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. 2026 Jan 3;26:197. doi: 10.1186/s12870-025-08060-x

Integrative physiological and transcriptomic insights into the low-phosphorus stress response of Pinus yunnanensis var. pygmaea

Feng Xiao 1, Yang Zhao 1,✉, Xiurong Wang 1, Xueyan Jian 2, Yao Yang 1
PMCID: PMC12866282  PMID: 41485057

Abstract

Phosphorus is an essential macronutrient regulating plant growth and development, yet the adaptive mechanisms to phosphorus deficiency in conifers remain poorly understood. In this study, we investigated the physiological and transcriptomic responses of Pinus yunnanensis var. pygmaea under prolonged low-phosphorus stress and compared its performance with Pinus massoniana and Pinus elliottii. Physiological analyses revealed that P. pygmaea is more sensitive to phosphorus deficiency, exhibiting greater metabolic impairment under stress conditions. Transcriptome profiling showed a time-dependent increase in differentially expressed genes enriched in amino acid metabolism, MAPK signaling, and flavonoid biosynthesis pathways. Weighted gene co-expression network analysis (WGCNA) identified potential regulatory interactions among phosphate-responsive genes, including SPX, ACP5, and ACP7, and transcription factors such as TALE, WRKY, and ERF, suggesting a coordinated role in maintaining ion homeostasis. These findings provide new insights into the molecular mechanisms of phosphorus stress adaptation in Pinus and offer a broader framework for understanding nutrient deficiency responses in woody plants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-08060-x.

Keywords: Pinus yunnanensis var. pygmaea, Low phosphorus, Transcriptomic, WGCNA

Introduction

Phosphorus is a crucial essential mineral element for the growth and development of plants, playing a significant role in processes such as root growth, nutrient absorption, and photosynthesis [1]. Although soil contains abundant phosphorus elements, around 80% of phosphorus exists in organic or mineral salt forms, which cannot be absorbed and utilized by plants [2]. The concentration of available phosphorus in most soils ranges from 0.1 to 10 mmol/L. During the growing season, phosphorus fertilizer utilization rate is only around 15%, with residual effects not exceeding 25% [3]. The excessive application of phosphorus fertilizers in pursuit of high yields can lead to environmental pollution, which is not in line with the principles of sustainable development. In response to low phosphorus stress, plants improve phosphorus uptake and homeostasis through morphological, physiological, and molecular changes, such as altering root morphology and structure [4]. To enhance phosphorus absorption, roots increase their length [5], activate insoluble phosphate in the soil by secreting organic acids, and utilize organic phosphorus in the rhizosphere through the secretion of acid phosphatases (ACP) [6]. Plants can enhance their antioxidant systems to protect themselves from oxidative damage and improve their adaptability to phosphorus deficiency [7, 8]. Differences in phosphorus utilization efficiency and genetic variation contribute to variations in the ability of different pine species to absorb and utilize phosphorus from the soil [9].

Pinus massoniana Lamb. is the most widely distributed species within the genus Pinus in China, mainly distributed from 21°41′N to 33°56′N and 102°10′E to 123°14′E [10]. Pinus yunnanensis var. pygmaea (P. pygmaea) is a variant of Pinus yunnanensis, exhibits a clear absence of apical dominance with an inconspicuous main trunk, the lack of CK and the massive accumulation of ABA and GA-related hormones may be the reasons for the loss of shoot apical dominance and the formation of multi-branching in P. pygmaea [11]. P. pygmaea has narrower niche and more adaption for extreme ecological conditions [12]. Compared with P. massoniana and P. elliottii, P. pygmaea exhibits stronger drought tolerance and greater recovery ability under prolonged drought conditions [13]. Phosphorus is the primary nutrient limiting factor for growth and biomass accumulation during the wet fertilization period of pine seedlings, and there are significant genotype differences in phosphorus efficiency [14]. Phosphorus stress increases the root malondialdehyde (MDA) of P. massoniana seedlings and affects their root vitality [15]. Phosphorus promoted the relative growth rate of P. massoniana seedlings [16]. The total biomass and aboveground biomass proportion increased with substrate phosphorus content in P. massoniana [17]. P. massoniana adapts to low phosphorus by developing shallower roots with a reduced branching intensity and promoting the conversion of starch to soluble sugar [18]. When facing low phosphorus environments, P. yunnanensis exhibits strong adaptability, including rhizosphere acidification, increased primary root length, which are considered effective strategies to cope with low phosphorus environments [19, 20]. Different pine trees exhibit differences in their ability to absorb and utilize phosphorus from the soil.

Pinus species exhibit significant genetic variation in phosphorus efficiency [21]. However, fewer comparative studies about low phosphorus have been conducted between pines, particularly involving P. pygmaea. In plant species lacking a reference genome or possessing only a partially sequenced one, transcriptome provides an effective approach to explore transcriptomic profiles and identify genes involved in specific biological processes [22]. Studying the response of pines to low phosphorus stress is of significant importance for understanding the absorption and utilization mechanisms of phosphorus in Pinus species, as well as the changes in gene expression and metabolites. In this study, the physiological responses of three pines (P. pygmaea, P. elliottii, P. massoniana) under low phosphorus conditions were assessed, and the transcriptome of P. pygmaea at different times of phosphorus stress was determined.Transcriptome analyses were conducted to identify differentially expressed genes under low phosphorus. Weighted gene co-expression network analysis (WGCNA) was conducted to identify pathways and central genes related associated with key physiological trait genes. Network co-expression analysis based on physiological and transcriptomic expression levels was conducted, aiming to provide a theoretical basis for understanding the dynamic response of P. pygmaea to low phosphorus, a foundation can be provided for identifying and developing the adaptive mechanisms of low-phosphorus-tolerant varieties.

Materials and methods

Experimental materials

An investigation and collection of early seed resources were conducted, and seeds from half-sib progeny of P. pygmaea, P. massoniana, and P. elliottii were sourced from the high-quality half-sibling progeny at Ma'anshan Seed Garden (26°16’ N, 107°31’ E) in Duyun City, Guizhou Province, China. These seeds were then planted in a greenhouse using a soil medium composed of a 1:3 mixture of humus and yellow loam. Three species were grown under identical nutrient and environmental conditions. One-year-old seedlings of P. massoniana, P. elliottii, P. pygmaea were selected as experimental subjects. The substrate was replaced with perlite, and the three pine seedlings were subjected to acclimatization treatment for three months. After acclimatization, they were subjected to severe low-phosphorus stress (nutrient solution P concentration of 0.5 mg/L) [23]. The nutrient solution used a modified Hoagland formula for the abundant elements [15]. Nutrient solution (100 mL per pot) was added every three days. Mature needles under different time points after low phosphorus stress treatment were collected. Sample collection was conducted between 9–10 a.m, to minimize the influence of circadian rhythms. Three biological replicates were collected for physiological and transcriptome sequencing. Samples were rinsed with double-distilled water and then flash-frozen in liquid nitrogen for at least half an hour, then storage at −80°C in an ultra-low temperature freezer.

The determination of physiological indicators

The determination of acid phosphatase (ACP) utilized p-nitrophenyl phosphate disodium (pNPP) [24]. Soluble protein was quantified by Coomassie Brilliant Blue G-250 staining [25], while soluble sugar (SS) content was measured usingthe phenol–sulfuric acid method [26]. Catalase (CAT) activity was assayed with the ammonium molybdate method [27], and starch concentration was measured by anthrone colorimetric method [28]. Non-structural carbohydrates (NSC) were calculated as the sum of soluble sugar and starch contents [29]. All measurements were conducted with three biological replicates. All statistical analyses were carried out in R. A least significant difference test (LSD) was used at a probability level of 0.05 to verify the significance between species at sample point.

RNA extraction and transcriptome sequencing

RNA was extracted using TRIzol reagent. RNA quality and integrity were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA) for quantification and an Agilent 5300 Bioanalyzer (Agilent Technologies, USA) for quality assessment. All RNA samples satisfied the required standards: total RNA ≥ 1 μg, concentration ≥ 35 ng/μL, A260/A280 ≥ 1.8, and A260/A230 ≥ 1.0. Sequencing libraries were prepared through mRNA extraction, cDNA synthesis, fragmentation, adapter ligation, size selection, PCR amplification, and purification, and subsequently sequenced on the NovaSeq 6000 platform. All raw Illumina sequencing data have been publicly deposited in the NCBI SRA database under BioProject accession number PRJNA1015740.

Gene functional annotation and DEGs analysis

Following quality control and filtering [30], the high-quality reads were mapped to the PacBio SMRT full-length transcriptome of P. pygmaea (PRJNA994856) [13] using Bowtie2 v2.4.3 [31]. the quantification of gene expression (calculated as fragments per kilobase of transcript per million mapped reads (FPKM) was performed using RSEM v1.2.12 [32]. Principal component analysis (PCA) was performed after normalizing the expression levels of all samples. Differentially expressed genes (DEGs) were identified using DESeq2 v1.48.2 [33] based on read counts, with thresholds set at an adjusted p-value < 0.05 and |fold change|> 2. Functional enrichment analyses of DEGs were performed with the ClusterProfiler v4.8.1 R package. Gene expression dynamics were further examined using STEM (Short Time-series Expression Miner) [34]. Gene set enrichment analysis (GSEA) was conducted based on the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Weighted gene co-expression network analysis (WGCNA) was conducted using the WGCNA v1.72.1 R package [35]. All DEGs with FPKM > 10 in at least one sample were included for network construction, with parameters set to minModuleSize = 30 and mergeCutHeight = 0.25.

Quantitative real-time PCR analysis (qRT-PCR)

qRT-PCR analysis of randomly selected genes was performed to examine the reliability of RNA-seq data. The primers were designed using primer Primer Premier and listed in Supplemental Tab S1. qRT-PCR was performed on a real-time CFX96 Touch Real-Time PCR System (Bio-Rad, USA). Each 50 μL reaction included: 25 μL of 2X SYBR® Green PCR Master Mix, 1μL of Forward/Reverse primer (10 μM), 2.0 μL of cDNA template, and 21 μL of RNase-free water. The PCR reaction conditions were as follows: preheating at 95 °C for 30 s, 40 cycles of heat denaturation at 95 °C for 5 s, annealing at 60 °C for 34 s. The relative gene expression was calculated by the 2−ΔΔCT method [36].

Results

The physiological response of three pine species under continuous low phosphorus conditions

The responses of three pine species under severe phosphorus stress were determined. The activity of ACP in P. massoniana was significantly higher than that in the other two pine species, and it showed an increasing trend with prolonged low-phosphorus stress (Fig. 1a). At 60 days under juvenile stress, the ACP activity of P. massoniana reached 0.36 ± 0.01 μmol/min/g.Meanwhile, the CAT activity in P. pygmaea showed a significant increase at 48 d and 60 d (Fig. 1b), which may be related to the higher content of H202 during the 24d-45d stress period (Fig. 1c). At 60 days, the CAT activity of P. pygmaea was 467.08 ± 28.44 μmol/min/g, significantly higher than 218.15 ± 5.35 at 0 days. The H202 content of P. pygmaea reached its peak of 17.38 ± 0.6 μmol/g at 36 days. The soluble sugar content in P. elliottii and P. pygmaea showed significant differences compared to 0D in the later stage of stress (Fig. 1d). P. massoniana exhibited a continuous increase in soluble sugars, starch (Fig. 1e), and NSC (Fig. 1f) over time.

Fig. 1.

Fig. 1

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Physiological changes of three pine trees under low phosphorus conditions. a Acid phosphatase (ACP); b Catalase (CAT); c Hydrogen peroxide (H2O2); d Soluble sugars (SS); e Starch; f Non structural carbohydrates (NSC); error bars indicate the standard error, the number of seedlings measured for each species was n = 3; the left Y-axis represented value of physiological traitors, “CK” group of each species was set as a reference to compare the mean by the Student's t-Test, “ns” means no difference,”*” P ≤ 0.05;”**” P ≤ 0.01;”***” P ≤ 0.001;”****” P ≤ 0.0001. The position of the significant difference marker was above the letter

Transcriptome PCA analysis and identification of DEGs

Samples of needles from P. pygmaea under different time points after low phosphorus stress treatment were collected, and a total of 18 libraries were established. RNA-seq sequencing was conducted based on the Illumina platform.After quality trimming, clean reads from 21102625 to 22465505 were generated, with an error rate of less than 0.01%. The Q20 and Q30 values of each sample were no less than 95.24% and 91.89%, respectively, and the duplication rate ranged from 4.23% to 7.08%. After quality control, alignment, and quantification, PCA analysis was performed based on the expression needles of samples. The results indicated good repeatability within the groups, and separation between the treatment and control groups, showing good specificity (Fig. 2a). DEGs identification was carried out using the R package DESeq2. Significant differences in gene expression were identified compared to 0 d at 12 d, 24 d, 36 d, 48 d, and 60 d (Fig. 2b). Compared to 0 d, 3293 (59.48%) down-regulated genes and 2243 (40.52%) up-regulated genes at 60d.

Fig. 2.

Fig. 2

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Transcriptome PCA and DEGs quantity analysis (a): PCA analysis; b Differential expression gene statistics. Note: in (a), different colors and shapes represent different combinations

STEM analysis and GO enrichment analysis

In order to investigate the gene expression trends under low-phosphorus stress, STEM was used to cluster all DEGs, resulting in 11 significantly distinct profiles (p-value < 0.05). Two profiles were selected for visualization. Profile 8 (0, −1, −2, −3, −4, −5) showed a continuous decreasing expression trend over time (Fig. 3a). GO enrichment analysis revealed that biological processes (BP) such as DNA unwinding involved in DNA replication, cytoskeleton-dependent cytokinesis, cellulose biosynthetic process, and cytokinesis were enriched (Fig. 3c). KEGG enrichment analysis showed pentose and glucuronate interconversions, cutin, suberine and wax biosynthesis, and cytoskeleton proteins [BR:ko04812] were enriched. Profile 39 (0, 1, 2, 3, 4, 5) exhibited a continuous increasing expression trend over time (Fig. 3b). GO enrichment analysis revealed BP terms such as aspartate family amino acid catabolic process, serine family amino acid biosynthetic process were enriched (Fig. 3d). KEGG enrichment analysis showed MAPK signaling pathway-plant, and flavonoid biosynthesis were enriched.

Fig. 3.

Fig. 3

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Stem analysis and GO enrichment analysis (a): Profile 8 trend chart; b Profile 39 trend chart; c GO enrichment in Profile 8 gene set; d GO enrichment in Profile 39 gene set

GSEA analysis of GO terms for different comparison groups revealed that compared to 0 d, the MAPK signaling pathway—plant was activated, while the Cell cycle was inhibited at 36 d, 48 d, and 60 d (Fig. 4a-c). Visualization of GSEA enrichment for multiple comparison groups of the same pathway showed that the reactive oxygen species metabolic process was significantly enriched (p-value < 0.05) in the P36_vs_P0, P48_vs_P0, and P60_vs_P0 groups, with NES > 1, indicating activation of this term. Cell growth was significantly enriched in the P36_vs_P0, P48_vs_P0, and P60_vs_P0 groups with NES < −1, indicating inhibition of this cell growth term. Meristem development was significantly enriched in the P12_vs_P0, P24_vs_P0, P36_vs_P0, P48_vs_P0, and P60_vs_P0 groups, with NES < −1, indicating inhibition of this meristem development term.

Fig. 4.

Fig. 4

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Gene Set Enrichment Analysis (GSEA) analysis. a P36_vs_P0 GSEA analysis; b P48_vs_P0 GSEA analysis; c P60_vs_P0 GSEA analysis; d Multiple combinations of reactive oxygen species metabolic processes enriched in GSEA; e Multiple combinations of cell growth GSEA enrichment; f Multiple combinations of meristem development GSEA enrichment

Co-expression analysis of needles in P. pygmaea

WGCNA analysis revealed a total of 11 co-expressed gene modules associated with traits (Fig. 5a). The paleturquoise module comprised 535 genes, which were significantly negatively correlated with ACP (r = −0.78, p = 0.00012), and exhibited significant correlation between genes within the module and traits (Fig. 5b). Genes within the module showed suppressed expression in the later stages of stress (Fig. 5f). GO enrichment analysis revealed regulation of pH (GO:0006885), regulation of cellular pH (GO:0030641), regulation of intracellular pH (GO:0051453), export across plasma membrane (GO:0140115), and response to toxic substance (GO:0009636) were enriched. KEGG enrichment analysis showed brassinosteroid biosynthesis, cytochrome P450, and cAMP signaling pathway were enriched. There were 339 genes in the green module, which were positively correlated with traits such as H2O2, SS, Starch, and NSC. GO enrichment analysis revealed cellular response to phosphate starvation and phosphate ion homeostasis were enriched. KEGG enrichment analysis showed the inositol phosphate metabolism pathway was enriched. Genes within the green module exhibited higher expression at 36 d (Fig. 5g). There were 367 genes in the lightyellow module, which showed a significant negative correlation with CAT and Starch. GO enrichment analysis revealed cellular response to light intensity, response to light intensity, and regulation of chlorophyll metabolic process were enriched. KEGG enrichment analysis showed Photosynthesis-antenna proteins, Lysosome, and Brassinosteroid biosynthesis were enriched. Genes within the lightyellow module exhibited a continuous decrease in relative expression after 24 d of stress (Fig. 5h). There were 437 genes in the steelblue module, which showed a significant negative correlation with ACP and CAT (Fig. 5i). GO enrichment analysis revealed that biological process terms such as polysaccharide biosynthetic process were enriched. KEGG enrichment analysis showed Phenylpropanoid biosynthesi, Pentose and glucuronate interconversions, and Fructose and mannose metabolism were enriched. To validate the accuracy of RNA-seq data, seven genes were randomly selected for the qRT-PCR analysis. The qRT-PCR results showed that the expression patterns of genes were similar to their results in the RNA-seq (Fig. S1).

Fig. 5.

Fig. 5

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Module-trait relationships from the WGCNA analysis. a Module-phenotypic correlation network; b a scatterplot of GS for ACP vs. MM in the paleturquoise module; c a scatterplot of GS for NSC vs. MM in the green module; d a scatterplot of GS for starch vs. MM in the lightyellow module; e a scatterplot of GS for CAT vs. MM in the steelblue module; f the eigengene expression of paleturquoise module; g the eigengene expression of green module; h the eigengene expression of lightyellow module; i the eigengene expression of steelblue module. Note: The correlation coefficients and corresponding correlation coefficients and p-values between modules and different traits were displayed in each cell; the left panel showed the 11 modules with different colors, the colour scale on right showed module-trait correlation from − 1 (blue) to 1 (red). The gene trend in the module was processed by logarithmic processing of the mean value of gene expression in the module

In the green module, 4 ACP5 (acid phosphatase, D1_transcript_95568, D1_transcript_2797, D1_transcript_124297, D1_transcript_23707), 4 ACP7 (D1_transcript_16258, D1_transcript_84124, D1_transcript_118602, D1_transcript_45236), and 2 SPX domain-containing proteins (D1_transcript_76918, D1_transcript_34172) were annotated. Module network visualization of SPX, ACP5, and ACP7-related genes in the green module revealed that TALE, WAKY, ERF, and other genes exhibited high weight values (Fig. 6), indicating their crucial regulatory roles with SPX, ACP5, and ACP7.

Fig. 6.

Fig. 6

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Part co-expression network of target genes in the green module. Note: Genes filled with different colors in the middle represent target genes. The circular arcs represent homologous genes, and 'Pp' was an abbreviation for 'D1_transcript_'. The outer ring conswasts of gene families connected, arranged in order of the number of associated gene families. The points within each gene family were randomly dwastributed within the circle, and the radius of the circle was related to the number of dwastributed points. The positions of the outer points were calculated using the 'chull' function and connected and filled. Connections between points with a weight < 0.3 were mapped to 'grey81', while connections between points with a weight > 0.3 were mapped to different colors based on the factor 'nodeName'. Connections between structural genes were mapped to 'red'

Discussion

Low phosphorus levels will reduce biomass and yield, and inhibit photosynthesis in leaves [37]. Under low phosphorus stress, the levels of proline, soluble sugars, and malondialdehyde, as well as the activities of antioxidant enzymes, significantly increased in the roots and leaves of three different sources of Pueraria lobate [38], the activity of ACP secreted by the roots of Larix gmelini increases [39]. Low phosphorus levels increase the activity of ACP and relocate soluble phosphorus from underground to aboveground [40]. The content of H2O2 and MDA, as well as the activities of SOD, POD, CAT, ACP, and PAP, will be influenced by different phosphorus stresses [41]. NSC exists more in the form of soluble sugar in needles in low-P conditions of P. massoniana [42]. NSC was allocated as starch in fine roots and as soluble sugar in needles [17]. The high growth and photosynthetic characteristics of P. massoniana seedlings vary significantly under different phosphorus concentrations [43]. Seedlings respond to low phosphorus stress by increasing root ACP activity, organic acid content, and antioxidant enzyme activity [44]. CAT activity in shoots was positively and significantly correlated with tiller number in both low phosphorus and normal conditions of rice [45]. Antioxidant enzyme activities, MAD content, acid phosphatase activity, and organic acid content all decreased with P addition in P. massoniana [46]. Low P availability reduced the activities of antioxidant enzymes and increased the content of malondialdehyde in the cotton genotypes [47]. Enzyme activity assays conducted on the needles of three pine species under severe low-phosphorus conditions indicate that the activity of ACP in P. massoniana was significantly higher than in the other two pine species. Moreover, with prolonged low-phosphorus stress, its activity shows an increasing trend (Fig. 1a). The soluble sugar content in both P. elliottii and P. massoniana showed significant differences in the late stages of stress compared to 0 days (Fig. 1d). The CAT activity in P. pygmaea showed a significant increase at 48 d and 60 d (Fig. 1b), which may be related to the higher levels of H2O2 during the 24d-45d stress period (Fig. 1c). Moreover, the H2O2 content was higher in P. pygmaea compared to P. massoniana and P. elliottii. This indicates that compared to P. massoniana and P. elliottii, P. pygmaea was more sensitive to low phosphorus and suffers more severe damage under low phosphorus conditions.

Samples of P. pygmaea needles under different levels of severe low phosphorus stress were collected and subjected to RNA-seq sequencing on the Illumina platform. Profile 8 (0, −1, −2, −3, −4, −5) exhibited a decreasing expression trend over time (Fig. 3a). GO enrichment analysis revealed enrichment in BP terms such as DNA unwinding involved in DNA replication, cytoskeleton-dependent cytokinesis, cellulose biosynthetic process, and cytokinesis (Fig. 3c). Profile 39 (0, 1, 2, 3, 4, 5) showed an increasing expression trend over time (Fig. 3b). KEGG enrichment analysis showed the MAPK signaling pathway-plant and flavonoid biosynthesis were enriched(Fig. 3b). GSEA analysis of different comparisons revealed activation of the MAPK signaling pathway-plant and suppression of the Cell cycle compared to 0 d at 36 d, 48 d, and 60 d (Fig. 4a-c). MAPKs can phosphorylate a range of substrates, including transcription factors, leading to changes in gene expression that enable the plant to respond to the initial signal [48]. For the sensitive species oxidative stress with accumulation of reactive oxygen species may not be balanced with a suitable antioxidation strategies either by enzymatic or non-enzymatic cascades [49]. Visualization of GSEA enrichment for multiple comparisons showed activation of the reactive oxygen species metabolic process term and suppression of cell growth and meristem development terms. This indicates that during P. pygmaea low phosphorus stress, processes such as cytokinesis, meristem development, cell growth, and the cell cycle were suppressed, while reactive oxygen species metabolic process and MAPK signaling pathway were activated.

WGCNA analysis revealed a total of 11 co-expression gene modules associated with traits (Fig. 5a). The green module contained 339 genes, positively correlated with traits such as H2O2, SS, starch, and NSC. GO enrichment analysis showed enrichment in processes like cellular response to phosphate starvation (GO:0016036) and phosphate ion homeostasis (GO:0055062). KEGG enrichment analysis indicated enrichment in the inositol phosphate metabolism pathway (ko00562). In the green module, gene expression is relatively high at 36 d (Fig. 5g). Within the green module, there were 4 genes annotated as acid phosphatase 5 (ACP5) (D1_transcript_95568, D1_transcript_2797, D1_transcript_124297, D1_transcript_23707), 4 ACP7 (D1_transcript_16258, D1_transcript_84124, D1_transcript_118602, D1_transcript_45236), and 2 genes annotated as SPX domain-containing proteins (D1_transcript_76918, D1_transcript_34172). Eight genes (SPX1, SPX3, SPX4, PHT1;1, PAP23, SQD1, SQD2, NPC4) and five genes (SPX1, SPX3, SPX4, PAP23, SQD1) were significantly up-regulated in the roots and leaves of both line 27 and line 9 under low-phosphorus stress in P. elliottii [50]. Transcription factors can bind to the promoter/enhancer regions of genes, regulating gene expression and playing crucial roles in responding to environmental stresses. Visualizing the module network of SPX, ACP5, and ACP7 related genes within the green module revealed that TALE, WRKY, and ERF genes had high weight values, indicating that they were key regulatory factors. Overexpression of GmACP1 in soybean hairy roots can increase phosphorus efficiency by 11–20% [51]. Inositol polyphosphates (InsPs) are a diverse group of phosphate storage and signaling molecules found widely in both animals and plants [52]. Proteins containing SPX domains are associated with phosphate sensing and metabolism in eukaryotes [53]. PmWRKY11,12, and 13 in P. massoniana were upregulated under low phosphorus concentrations of P1 (phosphorus content of 0.01 mM) and P2 (phosphorus content of 0.06 mM), indicating their involvement in the response to phosphorus starvation [10]. These findings suggest that SPX, ACP5, and ACP7 may interact with TALE, WRKY, and ERF in the cellular response to phosphate starvation, playing a crucial role in maintaining ion homeostasis. This integrated regulatory network highlights the complexity of phosphorus signaling and adaptation in conifers. Importantly, elucidating these molecular mechanisms provides a foundation for improving phosphorus-use efficiency in forest trees through genetic selection or molecular breeding. Future research should focus on functional validation of key regulatory genes via transgenic or genome-editing approaches, investigation of cross-talk between phosphorus signaling and other nutrient or hormonal pathways, and exploration of natural allelic variation in SPX- or WRKY-related genes among conifer species to better understand adaptive evolution under nutrient-limited environments.

Conclusions

Under severe low phosphorus conditions, differences in the physiological responses of P. massoniana, P. elliottii, and P. pygmaea needles to low phosphorus were observed. Compared to P. massoniana and P. elliottii, P. pygmaea was more sensitive to low phosphorus and suffers more severe damage under low phosphorus conditions. During P. pygmaea low phosphorus stress, processes such as cytokinesis, meristem development, cell growth, and the cell cycle were inhibited, while reactive oxygen species metabolic process and MAPK signaling pathway were activated. WGCNA analysis revealed that SPX, ACP5, and ACP7 may interact with TALE, WRKY, and ERF in the cellular response to phosphate starvation (Fig. 7), playing a crucial role in maintaining ion homeostasis. Collectively, these findings provide new insights into the molecular and physiological mechanisms underlying phosphorus deficiency adaptation in conifers. They not only enhance our understanding of nutrient stress resilience in pines but also offer potential molecular targets for improving nutrient use efficiency and promoting sustainable forestry management in phosphorus-poor ecosystems.

Fig. 7.

Fig. 7

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Response model of Pinus yunnanensis var. pygmaea to prolonged low-phosphorus stress

Supplementary Information

12870_2025_8060_MOESM1_ESM.xlsx (9.9KB, xlsx)

Supplementary Material 1: Table S1.Primers for genes in qRT-PCR.

12870_2025_8060_MOESM2_ESM.pdf (40.3KB, pdf)

Supplementary Material 2: Fig S1. Relative expressions of selected genes in P. pygmaea.

Acknowledgements

Not applicable.

Clinical trial

Not applicable.

Authors’ contributions

X.F: Writing – original draft, Data curation, Software. Y.Y.: Writing – original draft, Resources. W.X.R.: Review & editing. J.X.Y.: Software, Resources & editing, Methodology. Z.Y.: Writing – review and Resources, funding acquisition.

Funding

This research was funded by the Guizhou Provincial Major Scientific and Technological Program (QKEZDZX[2024]010); Guizhou Provincial Science and Technology Projects (QKHZHYD[2024]044); Guizhou Provincial Field Scientific Observation and Research Station (Qian Kehe Platform YWZ [2025] 006); National Natural Science Foundation of China (32060353).

Data availability

The raw reads generated from Illumina sequencing have been deposited in the NCBI SRA database (BioProject acc. PRJNA1015740).

Declarations

Ethics approval and consent to participate

All plants samples were collected on Ma'anshan Seed Garden, we had obtained the permissions from that unit. Experimental research and field studies on plants including the collection of plant material are comply with relevant guidelines and regulation. All samples were identified by the Professor Yang Zhao and Xiurong Wang, and all voucher specimens were deposited in the forestry college of Guizhou University (voucher ID numbers: Pp202212 for P. pygmaea, Pe202212 for P. elliottii, Pm202212 for P. massoniana).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12870_2025_8060_MOESM1_ESM.xlsx (9.9KB, xlsx)

Supplementary Material 1: Table S1.Primers for genes in qRT-PCR.

12870_2025_8060_MOESM2_ESM.pdf (40.3KB, pdf)

Supplementary Material 2: Fig S1. Relative expressions of selected genes in P. pygmaea.

Data Availability Statement

The raw reads generated from Illumina sequencing have been deposited in the NCBI SRA database (BioProject acc. PRJNA1015740).

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