Physiological, transcriptomic, and metabolomic analyses reveal the molecular regulatory mechanisms of walnut fruit in response to gibberellin and paclobutrazol

Physiological, transcriptomic, and metabolomic analyses reveal the molecular regulatory mechanisms of walnut fruit in response to gibberellin and paclobutrazol | 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 Physiological, transcriptomic, and metabolomic analyses reveal the molecular regulatory mechanisms of walnut fruit in response to gibberellin and paclobutrazol Yuanyuan Zhu, Han Li, Ziqian Fu, Xinyu Ding, Juanjuan Guo, Sihan Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8172269/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Gibberellins (GAs) and paclobutrazole (PP333) are crucial plant growth regulators, but their molecular mechanisms in walnut fruit development have yet to be elucidated. This study used ‘Lüling’ walnut plants, whose leaves were sprayed with different concentrations of GAs and PP333 during the fruit expansion period. The effects of those two treatments on the endogenous hormones, metabolite accumulation, and gene expression in the fruits were explored through a combined analysis of plant physiology, transcriptomics, and metabolomics. The results showed that the GAS treatment significantly increased the content of gibberellins (with a 321% maximal increase), cytokinins (KT, IP), and auxin (IAA), while reducing the level of abscisic acid (ABA). It also promoted the accumulation of soluble sugars (+ 50%), starch (+ 19.4%), and soluble proteins (+ 18.18%). The transcriptomic analysis revealed that GAS regulated fruit development by activating certain pathways, namely those for ABC transporters, carbon metabolism, and carotenoid biosynthesis. By contrast, PP333 inhibited the GA signaling pathway and downregulated the expression of genes involved in starch metabolism. Further, the WGCNA analysis identified genes related to amino acid synthesis, such as LOC109010970 . According to the transcription factor analysis, both MYB and bHLH families of transcription factors played a central regulatory role in hormone signal transduction. This study uncovered the mechanisms by which GAS and PP333 regulate the process of walnut fruit development through a ‘hormone–metabolism–gene’ network, providing a valuable theoretical basis for the precise application of growth regulators in walnut cultivation. metabolomics transcriptomics walnut fruit hormone signal transduction amino acid biosynthesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1 Introduction Walnut trees (Juglans spp.) are distributed widely throughout the world and have high economic and ecological value, especially J. regia L., long being an important crop in forestry and agriculture [ 1 – 2 ] . Walnut fruits are rich in proteins, polyunsaturated fatty acids (particularly linoleic acid and α-linolenic acid), vitamins, and minerals [ 3 – 4 ] . Not only are they a popular health food globally, but they are also a major source of edible oil and industrial raw materials, while walnut timber holds considerable commercial value due to its attractive texture and durability [ 5 ] . The yield of walnut nuts and their quality, including key traits such as uniformity, single-fruit weight, kernel yield, and nutritional flavor, directly determine tree planting efficiency and market competitiveness. The fruit expansion period represents a critical window for cell division and dry matter accumulation [ 6 – 7 ] . During this stage, the precise application of plant growth regulators, such as exogenous gibberellins (GAs), cytokinins, or their inhibitors (e.g., paclobutrazol, PP333), has emerged as an essential technique for scientifically regulating development of walnut fruits and improving their overall yield and quality [ 8 – 9 ] . Plant growth regulators, including GAs and PP333, have drawn considerable attention due to their substantial roles in modulating the growth and development of plants [ 10 ] .Both GAs and PP333 can profoundly influence the architecture and sink-source allocation efficiency of walnut trees—by affecting the rate of fruit expansion, inhibiting vegetative growth, and enhancing resistance to stress—via their antagonistic regulation of the endogenous hormone balance. The dynamic “promotion–inhibition” equilibrium established by these two regulators serves as a theoretical cornerstone for precise yield and quality management in modern walnut horticulture [ 11 – 13 ] . Gibberellins promote cell division and elongation by degrading DELLA proteins, thereby activating the expression of downstream genes [ 11 ] . In contrast, paclobutrazol (PP333) inhibits GA biosynthesis by blocking the oxidation of gibberellin precursors, leading to suppressed cell elongation and growth [ 14 ] . More specifically, PP333 mainly represses expression of the gene encoding GA20ox, a key enzyme in the GA biosynthesis pathway [ 15 ] ; concurrently, it upregulates GA2ox (a GA-inactivating enzyme) and DELLA family genes (e.g., RGL2 ), thus promoting the nuclear accumulation of DELLA proteins [ 16 – 17 ] . As central growth repressors, DELLA proteins inhibit cell elongation-related genes (e.g., EXPANSIN and XTH) by interfering with the interaction between the transcription factor PIF4 and its DNA-binding domain (bHLH domain), thereby reducing vegetative growth [ 18 ] . In terms of energy redistribution, treating plants with PP333 markedly increases their sorbitol content by activating sorbitol-6-phosphate dehydrogenase (S6PDH), which enhances the osmotic adjustment capacity of leaves. This improvement strengthens cellular osmotic regulation and water retention under adverse conditions, resulting in greater drought tolerance and stable photosynthetic efficiency in plants facing stress [ 15 ] . Applying PP333 also upregulates the fatty acid desaturase gene FAD2, augmenting the conversion of linoleic acid (C18:2) to oleic acid (C18:1) [ 19 – 20 ] . Gas chromatography–mass spectrometry (GC–MS) analyses have shown that increasing the oleic acid proportion in walnut kernel oil improves its lipid composition, thereby enhancing the oxidative stability and nutritional value of walnuts [ 21 ] . Furthermore, as reported by [ 22 ] , the sugar transporter genes SWEET13 and SWEET14 are specifically activated in phloem sieve tubes, forming an efficient sugar export pathway. This regulation bolsters the directional transport of photosynthetic products (e.g., sucrose and sorbitol) to walnut fruits, promoting their filling and kernel biomass accumulation [ 23 – 24 ] . Ultimately, these changes raise the soluble sugar content of kernels improving both walnut’s flavor and economic value [ 25 ] . Moreover, the dynamic interaction between GAs and PP333 reshapes the secondary metabolic network via ‘cross-talk’ with jasmonic acid/ethylene signaling. On one hand, it activates transcription of key phenylpropanoid pathway enzymes, such as phenylalanine ammonia-lyase (PAL) and chalcone synthase (CHS), facilitating the accumulation of flavonoid compounds like quercetin-3-O-rutinoside and strengthening disease resistance [ 26 – 27 ] . On the other hand, it suppresses the expression of ACC synthase (ACS), delaying ethylene release and extending the fruit storage life by 7–9 days [ 28 – 29 ] . As well, RGL2 ’s overexpression can increase canopy light transmittance by 30% [ 30 ] ,while the coordinated expression of FAD2 and PAL genes helps to regulate the balance between unsaturated fatty acids and polyphenols [ 31 – 32 ] . Nevertheless, the synergistic mechanisms through which GA and PP333 jointly regulate the process of walnut fruit development remain poorly understood. This study combines physiological, transcriptomic, and metabolomic analyses to systematically investigate how GAS and PP333 affect the endogenous hormones, metabolite profiles, and gene expression patterns during the period of walnut fruit expansion. This research aims to achieve the precise manipulation of four pertinent factors: endogenous hormone balance, minimal physiological-based fruit drop, greater nutrient allocation to fruits, and synchronize the shell hardening with kernel development. By addressing current limitations of conventional management practices and elucidating the underlying molecular regulatory networks, the findings provide a scientific basis for the rational application of growth regulators in walnut cultivation, thus contributing to efficient, controllable, and sustainable walnut production. 2 Materials and Methods 2.1 Materials and methods The experiment was conducted at Lvling Walnut Experimental Orchard(114°300–114°330 E, 37°290–37°320 N;), Lincheng County, Xingtai City, Hebei Province, P. R.China. All plants used in this study were 19-year-old 'Lvling' walnut trees ( Juglans regia L. ' Lvling ' ). Healthy, pest-free trees were selected for use in the experiment. These trees were planted in plots with a spacing of 3 m × 4.5 m; each tree was 19 years old, 3–4 m tall, with a crown width of approximately 451–472 cm (from north to south) and 468–518 cm (from east to west). Their fruits were harvested 30 days after pollination in 2022, all showing consistent growth, and then stored at -80°C. 2.2 Experimental design Walnut trees with uniform growth were selected at 30 days post-pollination. After a yearlong pilot experiment, a control (CK), 50 mg L − 1 gibberellin (GA), and 1000 mg L − 1 paclobutrazol (P) were selected as treatments. Foliar spraying was used to apply them. Under a 24-h weather forecast of calm wind and no rain, each treatment was applied to both the front and back surfaces of leaves before 11:00 AM. The spray dose was sufficient to fully wet the leaves and allow the solution to drip. Walnut fruit from each treatment was collected on the second, fourth, sixth, and tenth day after spraying for physiological measurements. The fruits from the sixth day under treatment were used for the transcriptome and metabolome analyses. 2.3 Test items and methods 2.3.1 Determination of soluble sugar content For this, the anthrone colorimetric method was used [ 1 ] . The steps for this procedure went as follows: Weigh 0.2 g of a sample and add to it 10 mL of 80% ethanol, then extract in an 80°C water bath for 30 min. Repeat the extraction step three times, combine the supernatants, and dilute to 50 mL. Add 5 mL of anthrone sulfuric acid reagent to 0.1 mL of the extract and allow this mixture react in a boiling water bath for 10 min. After cooling, measure the absorbance at 620 nm. Finally, calculate the soluble sugar content, using a glucose standard curve. 2.3.2 Determination of starch content For this, the perchloric acid hydrolysis method was used [ 1 ] . After the soluble sugar extraction, dry the residue, add 5 mL of distilled water and 6.5 mL of perchloric acid, and extract in a boiling water bath for 15 min. After centrifugation, collect the supernatant and dilute it to 50 mL. A 0.1 mL aliquot is then processed (according to the soluble sugar determination method) to calculate the starch content (using a conversion factor = 0.9). 2.3.3 Determination of soluble protein content For this, the Coomassie Brilliant Blue G-250 method was used [ 2 ] . The steps for this procedure went as follows: Weigh 0.3 g of fresh sample and add to it 2 mL of distilled water, then grind on ice, centrifuge, and collect the supernatant. To that, add 0.2 mL of the extract to 0.8 mL of distilled water and 5 mL of Coomassie Brilliant Blue reagent. After reacting for 5 min, measure the absorbance at 595 nm. Finally, calculate the protein content, using a bovine serum albumin standard curve. 2.3.4 Determination of endogenous hormone content For this, the HPLC method of Pan and Wang (2009) was used, as follows. A 1.5-g sample was extracted with methanol and cleaned up on a C18 solid-phase extraction cartridge. An Agilent 1260 HPLC system was employed, which was equipped with a DAD detector and a Zorbax SB-C18 column (250 mm × 4.6 mm, 5 µm). Detection wavelengths were 254 nm and 222 nm. The column temperature was set to 30°C, with a flow rate of 1.0 mL min − 1 , and an injection volume of 20 µL. Eight endogenous hormones, including abscisic acid (ABA), auxin (IAA), zeatin riboside (ZR), and isopentenyl adenine (IP), were quantitatively analyzed. 2.4 Omics analysis 2.4.1 Transcriptome sequencing Total RNA was extracted each sample, using the RNAprep Pure Total RNA Extraction Kit. After confirming the RNA quality was adequate, library preparation and sequencing tasks were outsourced to Beijing BGI Biotechnology Co., Ltd. Paired-end 150-bp sequencing was done on the Illumina NovaSeq 6000 platform. Raw data underwent quality control before alignment against the walnut reference genome. The differential gene expression analysis was performed using the DESeq2 package (criteria: |log 2 FC| ≥ 1 and padjust < 0.05). Total RNA was extracted using PrimeScript™ 1st strand cDNA Synthesis Kit, (Takara, Japan). Quantitative reverse transcription PCR (qRT-PCR) was performed using the 2X SG Fast qPCR Master Mix (High Rox, B639273, BBI) according to the manufacturer’s protocol. The specific primers used for qRT-PCR were designed using Primer Premier 5.0 (Table S1 ). Each 20 mL qRT-PCR reaction contained 7.2 mL ddH 2 O, 0.4 mL of 10 mM primers F, and R, 10 mL of 2×SybrGreen qPCR Master Mix, and 2 mL of diluted 6 selected DEGs cDNA. The amplification program was: initiation by a 95℃ for 3 min, followed by 45 cycles of 5s at 95°C, and 30s at 60°C, with a melting curve analysis program. Finally, the 96-well plate with the added samples was run on an ABI Step one plus fluorescent quantitative PCR instrument. Three technical replicates were performed for qRT-PCR.Raw RNA-seq reads were submitted to the NCBI Sequence Read Archive (SRA) under BioProject PRJNA1393871. 2.4.2 Metabolomics analysis This was carried out using LC-MS technology. From each sample, 50 mg was weighed, extracted with 400 µL of methanol (containing 2-chloro-L-phenylalanine internal standard), and filtered through a 0.22-µm membrane. It was then analyzed using a Thermo Vanquish UPLC system equipped with a HSS T3 column, coupled to a Thermo Orbitrap Exploris 120 mass spectrometer, in the ESI positive and negative ion modes. Raw data were processed using Compound Discoverer 3.3 and compared against databases such as HMDB and KEGG for metabolite identification. 2.5 Data processing and analysis One-way analysis of variance (ANOVA) was performed using SPSS 26.0 software, with Duncan’s multiple range test employed for significance testing (P < 0.05). Graphs were plotted using Origin 2022 software. 3 Results 3.1 Effects of exogenous GAS and PP333 on hormone levels of walnut fruit In the GAS-treated group, the gibberellin (GAS) content peaked on the sixth day after spraying, increasing by 321% relative to the control (CK). Cytokinin (KT) and IP and auxin (IAA) levels peaked on the fourth day, increasing by 260%, 259%, and 315%, respectively, before gradually declining; but their overall levels remained higher than those of the CK. Levels of zeatin riboside (ZR) and methylthioadenosine (MT) continued to rise, respectively increasing by 310% and 183% by the eighth day. Abscisic acid (ABA) and dihydrozeatin (DL) levels initially rose and then fell, with DL undergoing a significant decrease after the fourth day. By the eighth day, ABA and DL levels had respectively decreased by 4.9% and 40% compared to the CK. Treatment with paclobutrazol (PP333) resulted in the opposite effects of GAS. First, the GAS content exhibited a downward trend, reaching a 50% reduction vis-à-vis the CK by the sixth day. The KT, IAA, and IP levels all briefly increased before decreasing, such that they were similar to CK by the eighth day. However, both MT and DL levels were significantly lower than those of CK by the eighth day, decreasing by 29% and 16%, respectively. The ABA and ZR levels initially decreased before increasing, with the inhibitory effect being strongest on the sixth day, decreasing by 35% and 42%, respectively, relative to CK. These results suggested that GAS increases the levels of growth-promoting hormones (such as GAS, KT, IP, and IAA) and decreases the levels of growth-inhibiting hormones (such as ABA and DL), while PP333 reduces the levels of growth-promoting hormones by inhibiting the GAS signaling pathway. 3.2 Effects of exogenous GAS and PP333 on soluble sugar, starch, soluble protein and Ca2 + content in walnut fruit The GAS treatment significantly promoted the accumulation of soluble sugar, starch, and soluble protein in walnut fruits. Compared with CK, it increased the soluble sugar content by 50%, the starch content by 19.4%, and the soluble protein content by 18.18%. This indicated that GAS significantly enhanced the accumulation of nutrients in the fruit by activating specific pathways related to carbon metabolism and protein synthesis. By contrast, treatment with PP333 inhibited the accumulation of these nutrients. Compared with CK, it decreased the soluble sugar content by 10%, the starch content by 4.8%, and the soluble protein content by 36.36% (Fig. 2 A-C). Compared with CK, GAS treatment and PP333 treatment each increased the Ca2 + content of walnut fruits to varying degrees (Fig. 2 D). 3.3 Transcriptome analysis of exogenous GAS and PP333 effects on walnut fruit The eukaryotic reference transcriptome (RNA-seq) analysis of nine walnut fruit samples yielded a total of 61.79 Gb of clean data, with each sample generating 5.79 Gb of clean data and a Q30 base percentage of 94.04% or higher (Table S2 ). Clean reads from each sample were aligned to the designated reference genome, whose alignment efficiencies ranged from 88.12% to 95.87% (Table S3 ). These alignment results were then used for alternative splicing prediction, gene structure optimization, and novel gene discovery, resulting in the discovery of 3607 novel genes, of which 1648 were functionally annotated (Table S4 ). Analyzing the gene expression distribution within each sample revealed both symmetry and dispersion at very high expression levels (Fig. 3 A). Analysis of differentially expressed genes (DEGs) between the three treatment groups revealed 885 DEGs in CK1_vs_GA1, with 519 upregulated and 366 downregulated; 944 DEGs (549 upregulated, 395 downregulated genes) were found between CK1 and P1; and 1325 DEGs (596 upregulated, 729 downregulated) were identified between GA1 and P1 (Fig. 3 B). A Venn diagram revealed 17 DEGs shared between the three comparison groups, 190 DEGs shared by the CK1 and GA1 groups, and 720 DEGs unique to the GA1 and P1 groups (Fig. 3 C). The greater number of DEGs in CK1 versus P1 compared to CK1 versus GA1 suggested a higher transcriptional response in walnut fruit to growth regulators which inhibit plant growth. The GA1 versus P1 comparison harbored the highest number of DEGs, indicating that spraying with plant growth regulators can significantly alter the gene expression patterns in walnut fruit. Next, the DEGs’ functional enrichment analysis was performed using GO data ( http://www.geeontology.org ). The GO enrichment for the CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 groups focused cellular components, biological processes, and molecular functions. However, GO enrichment analysis showed that the DEGs among these three comparison groups were enriched most in the biological process and molecular function categories (Fig. 3 .D, E, F). As Fig. 3 D shows, for GA1_vs_P1 the dominant entries under the biological process category were metabolic process, cellular process, response to stimulus, biological regulation, multicellular organismal process, and signaling. To identify other important metabolic pathways, KEGG pathway analysis was used to determine the DEGs’ biological functions. Among the KEGG-enriched pathways for CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1, there were 105, 106, and 119 significantly enriched pathways, respectively (p < 0.05). Analysis of the top-50 KEGG significantly enriched pathways revealed that these pathways were chiefly enriched in cellular processes, environmental information processing, genetic information processing, metabolism, and organismal systems; however, the most DEGs were annotated under metabolism. In-depth analysis of the top-20 pathways with the most DEGs revealed that the main enriched pathways in the three comparison groups were plant hormone signal transduction (ko04075); plant–pathogen interaction (ko04626); phenylpropanoid biosynthesis (ko00940); protein processing in endoplasmic reticulum (ko04141); carbon metabolism (ko01200); starch and sucrose metabolism (ko00500); MAPK signaling pathway –plant (ko04016); biosynthesis of amino acids (ko01230); endocytosis (ko04144); and ABC transporter (ko00600). The pathways unique to the GA1_vs_P1 comparison group included the following: flavonoid biosynthesis (ko00941); glycine, serine, and threonine metabolism (ko00260); ubiquitin-mediated proteolysis (ko04120); cysteine ​​and methionine metabolism (ko00270); carbon fixation in photosynthetic organisms (ko00710); and glyoxylate and dicarboxylate metabolism (ko00630) (Fig. 4 ). 3.4 qRT-PCR verification To validate the RNA-Seq results, 12 DEGs were further analyzed for their expression, by qRT-PCR. As shown in Fig. 5 , the qRT-PCR results were highly consistent with the RNA-Seq data, confirming the reliability of the transcriptome analysis.. 3.5 Metabolomics analysis of exogenous GAS and PP333 effects on walnut fruit Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of nine leaf samples and quality control samples revealed robust correlation between replicates, with high correlation coefficients between samples within a treatment relative to those between treatments. Hence, the differential metabolites obtained were reliable and data confidence was high (Fig. S1 ). To investigate the metabolite changes in walnut fruit after spraying leaves with growth regulators, differential metabolites were analyzed in the CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 comparison groups. A total of 112 differential metabolites were identified in CK1_vs_GA1 (39 upregulated, 73 downregulated); 473 in CK1_vs_P1 (198 upregulated, 275 downregulated; and 575 differential metabolites were identified in GA1_vs_P1 (232 upregulated, 343 downregulated) (Fig. 6 B, C, D). In addition, 33 differentially abundant metabolites were shared among the three comparison groups, whereas 26 were unique to CK1_vs_GA1, 132 were exclusive to CK1_vs_P1, and 251 were restricted to GA1_vs_P1 (Fig. 6 A). KEGG pathways were enriched in 39, 69, and 81 entries in the CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 comparison groups, respectively. Further analysis of the top-20 most significantly enriched KEGG pathways among the differentially abundant metabolites detected in those groups revealed these shared metabolic pathways: porphyrin metabolism (ko00860), nitrogen metabolism (ko00910), and carotenoid biosynthesis (ko00906). The common pathways between CK1_vs_P1 and GA1_vs_P1 included fatty acid biosynthesis (ko00061); glycolysis/gluconeogenesis (ko00010); indole alkaloid biosynthesis (ko00901); lysine biosynthesis (ko00300); pantothenate and CoA biosynthesis (ko00770); cysteine ​​and methionine metabolism (ko00270); and arachidonic acid metabolism (ko00590) (Fig. 7 D, E, F). Importantly, the metabolic pathways of these KEGG significantly-enriched entries were similar to those for transcriptional results. Furthermore, analyzing the top-30 differentially expressed metabolites ranked by their p-value across the three comparison groups revealed that pseudoginsenoside RT5 and senecionine N-oxide were significantly enriched in all three groups. Differentially expressed metabolites found in common between CK1_vs_P1 and GA1_vs_P1 included apiferol, N-hydroxy-L-valine, hydroxyspheroidene, paederosidic acid, and 2,3,5,4'-tetrahydroxystilbene-2-O-glucoside; unlike that, the common differential metabolites between CK1_vs_GA1 and GA1_vs_P1 consisted of tropate, sapindoside B and 4-vinylphenol. The unique differential metabolites of GA1_vs_P1 were mucronine A, Corey PG-lactone diol, ginsenoside Rh2, thymidine, terpendole I, estradiol valerate, arjunic acid, roquefortine D, arginyl-glutamine, soyasaponin III, ganoderic acid Mi, naringenin 7-O-beta-D-glucoside, D-urobilin, LysoPE 18:1(2n isomer) isomer), 4alpha-carboxy-5alpha-cholesta-8,24-dien-3beta-ol, tripoxyrollin, casticin, and oleanolic acid-3-O-glucuronide. 3.6 Joint analysis of transcriptomics and metabolomics The combined transcriptomic and metabolomic analyses detected 31, 53, and 69 pathways shared between treatments for the CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 comparison groups, respectively (Fig. 8 ). Focusing the combined analysis on the top-30 differentially enriched pathways revealed that the three comparison groups all shared the following common pathways: ABC transporters; carbon metabolism; histidine metabolism; monoterpenoid biosynthesis; carotenoid biosynthesis; glyoxylate and dicarboxylate metabolism; isoquinoline alkaloid biosynthesis; arginine and proline metabolism; steroid biosynthesis; tryptophan metabolism; and amino acid biosynthesis. The shared metabolic pathways between CK1_vs_P1 and GA1_vs_P1 included flavonoid biosynthesis; glycine; serine and threonine metabolism; ubiquinone and other terpenoid-quinone biosynthesis; linoleic acid metabolism; zeatin biosynthesis; glycerolipid metabolism; and glycolysis/gluconeogenesis. The shared metabolic pathways between CK1_vs_GA1 and GA1_vs_P1 included phenylpropanoid biosynthesis, valine, leucine, and isoleucine degradation, beta-alanine metabolism, and anthocyanin biosynthesis. To sum up, the results of this joint analysis were consistent with those obtained for the DEGs and differentially accumulated metabolites in walnut fruit. Spraying with plant growth regulators affects the downstream signaling pathways of phytohormones, including auxin (auxin), cytokinin (CTK), abscisic acid (ABA), and brassinosteroids (BRs). The CTK signaling pathway consists of a histidine receptor kinase (CRE1), a phosphate transporter (AHP), and response regulators (type ARR family and type B ARR family). In the present study, genes regulating CRE1 and B-ARR were significantly upregulated in both CK1_vs_GA1 and GA1_vs_P1 comparison groups, but significantly downregulated in CK1_vs_P1 (Fig. 9 ). Genes regulating A-ARR were downregulated significantly in all comparison groups. Auxin signaling is mediated mainly via the TIR1/AFB (TIFY and AFB AUX/IAA proteins) receptor complex, which degrades IAA proteins through ubiquitination, thereby regulating the expression of downstream genes. Genes regulating AUX1 were found upregulated in every comparison group. AUX1/IAA was upregulated in the CK1_vs_GA1 group yet mostly downregulated in the CK1_vs_P1 group. However, genes regulating SAUR displayed the opposite pattern for all comparison groups, while genes regulating ARF and GH3 were mostly downregulated. ABA signal transduction is predominately mediated by PYR/PYL (pyrabactin resistance/pyrabactin resistance-like) proteins and SnRK2 (sucrose non-fermenting 1-related protein kinase 2) protein kinases, regulating the expression of ABA-responsive genes. In this study, genes regulating PP2C and PYR/PYL were significantly upregulated in the comparison groups CK1_vs_GA1 and CK1_vs_P1, but significantly downregulated in GA1_vs_P1; further, ABF was significantly upregulated in both CK1_vs_P1 and GA1_vs_P1, yet significantly downregulated in CK1_vs_GA1; however, SnRK2 was downregulated significantly in all three comparison groups. Regarding BR signals, they are sensed by BRI1 and BAK1 and transmitted to BSKs through a series of phosphorylation reactions. Specifically, BAK1 acts as a co-receptor in BR signal transduction and can directly interact with BRI1, to positively regulate BR signaling. Here, genes regulating BAK1 were significantly upregulated in the CK1_vs_GA1 and CK1_vs_P1 comparison groups; genes regulating BRI1, BSK, and CYC03 were also upregulated in CK1_vs_P1 and GA1_vs_P1; whereas, BKI1 and TCH4 were upregulated in all comparison groups. Spraying with growth regulators can also influence plant–pathogen interactions. This is largely due to pathogen-associated molecular templates triggering the recognition of the immune bacterial flagellin (flg22) by a receptor protein (FLS2). FLS2 activates the downstream mitogen-activated protein kinase (MEKK1), which then transmits signals to WRKY 22, activating the transcription of genes involved in immune defense responses. In the current study, genes regulating FLS2 and MEKKI were mostly downregulated in the comparison groups; conversely, WRKY 22 was significantly upregulated in each comparison group; FRK1 and PR1 were mostly upregulated significantly in CK1_vs_GA1 and CK1_vs_P1, yet downregulated in GA1_vs_P1. At the same time, with respect to the Ca2 + channels, genes regulating CNGCs and Rboh were mostly upregulated in the CK1_vs_GA1 and CK1_vs_P1 comparison groups; in general, the CDPK- and CaMCML-encoding genes were significantly upregulated in every group. This results indicated that spraying walnut trees with growth regulators led to greater Ca2 + signal transduction and an increased shell thickness. Amino acids are the building blocks of protein synthesis in organisms, and their synthesis pathways play a crucial role in cellular metabolism. In the present study, genes regulating TPI were upregulated in all comparison groups (Fig. 10 ). Genes regulating PGAM were significantly downregulated in both CK1_vs_GA1 and CK1_vs_P1 groups, but significantly upregulated in GA1_vs_P1. Genes regulating ALD and glyA were mostly upregulated in CK1_vs_GA1, while significantly downregulated in the other two comparison groups. Genes regulating ItaE also featured significant upregulation and downregulation. In particular, 3-phospho-D-glycerate, O-acetyl-L-serine, and S-sulfonyl-L-cysteine were each significantly upregulated in every comparison group, though by a significantly greater magnitude in CK1_vs_P1 than CK1_vs_GA1. The contents of 3-phospho-D-glycerate, O-acetyl-L-serine, and S-sulfo-L-cysteine in the P1 treatment were significantly higher than those in the GA1 and CK1 treatments. In summary, the results indicated that the application of growth regulators can promote amino acid biosynthesis, with the spraying of paclobutrazol (PP333) exerting a better regulatory effect. In addition, strong correlations were found between DEGs and DAMs (differentially abundant metabolites) in the amino acid biosynthesis pathway and brassinosteroid signaling pathway. In the former pathway (Fig. 11 A), S-sulfo-L-cysteine was negatively correlated with PGAM ( LOC108979616, LOC109016447 ) and ltaE ( LOC118343818 ), but positively correlated with TPI (NewGene_3568), ALDO ( LOC108994846 , LOC109006536 ), and ltaE ( LOC118346050 ). Regarding O-acetyl-L-serine, it was positively correlated with TPI (NewGene_3568), glyA ( LOC109012448 ), ltaE ( LOC118346050 ), and ALDO ( LOC108994846 ), though negatively correlated with ltaE ( LOC118343818) . Likewise, 3-Phospho-D-glycerate was negatively correlated with ltaE ( LOC118343818 ) yet positively correlated with ltaE ( LOC118346050 ). Finally, for the brassinolide signaling pathway (Fig. 9 B), while it was positively correlated with BAK1 ( LOC118344550 and LOC108997207 ), brassinolide was negatively correlated with the following: BAK1 ( LOC108998363 ), BRI1 ( LOC108991311 , LOC109007628 , and LOC109014172 ), BSK ( LOC108998493 ), and CYCD3 ( LOC108979248 and LOC108996078 ). To further investigate the gene regulatory network associated with walnut fruit size, a WGCNA analysis was conducted on all DEGs. The resulting modular gene clustering heatmap displays each dendrogram as a module, with each branch representing a gene (Fig. 12 A). There, the darker the color of each point, the stronger the connectivity between the two genes in the corresponding row and column. The follow-up correlation analysis with phenotypic data identified 11 key modules (Fig. 12 B). Pearson correlation coefficients ( r -values) were calculated between characteristic genes of each module and specific developmental stages of walnut fruit, along with their respective p-values. Based on the criteria of an r -value ≥ 0.8 and p-value ≤ 0.5, it was found that a yellow-green color was significantly correlated with metabolites related to amino acid biosynthesis. To further understand the functions of genes in that color module, a correlation network analysis of genes in this yellow-green module was performed (Fig. 12 D). This demonstrated that gene- LOC109010970 , gene- LOC109012088 , gene- LOC108998110 , gene- LOC118348515 , gene- LOC108981442 , and gene- LOC108981011 were all significantly correlated. Next, iTAK software based on the PlantTFDB database was used to predict transcription factors (TFs) for all transcripts, identifying 4498 genes from 214 gene families as candidate TFs. The families containing the largest number of TFs were FAR1 (260), MYB (223), RLK-Pelle_DLSV (179), AP2/ERF-ERF (171), bHLH (165), and C2H2 (147) (Fig. 13 A). The differential TFs in the different treatment comparison groups were analyzed using a cluster heat map (Fig. 13 B). Among them, gene-LOC108996157 and gene-LOC10899615 in the MYB gene family were upregulated significantly in GA1_vs_P1 and downregulated significantly in CK1_vs_GA1. Regarding the RLK-Pelle_SD-2b gene family, it was significantly downregulated in the comparison group GA1_vs_P1, but upregulated significantly in CK1_vs_GA1. Pearson correlations were tested between DEGs and differentially expressed TFs under the plant growth regulator treatments. As well, TFs related to amino acid biosynthesis processes possibly associated with walnut fruit size were analyzed (Fig. 13 C). Within the amino acid biosynthesis process, ALDO (LOC108994846) was positively correlated with MYB (LOC109006592), C2H2 (LOC108998795), and MYB-related (LOC108993213), but negatively correlated with C3H (LOC109012502) and RLK-Pelle_LRR-III (LOC108990728). ALDO (LOC109006536) was positively correlated with both MYB (LOC109006592) and MYB-related (LOC108993213), yet negatively correlated with C3H (LOC109012502) and RLK-Pelle_LRR-III (LOC108990728). For ltaE (LOC118346050), it was negatively correlated with MYB (LOC109009907), RLK-Pelle_DLSV (LOC109007473), bHLH (LOC108982135), C2H2 (LOC118344174), and RLK-Pelle_WAK (LOC108983353), but positively correlated with MYB (LOC109006592). While ltaE (LOC118343818) was positively correlated with bHLH (LOC109010157), C2H2 (LOC108984376), and C2H2 (LOC118344174), it was also negatively correlated with both C2H2 (LOC108997775) and bZIP (LOC109003262). Lastly, PGAM (LOC108979616P) was negatively correlated with MYB (LOC108987421) and C3H (LOC108991847), though positively correlated with MYB (LOC1089961579). Next, those TFs that could be related to IAA, CTK, BRs, ABA signal transduction, and plant–pathogen interaction pathways were also analyzed (Fig. 14 ). We found that in the BRs signal transduction pathway, BAK1 (LOC108998363) was negatively correlated with MYB (LOC109006592), MYB (LOC109008746) and RLK-Pelle_DLSV (LOC109000270), and positively correlated with MYB (LOC109009907), RLK-Pelle_DLSV (LOC109007473), bHLH (LOC108982135), RLK-Pelle_LRK10L-2 (LOC109008043) and RLK-Pelle_WAK (LOC108983353). Concerning BAK1 (LOC108997207), it was negatively correlated with MYB (LOC109008746), C3H (LOC109012502), and RLK-Pelle_WAK (LOC108983353), but positively correlated with MYB (LOC109015732), AP2/ERF-ERF (LOC108992157), C2H2 (LOC108998795, LOC109008851), and WRKY (LOC109001021). For the CTK signaling pathway analysis, CRE1 (LOC108993473) was found positively correlated with MYB (LOC108993912, LOC108996157, LOC109009907), bHLH (LOC108982135, LOC109010157), C2H2 (NewGene_1251), GRAS (LOC108985505), RLK-Pelle_LRK10L-2 (LOC109008043), and RLK-Pelle_WAK (LOC108983353), yet negatively correlated with MYB-related (LOC108993213). Analysis of the ABA signaling pathway showed that PYL (LOC108982697) was negatively correlated with C2H2 (NewGene_1251 and LOC109020061), GRAS (LOC108985505), and RLK-Pelle_LRK10L-2 (LOC108997207), though positively correlated with both MYB (LOC109015732) and NAC (LOC108996595). Concerning the IAA signaling pathway, IAA (LOC108984763) was negatively correlated with both MYB (LOC108987421, LOC108987421, and LOC108987421). Regarding plant–pathogen interaction pathways, CNGCs (LOC108991534) were negatively correlated with MYBs (LOC108993912, LOC109000871), NACs (LOC108992696), and GRASs (LOC108985505), and positively correlated with bZIPs (LOC109019104). CNGCs (LOC109009995) were positively correlated with MYBs (LOC108993912, LOC109000871, LOC109009907), bHLHs (LOC108982135), and GRASs (LOC108985505). Finally, CNGCs (LOC109008642) were positively correlated with C2H2 (LOC108997775), RLK-Pelle_LRR-XI-1 (LOC108993886), and bZIP (LOC109003262 and LOC109019104). 4 Discussion The fruit expansion period is a critical phase when fruit development shifts from cell division to the rapid expansion of cell volume and accumulation of cellular contents. In this context, the scientific application of exogenous growth regulators holds significant physiological and agronomic importance [ 33 ] . Applying specific exogenous growth regulators—such as GA, NAA, or BRs—can effectively induce the expression of expansins and promote cell wall relaxation and vacuolar water uptake through receptor-mediated signal transduction pathways involving the MAPK and calcium signaling cascades, thereby driving geometric increases in fruit volume [ 34 – 35 ] . On the fourth day after applying the GAS treatment, the contents of GAS, IAA, KT, IP, ZR, and MT in walnut fruit significantly exceeded those in the control (CK), whereas the ABA and DL contents were significantly lower under PP333 treatment relative to CK. The walnut fruit expansion period (lasting 35 to 42 days after flowering [DAF]) is characterized by active cell division in the endocarp along with the accumulation of dry matter, which directly influences both fruit morphology and nut development [ 36 ] . Treatment with PP333 may also increase the ABA content and bolster plants’ stress resistance,JrHDZ28 and JrbZIP40 was induced under salt and drought stress, which provided potential molecular evidence at the genetic regulation level for enhancing the stress resistance of walnuts by PP33335 [ 37 – 38 ] ..It is known that GA significantly promotes longitudinal fruit growth, alters the fruit shape index, and delays ripening of tomato by activating its cell elongation-related genes and inhibiting ethylene biosynthesis [ 38 ] . During grape’s seed-hardening stage, a treatment with PP333 modulates the expression of genes involved in cell wall synthesis and hormone signal transduction—including GA, IAA, and ABA pathways—to regulate seed development and fruit structure [ 40 ] . Applying PP333 also inhibits GA synthesis and synergistically regulates the dynamic balance between IAA and ABA, which reshapes the cell wall composition and seed mechanical strength, ultimately affecting fruit structure and development [ 41 ] . During fruit expansion period of ‘Red Globe’ grape, for example, GAS applied at 40 or 70 mg/L increased its fruit cell length, diameter, and volume, while levels of IAA and ZT increased considerably and vice versa for ABA [ 41 ] . Furthermore, treating sweet cherry promoted the accumulation of GAS and GA7 [ 42 ] , a finding largely consistent with the result of the present study. Expression patterns of genes in the CTK and ABA pathways—including those for CRE1, B-ARR, PP2C, and PYR/PYL—changed markedly during fruit development in the walnut W13 line [ 43 ] . Earlier work showed that a paclobutrazol (PBZ) treatment reduced the GA1 and GA4 contents of apple rootstock while increasing its ABA concentration [ 44 ] . In sweet sorghum, applying a PBZ treatment inhibited the activity of ent-kaurene oxidase (KO), blocking the early steps in GA biosynthesis which led to a substantial decrease in the GA4 content of the shoot apex [ 45 ] , consistent with our study’s results. In the cytokinin (CTK) signal transduction pathway of GAS-treated walnut fruit, we found that CRE1 and B-ARR genes were upregulated; however, related genes were downregulated by the PP333 treatment. In the ABA signaling pathway, genes such as PP2C and PYR/PYL were significantly upregulated by the GAS treatment, while ABF was strongly upregulated in response to PP333. The SnRK2 gene was downregulated by both GA and PP333 treatments. After GA binds to its receptor GID1, it promotes ubiquitination and degradation of DELLA proteins, enhancing cell elongation and division and thereby supporting plant growth and development—a mechanism widely validated in rice and other staple crops [ 46 ] . The research on apples has found that MdBLH14 physically interacts with another member of the TALE superfamily, MdKNOX19, and works together to inhibit the expression of MdGA20ox3, thereby preventing the accumulation of GA's activity [ 47 ] .In sweet cherry, an exogenous GA treatment not only upregulated the expression of NCED and ABA2, key genes in the synthesis of ABA, leading to the latter’s greater accumulation, but it also affected the expression of GA metabolism-related genes, such as GA2ox and CYP701, promoting the accumulation of GAS₃and GA7₇ [ 42 ] . Those findings align with the conclusions of our study of walnut fruit. In poplar, IAA and GA treatments upregulated the genes related to cellulose synthesis (e.g., *CesA8-B*) and cell wall relaxation (e.g., expansin [EXP] and xylan endotransglycosylase [XET]), potentially promoting cell wall expansion and elongation, thereby facilitating xylem formation and growth [ 48 ] . PBZ treatments are known to accelerate flower bud differentiation in citrus trees. For example, ABA levels initially decreased and then increased during the flower bud activation stage, mirroring the dynamics of ABA and ZR during fruit ripening [ 49 ] . In tandem, PBZ can regulate citrus fruit ripening by inhibiting GA biosynthesis [ 14 ] . Recent work shows that a cytokinin treatment increases the fruit set rate and fruit weight in sweet cherry [ 50 ] . The TFs belonging to the MYB family play a fundamental role in cytokinin-regulated cell division, promoting gains in size and weight of fruits during the early stages of their development [ 51 ] . A genome-wide analysis of GA metabolism and signaling genes in alfalfa identified 8 MtGA20ox , 2 MtGASox , and 13 MtGA2o x genes [ 52 ] . Applying a GAS treatment also affects the expression of multiple hormone signaling-related genes in grape berries, including GID2, SAUR, and ACS. In other work, an exogenous GA application upregulated the expression of CpGA2ox in the GA biosynthesis pathway and CpGAI in the signal transduction pathway in a dwarf mutant (dw) of Chimonanthus chinensis , thereby restoring its normal phenotype [ 53 ] . In response to a PBZ treatment, the endogenous IAA content increased, and there ensued an upregulated expression of polar auxin transport genes (e.g., MdPIN1 and MdLAX1 ) as well as that of the MdYUCCA10a biosynthesis gene [ 54 ] . Concurrently, the ABA concentration and expression of the ABA biosynthesis-related gene MdNCED1 both increased, while expression of the degradation gene MdCYP707A1 was inhibited [ 55 ] . However, a PBZPP333 treatment reduced the trans-zeatin (tZ) levels and downregulated the expression of the cytokinin biosynthesis gene MdIPT6 , indicating that PBZcould influence the phytohormonal balance and plant development by regulating hormone synthesis and transport genes [ 54 ] . In Chinese pear fruits, ethylene inhibits the expression of TFs, such as PpMYB10 and PpMYB114, thereby suppressing anthocyanin synthesis, whereas jasmonic acid promotes flavonoid accumulation by activating these TFs [ 56 ] . Additionally, ethylene affects its own biosynthetic pathway by regulating the expression of key enzymes, namely PpACS and PpACO [ 56 ] . In many plants, like grapevine, jasmonic acid and ethylene act synergistically to activate its defense responses [ 57 – 58 ] . Hormones including ABA, SA, and BRs collectively regulate the expression of defense-related genes—such as pathogenesis-related proteins (PRs), disease resistance proteins (RPSs), calcium-binding proteins (CMLs), and ethylene-responsive transcription factors (ERFs)—thereby enhancing plant resistance to pathogens [ 59 – 60 ] ,The WRKY transcription factors play a crucial role in the plant's response to abiotic stress,VQ-WRKY complex plays a crucial role in plants' response to biotic stresses, further enriching the molecular regulatory network for plant defense against biological stress37 [ 61 – 62 ] . In our study, in response to the GA and PP333 treatments, genes regulating Ca 2+ channels (CNGCs), Rboh, CDPK, and CaMCML were significantly upregulated in walnut fruit across the comparison groups, resulting increased shell thickness. Long-term spraying of PP333 not only inhibits GA synthesis and enhances the CK/ABA ratio, but is also accompanied by thickening of the peel in fruits [ 60 ] . Furthermore, the genes PpCDPK7 and PpRboh were co-upregulated under cold storage conditions in peach fruit, potentially reinforcing its fruit skin strength and cold resistance [ 63 – 64 ] . The nutritional quality of walnut fruit changes significantly during its development. Research indicates that fatty acid biosynthesis begins 13 weeks after pollination, with lipids, carbohydrates, amino acids, and their derivatives accumulating primarily in the kernel [ 67 ] . During walnut endosperm development, protein and amino acid contents are initially high but gradually decrease later on, suggesting their conversion into other compounds as the fruit matures [ 68 ] . In the present study, the GA treatment significantly increased the starch, soluble protein, and soluble sugar contents of walnut fruit, whereas carbohydrate content under the PP333 treatment was significantly lower than in CK. For kiwifruit, application of GAS promotes the accumulation of sugars and starch by upregulating the expression of sucrose phosphate synthase (SPS) and starch synthase (SS) genes [ 69 ] , which is consistent with our results. Similar findings have been reported for Torreya grandis nuts [ 70 ] . Treatment with PP333 can reduce α-amylase activity and inhibit starch degradation, thereby limiting the carbon source supply [ 71 – 72 ] . However, a paclobutrazol treatment can increase the soluble sugar content, enhance antioxidant enzyme activity, and improve both the chlorophyll and relative water content in leaves of Amorpha fruticosa , which improves its drought tolerance under water-deficient conditions [ 73 ] . This outcome may be attributed to carbohydrate storage in leaves to enhance plant resilience under stress conditions. Plant growth regulators can systematically improve the efficiency of assimilate transport—particularly sucrose and minerals—from source (leaves) to sink (fruits) organs. By enhancing the activity of sucrose transporters (SWEETs) and key enzymes such as sucrose synthase (SuSy) at phloem unloading sites in fruits, they ensure the continuous and efficient directional transport of photosynthetic products and nutrients into fruit [ 74 ] . A recent ¹³C isotope tracing study revealed patterns of carbohydrate assimilation and partitioning in walnut at different developmental stages, underscoring the essential role of leaf photosynthesis in dry matter accumulation in its fruit [ 75 ] . Later, Chen et al. (2022b) found that the SWEET13 gene in the C4 plant Setaria viridis is highly expressed in photosynthetic source tissues, particularly in phloem sieve tubes, pointing to its vital role in phloem loading of sucrose. Bezrutczyk et al. (2018) observed that mutants knocked out for three SWEET13 genes (ZmSWEET13a, 13b, and 13c) exhibited defective phloem loading, leading to sucrose accumulation in their leaves. Similar results have been reported in sugarcane [ 76 ] . Heterologous expression of SWEET13c in Arabidopsis reduced the sugar content of its leaves while enhancing its root and shoot growth, suggesting that SWEET13c may enhance the source-to-sink carbon flux by facilitating sucrose transport [ 76 ] . Treatment with GAS can enhance the expression of ABCG family ABC transporters, enabling the greater transport of photosynthetic products into fruit tissues and thereby supporting nutrient accumulation in them [ 77 ] . Growth regulators also substantially influence nutrient accumulation in plants by modulating key carbon and nitrogen metabolic pathways. Here, both GA and PP333 treatments increased the walnut fruit’s content of D-glycerol-3-phosphate, O-acetyl-L-serine, and S-sulfonyl-L-cysteine, with its amino acid content being significantly higher in PP333 than GA treatment. In a recent study of pear seedlings, Zhang et al. (2024) noted that besides increasing its hormone levels, a GAS treatment enhanced the activity of nitrogen metabolic enzymes and related genes (e.g., GS, GOGAT), which improved nitrogen uptake and carbon-nitrogen coordination to increase both crop growth and nutrient accumulation efficiency, which is consistent with our study’s conclusions. A paclobutrazol (PAC) treatment modified the carbon-nitrogen metabolic balance in corn, leading to its lower yield under limited fertilizer supply [ 78 ] , a finding also consistent with ours for walnut fruit. Various growth regulators, such as GAS, PBZ, ABA, and Ethrel impact the expression and activity of carbohydrate metabolic enzymes (e.g., SuSy and AGPase) in Lycoris radiata bulbs, thus influencing its nutrient accumulation [ 79 ] . In sum, GAS not only promotes nitrogen assimilation and root absorption but also enhances the carbon-nitrogen co-accumulation efficiency in fruits by upregulating their sugar metabolic enzymes and improving photosynthate transport into them. In contrast, PP333 inhibits GA signaling, resulting in reduced carbon and nitrogen metabolic activity [ 80 ] . Key genes involved in amino acid biosynthesis in walnut fruit (e.g., LOC109010970 and LOC108981442 ) encode sugar metabolism enzymes, such as TPI and PGAM. Recently, Sheng et al. (2024) found that upregulated expression of PGAM in octoploid strawberry fruit increased the accumulation of intermediate metabolites and amino acids, in line with the conclusions of our study. Additionally, MYB and bHLH family TFs were differentially expressed in both GAS- and PP333-treated groups of walnut fruit, consistent with the findings reported by Muhammad et al. (2025) on the regulation of anthocyanin and nutrient metabolism in fruits by the MYB–bHLH–WD40 complex. Moreover, MYB and bHLH have been shown to play central regulatory roles in strawberry fruit development and the integration of hormone signals (e.g., IAA, CTK, and ABA) [ 81 ] . In a study on sesame ( Sesamum indicum ), a paclobutrazol (PAC) treatment increased the contents of oleic acid (C18:1) and stearic acid (C18:0), while decreasing those of linoleic acid (C18:2) and linolenic acid (C18:3) [ 82 ] . A higher oleic acid content also improves the oxidative stability of oils [ 45 ] . Taken together, those findings suggest that paclobutrazol may augment the accumulation of monounsaturated fatty acids by regulating the activity of fatty acid desaturases such as FAD2, in this way optimizing the oxidative stability and nutritional value of edible oils. In their transcriptome analysis of walnut ( J. regia ) seed development, Shi et al. (2022) uncovered high expression levels of the FAD2 gene during the oil accumulation stage, indicating its important role in linoleic acid synthesis. In various crops, knocking out the FAD2 gene through CRISPR/Cas9 gene editing has successfully elevated the oleic acid content of their seed oil. For instance, in rapeseed ( Brassica napus ), the FAD2 knockout increased its oleic acid content from 74% to 80%; in tobacco ( Nicotiana tabacum ), it increased from 12% to 79% [ 83 ] . With respect to the unsaturated fatty acid-to-polyphenol ratio in seed kernels being governed by co-expression of FAD2 (fatty acid desaturase) and PAL (phenylalanine ammonia-lyase), RNA interference-mediated silencing of FAD2.2 and overexpression of SAD (stearoyl-ACP desaturase) in chicory ( Cynara cardunculus ) increased its oleic acid (C18:1) content and reduced its linoleic acid (C18:2) ratio, markedly optimizing the lipid composition profile [ 83 ] . After taking into account those reported results alongside our own for walnut fruit, we established a simple model for the mechanism underpinning how exogenous GA and PP333 affect walnut fruit during its expanding period. As depicted in Fig. 15 , amino acid biosynthesis, interaction between plants and pathogens and plant hormone signal transduction jointly regulate walnut fruit’s expansion through synergistic effects. Here, two key genes (encoding glyA and ItaE) control its fruit size by regulating the level of amino acid biosynthesis (Fig. 13 ). Spraying GAS at the expanding stage of walnut fruit can effectively amplify the accumulation of sugar, protein, and beneficial secondary metabolites, while PP333’s application is more suitable for growth inhibition. Therefore, we suggest that GAS should be used in the fruit expansion period to increase yield, and PP333 should be used in the vegetative growth period to control the vigor of walnut trees. Future research can combine CRISPR-Cas9 technology to edit key genes, notably PGAM and MYB, and further optimize the effect of plant growth regulators. For example, Chen et al. (2023) used CRISPR to knock out the DELLA gene in tomato, which greatly enhanced its GA signal’s transmission, resulting in a larger fruit size. As well, the mechanistic interactions between environmental factors (e.g., light, water) and growth regulators still requires in-depth exploration to precisely control walnut cultivation. In short, growth regulators regulate the development of walnut fruit through the multi-dimensional ‘hormone–metabolism–gene’ network. This empirical study provides important and timely theoretical support for the improvement of walnut fruit quality and the scientific application of plant growth regulators. Declarations Acknowledgments The authors extend their appreciation to Hebei Agricultural University, Baoding 071001, P. R. China. This work was supported by grants from the Development Program of China (2022YFD1600402-02)and Hebei Key R&D project (21326304D-2) the National Key Research. Auture contribution Yuanyuan Zhu:Writing-original draft, Methodology, Investigation,Formal analysis, Data curation.Xinyu Ding: Writing-original draft, Investigation,Formal anal- ysis, Data curation.Ziqian Fu:Writing-original draft, Re-sources, Methodology, Investigation,Formal analysis,Data curation. Guohui Qi: Writing-review & editing,Writing-original draft,Supervision, Re-sources, Project administration, Funding acquisition.Qinglong Dong: Writing-review & editing,Writing-original draft,Supervision, Re-sources. Data availability I used or generated research data in this study.Raw RNA-seq reads were submitted to the NCBI Sequence Read Archive (SRA) under BioProject PRJNA1393871. Funding Source This work was supported by grants from the Development Program of China (2022YFD1600402-02)and Hebei Key R&D project (21326304D-2) the National Key Research. Ethics approval and consent to participate All plant materials used in this study were artificially cultivated and provided by the Lvling Walnut Experimental Orchard affiliated to Hebei Agricultural University. No wild plant samples were involved in the experiment.This research does not involve experiments on animals or humans. Consent for publication All authors have read and approved the final version of the manuscript and consent to its publication in BMC Plant Biology . Declaration of Competing Interest The authors declare no conflict of interest. Competing interests The authors declare no competing interests. References Szalóki-Dorkó L, Kumar P, Székely D, Végvári G, Ficzek G, Simon G, Abrankó L, Tormási J, Bujdosó G, Máté M. 2024. Comparative Study of Different Walnut (Juglans regia L.) Varieties Based on Their Nutritional Values. Plants 13, 2097. 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Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx Fig.S1.png Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 24 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 08 Jan, 2026 Editor invited by journal 08 Jan, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":377348,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of the GAS and PP333 treatments at different times on walnut fruit’s hormone levels. ABA, DL, IAA, IP, KT, MT, ZR, and GA contents. CK, control. GA, gibberellin treatment. PP, paclobutrazol treatment. Likewise for the following figures.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/750259822763585161c8637b.png"},{"id":100362737,"identity":"31ec5072-2feb-46c6-b821-ab741108c6bc","added_by":"auto","created_at":"2026-01-16 07:47:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127104,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of exogenous hormones on walnut fruit’s soluble sugar, starch, soluble protein, and Ca2+ contents. Different lowercase letters indicate significant differences among treatments at the same time point (P \u0026lt; 0.05). CK, control. GA, gibberellin treatment. P, paclobutrazol treatment.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/79dc8baff8638236643b1e77.png"},{"id":100023875,"identity":"84db91e0-ff5f-4a5f-a680-d07626d46173","added_by":"auto","created_at":"2026-01-12 08:19:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174421,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed genes (DEGs) induced by plant growth regulators in walnut fruit. \u003cstrong\u003e(A)\u003c/strong\u003e Total gene expression levels in each sample based on log10 TPM values; \u003cstrong\u003e(B)\u003c/strong\u003eNumber of differentially expressed and downregulated genes; \u003cstrong\u003e(C)\u003c/strong\u003e Venn diagram of DEGs; \u003cstrong\u003e(D-F)\u003c/strong\u003e Overview of gene ontology (GO) categories of DEGs for the three treatment comparison groups: CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/4e58c590df572c507dc3a5f6.png"},{"id":100362284,"identity":"9bba354f-bd53-4c46-b7f9-49263bf6003c","added_by":"auto","created_at":"2026-01-16 07:46:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":352835,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of differentially expressed genes in walnut fruit after spraying trees with plant growth regulators. (\u003cstrong\u003eA)\u003c/strong\u003eKEGG functional enrichment of CK1_vs_GA1; \u003cstrong\u003e(B)\u003c/strong\u003eKEGG functional enrichment of CK1_vs_P1; (\u003cstrong\u003eC)\u003c/strong\u003eKEGG functional enrichment of GA1_vs_P1.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/ddfbbf34d4ee20b463741a4f.png"},{"id":100362591,"identity":"9cbf0c16-5f38-4bbc-9992-e278353144d9","added_by":"auto","created_at":"2026-01-16 07:47:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":311174,"visible":true,"origin":"","legend":"\u003cp\u003eExpression patterns of 12 genes in walnut fruit. The left y-axis represents the FPKM value obtained by RNA-Seq; the right y-axis shows the relative gene expression level analyzed by qRT-PCR. The x-axis represents the different treatment samples. Bars represent the mean ± SE of three replicates.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/9c4345107abe6d2e248ed1f1.png"},{"id":100363038,"identity":"9057925c-6c68-4e56-89aa-ac1ffa5398fb","added_by":"auto","created_at":"2026-01-16 07:48:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":116677,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential metabolite enrichment analysis of walnut fruit after spraying trees with plant growth regulators. \u003cstrong\u003e(A)\u003c/strong\u003e Venn diagram of differential metabolites in the three treatment comparison groups. \u003cstrong\u003e(B)\u003c/strong\u003e Volcano plot of differential metabolites \u003cstrong\u003e(B)\u003c/strong\u003e in CK1_vs_GA1, \u003cstrong\u003e(C)\u003c/strong\u003e in CK1_vs_P1, and \u003cstrong\u003e(D)\u003c/strong\u003e in GA1_vs_P1, with the log2 (fold-change) value along the horizontal axis, and -log10 (P-value) along the vertical axis.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/0a602e3fc650badd803c1df0.png"},{"id":100362608,"identity":"77735d6b-cc67-4db0-a295-6b332bd4bf99","added_by":"auto","created_at":"2026-01-16 07:47:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":253650,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of differentially expressed metabolites in walnut fruit after spraying trees with plant growth regulators. \u003cstrong\u003e(A-C)\u003c/strong\u003eZ-score plots of the top-30 differentially expressed metabolites ranked by P-value for the CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 treatment comparison groups. \u003cstrong\u003e(D-E)\u003c/strong\u003e KEGG functional enrichment statistics for differentially expressed metabolites in CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 comparison groups.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/77c11d8ee74fe3b6d6987958.png"},{"id":100362182,"identity":"56ecb0c9-fd87-40c8-b728-3e2bb5c3ade3","added_by":"auto","created_at":"2026-01-16 07:46:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":189671,"visible":true,"origin":"","legend":"\u003cp\u003eJoint analysis of transcriptional and metabolic KEGG pathway enrichments in walnut fruit after spraying trees with plant growth regulators. \u003cstrong\u003e(A-C)\u003c/strong\u003e KEGG enrichment histograms of the top-30 pathways significantly enriched for differentially expressed genes/metabolites in the three treatment comparison groups: CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 .\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/ee67333352b1be1d7f997b52.png"},{"id":100023878,"identity":"2db89af3-65b2-4d96-8b05-d8e68fbf5052","added_by":"auto","created_at":"2026-01-12 08:19:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":228264,"visible":true,"origin":"","legend":"\u003cp\u003eJoint analysis of differentially expressed genes (DEGs) and metabolites in walnut fruit after spraying trees with plant growth regulators. Left: \u003cstrong\u003e(A)\u003c/strong\u003e Plant hormone signaling pathway diagram; \u003cstrong\u003e(B)\u003c/strong\u003e Plant–pathogen interaction pathway diagram. Right: Heat map of log\u003csub\u003e2\u003c/sub\u003eFC values of corresponding DEGs in the comparison groups CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1, with red and blue indicating significant upregulation and downregulation, respectively.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/8551a579c31ac39ccb4822af.png"},{"id":100023895,"identity":"a80b734a-6e59-4b72-83f2-50e067944eed","added_by":"auto","created_at":"2026-01-12 08:19:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":182266,"visible":true,"origin":"","legend":"\u003cp\u003eJoint analysis of differentially expressed genes (DEGs) and metabolites in the amino acid biosynthesis pathway of walnut fruit after spraying trees with growth regulators. Right: Heat map based on the log\u003csub\u003e2\u003c/sub\u003eFC values of the corresponding DEGs in the treatment comparison groups: CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1, with red and blue indicating significant upregulation and downregulation, respectively.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/4910ba57a1b2855b6cbe05c6.png"},{"id":100362689,"identity":"4d1fd437-71d9-4bad-9886-02371c5ce8e2","added_by":"auto","created_at":"2026-01-16 07:47:54","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":151260,"visible":true,"origin":"","legend":"\u003cp\u003eGene-metabolite correlation network diagram. \u003cstrong\u003e(A)\u003c/strong\u003e Amino acid biosynthesis pathway. \u003cstrong\u003e(B)\u003c/strong\u003e Brassinosteroid signaling pathway. Blue diamonds represent genes; red circles represent metabolites. p \u0026lt; 0.01; |cor| \u0026lt; 0.6; solid lines indicate positive correlations; dashed lines indicate negative correlations.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/24a6c69c9b91051a0404f2d5.png"},{"id":100361586,"identity":"1d5d38d5-e74c-4042-8710-85d4d0c65329","added_by":"auto","created_at":"2026-01-16 07:45:20","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":801038,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression of DEGs between the control and treatment groups. \u003cstrong\u003e(A)\u003c/strong\u003eNetwork heatmap. \u003cstrong\u003e(B)\u003c/strong\u003e Cluster dendrogram. \u003cstrong\u003e(C)\u003c/strong\u003e Module-trait relationship. \u003cstrong\u003e(D)\u003c/strong\u003e Correlation network diagram of each differentially expressed gene in the yellow-green module. The upper limit value in the block represents the correlation coefficient, and the lower limit value represents the p-value.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/b94c16ceaeaef20de2868b3a.png"},{"id":100362448,"identity":"63592768-4440-4afc-978a-8b62848b8104","added_by":"auto","created_at":"2026-01-16 07:46:45","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":362329,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differentially expressed TF (transcription factors). \u003cstrong\u003e(A)\u003c/strong\u003e Statistical analysis of TF families. \u003cstrong\u003e(B)\u003c/strong\u003e Cluster analysis: Red and blue squares indicate up-regulated and down-regulated TFs, respectively. Network connecting DEGs (red circles), amino acid biosynthesis pathways \u003cstrong\u003e(C)\u003c/strong\u003e, and differentially expressed TFs (blue diamonds). Correlation coefficients were all greater than |0.8|, p \u0026lt; 0.05. Gray solid and dashed lines indicate positive and negative correlations, respectively.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/f4f3e1c1835bd78e4b698c54.png"},{"id":100363111,"identity":"838bbd30-6a3f-498f-ac51-567f180f3050","added_by":"auto","created_at":"2026-01-16 07:48:55","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":262820,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation network diagram. (\u003cstrong\u003eA-C\u003c/strong\u003e) Connection network between DEGs (red circles) involved in BRs signaling, CTK signaling, ABA signaling, and differentially expressed transcription factors (blue diamonds). (\u003cstrong\u003eD, E\u003c/strong\u003e) Connection network between DEGs (red circles) involved in plant–pathogen interaction pathways, IAA signaling, and differentially expressed transcription factors (blue diamonds). p \u0026lt; 0.01; |cor| \u0026lt; 0.8; solid lines indicate positive correlations; dashed lines indicate negative correlations.\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/efdf73a8b6f5af379f99b70a.png"},{"id":100023914,"identity":"a65fc016-2620-4c46-97a7-e3a904845a64","added_by":"auto","created_at":"2026-01-12 08:19:18","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":141716,"visible":true,"origin":"","legend":"\u003cp\u003eProposed model for exogenous GA and PP333 effects on walnut fruit. The model takes glyA and ItaE as the core genes, and promotes amino acid biosynthesis through positive regulation. Besides amino acid synthesis, it will also affect other biochemical pathways. The model also shows the regulatory effects of GA and PP333 on the walnut fruit development, with the red arrow indicating its promotion and the blue arrow indicating its inhibition.\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/f2c6358e3b1e2fe2f3701d6f.png"},{"id":100414266,"identity":"457f6c8b-1e14-4ac7-9cba-cc9dd8a07fc3","added_by":"auto","created_at":"2026-01-16 13:19:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4704275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/3e2064f6-af4d-4d76-8eca-e81061a44fbd.pdf"},{"id":100362853,"identity":"8e6fe805-aed1-4921-81ef-da0d73a8653d","added_by":"auto","created_at":"2026-01-16 07:48:10","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11230,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/188894923a7758876500243f.xlsx"},{"id":100406112,"identity":"7c8e7633-e2b8-4a37-abdf-304063077fee","added_by":"auto","created_at":"2026-01-16 12:40:49","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9775,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/9676817212e284d92024be3a.xlsx"},{"id":100362706,"identity":"dbd4a584-fe3e-491b-a470-80157fd01151","added_by":"auto","created_at":"2026-01-16 07:47:55","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10235,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/71b1848bd548efac994073a7.xlsx"},{"id":100362665,"identity":"b2c7eea6-f923-4e01-9b21-7a4f758df027","added_by":"auto","created_at":"2026-01-16 07:47:52","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10163,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/941e6394ff1544bafcd63b1f.xlsx"},{"id":100362552,"identity":"b1d5d166-3283-49cc-8154-60542cb807e4","added_by":"auto","created_at":"2026-01-16 07:47:01","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1100119,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.png","url":"https://assets-eu.researchsquare.com/files/rs-8172269/v1/14bc314b0892919048bbaf2b.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physiological, transcriptomic, and metabolomic analyses reveal the molecular regulatory mechanisms of walnut fruit in response to gibberellin and paclobutrazol","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWalnut trees \u003cem\u003e(Juglans\u003c/em\u003e spp.) are distributed widely throughout the world and have high economic and ecological value, especially \u003cem\u003eJ. regia\u003c/em\u003e L., long being an important crop in forestry and agriculture\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Walnut fruits are rich in proteins, polyunsaturated fatty acids (particularly linoleic acid and α-linolenic acid), vitamins, and minerals \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Not only are they a popular health food globally, but they are also a major source of edible oil and industrial raw materials, while walnut timber holds considerable commercial value due to its attractive texture and durability\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The yield of walnut nuts and their quality, including key traits such as uniformity, single-fruit weight, kernel yield, and nutritional flavor, directly determine tree planting efficiency and market competitiveness. The fruit expansion period represents a critical window for cell division and dry matter accumulation\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. During this stage, the precise application of plant growth regulators, such as exogenous gibberellins (GAs), cytokinins, or their inhibitors (e.g., paclobutrazol, PP333), has emerged as an essential technique for scientifically regulating development of walnut fruits and improving their overall yield and quality\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePlant growth regulators, including GAs and PP333, have drawn considerable attention due to their substantial roles in modulating the growth and development of plants\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.Both GAs and PP333 can profoundly influence the architecture and sink-source allocation efficiency of walnut trees\u0026mdash;by affecting the rate of fruit expansion, inhibiting vegetative growth, and enhancing resistance to stress\u0026mdash;via their antagonistic regulation of the endogenous hormone balance. The dynamic \u0026ldquo;promotion\u0026ndash;inhibition\u0026rdquo; equilibrium established by these two regulators serves as a theoretical cornerstone for precise yield and quality management in modern walnut horticulture\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Gibberellins promote cell division and elongation by degrading DELLA proteins, thereby activating the expression of downstream genes\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In contrast, paclobutrazol (PP333) inhibits GA biosynthesis by blocking the oxidation of gibberellin precursors, leading to suppressed cell elongation and growth\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. More specifically, PP333 mainly represses expression of the gene encoding GA20ox, a key enzyme in the GA biosynthesis pathway\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e; concurrently, it upregulates \u003cem\u003eGA2ox\u003c/em\u003e (a GA-inactivating enzyme) and DELLA family genes (e.g., \u003cem\u003eRGL2\u003c/em\u003e), thus promoting the nuclear accumulation of DELLA proteins\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. As central growth repressors, DELLA proteins inhibit cell elongation-related genes (e.g., EXPANSIN and XTH) by interfering with the interaction between the transcription factor PIF4 and its DNA-binding domain (bHLH domain), thereby reducing vegetative growth\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn terms of energy redistribution, treating plants with PP333 markedly increases their sorbitol content by activating sorbitol-6-phosphate dehydrogenase (S6PDH), which enhances the osmotic adjustment capacity of leaves. This improvement strengthens cellular osmotic regulation and water retention under adverse conditions, resulting in greater drought tolerance and stable photosynthetic efficiency in plants facing stress\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Applying PP333 also upregulates the fatty acid desaturase gene FAD2, augmenting the conversion of linoleic acid (C18:2) to oleic acid (C18:1)\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Gas chromatography\u0026ndash;mass spectrometry (GC\u0026ndash;MS) analyses have shown that increasing the oleic acid proportion in walnut kernel oil improves its lipid composition, thereby enhancing the oxidative stability and nutritional value of walnuts\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Furthermore, as reported by\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, the sugar transporter genes \u003cem\u003eSWEET13\u003c/em\u003e and \u003cem\u003eSWEET14\u003c/em\u003e are specifically activated in phloem sieve tubes, forming an efficient sugar export pathway. This regulation bolsters the directional transport of photosynthetic products (e.g., sucrose and sorbitol) to walnut fruits, promoting their filling and kernel biomass accumulation\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Ultimately, these changes raise the soluble sugar content of kernels improving both walnut\u0026rsquo;s flavor and economic value\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, the dynamic interaction between GAs and PP333 reshapes the secondary metabolic network via \u0026lsquo;cross-talk\u0026rsquo; with jasmonic acid/ethylene signaling. On one hand, it activates transcription of key phenylpropanoid pathway enzymes, such as phenylalanine ammonia-lyase (PAL) and chalcone synthase (CHS), facilitating the accumulation of flavonoid compounds like quercetin-3-O-rutinoside and strengthening disease resistance\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. On the other hand, it suppresses the expression of ACC synthase (ACS), delaying ethylene release and extending the fruit storage life by 7\u0026ndash;9 days\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. As well, \u003cem\u003eRGL2\u003c/em\u003e\u0026rsquo;s overexpression can increase canopy light transmittance by 30%\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e,while the coordinated expression of \u003cem\u003eFAD2\u003c/em\u003e and \u003cem\u003ePAL\u003c/em\u003e genes helps to regulate the balance between unsaturated fatty acids and polyphenols\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the synergistic mechanisms through which GA and PP333 jointly regulate the process of walnut fruit development remain poorly understood.\u003c/p\u003e \u003cp\u003eThis study combines physiological, transcriptomic, and metabolomic analyses to systematically investigate how GAS and PP333 affect the endogenous hormones, metabolite profiles, and gene expression patterns during the period of walnut fruit expansion. This research aims to achieve the precise manipulation of four pertinent factors: endogenous hormone balance, minimal physiological-based fruit drop, greater nutrient allocation to fruits, and synchronize the shell hardening with kernel development. By addressing current limitations of conventional management practices and elucidating the underlying molecular regulatory networks, the findings provide a scientific basis for the rational application of growth regulators in walnut cultivation, thus contributing to efficient, controllable, and sustainable walnut production.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials and methods\u003c/h2\u003e \u003cp\u003eThe experiment was conducted at Lvling Walnut Experimental Orchard(114\u0026deg;300\u0026ndash;114\u0026deg;330 E, 37\u0026deg;290\u0026ndash;37\u0026deg;320 N;), Lincheng County, Xingtai City, Hebei Province, P. R.China. All plants used in this study were 19-year-old 'Lvling' walnut trees (\u003cem\u003eJuglans regia\u003c/em\u003e L. \u003cem\u003e'\u003c/em\u003eLvling\u003cem\u003e'\u003c/em\u003e). Healthy, pest-free trees were selected for use in the experiment. These trees were planted in plots with a spacing of 3 m \u0026times; 4.5 m; each tree was 19 years old, 3\u0026ndash;4 m tall, with a crown width of approximately 451\u0026ndash;472 cm (from north to south) and 468\u0026ndash;518 cm (from east to west). Their fruits were harvested 30 days after pollination in 2022, all showing consistent growth, and then stored at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental design\u003c/h2\u003e \u003cp\u003eWalnut trees with uniform growth were selected at 30 days post-pollination. After a yearlong pilot experiment, a control (CK), 50 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e gibberellin (GA), and 1000 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e paclobutrazol (P) were selected as treatments. Foliar spraying was used to apply them. Under a 24-h weather forecast of calm wind and no rain, each treatment was applied to both the front and back surfaces of leaves before 11:00 AM. The spray dose was sufficient to fully wet the leaves and allow the solution to drip. Walnut fruit from each treatment was collected on the second, fourth, sixth, and tenth day after spraying for physiological measurements. The fruits from the sixth day under treatment were used for the transcriptome and metabolome analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Test items and methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Determination of soluble sugar content\u003c/h2\u003e \u003cp\u003eFor this, the anthrone colorimetric method was used\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The steps for this procedure went as follows: Weigh 0.2 g of a sample and add to it 10 mL of 80% ethanol, then extract in an 80\u0026deg;C water bath for 30 min. Repeat the extraction step three times, combine the supernatants, and dilute to 50 mL. Add 5 mL of anthrone sulfuric acid reagent to 0.1 mL of the extract and allow this mixture react in a boiling water bath for 10 min. After cooling, measure the absorbance at 620 nm. Finally, calculate the soluble sugar content, using a glucose standard curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Determination of starch content\u003c/h2\u003e \u003cp\u003eFor this, the perchloric acid hydrolysis method was used\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. After the soluble sugar extraction, dry the residue, add 5 mL of distilled water and 6.5 mL of perchloric acid, and extract in a boiling water bath for 15 min. After centrifugation, collect the supernatant and dilute it to 50 mL. A 0.1 mL aliquot is then processed (according to the soluble sugar determination method) to calculate the starch content (using a conversion factor\u0026thinsp;=\u0026thinsp;0.9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Determination of soluble protein content\u003c/h2\u003e \u003cp\u003eFor this, the Coomassie Brilliant Blue G-250 method was used\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The steps for this procedure went as follows: Weigh 0.3 g of fresh sample and add to it 2 mL of distilled water, then grind on ice, centrifuge, and collect the supernatant. To that, add 0.2 mL of the extract to 0.8 mL of distilled water and 5 mL of Coomassie Brilliant Blue reagent. After reacting for 5 min, measure the absorbance at 595 nm. Finally, calculate the protein content, using a bovine serum albumin standard curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Determination of endogenous hormone content\u003c/h2\u003e \u003cp\u003eFor this, the HPLC method of Pan and Wang (2009) was used, as follows. A 1.5-g sample was extracted with methanol and cleaned up on a C18 solid-phase extraction cartridge. An Agilent 1260 HPLC system was employed, which was equipped with a DAD detector and a Zorbax SB-C18 column (250 mm \u0026times; 4.6 mm, 5 \u0026micro;m). Detection wavelengths were 254 nm and 222 nm. The column temperature was set to 30\u0026deg;C, with a flow rate of 1.0 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and an injection volume of 20 \u0026micro;L. Eight endogenous hormones, including abscisic acid (ABA), auxin (IAA), zeatin riboside (ZR), and isopentenyl adenine (IP), were quantitatively analyzed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Omics analysis\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Transcriptome sequencing\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted each sample, using the RNAprep Pure Total RNA Extraction Kit. After confirming the RNA quality was adequate, library preparation and sequencing tasks were outsourced to Beijing BGI Biotechnology Co., Ltd. Paired-end 150-bp sequencing was done on the Illumina NovaSeq 6000 platform. Raw data underwent quality control before alignment against the walnut reference genome. The differential gene expression analysis was performed using the DESeq2 package (criteria: |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1 and padjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Total RNA was extracted using PrimeScript\u0026trade; 1st strand cDNA Synthesis Kit, (Takara, Japan). Quantitative reverse transcription PCR (qRT-PCR) was performed using the 2X SG Fast qPCR Master Mix (High Rox, B639273, BBI) according to the manufacturer\u0026rsquo;s protocol. The specific primers used for qRT-PCR were designed using Primer Premier 5.0 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Each 20 mL qRT-PCR reaction contained 7.2 mL ddH\u003csub\u003e2\u003c/sub\u003eO, 0.4 mL of 10 mM primers F, and R, 10 mL of 2\u0026times;SybrGreen qPCR Master Mix, and 2 mL of diluted 6 selected DEGs cDNA. The amplification program was: initiation by a 95℃ for 3 min, followed by 45 cycles of 5s at 95\u0026deg;C, and 30s at 60\u0026deg;C, with a melting curve analysis program. Finally, the 96-well plate with the added samples was run on an ABI Step one plus fluorescent quantitative PCR instrument. Three technical replicates were performed for qRT-PCR.Raw RNA-seq reads were submitted to the NCBI Sequence Read Archive (SRA) under BioProject PRJNA1393871.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Metabolomics analysis\u003c/h2\u003e \u003cp\u003eThis was carried out using LC-MS technology. From each sample, 50 mg was weighed, extracted with 400 \u0026micro;L of methanol (containing 2-chloro-L-phenylalanine internal standard), and filtered through a 0.22-\u0026micro;m membrane. It was then analyzed using a Thermo Vanquish UPLC system equipped with a HSS T3 column, coupled to a Thermo Orbitrap Exploris 120 mass spectrometer, in the ESI positive and negative ion modes. Raw data were processed using Compound Discoverer 3.3 and compared against databases such as HMDB and KEGG for metabolite identification.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data processing and analysis\u003c/h2\u003e \u003cp\u003eOne-way analysis of variance (ANOVA) was performed using SPSS 26.0 software, with Duncan\u0026rsquo;s multiple range test employed for significance testing (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Graphs were plotted using Origin 2022 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Effects of exogenous GAS and PP333 on hormone levels of walnut fruit\u003c/h2\u003e \u003cp\u003eIn the GAS-treated group, the gibberellin (GAS) content peaked on the sixth day after spraying, increasing by 321% relative to the control (CK). Cytokinin (KT) and IP and auxin (IAA) levels peaked on the fourth day, increasing by 260%, 259%, and 315%, respectively, before gradually declining; but their overall levels remained higher than those of the CK. Levels of zeatin riboside (ZR) and methylthioadenosine (MT) continued to rise, respectively increasing by 310% and 183% by the eighth day. Abscisic acid (ABA) and dihydrozeatin (DL) levels initially rose and then fell, with DL undergoing a significant decrease after the fourth day. By the eighth day, ABA and DL levels had respectively decreased by 4.9% and 40% compared to the CK.\u003c/p\u003e \u003cp\u003eTreatment with paclobutrazol (PP333) resulted in the opposite effects of GAS. First, the GAS content exhibited a downward trend, reaching a 50% reduction vis-\u0026agrave;-vis the CK by the sixth day. The KT, IAA, and IP levels all briefly increased before decreasing, such that they were similar to CK by the eighth day. However, both MT and DL levels were significantly lower than those of CK by the eighth day, decreasing by 29% and 16%, respectively. The ABA and ZR levels initially decreased before increasing, with the inhibitory effect being strongest on the sixth day, decreasing by 35% and 42%, respectively, relative to CK.\u003c/p\u003e \u003cp\u003eThese results suggested that GAS increases the levels of growth-promoting hormones (such as GAS, KT, IP, and IAA) and decreases the levels of growth-inhibiting hormones (such as ABA and DL), while PP333 reduces the levels of growth-promoting hormones by inhibiting the GAS signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Effects of exogenous GAS and PP333 on soluble sugar, starch, soluble protein and Ca2\u0026thinsp;+\u0026thinsp;content in walnut fruit\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe GAS treatment significantly promoted the accumulation of soluble sugar, starch, and soluble protein in walnut fruits. Compared with CK, it increased the soluble sugar content by 50%, the starch content by 19.4%, and the soluble protein content by 18.18%. This indicated that GAS significantly enhanced the accumulation of nutrients in the fruit by activating specific pathways related to carbon metabolism and protein synthesis. By contrast, treatment with PP333 inhibited the accumulation of these nutrients. Compared with CK, it decreased the soluble sugar content by 10%, the starch content by 4.8%, and the soluble protein content by 36.36% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Compared with CK, GAS treatment and PP333 treatment each increased the Ca2\u0026thinsp;+\u0026thinsp;content of walnut fruits to varying degrees (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Transcriptome analysis of exogenous GAS and PP333 effects on walnut fruit\u003c/h2\u003e \u003cp\u003eThe eukaryotic reference transcriptome (RNA-seq) analysis of nine walnut fruit samples yielded a total of 61.79 Gb of clean data, with each sample generating 5.79 Gb of clean data and a Q30 base percentage of 94.04% or higher (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Clean reads from each sample were aligned to the designated reference genome, whose alignment efficiencies ranged from 88.12% to 95.87% (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). These alignment results were then used for alternative splicing prediction, gene structure optimization, and novel gene discovery, resulting in the discovery of 3607 novel genes, of which 1648 were functionally annotated (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Analyzing the gene expression distribution within each sample revealed both symmetry and dispersion at very high expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Analysis of differentially expressed genes (DEGs) between the three treatment groups revealed 885 DEGs in CK1_vs_GA1, with 519 upregulated and 366 downregulated; 944 DEGs (549 upregulated, 395 downregulated genes) were found between CK1 and P1; and 1325 DEGs (596 upregulated, 729 downregulated) were identified between GA1 and P1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). A Venn diagram revealed 17 DEGs shared between the three comparison groups, 190 DEGs shared by the CK1 and GA1 groups, and 720 DEGs unique to the GA1 and P1 groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The greater number of DEGs in CK1 versus P1 compared to CK1 versus GA1 suggested a higher transcriptional response in walnut fruit to growth regulators which inhibit plant growth. The GA1 versus P1 comparison harbored the highest number of DEGs, indicating that spraying with plant growth regulators can significantly alter the gene expression patterns in walnut fruit.\u003c/p\u003e \u003cp\u003eNext, the DEGs\u0026rsquo; functional enrichment analysis was performed using GO data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geeontology.org\u003c/span\u003e\u003cspan address=\"http://www.geeontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GO enrichment for the CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 groups focused cellular components, biological processes, and molecular functions. However, GO enrichment analysis showed that the DEGs among these three comparison groups were enriched most in the biological process and molecular function categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.D, E, F). As Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD shows, for GA1_vs_P1 the dominant entries under the biological process category were metabolic process, cellular process, response to stimulus, biological regulation, multicellular organismal process, and signaling.\u003c/p\u003e \u003cp\u003eTo identify other important metabolic pathways, KEGG pathway analysis was used to determine the DEGs\u0026rsquo; biological functions. Among the KEGG-enriched pathways for CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1, there were 105, 106, and 119 significantly enriched pathways, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Analysis of the top-50 KEGG significantly enriched pathways revealed that these pathways were chiefly enriched in cellular processes, environmental information processing, genetic information processing, metabolism, and organismal systems; however, the most DEGs were annotated under metabolism. In-depth analysis of the top-20 pathways with the most DEGs revealed that the main enriched pathways in the three comparison groups were plant hormone signal transduction (ko04075); plant\u0026ndash;pathogen interaction (ko04626); phenylpropanoid biosynthesis (ko00940); protein processing in endoplasmic reticulum (ko04141); carbon metabolism (ko01200); starch and sucrose metabolism (ko00500); MAPK signaling pathway \u0026ndash;plant (ko04016); biosynthesis of amino acids (ko01230); endocytosis (ko04144); and ABC transporter (ko00600). The pathways unique to the GA1_vs_P1 comparison group included the following: flavonoid biosynthesis (ko00941); glycine, serine, and threonine metabolism (ko00260); ubiquitin-mediated proteolysis (ko04120); cysteine ​​and methionine metabolism (ko00270); carbon fixation in photosynthetic organisms (ko00710); and glyoxylate and dicarboxylate metabolism (ko00630) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 qRT-PCR verification\u003c/h2\u003e \u003cp\u003eTo validate the RNA-Seq results, 12 DEGs were further analyzed for their expression, by qRT-PCR. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the qRT-PCR results were highly consistent with the RNA-Seq data, confirming the reliability of the transcriptome analysis..\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Metabolomics analysis of exogenous GAS and PP333 effects on walnut fruit\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of nine leaf samples and quality control samples revealed robust correlation between replicates, with high correlation coefficients between samples within a treatment relative to those between treatments. Hence, the differential metabolites obtained were reliable and data confidence was high (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To investigate the metabolite changes in walnut fruit after spraying leaves with growth regulators, differential metabolites were analyzed in the CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 comparison groups. A total of 112 differential metabolites were identified in CK1_vs_GA1 (39 upregulated, 73 downregulated); 473 in CK1_vs_P1 (198 upregulated, 275 downregulated; and 575 differential metabolites were identified in GA1_vs_P1 (232 upregulated, 343 downregulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C, D). In addition, 33 differentially abundant metabolites were shared among the three comparison groups, whereas 26 were unique to CK1_vs_GA1, 132 were exclusive to CK1_vs_P1, and 251 were restricted to GA1_vs_P1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). KEGG pathways were enriched in 39, 69, and 81 entries in the CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 comparison groups, respectively. Further analysis of the top-20 most significantly enriched KEGG pathways among the differentially abundant metabolites detected in those groups revealed these shared metabolic pathways: porphyrin metabolism (ko00860), nitrogen metabolism (ko00910), and carotenoid biosynthesis (ko00906). The common pathways between CK1_vs_P1 and GA1_vs_P1 included fatty acid biosynthesis (ko00061); glycolysis/gluconeogenesis (ko00010); indole alkaloid biosynthesis (ko00901); lysine biosynthesis (ko00300); pantothenate and CoA biosynthesis (ko00770); cysteine ​​and methionine metabolism (ko00270); and arachidonic acid metabolism (ko00590) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, E, F). Importantly, the metabolic pathways of these KEGG significantly-enriched entries were similar to those for transcriptional results.\u003c/p\u003e \u003cp\u003eFurthermore, analyzing the top-30 differentially expressed metabolites ranked by their p-value across the three comparison groups revealed that pseudoginsenoside RT5 and senecionine N-oxide were significantly enriched in all three groups. Differentially expressed metabolites found in common between CK1_vs_P1 and GA1_vs_P1 included apiferol, N-hydroxy-L-valine, hydroxyspheroidene, paederosidic acid, and 2,3,5,4'-tetrahydroxystilbene-2-O-glucoside; unlike that, the common differential metabolites between CK1_vs_GA1 and GA1_vs_P1 consisted of tropate, sapindoside B and 4-vinylphenol. The unique differential metabolites of GA1_vs_P1 were mucronine A, Corey PG-lactone diol, ginsenoside Rh2, thymidine, terpendole I, estradiol valerate, arjunic acid, roquefortine D, arginyl-glutamine, soyasaponin III, ganoderic acid Mi, naringenin 7-O-beta-D-glucoside, D-urobilin, LysoPE 18:1(2n isomer) isomer), 4alpha-carboxy-5alpha-cholesta-8,24-dien-3beta-ol, tripoxyrollin, casticin, and oleanolic acid-3-O-glucuronide.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Joint analysis of transcriptomics and metabolomics\u003c/h2\u003e \u003cp\u003eThe combined transcriptomic and metabolomic analyses detected 31, 53, and 69 pathways shared between treatments for the CK1_vs_GA1, CK1_vs_P1, and GA1_vs_P1 comparison groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Focusing the combined analysis on the top-30 differentially enriched pathways revealed that the three comparison groups all shared the following common pathways: ABC transporters; carbon metabolism; histidine metabolism; monoterpenoid biosynthesis; carotenoid biosynthesis; glyoxylate and dicarboxylate metabolism; isoquinoline alkaloid biosynthesis; arginine and proline metabolism; steroid biosynthesis; tryptophan metabolism; and amino acid biosynthesis. The shared metabolic pathways between CK1_vs_P1 and GA1_vs_P1 included flavonoid biosynthesis; glycine; serine and threonine metabolism; ubiquinone and other terpenoid-quinone biosynthesis; linoleic acid metabolism; zeatin biosynthesis; glycerolipid metabolism; and glycolysis/gluconeogenesis. The shared metabolic pathways between CK1_vs_GA1 and GA1_vs_P1 included phenylpropanoid biosynthesis, valine, leucine, and isoleucine degradation, beta-alanine metabolism, and anthocyanin biosynthesis. To sum up, the results of this joint analysis were consistent with those obtained for the DEGs and differentially accumulated metabolites in walnut fruit.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpraying with plant growth regulators affects the downstream signaling pathways of phytohormones, including auxin (auxin), cytokinin (CTK), abscisic acid (ABA), and brassinosteroids (BRs). The CTK signaling pathway consists of a histidine receptor kinase (CRE1), a phosphate transporter (AHP), and response regulators (type ARR family and type B ARR family). In the present study, genes regulating CRE1 and B-ARR were significantly upregulated in both CK1_vs_GA1 and GA1_vs_P1 comparison groups, but significantly downregulated in CK1_vs_P1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Genes regulating A-ARR were downregulated significantly in all comparison groups.\u003c/p\u003e \u003cp\u003eAuxin signaling is mediated mainly via the TIR1/AFB (TIFY and AFB AUX/IAA proteins) receptor complex, which degrades IAA proteins through ubiquitination, thereby regulating the expression of downstream genes. Genes regulating AUX1 were found upregulated in every comparison group. AUX1/IAA was upregulated in the CK1_vs_GA1 group yet mostly downregulated in the CK1_vs_P1 group. However, genes regulating SAUR displayed the opposite pattern for all comparison groups, while genes regulating ARF and GH3 were mostly downregulated.\u003c/p\u003e \u003cp\u003eABA signal transduction is predominately mediated by PYR/PYL (pyrabactin resistance/pyrabactin resistance-like) proteins and SnRK2 (sucrose non-fermenting 1-related protein kinase 2) protein kinases, regulating the expression of ABA-responsive genes. In this study, genes regulating PP2C and PYR/PYL were significantly upregulated in the comparison groups CK1_vs_GA1 and CK1_vs_P1, but significantly downregulated in GA1_vs_P1; further, ABF was significantly upregulated in both CK1_vs_P1 and GA1_vs_P1, yet significantly downregulated in CK1_vs_GA1; however, SnRK2 was downregulated significantly in all three comparison groups.\u003c/p\u003e \u003cp\u003eRegarding BR signals, they are sensed by BRI1 and BAK1 and transmitted to BSKs through a series of phosphorylation reactions. Specifically, BAK1 acts as a co-receptor in BR signal transduction and can directly interact with BRI1, to positively regulate BR signaling. Here, genes regulating BAK1 were significantly upregulated in the CK1_vs_GA1 and CK1_vs_P1 comparison groups; genes regulating BRI1, BSK, and CYC03 were also upregulated in CK1_vs_P1 and GA1_vs_P1; whereas, BKI1 and TCH4 were upregulated in all comparison groups.\u003c/p\u003e \u003cp\u003eSpraying with growth regulators can also influence plant\u0026ndash;pathogen interactions. This is largely due to pathogen-associated molecular templates triggering the recognition of the immune bacterial flagellin (flg22) by a receptor protein (FLS2). FLS2 activates the downstream mitogen-activated protein kinase (MEKK1), which then transmits signals to WRKY 22, activating the transcription of genes involved in immune defense responses. In the current study, genes regulating FLS2 and MEKKI were mostly downregulated in the comparison groups; conversely, WRKY 22 was significantly upregulated in each comparison group; FRK1 and PR1 were mostly upregulated significantly in CK1_vs_GA1 and CK1_vs_P1, yet downregulated in GA1_vs_P1. At the same time, with respect to the Ca2\u0026thinsp;+\u0026thinsp;channels, genes regulating CNGCs and Rboh were mostly upregulated in the CK1_vs_GA1 and CK1_vs_P1 comparison groups; in general, the CDPK- and CaMCML-encoding genes were significantly upregulated in every group. This results indicated that spraying walnut trees with growth regulators led to greater Ca2\u0026thinsp;+\u0026thinsp;signal transduction and an increased shell thickness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmino acids are the building blocks of protein synthesis in organisms, and their synthesis pathways play a crucial role in cellular metabolism. In the present study, genes regulating TPI were upregulated in all comparison groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Genes regulating PGAM were significantly downregulated in both CK1_vs_GA1 and CK1_vs_P1 groups, but significantly upregulated in GA1_vs_P1. Genes regulating ALD and glyA were mostly upregulated in CK1_vs_GA1, while significantly downregulated in the other two comparison groups. Genes regulating ItaE also featured significant upregulation and downregulation. In particular, 3-phospho-D-glycerate, O-acetyl-L-serine, and S-sulfonyl-L-cysteine were each significantly upregulated in every comparison group, though by a significantly greater magnitude in CK1_vs_P1 than CK1_vs_GA1. The contents of 3-phospho-D-glycerate, O-acetyl-L-serine, and S-sulfo-L-cysteine in the P1 treatment were significantly higher than those in the GA1 and CK1 treatments. In summary, the results indicated that the application of growth regulators can promote amino acid biosynthesis, with the spraying of paclobutrazol (PP333) exerting a better regulatory effect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, strong correlations were found between DEGs and DAMs (differentially abundant metabolites) in the amino acid biosynthesis pathway and brassinosteroid signaling pathway. In the former pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA), S-sulfo-L-cysteine was negatively correlated with PGAM (\u003cem\u003eLOC108979616, LOC109016447\u003c/em\u003e) and ltaE (\u003cem\u003eLOC118343818\u003c/em\u003e), but positively correlated with TPI (NewGene_3568), ALDO (\u003cem\u003eLOC108994846\u003c/em\u003e, \u003cem\u003eLOC109006536\u003c/em\u003e), and ltaE (\u003cem\u003eLOC118346050\u003c/em\u003e). Regarding O-acetyl-L-serine, it was positively correlated with TPI (NewGene_3568), glyA (\u003cem\u003eLOC109012448\u003c/em\u003e), ltaE (\u003cem\u003eLOC118346050\u003c/em\u003e), and ALDO (\u003cem\u003eLOC108994846\u003c/em\u003e), though negatively correlated with ltaE (\u003cem\u003eLOC118343818)\u003c/em\u003e. Likewise, 3-Phospho-D-glycerate was negatively correlated with ltaE (\u003cem\u003eLOC118343818\u003c/em\u003e) yet positively correlated with ltaE (\u003cem\u003eLOC118346050\u003c/em\u003e). Finally, for the brassinolide signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), while it was positively correlated with BAK1 (\u003cem\u003eLOC118344550\u003c/em\u003e and \u003cem\u003eLOC108997207\u003c/em\u003e), brassinolide was negatively correlated with the following: BAK1 (\u003cem\u003eLOC108998363\u003c/em\u003e), BRI1 (\u003cem\u003eLOC108991311\u003c/em\u003e, \u003cem\u003eLOC109007628\u003c/em\u003e, and \u003cem\u003eLOC109014172\u003c/em\u003e), BSK (\u003cem\u003eLOC108998493\u003c/em\u003e), and CYCD3 (\u003cem\u003eLOC108979248\u003c/em\u003e and \u003cem\u003eLOC108996078\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the gene regulatory network associated with walnut fruit size, a WGCNA analysis was conducted on all DEGs. The resulting modular gene clustering heatmap displays each dendrogram as a module, with each branch representing a gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). There, the darker the color of each point, the stronger the connectivity between the two genes in the corresponding row and column. The follow-up correlation analysis with phenotypic data identified 11 key modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). Pearson correlation coefficients (\u003cem\u003er\u003c/em\u003e-values) were calculated between characteristic genes of each module and specific developmental stages of walnut fruit, along with their respective p-values. Based on the criteria of an \u003cem\u003er\u003c/em\u003e-value\u0026thinsp;\u0026ge;\u0026thinsp;0.8 and p-value\u0026thinsp;\u0026le;\u0026thinsp;0.5, it was found that a yellow-green color was significantly correlated with metabolites related to amino acid biosynthesis. To further understand the functions of genes in that color module, a correlation network analysis of genes in this yellow-green module was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eD). This demonstrated that gene-\u003cem\u003eLOC109010970\u003c/em\u003e, gene-\u003cem\u003eLOC109012088\u003c/em\u003e, gene-\u003cem\u003eLOC108998110\u003c/em\u003e, gene-\u003cem\u003eLOC118348515\u003c/em\u003e, gene-\u003cem\u003eLOC108981442\u003c/em\u003e, and gene-\u003cem\u003eLOC108981011\u003c/em\u003e were all significantly correlated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, iTAK software based on the PlantTFDB database was used to predict transcription factors (TFs) for all transcripts, identifying 4498 genes from 214 gene families as candidate TFs. The families containing the largest number of TFs were FAR1 (260), MYB (223), RLK-Pelle_DLSV (179), AP2/ERF-ERF (171), bHLH (165), and C2H2 (147) (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). The differential TFs in the different treatment comparison groups were analyzed using a cluster heat map (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB). Among them, gene-LOC108996157 and gene-LOC10899615 in the MYB gene family were upregulated significantly in GA1_vs_P1 and downregulated significantly in CK1_vs_GA1. Regarding the RLK-Pelle_SD-2b gene family, it was significantly downregulated in the comparison group GA1_vs_P1, but upregulated significantly in CK1_vs_GA1. Pearson correlations were tested between DEGs and differentially expressed TFs under the plant growth regulator treatments. As well, TFs related to amino acid biosynthesis processes possibly associated with walnut fruit size were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC). Within the amino acid biosynthesis process, ALDO (LOC108994846) was positively correlated with MYB (LOC109006592), C2H2 (LOC108998795), and MYB-related (LOC108993213), but negatively correlated with C3H (LOC109012502) and RLK-Pelle_LRR-III (LOC108990728). ALDO (LOC109006536) was positively correlated with both MYB (LOC109006592) and MYB-related (LOC108993213), yet negatively correlated with C3H (LOC109012502) and RLK-Pelle_LRR-III (LOC108990728). For ltaE (LOC118346050), it was negatively correlated with MYB (LOC109009907), RLK-Pelle_DLSV (LOC109007473), bHLH (LOC108982135), C2H2 (LOC118344174), and RLK-Pelle_WAK (LOC108983353), but positively correlated with MYB (LOC109006592). While ltaE (LOC118343818) was positively correlated with bHLH (LOC109010157), C2H2 (LOC108984376), and C2H2 (LOC118344174), it was also negatively correlated with both C2H2 (LOC108997775) and bZIP (LOC109003262). Lastly, PGAM (LOC108979616P) was negatively correlated with MYB (LOC108987421) and C3H (LOC108991847), though positively correlated with MYB (LOC1089961579).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, those TFs that could be related to IAA, CTK, BRs, ABA signal transduction, and plant\u0026ndash;pathogen interaction pathways were also analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e). We found that in the BRs signal transduction pathway, BAK1 (LOC108998363) was negatively correlated with MYB (LOC109006592), MYB (LOC109008746) and RLK-Pelle_DLSV (LOC109000270), and positively correlated with MYB (LOC109009907), RLK-Pelle_DLSV (LOC109007473), bHLH (LOC108982135), RLK-Pelle_LRK10L-2 (LOC109008043) and RLK-Pelle_WAK (LOC108983353). Concerning BAK1 (LOC108997207), it was negatively correlated with MYB (LOC109008746), C3H (LOC109012502), and RLK-Pelle_WAK (LOC108983353), but positively correlated with MYB (LOC109015732), AP2/ERF-ERF (LOC108992157), C2H2 (LOC108998795, LOC109008851), and WRKY (LOC109001021). For the CTK signaling pathway analysis, CRE1 (LOC108993473) was found positively correlated with MYB (LOC108993912, LOC108996157, LOC109009907), bHLH (LOC108982135, LOC109010157), C2H2 (NewGene_1251), GRAS (LOC108985505), RLK-Pelle_LRK10L-2 (LOC109008043), and RLK-Pelle_WAK (LOC108983353), yet negatively correlated with MYB-related (LOC108993213). Analysis of the ABA signaling pathway showed that PYL (LOC108982697) was negatively correlated with C2H2 (NewGene_1251 and LOC109020061), GRAS (LOC108985505), and RLK-Pelle_LRK10L-2 (LOC108997207), though positively correlated with both MYB (LOC109015732) and NAC (LOC108996595). Concerning the IAA signaling pathway, IAA (LOC108984763) was negatively correlated with both MYB (LOC108987421, LOC108987421, and LOC108987421). Regarding plant\u0026ndash;pathogen interaction pathways, CNGCs (LOC108991534) were negatively correlated with MYBs (LOC108993912, LOC109000871), NACs (LOC108992696), and GRASs (LOC108985505), and positively correlated with bZIPs (LOC109019104). CNGCs (LOC109009995) were positively correlated with MYBs (LOC108993912, LOC109000871, LOC109009907), bHLHs (LOC108982135), and GRASs (LOC108985505). Finally, CNGCs (LOC109008642) were positively correlated with C2H2 (LOC108997775), RLK-Pelle_LRR-XI-1 (LOC108993886), and bZIP (LOC109003262 and LOC109019104).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe fruit expansion period is a critical phase when fruit development shifts from cell division to the rapid expansion of cell volume and accumulation of cellular contents. In this context, the scientific application of exogenous growth regulators holds significant physiological and agronomic importance\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Applying specific exogenous growth regulators\u0026mdash;such as GA, NAA, or BRs\u0026mdash;can effectively induce the expression of expansins and promote cell wall relaxation and vacuolar water uptake through receptor-mediated signal transduction pathways involving the MAPK and calcium signaling cascades, thereby driving geometric increases in fruit volume\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the fourth day after applying the GAS treatment, the contents of GAS, IAA, KT, IP, ZR, and MT in walnut fruit significantly exceeded those in the control (CK), whereas the ABA and DL contents were significantly lower under PP333 treatment relative to CK. The walnut fruit expansion period (lasting 35 to 42 days after flowering [DAF]) is characterized by active cell division in the endocarp along with the accumulation of dry matter, which directly influences both fruit morphology and nut development\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Treatment with PP333 may also increase the ABA content and bolster plants\u0026rsquo; stress resistance,JrHDZ28 and JrbZIP40 was induced under salt and drought stress, which provided potential molecular evidence at the genetic regulation level for enhancing the stress resistance of walnuts by PP33335\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e..It is known that GA significantly promotes longitudinal fruit growth, alters the fruit shape index, and delays ripening of tomato by activating its cell elongation-related genes and inhibiting ethylene biosynthesis\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. During grape\u0026rsquo;s seed-hardening stage, a treatment with PP333 modulates the expression of genes involved in cell wall synthesis and hormone signal transduction\u0026mdash;including GA, IAA, and ABA pathways\u0026mdash;to regulate seed development and fruit structure\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Applying PP333 also inhibits GA synthesis and synergistically regulates the dynamic balance between IAA and ABA, which reshapes the cell wall composition and seed mechanical strength, ultimately affecting fruit structure and development\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDuring fruit expansion period of \u0026lsquo;Red Globe\u0026rsquo; grape, for example, GAS applied at 40 or 70 mg/L increased its fruit cell length, diameter, and volume, while levels of IAA and ZT increased considerably and vice versa for ABA\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Furthermore, treating sweet cherry promoted the accumulation of GAS and GA7\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, a finding largely consistent with the result of the present study. Expression patterns of genes in the CTK and ABA pathways\u0026mdash;including those for CRE1, B-ARR, PP2C, and PYR/PYL\u0026mdash;changed markedly during fruit development in the walnut W13 line\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Earlier work showed that a paclobutrazol (PBZ) treatment reduced the GA1 and GA4 contents of apple rootstock while increasing its ABA concentration\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. In sweet sorghum, applying a PBZ treatment inhibited the activity of ent-kaurene oxidase (KO), blocking the early steps in GA biosynthesis which led to a substantial decrease in the GA4 content of the shoot apex\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e, consistent with our study\u0026rsquo;s results.\u003c/p\u003e \u003cp\u003eIn the cytokinin (CTK) signal transduction pathway of GAS-treated walnut fruit, we found that \u003cem\u003eCRE1\u003c/em\u003e and \u003cem\u003eB-ARR\u003c/em\u003e genes were upregulated; however, related genes were downregulated by the PP333 treatment. In the ABA signaling pathway, genes such as \u003cem\u003ePP2C\u003c/em\u003e and \u003cem\u003ePYR/PYL\u003c/em\u003e were significantly upregulated by the GAS treatment, while ABF was strongly upregulated in response to PP333. The \u003cem\u003eSnRK2\u003c/em\u003e gene was downregulated by both GA and PP333 treatments. After GA binds to its receptor GID1, it promotes ubiquitination and degradation of DELLA proteins, enhancing cell elongation and division and thereby supporting plant growth and development\u0026mdash;a mechanism widely validated in rice and other staple crops\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. The research on apples has found that MdBLH14 physically interacts with another member of the TALE superfamily, MdKNOX19, and works together to inhibit the expression of MdGA20ox3, thereby preventing the accumulation of GA's activity\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e.In sweet cherry, an exogenous GA treatment not only upregulated the expression of NCED and ABA2, key genes in the synthesis of ABA, leading to the latter\u0026rsquo;s greater accumulation, but it also affected the expression of GA metabolism-related genes, such as GA2ox and CYP701, promoting the accumulation of GAS₃and GA7₇\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Those findings align with the conclusions of our study of walnut fruit. In poplar, IAA and GA treatments upregulated the genes related to cellulose synthesis (e.g., *CesA8-B*) and cell wall relaxation (e.g., expansin [EXP] and xylan endotransglycosylase [XET]), potentially promoting cell wall expansion and elongation, thereby facilitating xylem formation and growth\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePBZ treatments are known to accelerate flower bud differentiation in citrus trees. For example, ABA levels initially decreased and then increased during the flower bud activation stage, mirroring the dynamics of ABA and ZR during fruit ripening\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. In tandem, PBZ can regulate citrus fruit ripening by inhibiting GA biosynthesis\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Recent work shows that a cytokinin treatment increases the fruit set rate and fruit weight in sweet cherry\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. The TFs belonging to the MYB family play a fundamental role in cytokinin-regulated cell division, promoting gains in size and weight of fruits during the early stages of their development\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. A genome-wide analysis of GA metabolism and signaling genes in alfalfa identified 8 \u003cem\u003eMtGA20ox\u003c/em\u003e, 2 \u003cem\u003eMtGASox\u003c/em\u003e, and 13 \u003cem\u003eMtGA2o\u003c/em\u003ex genes\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Applying a GAS treatment also affects the expression of multiple hormone signaling-related genes in grape berries, including GID2, SAUR, and ACS. In other work, an exogenous GA application upregulated the expression of \u003cem\u003eCpGA2ox\u003c/em\u003e in the GA biosynthesis pathway and CpGAI in the signal transduction pathway in a dwarf mutant (dw) of \u003cem\u003eChimonanthus chinensis\u003c/em\u003e, thereby restoring its normal phenotype\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. In response to a PBZ treatment, the endogenous IAA content increased, and there ensued an upregulated expression of polar auxin transport genes (e.g., \u003cem\u003eMdPIN1\u003c/em\u003e and \u003cem\u003eMdLAX1\u003c/em\u003e) as well as that of the \u003cem\u003eMdYUCCA10a\u003c/em\u003e biosynthesis gene\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Concurrently, the ABA concentration and expression of the ABA biosynthesis-related gene \u003cem\u003eMdNCED1\u003c/em\u003e both increased, while expression of the degradation gene \u003cem\u003eMdCYP707A1\u003c/em\u003e was inhibited\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. However, a PBZPP333 treatment reduced the trans-zeatin (tZ) levels and downregulated the expression of the cytokinin biosynthesis gene \u003cem\u003eMdIPT6\u003c/em\u003e, indicating that PBZcould influence the phytohormonal balance and plant development by regulating hormone synthesis and transport genes \u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn Chinese pear fruits, ethylene inhibits the expression of TFs, such as PpMYB10 and PpMYB114, thereby suppressing anthocyanin synthesis, whereas jasmonic acid promotes flavonoid accumulation by activating these TFs\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Additionally, ethylene affects its own biosynthetic pathway by regulating the expression of key enzymes, namely PpACS and PpACO\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. In many plants, like grapevine, jasmonic acid and ethylene act synergistically to activate its defense responses\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Hormones including ABA, SA, and BRs collectively regulate the expression of defense-related genes\u0026mdash;such as pathogenesis-related proteins (PRs), disease resistance proteins (RPSs), calcium-binding proteins (CMLs), and ethylene-responsive transcription factors (ERFs)\u0026mdash;thereby enhancing plant resistance to pathogens\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e,The WRKY transcription factors play a crucial role in the plant's response to abiotic stress,VQ-WRKY complex plays a crucial role in plants' response to biotic stresses, further enriching the molecular regulatory network for plant defense against biological stress37\u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. In our study, in response to the GA and PP333 treatments, genes regulating Ca\u003csup\u003e2+\u003c/sup\u003e channels (CNGCs), Rboh, CDPK, and CaMCML were significantly upregulated in walnut fruit across the comparison groups, resulting increased shell thickness. Long-term spraying of PP333 not only inhibits GA synthesis and enhances the CK/ABA ratio, but is also accompanied by thickening of the peel in fruits\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the genes \u003cem\u003ePpCDPK7\u003c/em\u003e and \u003cem\u003ePpRboh\u003c/em\u003e were co-upregulated under cold storage conditions in peach fruit, potentially reinforcing its fruit skin strength and cold resistance\u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe nutritional quality of walnut fruit changes significantly during its development. Research indicates that fatty acid biosynthesis begins 13 weeks after pollination, with lipids, carbohydrates, amino acids, and their derivatives accumulating primarily in the kernel\u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. During walnut endosperm development, protein and amino acid contents are initially high but gradually decrease later on, suggesting their conversion into other compounds as the fruit matures\u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e. In the present study, the GA treatment significantly increased the starch, soluble protein, and soluble sugar contents of walnut fruit, whereas carbohydrate content under the PP333 treatment was significantly lower than in CK. For kiwifruit, application of GAS promotes the accumulation of sugars and starch by upregulating the expression of sucrose phosphate synthase (SPS) and starch synthase (SS) genes\u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e, which is consistent with our results. Similar findings have been reported for \u003cem\u003eTorreya grandis\u003c/em\u003e nuts\u003csup\u003e[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e. Treatment with PP333 can reduce α-amylase activity and inhibit starch degradation, thereby limiting the carbon source supply\u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e. However, a paclobutrazol treatment can increase the soluble sugar content, enhance antioxidant enzyme activity, and improve both the chlorophyll and relative water content in leaves of \u003cem\u003eAmorpha fruticosa\u003c/em\u003e, which improves its drought tolerance under water-deficient conditions\u003csup\u003e[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/sup\u003e. This outcome may be attributed to carbohydrate storage in leaves to enhance plant resilience under stress conditions.\u003c/p\u003e \u003cp\u003ePlant growth regulators can systematically improve the efficiency of assimilate transport\u0026mdash;particularly sucrose and minerals\u0026mdash;from source (leaves) to sink (fruits) organs. By enhancing the activity of sucrose transporters (SWEETs) and key enzymes such as sucrose synthase (SuSy) at phloem unloading sites in fruits, they ensure the continuous and efficient directional transport of photosynthetic products and nutrients into fruit\u003csup\u003e[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e. A recent \u0026sup1;\u0026sup3;C isotope tracing study revealed patterns of carbohydrate assimilation and partitioning in walnut at different developmental stages, underscoring the essential role of leaf photosynthesis in dry matter accumulation in its fruit\u003csup\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e. Later, Chen et al. (2022b) found that the \u003cem\u003eSWEET13\u003c/em\u003e gene in the C4 plant \u003cem\u003eSetaria viridis\u003c/em\u003e is highly expressed in photosynthetic source tissues, particularly in phloem sieve tubes, pointing to its vital role in phloem loading of sucrose. Bezrutczyk et al. (2018) observed that mutants knocked out for three SWEET13 genes (ZmSWEET13a, 13b, and 13c) exhibited defective phloem loading, leading to sucrose accumulation in their leaves. Similar results have been reported in sugarcane\u003csup\u003e[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/sup\u003e. Heterologous expression of SWEET13c in Arabidopsis reduced the sugar content of its leaves while enhancing its root and shoot growth, suggesting that SWEET13c may enhance the source-to-sink carbon flux by facilitating sucrose transport\u003csup\u003e[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/sup\u003e. Treatment with GAS can enhance the expression of ABCG family ABC transporters, enabling the greater transport of photosynthetic products into fruit tissues and thereby supporting nutrient accumulation in them\u003csup\u003e[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGrowth regulators also substantially influence nutrient accumulation in plants by modulating key carbon and nitrogen metabolic pathways. Here, both GA and PP333 treatments increased the walnut fruit\u0026rsquo;s content of D-glycerol-3-phosphate, O-acetyl-L-serine, and S-sulfonyl-L-cysteine, with its amino acid content being significantly higher in PP333 than GA treatment. In a recent study of pear seedlings, Zhang et al. (2024) noted that besides increasing its hormone levels, a GAS treatment enhanced the activity of nitrogen metabolic enzymes and related genes (e.g., GS, GOGAT), which improved nitrogen uptake and carbon-nitrogen coordination to increase both crop growth and nutrient accumulation efficiency, which is consistent with our study\u0026rsquo;s conclusions. A paclobutrazol (PAC) treatment modified the carbon-nitrogen metabolic balance in corn, leading to its lower yield under limited fertilizer supply\u003csup\u003e[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/sup\u003e, a finding also consistent with ours for walnut fruit. Various growth regulators, such as GAS, PBZ, ABA, and Ethrel impact the expression and activity of carbohydrate metabolic enzymes (e.g., SuSy and AGPase) in \u003cem\u003eLycoris radiata\u003c/em\u003e bulbs, thus influencing its nutrient accumulation\u003csup\u003e[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/sup\u003e. In sum, GAS not only promotes nitrogen assimilation and root absorption but also enhances the carbon-nitrogen co-accumulation efficiency in fruits by upregulating their sugar metabolic enzymes and improving photosynthate transport into them. In contrast, PP333 inhibits GA signaling, resulting in reduced carbon and nitrogen metabolic activity\u003csup\u003e[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eKey genes involved in amino acid biosynthesis in walnut fruit (e.g., \u003cem\u003eLOC109010970\u003c/em\u003e and \u003cem\u003eLOC108981442\u003c/em\u003e) encode sugar metabolism enzymes, such as TPI and PGAM. Recently, Sheng et al. (2024) found that upregulated expression of PGAM in octoploid strawberry fruit increased the accumulation of intermediate metabolites and amino acids, in line with the conclusions of our study. Additionally, MYB and bHLH family TFs were differentially expressed in both GAS- and PP333-treated groups of walnut fruit, consistent with the findings reported by Muhammad et al. (2025) on the regulation of anthocyanin and nutrient metabolism in fruits by the MYB\u0026ndash;bHLH\u0026ndash;WD40 complex. Moreover, MYB and bHLH have been shown to play central regulatory roles in strawberry fruit development and the integration of hormone signals (e.g., IAA, CTK, and ABA)\u003csup\u003e[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn a study on sesame (\u003cem\u003eSesamum indicum\u003c/em\u003e), a paclobutrazol (PAC) treatment increased the contents of oleic acid (C18:1) and stearic acid (C18:0), while decreasing those of linoleic acid (C18:2) and linolenic acid (C18:3)\u003csup\u003e[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/sup\u003e. A higher oleic acid content also improves the oxidative stability of oils\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Taken together, those findings suggest that paclobutrazol may augment the accumulation of monounsaturated fatty acids by regulating the activity of fatty acid desaturases such as FAD2, in this way optimizing the oxidative stability and nutritional value of edible oils. In their transcriptome analysis of walnut (\u003cem\u003eJ. regia\u003c/em\u003e) seed development, Shi et al. (2022) uncovered high expression levels of the \u003cem\u003eFAD2\u003c/em\u003e gene during the oil accumulation stage, indicating its important role in linoleic acid synthesis. In various crops, knocking out the \u003cem\u003eFAD2\u003c/em\u003e gene through CRISPR/Cas9 gene editing has successfully elevated the oleic acid content of their seed oil. For instance, in rapeseed (\u003cem\u003eBrassica napus\u003c/em\u003e), the FAD2 knockout increased its oleic acid content from 74% to 80%; in tobacco (\u003cem\u003eNicotiana tabacum\u003c/em\u003e), it increased from 12% to 79%\u003csup\u003e[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/sup\u003e. With respect to the unsaturated fatty acid-to-polyphenol ratio in seed kernels being governed by co-expression of FAD2 (fatty acid desaturase) and PAL (phenylalanine ammonia-lyase), RNA interference-mediated silencing of FAD2.2 and overexpression of SAD (stearoyl-ACP desaturase) in chicory (\u003cem\u003eCynara cardunculus\u003c/em\u003e) increased its oleic acid (C18:1) content and reduced its linoleic acid (C18:2) ratio, markedly optimizing the lipid composition profile\u003csup\u003e[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAfter taking into account those reported results alongside our own for walnut fruit, we established a simple model for the mechanism underpinning how exogenous GA and PP333 affect walnut fruit during its expanding period. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e, amino acid biosynthesis, interaction between plants and pathogens and plant hormone signal transduction jointly regulate walnut fruit\u0026rsquo;s expansion through synergistic effects. Here, two key genes (encoding glyA and ItaE) control its fruit size by regulating the level of amino acid biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). Spraying GAS at the expanding stage of walnut fruit can effectively amplify the accumulation of sugar, protein, and beneficial secondary metabolites, while PP333\u0026rsquo;s application is more suitable for growth inhibition. Therefore, we suggest that GAS should be used in the fruit expansion period to increase yield, and PP333 should be used in the vegetative growth period to control the vigor of walnut trees. Future research can combine CRISPR-Cas9 technology to edit key genes, notably PGAM and MYB, and further optimize the effect of plant growth regulators. For example, Chen et al. (2023) used CRISPR to knock out the DELLA gene in tomato, which greatly enhanced its GA signal\u0026rsquo;s transmission, resulting in a larger fruit size. As well, the mechanistic interactions between environmental factors (e.g., light, water) and growth regulators still requires in-depth exploration to precisely control walnut cultivation. In short, growth regulators regulate the development of walnut fruit through the multi-dimensional \u0026lsquo;hormone\u0026ndash;metabolism\u0026ndash;gene\u0026rsquo; network. This empirical study provides important and timely theoretical support for the improvement of walnut fruit quality and the scientific application of plant growth regulators.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation to Hebei Agricultural University, Baoding 071001, P. R. China. This work was supported by grants from the Development Program of China (2022YFD1600402-02)and Hebei Key R\u0026amp;D project (21326304D-2) the National Key Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuture contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuanyuan Zhu:Writing-original draft, Methodology, Investigation,Formal analysis, Data curation.Xinyu Ding: Writing-original draft, Investigation,Formal anal- ysis, Data curation.Ziqian Fu:Writing-original draft, Re-sources, Methodology, Investigation,Formal analysis,Data curation. Guohui Qi: Writing-review \u0026amp; editing,Writing-original draft,Supervision, Re-sources, Project administration, Funding acquisition.Qinglong Dong: Writing-review \u0026amp; editing,Writing-original draft,Supervision, Re-sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI used or generated research data in this study.Raw RNA-seq reads were submitted to the NCBI Sequence Read Archive (SRA) under BioProject PRJNA1393871.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Development Program of China (2022YFD1600402-02)and Hebei Key R\u0026amp;D project (21326304D-2) the National Key Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll plant materials used in this study were artificially cultivated and provided by the Lvling Walnut Experimental Orchard affiliated to Hebei Agricultural University. No wild plant samples were involved in the experiment.This research does not involve experiments on animals or humans.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript and consent to its publication in BMC Plant Biology .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSzal\u0026oacute;ki-Dork\u0026oacute; L, Kumar P, Sz\u0026eacute;kely D, V\u0026eacute;gv\u0026aacute;ri G, Ficzek G, Simon G, Abrank\u0026oacute; L, Torm\u0026aacute;si J, Bujdos\u0026oacute; G, M\u0026aacute;t\u0026eacute; M. 2024. 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Front Plant Sci. 2022;13:969844.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCappetta E, De Palma M, D\u0026rsquo;Alessandro R, Aiello A, Romano R, Graziani G, Ritieni A, Paolo D, Locatelli F, Sparvoli F, Docimo T, Tucci M. 2022. Development of a High Oleic Cardoon Cell Culture Platform by SAD Overexpression and RNAi-Mediated FAD2.2 Silencing. Frontiers in plant science 13\u0026ndash;2022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"metabolomics, transcriptomics, walnut fruit, hormone signal transduction, amino acid biosynthesis","lastPublishedDoi":"10.21203/rs.3.rs-8172269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8172269/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGibberellins (GAs) and paclobutrazole (PP333) are crucial plant growth regulators, but their molecular mechanisms in walnut fruit development have yet to be elucidated. This study used \u0026lsquo;L\u0026uuml;ling\u0026rsquo; walnut plants, whose leaves were sprayed with different concentrations of GAs and PP333 during the fruit expansion period. The effects of those two treatments on the endogenous hormones, metabolite accumulation, and gene expression in the fruits were explored through a combined analysis of plant physiology, transcriptomics, and metabolomics. The results showed that the GAS treatment significantly increased the content of gibberellins (with a 321% maximal increase), cytokinins (KT, IP), and auxin (IAA), while reducing the level of abscisic acid (ABA). It also promoted the accumulation of soluble sugars (+\u0026thinsp;50%), starch (+\u0026thinsp;19.4%), and soluble proteins (+\u0026thinsp;18.18%). The transcriptomic analysis revealed that GAS regulated fruit development by activating certain pathways, namely those for ABC transporters, carbon metabolism, and carotenoid biosynthesis. By contrast, PP333 inhibited the GA signaling pathway and downregulated the expression of genes involved in starch metabolism. Further, the WGCNA analysis identified genes related to amino acid synthesis, such as \u003cem\u003eLOC109010970\u003c/em\u003e. According to the transcription factor analysis, both MYB and bHLH families of transcription factors played a central regulatory role in hormone signal transduction. This study uncovered the mechanisms by which GAS and PP333 regulate the process of walnut fruit development through a \u0026lsquo;hormone\u0026ndash;metabolism\u0026ndash;gene\u0026rsquo; network, providing a valuable theoretical basis for the precise application of growth regulators in walnut cultivation.\u003c/p\u003e","manuscriptTitle":"Physiological, transcriptomic, and metabolomic analyses reveal the molecular regulatory mechanisms of walnut fruit in response to gibberellin and paclobutrazol","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 08:19:11","doi":"10.21203/rs.3.rs-8172269/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T13:20:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T09:54:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T15:37:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269670447733363552925212561918152547490","date":"2026-01-18T13:08:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24575155538455475776232437308664517683","date":"2026-01-13T01:46:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T02:29:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215516820060890659230326511119856045112","date":"2026-01-09T01:19:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T12:02:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T11:56:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-08T07:00:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T13:28:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-01-07T13:12:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3afb71d5-080d-4b01-af58-04ac93408dfd","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T12:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 08:19:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8172269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8172269","identity":"rs-8172269","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}