The CBL-CIPK Network Integrates Calcium and ABA Signaling to Mediate Drought Adaptation in Asparagus cochinchinensis

The CBL-CIPK Network Integrates Calcium and ABA Signaling to Mediate Drought Adaptation in Asparagus cochinchinensis | 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 The CBL-CIPK Network Integrates Calcium and ABA Signaling to Mediate Drought Adaptation in Asparagus cochinchinensis Zhengxi Long, Liu Tang, Shengbo Lu, Yuting Yang, Mingsheng Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8784662/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Asparagus cochinchinensis (Lour.) Merr. ( A. cochinchinensis ) is a precious traditional Chinese medicinal herb with significant economic value. However, its cultivation is severely constrained by environmental stresses, particularly drought. The Calcineurin B-Like (CBL) and CBL-Interacting Protein Kinase (CIPK) network constitutes a crucial calcium sensor system that decodes stress-induced Ca 2+ signatures in plants. Despite its importance, the molecular architecture and functional roles of the CBL-CIPK network in A. cochinchinensis remain largely uncharacterized. Results In this study, we generated a high-quality full-length transcriptome of A. cochinchinensis using PacBio Single-Molecule Real-Time (SMRT) sequencing, yielding 52,042 non-redundant transcripts. Based on this resource, we identified 35 AcCIPK and 13 AcCBL genes. Phylogenetic analysis revealed high conservation between AcCIPK24/AcCIPK23 and their Arabidopsis orthologs, while also uncovering species-specific alternative splicing events, including a truncated isoform of AcCIPK24.5 . Yeast two-hybrid assays confirmed a specific physical interaction between AcCBL10 and AcCIPK24. Expression profiling demonstrated that these genes exhibit tissue-specific and temporal responses to drought stress. Notably, while both genes were downregulated in roots and stems under drought, AcCBL10 was significantly upregulated in cladodes, suggesting complex regulatory mechanisms. Furthermore, hormone analysis revealed that drought stress induced endogenous ABA accumulation, and exogenous ABA application not only accelerated this peak but also enhanced the expression of AcCBL10 and AcCIPK24.5 , indicating a positive feedback loop between calcium signaling and ABA pathways. Conclusion This study provides the first comprehensive functional characterization of the CBL-CIPK network in A. cochinchinensis . The specific interaction between AcCBL10 and AcCIPK24 , coupled with the crosstalk between calcium signaling and ABA pathways, highlights a key molecular mechanism underlying drought adaptation in this medicinal plant. Asparagus cochinchinensis Drought stress Full-length transcriptome CBL-CIPK network ABA signaling Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introdution Calcium ions (Ca 2+ ) act as ubiquitous intracellular secondary messengers, integrating signaling pathways essential for plant growth, development, and stress responses (Zhang et al., 2022; Zhu et al., 2022). Specific sensor proteins, such as the plant-specific calcineurin B-like (CBL) family, detect these cytosolic Ca 2+ concentration fluctuations (Zhou et al., 2015). CBL proteins contain four conserved EF-hand domains that bind Ca 2+ , enabling them to decode Ca 2+ signals (Verma et al., 2021). These proteins selectively interact with CBL-interacting protein kinases (CIPKs), forming CBL-CIPK complexes that serve as central components of Ca 2+ signal transduction (Zhang et al., 2020). The subcellular localization of CBL determines its functional specificity, which is primarily governed by distinct N-terminal targeting sequences (Tang et al., 2020). The classical (type I) CBL contains a dual lipid modification motif (MGCXXS/T) and localizes to the plasma membrane (Weinl et al., 2009). The tonoplast-type (type II) CBL harbors a tonoplast targeting sequence (TTS) (Xu et al., 2006). The membrane-anchored (type III) CBL possesses a transmembrane helix domain that enables its localization to either the tonoplast or plasma membrane (Tang et al., 2020; Waadt et al., 2008). The interaction with CIPK is mediated by a shared C-terminal PFPF/FPSF motif containing a profound phosphorylation site that is essential for complex regulation (Aslam et al., 2019). CIPK contains an N-terminal serine/threonine kinase domain and a C-terminal regulatory domain (Shi et al., 2021). The regulatory domain features a highly conserved NAF/FISL motif (core residues: N, A, F, I, S, L) that functions both as the primary CBL-binding site and as an autoinhibitory domain (Wu et al., 2024). When Ca 2+ -activated CBL binds to this motif, it releases autoinhibition and activates the CIPK kinase domain in a calcium-dependent manner (Xiao et al., 2022). Some CIPKs also possess a less conserved protein phosphatase interaction site (PPI) adjacent to the NAF/FISL motif, potentially facilitating interactions with phosphatase regulators like ABI/PP2Cs (Yu et al., 2014). Plants inhabiting specific ecological niches, particularly those in karst regions like A. cochinchinensis , face unique water scarcity challenges due to the high permeability of limestone substrates. This medicinally important herb has evolved sophisticated mechanisms to cope with the fluctuating water availability in such environments. Given that calcium signaling is often the first responder to osmotic changes, it is hypothesized that the CBL-CIPK network in A. cochinchinensis may have undergone specific evolutionary adaptations to fine-tune its drought response. The CBL-CIPK signaling network plays a crucial role in regulating diverse physiological processes across the plant life cycle. This system mediates plant responses to multiple abiotic stresses, such as salinity, drought, low temperature, and heavy metal exposure (Kanwar et al., 2014; Ma et al., et al., 2020; Kaya et al., 2024). It also governs essential developmental stages including seedling establishment, flowering initiation, root architecture, pollen germination, and pollen tube elongation (Wang et al., 2020). Recent studies have further demonstrated its involvement in phytohormone pathways, particularly through interactions with ABA signaling, GA biosynthesis, and cold stress adaptation mechanisms (Kolukisaoglu et al., 2004). Drought stress severely limits plant growth, development, and agricultural productivity by disrupting water homeostasis, inducing osmotic stress, and impairing metabolic functions (González et al., 2023). Plants detect water deficit and activate adaptive responses, with the evolutionarily conserved CBL-CIPK signaling module serving as a key decoder of stress-induced Ca 2+ fluxes and playing a critical role in drought adaptation (Cheong et al., 2007). This complex regulates ion homeostasis by directly modulating plasma membrane and tonoplast ion channels and transporters (e.g., K + channels, H + -ATPases), thereby controlling essential drought tolerance mechanisms (Apse et al., 1999; Tang et al., 2015). The module also coordinates diverse cellular responses, such as osmoprotectant accumulation and hormone signaling pathway regulation. Studies across multiple plant species have demonstrated that specific CBL-CIPK interactions are indispensable for drought resistance. Overexpressing AtCIPK1/23 , AtCBL1/9 , OsCIPK3/12/15 , and MdCIPK22 enhances drought tolerance in Arabidopsis, rice, and apple, while loss-of-function mutants (e.g., cipk1 , cbl1 ) show increased drought sensitivity (Cho et al., 2018; Ma et al., 2019., Gao et al., 2019; Lara et al., 2020; Peng et al., 2021; Chao et al., 2022). These findings suggest that genetic manipulation of this signaling pathway could effectively improve crop drought resilience. A. cochinchinensis , a highly valued traditional Chinese medicinal herb renowned for its therapeutic properties, often faces cultivation challenges due to various environmental stresses (Wang et al., 2022; Yunmam et al., 2023). Although the medicinal properties and genomic resources of A. cochinchinensis have been recently advanced, the composition, structural diversity (such as alternative splicing variants), and functional specificity of the CBL-CIPK network in this species remain largely elusive. Furthermore, the crosstalk between the CBL-CIPK cascade and phytohormone signaling, particularly Abscisic Acid (ABA), which is central to drought adaptation, has not been elucidated in this non-model medicinal plant. In this study, we performed full-length transcriptome sequencing to generate a comprehensive genomic resource. We systematically identified and characterized the CBL and CIPK gene families, with a specific focus on evolutionary conservation and structural divergence. Furthermore, we validated the specific protein-protein interaction between key drought-responsive modules (AcCBL10 and AcCIPK24) and investigated their expression dynamics under drought stress. Our work aims to decipher the molecular architecture of the CBL-CIPK network and its interaction with ABA signaling, providing novel insights into the drought adaptation mechanisms of A. cochinchinensis . 2. Materials and Methods 2.1 Sample Collection and Processing In this experiment, the tested A. cochinchinensis samples were collected from Danzhai County (107°44′-108°08′E, 26°05′-26°26′N), Qiandongnan Prefecture, Guizhou Province, and authenticated by Professor Zhang Mingsheng, the Chief Scientist of Guizhou Province. The roots, stems, cladodes, flowers, and fruits of A. cochinchinensis were mixed, flash-frozen in liquid nitrogen, and stored at -80°C to ensure sample stability and subsequent experimental accuracy (Hajihashemi et al., 2018). The experiment was designed with three biological replicates to enhance the reliability and reproducibility of the results. For drought stress assays, A. cochinchinensis materials were cultivated in the greenhouse of Guizhou University and subjected to continuous treatment with 20% PEG6000 for 0, 2, 6, 12, and 24 h. The underground tissues were then collected for RNA extraction. Throughout the sampling period, water was replenished regularly to maintain PEG6000 concentration (Yao et al., 2022). Each treatment group consisted of three independent biological replicates, with 10 plants per replicate. Design specific primers (primer sequence shown in Table S1), using the internal reference gene Actin as a control, and calculate the relative expression levels of different tissues and organs as well as after drought stress treatment using the 2 ⁻ΔΔCt method. 2.2 RNA extraction, sequencing library construction and sequencing assembly Total RNA was isolated from tissue samples with TRIzol reagent (Life Technologies). The Agilent 2100 Bioanalyzer, agarose gel electrophoresis, and NanoDrop 2000 spectrophotometer confirmed RNA integrity (RIN ≥ 8.0), purity (OD260/280 = 1.8-2.0; OD260/230 ≥ 2.0), and concentration. Using the SMARTer approach (NEBNext® Single Cell/Low Input RNA Library Prep Kit), qualified RNA samples underwent double-stranded cDNA synthesis, PCR amplification, end repair, and PacBio SMRTbell adapter ligation (Cheng et al., 2019). The PacBio Sequel II platform sequenced quality-verified libraries by single-molecule real-time (SMRT) technology, achieving a target yield of ≥8Gb per sample. SMRT Link v10.2 produced circular consensus sequences (CCS, ≥3 full passes), from which full-length transcripts were identified by the presence of both 5'-primer (GCAATGAAGTCGCAGGGTTG) and 3'-primer (AAGCAGTGGTATCAACGCAGAGT). The iterative clustering and error correction (ICE) algorithm generated consensus sequences with ≥0.99 predicted accuracy for downstream analysis (Wang et al., 2023). 2.3 Annotation of full-length transcript The full-length transcript was aligned with the NCBI non-redundant proteins database (NCBI non-redundant proteins, Nr, http://www.ncbi.nlm.nih.gov), Swiss-Prot protein database (Swiss-Prot protein database, http://www.expasy.ch/sprot), Kyoto encyclopedia of genes and genomes database (KEGG, http://www.genome.jp/kegg), and COG/KOG database (COG/KOG database, http://www.ncbi.nlm.nih.gov/COG) using BLASTX alignment, with an E-value threshold set at 1E-5, to assess the sequence similarity between the full-length transcript of Asparagus and that of Asparagus cochinchinensis , and obtain basic annotations for each full-length transcript (Ning et al., 2024). Blast2GO software was used to perform GO annotation analysis on the Nr annotation results of the full-length transcript (Conesa et al., 2005). WEGO software was utilized for hierarchical functional classification of the full-length transcript (Ye et al., 2018). 2.4 Transcriptome structural feature analysis and gene function prediction The Astalavista software identified alternative splicing patterns in precursor mRNA (pre-mRNA) transcribed from each gene. We performed genome-wide simple sequence repeat (SSR) mining with the MicroSAtellite identification tool (MISA), applying these parameters: 1-12, 2-6, 3-5, 4-5, 5-4, and 6-4. SSRs separated by less than 100 bp were merged and treated as a single locus (Tandon et al., 2023). TransDecoder (v3.0.0) predicted reliable coding sequence (CDS) regions from transcript sequences by evaluating open reading frame (ORF) length, log-likelihood scores, and alignment of amino acid sequences against Pfam protein domains (Ramos et al., 2021). For novel transcripts, we predicted long non-coding RNAs (lncRNAs) by combining four computational approaches: Coding Potential Calculator (CPC), Coding-Non-Coding Index (CNCI), Pfam domain analysis, and Coding Potential Assessment Tool (CPAT). Transcripts were classified as lncRNAs only when all four methods agreed on their non-coding status (Duan et al., 2021). 2.5 Transcription Factor Analysis and Functional Annotation of Novel Transcripts Transcription factors (TFs) in A. cochinchinensis were identified using iTAK (http://itak.feilab.net/cgi-bin/itak/index.cgi), with subsequent validation using hmmscan to align query sequences against the Pfam database for TF quantification (Zheng et al., 2016). Following deduplication, gffcompare analyzed full-length transcripts in GFF3 format against the reference genome's annotations. The study detected novel transcripts and genes absent from the reference genome, which were then functionally annotated by DIAMOND (v2.0.15) through alignment with NR, SwissProt, GO, COG, KOG, Pfam, and KEGG databases (Sun et al., 2021). 2.6 Identification of the CBL and CIPK Gene Families in A. cochinchinensis Candidate genes were screened from the full-length transcriptome of Asparagus cochinchinensis ( A. cochinchinensis ) through BLASTP alignment against protein sequences of AtCBLs (e.g., AT4G17615) and AtCIPKs (e.g., AT3G17510) in A. thaliana , as well as those of A. cochinchinensis (Table S1). The conserved domains of CBL (PF00036 and PF13499) and CIPK (PF00069 and PF03822) were searched using HMMER 3.3, and domain integrity was further verified with the SMART database (Wu et al., 2023). A neighbor-joining tree was constructed using MEGA 12 with the p-distance model and 1000 bootstrap replicates. 2.7 Secondary and tertiary structure analysis of AcCBLs and AcCIPKs Conserved motifs in the AcCBL and AcCIPK protein sequences were identified using the MEME (v4.12.0; http://meme-suite.org/tools/meme), configured to detect up to 15 motifs with an optimum width of 6 - 200 residues. Only motifs satisfying a stringent significance threshold (e-value ≤ 1e-10) were retained. The motif distribution patterns were visualized with TBtools (Huang et al., 2022). We computationally characterized the AcCBL and AcCIPK proteins using the following tools: ExPASy for physicochemical properties, TMPRED for predicting transmembrane domains, SignalP 4.1 for identifying signal peptides, and Plant-mPLoc for inferring subcellular localizations (Wu et al., 2023). The tertiary structures of CBL and CIPK family members from A. cochinchinensis were predicted using the I-TASSER platform (Biasini et al., 2014). 2.8 Yeast Two Hybrid The yeast two-hybrid (Y2H) assay utilized the Saccharomyces cerevisiae strain Y2HGold, where AcCBL10 was cloned into pGADT7 as the prey and AcCIPK23/24 into pGBKT7 as the bait (Li et al., 2016). The positive control featured pGADT7-T co-transformed with pGBKT7-53, whereas pGADT7-T combined with pGBKT7-lam constituted the negative control. To assess transcriptional self-activation, pGADT7 was separately co-transformed with either pGBKT7-AcCBL10 or pGBKT7-AcCIPK23/24. All primer sequences are listed in Table S3 (Yang et al., 2013). The optimal 3-AT concentration for suppressing background growth was determined by culturing Y2HGold cells harboring the bait plasmid on dropout media (DDO, TDO, and QDO) containing a gradient of 3-AT concentrations at 30 °C for five days. Protein-protein interactions were confirmed by plating co-transformed Y2HGold cells on DDO, TDO supplemented with 15 mmol·L -1 3-AT, and QDO media, followed by incubation under the same conditions (Huang et al., 2023). 2.9 Plant Growth Conditions and Drought Stress Treatments The tissue culture seedlings of A. cochinchinensis were preserved at the Key Laboratory of Plant Resources Conservation and Germplasm Innovation in Mountainous Region, Guizhou University. Uniform and robust tissue-cultured seedlings were selected for transplanting. The cultivation substrate was a mixture of peat soil and perlite at a 3:1 ratio. The plants were maintained under conditions of approximately 95% relative humidity, a temperature of 20-28 °C, and no direct light. After one month of soil cultivation, uniformly growing and healthy plants were selected for the simulated drought treatment experiment. The experiment included two treatments: one group was treated with a 1/2 Hoagland nutrient solution containing 20% (w/v) PEG 6000 to simulate drought stress (Yao et al., 2022); the other group was treated with a 1/2 Hoagland nutrient solution containing both 20% (w/v) PEG 6000 and 100 mM ABA for a combined PEG and ABA treatment (Wu et al., 2019). A control group (CK) was set up using the basic 1/2 Hoagland nutrient solution under normal light/dark cycles. Each treatment had three biological replicates, with each replicate consisting of ten seedlings. Samples were collected at 0, 3, 6, and 12 hours after treatment, immediately frozen in liquid nitrogen, and stored for subsequent RNA extraction and analysis. 3. Results 3.1 Full-Length Transcriptome Assembly and Quality Assessment High-quality full-length transcripts were generated using PacBio Sequel II sequencing, yielding 12.6 Gb of raw data. After adapter trimming and quality filtering, 340,429 circular consensus sequences (CCS) were obtained. Among these, 307,748 reads (90.4%) contained intact 5’/3’ primers and poly A tails and were classified as full-length non-chimeric (FLNC) reads (Fig. S1A). Clustering the FLNC reads yielded 118,501 high-quality consensus isoforms (accuracy > 99%), and subsequent dereplication using cDNA_Cupcake resulted in 52,042 non-redundant transcripts with an N50 of 2.9 kb (Fig. S1B). BUSCO v3.0.2 analysis against the plantae_odb10 database assessed assembly completeness, recovering 94.2% of core genes (2.1% fragmented, 3.7% missing) and confirming high assembly integrity (Fig. S2). BLASTN analysis further validated the assembly by demonstrating 92.3% sequence identity with the A. cochinchinensis reference genome. 3.2 Functional Annotation of Transcripts Comprehensive annotation of the 52,042 non-redundant transcripts against multiple public databases successfully assigned functions to 37,827 (72.7%) transcripts in at least one database. Homology searches revealed that 37,794 (72.6%) transcripts had significant matches in the NR database, with the highest similarity to A. cochinchinensis (~71.5%), followed by Phoenix dactylifera and Elaeis guineensis (Fig. S3A). A total of 29,211 (56.1%) transcripts were matched to the manually curated Swiss-Prot database. Gene Ontology (GO) terms were assigned to 32,892 (63.2%) transcripts, which were predominantly enriched in the biological process categories of 'cellular process', 'metabolic process', and 'response to stimulus' (Fig. S3B). KEGG pathway analysis associated 29,388 transcripts (56.5%) with various pathways and identified 134 enriched pathways, providing a foundation for further investigation into Asparagus biological metabolism and its metabolic network (Table S1). Cluster of Orthologous Groups (COG/KOG) analysis classified 26,188 (50.3%) transcripts into 25 functional categories, where 'General function prediction only' (15.8%), 'Posttranslational modification, protein turnover, chaperones' (11.2%), and 'Signal transduction mechanisms' (10.5%) were the most abundant (Fig. S3C). Pfam domain analysis identified domains in 31,513 (60.6%) transcripts, with protein kinase, leucine-rich repeat, and EF-hand domains being the most prevalent. 3.3 Structural Characterization of Novel Transcripts In-depth transcriptome analysis identified 38,352 novel transcripts absent from the current reference annotation. A total of 31,724 simple sequence repeats (SSRs) were detected within 21,374 transcripts, with mononucleotide repeats (48.0%) being the most frequent, followed by di- (26.8%) and tri-nucleotide (23.3%) repeats (Fig. S4A). TransDecoder predicted 37,684 open reading frames (ORFs), of which 28,366 (75.3%) were complete, containing both start and stop codons (Fig. S4B). Alternative splicing (AS) analysis revealed extensive mRNA isoform diversity, identifying 13,580 gene loci and 1,382 novel loci. We cataloged 5,874 AS events, with intron retention (IR, 34.1%) and exon skipping (ES, 28.7%) representing the predominant types (Fig. S4C). Furthermore, 521 potential fusion transcripts were predicted, suggesting possible gene rearrangements or trans-splicing events. 3.4 Identification and Functional Prediction of LncRNAs A stringent pipeline integrating four computational tools (CPC2, CNCI, CPAT, and Pfam scan) identified 366 high-confidence long non-coding RNAs (LncRNAs) (Fig. S5A). These lncRNAs were classified according to their genomic context relative to protein-coding genes, with 58.7% designated as intergenic (lincRNAs), 21.3% as antisense, 12.0% as intronic, and 8.0% as sense overlapping (Fig. S5B). Target gene prediction, based on both cis-regulation (genomic proximity) and trans-regulation (sequence complementarity), indicated that these lncRNAs may regulate genes associated with transcription, stress response, and metabolic processes. 3.5 Transcription Factor Family Analysis Comprehensive analysis with the iTAK pipeline identified 7,565 transcripts encoding transcription factors (TFs) and transcriptional regulators (TRs), accounting for 14.5% of all transcripts. These TFs were classified into 58 distinct families. The most abundant families were bHLH (9.2%), ERF (8.5%), MYB-related (7.1%), NAC (6.3%), and C2H2 (5.8%), all of which are known to be critically involved in plant development, hormone signaling, and responses to abiotic stress (Fig. S6). 3.6 Genome-wide characterization of CBL-CIPK networks In order to comprehensively identify the CIPK and CBL genes in the full-length transcriptome data of Asparagus, the bidirectional BLAST method was used for identification. Members were named 48 members such as AcCIPK1 and AcCBL1 according to the genetic distance between them and the members of the Arabidopsis CIPK and CBL gene family (Table 1). The basic characteristics of gene coding sequence (CDS), protein molecular weight (MW), isoelectric point (PI) and subcellular localization were analyzed. Among the 35 identified CIPK protein members, their protein lengths ranged from 296 amino acids (aa) (AcCIPK24.3) to 485 aa (AcCIPK3.7), with corresponding molecular weights of 33,664.73 kDa (AcCIPK24.3) and 55,500.55 kDa (AcCIPK3.7), respectively. The CIPK family included 11 acidic proteins and 24 basic proteins, with isoelectric points (pI) distributed between 4.98 and 9.28. Subcellular localization analysis revealed that, with the exception of AcCIPK1.1, the remaining 34 members were localized to the cytoplasm. Notably, AcCIPK23.2 and AcCIPK23.3 were also predicted to localize to the cytoplasm. Among the 13 identified CBL protein members, protein lengths spanned from 64 aa (AcCBL4.2) to 283 aa (AcCBL9), with corresponding molecular weights ranging from approximately 7.17 kDa (AcCBL4.2) to 33.01 kDa (AcCBL9). The isoelectric points ranged from 4.51 (AcCBL4.2) to 5.23 (AcCBL9), and all members were acidic proteins. Subcellular localization results indicated diverse localization patterns for these 13 CBL members, including the nucleus (AcCBL2.1, AcCBL2.2), plasma membrane (AcCBL3.1, AcCBL3.2, AcCBL4.1, AcCBL8, AcCBL9, AcCBL10), and endoplasmic reticulum (AcCBL3.5), among others (Table 1). Table 1. List of CIPK and CBL Family Genes Identified in A. cochinchinensis Gene ID in A. cochinchinensis Gene Name in A. cochinchinensis Gene ID in A. thaliana Gene Name in A. thaliana Protein Length (aa) Molecular Weight (kDa) Protein Isoelectric Point (pI) Protein Subcellular Localization PB.8729 AcCIPK1 AT3G17510 AtCIPK1 446 50452.39 6.68 Nucleus PB.11531.2 AcCIPK2.1 AT5G45820 AtCIPK2 456 52218.28 9.24 Cytoplasm PB.1938.3 AcCIPK2.2 458 52116.97 8.79 Cytoplasm PB.1938.5 AcCIPK2.3 458 52116.97 8.79 Cytoplasm PB.2547.1 AcCIPK3.1 AT2G26980 AtCIPK3 436 49817.34 7.62 Cytoplasm PB.2547.4 AcCIPK3.2 268 30751.96 4.98 Cytoplasm PB.2547.6 AcCIPK3.3 441 50338.87 6.79 Cytoplasm PB.2547.7 AcCIPK3.4 441 50352.89 6.79 Cytoplasm PB.2547.8 AcCIPK3.5 474 54324.46 8.03 Cytoplasm PB.2547.9 AcCIPK3.6 441 50398.97 6.79 Cytoplasm PB.8993.3 AcCIPK3.7 485 55500.55 7.59 Cytoplasm. Nucleus. PB.8993.4 AcCIPK3.8 360 41035.43 5.14 Cytoplasm PB.2547.10 AcCIPK3.9 441 50398.97 6.79 Cytoplasm PB.12492.1 AcCIPK9.1 AT1G01140 AtCIPK9 435 49466.56 8.37 Cytoplasm PB.5252.1 AcCIPK9.2 358 40900.47 6.77 Cytoplasm PB.5252.2 AcCIPK9.3 436 49601.67 8.44 Cytoplasm PB.5252.5 AcCIPK9.4 372 42311.32 6.44 Cytoplasm PB.8410.7 AcCIPK10.1 AT5G58380 AtCIPK10 458 51650.75 9.20 Cytoplasm PB.8410.6 AcCIPK10.2 458 51635.70 9.20 Cytoplasm PB.5949.3 AcCIPK10.3 454 51336.75 9.28 Centrosome Cytoplasm Nucleus PB.5949.4 AcCIPK10.4 374 42197.81 8.92 Cytoplasm Nucleus PB.8410.1 AcCIPK10.5 458 51635.70 9.20 Cytoplasm PB.8410.3 AcCIPK10.6 458 51650.75 9.20 Cytoplasm PB.8410.4 AcCIPK10.7 354 39582.45 8.17 Cytoplasm PB.8410.5 AcCIPK10.8 458 51650.75 9.20 Cytoplasm PB.8137.6 AcCIPK23.1 AT1G30270 AtCIPK23 369 41492.54 6.25 Cytoplasm. Nucleus. PB.8137.1 AcCIPK23.2 445 50030.64 8.90 Centrosome Cytoplasm Nucleus PB.8137.3 AcCIPK23.3 445 50030.64 8.90 Centrosome Cytoplasm Nucleus PB.8137.4 AcCIPK23.4 390 44079.77 7.63 Cytoplasm. PB.8137.5 AcCIPK23.5 365 41565.23 9.04 Cytoplasm Nucleus PB.7948.1 AcCIPK24.1 AT5G35410 AtCIPK24 442 49949.86 8.56 Cytoplasm. PB.7948.3 AcCIPK24.2 422 47407.46 8.58 Cytoplasm. Nucleus. PB.7948.4 AcCIPK24.3 296 33664.73 6.19 Nucleus PB.7948.6 AcCIPK24.4 393 44592.75 8.86 Cytoplasm PB.7948.8 AcCIPK24.5 398 44525.02 7.68 Cytoplasm PB.6643.1 AcCBL2.1 AT5G55990 AtCBL2 80 9184.37 4.68 Nucleus PB.6643.2 AcCBL2.2 80 9184.37 4.68 Nucleus PB.295.4 AcCBL3.1 AT4G26570 AtCBL3 224 25757.43 4.83 Cell membrane PB.6643.4 AcCBL3.2 206 23602.97 4.76 Cell membrane. Cytoplasm PB.295.5 AcCBL3.3 148 16919.75 5.22 Extracell PB.295.6 AcCBL3.4 111 12608.98 5.03 Extracell. Nucleus. PB.295.3 AcCBL3.5 168 19356.32 4.79 Endoplasmic reticulum PB.6530.4 AcCBL4.1 AT5G24270 AtCBL4 214 24563.29 5.06 Cell membrane PB.6530.2 AcCBL4.2 64 7168.26 4.51 Cytoplasm. Nucleus. PB.295.1 AcCBL5 AT4G01420 AtCBL5 94 10835.59 4.82 Extracell PB.6530.1 AcCBL8 AT1G64480 AtCBL8 214 24563.29 5.06 Cell membrane PB.10318.1 AcCBL9 AT5G47100 AtCBL9 283 33008.04 5.23 Cell membrane. Extracell PB.4106.1 AcCBL10 AT4G33000 AtCBL10 170 19606.24 4.99 Cell membrane. Cytoplasm Most members clustered separately due to species differences, but CIPK3, CIPK9, CIPK23, and CIPK24 grouped into a single clade in Arabidopsis and A. cochinchinensis (Fig 1A 1 ). Similarly, AcCBL10 and AtCBL10 exhibited a close phylogenetic relationship. Based on these findings, it is hypothesized that the copies of these genes in A. cochinchinensis may possess functions similar to those of their homologs in Arabidopsis (Fig 1B 1 ). Regarding motif distribution, members within the same clade shared similar motif compositions. Specifically, the motif compositions of AcCIPK23.2 and AcCIPK23.3 were identical to that of AtCIPK23.2, and the motif composition of AcCIPK24.1 was the same as that of AtCIPK24 (Fig 1A 2 ). In addition, AcCBL10 lacked only motif14 compared with AtCBL10 (Fig 1B 2 ). These results suggest that the CIPK23, CIPK24 and CBL10 proteins are relatively conserved in terms of structural and functional integrity during evolution. 3.7 Tissue-Specific Expression Analysis of AcCIPK and AcCBL Genes under Drought Stress In plants, CBL-CIPK complexes are key signaling modules in drought responses (Arab et al., 2023). To investigate the potential functions of the key genes identified in this study in the drought response of A. cochinchinensis , seven representative genes, including AcCIPK24.5 and AcCBL10 were selected. Their expression dynamics in root, stem, and cladode under both normal and drought conditions were analyzed using qRT-PCR. The results indicated that drought stress significantly affected the expression levels of most tested genes, and this regulatory role exhibited distinct tissue specificity (Fig 2). In roots, the expression of AcCIPK10.3 , AcCIPK10.4 , AcCIPK24.5 , AcCBL10 , and AcCIPK2.1 was significantly suppressed by drought ( P < 0.05), with AcCIPK2.1 showing the greatest downregulation. In contrast, the expression of AcCIPK10.8 and AcCIPK9.3 showed no significant change. In stem, the expression of AcCIPK10.3 , AcCIPK10.4 , AcCIPK10.8 , and AcCBL10 was significantly downregulated by drought, while AcCIPK9.3 expression was significantly induced. The expression of AcCIPK24.5 and AcCIPK2.1 remained relatively stable. In cladodes, the expression of AcCIPK10.8 , AcCIPK24.5 , and AcCBL10 was significantly downregulated by drought, whereas AcCIPK9.3 expression was significantly upregulated. The expression of AcCIPK10.3 and AcCIPK10.4 showed no significant change. It is noteworthy that AcCIPK24.5 and AcCBL10 , which are highly conserved in phylogeny and whose homologs in Arabidopsis are known to interact, exhibited both correlated and specific expression patterns in response to drought stress. On one hand, their expression trends were consistent in root and stem tissues, both being suppressed by drought. On the other hand, in cladode tissues, AcCIPK24.5 expression was inhibited by drought, while AcCBL10 expression was significantly induced. This differential, even opposite, transcriptional response to the same stress signal in cladode tissues suggests that these two genes may be regulated by distinct upstream factors, indicating a complex functional relationship. 3.8 Interaction between AcCIPK24 and AcCBL10 Given the conserved homology and coordinated yet distinct expression patterns under stress, we hypothesized that AcCIPK24 and AcCBL10 might physically interact to form a functional module. This interaction was tested using a yeast two-hybrid (Y2H) assay. The constructs pGBKT7-AcCIPK24 and pGADT7-AcCBL10 were co-transformed into the yeast strain Y2HGold. As shown in Figure 3, the co-transformants grew robustly on SD/-Trp-Leu-His selective medium across serial dilutions 10 to 10 -3 , with growth comparable to that of the positive control (pGADT7-T + pGBKT7-P53). In contrast, the two negative controls (pGADT7-T + pGBKT7-Lam and pGBKT7-AcCIPK24 + empty pGADT7) showed almost no colony formation under the same selective conditions. These results indicate that AcCIPK24 and AcCBL10 specifically interact in yeast cells, and that this interaction is not due to autoactivation or nonspecific binding of either protein alone. 3.9 Effects of Drought Stress and Exogenous ABA Treatment on Endogenous ABA Content in Plants To investigate whether ABA signaling is involved in the response of A. cochinchinensis to drought stress and to elucidate its potential regulatory mode, we first measured the dynamic changes in endogenous ABA content in plants under PEG6000 simulated drought conditions. Subsequently, we analyzed the feedback regulation of exogenous ABA on endogenous ABA homeostasis. Under PEG6000 simulated drought stress, the endogenous ABA content in A. cochinchinensis plants exhibited a dynamic pattern of initial increase followed by a slight decrease over time. Compared to the 0-hour control, ABA content increased significantly ( P < 0.05) at 3 hours of stress treatment, peaked at 6 hours ( P < 0.001), and although slightly decreased by 12 hours, it remained significantly higher than the control level ( P < 0.001) (Fig 4A). This indicates that drought stress effectively activates ABA biosynthesis or accumulation in A. cochinchinensis . To further explore the role of ABA signaling in this process, exogenous ABA was applied in combination with PEG6000 treatment. The introduction of exogenous ABA significantly altered the accumulation dynamics of endogenous ABA. Compared to the drought-only treatment group (Fig 4A), plants subjected to combined PEG and ABA treatment exhibited a higher basal ABA level even at 0 hours. After 3 hours of stress treatment, their endogenous ABA content rapidly reached its highest value ( P < 0.001), and the timing of this peak occurred significantly earlier than in the drought-only group (6 hours). At 6 and 12 hours, endogenous ABA content gradually declined but remained significantly higher than the control ( P < 0.001) (Fig 4B). This suggests that exogenous ABA potentiates the drought-induced ABA response, possibly via a positive feedback mechanism. Discussion The calcium signaling network is a core regulatory system for plant responses to environmental stress. The CBL-CIPK module, as a key calcium signal decoder, converts intracellular calcium signals into downstream physiological responses through specific interactions (Batistič and Kudla, 2012; Weinl & Kudla, 2009 ). Based on the full-length transcriptome and expression analysis of A. cochinchinensis , this study systematically analyzed the molecular characteristics and expression patterns of its CBL-CIPK network under drought stress, providing experimental evidence for understanding the adaptation mechanisms of medicinal plants in karst habitats. Phylogenetic analysis revealed that the CBL and CIPK family members in A. cochinchinensis exhibit both conservation and diversity in evolution. AcCIPK24 and AcCBL10 clustered into the same branch as the core components of the Arabidopsis SOS pathway, AtCIPK24/SOS2 and AtCBL10/SOS3, respectively (Fig. 1 ), suggesting functional conservation of this interaction module in regulating ion homeostasis in dicotyledonous plants (Halfter et al., 2000 ). Notably, this study identified the presence of alternatively spliced variants of AcCIPK24.5 , whose structural differences may be involved in the fine-tuning of signaling pathways. Similar phenomena have been reported for Arabidopsis CIPK3 (Liu et al., 2022 ; Sanyal et al., 2021), implying that the "one gene, multiple transcripts" strategy may be a common mechanism for plants to adapt to complex environments. Considering the karst habitat background of A. cochinchinensis , the structural diversity of this network may be related to the selective pressure from periodic drought. Expression pattern analysis revealed significant tissue specificity and differential stress responses among CBL-CIPK members. Under drought treatment, the expression of AcCIPK24.5 and AcCBL10 was suppressed in roots and stems, while showing differential regulation in cladodes: AcCIPK24.5 was downregulated, whereas AcCBL10 was significantly upregulated (Figs. 2 and 3 ). This expression decoupling phenomenon indicates that the transcriptional regulation of CBL and CIPK is not simply coordinated but follows a tissue-specific regulatory logic. Considering that cladodes are the main photosynthetic and water exchange organs in A. cochinchinensis , the specific induction of AcCBL10 may be involved in local regulation of stomatal behavior or photosynthetic protection. This aligns with functional reports on SlCBL1 in tomato leaves (Sanyal et al., 2017 ; Kolukisaoglu et al., 2004 ), suggesting that CBL members may play specific roles in optimizing water use efficiency in photosynthetic tissues. Further analysis, combined with hormone treatment and endogenous hormone determination results (Fig. 4 ), revealed that exogenous ABA significantly induced the expression of AcCBL10 and AcCIPK24.5 while promoting endogenous ABA accumulation with an earlier peak. This result suggests a functional association between the CBL-CIPK module and the ABA signaling pathway, potentially forming a coordinated response loop, the CBL-CIPK complex may act both as a downstream effector unit of ABA signaling and participate in the regulation of ABA dynamic accumulation through feedback modulation (Zhu et al., 2007 ). Furthermore, endogenous ABA under drought stress exhibited a dynamic change of first increasing then decreasing. This pulsatile response may help plants initiate stress defense while avoiding excessive growth inhibition caused by sustained high ABA levels, reflecting a strategy of temporally precise regulation in stress responses. In summary, based on the available experimental evidence, this study proposes that in A. cochinchinensis , drought signals may activate the AcCBL10-AcCIPK24 interaction module, coordinating with the ABA pathway to achieve spatiotemporal regulation of stress responses. The differential expression of this network in roots/stems versus cladodes may serve to maintain ion homeostasis and protect photosynthetic organs, respectively, collectively supporting its adaptive capacity in the karst drought environment. This work not only expands the understanding of the CBL-CIPK network in non-model medicinal plants but also provides a reference case for deciphering the molecular basis of plant environmental adaptation. Declarations Consent for publication Not applicable. Availability of data and materials All data sheets and codes to process data are available upon request to the corresponding author, miaoliu ( [email protected] ). Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China (32360487); Department of Science and Technology of Guizhou Province (Qiankehe Fundamentals ZK [2023] General 114). Modern Industrial Technology System of Traditional Chinese Medicine in Guizhou Province (GZCYTX2024). Authors' contributions Liu Miao and Ming-sheng Zhang: conceptualization, methodology and funding acquisition, Zheng-xi Long, and Liu Tang: analysis, validation, Sheng-bo Lu and Yu-ting Yang: writing-original draft preparation, Liu Miao and Zheng-xi Long: discussion, writing-reviewing and editing, All authors read and approved the final manuscript. References Apse MP, Aharon GS, Snedden WA, Blumwald E. 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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-8784662","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589328187,"identity":"7413310b-4763-495b-831d-4d90d0ad2732","order_by":0,"name":"Zhengxi Long","email":"","orcid":"","institution":"School of Life Sciences/Key laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhengxi","middleName":"","lastName":"Long","suffix":""},{"id":589328188,"identity":"7c046104-13e2-42c3-a711-d772f432653d","order_by":1,"name":"Liu Tang","email":"","orcid":"","institution":"School of Life Sciences/Key laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Tang","suffix":""},{"id":589328189,"identity":"ff7e2676-b9ad-419b-996f-1ac81a86c040","order_by":2,"name":"Shengbo Lu","email":"","orcid":"","institution":"Qiannan Academy of Agricultural Sciences (The academy of Duyun Maojian tea)","correspondingAuthor":false,"prefix":"","firstName":"Shengbo","middleName":"","lastName":"Lu","suffix":""},{"id":589328190,"identity":"a757fe0f-f3ca-4e85-912f-aa6139975bfe","order_by":3,"name":"Yuting Yang","email":"","orcid":"","institution":"Qiannan Academy of Agricultural Sciences (The academy of Duyun Maojian tea)","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Yang","suffix":""},{"id":589328191,"identity":"a2993937-f667-4090-997e-59420435ed81","order_by":4,"name":"Mingsheng Zhang","email":"","orcid":"","institution":"School of Life Sciences/Key laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Mingsheng","middleName":"","lastName":"Zhang","suffix":""},{"id":589328192,"identity":"37c9eea3-fdfc-4278-9df4-47a4a0edabf1","order_by":5,"name":"miao liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3PsQrCMBCA4YNCuwTr2IL4DAdCQRT6KsnkJAguHRyUSjpY30U3RyVgHSKuHdM30EUcHGx1VBrdHPIPN93HcQAm0x+GohwKgbjObKZoNNGTbkUoQstPRYxK7vUk3FYEoI/5gPvF3NKTILMKRUeCYM54xKY2uMmC1hNhd5CiIL4seM42LfDkcaUhEHgVaWSsJNIG9Ib1BIVzfRLYMj5i3PqGkNeV5oFx+JaMy18GxE93sUflnmh/wVO2Vud7L3SdpLjcoknbTZb15C3y27rJZDKZPvYA5iVOv0a5jDIAAAAASUVORK5CYII=","orcid":"","institution":"School of Life Sciences/Key laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), Guizhou University","correspondingAuthor":true,"prefix":"","firstName":"miao","middleName":"","lastName":"liu","suffix":""}],"badges":[],"createdAt":"2026-02-04 09:55:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8784662/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8784662/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102651058,"identity":"5a809dd7-363e-4619-b8e3-d09a2df13c38","added_by":"auto","created_at":"2026-02-14 06:40:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":541856,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic relationships, motif of CBL and CIPK proteins in Arabidopsis and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. cochinchinensis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, the amino acid sequences of AtCBL and AcCBL, AtCIPK and AcCIPK were aligned using ClustalW2. \u003c/strong\u003e(A\u003csub\u003e1\u003c/sub\u003e) Phylogenetic analysis of CIPK proteins in in Arabidopsis and \u003cem\u003eA. cochinchinensis\u003c/em\u003e. (B\u003csub\u003e1\u003c/sub\u003e) Phylogenetic analysis of CBL proteins in in Arabidopsis and \u003cem\u003eA. cochinchinensis\u003c/em\u003e. The phylogenetic tree is constructed by MEGA 12 using the neighbor-joining method with 1000 bootstrap replicates and displayed using FigTree v1.4.0. At: Arabidopsis; Ac: \u003cem\u003eA. cochinchinensis\u003c/em\u003e. (B\u003csub\u003e1\u003c/sub\u003e) Conservative motifs of AtCIPK and AcCIPK proteins identifed by MEME. (B\u003csub\u003e2\u003c/sub\u003e) Conservative motifs of AtCBL and AcCBL proteins identifed by MEME. The motifs were indicated by colored boxes and their numbers are shown in the scale the diagram.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8784662/v1/1a7abacf4f824531728e6f81.png"},{"id":102651063,"identity":"9a827ca3-4746-4718-a202-f0eae87b83d6","added_by":"auto","created_at":"2026-02-14 06:40:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":499863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMorphological and Specific Gene Expression Responses of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. cochinchinensis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e to Drought Stress.\u003c/strong\u003e (A) Morphology of \u003cem\u003eA. cochinchinensis\u003c/em\u003e after 3 days of drought stress. (B) Relative expression analysis of \u003cem\u003eAcCBL10\u003c/em\u003e under drought stress at 0, 3, 6, and 12 h.(C) Relative expression analysis of \u003cem\u003eAcCIPK\u003c/em\u003e under drought stress at 0, 3, 6, and 12 h. CK: 0 h drought stress (control). ** denotes a significant difference at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, as determined using Student’s t test, Bars represent SD of the average, mean ± SD.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8784662/v1/67b7a09525c0ae0797c1fe24.png"},{"id":102651013,"identity":"fa613173-a821-428e-a129-0d7b371cb21b","added_by":"auto","created_at":"2026-02-14 06:40:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":305530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYeast-two-hybrid (Y2H) validation of the interaction between AcCBL10 and AcCIPK24.\u003c/strong\u003e Positive control: pGBKT7-p53 + pGADT7-T; negative control: pGBKT7-Lam + pGADT7-T. The rest are experimental groups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8784662/v1/874365079789c11cc0424577.png"},{"id":102651033,"identity":"d1988984-1559-4a66-a30f-fc2854f8832f","added_by":"auto","created_at":"2026-02-14 06:40:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of PEG 6000 and ABA treatments on ABA content in cladodes of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. cochinchinensis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e (A) Changes in endogenous ABA content in cladodes after treatment with 20% (w/v) PEG 6000. (B) Changes in ABA content in cladodes under combined treatment with 20% PEG 6000 and 100 mM ABA.Samples were collected at 0, 3, 6, and 12 h after treatment. Data are presented as mean ± SD of at least three biological replicates. Statistical significance was determined by one-way ANOVA (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8784662/v1/e1ea58589cf96ec45d7be770.png"},{"id":104835372,"identity":"98329253-df1a-4e84-9b8d-51be780dcd52","added_by":"auto","created_at":"2026-03-17 17:44:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3765221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8784662/v1/1806a1da-b233-4573-838f-bed2602493d6.pdf"},{"id":102651072,"identity":"2aa631d5-f40d-40fc-ab56-0f30fb344e77","added_by":"auto","created_at":"2026-02-14 06:40:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14090,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8784662/v1/dd207d3518c34cd4e5eebabd.docx"},{"id":102651075,"identity":"eb9e333a-ebbb-42ab-a5d2-95899d6e3f8c","added_by":"auto","created_at":"2026-02-14 06:40:33","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":981727,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarypictures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8784662/v1/8925989863cdf82183d8e68f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The CBL-CIPK Network Integrates Calcium and ABA Signaling to Mediate Drought Adaptation in Asparagus cochinchinensis","fulltext":[{"header":"1. Introdution","content":"\u003cp\u003eCalcium ions (Ca\u003csup\u003e2+\u003c/sup\u003e) act as ubiquitous intracellular secondary messengers, integrating signaling pathways essential for plant growth, development, and stress responses (Zhang et al., 2022; Zhu et al., 2022). Specific sensor proteins, such as the plant-specific calcineurin B-like (CBL) family, detect these cytosolic Ca\u003csup\u003e2+\u003c/sup\u003e concentration fluctuations (Zhou et al., 2015). CBL proteins contain four conserved EF-hand domains that bind Ca\u003csup\u003e2+\u003c/sup\u003e, enabling them to decode Ca\u003csup\u003e2+\u003c/sup\u003e signals (Verma et al., 2021). These proteins selectively interact with CBL-interacting protein kinases (CIPKs), forming CBL-CIPK complexes that serve as central components of Ca\u003csup\u003e2+\u003c/sup\u003e signal transduction (Zhang et al., 2020).\u003c/p\u003e\n\u003cp\u003eThe subcellular localization of CBL determines its functional specificity, which is primarily governed by distinct N-terminal targeting sequences (Tang et al., 2020). The classical (type I) CBL contains a dual lipid modification motif (MGCXXS/T) and localizes to the plasma membrane (Weinl et al., 2009). The tonoplast-type (type II) CBL harbors a tonoplast targeting sequence (TTS) (Xu et al., 2006). The membrane-anchored (type III) CBL possesses a transmembrane helix domain that enables its localization to either the tonoplast or plasma membrane (Tang et al., 2020; Waadt et al., 2008). The interaction with CIPK is mediated by a shared C-terminal PFPF/FPSF motif containing a profound phosphorylation site that is essential for complex regulation (Aslam et al., 2019).\u003c/p\u003e\n\u003cp\u003eCIPK contains an N-terminal serine/threonine kinase domain and a C-terminal regulatory domain (Shi et al., 2021). The regulatory domain features a highly conserved NAF/FISL motif (core residues: N, A, F, I, S, L) that functions both as the primary CBL-binding site and as an autoinhibitory domain (Wu et al., 2024). When Ca\u003csup\u003e2+\u003c/sup\u003e-activated CBL binds to this motif, it releases autoinhibition and activates the CIPK kinase domain in a calcium-dependent manner (Xiao et al., 2022). Some CIPKs also possess a less conserved protein phosphatase interaction site (PPI) adjacent to the NAF/FISL motif, potentially facilitating interactions with phosphatase regulators like ABI/PP2Cs (Yu et al., 2014).\u003c/p\u003e\n\u003cp\u003ePlants inhabiting specific ecological niches, particularly those in karst regions like \u003cem\u003eA. cochinchinensis\u003c/em\u003e, face unique water scarcity challenges due to the high permeability of limestone substrates. This medicinally important herb has evolved sophisticated mechanisms to cope with the fluctuating water availability in such environments. Given that calcium signaling is often the first responder to osmotic changes, it is hypothesized that the CBL-CIPK network in \u003cem\u003eA. cochinchinensis\u003c/em\u003e may have undergone specific evolutionary adaptations to fine-tune its drought response. The CBL-CIPK signaling network plays a crucial role in regulating diverse physiological processes across the plant life cycle. This system mediates plant responses to multiple abiotic stresses, such as salinity, drought, low temperature, and heavy metal exposure (Kanwar et al., 2014; Ma et al., et al., 2020; Kaya et al., 2024). It also governs essential developmental stages including seedling establishment, flowering initiation, root architecture, pollen germination, and pollen tube elongation (Wang et al., 2020). Recent studies have further demonstrated its involvement in phytohormone pathways, particularly through interactions with ABA signaling, GA biosynthesis, and cold stress adaptation mechanisms (Kolukisaoglu et al., 2004).\u003c/p\u003e\n\u003cp\u003eDrought stress severely limits plant growth, development, and agricultural productivity by disrupting water homeostasis, inducing osmotic stress, and impairing metabolic functions (Gonz\u0026aacute;lez et al., 2023). Plants detect water deficit and activate adaptive responses, with the evolutionarily conserved CBL-CIPK signaling module serving as a key decoder of stress-induced Ca\u003csup\u003e2+\u003c/sup\u003e fluxes and playing a critical role in drought adaptation (Cheong et al., 2007). This complex regulates ion homeostasis by directly modulating plasma membrane and tonoplast ion channels and transporters (e.g., K\u003csup\u003e+\u0026nbsp;\u003c/sup\u003echannels, H\u003csup\u003e+\u003c/sup\u003e-ATPases), thereby controlling essential drought tolerance mechanisms (Apse et al., 1999; Tang et al., 2015). The module also coordinates diverse cellular responses, such as osmoprotectant accumulation and hormone signaling pathway regulation. Studies across multiple plant species have demonstrated that specific CBL-CIPK interactions are indispensable for drought resistance. Overexpressing \u003cem\u003eAtCIPK1/23\u003c/em\u003e, \u003cem\u003eAtCBL1/9\u003c/em\u003e, \u003cem\u003eOsCIPK3/12/15\u003c/em\u003e, and \u003cem\u003eMdCIPK22\u003c/em\u003e enhances drought tolerance in Arabidopsis, rice, and apple, while loss-of-function mutants (e.g., \u003cem\u003ecipk1\u003c/em\u003e, \u003cem\u003ecbl1\u003c/em\u003e) show increased drought sensitivity (Cho et al., 2018; Ma et al., 2019., Gao et al., 2019; Lara et al., 2020; Peng et al., 2021; Chao et al., 2022). These findings suggest that genetic manipulation of this signaling pathway could effectively improve crop drought resilience.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA. cochinchinensis\u003c/em\u003e, a highly valued traditional Chinese medicinal herb renowned for its therapeutic properties, often faces cultivation challenges due to various environmental stresses (Wang et al., 2022; Yunmam et al., 2023). Although the medicinal properties and genomic resources of \u003cem\u003eA. cochinchinensis\u003c/em\u003e have been recently advanced, the composition, structural diversity (such as alternative splicing variants), and functional specificity of the CBL-CIPK network in this species remain largely elusive. Furthermore, the crosstalk between the CBL-CIPK cascade and phytohormone signaling, particularly Abscisic Acid (ABA), which is central to drought adaptation, has not been elucidated in this non-model medicinal plant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we performed full-length transcriptome sequencing to generate a comprehensive genomic resource. We systematically identified and characterized the \u003cem\u003eCBL\u003c/em\u003e and \u003cem\u003eCIPK\u003c/em\u003e gene families, with a specific focus on evolutionary conservation and structural divergence. Furthermore, we validated the specific protein-protein interaction between key drought-responsive modules (AcCBL10 and AcCIPK24) and investigated their expression dynamics under drought stress. Our work aims to decipher the molecular architecture of the CBL-CIPK network and its interaction with ABA signaling, providing novel insights into the drought adaptation mechanisms of \u003cem\u003eA. cochinchinensis\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Sample Collection and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this experiment, the tested \u003cem\u003eA. cochinchinensis\u0026nbsp;\u003c/em\u003esamples were collected from Danzhai County (107\u0026deg;44\u0026prime;-108\u0026deg;08\u0026prime;E, 26\u0026deg;05\u0026prime;-26\u0026deg;26\u0026prime;N), Qiandongnan Prefecture, Guizhou Province, and authenticated by Professor Zhang Mingsheng, the Chief Scientist of Guizhou Province. The roots, stems, cladodes, flowers, and fruits of \u003cem\u003eA. cochinchinensis\u003c/em\u003e were mixed, flash-frozen in liquid nitrogen, and stored at -80\u0026deg;C to ensure sample stability and subsequent experimental accuracy (Hajihashemi et al., 2018). The experiment was designed with three biological replicates to enhance the reliability and reproducibility of the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor drought stress assays, \u003cem\u003eA. cochinchinensis\u003c/em\u003e materials were cultivated in the greenhouse of Guizhou University and subjected to continuous treatment with 20% PEG6000 for 0, 2, 6, 12, and 24 h. The underground tissues were then collected for RNA extraction. Throughout the sampling period, water was replenished regularly to maintain PEG6000 concentration (Yao et al., 2022). Each treatment group consisted of three independent biological replicates, with 10 plants per replicate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDesign specific primers (primer sequence shown in Table S1), using the internal reference gene Actin as a control, and calculate the relative expression levels of different tissues and organs as well as after drought stress treatment using the 2\u003csup\u003e⁻\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 RNA extraction, sequencing library construction and sequencing assembly\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was isolated from tissue samples with TRIzol reagent (Life Technologies). The Agilent 2100 Bioanalyzer, agarose gel electrophoresis, and NanoDrop 2000 spectrophotometer confirmed RNA integrity (RIN \u0026ge; 8.0), purity (OD260/280 = 1.8-2.0; OD260/230 \u0026ge; 2.0), and concentration. Using the SMARTer approach (NEBNext\u0026reg; Single Cell/Low Input RNA Library Prep Kit), qualified RNA samples underwent double-stranded cDNA synthesis, PCR amplification, end repair, and PacBio SMRTbell adapter ligation (Cheng et al., 2019). The PacBio Sequel II platform sequenced quality-verified libraries by single-molecule real-time (SMRT) technology, achieving a target yield of \u0026ge;8Gb per sample. SMRT Link v10.2 produced circular consensus sequences (CCS, \u0026ge;3 full passes), from which full-length transcripts were identified by the presence of both 5\u0026apos;-primer (GCAATGAAGTCGCAGGGTTG) and 3\u0026apos;-primer (AAGCAGTGGTATCAACGCAGAGT). The iterative clustering and error correction (ICE) algorithm generated consensus sequences with \u0026ge;0.99 predicted accuracy for downstream analysis (Wang et al., 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Annotation of full-length transcript\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe full-length transcript was aligned with the NCBI non-redundant proteins database (NCBI non-redundant proteins, Nr, http://www.ncbi.nlm.nih.gov), Swiss-Prot protein database (Swiss-Prot protein database, http://www.expasy.ch/sprot), Kyoto encyclopedia of genes and genomes database (KEGG, http://www.genome.jp/kegg), and COG/KOG database (COG/KOG database, http://www.ncbi.nlm.nih.gov/COG) using BLASTX alignment, with an E-value threshold set at 1E-5, to assess the sequence similarity between the full-length transcript of Asparagus and that of \u003cem\u003eAsparagus cochinchinensis\u003c/em\u003e, and obtain basic annotations for each full-length transcript (Ning et al., 2024). Blast2GO software was used to perform GO annotation analysis on the Nr annotation results of the full-length transcript (Conesa et al., 2005). WEGO software was utilized for hierarchical functional classification of the full-length transcript (Ye et al., 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Transcriptome structural feature analysis and gene function prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Astalavista software identified alternative splicing patterns in precursor mRNA (pre-mRNA) transcribed from each gene. We performed genome-wide simple sequence repeat (SSR) mining with the MicroSAtellite identification tool (MISA), applying these parameters: 1-12, 2-6, 3-5, 4-5, 5-4, and 6-4. SSRs separated by less than 100 bp were merged and treated as a single locus (Tandon et al., 2023). TransDecoder (v3.0.0) predicted reliable coding sequence (CDS) regions from transcript sequences by evaluating open reading frame (ORF) length, log-likelihood scores, and alignment of amino acid sequences against Pfam protein domains (Ramos et al., 2021). For novel transcripts, we predicted long non-coding RNAs (lncRNAs) by combining four computational approaches: Coding Potential Calculator (CPC), Coding-Non-Coding Index (CNCI), Pfam domain analysis, and Coding Potential Assessment Tool (CPAT). Transcripts were classified as lncRNAs only when all four methods agreed on their non-coding status (Duan et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Transcription Factor Analysis and Functional Annotation of Novel Transcripts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscription factors (TFs) in \u003cem\u003eA. cochinchinensis\u003c/em\u003e were identified using iTAK (http://itak.feilab.net/cgi-bin/itak/index.cgi), with subsequent validation using hmmscan to align query sequences against the Pfam database for TF quantification (Zheng et al., 2016). Following deduplication, gffcompare analyzed full-length transcripts in GFF3 format against the reference genome\u0026apos;s annotations. The study detected novel transcripts and genes absent from the reference genome, which were then functionally annotated by DIAMOND (v2.0.15) through alignment with NR, SwissProt, GO, COG, KOG, Pfam, and KEGG databases (Sun et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Identification of the \u003cem\u003eCBL\u003c/em\u003e and \u003cem\u003eCIPK\u003c/em\u003e Gene Families in \u003cem\u003eA. cochinchinensis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCandidate genes were screened from the full-length transcriptome of \u003cem\u003eAsparagus cochinchinensis\u003c/em\u003e (\u003cem\u003eA. cochinchinensis\u003c/em\u003e) through BLASTP alignment against protein sequences of AtCBLs (e.g., AT4G17615) and AtCIPKs (e.g., AT3G17510) in \u003cem\u003eA. thaliana\u003c/em\u003e, as well as those of \u003cem\u003eA. cochinchinensis\u003c/em\u003e (Table S1). The conserved domains of CBL (PF00036 and PF13499) and CIPK (PF00069 and PF03822) were searched using HMMER 3.3, and domain integrity was further verified with the SMART database (Wu et al., 2023). A neighbor-joining tree was constructed using MEGA 12 with the p-distance model and 1000 bootstrap replicates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Secondary and tertiary structure analysis of AcCBLs and AcCIPKs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConserved motifs in the AcCBL and AcCIPK protein sequences were identified using the MEME (v4.12.0; http://meme-suite.org/tools/meme), configured to detect up to 15 motifs with an optimum width of 6 - 200 residues. Only motifs satisfying a stringent significance threshold (e-value \u0026le; 1e-10) were retained. The motif distribution patterns were visualized with TBtools (Huang et al., 2022). We computationally characterized the AcCBL and AcCIPK proteins using the following tools: ExPASy for physicochemical properties, TMPRED for predicting transmembrane domains, SignalP 4.1 for identifying signal peptides, and Plant-mPLoc for inferring subcellular localizations (Wu et al., 2023). The tertiary structures of CBL and CIPK family members from \u003cem\u003eA. cochinchinensis\u003c/em\u003e were predicted using the I-TASSER platform (Biasini et al., 2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Yeast Two Hybrid\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe yeast two-hybrid (Y2H) assay utilized the Saccharomyces cerevisiae strain Y2HGold, where AcCBL10 was cloned into pGADT7 as the prey and AcCIPK23/24 into pGBKT7 as the bait (Li et al., 2016). The positive control featured pGADT7-T co-transformed with pGBKT7-53, whereas pGADT7-T combined with pGBKT7-lam constituted the negative control. To assess transcriptional self-activation, pGADT7 was separately co-transformed with either pGBKT7-AcCBL10 or pGBKT7-AcCIPK23/24. All primer sequences are listed in Table S3 (Yang et al., 2013). The optimal 3-AT concentration for suppressing background growth was determined by culturing Y2HGold cells harboring the bait plasmid on dropout media (DDO, TDO, and QDO) containing a gradient of 3-AT concentrations at 30 \u0026deg;C for five days. Protein-protein interactions were confirmed by plating co-transformed Y2HGold cells on DDO, TDO supplemented with 15 mmol\u0026middot;L\u003csup\u003e-1\u003c/sup\u003e 3-AT, and QDO media, followed by incubation under the same conditions (Huang et al., 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Plant Growth Conditions and Drought Stress Treatments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tissue culture seedlings of \u003cem\u003eA. cochinchinensis\u003c/em\u003e were preserved at the Key Laboratory of Plant Resources Conservation and Germplasm Innovation in Mountainous Region, Guizhou University. Uniform and robust tissue-cultured seedlings were selected for transplanting. The cultivation substrate was a mixture of peat soil and perlite at a 3:1 ratio. The plants were maintained under conditions of approximately 95% relative humidity, a temperature of 20-28 \u0026deg;C, and no direct light. After one month of soil cultivation, uniformly growing and healthy plants were selected for the simulated drought treatment experiment. The experiment included two treatments: one group was treated with a 1/2 Hoagland nutrient solution containing 20% (w/v) PEG 6000 to simulate drought stress (Yao et al., 2022); the other group was treated with a 1/2 Hoagland nutrient solution containing both 20% (w/v) PEG 6000 and 100 mM ABA for a combined PEG and ABA treatment (Wu et al., 2019). A control group (CK) was set up using the basic 1/2 Hoagland nutrient solution under normal light/dark cycles. Each treatment had three biological replicates, with each replicate consisting of ten seedlings. Samples were collected at 0, 3, 6, and 12 hours after treatment, immediately frozen in liquid nitrogen, and stored for subsequent RNA extraction and analysis.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Full-Length Transcriptome Assembly and Quality Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-quality full-length transcripts were generated using PacBio Sequel II sequencing, yielding 12.6 Gb of raw data. After adapter trimming and quality filtering, 340,429 circular consensus sequences (CCS) were obtained. Among these, 307,748 reads (90.4%) contained intact 5\u0026rsquo;/3\u0026rsquo; primers and poly A tails and were classified as full-length non-chimeric (FLNC) reads (Fig. S1A). Clustering the FLNC reads yielded 118,501 high-quality consensus isoforms (accuracy \u0026gt; 99%), and subsequent dereplication using cDNA_Cupcake resulted in 52,042 non-redundant transcripts with an N50 of 2.9 kb (Fig. S1B). BUSCO v3.0.2 analysis against the plantae_odb10 database assessed assembly completeness, recovering 94.2% of core genes (2.1% fragmented, 3.7% missing) and confirming high assembly integrity (Fig. S2). BLASTN analysis further validated the assembly by demonstrating 92.3% sequence identity with the \u003cem\u003eA. cochinchinensis\u003c/em\u003e reference genome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Functional Annotation of Transcripts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComprehensive annotation of the 52,042 non-redundant transcripts against multiple public databases successfully assigned functions to 37,827 (72.7%) transcripts in at least one database. Homology searches revealed that 37,794 (72.6%) transcripts had significant matches in the NR database, with the highest similarity to \u003cem\u003eA. cochinchinensis\u003c/em\u003e (~71.5%), followed by \u003cem\u003ePhoenix dactylifera\u003c/em\u003e and \u003cem\u003eElaeis guineensis\u003c/em\u003e (Fig. S3A). A total of 29,211 (56.1%) transcripts were matched to the manually curated Swiss-Prot database. Gene Ontology (GO) terms were assigned to 32,892 (63.2%) transcripts, which were predominantly enriched in the biological process categories of \u0026apos;cellular process\u0026apos;, \u0026apos;metabolic process\u0026apos;, and \u0026apos;response to stimulus\u0026apos; (Fig. S3B). KEGG pathway analysis associated 29,388 transcripts (56.5%) with various pathways and identified 134 enriched pathways, providing a foundation for further investigation into Asparagus biological metabolism and its metabolic network (Table S1). Cluster of Orthologous Groups (COG/KOG) analysis classified 26,188 (50.3%) transcripts into 25 functional categories, where \u0026apos;General function prediction only\u0026apos; (15.8%), \u0026apos;Posttranslational modification, protein turnover, chaperones\u0026apos; (11.2%), and \u0026apos;Signal transduction mechanisms\u0026apos; (10.5%) were the most abundant (Fig. S3C). Pfam domain analysis identified domains in 31,513 (60.6%) transcripts, with protein kinase, leucine-rich repeat, and EF-hand domains being the most prevalent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Structural Characterization of Novel Transcripts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn-depth transcriptome analysis identified 38,352 novel transcripts absent from the current reference annotation. A total of 31,724 simple sequence repeats (SSRs) were detected within 21,374 transcripts, with mononucleotide repeats (48.0%) being the most frequent, followed by di- (26.8%) and tri-nucleotide (23.3%) repeats (Fig. S4A). TransDecoder predicted 37,684 open reading frames (ORFs), of which 28,366 (75.3%) were complete, containing both start and stop codons (Fig. S4B). Alternative splicing (AS) analysis revealed extensive mRNA isoform diversity, identifying 13,580 gene loci and 1,382 novel loci. We cataloged 5,874 AS events, with intron retention (IR, 34.1%) and exon skipping (ES, 28.7%) representing the predominant types (Fig. S4C). Furthermore, 521 potential fusion transcripts were predicted, suggesting possible gene rearrangements or trans-splicing events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Identification and Functional Prediction of LncRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA stringent pipeline integrating four computational tools (CPC2, CNCI, CPAT, and Pfam scan) identified 366 high-confidence long non-coding RNAs (LncRNAs) (Fig. S5A). These lncRNAs were classified according to their genomic context relative to protein-coding genes, with 58.7% designated as intergenic (lincRNAs), 21.3% as antisense, 12.0% as intronic, and 8.0% as sense overlapping (Fig. S5B). Target gene prediction, based on both cis-regulation (genomic proximity) and trans-regulation (sequence complementarity), indicated that these lncRNAs may regulate genes associated with transcription, stress response, and metabolic processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Transcription Factor Family Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComprehensive analysis with the iTAK pipeline identified 7,565 transcripts encoding transcription factors (TFs) and transcriptional regulators (TRs), accounting for 14.5% of all transcripts. These TFs were classified into 58 distinct families. The most abundant families were bHLH (9.2%), ERF (8.5%), MYB-related (7.1%), NAC (6.3%), and C2H2 (5.8%), all of which are known to be critically involved in plant development, hormone signaling, and responses to abiotic stress (Fig. S6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Genome-wide characterization of CBL-CIPK networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to comprehensively identify the CIPK and CBL genes in the full-length transcriptome data of Asparagus, the bidirectional BLAST method was used for identification. Members were named 48 members such as AcCIPK1 and AcCBL1 according to the genetic distance between them and the members of the Arabidopsis CIPK and CBL gene family (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe basic characteristics of gene coding sequence (CDS), protein molecular weight (MW), isoelectric point (PI) and subcellular localization were analyzed. Among the 35 identified CIPK protein members, their protein lengths ranged from 296 amino acids (aa) (AcCIPK24.3) to 485 aa (AcCIPK3.7), with corresponding molecular weights of 33,664.73 kDa (AcCIPK24.3) and 55,500.55 kDa (AcCIPK3.7), respectively. The CIPK family included 11 acidic proteins and 24 basic proteins, with isoelectric points (pI) distributed between 4.98 and 9.28. Subcellular localization analysis revealed that, with the exception of AcCIPK1.1, the remaining 34 members were localized to the cytoplasm. Notably, AcCIPK23.2 and AcCIPK23.3 were also predicted to localize to the cytoplasm. Among the 13 identified CBL protein members, protein lengths spanned from 64 aa (AcCBL4.2) to 283 aa (AcCBL9), with corresponding molecular weights ranging from approximately 7.17 kDa (AcCBL4.2) to 33.01 kDa (AcCBL9). The isoelectric points ranged from 4.51 (AcCBL4.2) to 5.23 (AcCBL9), and all members were acidic proteins. Subcellular localization results indicated diverse localization patterns for these 13 CBL members, including the nucleus (AcCBL2.1, AcCBL2.2), plasma membrane (AcCBL3.1, AcCBL3.2, AcCBL4.1, AcCBL8, AcCBL9, AcCBL10), and endoplasmic reticulum (AcCBL3.5), among others (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e1.\u003c/strong\u003e \u003cstrong\u003eList\u003c/strong\u003e \u003cstrong\u003eof\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003eCIPK\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003eand \u003cem\u003eCBL\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003eFamily\u003c/strong\u003e \u003cstrong\u003eGenes\u003c/strong\u003e \u003cstrong\u003eIdentified\u003c/strong\u003e \u003cstrong\u003ein\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003eA. cochinchinensis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"695\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e \u003cstrong\u003eID\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eA. cochinchinensis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e \u003cstrong\u003eName\u003c/strong\u003e \u003cstrong\u003ein\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eA. cochinchinensis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e \u003cstrong\u003eID\u003c/strong\u003e \u003cstrong\u003ein\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eA. thaliana\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e \u003cstrong\u003eName\u003c/strong\u003e \u003cstrong\u003ein\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eA. thaliana\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Length (aa)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kDa)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein Isoelectric\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePoint (pI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein Subcellular\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Localization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT3G17510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCIPK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e50452.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eNucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.11531.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT5G45820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCIPK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e52218.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.1938.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e52116.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.1938.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e52116.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.2547.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT2G26980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCIPK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e49817.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.2547.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e30751.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.2547.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e50338.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.2547.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e50352.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.2547.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e54324.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.2547.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e50398.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8993.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e55500.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm. Nucleus.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8993.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e41035.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.2547.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e50398.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.12492.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT1G01140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCIPK9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e49466.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.5252.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e40900.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.5252.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e49601.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.5252.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e42311.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8410.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT5G58380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCIPK10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e51650.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8410.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e51635.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.5949.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e51336.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCentrosome\u003c/p\u003e\n \u003cp\u003eCytoplasm Nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.5949.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e42197.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm Nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8410.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e51635.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8410.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e51650.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8410.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e39582.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8410.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e51650.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8137.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT1G30270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCIPK23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e41492.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm. Nucleus.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8137.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e50030.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCentrosome\u003c/p\u003e\n \u003cp\u003eCytoplasm Nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8137.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e50030.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCentrosome Cytoplasm Nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8137.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e44079.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.8137.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e41565.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm Nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.7948.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT5G35410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCIPK24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e49949.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.7948.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e47407.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm. Nucleus.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.7948.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e33664.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eNucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.7948.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e44592.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.7948.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCIPK24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e44525.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.6643.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT5G55990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCBL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e9184.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eNucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.6643.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e9184.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eNucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.295.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT4G26570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCBL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e25757.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCell membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.6643.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e23602.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCell membrane. Cytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.295.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e16919.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eExtracell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.295.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e12608.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eExtracell. Nucleus.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.295.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e19356.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eEndoplasmic reticulum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.6530.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT5G24270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCBL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e24563.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCell membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.6530.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e7168.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCytoplasm. Nucleus.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.295.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT4G01420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCBL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e10835.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eExtracell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.6530.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT1G64480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCBL8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e24563.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCell membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.10318.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT5G47100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCBL9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e33008.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCell membrane. Extracell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ePB.4106.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAcCBL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAT4G33000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAtCBL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e19606.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCell membrane. Cytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMost members clustered separately due to species differences, but CIPK3, CIPK9, CIPK23, and CIPK24 grouped into a single clade in Arabidopsis and \u003cem\u003eA. cochinchinensis\u0026nbsp;\u003c/em\u003e(Fig 1A\u003csub\u003e1\u003c/sub\u003e). Similarly, AcCBL10 and AtCBL10 exhibited a close phylogenetic relationship. Based on these findings, it is hypothesized that the copies of these genes in \u003cem\u003eA. cochinchinensis\u003c/em\u003e may possess functions similar to those of their homologs in Arabidopsis (Fig 1B\u003csub\u003e1\u003c/sub\u003e). Regarding motif distribution, members within the same clade shared similar motif compositions. Specifically, the motif compositions of AcCIPK23.2 and AcCIPK23.3 were identical to that of AtCIPK23.2, and the motif composition of AcCIPK24.1 was the same as that of AtCIPK24 (Fig 1A\u003csub\u003e2\u003c/sub\u003e). In addition, AcCBL10 lacked only motif14 compared with AtCBL10 (Fig 1B\u003csub\u003e2\u003c/sub\u003e). These results suggest that the CIPK23, CIPK24 and CBL10 proteins are relatively conserved in terms of structural and functional integrity during evolution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Tissue-Specific Expression Analysis of \u003cem\u003eAcCIPK\u003c/em\u003e and \u003cem\u003eAcCBL\u003c/em\u003e Genes under Drought Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn plants, CBL-CIPK complexes are key signaling modules in drought responses (Arab et al., 2023). To investigate the potential functions of the key genes identified in this study in the drought response of \u003cem\u003eA. cochinchinensis\u003c/em\u003e, seven representative genes, including \u003cem\u003eAcCIPK24.5\u003c/em\u003e and \u003cem\u003eAcCBL10\u003c/em\u003e were selected. Their expression dynamics in root, stem, and cladode under both normal and drought conditions were analyzed using qRT-PCR.\u003c/p\u003e\n\u003cp\u003eThe results indicated that drought stress significantly affected the expression levels of most tested genes, and this regulatory role exhibited distinct tissue specificity (Fig 2). In roots, the expression of \u003cem\u003eAcCIPK10.3\u003c/em\u003e, \u003cem\u003eAcCIPK10.4\u003c/em\u003e, \u003cem\u003eAcCIPK24.5\u003c/em\u003e, \u003cem\u003eAcCBL10\u003c/em\u003e, and \u003cem\u003eAcCIPK2.1\u003c/em\u003e was significantly suppressed by drought (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), with \u003cem\u003eAcCIPK2.1\u003c/em\u003e showing the greatest downregulation. In contrast, the expression of \u003cem\u003eAcCIPK10.8\u003c/em\u003e and \u003cem\u003eAcCIPK9.3\u003c/em\u003e showed no significant change. In stem, the expression of \u003cem\u003eAcCIPK10.3\u003c/em\u003e, \u003cem\u003eAcCIPK10.4\u003c/em\u003e, \u003cem\u003eAcCIPK10.8\u003c/em\u003e, and \u003cem\u003eAcCBL10\u003c/em\u003e was significantly downregulated by drought, while \u003cem\u003eAcCIPK9.3\u003c/em\u003e expression was significantly induced. The expression of \u003cem\u003eAcCIPK24.5\u003c/em\u003e and \u003cem\u003eAcCIPK2.1\u003c/em\u003e remained relatively stable. In cladodes, the expression of \u003cem\u003eAcCIPK10.8\u003c/em\u003e, \u003cem\u003eAcCIPK24.5\u003c/em\u003e, and \u003cem\u003eAcCBL10\u003c/em\u003e was significantly downregulated by drought, whereas \u003cem\u003eAcCIPK9.3\u003c/em\u003e expression was significantly upregulated. The expression of \u003cem\u003eAcCIPK10.3\u003c/em\u003e and \u003cem\u003eAcCIPK10.4\u0026nbsp;\u003c/em\u003eshowed no significant change.\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that \u003cem\u003eAcCIPK24.5\u003c/em\u003e and \u003cem\u003eAcCBL10\u003c/em\u003e, which are highly conserved in phylogeny and whose homologs in Arabidopsis are known to interact, exhibited both correlated and specific expression patterns in response to drought stress. On one hand, their expression trends were consistent in root and stem tissues, both being suppressed by drought. On the other hand, in cladode tissues, \u003cem\u003eAcCIPK24.5\u003c/em\u003e expression was inhibited by drought, while \u003cem\u003eAcCBL10\u003c/em\u003e expression was significantly induced. This differential, even opposite, transcriptional response to the same stress signal in cladode tissues suggests that these two genes may be regulated by distinct upstream factors, indicating a complex functional relationship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Interaction between AcCIPK24 and AcCBL10\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the conserved homology and coordinated yet distinct expression patterns under stress, we hypothesized that AcCIPK24 and AcCBL10 might physically interact to form a functional module. This interaction was tested using a yeast two-hybrid (Y2H) assay. The constructs pGBKT7-AcCIPK24 and pGADT7-AcCBL10 were co-transformed into the yeast strain Y2HGold. As shown in Figure 3, the co-transformants grew robustly on SD/-Trp-Leu-His selective medium across serial dilutions 10 to 10\u003csup\u003e-3\u003c/sup\u003e, with growth comparable to that of the positive control (pGADT7-T + pGBKT7-P53). In contrast, the two negative controls (pGADT7-T + pGBKT7-Lam and pGBKT7-AcCIPK24 + empty pGADT7) showed almost no colony formation under the same selective conditions. These results indicate that AcCIPK24 and AcCBL10 specifically interact in yeast cells, and that this interaction is not due to autoactivation or nonspecific binding of either protein alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Effects of Drought Stress and Exogenous ABA Treatment on Endogenous ABA Content in Plants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate whether ABA signaling is involved in the response of \u003cem\u003eA. cochinchinensis\u003c/em\u003e to drought stress and to elucidate its potential regulatory mode, we first measured the dynamic changes in endogenous ABA content in plants under PEG6000 simulated drought conditions. Subsequently, we analyzed the feedback regulation of exogenous ABA on endogenous ABA homeostasis. Under PEG6000 simulated drought stress, the endogenous ABA content in \u003cem\u003eA. cochinchinensis\u003c/em\u003e plants exhibited a dynamic pattern of initial increase followed by a slight decrease over time. Compared to the 0-hour control, ABA content increased significantly (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) at 3 hours of stress treatment, peaked at 6 hours (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and although slightly decreased by 12 hours, it remained significantly higher than the control level (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Fig 4A). This indicates that drought stress effectively activates ABA biosynthesis or accumulation in \u003cem\u003eA. cochinchinensis\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTo further explore the role of ABA signaling in this process, exogenous ABA was applied in combination with PEG6000 treatment. The introduction of exogenous ABA significantly altered the accumulation dynamics of endogenous ABA. Compared to the drought-only treatment group (Fig 4A), plants subjected to combined PEG and ABA treatment exhibited a higher basal ABA level even at 0 hours. After 3 hours of stress treatment, their endogenous ABA content rapidly reached its highest value (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and the timing of this peak occurred significantly earlier than in the drought-only group (6 hours). At 6 and 12 hours, endogenous ABA content gradually declined but remained significantly higher than the control (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Fig 4B). This suggests that exogenous ABA potentiates the drought-induced ABA response, possibly via a positive feedback mechanism.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe calcium signaling network is a core regulatory system for plant responses to environmental stress. The CBL-CIPK module, as a key calcium signal decoder, converts intracellular calcium signals into downstream physiological responses through specific interactions (Batistič and Kudla, 2012; Weinl \u0026amp; Kudla, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Based on the full-length transcriptome and expression analysis of \u003cem\u003eA. cochinchinensis\u003c/em\u003e, this study systematically analyzed the molecular characteristics and expression patterns of its CBL-CIPK network under drought stress, providing experimental evidence for understanding the adaptation mechanisms of medicinal plants in karst habitats.\u003c/p\u003e \u003cp\u003ePhylogenetic analysis revealed that the CBL and CIPK family members in \u003cem\u003eA. cochinchinensis\u003c/em\u003e exhibit both conservation and diversity in evolution. AcCIPK24 and AcCBL10 clustered into the same branch as the core components of the Arabidopsis SOS pathway, AtCIPK24/SOS2 and AtCBL10/SOS3, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting functional conservation of this interaction module in regulating ion homeostasis in dicotyledonous plants (Halfter et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Notably, this study identified the presence of alternatively spliced variants of \u003cem\u003eAcCIPK24.5\u003c/em\u003e, whose structural differences may be involved in the fine-tuning of signaling pathways. Similar phenomena have been reported for Arabidopsis CIPK3 (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sanyal et al., 2021), implying that the \"one gene, multiple transcripts\" strategy may be a common mechanism for plants to adapt to complex environments. Considering the karst habitat background of \u003cem\u003eA. cochinchinensis\u003c/em\u003e, the structural diversity of this network may be related to the selective pressure from periodic drought.\u003c/p\u003e \u003cp\u003eExpression pattern analysis revealed significant tissue specificity and differential stress responses among CBL-CIPK members. Under drought treatment, the expression of AcCIPK24.5 and AcCBL10 was suppressed in roots and stems, while showing differential regulation in cladodes: AcCIPK24.5 was downregulated, whereas AcCBL10 was significantly upregulated (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This expression decoupling phenomenon indicates that the transcriptional regulation of \u003cem\u003eCBL\u003c/em\u003e and \u003cem\u003eCIPK\u003c/em\u003e is not simply coordinated but follows a tissue-specific regulatory logic. Considering that cladodes are the main photosynthetic and water exchange organs in \u003cem\u003eA. cochinchinensis\u003c/em\u003e, the specific induction of \u003cem\u003eAcCBL10\u003c/em\u003e may be involved in local regulation of stomatal behavior or photosynthetic protection. This aligns with functional reports on SlCBL1 in tomato leaves (Sanyal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kolukisaoglu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), suggesting that CBL members may play specific roles in optimizing water use efficiency in photosynthetic tissues.\u003c/p\u003e \u003cp\u003eFurther analysis, combined with hormone treatment and endogenous hormone determination results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), revealed that exogenous ABA significantly induced the expression of \u003cem\u003eAcCBL10\u003c/em\u003e and \u003cem\u003eAcCIPK24.5\u003c/em\u003e while promoting endogenous ABA accumulation with an earlier peak. This result suggests a functional association between the CBL-CIPK module and the ABA signaling pathway, potentially forming a coordinated response loop, the CBL-CIPK complex may act both as a downstream effector unit of ABA signaling and participate in the regulation of ABA dynamic accumulation through feedback modulation (Zhu et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Furthermore, endogenous ABA under drought stress exhibited a dynamic change of first increasing then decreasing. This pulsatile response may help plants initiate stress defense while avoiding excessive growth inhibition caused by sustained high ABA levels, reflecting a strategy of temporally precise regulation in stress responses.\u003c/p\u003e \u003cp\u003eIn summary, based on the available experimental evidence, this study proposes that in \u003cem\u003eA. cochinchinensis\u003c/em\u003e, drought signals may activate the AcCBL10-AcCIPK24 interaction module, coordinating with the ABA pathway to achieve spatiotemporal regulation of stress responses. The differential expression of this network in roots/stems versus cladodes may serve to maintain ion homeostasis and protect photosynthetic organs, respectively, collectively supporting its adaptive capacity in the karst drought environment. This work not only expands the understanding of the CBL-CIPK network in non-model medicinal plants but also provides a reference case for deciphering the molecular basis of plant environmental adaptation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data sheets and codes to process data are available upon request to the corresponding author, miaoliu ([email protected]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32360487); Department of Science and Technology of Guizhou Province (Qiankehe Fundamentals ZK [2023] General 114). Modern Industrial Technology System of Traditional Chinese Medicine in Guizhou Province (GZCYTX2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiu Miao and Ming-sheng Zhang:\u0026nbsp;\u003c/strong\u003econceptualization, methodology and funding acquisition,\u003cstrong\u003e\u0026nbsp;Zheng-xi Long, and Liu Tang:\u0026nbsp;\u003c/strong\u003eanalysis,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003evalidation,\u003cstrong\u003e\u0026nbsp;Sheng-bo Lu and Yu-ting Yang:\u0026nbsp;\u003c/strong\u003ewriting-original draft preparation,\u0026nbsp;\u003cstrong\u003eLiu Miao and Zheng-xi Long:\u0026nbsp;\u003c/strong\u003ediscussion, writing-reviewing and editing, All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eApse MP, Aharon GS, Snedden WA, Blumwald E.\u003c/strong\u003e Salt tolerance conferred by overexpression of a vacuolar Na\u003csup\u003e+\u003c/sup\u003e/H\u003csup\u003e+\u003c/sup\u003e antiport in Arabidopsis. 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BMC Plant Biol. 2022 Nov 3;22(1):512. doi: 10.1186/s12870-022-03884-3.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eZheng Y, Jiao C, Sun H, Rosli HG, Pombo MA, Zhang P, Banf M, Dai X, Martin GB, Giovannoni JJ, Zhao PX, Rhee SY, Fei Z.\u003c/strong\u003e iTAK: A Program for Genome-wide Prediction and Classification of Plant Transcription Factors, Transcriptional Regulators, and Protein Kinases. Mol Plant. 2016 Dec 5;9(12):1667-1670. doi: 10.1016/j.molp.2016.09.014. Epub 2016 Oct 5. PMID: 27717919.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eZhou L, Lan W, Chen B, Fang W, Luan S.\u003c/strong\u003e A calcium sensor-regulated protein kinase, CALCINEURIN B-LIKE PROTEIN-INTERACTING PROTEIN KINASE19, is required for pollen tube growth and polarity. Plant Physiol. 2015 Apr;167(4):1351-60. doi: 10.1104/pp.114.256065.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eZhu K, Fan P, Liu H, Tan P, Ma W, Mo Z, Zhao J, Chu G, Peng F.\u0026nbsp;\u003c/strong\u003eInsight into the CBL and CIPK gene families in pecan (Carya illinoinensis): identification, evolution and expression patterns in drought response. BMC Plant Biol. 2022 Apr 28;22(1):221. doi: 10.1186/s12870-022-03601-0.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eZhu SY, Yu XC, Wang XJ, Zhao R, Li Y, Fan RC, Shang Y, Du SY, Wang XF, Wu FQ, Xu YH, Zhang XY, Zhang DP.\u003c/strong\u003e Two calcium-dependent protein kinases, CPK4 and CPK11, regulate abscisic acid signal transduction in Arabidopsis. Plant Cell. 2007 Oct;19(10):3019-36. doi: 10.1105/tpc.107.050666. Epub 2007 Oct 5. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Asparagus cochinchinensis, Drought stress, Full-length transcriptome, CBL-CIPK network, ABA signaling","lastPublishedDoi":"10.21203/rs.3.rs-8784662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8784662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAsparagus cochinchinensis\u003c/em\u003e (Lour.) Merr. (\u003cem\u003eA. cochinchinensis\u003c/em\u003e) is a precious traditional Chinese medicinal herb with significant economic value. However, its cultivation is severely constrained by environmental stresses, particularly drought. The Calcineurin B-Like (CBL) and CBL-Interacting Protein Kinase (CIPK) network constitutes a crucial calcium sensor system that decodes stress-induced Ca\u003csup\u003e2+\u003c/sup\u003e signatures in plants. Despite its importance, the molecular architecture and functional roles of the CBL-CIPK network in \u003cem\u003eA. cochinchinensis\u003c/em\u003e remain largely uncharacterized.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, we generated a high-quality full-length transcriptome of \u003cem\u003eA. cochinchinensis\u003c/em\u003e using PacBio Single-Molecule Real-Time (SMRT) sequencing, yielding 52,042 non-redundant transcripts. Based on this resource, we identified 35 \u003cem\u003eAcCIPK\u003c/em\u003e and 13 \u003cem\u003eAcCBL\u003c/em\u003e genes. Phylogenetic analysis revealed high conservation between \u003cem\u003eAcCIPK24/AcCIPK23\u003c/em\u003e and their Arabidopsis orthologs, while also uncovering species-specific alternative splicing events, including a truncated isoform of \u003cem\u003eAcCIPK24.5\u003c/em\u003e. Yeast two-hybrid assays confirmed a specific physical interaction between AcCBL10 and AcCIPK24. Expression profiling demonstrated that these genes exhibit tissue-specific and temporal responses to drought stress. Notably, while both genes were downregulated in roots and stems under drought, \u003cem\u003eAcCBL10\u003c/em\u003e was significantly upregulated in cladodes, suggesting complex regulatory mechanisms. Furthermore, hormone analysis revealed that drought stress induced endogenous ABA accumulation, and exogenous ABA application not only accelerated this peak but also enhanced the expression of \u003cem\u003eAcCBL10\u003c/em\u003e and \u003cem\u003eAcCIPK24.5\u003c/em\u003e, indicating a positive feedback loop between calcium signaling and ABA pathways.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study provides the first comprehensive functional characterization of the CBL-CIPK network in \u003cem\u003eA. cochinchinensis\u003c/em\u003e. The specific interaction between \u003cem\u003eAcCBL10\u003c/em\u003e and \u003cem\u003eAcCIPK24\u003c/em\u003e, coupled with the crosstalk between calcium signaling and ABA pathways, highlights a key molecular mechanism underlying drought adaptation in this medicinal plant.\u003c/p\u003e","manuscriptTitle":"The CBL-CIPK Network Integrates Calcium and ABA Signaling to Mediate Drought Adaptation in Asparagus cochinchinensis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-14 06:39:54","doi":"10.21203/rs.3.rs-8784662/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-20T03:28:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T22:44:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2631609176575623165006512777943918490","date":"2026-02-10T19:06:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T10:38:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79725643776649269038902855739472829721","date":"2026-02-08T07:29:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-08T05:30:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-08T01:16:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-07T14:30:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chemical and Biological Technologies in Agriculture","date":"2026-02-06T09:39:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"056d586d-9519-45d5-b61b-d128b006f130","owner":[],"postedDate":"February 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-04T01:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-14 06:39:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8784662","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8784662","identity":"rs-8784662","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}