi in soil environments may lead to more efficient phosphorus acquisition under field conditions. Therefore, future research could consider conducting phosphorus deficiency experiments in more complex soil environments to explore how plants adapt to fluctuating nutrient availability in natural systems under plant–soil–microbe interactions, which would provide a more comprehensive understanding.

5. Conclusions

This study demonstrates that phosphorus deficiency significantly impacts the growth, hormonal balance, metabolome, and transcriptome of sweet potato. While low phosphorus conditions reduced both above- and below-ground biomass, they did not affect tuber number, indicating a complex response to nutrient stress. The alterations in endogenous phytohormones, particularly the fluctuations in ABA and IAA levels, suggest that hormonal regulation plays a crucial role in mediating plant responses to phosphorus deficiency. Transcriptomic analysis revealed a substantial number of differentially expressed genes associated with stress response and nutrient signaling pathways, highlighting the molecular mechanisms underlying these physiological changes. Moreover, the metabolomic shifts observed in key metabolites indicate a reprogramming of metabolic pathways in response to phosphorus stress, which may further influence plant resilience and adaptation. Collectively, these findings underscore the intricate interplay between growth, hormonal regulation, metabolic processes, and gene expression in sweet potato under phosphorus-deficient conditions. Understanding the intricate interplay is essential for developing strategies to enhance sweet potato cultivation in low-phosphorus soils, ultimately contributing to sustainable agricultural practices and improved food security.