IntroductionPhosphorus is an essential macronutrient for plant development, playing a crucial role in numerous biological processes, such as nucleic acid biosynthesis, energy metabolism, cellular compartmentalization, and various signaling pathways1,2. However, bioavailable phosphate (Pi) is limited in most soils worldwide, as it is often present as insoluble inorganic phosphates or organic forms that plant roots cannot absorb directly3,4. In response to Pi starvation, plants have evolved several adaptive mechanisms. These include alterations in root system architecture, such as reduced primary root growth and enhanced lateral root initiation5, metabolic adjustments like anthocyanin accumulation to protect nucleic acids6,7, and induction of a series of Pi starvation-inducible (PSI) genes to regulate Pi homeostasis7.The transcription factor PHOSPHATE RESPONSE1 (PHR1) plays a central role in Pi starvation responses in Arabidopsis thaliana8,9. Under Pi starvation conditions, it binds to the cis-element P1BS (GNATATNC) in the promoters of hundreds of PSI genes, activating their expression9,10. Similarly, in rice, OsPHR2, the orthologue of AtPHR1, binds to the P1BS element to regulate PSI gene expression11. Loss-of-function mutations in PHR genes result in decreased anthocyanin accumulation and reduced induction of PSI genes8,10. In contrast, overexpression of PHR genes enhances Pi uptake and accumulation12,13. Additionally, PHRs are also essential for mycorrhizal symbiosis by regulating the expression of symbiosis-related genes14.SPX, named after Saccharomyces cerevisiae SYG1/Pho81/mammalian Xpr1, is a hydrophilic and poorly conserved domain found in many proteins related to Pi homeostasis in various species15,16. Crystal structures of the SPX domains from human, yeast, and Chaetomium thermophilum demonstrate that several conserved amino acid residues in the α1, α2 and α4 helices form a positively charged surface, facilitating the binding of inositol polyphosphates17. In Arabidopsis and rice, SPX domain proteins SPX1 and SPX2 are localized in the nucleus where they interact with PHR1 (PHR2 in rice) in a Pi-dependent manner, indicating their roles as Pi sensors18,19. In 2019, we identified inositol pyrophosphate InsP8 as the intracellular Pi signaling molecule20. Under Pi-replete conditions, InsP8 directly binds to the SPX domain, promoting the interaction between SPX1 and PHR1, thereby repressing PHR1 function20,21. Recent studies have shown that InsP8 also functions as a bona fide Pi signaling molecule in mammals and fungi22,23,24. Both Arabidopsis PHR1 and rice PHR2 form a dimer to bind the P1BS element9,25. We determined the structure of the rice SPX1-PHR2 complex and found that the Pi signaling molecule stabilizes helix α1 of SPX1. When two SPX1 molecules bind to the PHR2 dimer, the presence of two α1 helices imposes a steric hindrance, which allosterically decouples the PHR2 dimer and stabilizes the SPX1-PHR2 heterodimer25. SPX4 is another member of the SPX domain-containing protein family that plays a distinct role in Pi homeostasis in plants. Unlike SPX1 and SPX2, which are primarily in the nucleus, SPX4 is localized in the cytoplasm, where it interacts with PHR2, preventing its translocation into the nucleus when Pi levels are sufficient26. However, under Pi deficiency conditions, two E3 ubiquitin ligases, SDEL1 and SDEL2, directly ubiquitinate SPX4 to regulate its degradation via the 26S proteasome, resulting in the release of PHR227. SPX4 also interacts with the nitrate sensor NRT1.1B, and this interaction promotes SPX4 ubiquitination and degradation by NRT1.1B-associated E3 ubiquitin ligase NBIP128. Given the critical roles of SPX proteins in Pi homeostasis, understanding the regulatory mechanisms controlling their expression is essential. One such mechanism is alternative polyadenylation (APA), which modulates gene expression by generating mRNA isoforms.APA is a widespread regulatory mechanism in eukaryotic gene expression that involves the selection of different polyadenylation [poly(A)] sites on a pre-mRNA molecule to generate mRNA isoforms with varying 3′ termini29,30. Most APA sites are found within the 3′UTRs, which affects post-transcriptional gene regulation by modulating mRNA stability, translation, and nuclear export31,32,33. However, when APA sites are located in introns or coding exons, they can alter the coding sequence of the gene34. Studies have shown that APA occurs in over 70% of genes in Arabidopsis thaliana and rice35,36, indicating its importance in regulating gene expression in plants. The choice of poly(A) sites is influenced by various factors, including developmental cues, environmental stimuli, and the expression levels of polyadenylation factors. APA in plants is a dynamic process that contributes significantly to the diversity and complexity of the transcriptome, allowing plants to fine-tune gene expression in response to various internal and external cues37,38,39. While APA significantly contributes to transcript diversity and gene regulation, the factors influencing APA site selection under nutrient stress conditions like Pi starvation are not fully understood. Emerging evidence suggests that noncoding RNAs, such as cis-natural antisense transcripts (cis-NATs), play important roles in regulating gene expression. However, their potential involvement in modulating APA of their cognate genes remains unexplored. cis-NATs are a class of long noncoding RNAs that overlap with protein-coding genes, typically transcribed from the opposite DNA strand. They are widespread in eukaryotes and play important roles in regulating gene expression at both transcriptional and post-transcriptional levels40. Studies have shown that cis-NATs can impact mRNA stability, splicing, and translation, which are crucial for modulating plant responses under nutrient-deprivation conditions, including Pi starvation41. A cis-NAT associated with the Pi exporter gene Phosphate 1;2 (PHO1;2) enhances mRNA translation without altering the steady-state mRNA level, leading to increased Pi transporter protein accumulation in rice under Pi deficiency42. Similarly, cis-NATs that showed positive or negative correlations with the translation efficiency of their cognate sense mRNA under Pi starvation were also identified in Arabidopsis40.In tomato, there are five SPX domain proteins: SlSPX1 to SlSPX5. Previous studies have demonstrated that SlSPX1 and SlSPX2 interact with several SlPHRs to regulate Pi homeostasis and arbuscular mycorrhizal symbiosis43,44. However, the functions and regulatory mechanisms of other SlSPX proteins remain largely unexplored. In this study, we used poly(A) tag sequencing (PAT-seq) to investigate the APA profile in response to Pi starvation in tomato, focusing on a putative Pi sensor gene SlSPX5, which undergoes APA. We demonstrate that SlSPX5 interacts with SlPHL1 in the cytosol, preventing its translocation into the nucleus. Moreover, we show that SlPHL1 does not form a dimer and that the interaction between SlSPX5 and SlPHL1 is independent of Pi availability. Finally, we reveal that a cis-NAT is induced by Pi starvation at the SlSPX5 locus, promoting proximal polyadenylation of SlSPX5.ResultsIdentification and profiling of tomato poly(A) sites regulated by Pi-starvationTo study the impact of Pi starvation on RNA polyadenylation in tomato, pre-germinated tomato (Ailsa Craig) seedlings were grown under Pi-replete or Pi-depleted conditions for 7 days. Under Pi-depleted conditions, the tomato seedlings accumulated significant amounts of anthocyanin, accompanied by the upregulation of genes involved in anthocyanin biosynthesis (Supplementary Fig. 1a–c). Additionally, Pi content in both the roots and shoots of the 7-day-old seedlings were significantly reduced under Pi-depleted conditions (Supplementary Fig. 1d). The expression of PSI genes was also upregulated under Pi deficient conditions (Supplementary Fig. 1e). Poly(A) tag sequencing (PAT-seq) is an efficient method for analyzing poly(A) site usage, mature transcripts abundance, and functional gene expression on a genome-wide scale45. Therefore, we used PAT-seq to study poly(A) site usage of the tomato samples described above (Supplementary Fig. 2). A summary of the raw reads and mapped poly(A) tags (PATs) for each library can be found in Supplementary Fig. 3a. To ensure high-quality data, we filtered out reads with a quality score below Q20. Using a Perl script45,46 (http://www.bmibig.cn/plantAPAdb/about.php#help=pipeline), we removed sequences lacking a poly(T) tail or those with short or low-quality poly(T) to obtain valid poly(T) sequences. These reads were then mapped to the tomato genome (version Sl3.0). The principal component analysis (PCA) and hierarchical clustering of the samples are shown in Supplementary Fig. 3b.The mapped reads were counted, and PATs at the same position were merged to obtain effective PATs. About 80% of the PATs were located in 3′UTRs, while 5–15% were found in intergenic regions (excluding the promoter region), and 5–15% were located within introns. The remaining PATs were located in promoter regions. Poly(A) sites within 24 bases of each other were grouped into poly(A) clusters (PACs). To ensure reliable results, we considered only PACs with more than 11 PATs for further analysis. In total, we identified 64173 PACs across 20903 genes, of which 14633 genes (70%) were classified as APA genes, defined as those using two or more poly(A) sites. The number and distribution of PACs for each sample are shown in Fig. 1a, b. Over 60% of the PACs were found in 3′UTRs, 10% in CDSs, 10% in intergenic regions, and 10-15% in introns. The genome-wide proportion of APA genes did not differ significantly between Pi-replete and Pi-depleted conditions (Fig. 1c). To further explore the role of APA genes in regulatory processes under varying Pi concentrations, we calculated the poly(A) usage rate as the ratio of the expression level of a specific transcript to the expression level of all transcripts from the same gene. A cumulative distribution function was then plotted to show the poly(A) site usage ratio (Fig. 1d). The starting point of the cumulative curve for plants grown under Pi-replete conditions is around 14%, while for plants under Pi-depleted conditions, it was around 11%. These findings suggest that there is significant difference in the cumulative poly(A) site profiles between Pi-replete and –depleted conditions.Fig. 1: Identification of Poly(A) sites regulated by Pi-starvation in tomato.a Tomato genes grouped according to their number of Poly(A) sites. The X-axis indicates the number of Poly(A) sites. The Y-axis shows the number of genes belonging to each group. b Distribution of PACs among the tomato genome in each sample. The color legend at the bottom indicates the different locations of the PACs among the genome: 3′UTR, 5′UTR, CDS, intergenic and introns. The columns on the X-axis indicate the different samples. The Y-axis shows the percent scale for each location. c Genome-wide percentages of APA and non-APA genes in samples grown under Pi-replete (P+) conditions compared to samples grown under Pi-depleted (Pi-) conditions. The column on the right represents the proportions of APA and non-APA genes among the differentially expressed PAC genes (DE-PAC gene) between the two conditions. d Cumulative distribution function (CDF) curve indicating the Poly(A) site usage ratio between samples grown under Pi-replete (P+) and -depleted (P-) conditions. The X-axis shows the log values for the Poly(A) site usage ratio (PAU). The Y-axis shows the values for the CDF. e Differential expression analysis between samples grown under Pi-replete or Pi-depleted conditions. Green dots indicate the down-regulated (P+ vs P-) PACs, whereas red dots show the up-regulated PACs. f Differential 3′UTR length analysis between samples grown under Pi-replete or Pi-depleted conditions. The X-axis shows the strength of 3′UTR shortening (r 0). Green dots indicate genes with a shorter 3′UTR region under Pi-replete conditions than under Pi-depleted conditions, whereas red dots show genes with a longer 3′UTR region under Pi-replete conditions than under Pi-depleted conditions. Wald test of DEseq2 was used for differential gene expression analysis between P+ vs P- conditions. Padj, Benjamini-Hochberg adjusted p value for multiple test correction; Padj