a article accepted - genedenovo.com · systematic identification and analysis of...

Systematic identification and analysis of heat-stress-responsive lncRNAs, circRNAs and

miRNAs with associated co-expression and ceRNA networks in cucumber (Cucumis sativus L.)

Xueying Hea, Shirong Guo

a, Ying Wang

a, Liwei Wang

a, Sheng Shu

a and Jin Sun

a,b,*

a College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China

b Nanjing Agricultural University (Suqian) Academy of Protected Horticulture, Suqian 223800, China

Correspondence

*Corresponding author,

e-mail: [email protected]

Researchers have shown that long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) act as

competitive endogenous RNAs (ceRNAs) and are mutually regulated by competition for binding to

common microRNA response elements (MREs). However, a comprehensive identification and

analysis of lncRNAs and circRNAs as ceRNAs have not yet been completed in cucumber (Cucumis

sativus L.) exposed to high-temperature stress. In our study, 32 663 coding transcripts, 2085 lncRNAs,

2477 circRNAs and 348 differentially expressed miRNAs were identified using RNA sequencing. In

addition, six heat-stress-responsive miRNAs (five known and one novel miRNAs) and eight lncRNAs

were selected for qPCR to confirm their expression profiles. By analyzing the cis effects of lncRNAs,

we constructed a lncRNA-mRNA co-expression network. Based on the results, the corresponding

lncRNAs play a regulatory role in the stress response in cucumber plants. In our study, the PatMatch

software was used to predict the potential function of lncRNAs and circRNAs as ceRNAs. A total of

18 lncRNAs and seven circRNAs were predicted to bind to 114 differentially expressed miRNAs and

compete with 359 mRNAs for miRNA binding sites. These mRNAs are predicted to be involved in

various pathways, such as plant hormone signal transduction, plant-pathogen interaction and

glutathione metabolism. Among them, TCONS_00031790, TCONS_00014332, TCONS_00014717,

TCONS_00005674, novel_circ_001543 and novel_circ_000876 may interact with miR9748 by plant

hormone signal transduction pathways in response to high-temperature stress. Moreover,

indole-3-acetic acid (IAA) and 1-aminocyclopropane-l-carboxylic acid (ACC) levels decreased in the

high-temperature treatment group, indicating that IAA and ethylene signaling might be involved in

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This article is protected by copyright. All rights reserved.

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/ppl.12997

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response to high-temperature stress. In this study, we conducted a full transcriptomic analysis in

response to high-temperature stress in cucumber and, for the first time, integrated the potential ceRNA

functions of lncRNAs/circRNAs. The results provide a basis for studying the potential functions of

lncRNAs/circRNAs in response to high-temperature stress.

Introduction

High temperature is an important factor that limits plant growth and productivity. Heat stress (high

temperature) impedes intracellular homeostasis and can lead to leaf lesions, severe delays in growth

and development, risk of disease and even death (Bita and Gerats 2013, Liu et al. 2013). Studies have

shown that under high-temperature stress, the bioaccumulation of cucumber seedling leaves is

significantly inhibited, and the chlorophyll concentration is reduced (Zhou et al. 2016). As a result,

plants have developed specific adaptation mechanisms to address high-temperature stress.

A large number of studies have confirmed that miRNA and long noncoding RNA (lncRNA) play an

important role in plant stress, and their regulatory mechanisms have been revealed in plants (Xin et al.

2011, Zhu and Wang 2012). Under the induction of high temperature, miRNA affects and participates

in the process of plant growth by interacting with multiple genes. Genes respond to high-temperature

stress via changes in expression levels. Wang identified the first set of miRNAs associated with the

exogenous Spd-mediated improvement of high-temperature tolerance in cucumber seedlings (Wang et

al. 2018). For example, 34 specific lncRNAs were identified under heat stress and 192 target genes

were regulated by lncRNAs, most of which were thermos-responsive genes (Song et al. 2016a). Heat

stress does not induce lnc-173 expression, although its target gene SUCROSE SYNTHASE4, responds

to high temperatures (Di et al. 2014). In Populus simonii, the expression level of

PsiLncRNA00268512 is dynamic in response to heat stress (Song et al. 2016b). In addition to

participating in high-temperature stresses, lncRNAs are also involved in a series of physiological and

biochemical metabolic pathways during plant growth and development. In rice RNA-Seq data, most of

the 2063 lncRNAs were preferentially expressed during rice reproduction, and some lncRNAs also

induced rice reproductive defects (Zhang and Chen 2013). CSM10-lncRNA is differentially expressed

in different tissues, different developmental stages and different photoperiods of cucumber and may be

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involved in the regulation of growth and development of these tissues (Cho et al. 2005). However, it is

unclear whether lncRNA plays a specific physiological role in plants. Although some work has been

done on the roles of lncRNAs in plants, little is known about the functions of heat-stress-responsive

lncRNAs in cucumbers.

At present, an increasing number of studies have shown that circular RNA (circRNA) is involved in

the regulation of plant response to various biotic and abiotic stresses. Wang et al. (2017a) identified 88

circRNAs in wheat, of which 62 were differentially expressed under water stress, indicating that these

circRNAs may play a role in response to water stress. In tomato, Zuo et al. (2016) identified 854

circRNAs, of which 163 circRNAs were differentially expressed in tomato fruits under control and

chilling treatments, and 102 circRNAs could be combined as molecular sponges with 24 miRNAs.

Plant circRNAs showed different expression patterns, and 27 rice exonic circRNAs were found to be

differentially expressed under phosphate-sufficient and -starvation conditions (Ye et al. 2015).

Overexpression of Vv-circATS1, a circRNA derived from glycerol-3-P acyltransferase (ATS1),

improved cold tolerance in Arabidopsis, while the linear RNA derived from the same sequence is not

able to do the same (Gao et al. 2019). Recently, transcriptome analyses of plant drought response and

transgenic studies in Arabidopsis thaliana have revealed a relationship between circRNA expression

and drought resistance, indicating that circRNAs play a key role in plant drought resistance responses

and can also be used as effective biomarkers for genetic improvement of crop drought resistance

(Zhang et al. 2019). Heat-induced circRNAs might participate in plant response to heat stress through

circRNA-mediated competitive endogenous RNA (ceRNA) networks (Pan et al. 2018). In addition to

participating in biotic and abiotic stresses, circRNAs are also involved in a series of physiological and

biochemical metabolic pathways during plant growth and development. Recently, Wang used

'Zhongcai No. 4' and LeERF1 transgenic tomato plants to verify the possible role of circRNA in

regulating ethylene metabolism-related pathways in tomato fruits. The results showed that 102 target

mRNAs of 39 circRNAs were involved in the pathways involved in ethylene synthesis and signal

transduction (Wang et al. 2017b). The research showed that circRNA is involved in the regulation of

pigment accumulation during tomato fruit ripening and overexpression of circRNA related to pigment

synthesis can significantly reduce the expression level of the parental gene and the color of the

response (Tan et al. 2017). The research has found that circular RNA might play an important role in

development of moso bamboo by regulating the splicing of several rapid-growth related genes (Wang

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et al. 2019). It has been reported that 53 differentially expressed circRNAs were predicted as the

corresponding nine miRNAs sponges and play a role in sea buckthorn fruit ripening process (Zhang et

al. 2019). Similar research found that circRNAs play an important role in the regulation and control of

tomato fruits (Yin et al. 2018). CircRNA is widely distributed in plants and has been identified in

several plants such as Arabidopsis, rice, tomato and soybean using deep RNA-seq and bioinformatics

tools (Lu et al. 2015, Chen et al. 2017, Conn et al. 2017, Tan et al. 2017, Zhao et al. 2017, Zhou et al.

2018). However, at present, comprehensive studies of circRNA in plants are lacking, and the functions

of circRNA have not yet been clarified.

RNAs have been reported to regulate each other via competition in combination with common

microRNA response elements (MREs), which constitute a ceRNA (Salmena et al. 2011). The theory of

competing endogenous RNAs has been demonstrated and is now widely accepted (Salmena et al. 2011,

Ala et al.2013, Xu et al. 2016), and it includes protein-coding RNA and noncoding RNA, such as

pseudogene transcripts, lncRNA, and circRNA. The first example of ceRNA was found in Arabidopsis,

and the study showed that the noncoding RNA IPS1 could influence the expression level of PHO2 by

binding to miR399 (Franco-Zorrilla et al. 2007). Recent studies have also shown that circRNAs could

act as ceRNAs. For example, two circRNAs, ciRs-7/CDR1 and Sry, have been reported to be miRNA

sponges in humans (Hansen et al. 2013a, 2013b, Sebastian 2013). In addition, circRNAs, as ceRNAs,

are involved in the complex regulation of ethylene in tomato fruits (Wang et al. 2017b). Competition

of lncRNAs with other miRNA sponges has an important role in plants and animals (Franco-Zorrilla et

al. 2007, Sumazin et al. 2011, Wang et al. 2013, Wu et al. 2013, Zhang et al. 2014). Many studies have

demonstrated that lncRNAs play an important role as a competitive platform for miRNAs and mRNAs

in pathological and physiologically relevant processes (Tay et al. 2014). However, to the best of our

knowledge, the ceRNA network has not been used to study the function of lncRNAs/circRNAs in

cucumber.

Based on the hypothesis that lncRNAs/circRNAs compete with genes to play an important role in

cucumbers undergoing heat stress, we used the ceRNA network to study the functions of these

lncRNAs/circRNAs. Using high-throughput sequencing and bioinformatics analysis, we constructed

ceRNA networks of lncRNAs, circRNAs, miRNAs and mRNAs and analyzed their potential

regulatory roles with GO and KEGG. In this study, we conducted a transcriptome analysis of

cucumbers in response to high-temperature stress and integrated, for the first time, the potential

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ceRNA functions of lncRNAs/circRNAs in the process of cucumber hyperthermia stress, which

provided a basis for studying the potential functions of lncRNAs/circRNAs in response to

high-temperature stress.

Materials and methods

Plant material and growth conditions

The Cucumis sativus cultivar ‘Improved Jinchun 2’ was used in this study. The surface-sterilized seeds

were grown in pots containing nursery substrates in a controlled-environment growth chamber

programmed for 12/12 h at 28/18°C for day/night. Seedlings at the three-leaf stage were transferred to

growth chambers set to 42/32°C as the high-temperature treatment and 28/18°C as the control for 7 d.

Each treated sample was obtained by homogeneously mixing the completely expanded third leaves of

eight seedlings for two biological replicates (Li et al. 2014). All leaf samples were collected from

control and treated plants, frozen in liquid nitrogen and stored at -80°C for RNA extraction.

Strand-specific library construction and sequencing

After total RNA was extracted, rRNAs were removed to retain mRNAs and ncRNAs. The enriched

mRNAs and ncRNAs were fragmented into short fragments using fragmentation buffer, and reverse

transcribed into cDNA with random primers. Second-strand cDNA was synthesized by DNA

polymerase I, RNase H, dNTP (dUTP instead of dTTP) and the appropriate buffer. Next, the cDNA

fragments were purified with a QiaQuick PCR extraction kit (Qiagen), and they were then end

repaired, supplemented with poly(A), and ligated to Illumina sequencing adapters. Then, UNG

(Uracil-N-Glycosylase) was used to digest the second-strand cDNA. The digested products were size

selected by agarose gel electrophoresis, PCR amplified, and sequenced using an Illumina HiSeqTM

2500 (Gene Denovo Biotechnology Co.). Raw reads were processed by deleting adapter reads and

low-quality labels, with all subsequent analyses performed using clean reads.

Small RNA libraries construction and sequencing

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Total RNA was extracted from three replicates of each treatment with TRIzol reagent (Invitrogen).

High-purity (OD260/280 between 1.8 and 2.2) and high-integrity (RNA integrity number, RIN ≥ 7.5)

RNA samples from the CW (control treated with water) and HW (high temperature treated with water)

groups were selected to construct the sRNA libraries (CW and HW). Then, high-throughput

sequencing was performed on a HiSeq 2000 instrument (Illumina). The data were processed by the

following steps: (1) Total RNA was extracted from the samples via PAGE gel separation of RNA

segments of different size. A stripe between 18 and 30 nt was cut out and small RNAs were recovered.

(2) A 3' connection system was constructed, blending and centrifugation with 5000 rpm were

performed. Then 3' adaptors were ligated to the small RNAs based on a suitable temperature within a

specific period of time. (3) The same steps as in (2) were done for the 5' adaptors. (4) On a PCR

machine, the adaptor-ligated products were reverse-transcribed into double-stranded sequences, and

these double-stranded sequences were then PCR-amplified according to certain procedures, which

were: denaturation at 98 °C for 30s, followed by 6-16 cycles at 98°C for 10s, 65 °C for 30s, and 72 °C

for 30s, and a final extension at 72 °C for 5 min. (5) PAGE gel recycling and PCR product purification

were performed, and the product was placed in EB solution (10 mM Tris-HCL, pH of 8.0). (6) An

Agilent 2100 Bioanalyzer (Agilent Technologies) and ABI Step One Plus Real-Time PCR System

(Applied Biosystems) were used to determine the quality and yield of the RNA libraries.

Identification of differentially expressed lncRNA, circRNA and mRNA

Using the TopHat version 2.0.9, clean reads from both cDNA libraries were mapped to version 2 of the

cucumber genome sequence (http://www.icugi.org/cgi-bin/ICuGI/genome/home.cgi?ver=2&organism

= cucumber & cultivar = Chinese-long; Daehwan 2013). The known mRNAs were identified

according to the cucumber genomic sequence annotation. The protein coding potential of the new

transcripts was assessed using CNCI (version 2) and CPC (http://cpc.cbi.pku.edu.cn/; Kong et al. 2007,

Sun et al. 2013). The intersection of the two results was selected as the lncRNA. Cuffdiff was used to

calculate the fragments per kilobase of exon per million mapped reads (FPKM) scores for transcripts

in each library (Trapnell et al. 2012). Differentially expressed lncRNAs and mRNAs between the two

libraries were identified by edgeR (Robinson et al. 2010).

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Then, 20-mers from both ends of the unmapped reads were extracted and aligned to the reference

genome to find unique anchor positions within the splice site. Anchor reads that aligned in the reverse

orientation (head to tail) indicated circRNA splicing and were subjected to find_circ to identify

circRNAs (Sebastian 2013). The anchor alignments were then extended such that the complete read

alignments and the breakpoints were flanked by GU/AG splice sites. A candidate circRNA was

identified if it was supported by at least two unique back-spliced reads at least in one sample.

To quantify circRNAs, back-spliced junction reads were scaled to RPM (reads per million mapped

reads), and the formula is shown as follows:

Eqn. 1

where C is the number of back-spliced junction reads that uniquely aligned to a circRNA, and N is the

total number of back-spliced junction reads. The RPM method is able to eliminate the influence of

different amounts of sequencing data on the calculation of circRNA expression. Therefore, the

calculated expression can be directly used to compare differential expression among samples.

We used FDR < 0.05 and | log2(FC) | > 1 as a threshold for assessing significantly differentially

expressed lncRNAs, circRNAs and mRNAs. Then, a bioinformatics analysis of candidate lncRNAs,

circRNAs and mRNAs was performed.

Screening of miRNAs responsive to high-temperature stress

The expression levels of known miRNAs and novel miRNAs in the two libraries were calculated and

normalized as transcripts per million according to the following formula: normalized expression =

actual miRNA count/total count of clean reads × 1 000 000. The expression of miRNAs with an

abundance of zero was modified to 0.01 for further analysis (Murakami et al. 2006). Then, the

normalized results were used to calculate the fold change and P-value. To avoid errors, miRNAs only

expressed in one library were removed and not involved in the differential expression analysis.

We analyzed the expression of the miRNAs in the two libraries (CW and HW). Then, these libraries

were compared pairwise to find the differentially expressed miRNAs. The differential expression of

miRNAs was calculated by the following formula: fold change = log2 (HW/CW). A miRNA was

considered to be differentially expressed between the two compared libraries in each comparison pair

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when | fold change | = | (log2 (HW/CW) | > 1 and P-value < 0.05. The p-value was calculated as

previously described (Audic and Claverie 1997).

Target gene prediction and functional enrichment analysis

The cist-acting lncRNAs targeted neighboring genes (Ponjavic et al. 2009, Orom et al. 2010). We

searched for 10 kb of coding genes upstream and downstream of all identified lncRNAs and predicted

their function. The miRNA-mRNA, miRNA-lncRNA and miRNA-circRNA target genes were

predicted by patmatch_v1.2 software using small RNA sequencing and RNA-seq data (Yan et al.

2005).

All differentially expressed mRNAs were studied using GO annotation and KEGG pathway analyses

as described previously (Zhao et al. 2015, Chen et al. 2016). GO terms enrichment was determined

with Blast2GO by reference to the GO database (Conesa et al. 2005). At the same time, a KEGG

pathway analysis was performed with reference to the KEGG pathway database.

Network visualization

The ceRNA network was constructed based on ceRNA theory as follows: (1) The correlations in

expression between mRNA and miRNA or lncRNA and miRNA were evaluated using the Spearman

Rank correlation coefficient (SCC). Pairs with SCC < -0.7 were selected as negatively co-expressed

lncRNA–miRNA pairs or mRNA-miRNA pairs, where both mRNA and lncRNA were miRNA target

genes, and all RNAs were differentially expressed. (2) The correlation in expression between lncRNA

and mRNA was evaluated using the Pearson correlation coefficient (PCC). Pairs with PCC > 0.9 were

selected as co-expressed lncRNA–mRNA pairs, where both the mRNA and lncRNA in each pair were

targeted and co-expressed negatively with a common miRNA. As a result, only the gene pairs with a

P-value less than 0.05 were selected.

𝑝 − 𝑣𝑎𝑙𝑢𝑒 1 − 𝐹(𝑥/𝑈, ,𝑁) Eqn. 2

𝑝 − 𝑣𝑎𝑙𝑢𝑒 1 − ∑(𝑀𝑖 )(

𝑈−𝑀𝑁−𝑖 )

(𝑈𝑁)

𝑥− 𝑖= Eqn. 3

For a given gene pair (A,B), we denoted all their regulatory miRNAs as miRNA sets C (regulating

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gene A) and D (regulating gene B). In the above equations, x stands for the number of common

miRNAs that regulate both genes, U is the total number of miRNAs in this work, M is the size of

miRNA set C, and N is the size of miRNA set D.

The lncRNA-mRNA co-expression and ceRNA regulatory network was constructed by assembling all

co-expression competing triplets, which were identified above, and visualized using Cytoscape 3.3.0

software (The Cytoscape Consortium, USA). Nodes in the ceRNA network include miRNAs, mRNAs,

lncRNAs, and circRNAs.

Quantitative real-time PCR (qRT-PCR)

We performed qRT-PCR to confirm the quality of the high-throughput sequencing and the expression

patterns of miRNA and lncRNA. The main steps for the verification of miRNAs were as follows.

Small RNAs were extracted from the leaves of plants in the CK and HT groups using the miRcute

miRNA isolation kit (Tiangen). A Mir-X miRNA First-Strand Synthesis Kit (TaKaRa) was used for

first-strand cDNA synthesis from miRNAs. U6 snRNA served as the internal control for the miRNA

expression analysis (Li et al. 2014). qRT-PCR was performed with a SYBR PrimeScriptTM

RT-PCR

Kit (TaKaRa) on a Step One™ Real-time PCR System (Applied Biosystems) based on the

manufacturer’s instructions.

The main steps for the validation of lncRNAs were performed as follows. Total RNAs were extracted

from the leaves of two treated samples using an RNA simple Total RNA kit (Tiangen). Then, the total

RNA was used for first-strand cDNA synthesis using a PrimeScript™ II First Strand cDNA Synthesis

Kit (TaKaRa). Finally, real-time PCR was performed on a Step One ™ Real-time PCR System

(Applied Biosystems) using a SYBR PrimeScriptTM

RT-PCR Kit (TaKaRa). The lncRNA primers were

designed by Beacon Designer 7.9. The cucumber actin gene was used as an internal reference to

normalize the qRT-PCR data.

The primers used are shown in Table S1. The relative expression level (fold change) was expressed as

2-ΔΔCt

. The data were statistically analyzed using SAS Version 9.0 software (SAS Institute). The

significance level was set to a P-value < 0.05. All qRT-PCRs were performed in triplicates.

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Measurement of endogenous hormone contents

Approximately 500 mg of frozen leaf samples from the control and high temperature treatment were

used for endogenous phytohormone extraction. To link the RNA sequencing results to endogenous

hormone contents, the examined leaves were subjected to the same treatment used for sequencing. The

auxin indole-3-acetic acid (IAA) and the direct biosynthetic precursor of ethylene,

1-aminocyclopropane-l-carboxylic acid (ACC), contents were measured by previously described

methods (Dobrev and Vankova 2012). The hormones were isolated based on a previously published

protocol (Ağar et al. 2006). High-performance liquid chromatography-mass spectrometry (AB 5500)

was used to detect and quantify the hormones following a previously reported protocol (Pan et al.

2010). Standard IAA samples were purchased from Sigma-Aldrich, and standard ACC samples were

purchased from J&K Scientific. The results were analyzed using three replicates.

Results

Identification of lncRNAs and mRNAs responsive to high-temperature stress and their function

analysis

Identification of differentially expressed lncRNAs and mRNAs

To identify the lncRNAs involved in response to high-temperature stress in cucumber leaves, we

constructed four cDNA libraries (HT-1, HT-2, CK-1, and CK-2) and sequenced the libraries using the

Illumina HiSeq ™ 2500 platform (Fig. 2A). A total of 63 966 532 800 raw reads were generated for all

four libraries. After discarding the adaptor sequences and low-quality reads, we obtained

62 219 581 464 clean reads (Table 1). After removing the rRNA genes, the clean reads were mapped to

version 2 of the cucumber genome sequence. The percentage of clean reads in each library ranged

from 76.91-79.80% (Table 2). The mapping sequences in each library were assembled, and a total of

25 600 unique assembly transcripts were obtained. The mapping rate was low, which can be explained

by the following factors: (1) the V2 version of the cucumber reference genome sequence has only the

nuclear genome and does not contain the chloroplast genome; and (2) sampling cucumber leaves

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yields a relatively high chloroplast content.

The expression levels of lncRNAs and mRNAs were estimated by the FPKM value using Cuffdiff. We

identified a total of 32 663 mRNAs and 2085 lncRNAs. Compared with the control group, 108

lncRNAs and 2130 mRNAs were differentially expressed in the leaves of cucumber treated with high

temperature. Fifty-six lncRNAs and 1533 mRNAs were upregulated, while 52 lncRNAs and 597

mRNAs were downregulated (Fig. 1A and Table S2). The apparent variations in lncRNAs and mRNAs

between the two groups are visually displayed with heatmaps (Fig. 1B, C). During the

high-temperature treatment, the number of upregulated mRNAs was greater than that of

downregulated mRNAs, while the upregulated and downregulated lncRNAs presented similar

numbers.

Next, the identified lncRNAs were compared to the genomic characteristics of the protein-coding

genes in cucumber. The average exon length of the mRNAs was longer than that of the lncRNAs (Fig.

2B). Most lncRNAs contained only one exon, whereas over 90% of the mRNAs had multiple exons

(Fig. 2C). Meanwhile, the lncRNAs in cucumbers had fewer and shorter exons than the mRNAs. The

GC content of the lncRNAs was also lower than that of the mRNAs (Fig. 2D).

GO/KEGG pathway analysis of differentially expressed mRNAs

Differentially expressed mRNAs were significantly enriched in 18 GO terms under biological process,

13 GO terms under cellular component and 10 GO terms under molecular function in the

high-temperature treatment of cucumber leaves (Fig. 3A). In our study, we found that upregulated and

downregulated mRNAs were most abundant in GO terms such as metabolic process, cellular process,

single-organism process and catalytic activity. In addition, the GO analysis indicated that these

differentially expressed mRNAs may be involved in many biological processes (including response to

organic substance, response to endogenous stimulus, response to stimulus, biological regulation,

regulation of biological process, response to stress, etc.). A bioinformatics analysis was performed to

predict the potential function of lncRNA by a GO and KEGG enrichment analysis of mRNA, and the

results showed that some lncRNAs may regulate the mRNA response to high-temperature stress (GO:

0050896 response to stimulus, GO: 0006950 response to stress, GO: 0009408 response to heat).

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The differentially expressed mRNAs were enriched in 114 KEGG pathways, and the results showed

that three significant enrichment pathways were photosynthesis-antenna proteins, glutathione

metabolism and photosynthesis. Following these three groups, mRNAs were enriched in histidine

metabolism, brassinosteroid biosynthesis, phenylpropanoid biosynthesis, plant hormone signal

transduction and protein processing in the endoplasmic reticulum (Fig. 3B).

Co-expression analysis of lncRNAs and mRNAs and functional prediction

One of the functions of lncRNAs is cis-regulation of their neighboring genes on the same allele,

thereby regulating transcriptional or posttranscriptional gene expression. Because cis-acting lncRNAs

target neighboring genes (Ponjavic et al. 2009, Orom et al. 2010), we searched for 10 kb of coding

genes upstream and downstream of all identified lncRNAs and predicted their functions. By analyzing

the cis-regulation of lncRNAs, we constructed a co-expression network of lncRNAs and mRNAs.

Most mRNAs and lncRNAs were one-to-one matches. However, there were also one-to-many matches

between the lncRNAs and mRNAs (Table S3). Considering that graphics cannot display the enormous

amount of network information between lncRNAs and mRNAs, we selected more mRNAs for

co-expression with lncRNAs to make the network diagram (Fig. 4). As shown in Fig. 4, one lncRNA

interacted with nine mRNAs, two different lncRNAs interacted with seven mRNAs, and two different

lncRNAs interacted with six mRNAs.

LncRNAs located upstream of a protein-coding gene may overlap with a promoter region or

cis-regulate the element and may regulate the expression of genes in their vicinity at the transcriptional

or posttranscriptional level. LncRNAs located downstream of protein-coding genes can initiate

transcription from 3'UTRs or downstream regions and may be involved in intergenic regulatory

interactions. To predict the functions of lncRNAs, the upstream and downstream genes of lncRNAs

were analyzed. GO and KEGG enrichment analyses of lncRNAs and their upstream and downstream

differentially expressed genes were performed. The results showed that these genes were enriched in

the photosystem, response to endogenous stimulus, defense response, signal transduction and

regulation of biological process GO terms (Table S4). Interestingly, we found that protein-coding

genes, such as Csa4M314390.1 (ERF) and Csa5M613470.1 (MYB_related), were involved in

response to stimulus (GO:0050896), and these results showed the possible role of lncRNA in

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transcriptional regulation of gene expression. The KEGG enrichment analysis showed that these genes

were enriched in photosynthesis-antenna proteins, glutathione metabolism, photosynthesis, histidine

metabolism and other pathways, indicating that high-temperature stress had a great impact on plant

photosynthesis (Table S4).

Identification of circRNAs responsive to high-temperature stress and functional analysis

To identify circRNAs and analyze their functions, high-throughput sequencing was performed in the

control and high-temperature treatment cucumber leaves using the Illumina HiSeq ™ 2500 platform.

After screening, we found a total of 2477 novel circRNAs (Table S5). Cuffdiff was used to assess their

expression level by FPKM, and the results showed that in the high-temperature treatment group, there

were five upregulated and one downregulated circRNA. To further understand the potential functions

of circRNAs, GO and KEGG analyses of the source genes of circRNAs were performed. The results

showed that the circRNAs were enriched in 44 GO terms (18 GO terms under biological process, 15

GO terms under cellular component and 11 GO terms under molecular function), suggesting that these

circRNAs may be involved in the regulation of many biological processes (including growth, response

to stimulus, biological regulation, and signaling). Thus, it was predicted that circRNAs may regulate

gene response to high-temperature stress (GO: 0050896 response stimulus; Table S6). The source

genes of circRNAs were involved in 112 KEGG pathways and significantly enriched in four KEGG

pathways, photosynthesis-antenna proteins, carbon metabolism, glyoxylate and dicarboxylate

metabolism and photosynthesis (Table S6).

Identification of miRNAs responsive to high-temperature stress and functional analysis

To explore the expression patterns of small RNAs (sRNAs) in response to high temperature, two RNA

libraries were constructed, i.e., CW and HW, and these two libraries generated 11 255 470 and

11 296 147 raw reads, respectively (Table 3). Of these raw reads, 10 865 804 and 10 854 344 were

retained after contaminants and low-mass sequences were removed. Among the HW/CW comparison

pairs, there were 19 428 531 consensus sequences that accounted for 89.45% of the total reads, and

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these sequences were classified as 182 874 species, which accounting for 11.52%. In this comparison

pair, 546 579 sequences were HW-specific and 857 319 were CW-specific. To find miRNAs that

responded to high-temperature stress and identify their expression patterns, we compared the HW and

CW libraries. One hundred fifteen differentially expressed known miRNAs and 233 differentially

expressed novel miRNAs were obtained in the HW/CW comparison (Table S7).

To further understand the potential functions of miRNAs, we conducted GO and KEGG analyses of

the target genes of miRNAs. The results showed that all target genes were successfully assigned to the

corresponding 32 GO terms. The main subcategories were catalytic activity (GO: 0003824), binding

(GO: 0005488), metabolic process (GO: 0044710), and cellular process (GO: 0009987; Table S8). The

target genes of the miRNAs were involved in 30 KEGG pathways, which were significantly enriched

in the four KEGG pathways, including ribosome biogenesis in eukaryotes (ko03008), RNA

degradation (ko03018), RNA transport (ko03013), the mRNA surveillance pathway (ko03015) and

other metabolites. However, the p-values of the other pathways were greater than 0.05, indicating no

statistically significant difference (Table S8).

Construction of ceRNA network

At the transcriptome level, the mechanism by which ncRNAs regulate gene expression is revealed

through the ceRNA regulatory network. Based on the theory of ceRNA, lncRNAs, circRNAs and

mRNAs with the same miRNA binding sites were searched, and miRNAs were used as the core and

lncRNAs/circRNAs and mRNAs as target ceRNA regulatory networks. Thus, the ceRNA network was

constructed by integrating the expression profiles and regulatory relationships of mRNA, lncRNA,

circRNA and miRNA from RNA-seq and small RNA sequencing data. We found that 114 differentially

expressed miRNAs were associated with 359 mRNAs as well as eighteen lncRNAs and seven

circRNAs. Considering that graphics cannot display the enormous amount of network information

between miRNAs, lncRNAs, circRNAs and mRNAs, we selected miRNAs associated with more

mRNAs to make the ceRNA network diagram (Fig. 5). The ceRNA regulatory network contains 433

lncRNA-miRNAs, circRNA-miRNAs or mRNA-miRNAs pairs with 253 negative correlations and

180 positive correlations (Table S9).

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To reveal their potential function, we performed GO and KEGG pathway analyses of 359 differentially

expressed mRNAs to predict the potential regulatory role of lncRNAs/circRNAs in the ceRNA

network (Fig. 6 and Table S10). As shown in Fig. 6A, all 359 differentially expressed mRNAs were

enriched in 17 GO terms under biological process, 11 GO terms under cellular component, and 10 GO

terms under molecular function. The results showed that these mRNAs were involved in the regulation

of many biological processes (including biological regulation, cellular process, response to stimulus,

and metabolic process), which predict lncRNAs/circRNAs targeted by miRNAs that may regulate

gene responses to high-temperature stress. It is noteworthy that in these GO terms, 40 mRNAs were

significantly enriched in response to stimulus (GO: 0050896; Fig. 6A and Table S10). Based on the

KEGG analysis, mRNAs were predicted to be involved in 48 pathways in which plant hormone signal

transduction, plant-pathogen interactions and glutathione metabolism were closely linked to

high-temperature stress (Fig. 6B and Table S10). TCONS_00002425, TCONS_00011544,

TCONS_00031257, Csa1M690240.1, Csa6M091930.1 and Csa7M405830.1 were involved in plant

hormone signal transduction pathways. Csa2M286450.1, Csa3M130890.2, Csa3M727960.1, and

Csa3M823060.1 were involved in plant-pathogen interaction pathways. Csa3M889840.1 was involved

in the glutathione metabolism pathway. Csa3M823060.1 was predicted to be the target gene for

miR6196, and the rest of the mRNAs were targeted by miR9748, while the lncRNAs

TCONS_00031790, TCONS_00014332, TCONS_00014717 and TCONS_00005674, circRNAs

novel_circ_001543 and novel_circ_000876 were predicted to bind to miR9748 (Fig. 5).

Based on the above results, we selected lncRNAs, circRNAs, miRNAs and mRNAs related to the

plant hormone signal transduction pathway (the level 1 classification of this pathway was

‘Environmental Information Processing’ and the level 2 classification was ‘Signal transduction’ in the

KEGG database) to further examine the ceRNA network (Fig. 7). TCONS_00031790,

TCONS_00014332, TCONS_00014717, TCONS_00005674, novel_circ_001543 and

novel_circ_000876 were predicted to bind to miR9748. Csa1M690240.1 (auxin-responsive protein

IAA16), Csa6M091930.1 (protein TIFY 9-like) and Csa7M405830.1 (ethylene response sensor 1)

were predicted as target genes of miR9748. These three mRNAs were the pivotal genes of the plant

hormone signal transduction pathway according to the KEGG analysis. This complex ceRNA network

indicated that TCONS_00031790, TCONS_00014332, TCONS_00014717, TCONS_00005674,

novel_circ_001543 and novel_circ_000876 may play regulatory roles in the plant hormone signal

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transduction pathway through miR9748 and its target genes in response to high-temperature stress.

Validation of differentially expressed miRNAs and lncRNAs by qRT-PCR

To confirm the quality of the high-throughput sequencing and the expression patterns of miRNA and

lncRNA in the CK and HT groups, six heat-stress-responsive miRNAs (five known miRNAs and one

novel miRNA) and eight lncRNAs were randomly selected for qRT-PCR analysis. The expression

profiles are displayed in Fig. 8, and the primers are listed in Table S1. As shown in Fig. 8A, a similar

tendency was observed between the qRT-PCR and high-throughput sequencing results of the selected

miRNAs and lncRNAs expression, suggesting that the results of the high-throughput sequencing were

reliable. The expression of two miRNAs (miR172c, miR6196) was increased and reduced for four

miRNAs (miR827b, miR8597, miR9484 and novel_mir_261). The results suggested that the levels of

the tested miRNAs varied significantly during the process of high-temperature stress. Furthermore,

some miRNAs showed stage-specific expression, which was probably involved in the response to

high-temperature stress.

Additionally, we also validated the expression patterns of eight heat-stress-responsive lncRNAs. The

results showed that the differential expression of lncRNAs by qRT-PCR was consistent with the

high-throughput sequencing results. The expression profiles are displayed in Fig. 8B, and the primers

are listed in Table S1. For example, four lncRNAs (TCONS_00005674, TCONS_00014332,

TCONS_00000514 and TCONS_00017799) were significantly downregulated, and two lncRNAs

(TCONS_00031790 and TCONS_00011359) were significantly upregulated in the HT group,

consistent with the high-throughput sequencing results. In other words, the high-throughput

sequencing results were credible.

Endogenous hormone measurements

As mentioned above, the ceRNA network indicated that TCONS_00031790, TCONS_00014332,

TCONS_00014717, TCONS_00005674, novel_circ_001543 and novel_circ_000876 may play

regulatory roles in the plant hormone signal transduction pathway through miR9748 and its target

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genes in response to high-temperature stress. To further investigate the roles of endogenous hormones

in response to high-temperature stress, the IAA and ACC contents were measured in cucumber.

Samples were collected from the leaves in the CK and HT groups. As shown in Fig. 9, both the IAA

and ACC levels decreased in the HT group, indicating that IAA and ethylene signaling might be

involved in response to high-temperature stress.

Discussion

Researchers have shown that lncRNAs and circRNAs act as ceRNAs and are mutually regulated by

competition for binding to common MREs. LncRNAs, circRNAs, miRNAs and mRNAs form

large-scale ceRNA cross-talk networks through MREs, which has exciting implications for gene

regulation at the posttranscriptional level during multiple physiological and pathophysiological

processes (Salmena et al. 2011, Ala et al. 2013). Although lncRNAs and circRNAs have been

identified and studied in plants, the function of most circRNAs remains unknown (Lin 2014, Wang et

al. 2014, Lu et al. 2015, Muthusamy et al. 2015). To investigate the function of lncRNAs and

circRNAs in response to high-temperature stress, we performed a full transcriptome analysis of

lncRNAs, circRNAs, miRNAs and mRNAs by high-throughput sequencing.

A total of 2085 lncRNAs and 2477 circRNAs were identified in cucumber leaves. Compared with

mRNAs, lncRNAs are shorter and present fewer exons and lower GC content. All the 2477 circRNAs

identified by high-throughput sequencing were novel circRNAs. Compared with the control group,

five of the circRNAs were significantly upregulated, and one of them was significantly downregulated.

To date, the function of most lncRNAs is not fully understood. Constructing a co-expression network

of lncRNAs and mRNAs can facilitate lncRNA functional prediction (Cui et al. 2017). By analyzing

the cis effect of lncRNA, we constructed a lncRNA-mRNA co-expression network to further identify

the relationship between lncRNA and mRNA. The GO analysis showed that target genes were

enriched in the photosystem, response to endogenous stimulus, defense response, signal transduction,

and regulation of biological process. Interestingly, we found that the protein-coding genes

Csa4M314390.1 (ERF) and Csa5M613470.1 (MYB_related) were enriched in response to stimuli, and

the results showed the possible role of lncRNA in transcriptional regulation of gene expression, which

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means that the corresponding lncRNAs play a regulatory role in stress response. Recent studies have

also shown that lncRNAs are involved in stress response (Aversano et al. 2015, Song et al. 2016a). A

KEGG pathway analysis of the target genes revealed that they are involved in photosynthesis-antenna

proteins, glutathione metabolism and photosynthesis, which indicated that high-temperature stress has

a large impact on plant photosynthesis. For example, Csa2M079660.1 (photosystem I reaction center

PsaE) and Csa3M060980.1 (photosystem I reaction center PsaG) are enriched in the photosynthetic

pathway. This suggested that they are involved in plant photosynthesis, which is also consistent with

previous reports (Zhao et al. 1993, O'Neill et al. 1994).

Reports have indicated that circRNAs play important roles in miRNA-mediated posttranscriptional

regulation of gene expression by acting as ceRNAs (Hansen et al. 2013a, 2013b, Sebastian 2013,

Wang et al. 2017b). Expression profiles of some plant circRNAs showed a positive correlation with

their parental genes (Ye et al. 2015, Pan et al. 2018). Parent genes of over 700 exonic circRNAs were

orthologues between rice and Arabidopsis, suggesting conservation of circRNAs in plants (Ye et al.

2015). Heat-induced circRNAs might participate in plant response to heat stress through

circRNA-mediated ceRNA networks (Pan et al. 2018). To date, the ceRNAs involved in the

high-temperature stress response of cucumbers have not been reported. Here, for the first time, we

constructed a ceRNA regulatory network of lncRNA/circRNA-miRNA-mRNA that responds to

high-temperature stress in cucumbers based on high-throughput sequencing data. Through the GO

analysis, 359 differentially expressed mRNAs were shown to be involved in the regulation of many

biological processes (including biological regulation, cellular process, response to stimulus, metabolic

process, etc.). In these GO terms, 40 mRNAs were significantly enriched in response to stimulus (GO:

0050896). These key response-stimulated genes, lncRNAs and circRNAs, form ceRNA networks by

targeting common miRNAs, and these networks may provide new evidence of the regulatory

mechanisms in response to high-temperature stress in cucumbers. The KEGG analysis predicted that

mRNAs of the ceRNA network were involved in plant hormone signal transduction, plant-pathogen

interactions and glutathione metabolism in response to high-temperature stress. Previous studies have

shown that plant-pathogen interactions were associated with plant hyperthermia (Chen et al. 2014). In

addition, Song et al. (2016a) studied the co-expression of lncRNAs with genes under different

temperature treatments in non-heading Chinese cabbage and found that plant hormone signal

transduction pathways were enriched according to KEGG analysis. Moreover, plant hormones have

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been shown to induce abiotic stress tolerance through biosynthesis and signal transduction (Tran and

Pal 2014). Studies have shown that GSH synthesis activity in maize root cells increases during

high-temperature stress and increases GSH synthesis, which may be related to the ability of cells to

respond to high-temperature stress conditions (Nietosotelo and Ho 1986). Csa2M286450.1 (probable

calcium-binding protein CML32), Csa3M130890.2 (probable calcium-binding protein CML22),

Csa3M727960.1 (calcium-binding protein CML42) and Csa3M823060.1 (probable calcium-binding

protein CML35) were involved in the plant-pathogen interaction pathway, while Csa3M889840.1

(thylakoid lumenal 29 kDa protein) was involved in the glutathione metabolism pathway. It was

predicted that Csa3M823060.1 was a target gene of miR6196, while other mRNAs were targeted by

miR9748. Studies had shown that CML42 acts as a negative regulator of plant defenses by decreasing

COI1-mediated JA sensitivity and the expression of JA-responsive genes and is independent of

herbivore-induced JA biosynthesis; thus, CML42 might serve as a Ca2+

sensor that has multiple

functions in insect herbivory defense and abiotic stress responses (Vadassery et al. 2012). The CML37,

-38, and -39 transcripts are regulated by biotic and abiotic stress as well as hormone and chemical

treatment (Vanderbeld and Snedden 2007). CML9 and CML20 alters plant responses to ABA and

abiotic stress (Magnan et al. 2008, Wu et al. 2017). Mutum et al (2016) identified miR6196 from the

drought-tolerant rice variety Nagina 22. Yang et al (2017) found that miR6196 showed significant

differential expression under cold stress and was specifically differentially expressed in sugarcane

cultivars ROC22 (relatively cold-sensitive). We constructed the ceRNA network of TCONS_00031790,

TCONS_00014332, TCONS_00014717 and TCONS_00005674, novel_circ_001543,

novel_circ_000876, Csa1M690240.1, Csa6M091930.1, Csa7M405830.1 and miR9748. These mRNAs

were identified as important elements of the plant hormone signal transduction pathway based on the

KEGG analysis. Csa1M690240.1, Csa6M091930.1 and Csa7M405830.1 are the target genes of

miR9748. Studies have shown that chloroplast heat-shock protein 90 (HSP90), which plays a role in

protein processing in the endoplasmic reticulum, was affected by miR9722 and miR9748 (Cakir et al.

2016). The transcription factor MYC2 is involved in environmental information processing and plant

hormone signal transduction and is affected by miR9748 (Cakir et al. 2016). Csa1M690240.1 codes

for the cucumber auxin protein IAA16, and Csa7M405830.1 codes for the cucumber ethylene

response sensor 1 (ERS1) gene. Studies have shown that a high concentration of IAA can weaken the

cotton anther defense response to high-temperature stress (Min et al. 2014). High temperatures can

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limit ethylene production (Kawakami et al. 2013), and ERF1 has been reported to play an active role

in regulating tolerance to salt stress, drought stress and heat stress by regulating specific genes that

respond to stress, thereby regulating the integration of JA, ET and abscisic acid signals (Cheng et al.

2013).

As mentioned above, the ceRNA network indicated that TCONS_00031790, TCONS_00014332,

TCONS_00014717, TCONS_00005674, novel_circ_001543 and novel_circ_000876 may play

regulatory roles in the plant hormone signal transduction pathway through miR9748 and its target

genes in response to high-temperature stress. Most of the target genes were involved in the ethylene-

and IAA-mediated signaling pathways. To further investigate the roles of endogenous hormones in

response to high-temperature stress, the IAA and ACC contents were measured in cucumber. The

results revealed that both the IAA and ACC levels were reduced in the HT group (Fig. 9), suggesting

that heat stress could reduce IAA and ethylene. We speculate that miR9748 regulated Csa1M690240.1

and Csa7M405830.1 alter plant IAA and ethylene responses. Ethylene synthesis by plants is very

sensitive to changes in temperature (Field 1985) and at high temperatures may be greatly reduced (Yu

et al. 1980, Robinson and Biddington 1990). Research has shown that high-temperature treatments

reduce ethylene production from filaments alone and from filaments with anthers attached (Biddington

and Robinson 1993). The ethylene precursor ACC inhibited filament growth in Fuchsia hybrida at

32°C (Jones and Koning 1986) and of Ipomoea nil at 30°C (Koning and Raab 1987). Compared with

the control, the high temperature treatment reduced the endogenous hormone content of IAA in

rapeseed plants (Zhou and Leul 1999). Wu found that high-temperature stress reduced IAA in rice (Wu

et al. 2016). Heat-induced reductions in IAA were reported in the anthers and developing grains of rice

(Wang et al. 2006, Tang et al. 2008). High temperature generally suppresses IAA biosynthesis (Sakata

et al. 2010). Moreover, Sakata et al. (2010) reported that IAA regulated pollen development and male

sterility under high temperature. Our findings showed that miR9748 mainly acted on interactions

between heat-stress-responsive and hormone pathways rather than directly acting on heat-stress

response pathways.

Through the GO and KEGG pathway analyses, competitive lncRNA/circRNA-miRNA-mRNA

regulatory networks were comprehensively integrated and predicted to respond to high-temperature

stress. In addition, TCONS_00031790, TCONS_00014332, TCONS_00014717 and

TCONS_00005674, novel_circ_001543 and novel_circ_000876 were predicted to interact with

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miR9748 to regulate the heat shock response through the plant hormone signaling pathway. Our

research showed that specific lncRNAs and circRNAs might function as ceRNAs in response to

high-temperature stress. In this study, we conducted a full transcriptomic analysis in response to

high-temperature stress in cucumber and integrated, for the first time, the potential ceRNA function of

lncRNAs/circRNAs, laying a foundation for studying the potential functions and mechanisms of

lncRNAs/circRNAs in response to high-temperature stress.

Author contributions

X.H. performed the experiments, analyzed the data and wrote the manuscript. Y.W. and L.W.

contributed significantly to analyzing the data and preparing the manuscript. S.S. performed analyses

and contributed to constructive discussions. S.G. and J.S. conceived and designed the experiments. All

authors contributed to revising the manuscript. All authors read and approved the final manuscript.

Acknowledgements – We thank Gene Denovo Biotechnology Co. (Guangzhou, China) for their help

with the RNA-Seq and bioinformatics analyses. This work was supported by the National Key

Research and Development Program of China (2018YFD1000800) and National Natural Science

Foundation of China (31872152).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon

reasonable request.

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Supporting Information

Table S1. Primers used for qRT-PCR of miRNAs and lncRNAs.

Table S2. List of differentially expressed lncRNAs and mRNAs from the two treatment groups.

Table S3. Differentially expressed protein-coding genes detected 10-kb upstream and downstream of

the lncRNAs.

Table S4. GO and KEGG enrichment analysis of protein-coding genes targeted by cis-acting

lncRNAs.

Table S5. List of circRNAs identified in the cucumber leaf libraries.

Table S6. GO and KEGG enrichment analysis of circRNA source genes.

Table S7. List of known and novel miRNAs identified in the cucumber leaf libraries.

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Table S8. GO and KEGG enrichment analysis of protein-coding genes targeted by miRNAs.

Table S9. LncRNAs, mRNAs and circRNAs predicted to bind to miRNAs.

Table S10. GO terms and pathways enriched by target mRNAs through GO and KEGG analyses of

the ceRNA network.

Figure legends

Fig. 1. Differentially expressed lncRNAs and mRNAs in cucumber. (A) Number of upregulated and

downregulated lncRNAs and mRNAs. (B) and (C) Heatmap of differentially expressed mRNAs and

lncRNAs from four libraries (HT-1, HT-2, CK-1, and CK-2).

Fig. 2. Basic characteristics of lncRNAs in cucumber. (A) Flow chart of the method used to identify

the lncRNAs. (B) Distribution of exon lengths in lncRNAs and mRNAs. (C) Proportions of exon

numbers per transcript for lncRNAs and mRNAs. (D) GC contents of the lncRNAs and mRNAs.

Fig. 3. GO and KEGG enrichment analysis of differentially expressed mRNAs. (A) mRNAs were

significantly enriched in 41 GO terms (P-value < 0.05). Red indicate an increased level of expression,

while blue indicate a decreased level of expression. (B) KEGG pathways involving the top 20 terms.

QValue ranges from 0 to 1. The closer to zero the QValue is, the more significant the enrichment is. A

larger RichFactor value indicates a higher degree of enrichment.

Fig. 4. Multiple mRNAs interacted with one lncRNA. The lines indicate interaction.

Fig. 5. CeRNA regulatory network in cucumber. The ceRNA network is based on lncRNA/miRNA,

circRNA/miRNA, and miRNA/mRNA interactions. The lines represent sequence matching, and

lncRNAs or circRNAs connect expression correlated mRNAs via miRNAs.

Fig. 6. GO annotations and KEGG pathway analyses of 359 differentially expressed mRNAs. (A)

mRNAs were significantly enriched in 38 GO terms (P-value < 0.05). (B) KEGG pathways involving

the top 20 terms. QValue ranges from 0 to 1. The closer to zero the QValue is, the more significant the

enrichment is. A larger RichFactor value indicates a higher degree of enrichment.

Fig. 7. CeRNA network of lncRNAs/circRNAs-miRNAs-mRNA involved in KEGG pathway of Plant

hormone signal transduction. The lncRNAs coded as TCONS_00031790, TCONS_00014332,

TCONS_00014717 and TCONS_00005674, as well as novel_circ_001543 and novel_circ_000876,

were predicted to interact with miR9748. Csa1M690240.1, Csa6M091930.1, Csa7M405830.1 were

predicted to be target mRNAs of miR9748. These three mRNAs are pivotal genes in the Plant

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hormone signal transduction pathway according to KEGG analysis.

Fig. 8. qRT-PCR analysis of several miRNAs (A) and lncRNAs (B). * P-value < 0.05, ** P-value <

0.01.

Fig. 9. Direct biosynthetic precursor of ethylene ACC (A) and auxin IAA (B) contents in leaves from

the CK and HT groups in cucumber.

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Table 1. Base statistics before and after filtering form HT-1, HT-2, CK-1 and CK-2 lncRNA libraries.

Before Filter After FilterSample

total reads Q20 (%) Q30 (%) N (%) GC (%) clean reads Q20 (%) Q30 (%) N (%) GC (%)

HT-1

HT-2

CK-1

CK-2

16 640 895 000

15 811 860 600

15 123 343 500

16 390 433 700

15 652 223 999

(94.06%)

14 898 898 006

(94.23%)

14 259 408 356

(94.29%)

15 414 566 471

(94.05%)

14 559 951 092

(87.50%)

13 879 986 382

(87.78%)

13 292 965 086

(87.90%)

14 334 505 931

(87.46%)

8 144 531

(0.05%)

7 745 578

(0.05%)

7 347 134

(0.05%)

8 000 888

(0.05%)

6 974 562 474

(41.91%)

6 664 377 487

(42.15%)

6 359 434 947

(42.05%)

6 917 448 309

(42.20%)

16 168 783 366

15 369 275 680

14 747 065 130

15 934 457 288

15 333 665 497

(94.83%)

14 594 514 883

(94.96%)

14 008 253 421

(94.99%)

15 109 145 507

(94.82%)

14 323 684 725

(88.59%)

13 650 487 530

(88.82%)

13 107 755 080

(88.88%)

14 109 446 548

(88.55%)

7 638 541

(0.05%)

7 287 130

(0.05%)

6 931 190

(0.05%)

7 506 657

(0.05%)

6 753 978 148

(41.77%)

6 456 008 376

(42.01%)

6 183 504 648

(41.93%)

6 702 578 867

(42.06%)

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Table 2. Summary of RNA-seq data and reads mapped to the Cucumis sativus reference genome.

Sample Total Reads Unmapped Reads Unique Mapped Reads Multiple Mapped reads Mapping Ratio

HT-1

HT-2

CK-1

CK-2

108 425 230

103 190 136

98 789 534

106 824 530

25 030 450 (23.09%)

23 280 734 (22.56%)

19 950 633 (20.20%)

21 676 892 (20.29%)

82 062 106 (75.69%)

78 560 258 (76.13%)

77 720 689 (78.67%)

83 946 280 (78.58%)

1 332 674 (1.23%)

1 349 144 (1.31%)

1 118 212 (1.13%)

1 201 358 (1.12%)

76.91%

77.44%

79.80%

79.71%

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Table 3. Summary of cleaning data from CW and HW sRNA libraries. CW means control treated

with water and HW means high temperature treated with water.

CW HWType

Count Percent (%) Count Percent (%)

Total reads

High quality

3'adapter null

Insert null

5'adapter contaminants

Smaller than 18nt

Poly(A)

Clean reads

11 255 470

11 245 483

36 410

863

9749

332 565

92

10 865 804

100

0.32

0.01

0.09

2.96

0.00

96.62

11 296 147

11 284 130

48 705

2094

14 824

364 094

69

10 854 344

100

0.43

0.02

0.13

3.23

0.00

96.19

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