Indian Horticulture Journal 8(2/3): 45-51, April-September (2018)

ISSN: 2249-6823

©Indian Society of Advanced Horticulture

Research Paper

www.ihj.ind.in 544-18-1304-008

Comparative Transcriptome Sequencing of Cucumber Cultivars with Different Hypocotyl Length

Min Wang#1,2, Biao Jiang#1,2, Wenrui Liu1, Xiaoming He1, Qingwu Peng1, Zhaojun Liang1, Yu’e Lin*1

1Vegetable Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou - 510 640, China 2Guangdong Key Laboratory for New Technology Research of Vegetables, Guangzhou - 510 640, China

Received: 25 February 2018; Revised accepted: 30 May 2018

#These authors contributed equally to this work

*Corresponding author; e-mail: 1072816328@qq.com A B S T R A C T

Cucumber (Cucumis sativus L.; 2n = 2x = 14), which belongs to the Cucurbitaceae, is one of the agriculturally and economically important vegetable crops around the world. It’s appropriate hypocotyl length is beneficial for establishing strong seedlings before transplanting. However, little knowledge is known about the molecular mechanism underlying hypocotyl elongation in the seedling traits. In our study, comparative analysis of hypocotyl transcriptome between long hypocotyl (B80) cultivar and short hypocotyl (JSH) cultivar would unravel novel genetic regulatory mechanisms involved in hypocotyl elongation. Using BGISEQ-500 platform, about 23.74 M reads per sample was averagely generated. The average mapping ratio with reference genome is 95.12% and the average mapping ratio with gene is 58.58%. A total of 20,861 genes were detected, among them, 1089 differentially expressed genes (DEGs) were detected between the two samples including 646 up-regulated genes and 443 down-expressed genes and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the main activated genes in hypocotyl development were predominately involved in metabolic processes, biosynthesis of secondary metabolite pathways, catalytic activity, binding protein and other cellular components. In addition, the majority of related transcription factors (TFs) such as MYB, MYB related genes, and AP2-EREBP were identified in transcriptome. Multiple candidate cucumber genes in the hypocotyl development related pathways were also discovered. In all, these results offer specific genotype-dependent genes for genetic research of hypocotyl elongation in cucumber.

Key words: Cucumis sativus L, RNA-sequence, DEGs, Hypocotyl, Transcription factors Abbreviations: RNA seq: RNA sequencing DEGs: Differently expressed genes KEGG: Kyoto Encyclopedia of Genes and Genomes GO: Gene Ontology

ypocotyl, as an embryonic stem connecting

cotyledons and the primary root, has a relatively

simple architecture (Scheres et al. 1994). In Arabidopsis,

hypocotyl growth mainly relies on longitudinal cell

elongation after germination (Gendreau et al. 1997).

Cucumber (Cucumis sativus L.; 2n = 2x = 14), which

belongs to the Cucurbitaceae, is one of the agriculturally and

economically important vegetable crops around the world.

In general, cucumber is always cultivated by transplantation

way at seedling stage, thus, the seedling trait (hypocotyl

length, cotyledon size, the first female flower node) would

significantly influence plant development, flower bud

differentiation, and fruit development, finally impacting

plant production and quality (Ming et al. 2011).

Previous studies reported seedling related genes by

molecular genetic assay (Miao et al. 2012, Wang et al.

2016). At present, studies of hypocotyl length mainly focus

on the physiological (Zhou et al. 2010, Ming et al. 2011)

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and genetic analysis under adversity stress. Several

researches demonstrated that is a quantitative trait controlled

by seveal genes and detected some QTLs (quantitative trait

locus) related to the trait (Jin et al. 2009, Zhang et al. 2011,

Miao et al. 2012, Wang et al. 2016). About 15 QTLs with

hypocotyl length were detected in low light environment

(Zhang et al. 2011). Two QTLs (Hl5.1 and Hl6.1) associated

with hypocotyl length was located at SSR23750-SSR00193

and SSR15818-SSR06003 of chromosome 5 and 6,

respectively (Miao et al. 2012). Combing recombinant

inbred lines population, we previously detected 10 QTLs of

hypocotyl length with the highest observed variation 15.1%

(Wang et al. 2016). A recent research isolated and

characterized a short hypocotyl gene (SH1), which regulated

low-dosage UVB-dependent hypocotyl elongation by

modulating UVR8 signaling pathway (Bo et al. 2016). The

appropriate hypocotyl length of cucumber is beneficial for

establishing strong seedlings before transplanting. During

seedling stage, cucumber with short hypocotyl shows

robuster and higher wind resistance than that with long

hypocotyl. Short cucumber hypocotyl has great application

value in the field cultivation and factory farming (Chen et

al. 2015).

RNA-Seq, combined with appropriate bioinformatics

tools, offers a pretty precise measurement of transcriptive

levels and their isoforms (Wang et al. 2010). And its high

accuracy and sensitivity makes suitable to study the whole

transcriptome (Jain et al. 2012). In addition, the RNA-Seq

allows identification of alternative splicing (AS) events,

novel transcripts and digital gene expression at the isoform

level in contrast to other methods such as microarray and tag

sequencing (Wang et al. 2010, Filichkin et al. 2010, Zhang

et al. 2010). In recent years, RNA-seq has been widely

applied to excavate transcriptomes of several plants under

different stress (Garg et al. 2014, Zhou et al. 2016, Song et

al. 2016). However, few studies under normal condition

were reported to analyze the transcriptive profiling between

different cultivars. Here, using deep RNA-Seq and digital

gene expression profile (DGE) analysis, we could rapidly

identify important genes expressed differently. And two

sequencing libraries prepared from long hypocotyl and short

hypocotyl samples were sequenced using a BGISEQ-500

platform. Overall, the research could provide a

comprehensive overview of transcriptional regulation and

complexity in hypocotyl elongation, which would offer

basic thesis for molecular mechanism of hypocotyl

elongation.

MATERIALS AND METHODS

Plant materials and growth conditions

Seeds of JSH short for Jin Shan (a South China type

cucumber variety) and B80 (North China type cucumber

variety) were germinated 48 hours at 37°C in a growth

chamber. Then we transplanted seedlings to soil-filled

containers and then incubated them in a greenhouse at 28°C and

60% relative humidity. Hypocotyl samples of three randomly

selected biological replicates were then collected from both

JSH and B80 plants (six samples in total). Plant tissues were

frozen in liquid nitrogen and stored at -80°C.

BGISEQ-500 library preparation and sequencing

A total of six samples (three replicates each of JSH and

B80 plants) were prepared for RNA extraction. High-quality

RNA was extracted from the leaves using Trizol reagent

(Invitrogen, USA) and then treated with DNase I

(Invitrogen). Total RNA was then purified and concentrated

using an RNeasy MinElute cleanup kit (Qiagen, Germany).

We prepared libraries using 2.5 μg total RNA of each

sample following the protocol described by Fehlmann et al.

(2016). Magnetic beads with Oligo (dT) were used to isolate

mRNA from total RNA. After mixing with fragmentation

buffer, mRNA was broken into short fragments. First-strand

cDNA was synthesized using random hexamer primers and

M-MuLV reverse transcriptase (RNase H). Second-strand

cDNA synthesis was then performed using DNA

polymerase I and RNase H. To select cDNA fragments 130-

160 bp in length, the PCR products were purified using an

AMPure XP system. Library quality was assessed on an

Agilent Bioanalyzer 2100 system.

Functional annotation and classification of the assembled

transcripts

Before assembly, adaptor sequences, empty reads,

low-quality sequences with an ‘N’ percentage over 10%

and sequences containing more than 50% bases with a Q-

value < 5 were removed using a custom Perl script. After

filtering, the remaining clean reads were used for

downstream bioinformatics analysis. Reads were mapped

to the Nipponbare reference genome using HISAT

software (Kim et al. 2015). Expression levels of each

gene were then calculated by quantifying the reads

according to the RPKM (reads per kilobase per million

reads) method (Li et al. 2011). Differential expression

analysis of the two conditions/groups was performed

using NOISeq, with different expression genes (DEGs)

identified according to the following criteria: fold change

≥ 2 and divergence probability ≥ 0.8.GO enrichment

analysis of DEGs was carried out using WEGO software

(Ye et al. 2006). To further understand DEG biological

functions, pathway enrichment analysis was performed

using the KEGG database (Kanehisa et al. 2008), the

major public pathway-related database.

Quantitative real-time PCR (qRT-PCR) validation of RNA-

Seq results

Three independent RNA samples per condition were

used to validate gene expression levels by qRT-PCR. Total

RNA was prepared from rice tissues using Trizol Reagent

(Life Tech). cDNA (20 µl) was synthesized from 1 µg of

RNA using a QuantiTect Reverse Transcription kit

(Qiagen). qRT-PCR amplifications were performed in 20 µl

reaction volumes containing 0.5 µl cDNA, 0.2 µM primer

mix and SYBR Premix Ex Taq (Takara) on an ABI PRISM

7900HT sequence detection system. The Actin gene was

used as an internal control. All primers used for qRT-PCR

were listed in (Table 1). Data was analyzed using the

relative quantification method (Livak and Schmittgen 2001).

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Indian Horticulture Journal 8(2/3)

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Table 1 Primers used in the assay of qRT-PCR Gens Forward primer Reverse Primer

Csa3G840450 TACGTCTTGCAGGACCAACT CTTGTGGACTTAGCCTCCCA

Csa7G060160 GCCTTCAAGCTGAGTCTGGT GAGTTCCAAGTACCGCAAGT

Csa4G629470 CAAGGCCTGGTTTCCCAATC CCAACCTTGCTGAGGGAGTA

Csa6G154500 CTCCATATCCTGCCGACCAT CATCATCGGCAAATGCAACG

Csa6G040640 GAAGCCACCATTGAAGCCAA TTGGTCCACGTGAAGGAACT

Csa6G505810 AGCCGATACATTGTCCCGA ACCAAACTCCTCCTCTGCTC

Csa2G237140 AGCCCACCAACAACTTTGAC TGGGTTGGTCTCACCAATGT

Csa7G378510 TGTTGCAATGGTTGATGGCA AGTGAAAGCATCCGCAATCC

Csa7G428260 CACTCAAAGGCACACAACCA TGCGCTTGTAATGTGTCTGG

Csa4G022900 ACAAGGCGTGTAACGAATGG TCGGTAACGGCTCTGAGTTT

Csa3G144140 GTAGGCGTACGTCTCTCCAA CTGACATTGGGAGGACCAGT

Csa2G094390 TGTCAATGCTCACTGGAGGT CGAATCTTTGGTGGTGCCAT

RESULTS

Phenotypic characteristic analysis

After seeding for one week, we observed the seedling

trait of JSH and B80. Results showed that the hypocotyl of

B80 is much longer than JSH (Fig 1A, B), which reached to

the significant different level (Fig 1C). In addition, the

cotyledon size between them also shows different. In detail,

the cotyledon length and width of JSH were 3.4 cm and 1.8

cm, respectively, while they were prominently larger in

contrast to B80 (Fig 1D).

Sequencing statistics

To further understand the molecular mechanism of

hypocotyl elongation in B80 plants, we carried out our

RNA-seq to explore detect gene expression. cDNA libraries

were prepared from leaves of cucumber seedlings to RNA-

Seq analysis on the platform of BGISEQ-500. Average

23.74 M reads were obtained in JSH and B80 seedling

leaves, respectively (Table 2). After compared with Chinese

Long genome for the references genome to map these reads,

the average mapping ratio with reference genome is 95.12%,

the average mapping ratio with gene is 58.58%. And a total

of 20,861 genes were detected.

Fig 1 Hypocotyl observation of JSH and B80

(A, B) Seedlings of JSH and B80 at cotyledon stage (C) Measurement of hypocotyl length (D) Measurement of cotyledon length and width Bar in (A, B) 3 cm Data is presented as the mean ± standard deviation (n = 9) *0.01≤P≤0.05; **P≤0.01; Student’s t test

Table 2 Summary of RNA-Seq results from hypocotyl tissues between JSH and B80

Sample Total raw

Reeds (Mb) Total clean reads

(Mb) Total clean bases (Gb)

Clean reads Q20 (%)

Clean reads Q30 (%)

Clean reads ratio (%)

B80-1 23.78 23.73 1.19 97.99 90.93 99.78 B80-2 23.75 23.69 1.18 97.67 89.87 99.76 B80-3 23.83 23.78 1.19 97.78 90.07 99.78 JSH-1 23.78 23.72 1.19 97.74 90.26 99.74 JSH-2 23.86 23.8 1.19 97.75 90.14 99.73 JSH-3 23.76 23.7 1.18 97.66 89.81 99.75

DEGs uncovered by RNA-Seq

Gene expression levels were quantified using RSEM

software (Li et al. 2011) and the FPKM (fragments per

kilobase of transcript per million mapped reads) method.

Putative differently expressed genes (DEGs) between B80

vs. JSH (B80_1 vs.JSH_1, B80_2 vs.JSH_2and B80_3

vs.JSH_3) were identified according to the following default

criteria: fold change ≥ 2 (B80/JSH) and divergence

probability ≥ 0.8. Results showed that a total of 646

transcripts demonstrated significant up- regulated, while 443

transcripts were predominantly down-regulated (Fig 2,

Table S1).

GO functional classification of DEGs

The GO standardized classification system for gene

function was used to analyze DEGs and understand the

molecular events involved in hypocotyl elongation. WebGO

tool was used to classify these detected significant DEGs

into three categories: “biological process,” “molecular

function”, and “cellular components” (Fig 3). In many cases,

a contig was associated with multiple functions. Under

biological process, the two largest subcategories were

metabolic process (375) and cellar process (278), which

followed by subcategories such as single-organism process,"

biological function, response to stimulus. Major

Comparative Transcriptome Sequencing of Cucumber Cultivars

47

subcategories under molecular function were “binding” and

“catalytic activity”. Finally, “membrane”, “cell”, and “cell

part”, followed by “organelle”, were the predominant

subcategories in the cellular component category in

hypocotyl.

KEGG pathway analysis

To examine DEG-associated pathways, DEGs were

searched against the KEGG pathway database. The top 20

obviously enriched pathways are shown in (Fig 4). The main

pathways enriched in DEGs relative to the B80 were

“metabolic pathways”, “biosynthesis of secondary

metabolites”, “plant hormone signal transduction”, and

“protein processing in endoplasmic reticulum”, followed by

pathways such as “phenylpropanoid biosynthesis”, “ABC

transporters”, and “flavonoid biosynthesis”. These enriched

pathways and processes might thus participate in

metabolism. Under plant hormone signal transduction, genes

such as gibberellin 20-oxidase (down regulated 2.11-fold)

and brassinosteriod insensitive 1 BRI1 (up regulated 8.5-

fold) were identified in GA and BR signal pathways. From

the above results, metabolism pathways in B80 plants lines

may be affected to increase hypocotyl length.

Fig 2 Comparison of different genes expression (DEGs) in leaves between JSH and B80

X- and y-axes represent log2 values of gene expression. Blue, orange and brown correspond to down-regulated, up-regulated and unaltered gene expression, respectively. If a gene was expressed in just one sample, its expression value in another sample was replaced by the minimum value of all expressed genes in control and case samples. The screening threshold is given at the top of the figure.

Fig 3 Histogram of GO terms assigned to DEGs in leaves between JSH and B80

The x-axis indicates the square root of the number of DEGs. The y-axis represents GO terms. All GO terms are grouped into three ontologies: blue for biological process, brown for cellular component, and orange for molecular function.

Fig 4 KEGG enrichments of annotated DEGs across three comparisons

The y-axis indicates the KEGG pathway and the x-axis indicates the enrichment factor. A high q-value is represented by light blue, and a low q-value is represented by dark blue.

Transcription factor analysis

Transcription factors (TFs) are involved in multiple

biological processes in plant such as plant height (Chen et

al. 2015, Zhang et al. 2017), heading date (Nemoto et al.

2016), and other metabolic reactions and defense (review by

Feller et al. 2011). Thus, according to the detected DEGs in

the transcriptome data, we classify the family of

transcription factors to which the differentially expressed

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genes belong, and the results are shown in (Fig 5). In detail,

these TFs were classified into 59 families and the largest

group of differentially expressed TFs was MYB (215),

MYB-related (165), followed by the AP2-EREBP (136),

bHLH (122), NAC (83), WRKY (60) and others (Fig 5).

Among them, the representative TFs were listed in the

(Table S2). From the analysis, hypocotyl elongation might

be mainly attributed by MYB and MYB-related TFs.

Fig 5 DEGs classification on TF family

Verification of several genes among the DEGs

In order to confirm the accuracy of RNA-seq result, a

total of 12 genes were selected randomly based on

transcription upregulated and down regulated. These genes

encoded E3 ubiquitin-protein ligase (Csa3G840450),

BRASSINOSTEROID INSENSITIVE 1-associated receptor

kinase 1 (Csa7G060160), aminotransferase TAT2

(Csa4G629470), stem-specific protein TSJT1

(Csa6G154500), transcription factor MYB108-like

(Csa6G040640), auxin-induced protein (Csa6G505810),

salicylic acid-binding (Csa2G237140), LRR-repeat protein

(Csa7G378510), zinc finger protein WIP2-like

(Csa7G428260), gibberellin 20 oxidase (Csa4G022900), 3-

ketoacyl-CoA synthase (Csa3G144140), and sugar transport

protein (Csa2G094390). Expression quantities of the

selected genes using qRT-PCR were consistent with the

results obtained with RNA-Seq analysis, which means the

RNA-seq data were credible (Fig 6).

DISCUSSION

The study first represents the broad-scale gene

expression analysis of hypocotyl in cucumber to investigate

related genes expression profile changes in two materials

with differences in hypocotyl length. By this method, we

aimed to identify genes regulating hypocotyl length and

through these genes to explore what signal transduction

pathways and metabolism or physiological processes

involved in the hypocotyl development.

Fig 6 Quantitative real-time PCR (qRT-PCR) validation of several genes between JSH and B80 among DEGs from RNA-Seq results

Data is presented as the mean ± standard deviation (n = 9). *0.01≤P≤0.05; **P≤0.01; Student’s t test.

Multiple studies in plant could explore related genes

controlling the agricultural trait mainly by transcriptome

sequencing. For example, a study identified five cucumber

DEGs involved in gray mold combing the large-scale

transcriptome analysis during infection (Kong et al. 2015).

Heat shock proteins (HSPs) genes were reported to be

involved in the heat stress tolerance by an RNA-sequencing

approach of heat- and control-treated reproductive tissues in

rice (Gonzã et al. 2016). Genes of anthocyanin synthesis,

photosynthetic system reprogramming, cell wall remodeling,

and ascorbic acid (AsA) metabolism were involved in the

early nitrogen deficiency response in cucumber seedlings by

RNA-Seq-based transcriptome profiling (Zhao et al. 2015).

Similarly, combing the method, our results identified 1089

different expressed genes between JSH and B80, which

were largely enriched in the catalytic activity, binding,

metabolic process, cellular process, single-organism process,

membrane, and cell (Fig 3). These results indicate that

catalytic activity and metabolic process related genes play

essential roles in the growth and development of hypocotyl.

Gibberellin (GA) is essential for hypocotyl

development, which has been deeply studied in Arabidopsis

(Gomi et al. 2003, Alabadí et al. 2004, Stamm et al. 2017).

According to the KEGG analysis, we found that the

gibberellin receptor GID1 was prominently down-regulated

by 122.6-fold in B80 and the expression of gibberellin 20-

oxidase was also significantly decreased for 2.11-fold in

Comparative Transcriptome Sequencing of Cucumber Cultivars

49

B80, indicating the GA signaling transduction and

biosynthesis was affected in B80, resulting the different

hypocotyl length between JSH and B80.

MYB proteins are key factors in regulatory networks

controlling plant development, metabolism and responses to

biotic and abiotic stresses (review by Dubos et al. 2010).

Previous studies reported that MYB such as MYB46 and

MYB83 in Arabidopsis are regulators of the biosynthesis of

lignin, xylan and cellulose, involved in the secondary cell

wall thickening (Zhong et al. 2008, 2012, Hua et al. 2013).

In rice, OsMYB103L and MYB61 participated in the

secondary wall biosynthesis mediated by the GA pathway,

which can affect leaf shape, cellulose synthesis and

mechanical strength (Yafeng et al. 2015). OsMPH1, the

MYB-like transcription, factor regulates plant height and

improves grain yield in rice (Zhang et al. 2017). Our results

found that the MYB and MYB-related genes were mostly

enriched during these detected TFs (Fig 5) and the

expression of most MYBs was significantly increased in

B80 such as Csa6G040640 (up- regulated 5-fold),

Csa5G610490 (up-regulated 4.4-fold) and so on (Table S3).

In addition, we detected 12 genes expression level, which

was consistent with the transcriptome data, indicating these

expression changes of DEGs between JSH and B80 were

credible.

In conclusion, we hypothesized that higher expression

of MYBs in B80 might influence the synthesis of secondary

cell wall, resulting longer hypocotyl.

Acknowledgments

This work was supported by the Presidential foundation

of Guangdong academy of agricultural sciences (201813).

Conflicts of interest

This study is not related to any potentially competing

financial or other interest.

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