Effects of light intensity on apple bud differentiation analyzed by transcriptome and proteome

Effects of light intensity on apple bud differentiation analyzed by transcriptome and proteome | 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 Effects of light intensity on apple bud differentiation analyzed by transcriptome and proteome Niu Junqiang, Yin Xiaoning, Yang Zehua, Ma Ming, Dong Tie, Sun Wentai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4536836/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fuji, a major cultivar group of apple ( Malus domestica ), is extensively grown in China, Japan, and the USA. However, it has been experiencing prolonged differentiation of flower buds. and the potential mechanisms are largely unknown. Thus, for better comprehend the differentiation of apple flower buds, we performed a comparative transcriptomic and proteomic analysis between the closed (CK) and well-ventilated apple orchards (T) of 15-year-old ‘Nagano Fuji No.2’. In total, 12,211 and 8,290 differentially expressed genes (DEGs) and 473 and 534 differentially expressed proteins (DEPs) were identified in the CK group and T group, respectively. In both the expressional and translational levels, 14 up- and 156 down-regulated members were found in samples after flowering compared to pre-flowering in the CK group, respectively. In contrast, 31 up- and 131 down-regulated members were found in the T group. These members were mainly enriched in several Gene Ontology (GO) terms, such as "glycolytic process," "glucan biosynthetic process," and "response to water." These pathways were involved in the differentiation of flower buds regulated by light. Several genes, including MD13G1093200 , MD06G1122100 , MD15G1253900 , MD13G1161400 , MD07G1279200 , MD15G1253900 , and MD10G1289200 , exhibited differential expression patterns between the CK and T groups, making them potential key candidates for additional functional analysis. Our findings provide a foundation for further research on the molecular mechanisms of light in flower bud differentiation. apple light flower bud differentiate transcriptomics proteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Apple ( Malus domestia ) is regarded as one of the staple fruit crop in the world. Fuji varieties, with the high quality of fruit characteristics, are widely cultivated in all production area in china. However, the plantation was suffered from prolonged juvenescent phase due to low ratio of bud differentiation. In addition, excessive density, irrational pruning displays adverse effects on flowering of Fuji apples, which further result in large variations in fruit coloring and decreasing of production (Li et al. 2024 ; Win et al. 2023 ). As an symbol for the transitioning from vegetative growth to reproductive growth, bud differentiation was considered as a crucial process in apple cultivation (Ferree et al. 2015 ; Meng et al. 2020 ). Extensive investigations focus on explaining the potential mechanisms and regulators of bud differentiation. It is confirmed that various external and internal factors, such as light, temperature, hormones and carbohydrate, regulate the forming of buds. Under suitable environmental and nutritional conditions, the stem growth cone no longer turns into a leaf bud, but forms leaf primordia and axillary primordia; instead, it will form flower primordia and inflorescence primordia, gradually developing into a flower bud (Meng et al. 2009 ). Prang and otherss used radioimmunoassay to show that during the bud development period, developing Golden Delicious, Red Delicious, and Jonathan apple seeds produce a large amount of GA3, which inhibits bud development (Prang et al. 1997). In contrast, seedless Spencer apples mainly produce GA4, which can lead to flowering. Mature apple trees produce a large amount of GA3 in their seeds (Stephan et al. 1999 ; Ramirez et al. 2000 ). Moreover, several flowering related genes were successfully identified, such as mdfs1 and mdfs2 (Watillon et al. 1998 ). FT protein, which is known as the floral integrator and is a key point of integration for plant flowering, also can integrate multiple flowering pathways signals and transmit them to downstream factors, thereby regulating plant flowering (Song 2016 ; Kushanov et al. 2017 ). Apart from this, plant photoreceptors phyA, phyB, and cry2 are important light sensors that regulate flowering (Muntha et al. 2019 ) Unfortunately, our acknowledgment of the mechanism on flowering is largely limited because of the complex regulating factors and molecular networks. The molecular mechanism of apple flower formation has always been a focus of research. Currently, various regulatory pathways have been established in regulating the molecular mechanism of apple flower formation. These pathways interact with each other and converge on integrating factors, inducing gene expression in floral meristem tissues, ultimately leading to plant flowering (Srikanth et al. 2011; Bouche et al. 2016 ; Cho et al. 2017 ) Considering the challenge of Fuji apple trees in producing flowers, ultiple agronomic practices have been carry out to to tackle the problem. These include implementing pruning and thinning to enhance flower bud formation by optimizing light exposure (Reig et al. 2019 ). In the Loess Plateau region, thinning is a crucial method to boost light exposure, encourage flowering, and improve fruit quality (Khalil et al. 2023 ). Nevertheless, it remains uncertain how adjusting gene expression patterns to enhance light exposure can stimulate flower bud differentiation. Therefore, studying the effect of ventilation and light transmission conditions on flower bud differentiation of the ‘Nagano Fuji No.2' fruit tree can not only increase the yield of apples but also effectively solve practical production problems such as excessive pesticide use and high rates of fruit rot in fruit orchards. This research is of great significance in achieving high quality and yield. In the past few years, advancements in high-resolution mass spectrometry and high-throughput detection methods have led to the widespread adoption of technologies like metabolomics, transcriptomics, proteomics, and genomics in plant biology research (Atsuhiro and Mami 2016 ). While these omics research methods vary, they are interconnected. By combining multi-omics data at a systemic level, researchers can better understand biological processes and discover new approaches for exploring plant metabolome diversity (Yang et al. 2021 ). For example, Bai and others analyzed bagging-treated pear fruits using transcriptomics technology, identified some anthocyanin synthesis transcription factors, and revealed the mechanism of light affecting pear anthocyanin synthesis (Bai et al. 2017 ). Not all the DEGs identified through transcriptomics can be translated into proteins and perform functions in proteomics. Therefore, the joint analysis of transcriptomics and proteomics can connect all the stages of gene expression, provide a comprehensive view of expression and regulation, and yield findings that cannot be analyzed through genomics alone (Kumar et al. 2022 ; Feng et al. 2023 ). To fully understand variations in transcriptional levels, translation, post-translational modifications, and the underlying connections between histologies, a thorough analysis that integrates results from proteomics and transcriptomics sequencing is necessary (Xu et al. 2022 ; Li et al. 2021 ). Based on this, this study analyzed the impact of ventilation and light transmission conditions on the differentiation of apple flower buds through the correlation of transcriptomics and proteomics. The research results will provide a theoretical basis for optimizing apple flower bud differentiation and further support the development of superior apple varieties. 2. Materials and Methods 2.1 Plant material and cultivation conditions The experiment was conducted in Chengchuan Town, Jingning Demonstration County, Pingliang Apple Comprehensive Experimental Station of the National Apple Industry Technology System. A closed and low-light, and a well-ventilated ‘Nagano Fuji No.2’ apple orchard covers an area of more than 8 acres and trees about 15 years old. The indicator variety is ‘Nagano Fuji No.2’ apple, which is planted at a density of 3 m x 4 m. 2.1.1 Test method The experiment utilized the single-factor random design method with a closed, low-light apple orchard (CK group) and a well-ventilated, light-permeable orchard (T group) as the two samples. Each sample area is 20 m x 25 m, totaling 500 m², with three replicates (five representative trees per replicate). Orchard management is consistent, and fertilization is applied to individual trees; thus, the fertilization level of each tree in each treatment remains uniform. 2.1.2 Sampling Samples were taken on June 10th (the physiological prophase of apple flower bud differentiation) and August 12th (the physiological anaphase of apple flower bud differentiation), respectively. The collected samples were frozen in liquid nitrogen and transported back to the laboratory for cryopreservation. Subsequently, the samples were utilized for RNA and protein extraction. 2.2 Transcriptome sequencing The collected apple flower bud samples were used for transcriptome sequencing. TRIzol (Thermo Fisher Scientific, USA) was used to isolate and purify the RNA from the entire sample, following the protocol provided by the manufacturer. The quantity and purity of total RNA were assessed using NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA), while the integrity of RNA was evaluated with Bioanalyzer 2100 (Agilent, USA). Oligo (dT) magnetic beads (Thermo Fisher, USA) were used to specifically capture the mRNA containing PolyA (Polyadenylic Acid) through two rounds of purification. The captured mRNA was fragmented using the NEBNext Magnesium RNA Fragmentation Module (NEB, USA) at a high temperature for 5–7 minutes at 94°C. The fragmented RNA was synthesized into cDNA using Invitrogen SuperScript™ II Reverse Transcriptase (Thermo Fisher Scientific, USA). Then E. coli DNA polymerase I (NEB, USA) and RNase H (NEB, USA) were used for double-stranded synthesis. The resulting compound of double-stranded DNA and RNA was transformed into double-stranded DNA. At the same time, dUTP Solution (Thermo Fisher Scientific, USA) was added to the double-stranded DNA to fill in the ends of the double-stranded DNA to create blunt ends. Then, an 'A' base was added to each end of the fragment to link it with a linker containing a T base at the end. The fragment size was then screened and purified using magnetic beads. The two chains were digested with the UDG enzyme (NEB, USA), then pre-denatured at 95°C for 3 min, denatured at 98°C for a total of 8 cycles of 15 s each, annealed at 60°C for 15 s, extended at 72°C for 30 s, and finally extended at 72°C for 5 min. A library with a fragment size of 300 bp ± 50 bp was formed. Finally, we followed standard procedures for double-end sequencing using Illumina NovaSeq™ 6000 in PE150 mode. 2.3 Proteome sequencing Weighed and ground the appropriate amount of apple flower buds, then added the cracking buffer for ultrasonic cracking. After centrifugation, the supernatant was transferred to a new centrifuge tube, and the protein concentration was determined using the BCA kit. Proteins in the samples were extracted, enzymatically digested, enriched, and separated. The peptide segments were detected using high-performance liquid chromatography (HPLC) coupled with tandem high-resolution mass spectrometry. A substantial volume of mass spectrometry data was produced. Proteins in the samples were identified using MaxQuant (v2.1.4.0) software. The identification conditions were as follows: PSM FDR < 0.01 and protein FDR < 0.01. The database used for this protein identification was the Apple Uniprot library, which contained a total of 42,480 sequences. Data library sequence file: data/1.identification/uniprot_apple_UP000290289. fasta. All verified protein IDs and intensity information can be found at: data/1.identification/protein.raw.intensity.xls. MaxQuant's original output and results can be found in data/1.identification/MaxQuant_output/. 2.4 Transcriptome data analysis Remove adapter-containing, reads with undetermined base information, low-quality, and uncertain reads from the original sequencing data. The clean reads obtained through filtering are used to calculate Q20, Q30, and GC content. Use HISAT2 software to quickly and accurately compare clean reads with the reference genome to obtain the positioning information of reads on the reference genome. When the fragments per kilobase of transcript per million mapped reads (FPKM) is greater than 1, the gene is considered expressed. Create Venn diagrams using the Novogene platform, and after evaluating the expression levels based on log-transformed FPKM values, draw a gene expression heatmap. Use ClusterProfile software for gene ontology (GO) functional enrichment analysis of differentially expressed genes, with a padj less than 0.05 as the threshold for significant enrichment. From the GO enrichment analysis results, select the most significant 8 Terms to draw a bar graph for display. If there are fewer than 8 Terms, draw all Terms. 2.5 Proteome data analysis After the generation of the original mass spectrometry data, proteins are identified and quantitatively analyzed using Mascot 2.3.02 (Matrix Science, London, UK). Based on the protein abundance levels, proteins are defined as differentially expressed proteins (DEPs) if the ratio is ≤ 0.833 or ≥ 1.20, and the p-value is less than 0.05. Finally, the identified DEPs are subjected to Gene Ontology (GO) functional enrichment analysis ( http://geneontology.org/ ). 2.4 qRT-PCR assay The expression pattern of 6 DEGs involved in enriched pathways was further analyzed by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) with five independent biological replicates. cDNA synthesis was performed on the above sample RNAs using a PrimeScript™ RT Reagent Kit with a gDNA Eraser (TaKaRa). Primer 3.0 ( http://primer3.ut.ee/ ) was utilized to design gene-specific primers, while the primers for the internal reference gene Actin were adopted from the study by Zuo et al ( 2020 ). For the qRT-PCR assays, we utilized a LightCycler® 96 Instrument (Roche) and SYBR ® Green Premix Pro Taq HS qPCR Kit. Relative transcript levels were determined using the modified 2 −∆∆CT method (Livak et al. 2001). The above experiments were conducted with three biological replicates. 2.6 Statistical Analysis Use Microsoft Excel 2010 to perform statistical analysis and calculate the mean and standard deviation of the data, conduct significance testing of differences between samples using the Student’s t-test, and create graphs using Origin 8.5. 3. Results 3.1 Data quality control analyses After quality control and filtering of the transcriptome sequencing data, we obtained more than 37 million valid data points from each sample. The values of Q20% and Q30% were greater than 99.9% and 97.4%, respectively. Furthermore, the GC content of each sample ranged from 46–47% (Table 1 ). After PCA analysis, it was found that the differences between each sample in both the transcriptome (Fig. 1 A) and proteome (Fig. 1 B) were small, and the similarity between the three replicates of each treatment was high. Table 1 Summary data for reads in each sample. Sample Raw Data Base Valid Data Base Valid Ratio (reads) Q20% Q30% GC content % Read Read CK11 43722020 6.56G 42278234 6.34G 96.70 99.96 97.53 46.50 CK12 44764486 6.71G 43442000 6.52G 97.05 99.96 97.44 46.50 CK13 47019676 7.05G 45638572 6.85G 97.06 99.96 97.57 46.50 T11 46517346 6.98G 45121038 6.77G 97.00 99.96 97.43 46.50 T12 45081260 6.76G 43678850 6.55G 96.89 99.96 97.58 46.50 T13 41027260 6.15G 39799810 5.97G 97.01 99.97 97.56 47 CK21 39227908 5.88G 37906168 5.69G 96.63 99.94 97.78 46 CK22 39455054 5.92G 38074624 5.71G 96.50 99.95 97.94 46 CK23 39814576 5.97G 38495894 5.77G 96.69 99.94 97.82 46 T21 39912808 5.99G 38455186 5.77G 96.35 99.93 97.77 46 T22 40844696 6.13G 39355720 5.90G 96.35 99.93 97.75 46 T23 41225376 6.18G 39669346 5.95G 96.23 99.94 97.75 46.50 3.2 Screening of differentially expressed genes (DEGs) and Proteins (DEPs) At the transcriptional level, a total of 3790 up- and 8421 down-regulated genes were identified in samples after compared to pre-flowering in the CK group (CK2-VS-CK1), respectively. In the T group (T2-VS-T1), 3057 up- and 5233 down-regulated genes were determined (Fig. 2 A). At the translation level, 124 up- and 349 down-regulated proteins were found in CK2-VS-CK1, respectively, while 143 up- and 391 down-regulated proteins were identified in T2-VS-T1 (Fig. 2 B). A Venn diagram analysis of the DEGs and DEPs showed that there were 14 up- and 156 down-regulated shared elements in CK2-VS-CK1, respectively (Fig. 3 A, B), while there were 31 up- and 131 down-regulated shared elements in T2-VS-T1 (Fig. 3 C, D). 3.3 GO functional clustering of DEGs and DEPs Inspired by this, we examined the changes in biological functions pre- and after-flowering in orchards with good ventilation and light transmission conditions, as well as in orchards with low light. GO enrichment was performed for the 332 DEGs at the intersection of the transcriptome and proteome described above. It was found that DEGs in the pathways of “DNA integration”, “glycolytic process”, “response to stress” and “protein folding” pathways were up-regulated in both CK2-VS-CK1 and T2-VS-T1. DEGs in the pathways “biosynthetic process”, “negative regulation of catalytic activity” and “lipid metabolic process” were down-regulated in both CK2-VS-CK1 and T2-VS-T1. However, DEGs in the pathways of “response to water”, “glucan biosynthetic process” and “cell redox homeostasis” were up-regulated only in T2-VS-T1 (Fig. 4 ). 3.4 Expression analysis To this end, we next investigated the expression patterns of DEGs and DEPs in the shared and unique GO-enrichment pathways of CK2-VS-CK1 and T2-VS-T1. It was found that in T2-VS-T1, the expression of genes MD16G1160600 and MD16G1159900 in the "response to biological stimuli" pathway, gene MD14G1010300 in the "response to oxidative stress" pathway, gene MD09G1199700 in the "lipid metabolism process" pathway, and gene MD07G1279200 in the "response to stress" pathway was significantly lower than CK2-VS-CK1. Conversely, the expression of gene MD10G1289200 in the "protein folding" pathway, gene MD13G1161400 in the "response to biotic stimulus" pathway, gene MD01G1185500 in the "glycolytic process" pathway, and gene MD15G1253900 in the "response to stress" pathway was significantly higher than CK2-VS-CK1 (Fig. 5 ). 3.5 qRT-PCR validation of the DEGs qRT-PCR analysis was performed on the genes MD13G1161400 , MD14G1010300 , MD01G1185500 , MD07G1279200 , MD15G1253900 and MD10G1289200 , which are involved in the “biostimulus response", “oxidative stress response", “glycolysis process", “stress response" and “protein folding", respectively. The expression patterns were consistent with the transcriptomics and proteomics data (Fig. 6 ). 4. Discussion Transcriptome sequencing and proteome sequencing can not only achieve many genomic studies of plants and animals, but also accurately study some complex insect genomes (Ozsolak and Milos 2011 ). The Illumina platform can obtain deeper coverage and higher accuracy, and RNA-Seq technology is a useful tool for differential gene screening and analysis. In this experiment, transcriptome sequencing was completed, obtaining a total of 76.28G CleanData, with effective data distribution ranging from 5.88–7.05 G for each sample, Q30 base distribution ranging from 97.43–97.94%, and an average GC content of 46.33%. Candidate genes have gained a more complete understanding in cellular and metabolic categories through GO and KEGG, providing classifications such as biological functions, gene metabolic pathways, and signal transduction, laying the foundation for subsequent analysis. GO functional analysis and expression analysis were conducted on the screened differential genes and proteins, with some genes showing significant differences in treatment, providing a theoretical basis for subsequent experimental research. Light plays a crucial role in the process of apple flower bud differentiation. When there is sufficient light, apple branches and leaves grow robustly. Photosynthesis is strengthened, organic nutrients promote tree growth, enhance fruit quality, and facilitate bud and flower differentiation. Additionally, it improves the efficient use of water and fertilizer (Abed et al. 2019 ; Feng et al. 2023 ; Wertheim et al. 2001 ). Insufficient light can lead to thinner leaves, increased transpiration, weak branches, poor flower bud differentiation, reduced fruiting rate, yellowing of the crown, decreased root absorption ability, reduced sugar and vitamin content in the fruit, affected formation of anthocyanins, decreased number of flowers in the inflorescence, increased number of incomplete flowers, resulting in poor coloring of varieties (Paponov et al. 2023 ; Widmer and Krebs 2001 ). During the flowering period, plants require more light than during the germination period, and during the growing period, they need more light than during the dormant period. Reproductive organs require more light than nutrient organs (Varinder et al. 2021 ). Apple orchard light can be categorized into upper light, front light, lower light, and back light, all of which can be utilized (Zhang et al. 2018 ). It was found that the organic combination of dextran and boron, along with the biostimulation of dextran, could address issues related to frost damage, drought, high temperatures, and high humidity during flowering. This combination also enhances the effectiveness of boron fertilizer. Furthermore, the active substances like nucleic acid and mannan found in dextran have a significant impact on flowering. Dextran can also enhance flower bud differentiation, boost fruit setting rate, enhance fruit quality, and improve crop stress resistance (Li et al. 2021 ). Furthermore, the maintenance of intracellular redox homeostasis is critical for stem cell fate during plant growth and development. This process also involves the precise regulation of the function of key transcription factors, such as FASCIATED EAR4 (FEA4). FEA4 plays a crucial role in regulating maize inflorescence. Its redox state and transcriptional activity are controlled by Glutaredoxins (GRXs) (Yang et al. 2021 ). Our study also found that three pathways, "response to water," "glucan biosynthetic process," and "cell redox homeostasis," were up-regulated only in orchards with good light and ventilation. Flavonoids are secondary metabolites with high biological activity in plants, which play an important role in the growth and development of plants (Yu et al. 2024 ). These are the primary substances that influence flower colors, regulate seed germination, root growth, photosynthetic pigment synthesis, and negatively regulate auxin transport(Brown et al. 2001 ; Shi and Xie 2014 ; Tan et al. 2019 ). For example, flavonoids may alter the transport of auxin by influencing the PIN-FORMED protein, thereby impacting the growth and development of plants (Peer et al. 2004 ). Different proportions of red and blue light can further alter the flower bud differentiation process by regulating the biosynthesis of multiple hormones (Liu et al. 2021 ; Liu et al. 2021 ). In this study, the sealed 'Nagano Fuji No.2' apple orchard, approximately 15 years old, was selected as the research subject. In well-light and low-light orchards, the transcriptome and proteome of the two developmental stages of apple flower bud differentiation were analyzed together. We divided the results into two comparison groups: CK1-VS-CK2 and T1-VS-T2, and identified 20,501 DEGs and 1,007 DEPs. By conducting GO enrichment analysis, a total of 18 pathways were identified in the two comparison groups. Among these, the pathways "response to water," "glucan biosynthetic process," and "cell redox homeostasis" were specifically regulated only in T2-VS-T1. Additionally, 3 DEGs were discovered within these three pathways. This study provides a theoretical basis for further discussion on flower bud differentiation. Declarations Acknowledgments: This study is grateful to the National Natural Science Foundation of China (32160683, 31760556) and Project of Scientific Observation and Experiment Station of Fruit Trees in Northwest Area of Ministry of Agriculture (S-10-18). Author Contributions: J.N., X.Y. and Z.Y. designed and conceived this study. J.N., M.M., T.D. and W.S participated in the experiments and performed the data collection and collation. J.N., Z.Y., M.M. and W.S. jointly revised the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by the National Natural Science Foundation of China (32160683, 31760556); Project of Scientific Observation and Experiment Station of Fruit Trees in Northwest Area of Ministry of Agriculture (S-10-18). Data Availability Statement: All data supporting the findings of this study are available within the paper. Conflicts of Interest: The authors declare no conflict of interest. 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Acta Hort 514: 245-248. https://doi.org/ 10.17660/ActaHortic.2000.514.28 Reig G, Lordan J, Sazo MM, Hoying S, Fargione M, Reginato G (2019) Long-term performance of ‘Gala’, ‘Fuji’ and ‘Honeycrisp’ apple trees grafted on Geneva® rootstocks and trained to four production systems under New York State climatic conditions. Sci Hortic 244: 277-293. https://doi.org/10.1016/j.scienta.2018.09.025 Shi MZ, Xie DY (2014) Biosynthesis and metabolic engineering of anthocyanins in Arabidopsis thaliana . Recent Pat Biotechnol 8: 47-60. https://doi.org/10.2174/1872208307666131218123538 Song YH (2016) The effect of fluctuations in photoperiod and ambient temperature on the timing of flowering: time to move on natural environmental conditions. Mol Cells 39: 715-721. https://doi.org/ 10.14348/molcells.2016.0237 Srikanth A, Schmid M (2011) Regulation of flowering time:all roads lead to rome. Cellular and Mol Life Sci 68(12): 2013-2037. https://doi.org/10.1007/s00018-011-0673-y Stephan M, Bangerth F, Schneider G (1999) Quantification of endogenous aibberellins in exudates from fruits of Malus domestica . Plant Growth Regul 28: 55-58. https://doi.org/ 10.1023/A:1006211309707 Tan H, Man C, Xie Y, Yan JJ, Chu JF, Huang JR (2019) A crucial role of GA-regulated flavonol biosynthesis in root growth of Arabidopsis . MoL Plant 12(4): 521-537. https://doi.org/10.1016/j.molp.2018.12.021 Varinder S, Valérie EB, Marianne DL, Valérie J (2021) Effect of light quality and extended photoperiod on flower bud induction during transplant production of day-neutral strawberry cultivars. Can . J Plant Sci 102(2): 356-367. https://doi.org/10.1139/CJPS-2021-0081 Watillon B, Kettmann R, Arredouani A,，J, Hecquet F, Burny A (1998) Apple messenger RNAs related to bacterial lignostilbene dioxygenase and plant SAUR genes are preferentially expressed in flowers. Plant Mol Biol 36(6): 909-915. https://doi.org/ 10.1023/A:1005914506110 Wertheim S, Wagenmakers P, Bootsma J, Groot MJ (2001) Orchard systems for apple and pear: conditions for success. Acta. Hortic (557): 209-228. https://doi.org/10.17660/ActaHortic.2001.557.28 Widmer A, Krebs C (2001) Influence of planting density and tree form on yield and fruit quality of ‘golden delicious’ and ‘royal gala’ apples. Acta Hortic (557): 235-242. https://doi.org/10.17660/ActaHortic.2001.557.30 Win MN, Song YY, Nam CJ, Yoo J, Kang IK, Young SC, Yang SJ, Juhyeon P (2023) Influence of mechanical flower thinning on fruit set and quality of ‘arisoo’ and ‘Fuji’ apples. International J Plant Biol 14(2): 503-511. https://doi.org/10.3390/ijpb14020039 Xu XX, Qin HH, Liu CL, Liu JQ, Lyu MX, Wang F, Xing Y, Tian G, Zhu ZL, Jiang YM, Ge SF (2022) Transcriptome and metabolome analysis reveals the effect of nitrogen-potassium on anthocyanin biosynthesis in Fuji apple. J Agr Food Chem 70(48): 15057-15068. https://doi.org/10.1021/acs.jafc.2c06287 Yang RS, Xu F, Wang YM, Zhong WS, Dong L, Shi YN, Tang TJ, Sheng HJ, Jackson D, Yang F (2021) Glutaredoxins regulate maize inflorescence meristem development via redox control of TGA transcriptional activity. Nat Plant 7(12): 1589-1601. https://doi.org/10.1038/s41477-021-01029-2 Yu CM, Liu GY, Qin J, Wan X, Guo AF, Wei H, Chen YH, Lian B, Zhong F, Zhang J (2024) Genomic and transcriptomic studies on flavonoid biosynthesis in Lagerstroemia indica. BMC Plant Biol 24 (1). https://doi.org/10.1186/s12870-024-04776-4 Zhang L, Koc BA, Wang NX, Jiang YX (2018) A review of pruning fruit trees. Earth Environ Sci 153(6): 062029. https://doi.org/10.1088/1755-1315/153/6/062029 Zuo CW, Liu H, Lv QQ, Chen ZJ, Tian YZ, Mao J, Chu MY, Ma ZH, An ZS, Chen BH (2020) Genome-wide analysis of the apple ( Malus domestica ) cysteine-rich receptor-like kinase (CRK) family: Annotation, genomic organization, and expression profiles in response to fungal infection. Plant Mol Bio Rep 38: 14-24. https://doi.org/10.1007/s11105-019-01179-w Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4536836","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314504020,"identity":"93328103-89bf-486b-88bc-276badbd6308","order_by":0,"name":"Niu Junqiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACZgYGA4YKGzl+BsYGUrScSTOWbCBaCwgwth1O3HCAWNXm7DwGBW/YmBk3nz/c9uAHg52cLiHLLJt5DAzn8LAxm91IbDfsYUg2NiNkncFhHgNjHgkeNrMbjG0SPAwHErcRp8VAgse4/2Cb5B/itSQYSBgwJLZJE2kLW4HhnANAPTeAWmQMiPHL+cPbDN7++1/f33/8meSbCjs5glqAgM2AB2ECYeUgwPyAh7CiUTAKRsEoGMkAANYTPK755u2GAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Niu","middleName":"","lastName":"Junqiang","suffix":""},{"id":314504021,"identity":"785775e6-a149-454f-a18f-18528b1ae2a6","order_by":1,"name":"Yin Xiaoning","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Xiaoning","suffix":""},{"id":314504022,"identity":"4b6d6a44-4bc9-43c2-9136-e913c98489ed","order_by":2,"name":"Yang Zehua","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zehua","suffix":""},{"id":314504023,"identity":"0ac00508-571b-4db1-bd6a-502db167e1f0","order_by":3,"name":"Ma Ming","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ma","middleName":"","lastName":"Ming","suffix":""},{"id":314504024,"identity":"0fb6aca8-cca1-4a0b-ae2d-9b21265c0803","order_by":4,"name":"Dong Tie","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Tie","suffix":""},{"id":314504025,"identity":"5a8c48cf-990f-4fd7-ae70-352124f005a8","order_by":5,"name":"Sun Wentai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Wentai","suffix":""}],"badges":[],"createdAt":"2024-06-06 02:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4536836/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4536836/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58727465,"identity":"4ae6e843-4494-45a3-aaa2-afc34fd156ec","added_by":"auto","created_at":"2024-06-20 10:20:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88208,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of the transcriptome (A) and proteome (B) of low-light apple orchards and well-ventilated apple orchards was conducted before and after flowering. Among them, low-light orchards were recorded as CK1 before flowering and CK2 after flowering. Well-ventilated orchards weremarked as T1 before flowering and T2 after flowering.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4536836/v1/0841a237d04bc85636a010b9.png"},{"id":58727464,"identity":"1c87b82e-68bd-4279-a69c-d0ead2eb8411","added_by":"auto","created_at":"2024-06-20 10:20:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80863,"visible":true,"origin":"","legend":"\u003cp\u003eThe number of DEGs (A) and DEPs (B) in CK2-VS-CK1 and T2-VS-T1.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4536836/v1/955c59c07805242dbf740968.png"},{"id":58727030,"identity":"8fa2e23c-40b9-4336-b5b4-ebe20637fa83","added_by":"auto","created_at":"2024-06-20 10:12:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107371,"visible":true,"origin":"","legend":"\u003cp\u003eA venn diagram illustrating DEGs and DEPs in low-light orchards compared to high-light orchards before and after flowering. (A, B) Venn diagrams illustrate the up- and down-regulated expression of DEGs and DEPs in low-light orchards before and after flowering, respectively. (C, D) Venn diagrams illustrating the up- and down-regulated expression of DEGs and DEPs in well-ventilated orchards before and after flowering are shown.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4536836/v1/77d2ded53cfd6a63c1a5683a.png"},{"id":58727035,"identity":"d2dbd2ac-7786-40dc-ac68-4ee7f05ef568","added_by":"auto","created_at":"2024-06-20 10:12:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168649,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) enrichment analysis of the above proteomic and transcriptomic venn diagram intersection genes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4536836/v1/6f664809e58a1f15453331d3.png"},{"id":58727968,"identity":"ab14a547-c01c-4b13-abbf-213dd2b81066","added_by":"auto","created_at":"2024-06-20 10:28:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":390378,"visible":true,"origin":"","legend":"\u003cp\u003eExpression patterns of DEGs and DEPs with the same GO-enriched terms and unique GO-enriched terms in CK2-VS-CK1 and T2-VS-T1 in Fig. 4.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4536836/v1/74038e346d1029e7783ef0b5.png"},{"id":58727028,"identity":"e31aa708-1b55-492a-bbe5-f007135e03db","added_by":"auto","created_at":"2024-06-20 10:12:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80245,"visible":true,"origin":"","legend":"\u003cp\u003eRelative expression levels of \u003cem\u003eMD13G1161400\u003c/em\u003e, \u003cem\u003eMD14G1010300\u003c/em\u003e, \u003cem\u003eMD01G1185500\u003c/em\u003e, \u003cem\u003eMD07G1279200\u003c/em\u003e, \u003cem\u003eMD15G1253900\u003c/em\u003e, \u003cem\u003eMD10G1289200 \u003c/em\u003ein T2-VS-T1 and CK2-VS-CK1.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4536836/v1/f82a38c2987f99b3060b9d67.png"},{"id":68516634,"identity":"d46c6298-21ba-474b-b743-92dfab2ca391","added_by":"auto","created_at":"2024-11-08 07:02:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1417317,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4536836/v1/b4d970af-0ea2-45e0-be50-3523d8cdd136.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEffects of light intensity on apple bud differentiation analyzed by transcriptome and proteome\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eApple (\u003cem\u003eMalus domestia\u003c/em\u003e) is regarded as one of the staple fruit crop in the world. Fuji varieties, with the high quality of fruit characteristics, are widely cultivated in all production area in china. However, the plantation was suffered from prolonged juvenescent phase due to low ratio of bud differentiation. In addition, excessive density, irrational pruning displays adverse effects on flowering of Fuji apples, which further result in large variations in fruit coloring and decreasing of production (Li et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Win et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As an symbol for the transitioning from vegetative growth to reproductive growth, bud differentiation was considered as a crucial process in apple cultivation (Ferree et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Meng et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Extensive investigations focus on explaining the potential mechanisms and regulators of bud differentiation. It is confirmed that various external and internal factors, such as light, temperature, hormones and carbohydrate, regulate the forming of buds. Under suitable environmental and nutritional conditions, the stem growth cone no longer turns into a leaf bud, but forms leaf primordia and axillary primordia; instead, it will form flower primordia and inflorescence primordia, gradually developing into a flower bud (Meng et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Prang and otherss used radioimmunoassay to show that during the bud development period, developing Golden Delicious, Red Delicious, and Jonathan apple seeds produce a large amount of GA3, which inhibits bud development (Prang et al. 1997). In contrast, seedless Spencer apples mainly produce GA4, which can lead to flowering. Mature apple trees produce a large amount of GA3 in their seeds (Stephan et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Ramirez et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Moreover, several flowering related genes were successfully identified, such as mdfs1 and mdfs2 (Watillon et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). FT protein, which is known as the floral integrator and is a key point of integration for plant flowering, also can integrate multiple flowering pathways signals and transmit them to downstream factors, thereby regulating plant flowering (Song \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kushanov et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Apart from this, plant photoreceptors phyA, phyB, and cry2 are important light sensors that regulate flowering (Muntha et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Unfortunately, our acknowledgment of the mechanism on flowering is largely limited because of the complex regulating factors and molecular networks.\u003c/p\u003e \u003cp\u003eThe molecular mechanism of apple flower formation has always been a focus of research. Currently, various regulatory pathways have been established in regulating the molecular mechanism of apple flower formation. These pathways interact with each other and converge on integrating factors, inducing gene expression in floral meristem tissues, ultimately leading to plant flowering (Srikanth et al. 2011; Bouche et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cho et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) Considering the challenge of Fuji apple trees in producing flowers, ultiple agronomic practices have been carry out to to tackle the problem. These include implementing pruning and thinning to enhance flower bud formation by optimizing light exposure (Reig et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the Loess Plateau region, thinning is a crucial method to boost light exposure, encourage flowering, and improve fruit quality (Khalil et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, it remains uncertain how adjusting gene expression patterns to enhance light exposure can stimulate flower bud differentiation. Therefore, studying the effect of ventilation and light transmission conditions on flower bud differentiation of the \u0026lsquo;Nagano Fuji No.2' fruit tree can not only increase the yield of apples but also effectively solve practical production problems such as excessive pesticide use and high rates of fruit rot in fruit orchards. This research is of great significance in achieving high quality and yield.\u003c/p\u003e \u003cp\u003eIn the past few years, advancements in high-resolution mass spectrometry and high-throughput detection methods have led to the widespread adoption of technologies like metabolomics, transcriptomics, proteomics, and genomics in plant biology research (Atsuhiro and Mami \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While these omics research methods vary, they are interconnected. By combining multi-omics data at a systemic level, researchers can better understand biological processes and discover new approaches for exploring plant metabolome diversity (Yang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, Bai and others analyzed bagging-treated pear fruits using transcriptomics technology, identified some anthocyanin synthesis transcription factors, and revealed the mechanism of light affecting pear anthocyanin synthesis (Bai et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Not all the DEGs identified through transcriptomics can be translated into proteins and perform functions in proteomics. Therefore, the joint analysis of transcriptomics and proteomics can connect all the stages of gene expression, provide a comprehensive view of expression and regulation, and yield findings that cannot be analyzed through genomics alone (Kumar et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Feng et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To fully understand variations in transcriptional levels, translation, post-translational modifications, and the underlying connections between histologies, a thorough analysis that integrates results from proteomics and transcriptomics sequencing is necessary (Xu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on this, this study analyzed the impact of ventilation and light transmission conditions on the differentiation of apple flower buds through the correlation of transcriptomics and proteomics. The research results will provide a theoretical basis for optimizing apple flower bud differentiation and further support the development of superior apple varieties.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant material and cultivation conditions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe experiment was conducted in Chengchuan Town, Jingning Demonstration County, Pingliang Apple Comprehensive Experimental Station of the National Apple Industry Technology System. A closed and low-light, and a well-ventilated \u0026lsquo;Nagano Fuji No.2\u0026rsquo; apple orchard covers an area of more than 8 acres and trees about 15 years old. The indicator variety is \u0026lsquo;Nagano Fuji No.2\u0026rsquo; apple, which is planted at a density of 3 m x 4 m.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Test method\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe experiment utilized the single-factor random design method with a closed, low-light apple orchard (CK group) and a well-ventilated, light-permeable orchard (T group) as the two samples. Each sample area is 20 m x 25 m, totaling 500 m\u0026sup2;, with three replicates (five representative trees per replicate). Orchard management is consistent, and fertilization is applied to individual trees; thus, the fertilization level of each tree in each treatment remains uniform.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Sampling\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSamples were taken on June 10th (the physiological prophase of apple flower bud differentiation) and August 12th (the physiological anaphase of apple flower bud differentiation), respectively. The collected samples were frozen in liquid nitrogen and transported back to the laboratory for cryopreservation. Subsequently, the samples were utilized for RNA and protein extraction.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Transcriptome sequencing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe collected apple flower bud samples were used for transcriptome sequencing. TRIzol (Thermo Fisher Scientific, USA) was used to isolate and purify the RNA from the entire sample, following the protocol provided by the manufacturer. The quantity and purity of total RNA were assessed using NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA), while the integrity of RNA was evaluated with Bioanalyzer 2100 (Agilent, USA). Oligo (dT) magnetic beads (Thermo Fisher, USA) were used to specifically capture the mRNA containing PolyA (Polyadenylic Acid) through two rounds of purification. The captured mRNA was fragmented using the NEBNext Magnesium RNA Fragmentation Module (NEB, USA) at a high temperature for 5\u0026ndash;7 minutes at 94\u0026deg;C. The fragmented RNA was synthesized into cDNA using Invitrogen SuperScript\u0026trade; II Reverse Transcriptase (Thermo Fisher Scientific, USA). Then E. coli DNA polymerase I (NEB, USA) and RNase H (NEB, USA) were used for double-stranded synthesis. The resulting compound of double-stranded DNA and RNA was transformed into double-stranded DNA. At the same time, dUTP Solution (Thermo Fisher Scientific, USA) was added to the double-stranded DNA to fill in the ends of the double-stranded DNA to create blunt ends. Then, an 'A' base was added to each end of the fragment to link it with a linker containing a T base at the end. The fragment size was then screened and purified using magnetic beads. The two chains were digested with the UDG enzyme (NEB, USA), then pre-denatured at 95\u0026deg;C for 3 min, denatured at 98\u0026deg;C for a total of 8 cycles of 15 s each, annealed at 60\u0026deg;C for 15 s, extended at 72\u0026deg;C for 30 s, and finally extended at 72\u0026deg;C for 5 min. A library with a fragment size of 300 bp\u0026thinsp;\u0026plusmn;\u0026thinsp;50 bp was formed. Finally, we followed standard procedures for double-end sequencing using Illumina NovaSeq\u0026trade; 6000 in PE150 mode.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Proteome sequencing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWeighed and ground the appropriate amount of apple flower buds, then added the cracking buffer for ultrasonic cracking. After centrifugation, the supernatant was transferred to a new centrifuge tube, and the protein concentration was determined using the BCA kit. Proteins in the samples were extracted, enzymatically digested, enriched, and separated. The peptide segments were detected using high-performance liquid chromatography (HPLC) coupled with tandem high-resolution mass spectrometry. A substantial volume of mass spectrometry data was produced. Proteins in the samples were identified using MaxQuant (v2.1.4.0) software. The identification conditions were as follows: PSM FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and protein FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01. The database used for this protein identification was the Apple Uniprot library, which contained a total of 42,480 sequences. Data library sequence file: data/1.identification/uniprot_apple_UP000290289. fasta. All verified protein IDs and intensity information can be found at: data/1.identification/protein.raw.intensity.xls. MaxQuant's original output and results can be found in data/1.identification/MaxQuant_output/.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Transcriptome data analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRemove adapter-containing, reads with undetermined base information, low-quality, and uncertain reads from the original sequencing data. The clean reads obtained through filtering are used to calculate Q20, Q30, and GC content. Use HISAT2 software to quickly and accurately compare clean reads with the reference genome to obtain the positioning information of reads on the reference genome. When the fragments per kilobase of transcript per million mapped reads (FPKM) is greater than 1, the gene is considered expressed. Create Venn diagrams using the Novogene platform, and after evaluating the expression levels based on log-transformed FPKM values, draw a gene expression heatmap. Use ClusterProfile software for gene ontology (GO) functional enrichment analysis of differentially expressed genes, with a padj less than 0.05 as the threshold for significant enrichment. From the GO enrichment analysis results, select the most significant 8 Terms to draw a bar graph for display. If there are fewer than 8 Terms, draw all Terms.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Proteome data analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter the generation of the original mass spectrometry data, proteins are identified and quantitatively analyzed using Mascot 2.3.02 (Matrix Science, London, UK). Based on the protein abundance levels, proteins are defined as differentially expressed proteins (DEPs) if the ratio is \u0026le;\u0026thinsp;0.833 or \u0026ge;\u0026thinsp;1.20, and the p-value is less than 0.05. Finally, the identified DEPs are subjected to Gene Ontology (GO) functional enrichment analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org/\u003c/span\u003e\u003cspan address=\"http://geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 qRT-PCR assay\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe expression pattern of 6 DEGs involved in enriched pathways was further analyzed by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) with five independent biological replicates. cDNA synthesis was performed on the above sample RNAs using a PrimeScript\u0026trade; RT Reagent Kit with a gDNA Eraser (TaKaRa). Primer 3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://primer3.ut.ee/\u003c/span\u003e\u003cspan address=\"http://primer3.ut.ee/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to design gene-specific primers, while the primers for the internal reference gene Actin were adopted from the study by Zuo et al (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For the qRT-PCR assays, we utilized a LightCycler\u0026reg; 96 Instrument (Roche) and SYBR\u003csup\u003e\u0026reg;\u003c/sup\u003e Green Premix Pro Taq HS qPCR Kit. Relative transcript levels were determined using the modified 2\u003csup\u003e\u0026minus;∆∆CT\u003c/sup\u003e method (Livak et al. 2001). The above experiments were conducted with three biological replicates.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUse Microsoft Excel 2010 to perform statistical analysis and calculate the mean and standard deviation of the data, conduct significance testing of differences between samples using the Student\u0026rsquo;s t-test, and create graphs using Origin 8.5.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data quality control analyses\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter quality control and filtering of the transcriptome sequencing data, we obtained more than 37\u0026nbsp;million valid data points from each sample. The values of Q20% and Q30% were greater than 99.9% and 97.4%, respectively. Furthermore, the GC content of each sample ranged from 46\u0026ndash;47% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After PCA analysis, it was found that the differences between each sample in both the transcriptome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and proteome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) were small, and the similarity between the three replicates of each treatment was high.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary data for reads in each sample.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValid Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eValid Ratio (reads)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQ20%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQ30%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGC content %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRead\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRead\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43722020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.56G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42278234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.34G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44764486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.71G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43442000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.52G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47019676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.05G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45638572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.85G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46517346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.98G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45121038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.77G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45081260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.76G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43678850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.55G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41027260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.15G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39799810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.97G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39227908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.88G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37906168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.69G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39455054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.92G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38074624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.71G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39814576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.97G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38495894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.77G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39912808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.99G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38455186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.77G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40844696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.13G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39355720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.90G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41225376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.18G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39669346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.95G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Screening of differentially expressed genes (DEGs) and Proteins (DEPs)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAt the transcriptional level, a total of 3790 up- and 8421 down-regulated genes were identified in samples after compared to pre-flowering in the CK group (CK2-VS-CK1), respectively. In the T group (T2-VS-T1), 3057 up- and 5233 down-regulated genes were determined (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). At the translation level, 124 up- and 349 down-regulated proteins were found in CK2-VS-CK1, respectively, while 143 up- and 391 down-regulated proteins were identified in T2-VS-T1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A Venn diagram analysis of the DEGs and DEPs showed that there were 14 up- and 156 down-regulated shared elements in CK2-VS-CK1, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B), while there were 31 up- and 131 down-regulated shared elements in T2-VS-T1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 GO functional clustering of DEGs and DEPs\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eInspired by this, we examined the changes in biological functions pre- and after-flowering in orchards with good ventilation and light transmission conditions, as well as in orchards with low light. GO enrichment was performed for the 332 DEGs at the intersection of the transcriptome and proteome described above. It was found that DEGs in the pathways of \u0026ldquo;DNA integration\u0026rdquo;, \u0026ldquo;glycolytic process\u0026rdquo;, \u0026ldquo;response to stress\u0026rdquo; and \u0026ldquo;protein folding\u0026rdquo; pathways were up-regulated in both CK2-VS-CK1 and T2-VS-T1. DEGs in the pathways \u0026ldquo;biosynthetic process\u0026rdquo;, \u0026ldquo;negative regulation of catalytic activity\u0026rdquo; and \u0026ldquo;lipid metabolic process\u0026rdquo; were down-regulated in both CK2-VS-CK1 and T2-VS-T1. However, DEGs in the pathways of \u0026ldquo;response to water\u0026rdquo;, \u0026ldquo;glucan biosynthetic process\u0026rdquo; and \u0026ldquo;cell redox homeostasis\u0026rdquo; were up-regulated only in T2-VS-T1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Expression analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo this end, we next investigated the expression patterns of DEGs and DEPs in the shared and unique GO-enrichment pathways of CK2-VS-CK1 and T2-VS-T1. It was found that in T2-VS-T1, the expression of genes \u003cem\u003eMD16G1160600\u003c/em\u003e and \u003cem\u003eMD16G1159900\u003c/em\u003e in the \"response to biological stimuli\" pathway, gene \u003cem\u003eMD14G1010300\u003c/em\u003e in the \"response to oxidative stress\" pathway, gene \u003cem\u003eMD09G1199700\u003c/em\u003e in the \"lipid metabolism process\" pathway, and gene \u003cem\u003eMD07G1279200\u003c/em\u003e in the \"response to stress\" pathway was significantly lower than CK2-VS-CK1. Conversely, the expression of gene \u003cem\u003eMD10G1289200\u003c/em\u003e in the \"protein folding\" pathway, gene \u003cem\u003eMD13G1161400\u003c/em\u003e in the \"response to biotic stimulus\" pathway, gene \u003cem\u003eMD01G1185500\u003c/em\u003e in the \"glycolytic process\" pathway, and gene \u003cem\u003eMD15G1253900\u003c/em\u003e in the \"response to stress\" pathway was significantly higher than CK2-VS-CK1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 qRT-PCR validation of the DEGs\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eqRT-PCR analysis was performed on the genes \u003cem\u003eMD13G1161400\u003c/em\u003e, \u003cem\u003eMD14G1010300\u003c/em\u003e, \u003cem\u003eMD01G1185500\u003c/em\u003e, \u003cem\u003eMD07G1279200\u003c/em\u003e, \u003cem\u003eMD15G1253900\u003c/em\u003e and \u003cem\u003eMD10G1289200\u003c/em\u003e, which are involved in the \u0026ldquo;biostimulus response\", \u0026ldquo;oxidative stress response\", \u0026ldquo;glycolysis process\", \u0026ldquo;stress response\" and \u0026ldquo;protein folding\", respectively. The expression patterns were consistent with the transcriptomics and proteomics data (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTranscriptome sequencing and proteome sequencing can not only achieve many genomic studies of plants and animals, but also accurately study some complex insect genomes (Ozsolak and Milos \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The Illumina platform can obtain deeper coverage and higher accuracy, and RNA-Seq technology is a useful tool for differential gene screening and analysis. In this experiment, transcriptome sequencing was completed, obtaining a total of 76.28G CleanData, with effective data distribution ranging from 5.88\u0026ndash;7.05 G for each sample, Q30 base distribution ranging from 97.43\u0026ndash;97.94%, and an average GC content of 46.33%. Candidate genes have gained a more complete understanding in cellular and metabolic categories through GO and KEGG, providing classifications such as biological functions, gene metabolic pathways, and signal transduction, laying the foundation for subsequent analysis. GO functional analysis and expression analysis were conducted on the screened differential genes and proteins, with some genes showing significant differences in treatment, providing a theoretical basis for subsequent experimental research.\u003c/p\u003e \u003cp\u003eLight plays a crucial role in the process of apple flower bud differentiation. When there is sufficient light, apple branches and leaves grow robustly. Photosynthesis is strengthened, organic nutrients promote tree growth, enhance fruit quality, and facilitate bud and flower differentiation. Additionally, it improves the efficient use of water and fertilizer (Abed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Feng et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wertheim et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Insufficient light can lead to thinner leaves, increased transpiration, weak branches, poor flower bud differentiation, reduced fruiting rate, yellowing of the crown, decreased root absorption ability, reduced sugar and vitamin content in the fruit, affected formation of anthocyanins, decreased number of flowers in the inflorescence, increased number of incomplete flowers, resulting in poor coloring of varieties (Paponov et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Widmer and Krebs \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). During the flowering period, plants require more light than during the germination period, and during the growing period, they need more light than during the dormant period. Reproductive organs require more light than nutrient organs (Varinder et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Apple orchard light can be categorized into upper light, front light, lower light, and back light, all of which can be utilized (Zhang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt was found that the organic combination of dextran and boron, along with the biostimulation of dextran, could address issues related to frost damage, drought, high temperatures, and high humidity during flowering. This combination also enhances the effectiveness of boron fertilizer. Furthermore, the active substances like nucleic acid and mannan found in dextran have a significant impact on flowering. Dextran can also enhance flower bud differentiation, boost fruit setting rate, enhance fruit quality, and improve crop stress resistance (Li et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the maintenance of intracellular redox homeostasis is critical for stem cell fate during plant growth and development. This process also involves the precise regulation of the function of key transcription factors, such as FASCIATED EAR4 (FEA4). FEA4 plays a crucial role in regulating maize inflorescence. Its redox state and transcriptional activity are controlled by Glutaredoxins (GRXs) (Yang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our study also found that three pathways, \"response to water,\" \"glucan biosynthetic process,\" and \"cell redox homeostasis,\" were up-regulated only in orchards with good light and ventilation.\u003c/p\u003e \u003cp\u003eFlavonoids are secondary metabolites with high biological activity in plants, which play an important role in the growth and development of plants (Yu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These are the primary substances that influence flower colors, regulate seed germination, root growth, photosynthetic pigment synthesis, and negatively regulate auxin transport(Brown et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Shi and Xie \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tan et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, flavonoids may alter the transport of auxin by influencing the PIN-FORMED protein, thereby impacting the growth and development of plants (Peer et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Different proportions of red and blue light can further alter the flower bud differentiation process by regulating the biosynthesis of multiple hormones (Liu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the sealed 'Nagano Fuji No.2' apple orchard, approximately 15 years old, was selected as the research subject. In well-light and low-light orchards, the transcriptome and proteome of the two developmental stages of apple flower bud differentiation were analyzed together. We divided the results into two comparison groups: CK1-VS-CK2 and T1-VS-T2, and identified 20,501 DEGs and 1,007 DEPs. By conducting GO enrichment analysis, a total of 18 pathways were identified in the two comparison groups. Among these, the pathways \"response to water,\" \"glucan biosynthetic process,\" and \"cell redox homeostasis\" were specifically regulated only in T2-VS-T1. Additionally, 3 DEGs were discovered within these three pathways. This study provides a theoretical basis for further discussion on flower bud differentiation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e This study is grateful to the National Natural Science Foundation of China (32160683, 31760556) and Project of Scientific Observation and Experiment Station of Fruit Trees in Northwest Area of Ministry of Agriculture (S-10-18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e J.N., X.Y. and Z.Y. designed and conceived this study. J.N., M.M., T.D. and W.S participated in the experiments and performed the data collection and collation. J.N., Z.Y., M.M. and W.S. jointly revised the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by the National Natural Science Foundation of China (32160683, 31760556); Project of Scientific Observation and Experiment Station of Fruit Trees in Northwest Area of Ministry of Agriculture (S-10-18).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e All data supporting the findings of this study are available within the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbed A, Bonhomme M, Lacointe A, Bourgeois G, BaaliCherif D (2019) Climate change effect on the bud break and flowering dates of the apple trees in mountainous and plain regions of Algeria. 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Plant Mol Bio Rep 38: 14-24. https://doi.org/10.1007/s11105-019-01179-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"apple, light, flower bud differentiate, transcriptomics, proteomics","lastPublishedDoi":"10.21203/rs.3.rs-4536836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4536836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFuji, a major cultivar group of apple (\u003cem\u003eMalus domestica\u003c/em\u003e), is extensively grown in China, Japan, and the USA. However, it has been experiencing prolonged differentiation of flower buds. and the potential mechanisms are largely unknown. Thus, for better comprehend the differentiation of apple flower buds, we performed a comparative transcriptomic and proteomic analysis between the closed (CK) and well-ventilated apple orchards (T) of 15-year-old \u0026lsquo;Nagano Fuji No.2\u0026rsquo;. In total, 12,211 and 8,290 differentially expressed genes (DEGs) and 473 and 534 differentially expressed proteins (DEPs) were identified in the CK group and T group, respectively. In both the expressional and translational levels, 14 up- and 156 down-regulated members were found in samples after flowering compared to pre-flowering in the CK group, respectively. In contrast, 31 up- and 131 down-regulated members were found in the T group. These members were mainly enriched in several Gene Ontology (GO) terms, such as \"glycolytic process,\" \"glucan biosynthetic process,\" and \"response to water.\" These pathways were involved in the differentiation of flower buds regulated by light. Several genes, including \u003cem\u003eMD13G1093200\u003c/em\u003e, \u003cem\u003eMD06G1122100\u003c/em\u003e, \u003cem\u003eMD15G1253900\u003c/em\u003e, \u003cem\u003eMD13G1161400\u003c/em\u003e, \u003cem\u003eMD07G1279200\u003c/em\u003e, \u003cem\u003eMD15G1253900\u003c/em\u003e, and \u003cem\u003eMD10G1289200\u003c/em\u003e, exhibited differential expression patterns between the CK and T groups, making them potential key candidates for additional functional analysis. Our findings provide a foundation for further research on the molecular mechanisms of light in flower bud differentiation.\u003c/p\u003e","manuscriptTitle":"Effects of light intensity on apple bud differentiation analyzed by transcriptome and proteome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 10:12:35","doi":"10.21203/rs.3.rs-4536836/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e010ae9f-6101-472e-9832-e75e2e3293d0","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-08T06:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-20 10:12:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4536836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4536836","identity":"rs-4536836","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}