Ethylene-mediated regulation of root responses to deficient-phosphate stress based on transcriptomic and metabolomic analyses in alfalfa

Ethylene-mediated regulation of root responses to deficient-phosphate stress based on transcriptomic and metabolomic analyses in alfalfa | 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 Ethylene-mediated regulation of root responses to deficient-phosphate stress based on transcriptomic and metabolomic analyses in alfalfa Zhenyi Li, Na Guo, Jiarong Li, Xiaotong Duan, Dun Ao, Fang Tang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9350064/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Phosphorus is an essential nutrient for plant growth and development. However, the deficient available phosphorus in soil has become a critical factor limiting the improvement of forage yield and quality. Although ethylene is known to participate in plant responses to phosphorus starvation stress, its regulatory roles in alfalfa ( Medicago sativa ) remain poorly understood. In this study, the alfalfa roots were used to investigate the morphology and physiological traits, gene expression, and metabolite profiles after seven days of treatments under normal phosphorus (NP), deficient phosphate (DP) and DP supplemented with 1, 10, and 100 µM of the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC). The results showed that ACC significantly inhibited alfalfa root growth under DP conditions, reducing total root length, while concurrently stimulating root hair formation. ACC promoted the accumulation of starch, sucrose, and organic acids, and altered the levels of hormones such as abscisic acid (ABA), gibberellin (GA), and salicylic acid (SA). Transcriptome analysis identified 761, 2142, 2488, and 2607 differentially expressed genes (DEGs) in TDP, TACC1, TACC10 and TACC100 groups compared with TNP, respectively. ACC induced the expression of genes such as the glycerol-3-phosphate transporter , purple acid phosphatase , phosphate transporters . A total of 926 significantly differentially accumulated metabolites (DAMs), with a total of 266, 378, 504, and 570 identified in the groups of DP, ACC1, ACC10 and ACC100, respectively, versus NP conditions. ACC induced the accumulation of metabolites such as sucrose in the carbohydrate category, while it suppressed the accumulation of fatty acyls, sphingolipids, and the hormones ABA and SA. Integrated transcriptome and metabolome analysis revealed that DP and ACC co-regulated metabolic pathways including starch and sucrose metabolism, glutathione metabolism, flavonoid biosynthesis, fatty acid degradation, and hormone signal transduction. Ethylene also specifically induced glycolysis, the citrate cycle, tryptophan metabolism, and inositol phosphate metabolism in response to DP stress. These findings will provide a theoretical framework for improving phosphorus utilization efficiency in alfalfa. Medicago sativa Deficient phosphate Ethylene Phosphorous starvation response Hormone signal transduction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Approximately 5.7 billion hectares of land worldwide are characterized by phosphorus (P) deficiency, with plant-available phosphorus concentrations in soil being less than 10 µmol L − 1 (Batjes 1997 ), far below the threshold required for optimal crop growth. Due to its low mobility, phosphate reaches plant roots primarily through diffusion. Phosphate is readily fixed by calcium, iron, aluminum, and soil clay particles, forming insoluble phosphorus compounds or organic phosphorus (Po). As a result, despite high total P levels, only about 6% (1.5%~11%) is directly accessible to plants, while the remainder is present in the soil in unavailable forms (Lopez-Arredondo et al., 2014 ; Menezes-Blackburn et al., 2017 ). Traditional agricultural systems have relied on high fertilizer inputs to achieve high crop yields and quality. However, this increasing demand for phosphate fertilizers has led to issues such as soil degradation, eutrophication of aquatic ecosystems, and accelerated depletion of finite phosphate rock reserves (George et al., 2016 ). In response to these issues, advancements in science and technology are transforming conventional agricultural practices to promote sustainability and resource conservation (George et al., 2016 ). Phosphorus is an essential structural component of various biological macromolecules such as DNA, RNA and phospholipids. P is extensively involved in processes including plant photosynthesis, respiration, energy metabolism, and signal transduction (Chiou and Lin 2011 ). Under phosphorus starvation stress, plants exhibit slow growth, stunted stature, and impaired development. Plants have developed extremely complex signaling networks to maintain internal phosphorus homeostasis, primarily involving strategies that reduce phosphorus consumption and enhance exogenous phosphorus acquisition (Lin et al., 2014 ). Prominent adaptive responses of plants under deficient-phosphorus conditions are manifested through modifications in root system architecture and expansion of root surface area (Liu 2021 ). Upregulation of phosphate transporters facilitates increased phosphorus uptake by plants under deficient-phosphorus conditions (Fan et al., 2013 ; Liu et al., 2008 ; Wang et al., 2022b ). Plants also solubilize insoluble phosphorus in the rhizosphere by secreting organic acids such as citric acid, malic acid, oxalic acid, succinic acid, and acetic acid from their roots (Wang et al., 2018 ). As well as acid phosphatases and ribonucleases to facilitate the decomposition of inorganic phosphorus (Pi) and Po (Lopez-Arredondo et al., 2014 ; Zhang et al., 2014 ). Symbiotic associations with arbuscular mycorrhizal fungi (AMF) further augment phosphorus acquisition (Chiu and Paszkowski 2019 ). To facilitate the internal recycling of phosphorus, plants utilize galactolipids and sulfolipids to replace phospholipids within membrane systems under low-phosphorus conditions and maintain their functions (Liu 2021 ). Thereby releasing phosphorus for utilization in other metabolic processes. Ethylene is an important mediator of plant responses to phosphorus deficiency (Lei et al., 2011 ; Roldan et al., 2013 ). Conversely, phosphorus deficiency not only promotes ethylene synthesis but also increases root sensitivity to ethylene (Nagarajan and Smith 2012 ). Under low-phosphorus conditions, ethylene content significantly increases in the roots of common bean ( Phaseolus vulgaris ), white lupin ( Lupinus albus ), and Medicago falcata (Li et al., 2009 ; Neumann 2015 ). Ethylene participates in both local and systemic signal responses. The ethylene signaling pathway interacts with local response pathways to coordinately regulate root development in Arabidopsis and other plants (Crombez et al., 2019 ; Neumann 2015 ; Song and Liu 2015 ; Song et al., 2016 ). The ethylene precursor 1-Aminocyclopropane-1-carboxylic acid (ACC) strongly promotes lateral root formation in Trifolium repens under sufficient phosphorus conditions (Dinh et al., 2012 ). Whereas ethylene inhibitors reduce root hair density and length in Arabidopsis under low-phosphorus stress (Song and Liu 2015 ). In M. falcata , ethylene enhances phosphorus acquisition by increasing root acid phosphatase (ACP) activity and inducing the expression of phosphate transporter genes under deficient phosphorus conditions (Li et al., 2011 ). Ethylene perception is mediated by a family of receptors located on the endoplasmic reticulum, namely ethylene receptor 1 ( ETR1 ), ethylene response sensor 1 ( ERS1 ), ETR2 , ERS2 , and ethylene-insensitive 4 ( EIN4 ). Upon ethylene binding, these receptors become inactivated, leading to the suppression of constitutive triple response 1 (CTR1) kinase activity (Wen et al., 2012 ). This suppression enables EIN2 to relay the signal to the transcription factors EIN3/EIN3-Like 1 ( EIL1 ) and ethylene response factors ( ERFs ). ERFs inhibit the translation of EIN3-binding F-box protein 1 ( EBF1 ) and EBF2 , ultimately promoting the accumulation of EIN3/EIL1 proteins, thereby triggering a series of ethylene-induced plant growth and developmental (Binder 2020 ; Li et al., 2015 ). Core genes involved in phosphorus starvation response (PSR) are highly conserved across plant species and are primarily regulated by a signaling network centered on phosphate starvation response 1 ( PHR1 )/ SPX (Wang et al., 2026 ; Wang et al., 2021 ; Wang et al., 2024 ). ACC induce the expression of PHR1 , a corer regulator of the PSR, which subsequently modulates a series of genes involved in phosphorus uptake, transport, and PSR (Chiou and Lin 2011 ; Nagarajan and Smith 2012 ). Ethylene also modulates the expression of high-affinity phosphate transporters ( PHT ). In the ethylene-insensitive mutant ein2-5 , the Pht1;1 and Pht1;4 are reduced under low-phosphorus conditions (Lei et al., 2011 ). Under phosphorus sufficiency, ACC induces the expression of the phosphate transporter genes MfPT1 ( phosphate transporter 1 ) and MfPT5 . Conversely, ethylene synthesis inhibitors aminoethoxyvinylglycine (AVG) and Co²⁺ suppress the phosphorous starvation induced expression of these transporter genes (Li et al., 2011 ). Alfalfa ( Medicago sativa ), a globally important legume forage, is valued for its high yield and superior nutritional quality and is cultivated extensively worldwide (Chen et al., 2020 ; Cota-Ruiz et al., 2020 ). Although previous studies have implicated ethylene in the PSR processes of M . falcata and alfalfa (Li et al., 2009 ; Li et al., 2022a ). However, the specific regulatory mechanisms through which ethylene modulates the PSR in alfalfa remain largely unresolved. This study aims to elucidate the response mechanisms and regulatory pathways to DP stress in alfalfa through an integrated analysis of root morphology and physiological traits under ethylene treatment, with metabolomic and transcriptomic analyses. The findings will provide important insights into improving phosphorus utilization efficiency (PUE) in alfalfa, which will help reduce fertilizer inputs, alleviate the depletion of phosphorus resources, and achieve sustainable phosphorus use in agricultural systems. 2. Materials and methods 2.1 Cultivation and treatment of alfalfa plants The alfalfa ( Medicago sativa ‘Zhongmu No.3’) used in this study is a cultivated variety bred by the Institute of Animal Science of Chinese Academy of Agricultural Sciences (CAAS), Beijing, China. Alfalfa seeds were kindly provided by Yang Lab in the Institute of Animal Science of CAAS. The seeds were germinated under dark conditions at 25°C. Seedlings bearing their first true leaf were transferred to the Hoagland nutrient solution. After 7 days of growth, uniformly developed seedlings were selected and subjected to either normal-phosphate (NP, 1000 µM KH 2 PO 4 ) or deficient-phosphate (DP, 100 µM KH 2 PO 4 ) treatments. Simultaneously, seedlings were also treated with 1, 10, and 100 µM ACC (designated as ACC1, ACC10, and ACC100, respectively) under DP treatment. Potassium sulfate was added to the DP nutrient solution to maintain potassium levels equal to those in the NP treatment. The seedlings were cultivated in a greenhouse under controlled conditions: a 14 h/10 h day/night cycle at 24°C/22°C, 60% relative humidity, and a light intensity of 250 µmol/m 2 ·s. 2.2 Determination of morphology, physiological, and biochemical parameters of alfalfa roots After 7 days of cultivation under different treatments, changes in root morphological and physiological indicators associated with PSR were assessed in laboratory. Root architecture traits, including total root length, surface area, and lateral root number, were analyzed using the WinRHIZO root analysis system (v.5.0, Regent Instruments, Canada). Root hairs were observed using a microscope (BX41, Olympus, Japan). Fresh root samples were collected and stored at -80°C temperature freezer for subsequent physiological and biochemical analyses. Starch and sucrose contents were determined using corresponding assay kits (A148-1-1, A099-1-1, Nannjing Jiancheng bioengineering Institute, China). Catalase (CAT) and ACP activities were assayed using commercial kits (A007-1-1, A060-2-1), respectively. For total nitrogen and phosphorus contents determination, plant tissues were oven‑dried at 65°C to constant weight. The total phosphorus (TP) content was determined using the phosphomolybdic acid colorimetric method (Ames 1966 ), and total nitrogen content was quantified using the Kjeldahl method (Chen et al., 2019 ). All the above parameters were assessed in four biological replicates. Succinic acid and malic acid were quantified using high-performance liquid chromatography (HPLC). Analysis was performed using an HPLC instrument (e-2695, Waters Corp., USA). The detection conditions were as follows: mobile phase A was 0.1% phosphoric acid; mobile phase B was acetonitrile; column temperature was maintained at 35 ± 2℃; detection wavelength was 210 nm; injection volume was 1 µL; flow rate was 0.8 mL/min. Indole-3-acetic acid (IAA) content was determined using liquid chromatography-mass spectrometry (LC-MS). A 100 mg sample was homogenized in 500 µL of 0.02 mol/L NaH₂PO₄ (pH 2.2). The mixture was subjected to ultrasonic disruption, and the filtrate was collected for analysis. LC-MS analysis was performed (TQ-S, Waters Corp., USA), with the mobile phase A consisting of 0.1% formic acid, and the mobile phase B consisting of acetonitrile. The injection volume was 2 µL, the column temperature was 30°C, and the flow rate was 1 mL/min. 2.3 Transcriptome analysis After 7 days of DP and different ACC treatments, alfalfa seedling roots were collected for metabolite profiling and gene expression analysis. Total RNA was extracted using TRIzol reagent. cDNA libraries were subsequently constructed and sequenced using the Illumina NovaSeq X Plus system (2×150 bp). The obtained raw data were processed with Fastp ( https://github.com/OpenGene/fastp ), and the resulting clean reads were mapped to the alfalfa ( Medicago sativa L. cv. Zhongmu NO.1) reference genome ( https://figshare.com/articles/dataset/Medicago_sativa_genome_and_annotation_files/12623960?file=23754059 ) using HISAT2 (Shen et al., 2020 ). Unigenes were then annotated using the Gene Ontology (GO, http://www.geneontology.org ), KEGG ( http://www.genome.jp/kegg/ ), and NCBI non-redundant ( ftp://ftp.ncbi.nlm.nih.gov/blast/db/ ) protein databases. Transcript abundance was quantified using RSEM to obtain transcripts per million reads (TPM) (Zhou et al., 2016 ). The compared group contained TDP vs TNP (DP vs NP), TACC1 vs TNP (1 µM ACC vs NP), TACC10 vs TNP (10 µM ACC vs NP) and TACC100 vs TNP (100 µM ACC vs NP). Differentially expressed genes (DEGs) were identified using the criteria log|(fold change)| > 1 and p < 0.05, followed by functional enrichment analysis. 2.4 Detection and analysis of metabolites in alfalfa roots The root samples used for transcriptomics were also subjected to metabolomics analysis. Metabolite extraction and analysis were performed following previously described methods, with six biological replicates for each treatment. Sample preparation for metabolomics analysis and subsequent bioinformatics analysis were performed by Shanghai Majorbio Bio-Pharm Technology Co., Ltd. according to standard protocols. Metabolite extracts were analyzed on a liquid chromatography-mass spectrometry (LC/MS) instrument (Q-Exactive HF-X, Thermo Scientific, USA) (Xie et al., 2019 ). Raw data were imported into the metabolomics processing software ProgenesisQI v3.0 (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment. The MS and MS/MS mass spectrometry data were matched against public metabolite databases, including HMDB ( http://www.hmdb.ca/ ) and Metlin ( https://metlin.scripps.edu/ ), as well as the Majorbio in-house database, to obtain metabolite identification information. Data preprocessing included retention variables with non-zero values in at least 80% of samples within at least one group, followed by imputation of missing values. The mass spectral peak areas were normalized using the sum normalization method to obtain a normalized data matrix. Significantly differentially accumulated metabolites (DAMs) were identified using variable influence on projection (VIP) values from the OPLS-DA model and student’s t-test p -values. Metabolites satisfying the criteria of VIP > 1 and p < 0.05 were selected as DAMs. The compared group contained DP vs NP, ACC1 vs NP (1 µM ACC vs NP), ACC10 vs NP (10 µM ACC vs NP) and ACC100 vs NP (100 µM ACC vs NP). Differential metabolites were mapped to metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify their associated pathways. 2.5 Validation of DEGs expression by qRT-PCR The RNA samples used for transcriptome sequencing were further utilized for qRT-PCR analysis. Reverse transcription of mRNA was performed using the cDNA Synthesis Kit (R212, Vazyme, China), following the manufacturer’s instructions. qRT-PCR was conducted using a qPCR mix reagent, and β-actin was used as the internal reference gene. Gene-specific primers were designed using Primer-BLAST ( https://www.ncbi.nlm.nih.gov/tools/primer-blast ) and are listed in Table S1 . For this analysis, each sample included three biological replicates, and each biological replicate consisted of three technical replicates. 2.6 Integrated transcriptome and metabolome analysis Multivariate regression analysis was employed to assess the correlation between metabolomic and transcriptomic data (Bylesjo et al., 2007 ). KEGG pathway enrichment was evaluated using Fisher’s exact test for both data types. The enriched pathways and biological processes were visualized. 3. Results 3.1 Effects of ethylene on alfalfa root morphology and physiological traits under DP conditions To investigate the change by which ACC influences alfalfa morphological and physiological responses to DP, seedlings were treated with different ACC concentrations under DP conditions. Treatments with different ACC concentrations showed similar effects on root growth. Specifically, total root length, root surface area, number of lateral roots, and root dry weight were all reduced relative to both NP and DP treatments (Fig. 1 A-D). Compared to the DP treatment alone, total root length under ACC1, ACC10, and ACC100 treatments decreased by 12.97%, 34.03%, and 19.81%, respectively (Fig. 1 A). However, the root hairs in the maturation zone of the root tip are significantly induced by ACC treatment, and form a bend (Fig. S1 ). Starch and sucrose serve as key metabolic and signaling molecules under DP conditions, and their concentrations were significantly increased by ACC ( P < 0.05). Compared to DP condition, sucrose content increased by 2.80-fold and 2.68-fold under ACC10 and ACC100 treatments, respectively (Fig. 1 F). Both DP and ACC treatments enhanced succinic acid accumulation. whereas only malic acid was significantly increased by ACC100 treatment. As shown in Fig. 2 G, H, compared to the DP treatment, succinic acid and malic acid contents increased by 117.78% and 58.32%, respectively, under the ACC100 treatment. Compared to the DP treatment, ACC1 and ACC10 treatments increased total P content in the roots. Interestingly, while DP stress led to a decrease in total nitrogen content, increasing concentrations of ACC treatment progressively increased total nitrogen content in alfalfa roots. Both DP and ACC treatments (except ACC1 treatment) increased the activity of ACP. ACC10 and ACC100 treatments significantly enhanced the activity of CAT, compared to DP treatment. CAT activity increased progressively with increasing ACC concentrations, and increased by 4.69%, 58.45%, and 160.56% under ACC1, ACC10, and ACC100 treatments, respectively (Fig. 1 K, I). Both DP and ACC treatments decreased the concentrations of ABA and GA 3 . Compared to the NP treatment, ABA content decreased by 25.00%, 18.75%, and 50.00% under ACC1, ACC10, and ACC100 treatments, respectively. IAA and SA concentrations showed a downward trend under DP and ACC treatments (Fig. 1 M-P). 3.2 Transcriptional profiling of alfalfa roots under DP conditions with ethylene treatment 3.2.1 Overview of transcriptome data A total of 15 cDNA libraries were constructed, comprising three biological replicates per treatment, with each library yielding 42.42 to 53.09 million raw reads. The sequencing data have been deposited in the NCBI database (PRJNA1419934). Each library generated more than 42.06 million clean reads, after filtering out low-quality sequences. Clean bases ranged from 6.26 to 7.84 Gb, with Q20 and Q30 values exceeding 98.47% and 95.25%, respectively (Table S2 A). Approximately 73.73% to 76.16% of the clean reads were successfully mapped to the alfalfa reference genome (Table S2 B), indicating satisfactory sequencing depth and quality. Gene expression levels were quantified as Transcripts Per Million (TPM). Pearson correlation analysis revealed high reproducibility of gene expression patterns among different libraries within the same treatment group, while correlation coefficients were relatively low between different treatment groups (Fig. 2 A). Principal omponent analysis (PCA) distinctly separated different treatments into different clusters (Fig. 2 B), indicating divergent gene expression patterns among different libraries. DEGs between comparison groups were identified using the screening criteria of |log2Fold Change| > 1 and false discovery rate (FDR) < 0.05. A total of 761 (639 up-regulated, 122 down-regulated), 2142 (1269 up-regulated, 873 down-regulated), 2488 (1386 up-regulated, 1102 down-regulated), and 2607 (1279 up-regulated, 1328 down-regulated) DEGs were identified in the TDP vs TNP, TACC1 vs TNP, TACC10 vs TNP, and TACC100 vs TNP comparison groups, respectively (Fig. 2 C, Fig. S2 ). Venn diagram analysis revealed 187 common DEGs shared among all four comparison groups (27 down-regulated, 160 up-regulated), and 420 DEGs common to all three ACC treatments (185 up-regulated, 235 down-regulated) (Fig. 2 D, Table S3 ). The DEGs were grouped into 10 clusters based on their expression patterns by K-means clustering (Fig. 3). Subcluster 1 (89 genes), subcluster 2 (296 genes), subcluster 5 (1055 genes), and subcluster 6 (1017 genes) exhibited positive responses relative to NP treatments, whereas subcluster 4 (1960 genes), subcluster 7 (36 genes), subcluster 8 (34 genes), and subcluster 9 (21 genes) showed negative responses. ACC treatment modulated the expression of key genes associated with DP responses. ACC consistently induced the expression of glycerol-3-phosphate transporter 1 and purple acid phosphatase across all concentrations. However, only the ACC1 upregulated the phosphate transporter PHO1 , SPX domain-containing protein , low affinity inorganic phosphate transporter 1 and inorganic phosphate transporter 1–4 involved in phosphorus uptake. ACC10 induced glucose-6-phosphate/phosphate translocator 2 ( GPT2 ) (Table S3 ), a gene that plays a crucial role in metabolic connectivity between the plastid stroma and the cytosol by facilitating the exchange of phosphate with phosphorylated intermediates. In contrast, ACC suppressed the expression of the PHO1 and aluminum-activated malate transporter 12 . Furthermore, although DP induced the expression of mitochondrial phosphate carrier protein 3 , this induction was absent in ACC‑treated plants (Table S3 ). 3.2.2 GO classification and KEGG pathway enrichment analysis of DEGs GO classification analysis was performed to characterize the functional categories of DEGs. GO categories were assigned into three major contained molecular function (MF), cellular component (CC), and biological process (BP). The MF category enriched terms included nucleoside phosphate binding, transferase activity, transferring phosphorus-containing groups, and protein kinase activity (Fig. 4A). The CC terms included plasma membrane, membrane-bounded organelle, intracellular organelle. And BP terms included catabolic process, defense response, biosynthetic process, phosphorus metabolic process, and regulation of cellular process, among others (Fig. 4A). Functional enrichment analysis was performed on the DEGs identified from different compared groups in alfalfa roots under DP conditions with ACC treatment. Within the top 30 enriched KEGG pathways, a substantial proportion of DEGs from the union set were associated with flavonoid biosynthesis, phenylpropanoid biosynthesis, plant hormone signal transduction, MAPK signaling pathway, among others (Fig. 4B). Furthermore, KEGG pathways commonly enriched under both DP and ACC treatment included starch and sucrose metabolism, fructose and mannose metabolism, glycerophospholipid metabolism, nitrogen metabolism, flavonoid biosynthesis, glutathione metabolism, plant hormone signal transduction. In contrast, the pathways specifically induced by ACC treatment included the pentose phosphate pathway, glycolysis, inositol phosphate metabolism, the citrate (TCA) cycle, pyrimidine metabolism, glycosphingolipid biosynthesis and tryptophan metabolism, among others (Fig. 4B). Additionally, to validate the reliability of the RNA-seq results, 9 DEGs were randomly selected from the TNP, TDP, TACC1, TACC10, TACC100 group for qRT-PCR analysis. As the results showed that qRT-PCR expression patterns including SPX domain-containing protein (MsG0180004121.01), inorganic phosphate transporter 1–4 (MsG0780040738.01), ethylene-responsive transcription factor 2 (MsG0780041015.01), probable WRKY transcription factor 40 (MsG0280008324.01), sucrose synthase 5-like isoform X5 (MsG0280006583.01) and other genes (Fig. S3 ), were largely consistent with RNA‑seq data. The transcriptional profiles of the selected DEGs exhibited similar trends, confirming the reliability of the RNA-seq data. 3.3 Metabolite profiling of roots under DP conditions with ethylene treatment 3.3.1 Overview of metabolite profiling The changes in alfalfa root metabolite profiles under DP and ACC treatments were also analyzed, with six biological replicates per treatment. Correlation heatmaps revealed substantial differences in metabolite composition and abundance across treatments (Fig. S4 ). PCA separated the 30 samples (encompassing six independent replicates) along the first two principal components, which accounted for 33.7% (PC1) and 10.5% (PC2) of the total variance, respectively (Fig. 5A). Samples within the same treatment clustered closely, indicating high reproducibility and providing a reliable basis for downstream analyses. Based on the criteria of VIP ≥ 1 and fold-change ≥ 2 or ≤ 0.5, a total of 926 significantly DAMs were identified (Table S4 ). 3.3.2 Expression patterns of DAMs Based on HMDB superClass annotations, the 926 DAMs were categorized into 13 classes (Fig. 6 A). Among these DAMs, 266 were identified in the DP vs NP comparison group, comprising 214 up-regulated and 52 down-regulated metabolites. 378 DAMs were identified in the ACC1 vs NP comparison group, comprising 285 up-regulated and 95 down-regulated DAMs. 504 DAMs were identified in the ACC10 vs NP comparison group, comprising 310 up-regulated and 194 down-regulated DAMs. And 570 DAMs were identified in the ACC100 vs NP comparison group, comprising 335 up-regulated and 235 down-regulated DAMs (Fig. 5B, C; Fig. S5 ). Cluster analysis revealed distinct metabolite response patterns among DAMs across the five treatment comparisons (Fig. S6 ). The results showed that the subcluster trend plots indicated that subcluster 1 DAMs accumulated to higher levels during ACC treatments compared to the NP and DP treatments. Conversely, subcluster 2, subcluster 4, and subcluster 5 showed opposite trends, with DAMs significantly decreasing following ACC treatment (Fig. S6 ). Subcluster 1 DAMs predominantly included carboxylic acids and derivatives, flavonoids, isoflavonoidsand and other related compounds (Table S5 ). Subcluster 2, 4, and 5 DAMs were dominated by fatty acyls, organooxygen compounds, sphingolipids and others. ACC treatment increased the accumulation of specific amino acids such as L-ornithine, L-Cysteine, L-Proline and others. While carbohydrates, including sucrose, trehalose, sucrosewere and others also elevated. Notably, significant changes were observed in the levels of lipids and flavonoids under ACC treatment (Table S5 ). Specifically, lipid-related metabolites such as malonic acid, Phosphatidylethanolamine (Pe), Lysophosphatidylcholine (Lysopc), glycerophosphoinositol, flavonoid glycosides including poncirin, liquiritin, baicalin, fatty acids such as undecylenic acid, traumatic acid, traumatin and the hormones cotained ABA and SA exhibited significant alterations in response to ACC treatment. 3.3.3 KEGG enrichment of DAM‑associated regulatory pathways To identify potential regulatory pathways associated with DAMs, KEGG enrichment analysis was performed. In the DP vs NP comparison, the top 30 significantly enriched KEGG pathways included nucleotide metabolism, flavonoid biosynthesis, zeatin biosynthesis, fructose and mannose metabolismitrate, TCA cycle and so on. In ACC1 vs NP, the top 20 enriched pathways included flavonoid biosynthesis, alanine, aspartate and glutamate metabolism, plant hormone signal transduction, sphingolipid metabolism, zeatin biosynthesis and others. In ACC10 vs NP, the top 20 pathways included favonoid biosynthesis, nucleotide metabolism, galactose metabolism, plant hormone signal transduction, glycerophospholipid metabolism and others. In ACC100 vs NP, the top 20 pathways included flavonoid biosynthesis, glutathione metabolism, plant hormone signal transduction, cyanoamino acid metabolism and others (Fig. 6 B; Table S6 ). Flavonoid biosynthesis, isoflavonoid biosynthesis, valine, leucine and isoleucine biosynthesis, pyrimidine metabolism, glycine, serine and threonine metabolism, zeatin biosynthesis, galactose metabolism, glycerophospholipid metabolism was significantly enriched across all four comparison groups, suggesting its central role in the regulatory response network under combined DP and ethylene stress. Furthermore, pathways including glycosylphosphatidylinositol (GPI)-anchor biosynthesis, tryptophan metabolism, biotin metabolism, arginine biosynthesis, arachidonic acid metabolism, plant hormone signal transduction, sphingolipid metabolism and others were implicated in the regulatory response networks under varying ACC concentrations in addition to the DP treatment (Fig. 6 B, Table S6 ). 3.4 Integrative analysis of regulatory pathways involving DEGs and DAMs To deeply explore the relationships between transcriptomic and metabolomic responses, a co-expression network analysis integrating both datasets was conducted. Procrustes result based on both datasets showed tight clustering of samples within each treatment and clear separation between treatments (Fig. S7 A). KEGG pathway analysis indicated that these DEGs and DAMs were co-enriched in pathways including flavonoid biosynthesis, isoflavonoid biosynthesis, and phenylpropanoid biosynthesis (Fig. S7 B-E). Both common and specific regulatory pathways were identified under ACC treatment in conjunction with DP conditions, indicating that ethylene modulates multiple aspects of the DP response. Common regulatory pathways shared by DP and ACC treatments included starch metabolism, the TCA cycle, glycolysis, glycerophospholipid metabolism, glycerolipid metabolism, glutathione metabolism, and flavonoid biosynthesis (Fig. S7 B-E). In TCA cycle, citrate content significantly decreased under DP and ACC treatments (Fig. 7 A). The expression of phosphoenolpyruvate carboxykinase (ATP) 1 ( PEPCK1 ) and citrate synthase ( CS ) was downregulated, while aconitate hydratase ( ACO ) only increased under ACC100 treatment. malate dehydrogenase ( MDH ) expression was either increased or decreased under ACC treatment (Fig. 7 A). The inositol phosphate pathway was specifically enriched under ACC treatment. In this pathway, the expression of type I inositol-1,4,5-trisphosphate 5-phosphatase ( IP3P1 ), inositol-tetrakisphosphate 1-kinase 3 ( ITPK3 ), inositol 1,3,4-trisphosphate 5/6-kinase ( ITPK5/6 ), and inositol oxygenase 2 ( IOX2 ) was suppressed only by ACC treatment, whereas phosphoinositide phospholipase C6 ( PLC6 ), type IV inositol polyphosphate 5-phosphatase 9 ( IP5P9 ), and phosphatidylinositol 4-phosphate 5-kinase 1 ( PIP5K1 ) were induced exclusively under ACC treatment (Fig. 7 B). The results indicate that ethylene is involved in the regulation of the inositol phosphate pathway under DP conditions. The hormone signaling pathways were enriched under DP and ACC treatments. The synthesis of ABA and SA was inhibited by ACC. (Fig. 8A). The auxin signalling pathway was suppressed under ACC treatment, with auxin transporter ( AUX1 ), transport inhibitor response 1/ABA-responsive element-binding factor ( TIR1/AFB ), Auxin/auxin-induced protein ( AUX/IAA ), auxin response factor ( ARF ), and indole-3-acetic acid-amido synthetase ( CH3 ) being inhibited specifically under ethylene treatment (Fig. 8A). However, auxin-mediated signal transduction involves the induction of mitogen-activated protein kinase 3/6 ( MPK3/6 ) and the inhibition of the ABA response. And the key genes such as ransmembrane kinase 1/4 ( TMK1/4 ), MPK3/6 and ABA insensitive 1/2 ( ABI1/2 ) were induced by DP and ACC. The cytokinin pathway was induced by ACC, with key genes histidine-containing phosphotransfer ( AHP ) and type-A two-component response regulator ( A-ARR ) showing induced expression upon ACC treatment (Fig. 8C). ACC suppressed the ABA signaling pathway under DP conditions, with decreasing ABA content, and inhibited genes such as abscisic acid receptor PLR/PYL ( PLR/PYL ) and abscisic acid-insensitive ( ABF ) (Fig. 8D). Exogenous ACC treatment affected the transduction of the ethylene signaling pathway, with key genes such as ethylene receptor ( ETR ), EIN3-binding F-box protein ( EBF ) and ethylene response factor1/2 ( ERF1/2 ) showing increased expression (Fig. 8E). Both DP and ACC influenced the jasmonic acid (JA) signaling pathway by promoting the jasmonate ZIM-domain protein ( JAZ ) and transcription factor MYC2 ( MYC2 ) (Fig. 8F). ACC reduced SA content while promoting the expression of nonexpressor of pathogenesis-related genes 1 ( NPR1 ) and pathogenesis-related protein 1 ( PR-1 ) (Fig. 8G). 3.5 Proposed regulatory model of ethylene-modulated responses to DP stress in alfalfa roots Through integrated analysis of root phenotypes, physiological traits, key metabolites, and gene expression profiles, we have identified indirect regulatory pathways potentially associated with phosphorus uptake or abiotic stress responses. These pathways can be categorized into those co-induced by DP and ACC, and those specifically induced by ACC. Both DP and ACC treatments suppressed root growth and modulated multiple metabolic pathways, including flavonoid, starch and sucrose, glutathione, fructose and mannose, and galactose metabolism, as well as fatty acid degradation and glycerolipid/glycerophospholipid metabolism (Fig. 9 ). Additionally, these treatments activated ethylene and salicylic acid (SA) signaling pathways. Furthermore, ethylene precursor application markedly accelerated several of these responses under DP deficiency stress, particularly regarding glutathione and flavonoid metabolism. ACC also triggered root hair formation and uniquely activated glycolysis, the TCA cycle, glycosphingolipid metabolism, tryptophan metabolism, pyrimidine metabolism, and inositol phosphate metabolism, while concurrently suppressing auxin and ABA signaling pathways (Fig. 9 ) 4. Discussion Although soils contain substantial phosphorus, most of it exists in forms unavailable for plant uptake (Yan et al., 2023 ). The orthophosphate that plants can directly take up is scarce in soil, making phosphorus a limiting nutrient for plant growth and development (Lopez-Arredondo et al., 2014 ). Furthermore, the plant utilization efficiency of phosphorus fertilizers is only 15%-30% (Vance et al., 2003 ). Cope with phosphorus deficiency in soil, plants enhance their phosphorus acquisition and internal utilization efficiency by modulating growth, development, and metabolic processes (Hu et al., 2025 ; Lin et al., 2009 ; Lopez-Arredondo et al., 2014 ). Therefore, elucidating the mechanisms of phosphorus uptake is of great significance for improving plant PUE. Ethylene plays a key role in regulating the local response of plant roots to phosphorus deficiency (Nagarajan and Smith 2012 ). However, the mechanisms by which exogenous ethylene participates in regulating phosphorus uptake in alfalfa roots have not been previously elucidated. This study primarily investigated the transcripts, metabolites, and regulatory pathways involved in the ethylene‑induced DP response in alfalfa roots. The DP stress response induced by ethylene can be categorized into local and systemic responses. Among these, local Pi sensing regulates the expression of a large set of PSR genes, initiating a series of adaptive responses to remodel root architecture that enhance phosphorus acquisition (Chiou and Lin 2011 ). Systemic Pi responses include the induction of high-affinity Pi transporters (Nagarajan and Smith 2012 ), which augment the acquisition capacity of available phosphorus through direct or indirect mechanisms in planta. 4.1 Effects of ethylene on root growth and phosphatase activity in alfalfa under DP conditions Ethylene signaling plays a central role in regulating plant adaptive responses to phosphorus starvation stress (Li et al., 2011 ), typically manifested as inhibition of the primary root and promotion of lateral root and root hair development (Neumann 2015 ; Roldan et al., 2013 ; Shukla 2025 ; Song and Liu 2015 ). In this study, both DP conditions and exogenous ACC inhibited root growth, with no stimulatory effect observed on roots, consistent with the effects of ethephon on wheat roots (Wang et al., 2025 ). However, ACC significantly promoted root hair formation, and this observation may be related to the specific ACC concentrations used in our study. Achieving simultaneous enhancement of both root system and root hair growth is especially important for enhancing nutrient uptake efficiency in alfalfa. EXPANSIN proteins mediate plant cell wall loosening and are key regulators of root hair initiation and elongation (Lin et al., 2011 ). In this study, the root hairs were only induced by ACC treatment. Correspondingly, the increase in root hairs enhances the absorptive surface area. And the expression of EXPANSIN -related genes was upregulated to varying degrees under ACC treatment, suggesting their induction may contribute to the dense root hair phenotype observed. Phosphate deficiency response 2 ( PDR2 ) and aluminum-activated malate transporter 1 ( ALMT1 ), in coordination with LPR1 , regulate the differentiation process of root apical meristems (Balzergue et al., 2017 ). In this study, aluminum-activated malate transporter 12/2 showed varying degrees of downregulation under ACC treatment, which may partially explain the observed inhibition of primary root growth. Auxin activates the MKK4/5-MPK3/6 signaling cascade via TMK1/4, thereby regulating cell division patterns during lateral root development (Huang et al., 2019 ). Consistent with this, our results revealed the induction of TMK1/4 and MPK3/6 expression within the MAPK signaling pathway, suggesting their potential involvement in lateral root development. Ethylene also increases the expression of phosphate transporters in the PHT1 family (Feng et al., 2017 ). The expression levels of alfalfa PHT1 family genes were significantly increased under phosphorus-deficient conditions to enhance root phosphate uptake (Li et al., 2022b ). The PHO1 protein mediates phosphate transport from roots to shoots, and loss-of-function mutants of PHO1 exhibit significantly reduced shoot phosphorus content and delayed flowering (Dai et al., 2024 ). In this study, only ACC1 induced the expression of PHO1 and PHT1;4 under DP conditions, potentially promoting phosphorus uptake, consistent with results reported by (Roldan et al., 2013 ). Ethylene downregulates the expression of the nitrate transporter gene NRT2.1 , reducing the nitrate uptake in plants under low-nitrogen conditions (Zheng et al., 2013 ). In the present study, total nitrogen content decreased under DP conditions. However, ACC application increased total nitrogen content, while the expression of the nitrogen transport-associated high-affinity nitrate transporter 2.1 was also increased. These findings suggest that ACC may simultaneously promote phosphorus and nitrogen uptake under DP conditions. Ethylene is a positive regulator of APase activity (Lei et al., 2011 ). Exogenous ACC application induces the expression of phosphorus starvation-induced ( PSI ) genes and significantly upregulates root ACP activity (Nagarajan and Smith 2012 ). Secreted APases closely associated with the root surface facilitate the mobilization and utilization of soil‑bound phosphate. Overexpression of the GmACP2 in soybean hairy roots effectively increased endogenous ACP activity and improved phosphorus use efficiency by 15.43%-24.54% (Zhang et al., 2016 ). In this study, both DP and ACC treatment increased phosphatase activity, and transcriptome data indicated that the expression of acid phosphatase-encoding genes was mostly upregulated under ACC treatment. 4.2 Regulation of DP stress by the ethylene signaling pathway Ethylene biosynthesis and signal transduction are modulated by systemic phosphorus signals, and AP2 / ERF genes may regulate the expression of PSI genes (Bustos et al., 2010 ). A critical step in ethylene signaling involves the stabilization and activation of EIN3 and EIL1 transcription factors following ethylene perception. Activated EIN3 and EIL1 bind to the promoters of ethylene-responsive genes, including ERFs , thereby activating or repressing downstream gene expression (Shukla 2025 ). The functional diversity of the ERF gene family underlies the broad spectrum of ethylene‑mediated responses, ranging from growth regulation to responses to biotic and abiotic stresses (Zhao et al., 2021 ). In the current study, the ethylene signaling pathway was significantly enriched under DP and ACC treatments. However, further analysis is required to determine how downstream ERFs interact with GCC-box motifs in the promoters of ethylene-sensitive genes. Under DP conditions, ethylene upregulates the expression of the EIN3 / EIL1 transcription factor, which in turn induces the expression of the phosphate transporter gene PHT1 , promoting the formation of longer and denser root hairs to enhance the phosphorus uptake capacity of plants (Feng et al., 2017 ). In the phosphorus signaling pathway, SPX proteins do not directly sense inorganic phosphorus signals under phosphorus starvation conditions, but instead respond to soluble inositol polyphosphates (Wild et al., 2016 ). The InsP8-SPX complex, formed by the binding of InsP8 with SPX proteins, binds to the coiled‑coil (CC) domain of PHR transcription factors to regulate phosphorus homeostasis (Ried et al., 2021 ). Reduced InsP8 content weakens the interaction between SPX1 and PHR1 , thereby activating the expression of PSI genes and promoting phosphorus uptake and accumulation in plants (Poirier et al., 2022 ). Low-phosphorus signals can induce PHR1 to activate downstream PSR genes, while the expression of certain genes regulating phosphorus uptake, transport, and starvation responses partially depends on ethylene biosynthesis and signaling changes (Liu et al., 2017 ). In this study, only ACC1 promoted the expression of the key phosphorus uptake gene encoding a putative SPX domain-containing protein , and genes involved in inositol phosphate metabolism were significantly enriched under ACC treatment. These observations suggest that ACC enhances phosphorus uptake by promoting SPX‑mediated signaling and inositol phosphate metabolism. In this study, PHR1 gene expression was not detected. However, the MYB family transcription factor PHL5 isoform X2 was induced under both DP and ACC treatments, suggesting its potential involvement in phosphorus uptake. The absence of detectable PHR1 expression may be attributable to the relatively higher phosphorus (100 µM) used in this study, to assess phosphorus-limiting conditions, which is greater than10 µM or 5 µM KH₂PO₄ used in other studies, potentially resulting in relatively low expression of DP stress-responsive genes. 4.3 Ethylene participates in the metabolism of sugars and lipids under DP conditions Under DP conditions, plants accumulate sugars and starch in leaf tissues, and the expression of PSI genes increases in response to elevated exogenous sugar levels (Nilsson et al., 2007 ). Enhanced sucrose loading in leaves and its subsequent transport from shoots to roots promote the expression of PSI genes (Lei et al., 2011 ). The findings confirm that sugars function as systemic signals in the phosphorus signaling network(Shu Yi et al., 2024 ). Phosphorus limitation leads to reduced photosynthesis, accompanied by elevated levels of sugars and starch. Beyond serving as metabolic substrates, sucrose and other carbohydrates act as signaling molecules in the PSR and are also transported from shoots to roots via the phloem (Karthikeyan et al., 2007 ). Carbohydrates can also induce the expression of other genes responsive to phosphorus starvation stress (Hammond and White 2008 ; Karthikeyan et al., 2007 ). In rice, sugars such as sucrose, trehalose, and melibiose accumulate during phosphorus starvation and actively participate in downstream signaling (Yan et al., 2022 ). In this study, only a single gene was enriched in the glycolysis pathway under DP conditions. In contrast, a large number of genes were enriched in this pathway under ACC treatment, suggesting that ACC accelerated the glycolytic flux in alfalfa roots. Our results in alfalfa root indicate that the contents of sucrose, starch, and other sugars increased, and sucrose was significantly induced by ACC, suggesting that ACC promotes sugar accumulation and accelerates the DP stress response. To cope with phosphorus starvation, plants initiate a key process of lipid remodeling. During this process, membrane phospholipids are dephosphorylated to serve as an internal phosphorus source, while galactolipids and sulfolipids are incorporated into membrane systems to maintain functional integrity (Verma et al., 2021 ). Major phospholipids involved in this process include phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), lysophosphatidylcholine (lysoPC), diacylglycerol (DAG), and triacylglycerol (TAG) (Hu et al., 2025 ; Verma et al., 2021 ). Additional responses include the substitution of phospholipids with galactolipids and sulfolipids, the activation of metabolic bypasses to conserve ATP, and the maintenance of cytosolic phosphorus homeostasis through the regulation of vacuolar phosphorus storage and release (Shu Yi et al., 2024 ). In this study, ACC accelerated the decline in PC, promoted glycerophospholipid turnover, and enhanced the synthesis of galactolipids and sulfolipids, consistent with previously reported lipid remodeling under phosphorus deficiency. Our analysis identified a greater number of genes or metabolites enriched in these pathways, suggesting that ACC accelerates sugar and lipid metabolism as part of the adaptive response to DP stress in alfalfa roots. 4.4 Ethylene participates in protective responses under DP conditions Phosphorus deficiency induces the accumulation of reactive oxygen species (ROS) in the roots of Arabidopsis seedlings (Tyburski et al., 2009 ). Low-phosphorus stress increase the activities of antioxidant enzymes in soybean roots. Ethylene enhances ROS even under phosphorus‑sufficient conditions and elevates superoxide dismutase (SOD) and ascorbate peroxidase (APX) activities to levels comparable to those under phosphorus-deprived conditions (Yang et al., 2020 ). Ethylene can regulate the activities of respiratory enzymes, including cytochrome oxidase and alternative oxidase, thereby influencing electron transport efficiency and cellular energy production (Xu et al., 2012 ). In this study, among several antioxidant enzyme activities assayed in alfalfa roots, only CAT activity showed an increasing trend under ACC treatment, and the expression of oxidase genes was significantly induced by ACC. These results suggest that ethylene‑mediated activation of ROS‑related pathways may enhance plant tolerance to DP stress. Phosphorus starvation induces the synthesis of secondary metabolites with antimicrobial and protective functions, such as flavonoids and glucosinolate (Pant et al., 2015 ), which regulate plant immunity. Low-phosphorus conditions significantly affected the flavonoid biosynthesis pathway. Various flavonoids and their regulatory genes were found to be enriched, and they have been previously reported to be tightly associated with tolerance to phosphorus-limited stress in multiple plant species (Li et al., 2022a ; Wang et al., 2026 ; Wang et al., 2022a ). Flavonoids are considered a secondary antioxidant system (Agati et al., 2012 ), typically increasing during nutrient stress to mitigate oxidative damage (Malusà et al., 2007 ). The SPX1/3-PHR2 regulatory network governing PSRs in Medicago truncatula modulates flavonoid biosynthesis, thereby recruiting nitrogen-fixing microorganisms to facilitate nitrogen acquisition (Wang et al., 2026 ). In this study, most flavonoids showed increased accumulation in alfalfa roots, potentially participating in ROS scavenging and contributing to the establishment of beneficial AMF relationships under phosphorus limitation conditions. Although our experiment was conducted under hydroponic culture conditions, the observed flavonoid increases are consistent with these known functions. The glutathione synthesis pathway in alfalfa was enriched under Pi deficiency conditions (Li et al., 2022a ), indicating its involvement in the stress response. Furthermore, under ACC treatment, an even greater number of genes were enriched in this pathway compared to those under DP treatment alone, suggesting that ACC enhances the glutathione pathway in response to DP stress. 4.5 Ethylene participates in hormone‑mediated responses to DP stress Multiple plant hormones participate in the response to phosphorus starvation (Chiou and Lin 2011 ). Under different phosphorus nutritional conditions, hormones such as strigolactones (SL), SA, JA, and ABA play distinct roles in biotic stress responses (Chan et al., 2021b ). Furthermore, the ethylene signaling pathway can interact with auxin, GA, ABA, JA and SA (Shukla 2025 ), thereby coordinately regulating plant growth and stress responses. Phosphorus starvation also induces the synthesis of defense hormones, such as SA and JA (Castrillo et al., 2017 ; Morcillo et al., 2020 ), which regulate cellular phosphorus status or modulate PSR mechanisms to enhance plant adaptability against pathogens (Chan et al., 2021a ). SA is considered a central integrator linking phosphorus starvation and immune activation (Chan et al., 2021a ). Under phosphorus-deficient conditions, plants can target genes in the JA and SA signaling pathways through PHR1 , thereby coordinating immune responses with PSR (Castrillo et al., 2017 ). Under such conditions, Arabidopsis PHR1 also interacts with the JA pathway repressor JAZ proteins and the MYC2 ( myelocytomatosis proteins 2 ) transcription factor to enhance JA signaling (He et al., 2023 ). In this study, SA and JA regulatory pathways were significantly enriched, and SA content decreased under ACC treatment. These results suggest that ACC activates defense mechanisms involving SA and JA in alfalfa roots. Auxin is the key regulator of low‑phosphorus responses in rice root. Under phosphorus deficiency, auxin transport in the root system is enhanced, promoting primary root elongation, increased lateral root length and density, and root hair formation to facilitate adaptation to low-phosphorus conditions (Ding et al., 2020 ). In this study, IAA signaling pathway and cysteine metabolism pathway were enriched. Although IAA metabolite levels did not change, it can be inferred that ACC influences the IAA signaling pathway under DP conditions. Ethylene and ABA often exhibit antagonistic interactions, with ABA suppressing ethylene-mediated growth responses under stress conditions. This antagonistic relationship helps achieve a dynamic balance between growth and stress adaptation depending on the specific environment (Yang et al., 2015 ). In this study, ABA signalling was suppressed, as evidenced by reduced ABA accumulation and downregulation of signal transduction-related genes. Furthermore, the ABA response was modulated via auxin-mediated induction of MTK1/4 and ABI1/2. Ethylene also interacts with GA to regulate growth, and the two hormones frequently exert opposing effects, such as in stem elongation and fruit ripening (Yang et al., 2015 ). The GA-DELLA signaling pathway regulates the architecture remodeling of the shoot/root system and root hair elongation under phosphorus starvation stress (Jiang et al., 2007 ). Our data on alfalfa indicate that within the GA signaling pathway, GA37 content increased in shoots, while DELLA protein accumulation increased in roots. In this study, hormone-related pathways such as IAA signaling were enriched. Notably, ABA and SA showed significant decreases, and genes involved in other hormone signal transduction pathways were detected. This directly or indirectly demonstrates the effect of ACC on these hormones under DP conditions. Furthermore, according to our findings, ABA and SA decreased across all three ACC concentrations applied, indicating that ACC inhibited ABA and SA. Although ACC appears to interact closely with several hormonal pathways under DP conditions, the precise mechanisms underlying these interactions require further in-depth investigation. 5. Conclusions This study investigated the mechanisms by which ACC regulates the response of alfalfa roots to DP stress through integrated morphological, physiological, transcriptomic, and metabolomic analyses. The results indicate that ACC remodels root system architecture under DP by inhibiting primary root elongation, while promoting root hair formation. Ethylene precursor promoted the accumulation of starch, sucrose, and organic acids, enhanced the activities of ACP and CAT, and increased total phosphorus and total nitrogen contents. Ethylene precursor induced the expression of a limited set of DP stress-responsive genes such as PHO1 and SPX genes. Both DP and ethylene precursor conditions modulated flavonoid, starch, sucrose, glutathione, fructose, mannose, galactose, glycerolipid, and glycerophospholipid metabolism, as well as fatty acid degradation. Furthermore, ethylene specifically promoted glycolysis, the TCA cycle, glycosphingolipid, tryptophan, pyrimidine, and inositol phosphate metabolism in response to DP stress. These findings provide a theoretical framework and identify key genes within ethylene signaling or metabolic pathways that may be targeted to improve PUE in alfalfa. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Zhenyi Li: Experiment design, Data collection, Writing and editing. Na Guo: Data collection and curation, Validation. Jiarong Li, Xiaotong Duan and Aodun: Investigation, Validation, Data collection and analysis,. Fang Tang, Zhiqiang Zhang and Fengling Shi: Experiment design, Writing-review. Acknowledgments This work was supported by the National Natural Science Foundation of China (32301484), Inner Mongolia Autonomous Region Natural Science Foundation General Program (2025MS03136), Grassland Talents Program of Inner Mongolia Autonomous Region, Scientific Research Foundation for Advanced Talents by Inner Mongolia Agricultural University (NDYB2022-51), The First-Class Discipline Scientific Research Program of Inner Mongolia (IMAUCXQJ2023015), Scientific Research Funding for Universities Directly under the Inner Mongolia Autonomous Region (BR22-12-07), 2025 Key Laboratory of Grass Seed Innovation and Sustainable Grassland Resource Utilization, Inner Mongolia Autonomous Region (2025KYPT0033). 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J Integr Plant Biol. 2021;63(1):102–25. Zheng D, Han X, An YI, et al. The nitrate transporter NRT2.1 functions in the ethylene response to nitrate deficiency in Arabidopsis . Plant Cell Environ. 2013;36(7):1328–37. Zhou Q, Guo JJ, He CT, et al. Comparative transcriptome analysis between low- and high-cadmium-accumulating genotypes of pakchoi ( Brassica chinensis L.) in response to cadmium stress. Environ Sci Technol. 2016;50(12):6485–94. Additional Declarations No competing interests reported. Supplementary Files Fig.S1Hairroots.jpg Fig. S1 Alfalfa root hairs under conditions of sufficient phosphate, deficient phosphate, and deficient phosphate with different concentrations of ACC treatments. FigS2VolcanoplotsofDEGsinTDPvsTNPTACC1vsTNPTACC10vsTNPTACC100vsTNP.jpg Fig. S2 Volcano plots of DEGs in TDP vs TNP (A), TACC1 vs TNP (B), TACC10 vs TNP (C), TACC100 vs TNP (D). FigS3qRTPCRvalidationofselectedgenes.jpg Fig. S3 qRT-PCR validation of selected genes. FigS4Correlationheatmapsamongdifferentsamples.jpg Fig. S4 Correlation heatmaps among different samples. FigS5VolcanoplotsofDAMsinDPvsNPACC1vsNPACC10vsNPACC100vsNP.jpg Fig. S5 Volcano plots of DAMs in DP vs NP (A), ACC1 vs NP (B), ACC10 vs NP (C), ACC100 vs NP (D). FigS6ClusteranalysisamongDAMsacrossthefivetreatmentcomparisons.jpg Fig S6 Cluster analysis among DAMs across the five treatment comparisons. FigS7IntegratedKEGGpathwayanalysisoftranscriptomicandmetabolomicdata.jpg Fig S7 IntegratedKEGG pathway analysis of transcriptomic and metabolomic data. (A) Procrustes result. (B-E) integrated KEGG pathway in DP vs NP (B), ACC1 vs NP (C), ACC10 vs NP (D), ACC100 vs NP (E). TableS1PrimersusedforqRTPCRvalidation.xlsx Table S1 Primers used for qRT-PCR validation. TableS2OverviewofRNAseqeuencingdataandcomparisonwithalfalfagenome.xlsx Table S2 Overview of RNA sequencing data and comparison with the alfalfa genome. TableS3DEGsindifferentgroups.xlsx Table S3 DEGs in different groups. TableS4differentiallyaccumulatedmetabolitesDAMsindifferenttreatments.xlsx Table S4 Differentially accumulated metabolites (DAMs) in different treatments. TableS5SubclustersofDAMsduringdifferentconcentrationofACCcomparedtotheNPtreatment.xlsx Table S5 Subclusters of DAMs during different concentration of ACC compared to the NP treatment. TableS6KEGGpathwayscoregulatedbytranscriptomeandmetabolomeintegratedanalysis.xlsx Table S6 KEGG pathways co-regulated by transcriptome and metabolome integrated analysis. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 14 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9350064","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629901944,"identity":"13bb45d4-81c8-4b87-8930-91b3286ba3f9","order_by":0,"name":"Zhenyi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACPmYGNhDNw8fAfODAhx9EaGGDaWFjYEs8OLOHGC0MEC1Aksf4MAcbMVrY2Z89+LjjsAybRM6Hwww8DPL8YgcIOizdcOaZwzxsErkbDhdYMBjOnJ1AUMsxad42qJYZPAwJBrcJamFsk/4L1pLzAEgSpYWZTZoRooWBWC1sbJK9bek8bDzPDICBLEHYL/z8x59J/GyztudnT3784cMPG3l+aQJaoKCZgUEArFKCKOUgUAe07wDRqkfBKBgFo2CEAQCFEDp1cfRnhgAAAABJRU5ErkJggg==","orcid":"","institution":"Inner Mongolia Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Zhenyi","middleName":"","lastName":"Li","suffix":""},{"id":629901945,"identity":"529d30e3-a495-409b-86f8-ba5a645e3eba","order_by":1,"name":"Na Guo","email":"","orcid":"","institution":"Inner Mongolia Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Guo","suffix":""},{"id":629901946,"identity":"7a02ad0f-bc2b-4710-86fd-cb80663dda35","order_by":2,"name":"Jiarong Li","email":"","orcid":"","institution":"Inner Mongolia Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jiarong","middleName":"","lastName":"Li","suffix":""},{"id":629901947,"identity":"2a5fe414-3987-48bb-8d2a-51e803c31dd7","order_by":3,"name":"Xiaotong Duan","email":"","orcid":"","institution":"Inner Mongolia Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Duan","suffix":""},{"id":629901951,"identity":"64f0ce47-741c-4a01-99bb-b17ef47f90e6","order_by":4,"name":"Dun Ao","email":"","orcid":"","institution":"Inner Mongolia Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dun","middleName":"","lastName":"Ao","suffix":""},{"id":629901954,"identity":"9f0545de-d8dc-44ce-9fd2-e2517ead59ef","order_by":5,"name":"Fang Tang","email":"","orcid":"","institution":"Inner Mongolia Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Tang","suffix":""},{"id":629901957,"identity":"d189587f-a33d-475c-b07d-08e11f76e4e4","order_by":6,"name":"Zhiqiang Zhang","email":"","orcid":"","institution":"Inner Mongolia Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Zhang","suffix":""},{"id":629901958,"identity":"f9899e55-2b86-4f65-a503-06ec9c858e37","order_by":7,"name":"Fengling Shi","email":"","orcid":"","institution":"Inner Mongolia Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Fengling","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2026-04-08 01:08:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9350064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9350064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108494310,"identity":"12873778-be6e-4c10-97e9-50a7dff1e953","added_by":"auto","created_at":"2026-05-05 10:03:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2578957,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in alfalfa root morphological, physiological, and biochemical traits after seven days of treatments deficient-phosphate (DP) and ethylene precursor ACC. A-P exhibits root length (A), root surface area (B), lateral root number (C), root dry weight (D), starch (E), sucrose (F), succinic acid (G), malic acid (H), total phosphorous content (I), total nitrogen content (J), acid phosphatase activity (K), catalase activity (L), Indole-3-acetic acid (IAA) content (M), abscisic acid (ABA) content (N), gibberellin 3 (GA\u003csub\u003e3\u003c/sub\u003e) content (O) and salicylic acid (SA) content (P), respectively. Error bars represent standard deviation (SD; n = 4 or 6). Different lowercase letter indicates significant difference (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig1260324.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/ed16f6a32d13d87e8cea1a50.jpg"},{"id":108494321,"identity":"2e223409-5307-4b95-8cbb-cb872a398d29","added_by":"auto","created_at":"2026-05-05 10:03:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1616099,"visible":true,"origin":"","legend":"\u003cp\u003eExpression characteristics of differentially expressed genes (DEGs) and their distribution across different libraries. (A) Correlation analysis of DEGs. (B) Principal component analysis (PCA) of all detected DEGs. (C) Number of DEGs in different comparison groups. (D) Venn diagram showing DEGs profiles under different treatments.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/3adb66a6b35f295fa5bc6352.jpg"},{"id":108491066,"identity":"e31ffd92-bf71-4bba-bfb1-fdadfa5d0dc9","added_by":"auto","created_at":"2026-05-05 09:51:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1490414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression pattern analysis of DEGs responsive to DP and ACC treatments in different subclusters.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/b7b139a01f8402e47cb1794f.jpg"},{"id":108494322,"identity":"8e896b4c-2128-42f3-b59a-0ff4c42cdae2","added_by":"auto","created_at":"2026-05-05 10:03:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1972584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTop 20 enriched GO terms and Top 30 enriched KEGG pathways from DEGs.\u003c/strong\u003e GO enrichment (A) and KEGG pathway enrichment (B) of DEGs in TDP vs TNP, TACC1 vs TNP, TACC10 vs TNP and TACC100 vs TNP comparison groups. In Fig. A, molecular function (MF), cellular component (CC) and biological process (BP) were exhibited in GO terms.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/18b493c869e66bc9b40cda91.jpg"},{"id":108494324,"identity":"cbcd895c-03b8-4463-bd83-5d6ab9d8b194","added_by":"auto","created_at":"2026-05-05 10:03:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":666326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA of differential metabolites in different treatment groups and venn diagrams under different groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/34912eab0f8dc76e13ea0cb4.jpg"},{"id":108494318,"identity":"ce0d7cae-9ca7-4e6e-b77f-0e9b5648dcca","added_by":"auto","created_at":"2026-05-05 10:03:54","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1820268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe DAMs were categorized into 13 classes by GO classification (A) and functional enrichment by KEGG (B).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig61.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/88e5bc97ccf0bae2a7f0c78c.jpg"},{"id":108494325,"identity":"2bc41585-387f-4013-858a-11f2e2042783","added_by":"auto","created_at":"2026-05-05 10:03:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1183776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of ethylene on the TCA cycle (A) and inositol phosphate metabolism pathway (B) under DP conditions.\u003c/strong\u003e Four consecutive boxes represent the group of DP vs NP, ACC1 vs NP, ACC10 vs NP and ACC100 vs NP, respectively. The heatmap of DEGs represents the fold change compared to the NP treatment. The heatmap of DAMs represents the fold change compared to the NP treatment. Green words represent decreased DAMs in roots. The same below. Abbreviations for genes are as follows: p\u003cem\u003ehosphoenolpyruvate carboxykinase \u003c/em\u003e(\u003cem\u003eATP\u003c/em\u003e)\u003cem\u003e 1 \u003c/em\u003e(\u003cem\u003ePEPCK1\u003c/em\u003e)\u003cem\u003e, citrate synthase \u003c/em\u003e(\u003cem\u003eCS\u003c/em\u003e)\u003cem\u003e, aconitate hydratase \u003c/em\u003e(\u003cem\u003eACO\u003c/em\u003e)\u003cem\u003e, malate dehydrogenase \u003c/em\u003e(\u003cem\u003eMDH\u003c/em\u003e)\u003cem\u003e \u003c/em\u003ein TCA cycle; \u003cem\u003ephosphoinositide phospholipase C1 \u003c/em\u003e(\u003cem\u003ePLC1\u003c/em\u003e)\u003cem\u003e, type I inositol-1,4,5-trisphosphate 5-phosphatase \u003c/em\u003e(\u003cem\u003eIP3P1\u003c/em\u003e)\u003cem\u003e, inositol-tetrakisphosphate 1-kinase 3 \u003c/em\u003e(\u003cem\u003eITPK3\u003c/em\u003e)\u003cem\u003e, inositol 1,3,4-trisphosphate 5/6-kinase \u003c/em\u003e(\u003cem\u003eITPK5/6\u003c/em\u003e)\u003cem\u003e, inositol oxygenase 2 \u003c/em\u003e(\u003cem\u003eIOX2\u003c/em\u003e)\u003cem\u003e, phosphatidylinositol 4-phosphate 5-kinase 1 \u003c/em\u003e(\u003cem\u003ePIP5K1\u003c/em\u003e) in inositol phosphate metabolism pathway.\u003c/p\u003e","description":"","filename":"Fig71.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/391c99fd30ac0d723b30dc9a.jpg"},{"id":108494319,"identity":"d5c75202-bc81-4273-a474-f1b0e4ee3f0f","added_by":"auto","created_at":"2026-05-05 10:03:54","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1546829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of ethylene on the hormone signalling pathways under DP conditions.\u003c/strong\u003e A-F represent the signaling pathways of auxin, cytokinine, gibberellin (GA), abscisic acid (ABA), ethylenen, jasmonic acid (JA) and salicylic acid (SA), respectively. Abbreviations for metabolites and genes are as follows:\u003cem\u003eauxin transporter \u003c/em\u003e(\u003cem\u003eAUX1\u003c/em\u003e)\u003cem\u003e, transport inhibitor response 1/ABA-responsive element-binding factor \u003c/em\u003e(\u003cem\u003eTIR1/ABF\u003c/em\u003e)\u003cem\u003e, Auxin/ auxin-induced protein \u003c/em\u003e(\u003cem\u003eAUX/IAA\u003c/em\u003e)\u003cem\u003e, auxin response factor \u003c/em\u003e(\u003cem\u003eARF\u003c/em\u003e)\u003cem\u003e, indole-3-acetic acid-induced protein ARG7 \u003c/em\u003e(\u003cem\u003eSAUR\u003c/em\u003e)\u003cem\u003e, indole-3-acetic acid-amido synthetase \u003c/em\u003e(\u003cem\u003eGH3\u003c/em\u003e)\u003cem\u003e, transmembrane kinase 1/4 \u003c/em\u003e(\u003cem\u003eTMK1/4\u003c/em\u003e)\u003cem\u003e, mitogen-activated protein kinase 3 \u003c/em\u003e(\u003cem\u003eMPK3/6) and ABA insensitive 1/2 \u003c/em\u003e(\u003cem\u003eABI1/2\u003c/em\u003e)\u003cem\u003e \u003c/em\u003ein auxin signaling pathway;\u003cem\u003e cytokinin receptor \u003c/em\u003e(\u003cem\u003eCRE\u003c/em\u003e)\u003cem\u003e, histidine-containing phosphotransfer \u003c/em\u003e(\u003cem\u003eAHP\u003c/em\u003e)\u003cem\u003e, type-A two-component response regulator ARR \u003c/em\u003e(\u003cem\u003eA-ARR\u003c/em\u003e)\u003cem\u003e and type-B two-component response regulator ARR \u003c/em\u003e(\u003cem\u003eB-ARR\u003c/em\u003e)\u003cem\u003e \u003c/em\u003ein cytokinine signaling pathway; \u003cem\u003egibberellin Insensitive dwarf \u003c/em\u003e(\u003cem\u003eGID1\u003c/em\u003e)\u003cem\u003e and DELLA domain-containing protein \u003c/em\u003e(\u003cem\u003eDELLA\u003c/em\u003e)\u003cem\u003e \u003c/em\u003ein GA pathway;\u003cem\u003e abscisic acid receptor PLR/PYL \u003c/em\u003e(\u003cem\u003ePLR/PYL\u003c/em\u003e)\u003cem\u003e, protein phosphatase 2C \u003c/em\u003e(\u003cem\u003ePP2C\u003c/em\u003e)\u003cem\u003e, serine/threonine-protein kinase SAPK2 \u003c/em\u003e(\u003cem\u003eSnPK2\u003c/em\u003e) \u003cem\u003eand abscisic acid-insensitive \u003c/em\u003e(\u003cem\u003eABF\u003c/em\u003e)\u003cem\u003e \u003c/em\u003ein ABA signaling pathway;\u003cem\u003e ethylene receptor \u003c/em\u003e(\u003cem\u003eETR\u003c/em\u003e)\u003cem\u003e, EIN3-binding F-box protein \u003c/em\u003e(\u003cem\u003eEBF\u003c/em\u003e)\u003cem\u003e, ethylene-insensitive 3 \u003c/em\u003e(\u003cem\u003eEIN3\u003c/em\u003e)\u003cem\u003eand ethylene response factor1/2 \u003c/em\u003e(\u003cem\u003eERF1/2\u003c/em\u003e)\u003cem\u003e \u003c/em\u003ein ethylene signaling pathway;\u003cem\u003e jasmonic acid-amino acid synthetase 1 \u003c/em\u003e(\u003cem\u003eJAR1\u003c/em\u003e)\u003cem\u003e, coronatine-insensitive protein 1 \u003c/em\u003e(\u003cem\u003eCOI1\u003c/em\u003e)\u003cem\u003e, jasmonate ZIM-domain protein \u003c/em\u003e(\u003cem\u003eJAZ\u003c/em\u003e)\u003cem\u003eand transcription factor MYC2 \u003c/em\u003e(\u003cem\u003eMYC2\u003c/em\u003e) in JA signaling pathway;\u003cem\u003e nonexpressor of pathogenesis-related genes 1 \u003c/em\u003e(\u003cem\u003eNPR1\u003c/em\u003e)\u003cem\u003e, transcription factor TGA \u003c/em\u003e(\u003cem\u003eTGA\u003c/em\u003e) \u003cem\u003eand pathogenesis-related protein 1 \u003c/em\u003e(\u003cem\u003ePR-1\u003c/em\u003e) in SA signaling pathway.\u003c/p\u003e","description":"","filename":"Fig81.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/7ad8191bf00d6a3cc68e8d77.jpg"},{"id":108494317,"identity":"b11deb50-84e4-4daf-934b-d27acc2df54f","added_by":"auto","created_at":"2026-05-05 10:03:54","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1454690,"visible":true,"origin":"","legend":"\u003cp\u003eResponse pathways of alfalfa roots to ACC treatment under DP stress. This model includes response pathways that are co-regulated by DP and ACC, as well as those specifically regulated by ACC.\u003c/p\u003e","description":"","filename":"Fig9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/5973f501cc0694a830615318.jpg"},{"id":108814326,"identity":"676885ed-7293-4e1e-85d1-5b861bdd25e9","added_by":"auto","created_at":"2026-05-08 16:17:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14871858,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/86f91917-6246-40eb-816f-b115cc1ded30.pdf"},{"id":108494376,"identity":"829efa54-0678-4015-982b-cbce7b0a4efc","added_by":"auto","created_at":"2026-05-05 10:04:20","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":842452,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1 \u003c/strong\u003eAlfalfa root hairs under conditions of sufficient phosphate, deficient phosphate, and deficient phosphate with different concentrations of ACC treatments.\u003c/p\u003e","description":"","filename":"Fig.S1Hairroots.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/1927def90b169d1898e55f2f.jpg"},{"id":108494375,"identity":"9e5f381a-a047-4872-a4cb-a92cd9c152f9","added_by":"auto","created_at":"2026-05-05 10:04:20","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1635388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S2\u003c/strong\u003e Volcano plots of DEGs in TDP vs TNP (A), TACC1 vs TNP (B), TACC10 vs TNP (C), TACC100 vs TNP (D).\u003c/p\u003e","description":"","filename":"FigS2VolcanoplotsofDEGsinTDPvsTNPTACC1vsTNPTACC10vsTNPTACC100vsTNP.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/69feff37b7aa45e03309ecad.jpg"},{"id":108494323,"identity":"29df4c06-3433-4d08-926d-7b0af67cc713","added_by":"auto","created_at":"2026-05-05 10:03:55","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":681915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S3\u003c/strong\u003e qRT-PCR validation of selected genes.\u003c/p\u003e","description":"","filename":"FigS3qRTPCRvalidationofselectedgenes.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/0ecae1330067a969509302ec.jpg"},{"id":108804015,"identity":"0d2ae4f1-fb57-4458-aae2-da4c2d6c59a8","added_by":"auto","created_at":"2026-05-08 15:14:29","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2226805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S4\u003c/strong\u003e Correlation heatmaps among different samples.\u003c/p\u003e","description":"","filename":"FigS4Correlationheatmapsamongdifferentsamples.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/8a7c848071a9effe6c5a4b26.jpg"},{"id":108494800,"identity":"0f9f5073-8b1a-4e0f-9a29-d283f2e5d96b","added_by":"auto","created_at":"2026-05-05 10:07:25","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1710304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S5\u003c/strong\u003e Volcano plots of DAMs in DP vs NP (A), ACC1 vs NP (B), ACC10 vs NP (C), ACC100 vs NP (D).\u003c/p\u003e","description":"","filename":"FigS5VolcanoplotsofDAMsinDPvsNPACC1vsNPACC10vsNPACC100vsNP.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/bd32e7c79bb7cd8537f45044.jpg"},{"id":108494373,"identity":"384d7b3e-424e-40b6-9075-8b8883a40834","added_by":"auto","created_at":"2026-05-05 10:04:20","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2461667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S6\u003c/strong\u003e Cluster analysis among DAMs across the five treatment comparisons.\u003c/p\u003e","description":"","filename":"FigS6ClusteranalysisamongDAMsacrossthefivetreatmentcomparisons.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/d23638bc96ae74a6d7772702.jpg"},{"id":108494326,"identity":"96ea1369-9a2b-415b-bf2f-2b813fe690a7","added_by":"auto","created_at":"2026-05-05 10:03:56","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2247109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S7 \u003c/strong\u003eIntegratedKEGG pathway analysis of transcriptomic and metabolomic data. (A) Procrustes result. (B-E) integrated KEGG pathway in DP vs NP (B), ACC1 vs NP (C), ACC10 vs NP (D), ACC100 vs NP (E).\u003c/p\u003e","description":"","filename":"FigS7IntegratedKEGGpathwayanalysisoftranscriptomicandmetabolomicdata.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/6453d17e56f304468cce561c.jpg"},{"id":108494315,"identity":"1b46369c-6728-42a4-a4f1-3f879d757fc8","added_by":"auto","created_at":"2026-05-05 10:03:53","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":12617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e Primers used for qRT-PCR validation.\u003c/p\u003e","description":"","filename":"TableS1PrimersusedforqRTPCRvalidation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/253c4093d43f65d784d466a3.xlsx"},{"id":108494309,"identity":"e18e3ab0-6cd0-4d0f-873a-bfdb37a80bb0","added_by":"auto","created_at":"2026-05-05 10:03:49","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":16586,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2\u003c/strong\u003e Overview of RNA sequencing data and comparison with the alfalfa genome.\u003c/p\u003e","description":"","filename":"TableS2OverviewofRNAseqeuencingdataandcomparisonwithalfalfagenome.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/c8e233330d33a2c0ce1825dc.xlsx"},{"id":108494329,"identity":"5f5c2669-fd8b-4423-9f25-6132b18ac71a","added_by":"auto","created_at":"2026-05-05 10:04:00","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1464898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3\u003c/strong\u003e DEGs in different groups.\u003c/p\u003e","description":"","filename":"TableS3DEGsindifferentgroups.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/2619d731e2f220e8fd9c21f7.xlsx"},{"id":108494316,"identity":"02ea8d39-aa2c-4674-96f4-fd78952c02d4","added_by":"auto","created_at":"2026-05-05 10:03:54","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":245051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S4\u003c/strong\u003e Differentially accumulated metabolites (DAMs) in different treatments.\u003c/p\u003e","description":"","filename":"TableS4differentiallyaccumulatedmetabolitesDAMsindifferenttreatments.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/504141064d789f9b817d4fc7.xlsx"},{"id":108494320,"identity":"ed8f40f0-0acc-460e-b04c-ce91f3ab4e5d","added_by":"auto","created_at":"2026-05-05 10:03:54","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":23062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S5\u003c/strong\u003e Subclusters of DAMs during different concentration of ACC compared to the NP treatment.\u003c/p\u003e","description":"","filename":"TableS5SubclustersofDAMsduringdifferentconcentrationofACCcomparedtotheNPtreatment.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/01f3a3d3071ff5e78401f174.xlsx"},{"id":108494374,"identity":"01d9d8a3-2cab-41eb-a4f8-2558bfb89f2a","added_by":"auto","created_at":"2026-05-05 10:04:20","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":69136,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S6\u003c/strong\u003e KEGG pathways co-regulated by transcriptome and metabolome integrated analysis.\u003c/p\u003e","description":"","filename":"TableS6KEGGpathwayscoregulatedbytranscriptomeandmetabolomeintegratedanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9350064/v1/30d9cc8677becb72b5233f15.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ethylene-mediated regulation of root responses to deficient-phosphate stress based on transcriptomic and metabolomic analyses in alfalfa","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eApproximately 5.7\u0026nbsp;billion hectares of land worldwide are characterized by phosphorus (P) deficiency, with plant-available phosphorus concentrations in soil being less than 10 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Batjes \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), far below the threshold required for optimal crop growth. Due to its low mobility, phosphate reaches plant roots primarily through diffusion. Phosphate is readily fixed by calcium, iron, aluminum, and soil clay particles, forming insoluble phosphorus compounds or organic phosphorus (Po). As a result, despite high total P levels, only about 6% (1.5%~11%) is directly accessible to plants, while the remainder is present in the soil in unavailable forms (Lopez-Arredondo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Menezes-Blackburn et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Traditional agricultural systems have relied on high fertilizer inputs to achieve high crop yields and quality. However, this increasing demand for phosphate fertilizers has led to issues such as soil degradation, eutrophication of aquatic ecosystems, and accelerated depletion of finite phosphate rock reserves (George et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In response to these issues, advancements in science and technology are transforming conventional agricultural practices to promote sustainability and resource conservation (George et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhosphorus is an essential structural component of various biological macromolecules such as DNA, RNA and phospholipids. P is extensively involved in processes including plant photosynthesis, respiration, energy metabolism, and signal transduction (Chiou and Lin \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Under phosphorus starvation stress, plants exhibit slow growth, stunted stature, and impaired development. Plants have developed extremely complex signaling networks to maintain internal phosphorus homeostasis, primarily involving strategies that reduce phosphorus consumption and enhance exogenous phosphorus acquisition (Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Prominent adaptive responses of plants under deficient-phosphorus conditions are manifested through modifications in root system architecture and expansion of root surface area (Liu \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Upregulation of phosphate transporters facilitates increased phosphorus uptake by plants under deficient-phosphorus conditions (Fan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Plants also solubilize insoluble phosphorus in the rhizosphere by secreting organic acids such as citric acid, malic acid, oxalic acid, succinic acid, and acetic acid from their roots (Wang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As well as acid phosphatases and ribonucleases to facilitate the decomposition of inorganic phosphorus (Pi) and Po (Lopez-Arredondo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Symbiotic associations with arbuscular mycorrhizal fungi (AMF) further augment phosphorus acquisition (Chiu and Paszkowski \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To facilitate the internal recycling of phosphorus, plants utilize galactolipids and sulfolipids to replace phospholipids within membrane systems under low-phosphorus conditions and maintain their functions (Liu \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thereby releasing phosphorus for utilization in other metabolic processes.\u003c/p\u003e \u003cp\u003eEthylene is an important mediator of plant responses to phosphorus deficiency (Lei et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Roldan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Conversely, phosphorus deficiency not only promotes ethylene synthesis but also increases root sensitivity to ethylene (Nagarajan and Smith \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Under low-phosphorus conditions, ethylene content significantly increases in the roots of common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e), white lupin (\u003cem\u003eLupinus albus\u003c/em\u003e), and \u003cem\u003eMedicago falcata\u003c/em\u003e (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Neumann \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Ethylene participates in both local and systemic signal responses. The ethylene signaling pathway interacts with local response pathways to coordinately regulate root development in \u003cem\u003eArabidopsis\u003c/em\u003e and other plants (Crombez et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Neumann \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Song and Liu \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The ethylene precursor 1-Aminocyclopropane-1-carboxylic acid (ACC) strongly promotes lateral root formation in \u003cem\u003eTrifolium repens\u003c/em\u003e under sufficient phosphorus conditions (Dinh et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Whereas ethylene inhibitors reduce root hair density and length in \u003cem\u003eArabidopsis\u003c/em\u003e under low-phosphorus stress (Song and Liu \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In \u003cem\u003eM. falcata\u003c/em\u003e, ethylene enhances phosphorus acquisition by increasing root acid phosphatase (ACP) activity and inducing the expression of \u003cem\u003ephosphate transporter\u003c/em\u003e genes under deficient phosphorus conditions (Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Ethylene perception is mediated by a family of receptors located on the endoplasmic reticulum, namely \u003cem\u003eethylene receptor 1\u003c/em\u003e (\u003cem\u003eETR1\u003c/em\u003e), \u003cem\u003eethylene response sensor 1\u003c/em\u003e (\u003cem\u003eERS1\u003c/em\u003e), \u003cem\u003eETR2\u003c/em\u003e, \u003cem\u003eERS2\u003c/em\u003e, and \u003cem\u003eethylene-insensitive 4\u003c/em\u003e (\u003cem\u003eEIN4\u003c/em\u003e). Upon ethylene binding, these receptors become inactivated, leading to the suppression of constitutive triple response 1 (CTR1) kinase activity (Wen et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This suppression enables \u003cem\u003eEIN2\u003c/em\u003e to relay the signal to the transcription factors \u003cem\u003eEIN3/EIN3-Like 1\u003c/em\u003e (\u003cem\u003eEIL1\u003c/em\u003e) and \u003cem\u003eethylene response factors\u003c/em\u003e (\u003cem\u003eERFs\u003c/em\u003e). \u003cem\u003eERFs\u003c/em\u003e inhibit the translation of \u003cem\u003eEIN3-binding F-box protein 1\u003c/em\u003e (\u003cem\u003eEBF1\u003c/em\u003e) and \u003cem\u003eEBF2\u003c/em\u003e, ultimately promoting the accumulation of EIN3/EIL1 proteins, thereby triggering a series of ethylene-induced plant growth and developmental (Binder \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCore genes involved in phosphorus starvation response (PSR) are highly conserved across plant species and are primarily regulated by a signaling network centered on \u003cem\u003ephosphate starvation response 1\u003c/em\u003e (\u003cem\u003ePHR1\u003c/em\u003e)/\u003cem\u003eSPX\u003c/em\u003e (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). ACC induce the expression of \u003cem\u003ePHR1\u003c/em\u003e, a corer regulator of the PSR, which subsequently modulates a series of genes involved in phosphorus uptake, transport, and PSR (Chiou and Lin \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nagarajan and Smith \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Ethylene also modulates the expression of \u003cem\u003ehigh-affinity phosphate transporters\u003c/em\u003e (\u003cem\u003ePHT\u003c/em\u003e). In the ethylene-insensitive mutant \u003cem\u003eein2-5\u003c/em\u003e, the \u003cem\u003ePht1;1\u003c/em\u003e and \u003cem\u003ePht1;4\u003c/em\u003e are reduced under low-phosphorus conditions (Lei et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Under phosphorus sufficiency, ACC induces the expression of the phosphate transporter genes \u003cem\u003eMfPT1\u003c/em\u003e (\u003cem\u003ephosphate transporter 1\u003c/em\u003e) and \u003cem\u003eMfPT5\u003c/em\u003e. Conversely, ethylene synthesis inhibitors aminoethoxyvinylglycine (AVG) and Co\u0026sup2;⁺ suppress the phosphorous starvation induced expression of these transporter genes (Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlfalfa (\u003cem\u003eMedicago sativa\u003c/em\u003e), a globally important legume forage, is valued for its high yield and superior nutritional quality and is cultivated extensively worldwide (Chen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cota-Ruiz et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although previous studies have implicated ethylene in the PSR processes of \u003cem\u003eM\u003c/em\u003e. \u003cem\u003efalcata\u003c/em\u003e and alfalfa (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). However, the specific regulatory mechanisms through which ethylene modulates the PSR in alfalfa remain largely unresolved. This study aims to elucidate the response mechanisms and regulatory pathways to DP stress in alfalfa through an integrated analysis of root morphology and physiological traits under ethylene treatment, with metabolomic and transcriptomic analyses. The findings will provide important insights into improving phosphorus utilization efficiency (PUE) in alfalfa, which will help reduce fertilizer inputs, alleviate the depletion of phosphorus resources, and achieve sustainable phosphorus use in agricultural systems.\u003c/p\u003e"},{"header":"2. Materials and methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cultivation and treatment of alfalfa plants\u003c/h2\u003e \u003cp\u003eThe alfalfa (\u003cem\u003eMedicago sativa\u003c/em\u003e \u0026lsquo;Zhongmu No.3\u0026rsquo;) used in this study is a cultivated variety bred by the Institute of Animal Science of Chinese Academy of Agricultural Sciences (CAAS), Beijing, China. Alfalfa seeds were kindly provided by Yang Lab in the Institute of Animal Science of CAAS. The seeds were germinated under dark conditions at 25\u0026deg;C. Seedlings bearing their first true leaf were transferred to the Hoagland nutrient solution. After 7 days of growth, uniformly developed seedlings were selected and subjected to either normal-phosphate (NP, 1000 \u0026micro;M KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e) or deficient-phosphate (DP, 100 \u0026micro;M KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e) treatments. Simultaneously, seedlings were also treated with 1, 10, and 100 \u0026micro;M ACC (designated as ACC1, ACC10, and ACC100, respectively) under DP treatment. Potassium sulfate was added to the DP nutrient solution to maintain potassium levels equal to those in the NP treatment. The seedlings were cultivated in a greenhouse under controlled conditions: a 14 h/10 h day/night cycle at 24\u0026deg;C/22\u0026deg;C, 60% relative humidity, and a light intensity of 250 \u0026micro;mol/m\u003csup\u003e2\u003c/sup\u003e\u0026middot;s.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Determination of morphology, physiological, and biochemical parameters of alfalfa roots\u003c/h2\u003e \u003cp\u003eAfter 7 days of cultivation under different treatments, changes in root morphological and physiological indicators associated with PSR were assessed in laboratory. Root architecture traits, including total root length, surface area, and lateral root number, were analyzed using the WinRHIZO root analysis system (v.5.0, Regent Instruments, Canada). Root hairs were observed using a microscope (BX41, Olympus, Japan).\u003c/p\u003e \u003cp\u003eFresh root samples were collected and stored at -80\u0026deg;C temperature freezer for subsequent physiological and biochemical analyses. Starch and sucrose contents were determined using corresponding assay kits (A148-1-1, A099-1-1, Nannjing Jiancheng bioengineering Institute, China). Catalase (CAT) and ACP activities were assayed using commercial kits (A007-1-1, A060-2-1), respectively. For total nitrogen and phosphorus contents determination, plant tissues were oven‑dried at 65\u0026deg;C to constant weight. The total phosphorus (TP) content was determined using the phosphomolybdic acid colorimetric method (Ames \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1966\u003c/span\u003e), and total nitrogen content was quantified using the Kjeldahl method (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All the above parameters were assessed in four biological replicates.\u003c/p\u003e \u003cp\u003eSuccinic acid and malic acid were quantified using high-performance liquid chromatography (HPLC). Analysis was performed using an HPLC instrument (e-2695, Waters Corp., USA). The detection conditions were as follows: mobile phase A was 0.1% phosphoric acid; mobile phase B was acetonitrile; column temperature was maintained at 35\u0026thinsp;\u0026plusmn;\u0026thinsp;2℃; detection wavelength was 210 nm; injection volume was 1 \u0026micro;L; flow rate was 0.8 mL/min. Indole-3-acetic acid (IAA) content was determined using liquid chromatography-mass spectrometry (LC-MS). A 100 mg sample was homogenized in 500 \u0026micro;L of 0.02 mol/L NaH₂PO₄ (pH 2.2). The mixture was subjected to ultrasonic disruption, and the filtrate was collected for analysis. LC-MS analysis was performed (TQ-S, Waters Corp., USA), with the mobile phase A consisting of 0.1% formic acid, and the mobile phase B consisting of acetonitrile. The injection volume was 2 \u0026micro;L, the column temperature was 30\u0026deg;C, and the flow rate was 1 mL/min.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Transcriptome analysis\u003c/h2\u003e \u003cp\u003eAfter 7 days of DP and different ACC treatments, alfalfa seedling roots were collected for metabolite profiling and gene expression analysis. Total RNA was extracted using TRIzol reagent. cDNA libraries were subsequently constructed and sequenced using the Illumina NovaSeq X Plus system (2\u0026times;150 bp). The obtained raw data were processed with Fastp (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003cspan address=\"https://github.com/OpenGene/fastp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the resulting clean reads were mapped to the alfalfa (\u003cem\u003eMedicago sativa\u003c/em\u003e L. cv. Zhongmu NO.1) reference genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/articles/dataset/Medicago_sativa_genome_and_annotation_files/12623960?file=23754059\u003c/span\u003e\u003cspan address=\"https://figshare.com/articles/dataset/Medicago_sativa_genome_and_annotation_files/12623960?file=23754059\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using HISAT2 (Shen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Unigenes were then annotated using the Gene Ontology (GO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and NCBI non-redundant (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.ncbi.nlm.nih.gov/blast/db/\u003c/span\u003e\u003cspan address=\"http://ftp://ftp.ncbi.nlm.nih.gov/blast/db/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) protein databases. Transcript abundance was quantified using RSEM to obtain transcripts per million reads (TPM) (Zhou et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The compared group contained TDP vs TNP (DP vs NP), TACC1 vs TNP (1 \u0026micro;M ACC vs NP), TACC10 vs TNP (10 \u0026micro;M ACC vs NP) and TACC100 vs TNP (100 \u0026micro;M ACC vs NP). Differentially expressed genes (DEGs) were identified using the criteria log|(fold change)| \u0026gt; 1 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, followed by functional enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Detection and analysis of metabolites in alfalfa roots\u003c/h2\u003e \u003cp\u003eThe root samples used for transcriptomics were also subjected to metabolomics analysis. Metabolite extraction and analysis were performed following previously described methods, with six biological replicates for each treatment. Sample preparation for metabolomics analysis and subsequent bioinformatics analysis were performed by Shanghai Majorbio Bio-Pharm Technology Co., Ltd. according to standard protocols. Metabolite extracts were analyzed on a liquid chromatography-mass spectrometry (LC/MS) instrument (Q-Exactive HF-X, Thermo Scientific, USA) (Xie et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRaw data were imported into the metabolomics processing software ProgenesisQI v3.0 (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment. The MS and MS/MS mass spectrometry data were matched against public metabolite databases, including 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) and 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), as well as the Majorbio in-house database, to obtain metabolite identification information. Data preprocessing included retention variables with non-zero values in at least 80% of samples within at least one group, followed by imputation of missing values. The mass spectral peak areas were normalized using the sum normalization method to obtain a normalized data matrix. Significantly differentially accumulated metabolites (DAMs) were identified using variable influence on projection (VIP) values from the OPLS-DA model and student\u0026rsquo;s t-test \u003cem\u003ep\u003c/em\u003e-values. Metabolites satisfying the criteria of VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected as DAMs. The compared group contained DP vs NP, ACC1 vs NP (1 \u0026micro;M ACC vs NP), ACC10 vs NP (10 \u0026micro;M ACC vs NP) and ACC100 vs NP (100 \u0026micro;M ACC vs NP). Differential metabolites were mapped to metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify their associated pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Validation of DEGs expression by qRT-PCR\u003c/h2\u003e \u003cp\u003eThe RNA samples used for transcriptome sequencing were further utilized for qRT-PCR analysis. Reverse transcription of mRNA was performed using the cDNA Synthesis Kit (R212, Vazyme, China), following the manufacturer\u0026rsquo;s instructions. qRT-PCR was conducted using a qPCR mix reagent, and \u003cem\u003eβ-actin\u003c/em\u003e was used as the internal reference gene. Gene-specific primers were designed using Primer-BLAST (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/tools/primer-blast\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/tools/primer-blast\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. For this analysis, each sample included three biological replicates, and each biological replicate consisted of three technical replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Integrated transcriptome and metabolome analysis\u003c/h2\u003e \u003cp\u003eMultivariate regression analysis was employed to assess the correlation between metabolomic and transcriptomic data (Bylesjo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). KEGG pathway enrichment was evaluated using Fisher\u0026rsquo;s exact test for both data types. The enriched pathways and biological processes were visualized.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Effects of ethylene on alfalfa root morphology and physiological traits under DP conditions\u003c/h2\u003e\n \u003cp\u003eTo investigate the change by which ACC influences alfalfa morphological and physiological responses to DP, seedlings were treated with different ACC concentrations under DP conditions. Treatments with different ACC concentrations showed similar effects on root growth. Specifically, total root length, root surface area, number of lateral roots, and root dry weight were all reduced relative to both NP and DP treatments (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-D). Compared to the DP treatment alone, total root length under ACC1, ACC10, and ACC100 treatments decreased by 12.97%, 34.03%, and 19.81%, respectively (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, the root hairs in the maturation zone of the root tip are significantly induced by ACC treatment, and form a bend (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Starch and sucrose serve as key metabolic and signaling molecules under DP conditions, and their concentrations were significantly increased by ACC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to DP condition, sucrose content increased by 2.80-fold and 2.68-fold under ACC10 and ACC100 treatments, respectively (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e\n \u003cp\u003eBoth DP and ACC treatments enhanced succinic acid accumulation. whereas only malic acid was significantly increased by ACC100 treatment. As shown in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eG, H, compared to the DP treatment, succinic acid and malic acid contents increased by 117.78% and 58.32%, respectively, under the ACC100 treatment. Compared to the DP treatment, ACC1 and ACC10 treatments increased total P content in the roots. Interestingly, while DP stress led to a decrease in total nitrogen content, increasing concentrations of ACC treatment progressively increased total nitrogen content in alfalfa roots.\u003c/p\u003e\n \u003cp\u003eBoth DP and ACC treatments (except ACC1 treatment) increased the activity of ACP. ACC10 and ACC100 treatments significantly enhanced the activity of CAT, compared to DP treatment. CAT activity increased progressively with increasing ACC concentrations, and increased by 4.69%, 58.45%, and 160.56% under ACC1, ACC10, and ACC100 treatments, respectively (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eK, I). Both DP and ACC treatments decreased the concentrations of ABA and GA\u003csub\u003e3\u003c/sub\u003e. Compared to the NP treatment, ABA content decreased by 25.00%, 18.75%, and 50.00% under ACC1, ACC10, and ACC100 treatments, respectively. IAA and SA concentrations showed a downward trend under DP and ACC treatments (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eM-P).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Transcriptional profiling of alfalfa roots under DP conditions with ethylene treatment\u003c/h2\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Overview of transcriptome data\u003c/h2\u003e\n \u003cp\u003eA total of 15 cDNA libraries were constructed, comprising three biological replicates per treatment, with each library yielding 42.42 to 53.09 million raw reads. The sequencing data have been deposited in the NCBI database (PRJNA1419934). Each library generated more than 42.06 million clean reads, after filtering out low-quality sequences. Clean bases ranged from 6.26 to 7.84 Gb, with Q20 and Q30 values exceeding 98.47% and 95.25%, respectively (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). Approximately 73.73% to 76.16% of the clean reads were successfully mapped to the alfalfa reference genome (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB), indicating satisfactory sequencing depth and quality.\u003c/p\u003e\n \u003cp\u003eGene expression levels were quantified as Transcripts Per Million (TPM). Pearson correlation analysis revealed high reproducibility of gene expression patterns among different libraries within the same treatment group, while correlation coefficients were relatively low between different treatment groups (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Principal omponent analysis (PCA) distinctly separated different treatments into different clusters (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), indicating divergent gene expression patterns among different libraries. DEGs between comparison groups were identified using the screening criteria of |log2Fold Change| \u0026gt; 1 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n \u003cp\u003eA total of 761 (639 up-regulated, 122 down-regulated), 2142 (1269 up-regulated, 873 down-regulated), 2488 (1386 up-regulated, 1102 down-regulated), and 2607 (1279 up-regulated, 1328 down-regulated) DEGs were identified in the TDP vs TNP, TACC1 vs TNP, TACC10 vs TNP, and TACC100 vs TNP comparison groups, respectively (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Venn diagram analysis revealed 187 common DEGs shared among all four comparison groups (27 down-regulated, 160 up-regulated), and 420 DEGs common to all three ACC treatments (185 up-regulated, 235 down-regulated) (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe DEGs were grouped into 10 clusters based on their expression patterns by K-means clustering (Fig. 3). Subcluster 1 (89 genes), subcluster 2 (296 genes), subcluster 5 (1055 genes), and subcluster 6 (1017 genes) exhibited positive responses relative to NP treatments, whereas subcluster 4 (1960 genes), subcluster 7 (36 genes), subcluster 8 (34 genes), and subcluster 9 (21 genes) showed negative responses.\u003c/p\u003e\n \u003cp\u003eACC treatment modulated the expression of key genes associated with DP responses. ACC consistently induced the expression of \u003cem\u003eglycerol-3-phosphate transporter 1\u003c/em\u003e and \u003cem\u003epurple acid phosphatase\u003c/em\u003e across all concentrations. However, only the ACC1 upregulated the \u003cem\u003ephosphate transporter PHO1\u003c/em\u003e, \u003cem\u003eSPX domain-containing protein\u003c/em\u003e, \u003cem\u003elow affinity inorganic phosphate transporter 1\u003c/em\u003e and \u003cem\u003einorganic phosphate transporter 1\u0026ndash;4\u003c/em\u003e involved in phosphorus uptake. ACC10 induced \u003cem\u003eglucose-6-phosphate/phosphate translocator 2\u003c/em\u003e (\u003cem\u003eGPT2\u003c/em\u003e) (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), a gene that plays a crucial role in metabolic connectivity between the plastid stroma and the cytosol by facilitating the exchange of phosphate with phosphorylated intermediates. In contrast, ACC suppressed the expression of the \u003cem\u003ePHO1\u003c/em\u003e and \u003cem\u003ealuminum-activated malate transporter 12\u003c/em\u003e. Furthermore, although DP induced the expression of \u003cem\u003emitochondrial phosphate carrier protein 3\u003c/em\u003e, this induction was absent in ACC‑treated plants (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 GO classification and KEGG pathway enrichment analysis of DEGs\u003c/h2\u003e\n \u003cp\u003eGO classification analysis was performed to characterize the functional categories of DEGs. GO categories were assigned into three major contained molecular function (MF), cellular component (CC), and biological process (BP). The MF category enriched terms included nucleoside phosphate binding, transferase activity, transferring phosphorus-containing groups, and protein kinase activity (Fig.\u0026nbsp;4A). The CC terms included plasma membrane, membrane-bounded organelle, intracellular organelle. And BP terms included catabolic process, defense response, biosynthetic process, phosphorus metabolic process, and regulation of cellular process, among others (Fig.\u0026nbsp;4A).\u003c/p\u003e\n \u003cp\u003eFunctional enrichment analysis was performed on the DEGs identified from different compared groups in alfalfa roots under DP conditions with ACC treatment. Within the top 30 enriched KEGG pathways, a substantial proportion of DEGs from the union set were associated with flavonoid biosynthesis, phenylpropanoid biosynthesis, plant hormone signal transduction, MAPK signaling pathway, among others (Fig.\u0026nbsp;4B).\u003c/p\u003e\n \u003cp\u003eFurthermore, KEGG pathways commonly enriched under both DP and ACC treatment included starch and sucrose metabolism, fructose and mannose metabolism, glycerophospholipid metabolism, nitrogen metabolism, flavonoid biosynthesis, glutathione metabolism, plant hormone signal transduction. In contrast, the pathways specifically induced by ACC treatment included the pentose phosphate pathway, glycolysis, inositol phosphate metabolism, the citrate (TCA) cycle, pyrimidine metabolism, glycosphingolipid biosynthesis and tryptophan metabolism, among others (Fig. 4B).\u003c/p\u003e\n \u003cp\u003eAdditionally, to validate the reliability of the RNA-seq results, 9 DEGs were randomly selected from the TNP, TDP, TACC1, TACC10, TACC100 group for qRT-PCR analysis. As the results showed that qRT-PCR expression patterns including \u003cem\u003eSPX domain-containing protein\u003c/em\u003e (MsG0180004121.01), \u003cem\u003einorganic phosphate transporter 1\u0026ndash;4\u003c/em\u003e (MsG0780040738.01), \u003cem\u003eethylene-responsive transcription factor 2\u003c/em\u003e (MsG0780041015.01), \u003cem\u003eprobable WRKY transcription factor 40\u003c/em\u003e (MsG0280008324.01), \u003cem\u003esucrose synthase 5-like isoform X5\u003c/em\u003e (MsG0280006583.01) and other genes (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), were largely consistent with RNA‑seq data. The transcriptional profiles of the selected DEGs exhibited similar trends, confirming the reliability of the RNA-seq data.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Metabolite profiling of roots under DP conditions with ethylene treatment\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Overview of metabolite profiling\u003c/h2\u003e\n \u003cp\u003eThe changes in alfalfa root metabolite profiles under DP and ACC treatments were also analyzed, with six biological replicates per treatment. Correlation heatmaps revealed substantial differences in metabolite composition and abundance across treatments (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). PCA separated the 30 samples (encompassing six independent replicates) along the first two principal components, which accounted for 33.7% (PC1) and 10.5% (PC2) of the total variance, respectively (Fig. 5A). Samples within the same treatment clustered closely, indicating high reproducibility and providing a reliable basis for downstream analyses. Based on the criteria of VIP\u0026thinsp;\u0026ge;\u0026thinsp;1 and fold-change\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5, a total of 926 significantly DAMs were identified (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Expression patterns of DAMs\u003c/h2\u003e\n \u003cp\u003eBased on HMDB superClass annotations, the 926 DAMs were categorized into 13 classes (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Among these DAMs, 266 were identified in the DP vs NP comparison group, comprising 214 up-regulated and 52 down-regulated metabolites. 378 DAMs were identified in the ACC1 vs NP comparison group, comprising 285 up-regulated and 95 down-regulated DAMs. 504 DAMs were identified in the ACC10 vs NP comparison group, comprising 310 up-regulated and 194 down-regulated DAMs. And 570 DAMs were identified in the ACC100 vs NP comparison group, comprising 335 up-regulated and 235 down-regulated DAMs (Fig. 5B, C; Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eCluster analysis revealed distinct metabolite response patterns among DAMs across the five treatment comparisons (Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). The results showed that the subcluster trend plots indicated that subcluster 1 DAMs accumulated to higher levels during ACC treatments compared to the NP and DP treatments. Conversely, subcluster 2, subcluster 4, and subcluster 5 showed opposite trends, with DAMs significantly decreasing following ACC treatment (Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSubcluster 1 DAMs predominantly included carboxylic acids and derivatives, flavonoids, isoflavonoidsand and other related compounds (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Subcluster 2, 4, and 5 DAMs were dominated by fatty acyls, organooxygen compounds, sphingolipids and others. ACC treatment increased the accumulation of specific amino acids such as L-ornithine, L-Cysteine, L-Proline and others. While carbohydrates, including sucrose, trehalose, sucrosewere and others also elevated. Notably, significant changes were observed in the levels of lipids and flavonoids under ACC treatment (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Specifically, lipid-related metabolites such as malonic acid, Phosphatidylethanolamine (Pe), Lysophosphatidylcholine (Lysopc), glycerophosphoinositol, flavonoid glycosides including poncirin, liquiritin, baicalin, fatty acids such as undecylenic acid, traumatic acid, traumatin and the hormones cotained ABA and SA exhibited significant alterations in response to ACC treatment.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 KEGG enrichment of DAM‑associated regulatory pathways\u003c/h2\u003e\n \u003cp\u003eTo identify potential regulatory pathways associated with DAMs, KEGG enrichment analysis was performed. In the DP vs NP comparison, the top 30 significantly enriched KEGG pathways included nucleotide metabolism, flavonoid biosynthesis, zeatin biosynthesis, fructose and mannose metabolismitrate, TCA cycle and so on. In ACC1 vs NP, the top 20 enriched pathways included flavonoid biosynthesis, alanine, aspartate and glutamate metabolism, plant hormone signal transduction, sphingolipid metabolism, zeatin biosynthesis and others. In ACC10 vs NP, the top 20 pathways included favonoid biosynthesis, nucleotide metabolism, galactose metabolism, plant hormone signal transduction, glycerophospholipid metabolism and others. In ACC100 vs NP, the top 20 pathways included flavonoid biosynthesis, glutathione metabolism, plant hormone signal transduction, cyanoamino acid metabolism and others (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB; Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFlavonoid biosynthesis, isoflavonoid biosynthesis, valine, leucine and isoleucine biosynthesis, pyrimidine metabolism, glycine, serine and threonine metabolism, zeatin biosynthesis, galactose metabolism, glycerophospholipid metabolism was significantly enriched across all four comparison groups, suggesting its central role in the regulatory response network under combined DP and ethylene stress. Furthermore, pathways including glycosylphosphatidylinositol (GPI)-anchor biosynthesis, tryptophan metabolism, biotin metabolism, arginine biosynthesis, arachidonic acid metabolism, plant hormone signal transduction, sphingolipid metabolism and others were implicated in the regulatory response networks under varying ACC concentrations in addition to the DP treatment (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Integrative analysis of regulatory pathways involving DEGs and DAMs\u003c/h2\u003e\n \u003cp\u003eTo deeply explore the relationships between transcriptomic and metabolomic responses, a co-expression network analysis integrating both datasets was conducted. Procrustes result based on both datasets showed tight clustering of samples within each treatment and clear separation between treatments (Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eA). KEGG pathway analysis indicated that these DEGs and DAMs were co-enriched in pathways including flavonoid biosynthesis, isoflavonoid biosynthesis, and phenylpropanoid biosynthesis (Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eB-E). Both common and specific regulatory pathways were identified under ACC treatment in conjunction with DP conditions, indicating that ethylene modulates multiple aspects of the DP response. Common regulatory pathways shared by DP and ACC treatments included starch metabolism, the TCA cycle, glycolysis, glycerophospholipid metabolism, glycerolipid metabolism, glutathione metabolism, and flavonoid biosynthesis (Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eB-E).\u003c/p\u003e\n \u003cp\u003eIn TCA cycle, citrate content significantly decreased under DP and ACC treatments (Fig. \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The expression of \u003cem\u003ephosphoenolpyruvate carboxykinase (ATP) 1\u003c/em\u003e (\u003cem\u003ePEPCK1\u003c/em\u003e) and \u003cem\u003ecitrate synthase\u003c/em\u003e (\u003cem\u003eCS\u003c/em\u003e) was downregulated, while \u003cem\u003eaconitate hydratase\u003c/em\u003e (\u003cem\u003eACO\u003c/em\u003e) only increased under ACC100 treatment. \u003cem\u003emalate dehydrogenase\u003c/em\u003e (\u003cem\u003eMDH\u003c/em\u003e) expression was either increased or decreased under ACC treatment (Fig. \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The inositol phosphate pathway was specifically enriched under ACC treatment. In this pathway, the expression of \u003cem\u003etype I inositol-1,4,5-trisphosphate 5-phosphatase\u003c/em\u003e (\u003cem\u003eIP3P1\u003c/em\u003e), \u003cem\u003einositol-tetrakisphosphate 1-kinase 3\u003c/em\u003e (\u003cem\u003eITPK3\u003c/em\u003e), \u003cem\u003einositol 1,3,4-trisphosphate 5/6-kinase\u003c/em\u003e (\u003cem\u003eITPK5/6\u003c/em\u003e), and \u003cem\u003einositol oxygenase 2\u003c/em\u003e (\u003cem\u003eIOX2\u003c/em\u003e) was suppressed only by ACC treatment, whereas \u003cem\u003ephosphoinositide phospholipase C6\u003c/em\u003e (\u003cem\u003ePLC6\u003c/em\u003e), \u003cem\u003etype IV inositol polyphosphate 5-phosphatase 9\u003c/em\u003e (\u003cem\u003eIP5P9\u003c/em\u003e), and \u003cem\u003ephosphatidylinositol 4-phosphate 5-kinase 1\u003c/em\u003e (\u003cem\u003ePIP5K1\u003c/em\u003e) were induced exclusively under ACC treatment (Fig. \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The results indicate that ethylene is involved in the regulation of the inositol phosphate pathway under DP conditions.\u003c/p\u003e\n \u003cp\u003eThe hormone signaling pathways were enriched under DP and ACC treatments. The synthesis of ABA and SA was inhibited by ACC. (Fig. 8A). The auxin signalling pathway was suppressed under ACC treatment, with \u003cem\u003eauxin transporter\u003c/em\u003e (\u003cem\u003eAUX1\u003c/em\u003e), \u003cem\u003etransport inhibitor response 1/ABA-responsive element-binding factor\u003c/em\u003e (\u003cem\u003eTIR1/AFB\u003c/em\u003e), \u003cem\u003eAuxin/auxin-induced protein\u003c/em\u003e (\u003cem\u003eAUX/IAA\u003c/em\u003e), \u003cem\u003eauxin response factor\u003c/em\u003e (\u003cem\u003eARF\u003c/em\u003e), and \u003cem\u003eindole-3-acetic acid-amido synthetase\u003c/em\u003e (\u003cem\u003eCH3\u003c/em\u003e) being inhibited specifically under ethylene treatment (Fig. 8A). However, auxin-mediated signal transduction involves the induction of \u003cem\u003emitogen-activated protein kinase 3/6\u003c/em\u003e (\u003cem\u003eMPK3/6\u003c/em\u003e) and the inhibition of the ABA response. And the key genes such as \u003cem\u003eransmembrane kinase 1/4\u003c/em\u003e (\u003cem\u003eTMK1/4\u003c/em\u003e), \u003cem\u003eMPK3/6\u003c/em\u003e and \u003cem\u003eABA insensitive 1/2\u003c/em\u003e (\u003cem\u003eABI1/2\u003c/em\u003e) were induced by DP and ACC.\u003c/p\u003e\n \u003cp\u003eThe cytokinin pathway was induced by ACC, with key genes \u003cem\u003ehistidine-containing phosphotransfer\u003c/em\u003e (\u003cem\u003eAHP\u003c/em\u003e) and \u003cem\u003etype-A two-component response regulator\u003c/em\u003e (\u003cem\u003eA-ARR\u003c/em\u003e) showing induced expression upon ACC treatment (Fig. 8C). ACC suppressed the ABA signaling pathway under DP conditions, with decreasing ABA content, and inhibited genes such as \u003cem\u003eabscisic acid receptor PLR/PYL\u003c/em\u003e (\u003cem\u003ePLR/PYL\u003c/em\u003e) and \u003cem\u003eabscisic acid-insensitive\u003c/em\u003e (\u003cem\u003eABF\u003c/em\u003e) (Fig. 8D). Exogenous ACC treatment affected the transduction of the ethylene signaling pathway, with key genes such as \u003cem\u003eethylene receptor\u003c/em\u003e (\u003cem\u003eETR\u003c/em\u003e), \u003cem\u003eEIN3-binding F-box protein\u003c/em\u003e (\u003cem\u003eEBF\u003c/em\u003e) and \u003cem\u003eethylene response factor1/2\u003c/em\u003e (\u003cem\u003eERF1/2\u003c/em\u003e) showing increased expression (Fig. 8E). Both DP and ACC influenced the jasmonic acid (JA) signaling pathway by promoting the \u003cem\u003ejasmonate ZIM-domain protein\u003c/em\u003e (\u003cem\u003eJAZ\u003c/em\u003e) and \u003cem\u003etranscription factor MYC2\u003c/em\u003e (\u003cem\u003eMYC2\u003c/em\u003e) (Fig. 8F). ACC reduced SA content while promoting the expression of \u003cem\u003enonexpressor of pathogenesis-related genes 1\u003c/em\u003e (\u003cem\u003eNPR1\u003c/em\u003e) and \u003cem\u003epathogenesis-related protein 1\u003c/em\u003e (\u003cem\u003ePR-1\u003c/em\u003e) (Fig. 8G).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Proposed regulatory model of ethylene-modulated responses to DP stress in alfalfa roots\u003c/h2\u003e\n \u003cp\u003eThrough integrated analysis of root phenotypes, physiological traits, key metabolites, and gene expression profiles, we have identified indirect regulatory pathways potentially associated with phosphorus uptake or abiotic stress responses. These pathways can be categorized into those co-induced by DP and ACC, and those specifically induced by ACC. Both DP and ACC treatments suppressed root growth and modulated multiple metabolic pathways, including flavonoid, starch and sucrose, glutathione, fructose and mannose, and galactose metabolism, as well as fatty acid degradation and glycerolipid/glycerophospholipid metabolism (Fig. \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Additionally, these treatments activated ethylene and salicylic acid (SA) signaling pathways. Furthermore, ethylene precursor application markedly accelerated several of these responses under DP deficiency stress, particularly regarding glutathione and flavonoid metabolism. ACC also triggered root hair formation and uniquely activated glycolysis, the TCA cycle, glycosphingolipid metabolism, tryptophan metabolism, pyrimidine metabolism, and inositol phosphate metabolism, while concurrently suppressing auxin and ABA signaling pathways (Fig. \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAlthough soils contain substantial phosphorus, most of it exists in forms unavailable for plant uptake (Yan et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The orthophosphate that plants can directly take up is scarce in soil, making phosphorus a limiting nutrient for plant growth and development (Lopez-Arredondo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, the plant utilization efficiency of phosphorus fertilizers is only 15%-30% (Vance et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Cope with phosphorus deficiency in soil, plants enhance their phosphorus acquisition and internal utilization efficiency by modulating growth, development, and metabolic processes (Hu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lopez-Arredondo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, elucidating the mechanisms of phosphorus uptake is of great significance for improving plant PUE.\u003c/p\u003e \u003cp\u003eEthylene plays a key role in regulating the local response of plant roots to phosphorus deficiency (Nagarajan and Smith \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, the mechanisms by which exogenous ethylene participates in regulating phosphorus uptake in alfalfa roots have not been previously elucidated. This study primarily investigated the transcripts, metabolites, and regulatory pathways involved in the ethylene‑induced DP response in alfalfa roots. The DP stress response induced by ethylene can be categorized into local and systemic responses. Among these, local Pi sensing regulates the expression of a large set of PSR genes, initiating a series of adaptive responses to remodel root architecture that enhance phosphorus acquisition (Chiou and Lin \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Systemic Pi responses include the induction of high-affinity Pi transporters (Nagarajan and Smith \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which augment the acquisition capacity of available phosphorus through direct or indirect mechanisms in planta.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Effects of ethylene on root growth and phosphatase activity in alfalfa under DP conditions\u003c/h2\u003e \u003cp\u003eEthylene signaling plays a central role in regulating plant adaptive responses to phosphorus starvation stress (Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), typically manifested as inhibition of the primary root and promotion of lateral root and root hair development (Neumann \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Roldan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Shukla \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Song and Liu \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this study, both DP conditions and exogenous ACC inhibited root growth, with no stimulatory effect observed on roots, consistent with the effects of ethephon on wheat roots (Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, ACC significantly promoted root hair formation, and this observation may be related to the specific ACC concentrations used in our study. Achieving simultaneous enhancement of both root system and root hair growth is especially important for enhancing nutrient uptake efficiency in alfalfa.\u003c/p\u003e \u003cp\u003eEXPANSIN \u003cem\u003eproteins\u003c/em\u003e mediate plant cell wall loosening and are key regulators of root hair initiation and elongation (Lin et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In this study, the root hairs were only induced by ACC treatment. Correspondingly, the increase in root hairs enhances the absorptive surface area. And the expression of \u003cem\u003eEXPANSIN -related genes\u003c/em\u003e was upregulated to varying degrees under ACC treatment, suggesting their induction may contribute to the dense root hair phenotype observed. \u003cem\u003ePhosphate deficiency response 2\u003c/em\u003e (\u003cem\u003ePDR2\u003c/em\u003e) and \u003cem\u003ealuminum-activated malate transporter 1\u003c/em\u003e (\u003cem\u003eALMT1\u003c/em\u003e), in coordination with \u003cem\u003eLPR1\u003c/em\u003e, regulate the differentiation process of root apical meristems (Balzergue et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, \u003cem\u003ealuminum-activated malate transporter 12/2\u003c/em\u003e showed varying degrees of downregulation under ACC treatment, which may partially explain the observed inhibition of primary root growth. Auxin activates the MKK4/5-MPK3/6 signaling cascade via TMK1/4, thereby regulating cell division patterns during lateral root development (Huang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consistent with this, our results revealed the induction of TMK1/4 and MPK3/6 expression within the MAPK signaling pathway, suggesting their potential involvement in lateral root development.\u003c/p\u003e \u003cp\u003eEthylene also increases the expression of phosphate transporters in the \u003cem\u003ePHT1\u003c/em\u003e family (Feng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The expression levels of alfalfa \u003cem\u003ePHT1\u003c/em\u003e family genes were significantly increased under phosphorus-deficient conditions to enhance root phosphate uptake (Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). The PHO1 protein mediates phosphate transport from roots to shoots, and loss-of-function mutants of \u003cem\u003ePHO1\u003c/em\u003e exhibit significantly reduced shoot phosphorus content and delayed flowering (Dai et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, only ACC1 induced the expression of \u003cem\u003ePHO1\u003c/em\u003e and \u003cem\u003ePHT1;4\u003c/em\u003e under DP conditions, potentially promoting phosphorus uptake, consistent with results reported by (Roldan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEthylene downregulates the expression of the nitrate transporter gene \u003cem\u003eNRT2.1\u003c/em\u003e, reducing the nitrate uptake in plants under low-nitrogen conditions (Zheng et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the present study, total nitrogen content decreased under DP conditions. However, ACC application increased total nitrogen content, while the expression of the nitrogen transport-associated \u003cem\u003ehigh-affinity nitrate transporter 2.1\u003c/em\u003e was also increased. These findings suggest that ACC may simultaneously promote phosphorus and nitrogen uptake under DP conditions.\u003c/p\u003e \u003cp\u003eEthylene is a positive regulator of APase activity (Lei et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Exogenous ACC application induces the expression of \u003cem\u003ephosphorus starvation-induced\u003c/em\u003e (\u003cem\u003ePSI\u003c/em\u003e) genes and significantly upregulates root ACP activity (Nagarajan and Smith \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Secreted APases closely associated with the root surface facilitate the mobilization and utilization of soil‑bound phosphate. Overexpression of the \u003cem\u003eGmACP2\u003c/em\u003e in soybean hairy roots effectively increased endogenous ACP activity and improved phosphorus use efficiency by 15.43%-24.54% (Zhang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this study, both DP and ACC treatment increased phosphatase activity, and transcriptome data indicated that the expression of acid phosphatase-encoding genes was mostly upregulated under ACC treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Regulation of DP stress by the ethylene signaling pathway\u003c/h2\u003e \u003cp\u003eEthylene biosynthesis and signal transduction are modulated by systemic phosphorus signals, and \u003cem\u003eAP2\u003c/em\u003e/\u003cem\u003eERF\u003c/em\u003e genes may regulate the expression of \u003cem\u003ePSI\u003c/em\u003e genes (Bustos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A critical step in ethylene signaling involves the stabilization and activation of \u003cem\u003eEIN3\u003c/em\u003e and \u003cem\u003eEIL1\u003c/em\u003e transcription factors following ethylene perception. Activated \u003cem\u003eEIN3\u003c/em\u003e and \u003cem\u003eEIL1\u003c/em\u003e bind to the promoters of ethylene-responsive genes, including \u003cem\u003eERFs\u003c/em\u003e, thereby activating or repressing downstream gene expression (Shukla \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The functional diversity of the \u003cem\u003eERF\u003c/em\u003e gene family underlies the broad spectrum of ethylene‑mediated responses, ranging from growth regulation to responses to biotic and abiotic stresses (Zhao et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the current study, the ethylene signaling pathway was significantly enriched under DP and ACC treatments. However, further analysis is required to determine how downstream \u003cem\u003eERFs\u003c/em\u003e interact with GCC-box motifs in the promoters of ethylene-sensitive genes. Under DP conditions, ethylene upregulates the expression of the \u003cem\u003eEIN3\u003c/em\u003e/\u003cem\u003eEIL1\u003c/em\u003e transcription factor, which in turn induces the expression of the phosphate transporter gene \u003cem\u003ePHT1\u003c/em\u003e, promoting the formation of longer and denser root hairs to enhance the phosphorus uptake capacity of plants (Feng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the phosphorus signaling pathway, SPX proteins do not directly sense inorganic phosphorus signals under phosphorus starvation conditions, but instead respond to soluble inositol polyphosphates (Wild et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The InsP8-SPX complex, formed by the binding of InsP8 with SPX proteins, binds to the coiled‑coil (CC) domain of PHR transcription factors to regulate phosphorus homeostasis (Ried et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Reduced InsP8 content weakens the interaction between \u003cem\u003eSPX1\u003c/em\u003e and \u003cem\u003ePHR1\u003c/em\u003e, thereby activating the expression of \u003cem\u003ePSI\u003c/em\u003e genes and promoting phosphorus uptake and accumulation in plants (Poirier et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Low-phosphorus signals can induce \u003cem\u003ePHR1\u003c/em\u003e to activate downstream \u003cem\u003ePSR\u003c/em\u003e genes, while the expression of certain genes regulating phosphorus uptake, transport, and starvation responses partially depends on ethylene biosynthesis and signaling changes (Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, only ACC1 promoted the expression of the key phosphorus uptake gene encoding a \u003cem\u003eputative SPX domain-containing protein\u003c/em\u003e, and genes involved in inositol phosphate metabolism were significantly enriched under ACC treatment. These observations suggest that ACC enhances phosphorus uptake by promoting SPX‑mediated signaling and inositol phosphate metabolism.\u003c/p\u003e \u003cp\u003eIn this study, \u003cem\u003ePHR1\u003c/em\u003e gene expression was not detected. However, the \u003cem\u003eMYB\u003c/em\u003e family \u003cem\u003etranscription factor PHL5 isoform X2\u003c/em\u003e was induced under both DP and ACC treatments, suggesting its potential involvement in phosphorus uptake. The absence of detectable \u003cem\u003ePHR1\u003c/em\u003e expression may be attributable to the relatively higher phosphorus (100 \u0026micro;M) used in this study, to assess phosphorus-limiting conditions, which is greater than10 \u0026micro;M or 5 \u0026micro;M KH₂PO₄ used in other studies, potentially resulting in relatively low expression of DP stress-responsive genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Ethylene participates in the metabolism of sugars and lipids under DP conditions\u003c/h2\u003e \u003cp\u003eUnder DP conditions, plants accumulate sugars and starch in leaf tissues, and the expression of \u003cem\u003ePSI\u003c/em\u003e genes increases in response to elevated exogenous sugar levels (Nilsson et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Enhanced sucrose loading in leaves and its subsequent transport from shoots to roots promote the expression of \u003cem\u003ePSI\u003c/em\u003e genes (Lei et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The findings confirm that sugars function as systemic signals in the phosphorus signaling network(Shu Yi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Phosphorus limitation leads to reduced photosynthesis, accompanied by elevated levels of sugars and starch. Beyond serving as metabolic substrates, sucrose and other carbohydrates act as signaling molecules in the PSR and are also transported from shoots to roots via the phloem (Karthikeyan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Carbohydrates can also induce the expression of other genes responsive to phosphorus starvation stress (Hammond and White \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Karthikeyan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In rice, sugars such as sucrose, trehalose, and melibiose accumulate during phosphorus starvation and actively participate in downstream signaling (Yan et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, only a single gene was enriched in the glycolysis pathway under DP conditions. In contrast, a large number of genes were enriched in this pathway under ACC treatment, suggesting that ACC accelerated the glycolytic flux in alfalfa roots. Our results in alfalfa root indicate that the contents of sucrose, starch, and other sugars increased, and sucrose was significantly induced by ACC, suggesting that ACC promotes sugar accumulation and accelerates the DP stress response.\u003c/p\u003e \u003cp\u003eTo cope with phosphorus starvation, plants initiate a key process of lipid remodeling. During this process, membrane phospholipids are dephosphorylated to serve as an internal phosphorus source, while galactolipids and sulfolipids are incorporated into membrane systems to maintain functional integrity (Verma et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Major phospholipids involved in this process include phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), lysophosphatidylcholine (lysoPC), diacylglycerol (DAG), and triacylglycerol (TAG) (Hu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Verma et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additional responses include the substitution of phospholipids with galactolipids and sulfolipids, the activation of metabolic bypasses to conserve ATP, and the maintenance of cytosolic phosphorus homeostasis through the regulation of vacuolar phosphorus storage and release (Shu Yi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, ACC accelerated the decline in PC, promoted glycerophospholipid turnover, and enhanced the synthesis of galactolipids and sulfolipids, consistent with previously reported lipid remodeling under phosphorus deficiency. Our analysis identified a greater number of genes or metabolites enriched in these pathways, suggesting that ACC accelerates sugar and lipid metabolism as part of the adaptive response to DP stress in alfalfa roots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Ethylene participates in protective responses under DP conditions\u003c/h2\u003e \u003cp\u003ePhosphorus deficiency induces the accumulation of reactive oxygen species (ROS) in the roots of \u003cem\u003eArabidopsis\u003c/em\u003e seedlings (Tyburski et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Low-phosphorus stress increase the activities of antioxidant enzymes in soybean roots. Ethylene enhances ROS even under phosphorus‑sufficient conditions and elevates superoxide dismutase (SOD) and ascorbate peroxidase (APX) activities to levels comparable to those under phosphorus-deprived conditions (Yang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ethylene can regulate the activities of respiratory enzymes, including cytochrome oxidase and alternative oxidase, thereby influencing electron transport efficiency and cellular energy production (Xu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this study, among several antioxidant enzyme activities assayed in alfalfa roots, only CAT activity showed an increasing trend under ACC treatment, and the expression of oxidase genes was significantly induced by ACC. These results suggest that ethylene‑mediated activation of ROS‑related pathways may enhance plant tolerance to DP stress.\u003c/p\u003e \u003cp\u003ePhosphorus starvation induces the synthesis of secondary metabolites with antimicrobial and protective functions, such as flavonoids and glucosinolate (Pant et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which regulate plant immunity. Low-phosphorus conditions significantly affected the flavonoid biosynthesis pathway. Various flavonoids and their regulatory genes were found to be enriched, and they have been previously reported to be tightly associated with tolerance to phosphorus-limited stress in multiple plant species (Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Flavonoids are considered a secondary antioxidant system (Agati et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), typically increasing during nutrient stress to mitigate oxidative damage (Malus\u0026agrave; et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The \u003cem\u003eSPX1/3-PHR2\u003c/em\u003e regulatory network governing PSRs in \u003cem\u003eMedicago truncatula\u003c/em\u003e modulates flavonoid biosynthesis, thereby recruiting nitrogen-fixing microorganisms to facilitate nitrogen acquisition (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). In this study, most flavonoids showed increased accumulation in alfalfa roots, potentially participating in ROS scavenging and contributing to the establishment of beneficial AMF relationships under phosphorus limitation conditions. Although our experiment was conducted under hydroponic culture conditions, the observed flavonoid increases are consistent with these known functions.\u003c/p\u003e \u003cp\u003eThe glutathione synthesis pathway in alfalfa was enriched under Pi deficiency conditions (Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e), indicating its involvement in the stress response. Furthermore, under ACC treatment, an even greater number of genes were enriched in this pathway compared to those under DP treatment alone, suggesting that ACC enhances the glutathione pathway in response to DP stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Ethylene participates in hormone‑mediated responses to DP stress\u003c/h2\u003e \u003cp\u003eMultiple plant hormones participate in the response to phosphorus starvation (Chiou and Lin \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Under different phosphorus nutritional conditions, hormones such as strigolactones (SL), SA, JA, and ABA play distinct roles in biotic stress responses (Chan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Furthermore, the ethylene signaling pathway can interact with auxin, GA, ABA, JA and SA (Shukla \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), thereby coordinately regulating plant growth and stress responses. Phosphorus starvation also induces the synthesis of defense hormones, such as SA and JA (Castrillo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morcillo et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which regulate cellular phosphorus status or modulate PSR mechanisms to enhance plant adaptability against pathogens (Chan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). SA is considered a central integrator linking phosphorus starvation and immune activation (Chan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Under phosphorus-deficient conditions, plants can target genes in the JA and SA signaling pathways through \u003cem\u003ePHR1\u003c/em\u003e, thereby coordinating immune responses with PSR (Castrillo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Under such conditions, \u003cem\u003eArabidopsis PHR1\u003c/em\u003e also interacts with the JA pathway repressor JAZ proteins and the \u003cem\u003eMYC2\u003c/em\u003e (\u003cem\u003emyelocytomatosis proteins 2\u003c/em\u003e) \u003cem\u003etranscription factor\u003c/em\u003e to enhance JA signaling (He et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, SA and JA regulatory pathways were significantly enriched, and SA content decreased under ACC treatment. These results suggest that ACC activates defense mechanisms involving SA and JA in alfalfa roots.\u003c/p\u003e \u003cp\u003eAuxin is the key regulator of low‑phosphorus responses in rice root. Under phosphorus deficiency, auxin transport in the root system is enhanced, promoting primary root elongation, increased lateral root length and density, and root hair formation to facilitate adaptation to low-phosphorus conditions (Ding et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, IAA signaling pathway and cysteine metabolism pathway were enriched. Although IAA metabolite levels did not change, it can be inferred that ACC influences the IAA signaling pathway under DP conditions. Ethylene and ABA often exhibit antagonistic interactions, with ABA suppressing ethylene-mediated growth responses under stress conditions. This antagonistic relationship helps achieve a dynamic balance between growth and stress adaptation depending on the specific environment (Yang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this study, ABA signalling was suppressed, as evidenced by reduced ABA accumulation and downregulation of signal transduction-related genes. Furthermore, the ABA response was modulated via auxin-mediated induction of MTK1/4 and ABI1/2. Ethylene also interacts with GA to regulate growth, and the two hormones frequently exert opposing effects, such as in stem elongation and fruit ripening (Yang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The GA-DELLA signaling pathway regulates the architecture remodeling of the shoot/root system and root hair elongation under phosphorus starvation stress (Jiang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Our data on alfalfa indicate that within the GA signaling pathway, GA37 content increased in shoots, while \u003cem\u003eDELLA protein\u003c/em\u003e accumulation increased in roots.\u003c/p\u003e \u003cp\u003eIn this study, hormone-related pathways such as IAA signaling were enriched. Notably, ABA and SA showed significant decreases, and genes involved in other hormone signal transduction pathways were detected. This directly or indirectly demonstrates the effect of ACC on these hormones under DP conditions. Furthermore, according to our findings, ABA and SA decreased across all three ACC concentrations applied, indicating that ACC inhibited ABA and SA. Although ACC appears to interact closely with several hormonal pathways under DP conditions, the precise mechanisms underlying these interactions require further in-depth investigation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study investigated the mechanisms by which ACC regulates the response of alfalfa roots to DP stress through integrated morphological, physiological, transcriptomic, and metabolomic analyses. The results indicate that ACC remodels root system architecture under DP by inhibiting primary root elongation, while promoting root hair formation. Ethylene precursor promoted the accumulation of starch, sucrose, and organic acids, enhanced the activities of ACP and CAT, and increased total phosphorus and total nitrogen contents. Ethylene precursor induced the expression of a limited set of DP stress-responsive genes such as \u003cem\u003ePHO1\u003c/em\u003e and \u003cem\u003eSPX\u003c/em\u003e genes. Both DP and ethylene precursor conditions modulated flavonoid, starch, sucrose, glutathione, fructose, mannose, galactose, glycerolipid, and glycerophospholipid metabolism, as well as fatty acid degradation. Furthermore, ethylene specifically promoted glycolysis, the TCA cycle, glycosphingolipid, tryptophan, pyrimidine, and inositol phosphate metabolism in response to DP stress. These findings provide a theoretical framework and identify key genes within ethylene signaling or metabolic pathways that may be targeted to improve PUE in alfalfa.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhenyi Li: Experiment design, Data collection, Writing and editing. Na Guo: Data collection and curation, Validation. Jiarong Li, Xiaotong Duan and Aodun: Investigation, Validation, Data collection and analysis,. Fang Tang, Zhiqiang Zhang and Fengling Shi: Experiment design, Writing-review.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32301484), Inner Mongolia Autonomous Region Natural Science Foundation General Program (2025MS03136), Grassland Talents Program of Inner Mongolia Autonomous Region, Scientific Research Foundation for Advanced Talents by Inner Mongolia Agricultural University (NDYB2022-51), The First-Class Discipline Scientific Research Program of Inner Mongolia (IMAUCXQJ2023015), Scientific Research Funding for Universities Directly under the Inner Mongolia Autonomous Region (BR22-12-07), 2025 Key Laboratory of Grass Seed Innovation and Sustainable Grassland Resource Utilization, Inner Mongolia Autonomous Region (2025KYPT0033). Key Laboratory of Grassland Ecological Protection, Inner Mongolia Autonomous Region Project-Identification, Evaluation and Creation of New Germplasm of High-Quality Forage (2025KYPT0148).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw sequence data reported in this paper have been deposited in the Genome Sequence Archive ( https://dataview.ncbi.nlm.nih.gov/object/PRJNA1419934 ).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCRediT authorship contribution statement\u003c/b\u003e \u003c/p\u003e \u003cp\u003eZhenyi Li: Experiment design, Data collection, Writing and editing. Na Guo: Data collection and curation, Validation. Jiarong Li and Xiaotong Duan: Investigation, Validation, Data collection and analysis,. Fang Tang, Zhiqiang Zhang and Fengling Shi: Experiment design, Writing-review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgati G, Azzarello E, Pollastri S, et al. 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Environ Sci Technol. 2016;50(12):6485\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Medicago sativa, Deficient phosphate, Ethylene, Phosphorous starvation response, Hormone signal transduction","lastPublishedDoi":"10.21203/rs.3.rs-9350064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9350064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhosphorus is an essential nutrient for plant growth and development. However, the deficient available phosphorus in soil has become a critical factor limiting the improvement of forage yield and quality. Although ethylene is known to participate in plant responses to phosphorus starvation stress, its regulatory roles in alfalfa (\u003cem\u003eMedicago sativa\u003c/em\u003e) remain poorly understood. In this study, the alfalfa roots were used to investigate the morphology and physiological traits, gene expression, and metabolite profiles after seven days of treatments under normal phosphorus (NP), deficient phosphate (DP) and DP supplemented with 1, 10, and 100 \u0026micro;M of the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC). The results showed that ACC significantly inhibited alfalfa root growth under DP conditions, reducing total root length, while concurrently stimulating root hair formation. ACC promoted the accumulation of starch, sucrose, and organic acids, and altered the levels of hormones such as abscisic acid (ABA), gibberellin (GA), and salicylic acid (SA). Transcriptome analysis identified 761, 2142, 2488, and 2607 differentially expressed genes (DEGs) in TDP, TACC1, TACC10 and TACC100 groups compared with TNP, respectively. ACC induced the expression of genes such as the \u003cem\u003eglycerol-3-phosphate transporter\u003c/em\u003e, \u003cem\u003epurple acid phosphatase\u003c/em\u003e, \u003cem\u003ephosphate transporters\u003c/em\u003e. A total of 926 significantly differentially accumulated metabolites (DAMs), with a total of 266, 378, 504, and 570 identified in the groups of DP, ACC1, ACC10 and ACC100, respectively, versus NP conditions. ACC induced the accumulation of metabolites such as sucrose in the carbohydrate category, while it suppressed the accumulation of fatty acyls, sphingolipids, and the hormones ABA and SA. Integrated transcriptome and metabolome analysis revealed that DP and ACC co-regulated metabolic pathways including starch and sucrose metabolism, glutathione metabolism, flavonoid biosynthesis, fatty acid degradation, and hormone signal transduction. Ethylene also specifically induced glycolysis, the citrate cycle, tryptophan metabolism, and inositol phosphate metabolism in response to DP stress. These findings will provide a theoretical framework for improving phosphorus utilization efficiency in alfalfa.\u003c/p\u003e","manuscriptTitle":"Ethylene-mediated regulation of root responses to deficient-phosphate stress based on transcriptomic and metabolomic analyses in alfalfa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 15:56:18","doi":"10.21203/rs.3.rs-9350064/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-05T16:29:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8488285581452270089938928517634483887","date":"2026-04-29T14:15:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216933456147902126765124471855727123735","date":"2026-04-29T10:01:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T08:14:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T06:24:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-14T16:41:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-04-14T15:51:24+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":"4d6ed32f-90f1-466c-ad0d-b7b9319f88cc","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T16:29:23+00:00","index":51,"fulltext":""},{"type":"reviewerAgreed","content":"8488285581452270089938928517634483887","date":"2026-04-29T14:15:57+00:00","index":49,"fulltext":""},{"type":"reviewerAgreed","content":"216933456147902126765124471855727123735","date":"2026-04-29T10:01:48+00:00","index":46,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T15:56:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 15:56:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9350064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9350064","identity":"rs-9350064","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}