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Source leaves are regulated by sink strengths through non-coding RNAs and alternative polyadenylation in cucumber (Cucumis sativus L.)

BMC Plant Biology volume 24, Article number: 812 (2024) Cite this article

Abstract

Background

The yield of major crops is generally limited by sink capacity and source strength. Cucumber is a typical raffinose family oligosaccharides (RFOs)–transporting crop. Non-coding RNAs and alternative polyadenylation (APA) play important roles in the regulation of growth process in plants. However, their roles on the sink‒source regulation have not been demonstrated in RFOs–translocating species.

Results

Here, whole–transcriptome sequencing was applied to compare the leaves of cucumber under different sink strength, that is, no fruit-carrying leaves (NFNLs) and fruit-carrying leaves (FNLs) at 12th node from the bottom. The results show that 1101 differentially expressed (DE) mRNAs, 79 DE long non–coding RNAs (lncRNAs) and 23 DE miRNAs were identified, which were enriched in photosynthesis, energy production and conversion, plant hormone signal transduction, starch and carbohydrate metabolism and protein synthesis pathways. Potential co–expression networks like, DE lncRNAs–DE mRNAs/ DE miRNAs–DE mRNAs, and competing endogenous RNA (ceRNA) regulation models (DE lncRNAs–DE miRNAs–DE mRNAs) associated with sink‒source allocation, were constructed. Furthermore, 37 and 48 DE genes, which enriched in MAPK signaling and plant hormone signal transduction pathway, exist differentially APA, and SPS (CsaV3_2G033300), GBSS1 (CsaV3_5G001560), ERS1 (CsaV3_7G029600), PNO1 (CsaV3_3G003950) and Myb (CsaV3_3G022290) may be regulated by both ncRNAs and APA between FNLs and NFNLs, speculating that ncRNAs and APA are involved in the regulation of gene expression of cucumber sink‒source carbon partitioning.

Conclusions

These results reveal a comprehensive network among mRNAs, ncRNAs, and APA in cucumber sink–source relationships. Our findings also provide valuable information for further research on the molecular mechanism of ncRNA and APA to enhance cucumber yield.

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Background

Plant yield is influenced by the capacity of source organs (such as fully developed leaves) to capture light and the strength of sink organs (such as roots, stem, fruits, and seeds) to store photosynthetic carbon assimilates [1, 2]. Previous studies have shown that the yield is generally determined by the assimilate allocation between source and sink, and it can be improved by decreasing the source-sink ratio appropriately [3,4,5]. Several evidences have revealed the plant hormones, assimilates produced by photosynthesis and environmental factors are important regulatory factors regulating the sink‒source carbon partitioning [6,7,8].

Non-coding RNAs (ncRNAs) including, long non–coding RNAs (lncRNAs), micro RNAs (miRNAs), small interfering RNAs (siRNAs), circular RNAs (circRNAs) and some others are widespread in plants and becoming key regulators of cell function [9, 10]. LncRNAs are transcripts > 200 nt in length, which could interact with DNA, RNA, and proteins, which help in regulating RNA metabolism, protein modification, and chromatin remodeling through either cis– or trans– activation at the transcriptional and post–transcriptional levels [11]. Plant miRNAs are endogenous regulatory molecules, which act as posttranscriptional regulators by targeting RNAs via sequence complementarity, usually resulting in the cleavage or degradation of targets [12, 13]. The involvement of numerous classes of ncRNA has been documented in the regulation of sink-source control, serving as possible signals for growth and differentiation. For instance, the utilization of lncRNA known as asCsSTS-RNAi has been shown to enhance the expression of the assimilate loading gene stachyose synthase (CsSTS, CsaV3_7G030090) in cucumber leaves, thereby promoting the growth of sink organs [14]. Transcription factor (TFs) of ZmMYB138/ZmMYB115 was found to be regulated by Zma-miR159k-3p, hence impacting the activity of the promoter of some starch synthesis genes [15]. Overexpression of miR166/165 in Arabidopsis inhibits the expression of TF class III homeodomain leucine–zipper (HD-ZIP III) and improves root growth [16, 17]. Additionally, the competing endogenous RNA (ceRNA) hypothesis posits that many types of RNA molecules, including mRNA, circRNA, lncRNA, and pseudogene transcripts with the same miRNA response element (MRE) are competitively bound to the same miRNA, serves as a model for the regulation of gene expression [12]. In recent years, the ceRNA network has been explored and constructed through utilization of high throughput sequencing technology in many plants, revealing the significant function of ceRNA network in various aspects of plant growth, including photosynthesis, carbohydrate transport and metabolism, as well as plant hormone signal transduction, throughout all phases of plant development [18,19,20,21,22]. However, the response of ceRNA regulatory network to sink‒source carbon allocation in plants, especially cucurbits, has not been systematically studied.

Alternative polyadenylation (APA) is highly prevalent in plants, with over 75% of the mRNA transcripts in Arabidopsis are subjected to APA–regulation [23]. During mRNA maturation process, the cleavage and polyadenylation specificity factor (CPSF) splits the pre-mRNAs by binding to different polyadenylation signals (PAS), followed by attachment of a polyadenylation (poly(A)) tail, leading to the production of multiple transcripts isoforms that vary in terms of their lengths and sequence compositions [24]. The most common polyadenylation event observed is 3′UTR–APA, which has the potential to impact various aspects of mRNA, including stability, localization, and translation efficiency. Moreover, APA also exists in the coding region of genes, specifically in introns or exons having potential of affecting the gene function by alter protein sequence [25]. To date, APA has been implicated to play an important role in post–transcriptional regulation in response to biotic and abiotic stresses, such as hypoxic, drought, salt stress, nitrogen starvation, temperature, pathogens, ROS and ABA treatments [26,27,28]. Nevertheless, there is scarcity of research regarding the role of APA in sink–source regulation. In a study conducted by Zhang [29], it has been reported three stachyose synthase (CsSTS) mRNAs with different 3′UTR namely CsSTS1, CsSTS2 and CsSTS3. Among these, CsSTS1 was found to be the most stable isoform and the expression of CsSTS1 was markedly upregulated in fruit-carrying node leaves, as compared to no fruit-carrying node leaves, thereby promoting the process of assimilate loading in source leaves.

The cucumber (Cucumis sativus L.) is an economically important crop that is grown in over 150 countries around the world and is a typical carrier of raffinose family oligosaccharides (RFOs) [30]. In cucumber leaves, three main genes, Galactitol synthase (CsGolS1), raffinose synthase (CsRS), and CsSTS are responsible for assimilate loading in phloem [31]. In recent years, researchers have studied the effects of different nitrogen concentration, CO2 concentration, temperature, salt and water stress on sink‒source relationship of cucumber [32,33,34,35,36]. However, do APA and lncRNA play roles on the cucumber sink‒source regulation remains unknown [14, 29]. In this study, whole–transcriptome sequencing between leaves carrying fruit (FNLs) and without fruit (NFNLs) of cucumber was carried out, aiming to identify the differentially expressed (DE) mRNAs, DE lncRNAs, DE miRNAs, DE lncRNAs–DE miRNAs–DE mRNAs network. Additionally, the study aimed to investigate the differentially APA events, and explore the role of ncRNA and APA in regulating gene expression associated with sink–source allocation. Our findings provide a way to increase cucumber production by regulating the sink–source relationship.

Materials and methods

Plant material and treatments

Seeds of the “Jinchun 5” variety of cucumber seeds were obtained from Tianjin Cucumber Institute of China, and planted in the growth chambers of Yangzhou University (119°42′E, 32°38′N) at 25℃/16 h of light and 16℃/ 8 h of dark with a relative humidity of 70% in 2022. A mixture of Peat-vermiculite in a volumetric ratio of 2:1 was placed in 40 × 40 cm plastic pots. Plants were watered every three days and received weekly fertilization using Yamazaki cucumber nutrient solution [37]. The experiments were conducted between no fruit–carrying leaves (NFNLs) and fruit-carrying leaves (FNLs) at the 12th node from the bottom of cucumber plants at eight days post anthesis. The cucumber plants with NFNL and FNL both had 20 true leaves and were healthy when sampled. The plants with NFNL removed all the female flowers, and the male flowers opened normally. The plants with FNL only retained the female flowers at the 12th node, and the other treatments were the same as the plants with NFNL. NFNLs/FNLs from five plants were mixed together as a sample stored at -80 °C. Six cDNA libraries were constructed from the control groups (NFNL-1, NFNL-2, NFNL-3) and treatment groups (FNL-1, FNL-2, FNL-3).

RNA extraction, library construction, and RNA sequencing

The extraction of total RNA from FNLs and NFNLs was performed using RNAiso Plus (Takara, Beijing, China). The RNA concentration and integrity were analyzed using a NanoDrop One/OneC spectrophotometer (Thermo Scientific, DE, USA). To identify lncRNAs and mRNAs, a strand-specific library was constructed by removing ribosomal RNA using the NEBNext Ultra™ Directional RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA). For small RNA sequencing, 15 µg of total RNA for three biological replicates of NFNLs/FNLs were performed for the construction of libraries using a Small RNA Library Preparation kit (Illumina Inc., San Diego, CA, USA). The Illumina HiSeq™ 2500 platform was used for RNA-seq by Biomarker company (Beijing, China).

lncRNA identification and analysis

Firstly, transcripts longer than 200 nt were selected as lncRNAs. Then, the protein-coding capacity was predicted by Coding Potential Calculator [38] and Coding Potential Assessment Tool [39]. The DE lncRNAs were selected with | log2 (fold change) | > 1 and FDR < 0.05. Cis–target genes could regulate nearby genes. Perl scripts (www.perl.org, Perl Foundation, 5.32.0) was used to search for target genes within 100 kb upstream and downstream of lncRNAs [40]. Trans–target genes of lncRNAs were screened by lncRNA and mRNA expression correlation analysis or co-expression analysis methods using LncTar [41]. The complementary sequence between lncRNAs and mRNAs were performed to calculate the free energy and normalized free energy of the pairing site. Those below the normalized free energy threshold (<–0.1) were selected as the trans–target genes of lncRNAs.

Identification of miRNAs and the prediction of their target genes

After filtering out low-quality bases, adapter dimers, common RNA families (rRNA, snRNAs, tRNA, snoRNA, etc.) and duplications in the original reads, miRBase (v22) [42] and MiRDeep2 [43] were used to predict known miRNAs and novel miRNAs, respectively. The miRNA expression levels were calculated by transcripts per million reads (TPM) method. The DESeq2 [44] was performed to screen the DE miRNAs with | log2 (fold change) | > 1 and FDR < 0.05. Also, the targeted genes of DE miRNAs were predicted by TargetFinder [45] with the default parameters.

mRNA identification and differential expression analysis

HISAT 2.2.1 [46] was used to map clean reads to the cucumber genome [47] after low-quality bases were removed. The DE mRNAs were analyzed by Edge R [48] with | log2 (fold change) | > 1 and FDR < 0.05 between FNLs and NFNLs. DE mRNAs and target genes regulated by DE lncRNAs and DE miRNAs were subjected to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using the BLAST2GO [49] and KOBAS–2.0 software [50], respectively. The significantly enriched GO terms and KEGG pathways were selected based on the p–value less than 0.05.

Construction of lncRNA–mRNA, miRNA–mRNA and ceRNA regulatory network

In this study, the mRNA–miRNA and mRNA–lncRNA pairs were predicted by psRobot [51] and evaluated by Spearman correlation coefficient (SCC) [52]. The SCC pairs with value less than − 0.7 were selected as co–expressed lncRNA–mRNA/mRNA–miRNA pairs. Then, ceRNA–score principle was used to screen ceRNAs according to previous reports [53, 54]. Cytoscape v3.8.2 [55] was used for visualization of interaction networks.

Quantitative reverse transcriptase–polymerase chain reaction (qRT‒PCR) validation of DE mRNAs, DE lncRNAs and DE miRNAs

In order to verify the reliability of the whole–transcriptome sequencing results, we carried out qRT‒PCR. Similary, total RNA was isolated from NFNLs and FNLs using an RNAprep pure Plant Kit (TIANGEN, Cat#DP432, China). Reverse transcription (RT) reactions of mRNAs and lncRNAs were performed with EasyScript® One–Step gDNA Removal and cDNA Synthesis SuperMix (TransGen Biotech, AE311–02, China). RT reactions of miRNAs were used for cDNA synthesis by a miRNA 1st Strand cDNA Synthesis Kit (Vazyme, Cat#MR101, China). The primers of mRNAs/lncRNAs for qRT‒PCR were designed using DNAMAN 6.0 (LynnonBiosoft, USA) whereas, the stem–loop primers for RT and specific primer (forward primer) for qRT‒PCR of miRNAs were designed using miRNA Design V1.01 (Vazyme, China). The genes of 18 S rRNA (GenBank accession No.: AF206894) and U6 snRNA (GenBank accession No.: XM_004149554) were served as the internal control for the mRNA/lncRNA and miRNA expression analysis, respectively. Primers and thermocycling conditions used in this study have been were listed in Table S1. Using CFX Connect Real–Time System (Bio–Rad, Hercules, CA), qRT‒PCR was performed for three technical replicates in each sample. The 2−ΔΔCT method was used to calculate the relative gene expression levels.

Full-length transcriptome sequencing, APA identification and differentially APA analysis between NFNLs and NFNLs

Full-length transcriptome technology was performed by Oxford Nanopore Technologies (ONT, UK) to identify APA sites. The two samples including NFNLs and FNLs which contained nine leaves for each were collected and put them in liquid nitrogen. The experimental procedure was performed in accordance with the standard protocol provided by ONT. It mainly includes the following steps: extracting RNA according to the method in 2.2, constructing library, adding sequencing connector, and sequencing using PromethION48 machine. The TAPIS pipeline, as described by Abdel-Ghany [56], was used to identify APA. To identify potential motif necessary for polyadenylation, the 50 bp upstream and 50 bp downstream sequences of poly(A) sites in all transcripts were analyzed by DREME [57]. The genome locations of the APA site with largest reads were compared between FNLs and NFNLs (Table S5), and the proximal or distal preference of APA sites was defined according to the genome location of the two APA sites with the largest reads of FNLs and NFNLs.

Measurement of the net photosynthetic rate, soluble sugar, starch and soluble protein contents

Net photosynthetic rate of cucumber between NFNLs and FNLs was measured by using photosynthesis–apparatus Li–6400 (LI–COR Inc. Lincoln, NE, USA). The measurements were conducted at light intensity of 1500 µmol m− 2 s− 1, leaf temperature of 26 °C, and CO2 of 410 ± 10µL L− 1 in the sample chamber [58, 59]. The content of soluble sugars, namely stachyose, raffinose, galactinol, sucrose, glucose and fructose were extracted and analyzed with HPLC according to our previous work [60]. Starch and soluble protein content were measured using anthrone colorimetry [61] and Coomassie Brilliant Blue G‒250 [62], respectively.

Statistical analysis

The data presented in this study consists of mean values ± standard deviation (SD) obtained from five biological replicates for NFNLs and FNLs in the experiment described in Sect. 2.9. For the whole-transcriptome sequencing and qRT-PCR analyses, the mean values ± standard deviation (SD) were calculated from three biological replicates. Statistical comparison was conducted using the student’s t-test in GraphPad Prism 9 (GraphPad Software, Co., Ltd, San Diego, CA, USA). Significant differences were determined based on the values of P < 0.05 (*) and P < 0.01 (**).

Results

Identification and characterization of mRNAs and ncRNAs in cucumber leaves

In this study, we performed whole-transcriptome sequencing of cucumber leaves for different sink strengths (no fruit-carrying leaves (NFNLs) and fruit-carrying leaves (FNLs)) (Fig. 1a). The quality control data of whole transcriptome sequencing shown in Table S2 indicated that whole transcriptome sequencing is of high quality and could be used for further analysis. A total of 25,070 mRNAs, 2743 lncRNAs and 218 miRNAs were detected between NFNLs and FNLs as shown in Fig. 1b. Interestingly, most of lncRNAs in cucumber were long intergenic noncoding RNAs (lincRNAs, 1695, 61.8%), followed by antisense–lncRNAs (715, 26.1%), sense–lncRNAs (190, 6.9%) and intronic–lncRNAs (143, 5.2%) (Fig. 1c). We found that the transcript length and open reading frame (ORF) length of lncRNAs were significantly shorter than that of mRNAs. Additionally, the exon number of lncRNAs was found not to be widely distributed as mRNAs (Fig. S1). Moreover, the expression level of lncRNAs were lower than that of mRNAs (Fig. 1d). Among 218 miRNAs, 84 known miRNAs and 134 putatively novel miRNAs were identified, accounting 21 nt and 24 nt for the most abundant miRNAs in the known miRNA and novel miRNA, respectively (Fig. 1e). Other non-coding RNAs, such as circular RNAs (circRNAs), have been identified rarely in FNLs and NFNLs, and hence these are not introduced in this study.

Fig. 1
figure 1

The identification and characteristics of mRNAs and non–coding RNAs (ncRNAs) between NFNLs and FNLs. (a) Cucumber leaves phenotypes of FNL and NFNL. (b) Numbers of mRNAs, lncRNAs and miRNAs. (c) Numbers of different types lncRNAs. (d) Expression levels (FPKM) of mRNAs and lncRNAs. (e) Length distribution of known miRNAs and novel miRNAs

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Identification of DE mRNAs, DE lncRNAs and DE miRNAs

In the comparison between FNLs vs. NFNLs comparison, a total of 1104 DE mRNAs, 75 DE lncRNAs and 23 DE miRNAs were differentially expressed (Fig. 2a). Among them, 759 mRNAs, 35 lncRNAs and 11 miRNAs were upregulated, whereas 345 mRNAs, 40 lncRNAs and 12 miRNAs were found to be downregulated in FNLs (Fig. 2a-d). Table S3 provides annotations of the DE mRNAs and DE ncRNAs target genes between NFNLs and FNLs, whereas, a circos plot clearly showed that DE lncRNAs and DE miRNAs were unevenly distributed across chromosomes (Fig. S2).

Fig. 2
figure 2

Identification of differentially expressed (DE) mRNAs and DE non–coding RNAs (ncRNAs) between NFNLs and FNLs. (a) Numbers of DE mRNAs, DE lncRNAs and DE miRNAs with up– and down–regulation. (b)–(d) Heatmaps of all DE mRNAs, DE lncRNAs and DE miRNAs

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Function enrichment analysis of DE mRNAs, targets of DE lncRNAs and DE miRNAs related to sink– source regulation

The majority of the DE mRNAs were annotated to the GO terms “sucrose metabolic process” under biological process (BP), to “cell wall” and “DNA replication factor” under cellular component (CC), and to “sucrose synthase activity” under molecular function (MF) (Fig. 3a, b, c), suggesting a potential role in the regulation of sink‒source in cucumber (Fig. 3a). These DE mRNAs were enriched in 19 significantly KEGG metabolic pathways (Fig. 3b), such as “photosynthesis‒antenna proteins”, “MAPK signaling pathway” and “DNA replication” (Fig. 4a).

Generally, lncRNAs mediate transcriptional regulation via cis– or trans–regulation [63]. In this study, a total of 1565 pairs of cis–regulatory genes were identified (Table S3–2). The cis–target genes of the DE lncRNAs were mainly involved in “cell proliferation” under BP, in “mitochondrion”, “chloroplast part”, and “NAD(P)H dehydrogenase complex (plastoquinone)” under CC, and in “ATP: ADP antiporter activity” and “mitochondrial ribosome binding” under MF (Fig. 3d, e, f). For KEGG analysis, these targeted genes were enriched in “plant hormone signal transduction”, “photosynthesis–antenna proteins” and “Photosynthesis” pathways, which may play an important role in sink‒source relationship (Fig. 4b). Besides, for the trans action of lncRNAs, computational prediction identified 204 potential target genes for 79 DE lncRNAs (Table S3–3). GO analysis revealed that trans‒target genes of DE lncRNAs were involved in “response to auxin” under BP (Fig. 3g), there are no CC and MF related to sink–source regulation (Fig. 3h, i) Similarly, two significantly enriched pathways, “plant hormone signal transduction” and “carbon metabolism” were identified by KEGG analysis (Fig. 4c).

In addition, 787 target genes of the DE miRNAs were mainly enriched in “nitrogen compound metabolic process” under BP, in “intracellular membrane − bounded organelle” under CC, and in “ADP binding”, and “ribosome binding” under MP (Fig. 3j, k, l, Table S3–4). Further, the KEGG enrichment analysis of the target genes showed a significantly enriched pathway “Starch and sucrose metabolism”, which may play a role in sink‒source regulation (Fig. 4d).

Fig. 3
figure 3

Gene ontology (GO) enrichment analysis for differentially expressed (DE) mRNAs, DE non–coding RNAs (lncRNAs) cis–targets, DE lncRNAs trans–targets and DE miRNAs targets under biological process (BP), cellular component (CC) and molecular function (MF). (a)–(c) GO enrichment analysis of DE mRNAs. (d)–(f) GO enrichment analysis of DE lncRNAs cis–targets. (g)–(i) GO enrichment analysis of DE lncRNAs trans–targets. (j)–(l) GO enrichment analysis of DE miRNAs target genes

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Fig. 4
figure 4

Functional enrichment analysis of differentially expressed (DE) mRNAs and targets of DE non–coding RNAs (ncRNAs) between NFNLs and FNLs. (a)–(d) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DE mRNAs, DE lncRNAs cis–targets, DE lncRNAs trans–targets and DE miRAs targets

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Construction of co–expression networks for DE lncRNAs and DE mRNAs related to sink‒source interactions

A total of 64 DE lncRNA–DE mRNA pairs, including 58 cis–regulated DE mRNAs and 33 DE lncRNAs, were constructed according to the location relationship (Fig. 5a, b and Table S4–1). The study revealed that there were 35 cis–targets of DE lncRNAs that exhibited positively co‒expression with those DE lncRNAs. As an instance, upregulated genes CsaV3_ (photosystem II protein) was targeted by MSTRG.1470.5, which may promote photosynthesis in FNLs. Upregulated genes CsaV3_3G025070 (mitochondrial arginine transporter BAC2) was targeted by MSTRG.7495.1, which may promote the synthesis of mitochondria in FNLs. In addition, a total of 30 cis–targets of DE lncRNAs were negatively co‒expressed with those DE lncRNAs. Among them, MSTRG.5231.1 and MSTRG.12014.2 negatively regulated their downregulated target genes CsaV3_2G033300 (sucrose–phosphate synthase, SPS) and CsaV3_5G001560 (Granule–bound starch synthase 1, GBSS1) in FNLs, respectively, indicating that they may be involved in the synthesis of sucrose and starch in leaves. The upregulated gene CsaV3_6G009850 (UDP–rhamnose/UDP–galactose transporter 4, UGT4) was targeted by MSTRG.15397.2, which may promote the synthesis of non-cellulosic polysaccharides and glycoproteins in the Golgi apparatus of FNL cells, thus facilitating cell wall synthesis [64]. CsaV3_7G029600 (ethylene response sensor 1, ERS1, targeted by MSTRG.19437.5) was upregulated in FNLs, indicating that it may involve in ethylene signal transduction pathway and promote FNL maturation. Upregulated gene CsaV3_3G032310 (Cytochrome P450), which was positively regulated by MSTRG.7966.2 and negatively regulated by MSTRG.7966.1, may led to photosynthetic enhancement [65, 66].

Fig. 5
figure 5

Co–expression network of differentially expressed long non–coding RNAs (DE lncRNAs) and DE mRNAs between NFNLs and FNLs. (a) Co–expression networks between the DE lncRNAs and cis–target DE mRNAs. Circle represents DE mRNAs, and diamond represents DE lncRNAs. Orange represents up–regulation, and green represents down–regulation. (b) Venn diagrams of DE mRNAs and DE lncRNAs cis–target genes

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Construction of co–expression networks for DE miRNAs and DE mRNAs related to sink‒source interactions

It has been demonstrated that miRNA can activate or repress gene expression, which may be related to the location of miRNA in the organism [67]. In the current investigation, a total of 40 DE miRNA–DE mRNA interactions were examined between FNLs and NFNLs. Among these interactions, it was shown that 7 DE miRNAs exhibited positive regulation, while 6 DE miRNAs exhibited negative regulation of the DE mRNAs. This finding is visually represented in Fig. 6a and b, and further details can be found in Table S4–2. In positive co-expression of miRNA-mRNA pairs, CsaV3_3G003950 (RNA–binding protein, PNO1, targeted by novel_miR_20) was upregulated in FNLs, which may be related to protein synthesis. In addition, upregulated gene CsaV3_3G022290 (Myb family transcription factors, Myb) was negatively targeted by novel_miR_94, which may involve in fructan synthesis and degradation of carbohydrate metabolism [68]. As shown in Fig. 6d, the precursors of novel DE miRNAs, which discovered in this study for the first time, contained a typical hairpin structure. miRNA and miRNA stars were generated by the same hairpin structure of two arms.

ceRNA network construction in the regulation of sink–source allocation

In order to elucidate the global regulatory network of protein–coding RNAs and ncRNAs under different sink strengths of cucumber leaves, ceRNA networks were constructed using DE miRNAs, DE mRNAs and DE lncRNAs based on ceRNA theory. In Fig. 6c, we observed the presence of two lncRNA-miRNA-mRNA pairs. These pairs consisted of two upregulated miRNAs, namely novel_miR_20 and novel_miR_34. Additionally, we identified seven DE mRNAs, of which CsaV3_3G004460, and CsaV3_3G003950 were upregulated, while CsaV3_3G003000, CsaV3_5G003790, CsaV3_UNG100750, CsaV3_3G039720, Cucumis_Sativus_newGene_267 were downregulated. Furthermore, two DE lncRNAs (MSTRG13116.1, MSTRG5394.5), were also identified in the comparison between NFNLs vs. FNLs (Fig. 6c and Table S4–3). It is worth mentioning that the CsaV3_UNG100750 (CBL–interacting serine/threonine–protein kinase 14, CBL–CIPK14) was downregulated in FNLs, indicating that it may be a negative regulator of FNLs growth [69, 70].

To verify the quality of the whole–transcriptome sequencing, we selected several DE mRNAs, DE lncRNAs and DE miRNAs for qRT–PCR (Fig. 7). In terms of the expression trend, most of qRT–PCR results were consistent with RNA–seq data.

Fig. 6
figure 6

Co–expression networks of differentially expressed (DE) miRNAs and DE mRNAs and construction of ceRNA networks between NFNLs and FNLs. (a) The DE miRNAs–DE mRNAs co–expression networks. Circle represents DE mRNAs, diamond represents DE lncRNAs, and triangle represents DE miRNAs. Compare with NFNLs, orange represents up–regulation, and green represents down–regulation. (b) Venn diagram of DE mRNAs and DE miRNA target genes. (c) CeRNA networks constructed with all DE mRNAs, DE lncRNAs and DE miRNAs related to the sink–source regulation. (d) The secondary structure prediction of the precursors of novel DE miRNAs. Red represents the mature sequence, yellow represents the ring structure, purple represents the star sequence predicted by miRDeep2 software, and bright blue represents the star sequence supported by sequencing reads

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Fig. 7
figure 7

Expression profiles analysis of sink–source related genes between NFNLs and FNLs. (a) Quantitative reverse transcriptase–polymerase chain reaction (qRT‒PCR) analysis for differentially expressed genes (DEGs) and DE non–coding RNAs (ncRNAs) between NFNLs and FNLs. Error bars represent the means ± standard deviation of three biological replicates. The asterisks represent significant differences between NFNLs and FNLs (*P < 0.05; **P < 0.01; student’s t-test). (b) The expression profiles of sink–source related genes between NFNLs and FNLs based on transcriptome data

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Profiling of differentially APA genes and poly(A) sites switching in DE mRNAs and DE ncRNA target genes associated with sink– source interactions

In order to gain a deeper understanding of the potential involvement of APA in cucumber leaves induced by different sink strengths, we explored the APA sites information from the full-length transcriptome sequencing data. The quality control data of full-length transcriptome sequencing was shown in Table S2. We detected APA events in 12,600 genes of NFNLs, and in 12,530 genes of FNLs (Table S5). As shown in Figs. 8a and 52.59% and 52.63% genes have more than 5 APA sites in NFNLs and FNLs, respectively. The nucleotide composition in the upstream (0 − 50 nts) and downstream (50–100 nts) of all poly(A) cleavage sites were analyzed. The findings demonstrated a strong and prominent A peak and low GC–content after the cleavage site (Fig. 8b). By analyzing the pattern of base composition around cluster (A) sites, we found that UGUA, AUAUAU, AAAGAAA and UUUGUUU are relatively conserved sequences, which are also the most common and functional motifs of polyadenylation signaling in plants (Fig. 8c) [71, 72].

Fig. 8
figure 8

Alternative polyadenylation analysis between NFNLs and FNLs and sequence elements analysis at cleavage sites. (a) Distribution of the number of poly(A) sites per gene in NFNLs and FNLs. (b) Nucleotide composition around poly(A) cleavage sites. The y-axis values are the percentage composition for each base. (c) DREME analysis identified the motifs of poly(A) signal in cucumber transcripts

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In comparison to NFNLs, a total of 10,816 differentially APA (D–APA) genes were identified in FNLs. Among these genes, 5817 genes tended to use proximal, while 4999 genes tended to utilize distal poly (A) sites. This observation suggests that implying APA may play a role in regulating sink–source balance (Fig. 9a and Table S6–1). Furthermore, our analysis revealed the presence of 512 D–APA genes in 1101 DE mRNAs, which includes both DE mRNAs– and D–APA–related genes. Among these, we observed that 174 upregulated genes and 85 downregulated genes tended to use proximal poly (A) sites. Similarly, 177 upregulated genes and 76 downregulated genes tended to use distal poly (A) sites, respectively (Fig. 9b, Table S6–2). KEGG enrichment analyses showed that 37 and 48 DE mRNAs– and D–APA–related genes were significantly enriched in MAPK signaling and plant hormone signal transduction pathway related to sink–source relationship, respectively (Fig. 9e).

In addition, in DE lncRNA–DE mRNA networks, we found a total of eight upregulated and four downregulated DE mRNAs tended to use proximal poly(A) sites, and twelve upregulated and five downregulated DE mRNAs tended to use distal poly(A) sites (Fig. 9c, Table S6–3). Similarly, in DE miRNA–DE mRNA networks, a total of eight upregulated and one downregulated DE mRNAs tended to use proximal poly(A) sites, whereas six upregulated and six downregulated DE mRNAs tended to use distal poly(A) sites (Fig. 9d, Table S6–4). Within the context of sink-source interaction, the genes SPS (CsaV3_2G033300), GBSS1 (CsaV3_5G001560), ERS1 (CsaV3_7G029600), PNO1 (CsaV3_3G003950) and Myb (CsaV3_3G022290) may be influenced by APA and ncRNAs, as discussed in Sect. 3.4 and 3.5. These results indicated that non–coding RNAs and APA may be important to regulate the assimilate allocation between source and sink of cucumber.

Fig. 9
figure 9

Poly(A) sites switching in DE mRNAs and the targets of DE ncRNAs associated with sink–source interactions. (a) Profiling of differentially APA genes between NFNLs and FNLs. (b), (c) and (d) Poly(A) sites switching in DE mRNAs, targets of DE lncRNAs and DE miRNAs associated with sink–source interactions. (e) KEGG enrichment analysis of DE mRNAs and differentially APA related genes

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Net photosynthetic rate, soluble sugar, starch and soluble protein content determination

Based on the results of transcriptome analysis, our follow–up research mainly focused on the role of photosynthesis, starch and carbohydrate metabolism, plant hormone signal transduction, energy metabolism and protein synthesis pathways response to source–sink regulation. As shown in Fig. 10, the net photosynthetic rate, and the levels of soluble sugars and soluble protein in FNLs were remarkably higher than that in NFNLs. Among soluble sugars; stachyose, raffinose and sucrose were higher in FNLs than those in NFNLs. However, higher level starch accumulation was detected in NFNLs comparing with FNLs. Taken together, fruit setting might enhance leaf photosynthesis, inhibit starch synthesis, and promote soluble sugar and protein synthesis in FNLs to accelerate assimilate synthesis and meet more metabolic requirements.

Fig. 10
figure 10

Net photosynthetic rate, soluble sugar, starch and soluble protein content determination between NFNLs and FNLs. (a) Net photosynthetic rate. (b) Soluble sugar content. (c) Starch content. (d) Soluble protein content. Error bars represent the means ± standard deviation of five biological replicates. The asterisks represent significant differences between NFNLs and FNLs (*P < 0.05; **P < 0.01; student’s t-test)

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Discussion

It is of great significance to identify the genes regulating sink–source relationship of cucumber and understand their molecular mechanisms for increasing yield. In this study, we comprehensively identified DE mRNAs, DE ncRNAs and D–APAs in cucumber leaves under different sink strengths by whole transcriptome sequencing. The influence of ncRNA and APA on the expression of genes involved in sink–source interaction was also carefully investigated. The functional information of genes was obtained by GO and KEGG analysis. Further determination of relevant physiological indicators confirmed the enrichment of DEGs in photosynthesis, energy production and conversion, plant hormone signal transduction, starch and carbohydrate metabolism and protein synthesis pathways that may be involved in source and sink regulation.

The sink–source relationship may be related to gene expression in pathways such as photosynthesis, energy production and conversion and protein synthesis. Higher yields are attributed to the higher rate of photosynthesis within the source organs [73]. A feedback-regulated inhibition is often observed in cucumbers, where weak sinks are thought to lead to a low sink/source ratio of carbohydrates, thereby inhibiting photosynthesis [74, 75]. Here, we found that the expression of photosystem II protein (CsaV3_1G025480) and net photosynthetic rate increased in FNLs, indicating that fast growing fruit promoted the photosynthesis rate of cucumber leaves (Fig. 5a). Santos-Merino [66] reported that the expression of cytochrome P450 improved photosynthesis in cyanobacteria. The homologous gene, P450 (CsaV3_3G032310), was upregulated in FNLs, suggesting that it might be involved in photosynthesis (Fig. 5a). Previous studies have confirmed that in the why1/3–var and OE-CIPK14–var lines, the synergistic regulation of CIPK14 and WHIRLY1/3 mediated pale yellowing of leaves in Arabidopsis, the content of chlorophyll and the photochemical efficiency of photosystem (PSII) (Fv/Fm) were decreased [76]. Therefore, we speculated that fruit setting led to an increase in photosynthesis and an up–regulation of PSII, but a down–regulation of CBL–CIPK14 (Fig. 6c). Another study showed that, through the phosphorylation-dependent ubiquitination and proteasomal degradation of 14-3-3 proteins, CIPK7, CIPK12 and CIPK14 regulated the function of a ubiquitin ligase ATL31 involved in the C/N-nutrient response in Arabidopsis [77]. In this study, CsCIPK14, a homologous gene of AtCIPK14, is predicted to be an influencing factor that might regulate sink–source carbon partitioning, which is similar to the results of Yasuda’s research [77]. In addition, it is common knowledge that if photosynthesis were boosted, energy generation and transformation would be boosted as well [78]. The transport of arginine to mitochondria in the energy production and conversion pathway has been attributed to the mitochondrial arginine transporter (BAC2, CsaV3_3G025070) [79]. In our work, BAC2 was upregulated in FNLs, so that we speculated that BAC2 participated in mitochondrial energy metabolism of cucumber leaves, thus to regulate the source activity (Fig. 5a). In addition, the ribosome is the main site of protein synthesis in most eukaryotes [80]. It has been reported that RNA–binding proteins (RBPs) Rnc1 play a major role during control of translation, ribosomal structure and biogenesis [81]. In FNLs, PNO1 (CsaV3_3G003950), which has the same K-homology (KH) RNA-binding domain as Rnc1, was upregulated and soluble protein content was found to be higher when compared with the NFNL groups, suggesting that it may promote protein synthesis related to photosynthesis and mitochondrial energy metabolism in FNLs (Fig. 6a).

In addition, both sugars and hormones can be regarded as signaling molecules to regulate plant growth and development, including carbon allocation in sink–source relationship. CsRS and CsSTS were upregulated in FNLs, confirming that the key role of two genes in loading assimilates in the leaves of the RFO synthesis pathway (Table S3–1, Fig. 7a). It has been reported that the expression of genes related to ethylene signal transduction pathway, such as ERS1, was up-regulated under the condition of low source/sink ratio, thus promoting leaf maturation [82]. Therefore, we speculated that ERS1 functioned by altering plant hormone signal transduction in FNLs to promote the growth of source leaves (Fig. 5a). Wei [68] demonstrated that the transcription factor CiMYB17 of chicory can regulate source–sink interactions by activating sucrose transporter and cell wall invertase gene promoters. In this study, three DE MYBs were identified between cucumber FNLs and NFNLs, suggesting that they may have similar roles in sink–source balance (Fig. 6a). The expression of SPS decreased in FNLs (Table S3–1), indicating that sucrose synthesis may be reduced. However, the increase of sucrose content is not simply determined by the amount of sucrose synthesis, and the amount of sucrose decomposition also needs to be considered [83]. In the transcriptome data, we found that the expression of sucrose synthase genes CsaV3_5G020420 and CsaV3_4G000970 were downregulated in FNLs, indicating that sucrose decomposition was also reduced. In addition, the decrease in starch content in FNLs may be due to the accelerated hydrolysis of starch into soluble sugars to provide more assimilates in the leaves.

With the development of whole transcriptome sequencing technology, plant ncRNAs, especially lncRNA–miRNA–miRNA ceRNA networks, have been reported playing an important role in sink‒source regulation. For example, miR164c‒3p and miR166e‒3p are involved in regulating photosynthesis in pineapple [84]. In this study, we found that lncRNAs MSTRG.1470.5 and MSTRG.7495.1 of cucumber leaves may have effect on photosynthesis (Fig. 5a). It has been shown that the Ca2+ sensor CBL4 interacts with CIPK15 to regulate a–amylase 3 (RAMY3D) expression in a Ca2+–dependent manner under low O2, and expression of RAMY3D was suppressed after silencing CBL4 with an artificial miRNA [85]. In this study, MSTRG5394.5 (down)–novel_miR_20 (up)–CIPK14 (down) ceRNA network involved in signal transduction mechanism was discovered in FNLs, compared with NFNLs, which might have influence on source and sink regulation (Fig. 6c). Additionally, Bazin [86] found that nuclear speckle RNA binding proteins (NSRs) were predicted to interact with lncRNAs to regulate lateral root formation in Arabidopsis. Therefore, we speculated that upregulated PNO1 (CsaV3_3G003950) in FNLs, targeted by novel_miR_20, may facilitate protein synthesis (Fig. 6a).

Functioning as a post-transcriptional regulation mechanism, APA modulates gene expression in eukaryotes [87]. APA can affect the stability and translation efficiency of the target mRNA. APA sites, which close to the stop codon, reduce the binding of potential regulatory elements, such as miRNAs and lncRNAs, ultimately leading to increased gene expression [88]. In this study (Figs. 8), 512 DE mRNAs– and D–APA–related genes, 29 DE lncRNAs targets– and D–APA–related genes and 21 DE miRNAs targets– and D–APA–related genes were identified between FNLs and NFNLs (Fig. 9c, d). Therefore, we speculated that some DEGs, such as SPS (CsaV3_2G033300), GBSS1 (CsaV3_5G001560), ERS1 (CsaV3_7G029600), PNO1 (CsaV3_3G003950) and Myb (CsaV3_3G022290), might be regulated by ncRNAs and APA in sink–source relationship.

In summary, our findings provide a comprehensively description of the effects of lncRNA, miRNA, ceRNA network and APA on the expression of genes in sink‒source carbon partitioning, which involved in photosynthesis, energy production and conversion, plant hormone signal transduction, starch and carbohydrate metabolism and protein synthesis in cucumber leaves (Fig. 11). This research has improved our understanding of the molecular mechanism behind assimilate allocation between source and sink of the raffinose family oligosaccharides (RFOs)–translocated plant cucumber.

Fig. 11
figure 11

A comprehensive scheme summarizing the changes of various genes, ncRNAs transcription and APA from NFNLs to FNLs related to sink‒source regulation. Orange and green colors represent upregulated and downregulated, respectively.

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Data availability

Data of whole–transcriptome sequencing can be found in NCBI (accession: PRJNA778451 and PRJNA883559). Data of full-length transcriptome sequencing can be found in NCBI (accession: PRJNA1125650).

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Acknowledgements

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Funding

This research was supported by the Key Research and Development Program of Jiangsu Province (grant number: BE2022425), the National Natural Science Foundation of China (grant number: 32072579) and Postgraduate Research and Practice Innovation Program in Jiangsu Province (grant number: KYCX22_3520).

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Authors and Affiliations

  1. College of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou, 225009, China

    Yudan Wang, Zhiping Zhang, Bing Hua, Jiexia Liu & Minmin Miao

  2. Jiangsu Yanjiang Institute of Agricultural Sciences, Nantong, 226541, China

    Huimin Zhang

  3. Joint International Research Laboratory of Agriculture and Agri–Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, 225009, China

    Minmin Miao

  4. Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Yangzhou University, Yangzhou, 225009, China

    Minmin Miao

Authors
  1. Yudan Wang
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  2. Huimin Zhang
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  3. Zhiping Zhang
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  4. Bing Hua
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  5. Jiexia Liu
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  6. Minmin Miao
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Contributions

M.M. initiated and designed the research; Y.W. and H.Z. performed the experiments and analyzed the data; Z.Z., B.H. and J.L. contributed reagents/materials/analysis tools; Y.W. wrote the paper; M.M. revised the paper. All the authors participated in the revision of the manuscript.

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Correspondence to Minmin Miao.

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Wang, Y., Zhang, H., Zhang, Z. et al. Source leaves are regulated by sink strengths through non-coding RNAs and alternative polyadenylation in cucumber (Cucumis sativus L.). BMC Plant Biol 24, 812 (2024). https://doi.org/10.1186/s12870-024-05416-7

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  • DOI: https://doi.org/10.1186/s12870-024-05416-7

Keywords

BMC Plant Biology

ISSN: 1471-2229

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