Transcriptome and metabolome conjoint analysis revealed that PaGLK affects photosynthesis and composition of root exudates in poplar

Transcriptome and metabolome conjoint analysis revealed that PaGLK affects photosynthesis and composition of root exudates in poplar | 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 Transcriptome and metabolome conjoint analysis revealed that PaGLK affects photosynthesis and composition of root exudates in poplar Xiang-dong Bai, Yu Zheng, Li Cao, Wei Wang, Jing Jiang, Qi-bin Yu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4293152/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2025 Read the published version in Plant Molecular Biology Reporter → Version 1 posted 9 You are reading this latest preprint version Abstract Background Photosynthetic carbon fixation is the main source of root exudates. GOLDEN2-LIKE ( GLK ) genes play an important role in photosynthetic carbon fixation. Previous studies have found that expression-inhibited the PaGLK in poplar reduce its net photosynthesis. However, the relationship between GLK genes, root exudates and photosynthetic carbon fixation and how photosynthesis affects root exudate in poplar are not clear. Result In this study, we performed comparative transcriptome and metabolome analyses of overexpression and suppression transgenic poplar. GO enrichment analysis showed that the downregulation of differentially expressed genes (DEGs) in suppression lines was mainly related to photosynthesis in biological processes. Specifically, photosynthesis-antenna proteins, porphyrin and chlorophyll metabolism, and photosynthesis were significantly enriched in KEGG pathways. Gene expression showed consistent trends in real time quantitative PCR (RT-qPCR) and transcriptome, indicating reliable transcriptome. Differentially expressed metabolites (DEMs) of root exudates were mainly enriched in amino acid metabolism, glucose metabolism and fatty acid metabolism pathways. After correlating DEGs and DEMs, we found that most genes and metabolites showed positive regulation. Conclusion This study shows that the new factors change composition of root exudates. Transcriptome Metabolome Root Exudate GOLDEN2-LIKE genes poplar Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Root exudate is the diverse array of substances that are secreted into the environment from plant roots[ 1 ]. It encompasses primary metabolites of amino acids, carbohydrates, and organic acids, as well as flavonoids, terpenes, auxins, and alkaloids[ 2 , 3 ]. Root exudate plays a significant role in plant growth and development and defense against stressors[ 4 , 5 ], and varies among different plant species. For instance, 8-hydroxyquinoline is a specific root exudate found only in Sebastiania corniculata and Centaurea diffusa and not in other plant species[ 6 ]. Plant in different developmental stages differs in composition of root exudates. Difference in amino acid secretion between the fast and slow growth stages was found in Arabidopsis thaliana [ 7 ]. Environmental conditions also influence the composition of root exudates. Plants secrete different root exudates are found under nutrient-deficient conditions[ 5 ]. In aluminum stress, plants secrete organic acids that chelate aluminum ions, thus detoxifying the environment[ 8 ]. In phosphate-deficient soil environments, beets increase the secretion of organic acids like citric acid and salicylic acid, which acidify the soil and alleviate the stress[ 9 ]. Carvalhais et al. found that maize secretes different root exudates when lacking different nutrients, such as releasing more glutamate, glucose, ribitol, and citrate in response to iron deficiency, or secreting γ-aminobutyric acid and carbohydrates under phosphorus deficiency. Potassium-deficient plants secrete less glycerol, ribitol, fructose, and maltose, while nitrogen-deficient plants produce smaller amounts of amino acids[ 10 ]. Furthermore, temperature influences the composition of root exudates. For instance, Pramanik et al. observed that cucumber secreted more benzoic and palmitic acids when exposed to a day/night temperature of 30/25°C instead of 25/20°C[ 11 ]. Consequently, studying the factors affecting root exudates is highly necessary. The Golden2-like (GLK) transcription factor plays a pivotal role in plant photosynthesis by regulating chloroplast development[ 12 , 13 ]. Studies on rice ( Oryza sativa ), moss ( Physcomitrella patens ), and Arabidopsis thalian have shown that glk mutants display light green leaves with decreased accumulation of chlorophyll precursors, leading to stunted chloroplast development[ 14 , 15 , 15 ]. Over-expressing AtrGLK in Arabidopsis thaliana glk mutants restored normal green color to the leaves and improved photosynthesis[ 12 , 14 ]. The photosynthetic capacity of white birch glk mutants was impaired and the introduction of GLK genes into them restored photosynthetic function[ 16 , 17 ]. Research has shown that about 25% of the carbon fixed through photosynthesis is released by plants as root exudates into soil, thus becoming a significant component of their secretion[ 18 , 19 ]. Therefore, there exists an intimate relationship between photosynthesis and root exudates. The formation of a microenvironment by root secretions has significant implications, studies on root exudates in poplar is scarce. Our previous studies showed that suppression of PaGLK1 of Populus alba × P. Berolinensis reduces chlorophyll content and have effects on root exudates, rhizosphere soil enzyme activities and soil microbial community composition [ 20 , 21 ]. The root exudates primarily originate from plant photosynthesis. In this study, we used conjoint analysis to study transcriptome and metabolome and provide novel insight into the relationship between photosynthesis and composition of root exudates in poplar. Materials and methods Plant materials PaGLK overexpressing lines (OE), PaGLK suppressing lines (RE), and wild-type (WT) were used as experimental materials at the State Key Laboratory of Tree Genetics and Breeding of Northeast Forestry University, China[ 20 ]. 1cm-long aseptic stem cuttings were propagated in rooting medium (1/2 MS (Murashige & Skoog) medium + 0.5 mg/L IBA (Indole-3-butyric Acid) + 30g/L sucrose + 6g/L agar). The transgenic plants grow at tissue culture room which was maintained at 26°C with a photoperiod of 16h light/8h dark. After 30 days, root exudates and transcriptome were measured. RNA extraction and transcriptome analysis Total RNA was extracted from the whole leaves of WT, OE, and RE transgenic plants using a universal plant total RNA extraction kit (BioTeke Corporation, Beijing, China). RNA samples were submitted for 150 bp paired − end reads on Illumina × 10 platform. After sequencing, low-quality sequences and adapter contamination were removed from the original raw data through fastq software[ 22 ]. High-quality sequences were then aligned to the Populus trichocarpa genome[ 23 ] using HISAT2 software[ 24 ] to obtain positional information on genes. Functional annotations were carried out based on various databases. Significantly differentially expressed genes (DEGs) were selected based on the criteria of log2 Fold change ≥ 2 and q < 0.05. Common DEGs between two comparison groups were selected, and results of their alignment in the GO database ( http://geneontology.org/ ) Pathway enrichment analysis was also performed using the KEGG database ( http://kobas.cbi.pku.edu.cn/genelist/ ). Metabolome Analysis Transgenic and non- transgenic line were placed in 50 mL centrifuge tube (two plantlets per tube), with 15 tubes for each line. Each tube contained 15 mL of pure water under conditions of 26°C with a photoperiod of 16h light / 8h dark for 30 days. Every 10 days, the culture solutions were collected from the test plants. Collected root culture solutions were analyzed by chromatographic separation method using the ACQUITY UPLC BEH C18 column (Waters™, USA). Raw data collected were pre-processed for peak extraction, alignment, calibration, and normalization with Compound Discoverer 3.0 software (Thermo Fisher Scientific, USA). Metabolite structures were identified using accurate mass (< 25ppm) and secondary spectrum matching methods, and were searched in databases. Significantly differential expressed metabolites (DEMs) were selected based on the criteria of log2 Fold change ≥ 2 and q < 0.05. Common DEMs between two comparison groups were selected, and results of their alignment in KEGG database ( http://kobas.cbi.pku.edu.cn/genelist/ ). Correlation Analysis between Metabolome and Transcriptome The correlation between the metabolome and transcriptome was calculated using the Pearson function. Data with R values ≥ 0.95 and P values ≤ 0.01 were selected for performing correlation analysis using R language nine-quadrant plot. The WGCNA package( https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/ ) is used to calculate correlation. Results PaGLK transgenic lines showed different gene expression patterns To explore the transcriptional expression characteristics of PaGLK transgenic poplar, the leaves of PaGLK overexpressing lines, PaGLK suppressing lines, and WT were subjected to RNA-seq sequencing. Due to abnormal clustering of samples OE3 and WT3, these outliers were removed, and a total of 307.9 million Clean Reads were obtained (39.8 ~ 47.9 million per library), with a Q30 base percentage above 92.97% and alignment rate to the Populus trichocarpa genome between 74.10% and 76.63% (Table S1 ), indicating reliable transcriptome data. Principal Component Analysis (PCA) showed that the clustering position of WT was closer to that of the overexpressing lines and far from that of the suppressing lines (Fig. 1 a). OE vs WT had 3052 DEGs, 1731 upregulated and 1321 downregulated. in RE vs WT had 1326 DEGs, 669 upregulated and 657 downregulated. OE vs RE had 4577 DEGs, 2376 upregulated and 2201 downregulated (Fig. 1 b). There were more upregulated DEGs than downregulated genes in all three comparison groups, with RE vs WT having the least DEGs, and the up/downregulated gene ratio in OE vs WT being uneven. There were 597 common DEGs (317 upregulated, 280 downregulated) in RE vs WT and RE vs OE. There were also 1705 common DEGs (942 upregulated, 763 downregulated) in OE vs WT and RE vs OE (Fig. 1 c). Functional analysis of DEGs GO functional enrichment analysis was performed mainly for biological processes on the DEGs in the three comparison groups. The common upregulated DEGs in RE vs WT and RE vs OE were enriched related to salt stress response, response to darkness, carbohydrate response, and water shortage (Fig. 2 a). The downregulated DEGs were related to photosynthetic electron transfer in photosystem I, photosynthesis, light harvesting in photosystem I, response to light stimulus, chloroplast development, and chloroplast organization (Fig. 2 b). The upregulated DEGs in OE vs WT and OE vs RE were related to cytokinesis during male meiosis, spindle assembly checkpoint signaling in mitosis, DNA replication initiation, and protection of germ cell differentiation (Fig. 2 c). The downregulated DEGs were related to regulation of cell fate determination, response to bacterial derived molecular patterns, protein autophosphorylation, and salicylic acid activation signaling pathway (Fig. 2 d). These results reveal that the photosynthetic capacity of the RE transgenic lines was negatively affected compared to the WT. Pathway analysis showed that the DEGs in OE vs WT and RE vs WT corresponded to 120 and 92 KEGG pathways, respectively. The photosynthesis-antenna proteins, photosynthesis, and porphyrin and chlorophyll metabolism pathways were significantly enriched in RE vs WT (Fig. 3 a). The ribosome pathway was significantly enriched in OE vs WT (Fig. 3 b). Validation of DEGs by RT-qPCR We found that downregulated DEGs in RE vs WT and RE vs OE were enriched in pathways related to photosynthesis, and focused on the DEGs in six biological processes related to photosynthesis. We chose ten genes from these processes for qPCR validation and found that the expression levels of these genes in the WT lines were higher than those in the RE lines (Fig. 4 ). Metabolome analysis The response intensity and retention time of each chromatographic peak in the QC sample total ion flow map overlapped substantially (Figure S1 ), indicating low variation due to instrumental error. A total of 5128 ion peaks were extracted from the metabolites, 2583 positive ions and 2545 negative ions. All extracted peaks were used for PCA analysis. PCA analysis of the metabolites showed a certain separation tendency on the PC1 and PC2 axis plots, and the distribution of the sample points was somewhat discrete, indicating differences in metabolites among the three groups of samples (Fig. 5 ). Analysis of DEMs KEGG pathway enrichment analysis of differential expressed metabolites (DEMs) found a total of 39 enriched differential metabolic pathways in RE vs WT. Ten of them were significantly enriched (P < 0.01), and mainly related to amino acid metabolism, carbohydrate metabolism, and fatty acid metabolism (Fig. 6 a). A total of 49 enriched differential metabolic pathways were found in OE vs WT, and 15 of them were significantly enriched (P < 0.01), mainly related to amino acid metabolism and carbohydrate metabolism (Fig. 6 b). Cluster analysis was performed on the metabolites with significant differences in the metabolic pathways involved in the three comparison groups (Fig. 7 ). Compared with the WT, more of the downregulated metabolites were found in the PaGLK overexpression lines, mainly including biphenols (catechol, epinephrine, 3,4-dihydroxyphenylethylene glycol, 3,4-dihydroxyphenylethanoic acid), methoxyphenols (vanillin, 3-methoxytyramine), and carbohydrates and carbohydrate-associated molecules (gomphrenin-I, arbutin). In the PaGLK suppression lines, the levels of dicarboxylic acids and their derivatives (fumaric acid, malic acid, succinic acid), eicosanoic acids (prostaglandin D2, prostaglandin G2, PGB2), and amino acids, peptides and analogues (yeast amino acid, O-carbamyl-D-serine) were upregulated, while the levels of these metabolites were downregulated in the WT. These results indicate that differential metabolites have different expression patterns in the transgenic and WT poplar. Transcriptome and metabolome conjoint analysis Pearson correlation analysis was done between transcriptome and metabolome. DEGs and DEMs were selected with threshold R-value ≥ 0.95 and P-value ≤ 0.01 for nine quadrant analysis. There were no correlated genes in either the positive or negative ion metabolites in the first quadrant. In the third quadrant, there were 236 regulatory relationships between positive ion metabolites and genes, and 193 regulatory relationships between negative ion metabolites and genes. In the seventh quadrant, there were 72 regulatory relationships between positive ion metabolites and genes, and 69 regulatory relationships between negative ion metabolites and genes. In the ninth quadrant, there were 19 regulatory relationships between positive ion metabolites and genes, and 15 regulatory relationships between negative ion metabolites and genes (Fig. 8 ). The results indicate that the DEMs have a positive regulatory relationship with DEGs. Discussion The rhizosphere microorganisms have a significant impact on plant growth and development[ 25 ]. The composition of rhizosphere microorganisms is not constant and is influenced by various factors[ 26 , 27 ]. Therefore, studying the factors that affect composition of these microorganisms is crucial. Previous studies have demonstrated that the addition of above- and below-ground pineapple residues in highly infested soils significantly reduced the number of pathogens in the soil, thus resulting in a lower disease incidence. This is because Aspergillus fumigatus and Fusarium solani , which are present in pineapples, exhibit antagonism against the pathogens, thereby altering the original microbial environment[ 28 ]. Another research use metagenomics information as an external quantitative phenotype to map the host genetic determinants of the rhizosphere microbiota in barley, and identify a small number of loci with a major effect on the composition of rhizosphere communities[ 29 ]. Salas-González et al. investigated the regulatory relationship between the root diffusion barriers in the endodermis and microbiota and found that genes controlling endodermal function in Arabidopsis thaliana contribute to the plant microbiome assembly[ 30 ]. Previous studies have shown significant differences in the activity of bacteria, fungi, and soil enzymes in the roots of transgenic poplar lines with overexpressed PaGLK, suppressed expression, and WT[ 21 ]. Furthermore, PaGLK gene expression alter the photosynthetic capacity of poplar[ 20 ]. Rhizosphere exudates are the primary source of soil carbon[ 19 ], on which most microorganisms rely for their carbon supply[ 31 ]. In this study, we found differences in metabolic pathways, including carbon metabolism and the TCA cycle, between transgenic lines and WT. Therefore, we hypothesize that PaGLK alters the composition of root exudates by changing C-related metabolic pathways. Furthermore, we conducted correlation analysis between DEMs and DEGs, and found a positive correlation between them, but a precise localization is lacking and needs further investigation. Overall, this study provides a new idea for studying the causes of root microbial community alterations. Conclusion In this study, it was found that the expression of PaGLK significantly altered the expression of photosynthesis-related genes, indicating that PaGLK is involved in the process of plant photosynthesis. Furthermore, it was observed that the types of root exudates between transgenic and wild-type plants were significantly different, with significant differences ( p < 0.01 ) in carbon metabolism and pyruvate metabolism in the suppressed expression transgenic lines compared with WT. These results suggest that PaGLK may regulate the expression of genes involved in carbon metabolism, pyruvate metabolism, and other pathways to alter the types of root exudates produced. Declarations Supplementary Information The online version contains supplementary material available at Acknowledgements We are grateful for the experimental platform provided by Northeast Forestry University. Authors' contributions XDB writes the first draft and analyzes the data. YZ and WW design experiments and complete them. LC participated in the experimental part. GFL and JJ supervised the entire experimental process and provided guidance. QBY and CPY have made revisions and improvements to the initial draft. Ethics approval and consent to participate Not applicable. Consent for publication All authors agreed to publish this manuscript. Availability of data and materials The datasets generated during the current study are available in the NCBI database https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1021575/. Competing Interests The authors declare that they have no competing interests. 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Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2025 Read the published version in Plant Molecular Biology Reporter → Version 1 posted Editorial decision: Revision requested 02 Jun, 2024 Reviews received at journal 02 Jun, 2024 Reviews received at journal 28 May, 2024 Reviewers agreed at journal 27 May, 2024 Reviewers agreed at journal 20 May, 2024 Reviewers invited by journal 18 May, 2024 Editor assigned by journal 22 Apr, 2024 Submission checks completed at journal 22 Apr, 2024 First submitted to journal 19 Apr, 2024 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-4293152","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":297071858,"identity":"c12a7a66-43b4-4ff3-aa6c-af704ce401da","order_by":0,"name":"Xiang-dong Bai","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xiang-dong","middleName":"","lastName":"Bai","suffix":""},{"id":297071863,"identity":"2fefc7e0-c8ad-452a-af21-a8587ca7f583","order_by":1,"name":"Yu Zheng","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zheng","suffix":""},{"id":297071868,"identity":"0b578d23-17e4-4323-a0ff-100b83d12b4e","order_by":2,"name":"Li Cao","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Cao","suffix":""},{"id":297071872,"identity":"a6342397-2a20-4178-a6f0-4a258b30b6e3","order_by":3,"name":"Wei Wang","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":297071874,"identity":"61472e6f-fea0-4650-8cc3-68d042fd8710","order_by":4,"name":"Jing Jiang","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Jiang","suffix":""},{"id":297071876,"identity":"69bae4b1-855a-4c45-b414-cb1f4e8b57fc","order_by":5,"name":"Qi-bin Yu","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Qi-bin","middleName":"","lastName":"Yu","suffix":""},{"id":297071878,"identity":"f1c45871-dd54-4950-9ba9-5535db098e36","order_by":6,"name":"Chuan-ping Yang","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Chuan-ping","middleName":"","lastName":"Yang","suffix":""},{"id":297071880,"identity":"86986497-9903-4ed8-8c29-576844ebd420","order_by":7,"name":"Gui-feng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACPmYQaSAHJJgPHPhQQYQWNogWYxAz8eCMM8RogVAgLTzGh3lbiNHCzmP8mqfAQM6cf82HA7wNDPL8YgcIOYzHzJrHwMDYcsbbDQckdzAYzpydQFiLMY/Bn8QNN85uOGB4hiHB4DZxWgzqN9w48+BAYhtxWowfA7UkGJzvYThwkDgtbGWMcwwMDDfcYDM42HBGgrBf+PkPb/7w5o+BvMH5w48//6mwkeeXJqAFZJEEmJIAq5QgqBwEmD9A7DtAlOpRMApGwSgYgQAAoB9BqsHsEdAAAAAASUVORK5CYII=","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Gui-feng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-04-19 12:27:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4293152/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4293152/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11105-025-01550-0","type":"published","date":"2025-03-04T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55784500,"identity":"2929e369-13a2-4560-a46a-95ee2f01666c","added_by":"auto","created_at":"2024-05-03 06:28:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150082,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram and principal component analysis of differentially expressed genes. (a) Principal component analysis of RNA-seq data. (b) Differentially expressed genes between groups. (c) Venn diagram of differential genes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4293152/v1/8aa004dabe7cc47bb6416611.png"},{"id":55784504,"identity":"b28e0b74-6c9a-4749-8c6c-a51234b8fff2","added_by":"auto","created_at":"2024-05-03 06:28:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":394790,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of DEGs using (a) Shared upregulated DEGs in RE vs WT and RE vs OE. (b) Shared downregulated DEGs in RE vs WT and RE vs OE. (c) Shared upregulated DEGs in OE vs WT and OE vs RE. (d) Shared downregulated DEGs in OE vs WT and OE vs RE.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4293152/v1/f5cda92b0aa788f427ba30bd.png"},{"id":55784499,"identity":"09ffa665-748f-4d65-8c60-e70d8d7f4f10","added_by":"auto","created_at":"2024-05-03 06:28:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56091,"visible":true,"origin":"","legend":"\u003cp\u003eThe KEGG pathway enrichment analysis using DEGs between groups: (a) RE vs WT, (b) OE vs WT.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4293152/v1/27622191b311117a29dc9ecd.png"},{"id":55784501,"identity":"19860c24-cbf2-4a82-9a3f-f9288f904775","added_by":"auto","created_at":"2024-05-03 06:28:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":546312,"visible":true,"origin":"","legend":"\u003cp\u003eExpression pattern of shared downregulated DEGs in RE vs WT and RE vs OE.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4293152/v1/54ef082a2781aeab2e00900c.png"},{"id":55784502,"identity":"aebf1f9e-b613-40d6-a650-1bf1f0c2c886","added_by":"auto","created_at":"2024-05-03 06:28:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":252640,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of the metabolomics data was performed for the positive and negative ion mode. (a) The positive ion mode, (b) The negtive ion mode. G4 represents OE, QC represents control, Y1 represents RE.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4293152/v1/e4c517ed379e07e2915f64a7.png"},{"id":55784506,"identity":"171153dd-0118-44ba-9e98-4c109d31df25","added_by":"auto","created_at":"2024-05-03 06:28:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":206880,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway enrichment analysis of differential metabolites, (a) RE vs WT, (b) OE vs WT.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4293152/v1/482842c2eb91d9e02aa4a7a7.png"},{"id":55784679,"identity":"4d63d20a-b807-4467-b2b8-d5e6bf9c0e8a","added_by":"auto","created_at":"2024-05-03 06:36:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":28445,"visible":true,"origin":"","legend":"\u003cp\u003eCluster heatmap of differential metabolites.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4293152/v1/16e52ce41596c09035ffa860.png"},{"id":55784505,"identity":"6abf7c32-cf36-4636-916e-cf62bb0706ec","added_by":"auto","created_at":"2024-05-03 06:28:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":143940,"visible":true,"origin":"","legend":"\u003cp\u003eNine-quadrant diagram of DEMs vs. DEGs. (a) Positive ion DEMs vs. DEGs. (b) Negative ion DEMs vs. DEGs.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4293152/v1/01f8744e40894b09f4b00dad.png"},{"id":78191426,"identity":"f13c8818-42e0-45b0-bc31-02061dc0d0b5","added_by":"auto","created_at":"2025-03-10 20:00:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2393826,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4293152/v1/b4169926-7860-4335-953a-348a0f3eb1ac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome and metabolome conjoint analysis revealed that PaGLK affects photosynthesis and composition of root exudates in poplar","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRoot exudate is the diverse array of substances that are secreted into the environment from plant roots[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It encompasses primary metabolites of amino acids, carbohydrates, and organic acids, as well as flavonoids, terpenes, auxins, and alkaloids[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Root exudate plays a significant role in plant growth and development and defense against stressors[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and varies among different plant species. For instance, 8-hydroxyquinoline is a specific root exudate found only in \u003cem\u003eSebastiania corniculata\u003c/em\u003e and \u003cem\u003eCentaurea diffusa\u003c/em\u003e and not in other plant species[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Plant in different developmental stages differs in composition of root exudates. Difference in amino acid secretion between the fast and slow growth stages was found in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Environmental conditions also influence the composition of root exudates. Plants secrete different root exudates are found under nutrient-deficient conditions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In aluminum stress, plants secrete organic acids that chelate aluminum ions, thus detoxifying the environment[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In phosphate-deficient soil environments, beets increase the secretion of organic acids like citric acid and salicylic acid, which acidify the soil and alleviate the stress[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Carvalhais et al. found that maize secretes different root exudates when lacking different nutrients, such as releasing more glutamate, glucose, ribitol, and citrate in response to iron deficiency, or secreting γ-aminobutyric acid and carbohydrates under phosphorus deficiency. Potassium-deficient plants secrete less glycerol, ribitol, fructose, and maltose, while nitrogen-deficient plants produce smaller amounts of amino acids[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, temperature influences the composition of root exudates. For instance, Pramanik et al. observed that cucumber secreted more benzoic and palmitic acids when exposed to a day/night temperature of 30/25\u0026deg;C instead of 25/20\u0026deg;C[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Consequently, studying the factors affecting root exudates is highly necessary.\u003c/p\u003e \u003cp\u003eThe Golden2-like (GLK) transcription factor plays a pivotal role in plant photosynthesis by regulating chloroplast development[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies on rice (\u003cem\u003eOryza sativa\u003c/em\u003e), moss (\u003cem\u003ePhyscomitrella patens\u003c/em\u003e), and \u003cem\u003eArabidopsis thalian\u003c/em\u003e have shown that \u003cem\u003eglk\u003c/em\u003e mutants display light green leaves with decreased accumulation of chlorophyll precursors, leading to stunted chloroplast development[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Over-expressing \u003cem\u003eAtrGLK\u003c/em\u003e in \u003cem\u003eArabidopsis thaliana glk\u003c/em\u003e mutants restored normal green color to the leaves and improved photosynthesis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The photosynthetic capacity of white birch \u003cem\u003eglk\u003c/em\u003e mutants was impaired and the introduction of \u003cem\u003eGLK\u003c/em\u003e genes into them restored photosynthetic function[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Research has shown that about 25% of the carbon fixed through photosynthesis is released by plants as root exudates into soil, thus becoming a significant component of their secretion[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, there exists an intimate relationship between photosynthesis and root exudates.\u003c/p\u003e \u003cp\u003eThe formation of a microenvironment by root secretions has significant implications, studies on root exudates in poplar is scarce. Our previous studies showed that suppression of \u003cem\u003ePaGLK1\u003c/em\u003e of \u003cem\u003ePopulus alba \u0026times; P. Berolinensis\u003c/em\u003e reduces chlorophyll content and have effects on root exudates, rhizosphere soil enzyme activities and soil microbial community composition [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The root exudates primarily originate from plant photosynthesis. In this study, we used conjoint analysis to study transcriptome and metabolome and provide novel insight into the relationship between photosynthesis and composition of root exudates in poplar.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePaGLK\u003c/em\u003e overexpressing lines (OE), \u003cem\u003ePaGLK\u003c/em\u003e suppressing lines (RE), and wild-type (WT) were used as experimental materials at the State Key Laboratory of Tree Genetics and Breeding of Northeast Forestry University, China[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. 1cm-long aseptic stem cuttings were propagated in rooting medium (1/2 MS (Murashige \u0026amp; Skoog) medium\u0026thinsp;+\u0026thinsp;0.5 mg/L IBA (Indole-3-butyric Acid)\u0026thinsp;+\u0026thinsp;30g/L sucrose\u0026thinsp;+\u0026thinsp;6g/L agar). The transgenic plants grow at tissue culture room which was maintained at 26\u0026deg;C with a photoperiod of 16h light/8h dark. After 30 days, root exudates and transcriptome were measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and transcriptome analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from the whole leaves of WT, OE, and RE transgenic plants using a universal plant total RNA extraction kit (BioTeke Corporation, Beijing, China). RNA samples were submitted for 150 bp paired\u0026thinsp;\u0026minus;\u0026thinsp;end reads on Illumina \u0026times; 10 platform. After sequencing, low-quality sequences and adapter contamination were removed from the original raw data through fastq software[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. High-quality sequences were then aligned to the \u003cem\u003ePopulus trichocarpa\u003c/em\u003e genome[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] using HISAT2 software[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] to obtain positional information on genes. Functional annotations were carried out based on various databases. Significantly differentially expressed genes (DEGs) were selected based on the criteria of log2 Fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 and q\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Common DEGs between two comparison groups were selected, and results of their alignment in the GO database (\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) Pathway enrichment analysis was also performed using the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kobas.cbi.pku.edu.cn/genelist/\u003c/span\u003e\u003cspan address=\"http://kobas.cbi.pku.edu.cn/genelist/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMetabolome Analysis\u003c/h2\u003e \u003cp\u003eTransgenic and non- transgenic line were placed in 50 mL centrifuge tube (two plantlets per tube), with 15 tubes for each line. Each tube contained 15 mL of pure water under conditions of 26\u0026deg;C with a photoperiod of 16h light / 8h dark for 30 days. Every 10 days, the culture solutions were collected from the test plants. Collected root culture solutions were analyzed by chromatographic separation method using the ACQUITY UPLC BEH C18 column (Waters\u0026trade;, USA). Raw data collected were pre-processed for peak extraction, alignment, calibration, and normalization with Compound Discoverer 3.0 software (Thermo Fisher Scientific, USA). Metabolite structures were identified using accurate mass (\u0026lt;\u0026thinsp;25ppm) and secondary spectrum matching methods, and were searched in databases. Significantly differential expressed metabolites (DEMs) were selected based on the criteria of log2 Fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 and q\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Common DEMs between two comparison groups were selected, and results of their alignment in KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kobas.cbi.pku.edu.cn/genelist/\u003c/span\u003e\u003cspan address=\"http://kobas.cbi.pku.edu.cn/genelist/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis between Metabolome and Transcriptome\u003c/h2\u003e \u003cp\u003eThe correlation between the metabolome and transcriptome was calculated using the Pearson function. Data with R values\u0026thinsp;\u0026ge;\u0026thinsp;0.95 and P values\u0026thinsp;\u0026le;\u0026thinsp;0.01 were selected for performing correlation analysis using R language nine-quadrant plot. The WGCNA package(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/\u003c/span\u003e\u003cspan address=\"https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is used to calculate correlation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePaGLK transgenic lines showed different gene expression patterns\u003c/h2\u003e \u003cp\u003eTo explore the transcriptional expression characteristics of \u003cem\u003ePaGLK\u003c/em\u003e transgenic poplar, the leaves of \u003cem\u003ePaGLK\u003c/em\u003e overexpressing lines, \u003cem\u003ePaGLK\u003c/em\u003e suppressing lines, and WT were subjected to RNA-seq sequencing. Due to abnormal clustering of samples OE3 and WT3, these outliers were removed, and a total of 307.9\u0026nbsp;million Clean Reads were obtained (39.8\u0026thinsp;~\u0026thinsp;47.9\u0026nbsp;million per library), with a Q30 base percentage above 92.97% and alignment rate to the \u003cem\u003ePopulus trichocarpa\u003c/em\u003e genome between 74.10% and 76.63% (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), indicating reliable transcriptome data.\u003c/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA) showed that the clustering position of WT was closer to that of the overexpressing lines and far from that of the suppressing lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). OE vs WT had 3052 DEGs, 1731 upregulated and 1321 downregulated. in RE vs WT had 1326 DEGs, 669 upregulated and 657 downregulated. OE vs RE had 4577 DEGs, 2376 upregulated and 2201 downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). There were more upregulated DEGs than downregulated genes in all three comparison groups, with RE vs WT having the least DEGs, and the up/downregulated gene ratio in OE vs WT being uneven.\u003c/p\u003e \u003cp\u003eThere were 597 common DEGs (317 upregulated, 280 downregulated) in RE vs WT and RE vs OE. There were also 1705 common DEGs (942 upregulated, 763 downregulated) in OE vs WT and RE vs OE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis of DEGs\u003c/h2\u003e \u003cp\u003eGO functional enrichment analysis was performed mainly for biological processes on the DEGs in the three comparison groups. The common upregulated DEGs in RE vs WT and RE vs OE were enriched related to salt stress response, response to darkness, carbohydrate response, and water shortage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The downregulated DEGs were related to photosynthetic electron transfer in photosystem I, photosynthesis, light harvesting in photosystem I, response to light stimulus, chloroplast development, and chloroplast organization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe upregulated DEGs in OE vs WT and OE vs RE were related to cytokinesis during male meiosis, spindle assembly checkpoint signaling in mitosis, DNA replication initiation, and protection of germ cell differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The downregulated DEGs were related to regulation of cell fate determination, response to bacterial derived molecular patterns, protein autophosphorylation, and salicylic acid activation signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). These results reveal that the photosynthetic capacity of the RE transgenic lines was negatively affected compared to the WT.\u003c/p\u003e \u003cp\u003ePathway analysis showed that the DEGs in OE vs WT and RE vs WT corresponded to 120 and 92 KEGG pathways, respectively. The photosynthesis-antenna proteins, photosynthesis, and porphyrin and chlorophyll metabolism pathways were significantly enriched in RE vs WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The ribosome pathway was significantly enriched in OE vs WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eValidation of DEGs by RT-qPCR\u003c/h2\u003e \u003cp\u003eWe found that downregulated DEGs in RE vs WT and RE vs OE were enriched in pathways related to photosynthesis, and focused on the DEGs in six biological processes related to photosynthesis. We chose ten genes from these processes for qPCR validation and found that the expression levels of these genes in the WT lines were higher than those in the RE lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMetabolome analysis\u003c/h2\u003e \u003cp\u003eThe response intensity and retention time of each chromatographic peak in the QC sample total ion flow map overlapped substantially (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), indicating low variation due to instrumental error. A total of 5128 ion peaks were extracted from the metabolites, 2583 positive ions and 2545 negative ions. All extracted peaks were used for PCA analysis.\u003c/p\u003e \u003cp\u003ePCA analysis of the metabolites showed a certain separation tendency on the PC1 and PC2 axis plots, and the distribution of the sample points was somewhat discrete, indicating differences in metabolites among the three groups of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of DEMs\u003c/h2\u003e \u003cp\u003eKEGG pathway enrichment analysis of differential expressed metabolites (DEMs) found a total of 39 enriched differential metabolic pathways in RE vs WT. Ten of them were significantly enriched (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and mainly related to amino acid metabolism, carbohydrate metabolism, and fatty acid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). A total of 49 enriched differential metabolic pathways were found in OE vs WT, and 15 of them were significantly enriched (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), mainly related to amino acid metabolism and carbohydrate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eCluster analysis was performed on the metabolites with significant differences in the metabolic pathways involved in the three comparison groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Compared with the WT, more of the downregulated metabolites were found in the \u003cem\u003ePaGLK\u003c/em\u003e overexpression lines, mainly including biphenols (catechol, epinephrine, 3,4-dihydroxyphenylethylene glycol, 3,4-dihydroxyphenylethanoic acid), methoxyphenols (vanillin, 3-methoxytyramine), and carbohydrates and carbohydrate-associated molecules (gomphrenin-I, arbutin). In the \u003cem\u003ePaGLK\u003c/em\u003e suppression lines, the levels of dicarboxylic acids and their derivatives (fumaric acid, malic acid, succinic acid), eicosanoic acids (prostaglandin D2, prostaglandin G2, PGB2), and amino acids, peptides and analogues (yeast amino acid, O-carbamyl-D-serine) were upregulated, while the levels of these metabolites were downregulated in the WT. These results indicate that differential metabolites have different expression patterns in the transgenic and WT poplar.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome and metabolome conjoint analysis\u003c/h2\u003e \u003cp\u003ePearson correlation analysis was done between transcriptome and metabolome. DEGs and DEMs were selected with threshold R-value\u0026thinsp;\u0026ge;\u0026thinsp;0.95 and P-value\u0026thinsp;\u0026le;\u0026thinsp;0.01 for nine quadrant analysis. There were no correlated genes in either the positive or negative ion metabolites in the first quadrant. In the third quadrant, there were 236 regulatory relationships between positive ion metabolites and genes, and 193 regulatory relationships between negative ion metabolites and genes. In the seventh quadrant, there were 72 regulatory relationships between positive ion metabolites and genes, and 69 regulatory relationships between negative ion metabolites and genes. In the ninth quadrant, there were 19 regulatory relationships between positive ion metabolites and genes, and 15 regulatory relationships between negative ion metabolites and genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The results indicate that the DEMs have a positive regulatory relationship with DEGs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe rhizosphere microorganisms have a significant impact on plant growth and development[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The composition of rhizosphere microorganisms is not constant and is influenced by various factors[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, studying the factors that affect composition of these microorganisms is crucial. Previous studies have demonstrated that the addition of above- and below-ground pineapple residues in highly infested soils significantly reduced the number of pathogens in the soil, thus resulting in a lower disease incidence. This is because \u003cem\u003eAspergillus fumigatus\u003c/em\u003e and \u003cem\u003eFusarium solani\u003c/em\u003e, which are present in pineapples, exhibit antagonism against the pathogens, thereby altering the original microbial environment[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Another research use metagenomics information as an external quantitative phenotype to map the host genetic determinants of the rhizosphere microbiota in barley, and identify a small number of loci with a major effect on the composition of rhizosphere communities[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Salas-Gonz\u0026aacute;lez et al. investigated the regulatory relationship between the root diffusion barriers in the endodermis and microbiota and found that genes controlling endodermal function in Arabidopsis thaliana contribute to the plant microbiome assembly[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Previous studies have shown significant differences in the activity of bacteria, fungi, and soil enzymes in the roots of transgenic poplar lines with overexpressed PaGLK, suppressed expression, and WT[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, PaGLK gene expression alter the photosynthetic capacity of poplar[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRhizosphere exudates are the primary source of soil carbon[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], on which most microorganisms rely for their carbon supply[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this study, we found differences in metabolic pathways, including carbon metabolism and the TCA cycle, between transgenic lines and WT. Therefore, we hypothesize that PaGLK alters the composition of root exudates by changing C-related metabolic pathways. Furthermore, we conducted correlation analysis between DEMs and DEGs, and found a positive correlation between them, but a precise localization is lacking and needs further investigation. Overall, this study provides a new idea for studying the causes of root microbial community alterations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, it was found that the expression of \u003cem\u003ePaGLK\u003c/em\u003e significantly altered the expression of photosynthesis-related genes, indicating that \u003cem\u003ePaGLK\u003c/em\u003e is involved in the process of plant photosynthesis. Furthermore, it was observed that the types of root exudates between transgenic and wild-type plants were significantly different, with significant differences (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e) in carbon metabolism and pyruvate metabolism in the suppressed expression transgenic lines compared with WT. These results suggest that \u003cem\u003ePaGLK\u003c/em\u003e may regulate the expression of genes involved in carbon metabolism, pyruvate metabolism, and other pathways to alter the types of root exudates produced. \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available at\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the experimental platform provided by Northeast Forestry University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXDB writes the first draft and analyzes the data. YZ and WW design experiments and complete them. LC participated in the experimental part. GFL and JJ supervised the entire experimental process and provided guidance. QBY and CPY have made revisions and improvements to the initial draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agreed to publish this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available in the NCBI\u0026nbsp;\u003c/p\u003e\n\u003cp\u003edatabase https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1021575/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Heilongjiang Touyan Innovation Team Program (Tree Genetics and Breeding Innovation Team).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBais HP, Weir TL, Perry LG, et al (2006) THE ROLE OF ROOT EXUDATES IN RHIZOSPHERE INTERACTIONS WITH PLANTS AND OTHER ORGANISMS. 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Journal of Experimental Botany 70:3125\u0026ndash;3138. https://doi.org/10.1093/jxb/erz128\u003c/li\u003e\n\u003cli\u003ePausch J, Kuzyakov Y (2018) Carbon input by roots into the soil: Quantification of rhizodeposition from root to ecosystem scale. Glob Chang Biol 24:1\u0026ndash;12. https://doi.org/10.1111/gcb.13850\u003c/li\u003e\n\u003cli\u003ePanchal P, Preece C, Pe\u0026ntilde;uelas J, Giri J (2022) Soil carbon sequestration by root exudates. Trends in Plant Science 27:749\u0026ndash;757. https://doi.org/10.1016/j.tplants.2022.04.009\u003c/li\u003e\n\u003cli\u003eLi Y, Gu C, Gang H, et al (2021) Generation of a Golden Leaf Triploid Poplar by Repressing the Expression of \u003cem\u003eGLK\u003c/em\u003e Genes. f 1:1\u0026ndash;7. https://doi.org/10.48130/FR-2021-0003\u003c/li\u003e\n\u003cli\u003eZheng Y, Lv GB, Chen K, et al (2022) Impact of PaGLK transgenic poplar on microbial community and soil enzyme activity in rhizosphere soil. 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Science 371:eabd0695. https://doi.org/10.1126/science.abd0695\u003c/li\u003e\n\u003cli\u003eWang W, Jia T, Qi T, et al (2022) Root exudates enhanced rhizobacteria complexity and microbial carbon metabolism of toxic plants. iScience 25:105243. https://doi.org/10.1016/j.isci.2022.105243\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-molecular-biology-reporter","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pmbr","sideBox":"Learn more about [Plant Molecular Biology Reporter](http://link.springer.com/journal/11105)","snPcode":"11105","submissionUrl":"https://submission.nature.com/new-submission/11105/3","title":"Plant Molecular Biology Reporter","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Transcriptome, Metabolome, Root Exudate, GOLDEN2-LIKE genes, poplar","lastPublishedDoi":"10.21203/rs.3.rs-4293152/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4293152/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePhotosynthetic carbon fixation is the main source of root exudates. \u003cem\u003eGOLDEN2-LIKE\u003c/em\u003e (\u003cem\u003eGLK\u003c/em\u003e) genes play an important role in photosynthetic carbon fixation. Previous studies have found that expression-inhibited the \u003cem\u003ePaGLK\u003c/em\u003e in poplar reduce its net photosynthesis. However, the relationship between GLK genes, root exudates and photosynthetic carbon fixation and how photosynthesis affects root exudate in poplar are not clear.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eIn this study, we performed comparative transcriptome and metabolome analyses of overexpression and suppression transgenic poplar. GO enrichment analysis showed that the downregulation of differentially expressed genes (DEGs) in suppression lines was mainly related to photosynthesis in biological processes. Specifically, photosynthesis-antenna proteins, porphyrin and chlorophyll metabolism, and photosynthesis were significantly enriched in KEGG pathways. Gene expression showed consistent trends in real time quantitative PCR (RT-qPCR) and transcriptome, indicating reliable transcriptome. Differentially expressed metabolites (DEMs) of root exudates were mainly enriched in amino acid metabolism, glucose metabolism and fatty acid metabolism pathways. After correlating DEGs and DEMs, we found that most genes and metabolites showed positive regulation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study shows that the new factors change composition of root exudates.\u003c/p\u003e","manuscriptTitle":"Transcriptome and metabolome conjoint analysis revealed that PaGLK affects photosynthesis and composition of root exudates in poplar","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-03 06:28:24","doi":"10.21203/rs.3.rs-4293152/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-02T13:47:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-02T13:27:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-28T17:02:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196314417657748958378206156670984777147","date":"2024-05-27T15:54:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89820587722510971155228383045009077651","date":"2024-05-20T13:25:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-18T21:28:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-22T07:11:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-22T07:11:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Molecular Biology Reporter","date":"2024-04-19T12:26:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-molecular-biology-reporter","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pmbr","sideBox":"Learn more about [Plant Molecular Biology Reporter](http://link.springer.com/journal/11105)","snPcode":"11105","submissionUrl":"https://submission.nature.com/new-submission/11105/3","title":"Plant Molecular Biology Reporter","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"12b3397d-d5ee-40cc-b347-1742d92939f6","owner":[],"postedDate":"May 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T20:00:43+00:00","versionOfRecord":{"articleIdentity":"rs-4293152","link":"https://doi.org/10.1007/s11105-025-01550-0","journal":{"identity":"plant-molecular-biology-reporter","isVorOnly":false,"title":"Plant Molecular Biology Reporter"},"publishedOn":"2025-03-04 15:57:44","publishedOnDateReadable":"March 4th, 2025"},"versionCreatedAt":"2024-05-03 06:28:24","video":"","vorDoi":"10.1007/s11105-025-01550-0","vorDoiUrl":"https://doi.org/10.1007/s11105-025-01550-0","workflowStages":[]},"version":"v1","identity":"rs-4293152","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4293152","identity":"rs-4293152","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}