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BioMed Research International

Volume 2014 (2014), Article ID 404578, 9 pages

Research Article

Gleditsia sinensis: Transcriptome Sequencing, Construction, and Application of Its Protein-Protein Interaction Network

1Institute of System Biology, Shanghai University, Shanghai 200444, China

2Yangzhou Breeding Biological Agriculture Technology Co. Ltd., Yangzhou 225200, China

3State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210093, China

Received 11 March 2014; Accepted 21 April 2014; Published 27 May 2014

Academic Editor: Lei Chen

Copyright © 2014 Liucun Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Gleditsia sinensis is a genus of deciduous tree in the family Caesalpinioideae, native to China, and is of great economic importance. However, despite its economic value, gene sequence information is strongly lacking. In the present study, transcriptome sequencing of G. sinensis was performed resulting in approximately 75.5 million clean reads assembled into 142155 unique transcripts generating 58583 unigenes. The average length of the unigenes was 900 bp, with an N50 of 549 bp. The obtained unigene sequences were then compared to four protein databases to include NCBI nonredundant protein (NRDB), Swiss-prot, Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Cluster of Orthologous Groups (COG). Using BLAST procedure, 31385 unigenes (53.6%) were generated to have functional annotations. Additionally, sequence homologies between identified unigenes and genes of known species in a protein-protein interaction (PPI) network facilitated G. sinensis PPI network construction. Based on this network construction, new stress resistance genes (including cold, drought, and high salinity) were predicted. The present study is the first investigation of genome-wide gene expression in G. sinensis with the results providing a basis for future functional genomic studies relating to this species.

1. Introduction

Gleditsia sinensis is a genus of deciduous tree in the family Caesalpinioideae, native to China. G. sinensis usually grows 15–30 m tall and is of economic and medicinal importance. The fruits of G. sinensis can serve as medicine, food, health products, cosmetics, and natural raw materials for cleaning products [1]; the seeds can be used as appetizing medicine [2, 3] and contain an important vegetable gum (guar gum) [2]; the thorns (soap-pin) contain flavonoid glycosides, phenols, and amino acids and have a high economic value [3]. However, up to October 2013, only 17 nucleotide sequences and eight protein sequences of G. sinensis were available in the NCBI database. This brings to question why such an economically valuable organism has been so understudied, making it important to generate more genetic sequence information to further study G. sinensis.

Plants are exposed to continuously changing environmental conditions under natural conditions. Various environmental stresses, such as heat, cold, drought, and high salinity, are major factors in affecting plant development, growth, and productivity [46]. The stress-induced transcriptomic responses of plants revealed that many molecular mechanisms had been evolved to help plants to adapt and survive under the harmful stresses. Usually, there is an initial activation/sensory stage followed by a physiological stage when the plant perceived and responded to the abiotic stress [6, 7]. Previous work in a variety of stresses has been studied in Arabidopsis thaliana [8, 9]. Compared to Arabidopsis thaliana, there is little known about how trees respond to abiotic stress. In recent years, the emergence of next-generation sequencing technology has provided a fast and effective approach to generation of transcriptome data of nonmodel organisms lacking a complete genome sequence [10, 11]. Compared with whole-genome sequencing, RNA sequencing (RNA-seq) is of low cost and high throughput, becoming an important part of functional genome research [12]. It provided an efficient way of identifying the expression level and new members of the genes [13, 14].

In the present study, the Illumina HiSeq 2000 platform was used for transcriptome sequencing in four tissue types collected from G. sinensis. A total of 7632619288 reads assembled into 58583 unigenes and their functional annotations were obtained. In addition, a protein-protein interaction (PPI) network comprising genes expressed in G. sinensis was constructed and utilized to identify some new potential drought, freezing, and salinity tolerance genes. These findings will provide a solid foundation for further investigation of the functional genomics of G. sinensis.

2. Materials and Method

2.1. RNA Preparation, Sequencing, and Assembly

The G. sinensis specimen used in the present study was wild-grown from the Jiangsu Province, China. Total RNA was extracted from four tissues: tender shoots, young leaves, flower buds, and round thorns, using TRIzol Reagent with qualification and quantification evaluated by Agilent 2100 Bioanalyzer Nanochips and NanoDrop 2000 Spectrophotometer. And then it was processed and used for Illumina sequencing [15].

Raw read sequences are uploaded in the Short Read Archive database from National Center for Biotechnology Information (NCBI) with the accession number SRR 1012862.

We used SeqPreq (https://github.com/jstjohn/SeqPrep) to remove sequencing adapters and then used sickle [16] to trim low-quality sequences with default parameters. After cleaning reads, all of the high-quality reads were used in assembling by Trinity (trinityrnaseq_r2013-02-25) [17, 18]. The k-mer was counted by jellyfish and the min_contig_length was set as 300. Then, RSEM [19] was used to measure expression levels of every unique transcript. Units of TPM (transcripts per million) were used to report results. After counting fraction of each isoform, length × isoform percent was defined as a standard to identify unigenes.

2.2. Functional Annotation

Unigenes larger than 300 bp were subjected to functional annotation for predicting putative gene descriptions, conserved domains, gene ontology (GO) terms, and association with metabolic pathways. First, BLAST procedure (ftp://ftp.ncbi.nih.gov/blast/executables/release/2.2.18/) was used to compare all unigenes against protein sequence databases to include NRDB, Swiss-prot, and Clusters of Orthologous Groups (COG) with an -value < . Based on BLAST best match results, gene function and protein-related information were predicted, with Blast2GO software [20] used for gene ontology (GO) annotation in terms of molecular function, cellular component, and biological process. After all GO annotations were obtained, Web Gene Ontology Annotation Plot (WEGO) software was used to construct comparative plots based on unigene classifications. Unigenes were also subjected to sequence comparison using the COG database for gene function prediction [21]. For Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation [22], the BLASTX software was used for sequence comparison of unigenes within the KEGG database and completed on the KASS website [23] (http://www.genome.jp/tools/kaas/). Following KEGG annotation, each gene acquired a KEGG Orthology (KO) number representing a node in a certain reference metabolic pathway in KEGG.

2.3. Construction and Analysis of PPI Networks

First, known PPI networks and protein sequences from six species (Arabidopsis thaliana, Arabidopsis lyrata, Oryza sativa subsp. japonica, Brachypodium distachyon, Populus trichocarpa, and Sorghum bicolor) were downloaded from the String database [24]. Then using the TBLASTN software, protein sequences from the downloaded PPIs were compared with G. sinensis unigenes to identify homologous sequences with an -value < . The criterion of candidate interacting genes of the network was the TBLASTN hits with identity >50% and covering query gene >80%. If two unigenes from G. sinensis corresponded to two homologous proteins in the known networks, the encoded proteins were considered to interact with each other. Concluding network construction, each node in the network was assigned to a degree , which is the number of connected neighboring nodes. The degree distribution of giant network branches was computed using the formula where is the number of nodes and denotes the number of nodes with degree [25].

Stress resistance genes and protein sequences of Arabidopsis thaliana proteins related to salinity, drought, and freezing tolerance were downloaded from the stress responsive transcription factor database [2628] (STIFDB; http://caps.ncbs.res.in/stifdb2/) and compared with the G. sinensis unigene library to search for homologous sequences potentially possessing the same functions in G. sinensis. Next, the PPI network was used to predict novel drought, freezing, and salinity tolerance genes. The specific predictive criterions are as follows. If a protein in the G. sinensis network connects directly with the homologous sequences of over four known stress resistance genes, with no homology among these genes, then that gene was predicted to be a potential stress resistance gene.

3. Result

3.1. Paired-End Sequencing and De Novo Assembly of G. sinensis Transcriptome

Genes are differentially expressed in different tissue types. In an effort to broaden the obtained gene expression profile in G. sinensis, total RNA was extracted from four different tissues (tender shoots, young leaves, flower buds, and round thorns) and mixed in equal parts for sequencing using the Illumina platform. This resulted in a total of 75.6 million high-quality clean reads containing 7632619288 nucleotides (nt) and an average read length of 101 bp (Table 1). Due to a lack of G. sinensis whole-genome sequence, Trinity software was used to assemble all high-quality clean reads into transcripts de novo. From all of the clean reads, a total of 142155 unique transcripts with an average length of 1537 bp were obtained with a N50 of 1202 bp and the majority of unique transcripts (31818) between 100 and 500 bp (Figure 1(a)). Then after removing redundant sequences, 58583 unigenes were obtained, with an average length of 900 bp. The lengths of the unigenes varied from 300 bp to over 3000 bp and the length distribution of unigenes was shown in Figure 1(b).

Table 1: Summary of sequence assembly by Trinity after Illumina sequencing.
Figure 1: Overview of the G. sinensis transcriptome assembly. The size distribution of the UTs (a) and unigenes (b) produced from de novo assembly of reads by Trinity.

3.2. Functional Annotation and Classification of G. sinensis Transcriptome

Nonredundant database (NRDB) built in NCBI contains large amounts of protein information. For annotation of the G. sinensis transcriptome, all unigenes were compared against the NRDB using BLASTX (an -value cut-off of 1 ) to reveal 31100 unigenes with sequence homology. Among them, 45.1% of unigenes have the best matches mapping soybean.

The distribution of -values based on sequence homology showed that 61.1% of unigenes had high homology (smaller than ) with the -values of the other matches varying from to (Figure 2(a)). The similarity distribution showed the majority of unigenes (93.0%) with homologous sequences having similarities between 40% and 100%, with only 7% of the unigenes with homologous sequences having similarities less than 40% (Figure 2(b)).

Figure 2: Unigene homology searches against NR and Swiss-prot databases shown by pie graphs. -value proportional frequency distribution of BLAST hits against the NR database (a) and Swiss-prot database (c). Proportional frequency distribution of UTs similarities against the NR database (b) and Swiss-prot database (d) based on the best BLAST hits ( -value ≤ ).

Swiss-prot, an annotated protein sequence database maintained by the European Bioinformatics Institute (EBI), was also employed for unigene comparison which revealed 22157 of 58583 unigenes (37.8%) with sequence homology at an -value threshold of ≤ (Table 2). Almost half of these unigenes (49.2%) had an -value ≤ , and the remaining had -values between and which showed a slightly different result compared to the NRDB query (Figure 2(c)). In Swiss-prot comparison, 73.4% of the unigenes had sequence homology in the range of 40% to 100%, with only 26.6% having homology <40% (Figure 2(d)). In short, when combining the results from Swiss-prot and NRDB comparisons, a total of 31131 unigenes were confirmed to have homologous sequences.

Table 2: Summary of annotation of G. sinensis unigenes.

3.3. Classification by Gene Ontology (GO) Annotation

Gene ontology (GO) terms were utilized to assign gene function classifications to each unigene based on sequence similarity comparisons from NRDB. Among the 58583 unigenes identified in G. sinensis, 15264 were categorized into at least one of the three main GO categories which could be further subdivided into 51 subcategories (Table 2, Figure 3, and Additional File 1; see Supplementary Material available online at http://dx.doi.org/10.1155/2014/404578). The number of unigenes in cellular component, biological process, and molecular function was 11942 (20.4%), 11683 (19.9%), and 11129 (19.0%), respectively. The subcategory at the “cell,” “cell part,” “cellular process,” “organelle,” “metabolic process,” “binding,” and “catalytic activity” level included the highest number of unigenes relative to other subcategories, with the biological processes category also showing large numbers relating to cellular processes (9353) and metabolic processes (9039). This suggested an abundance of metabolic activities occurring at the time of sampling.

Figure 3: Gene ontology classification of the G. sinensis transcriptome. Gene ontology (GO) term assignments to G. sinensis unigenes based on significant hits against the NR database are summarized into three main GO categories (biological process, cellular component, and molecular function) and 51 subcategories.
3.4. Functional Classification by KEGG

To further predict the metabolic pathways of G. sinensis, Arabidopsis thaliana and Oryza sativa were used as references and each assembled unigene was annotated in KAAS to obtain the corresponding enzyme commission (EC) numbers. KEGG is considered a basic platform for systematic functional analysis based on constructed networks comprising gene products. KEGG analysis mapped a total of 2914 unigenes to 307 metabolic pathways encompassing five KEGG categories, including metabolism, genetic information processing, environmental information processing, cellular processes, and organismal systems (Additional File 2). The “metabolic” pathways show the highest representation (1357 members, 46.6%), followed by ribosome (180 members, 6.2%, ko03010), biosynthesis of amino acids (147 members, 5.0%, ko01230), carbon metabolism (133 members, 4.6%, ko01200), plant hormone signal transduction (129 members, 4.4%, ko4075), spliceosome (127 members, 4.3%, ko03040), protein processing in the endoplasmic reticulum (125 members, 4.3%, ko04141), purine metabolism (109 members, 3.7%, ko00230), and RNA transport (100 members, 3.5%, ko03013).

3.5. Classification by COG

Cluster of Orthologous Groups (COG) database containing proteins encoded by 66 complete genomes of bacteria, algae, and eukaryotes was constructed according to phylogenetic relationships. COG is a very useful tool for predicting the functionality of the individual proteins or all proteins in a new genome. In the present study, all obtained unigenes were compared with proteins in COG and classified into appropriate COG clusters. The results identified 6413 genes displaying significant sequence homology with COG database proteins, accounting for 11.0% of all unigenes. Some unigenes were shown to have multiple COG functions with a total of 6582 functional annotations noted and grouped into 21 COG function sets with -values ≤ (Table 2, Figure 4, and Additional File 3). The five sets including the largest number of unigenes were (1) “general function prediction only” (14.8%); (2) “replication, recombination, and repair” (10.6%); (3) “translation, ribosomal structure, and biogenesis” (10.2%); (4) “posttranslational modification, protein turnover, and chaperones” (9.8%); and (5) “amino acid transport and metabolism” (8.1%). The “RNA processing and modification” and “chromatin structure and dynamics” sets contained the least numbers of unigenes, 21 and 13, respectively.

Figure 4: COG function classification of the G. Sinensis transcriptome. A total of 6413 unigenes with significant homologies to the COG database ( -values ≤ ) were classified into 21 COG categories.

In summary, 31385 unigenes were annotated using NR, Swiss-prot, COG, and KEGG databases with -values ≤ deemed significant. Among these unigenes, 1433 showed BLAST match results in all four public databases demonstrating a strong functional annotation. These annotations provide valuable resources for further study of the specific metabolic activities, gene structures, and functions and pathways of G. sinensis.

3.6. Construction of Protein-Protein Interaction Network in G. sinensis

Using TBLASTN, similarities between G. sinensis unigenes and genes in a PPI network consisting of six String database genomes (Arabidopsis thaliana, Arabidopsis lyrata, Oryza sativa subsp. japonica, Brachypodium distachyon, Populus trichocarpa, and Sorghum bicolor) facilitated the construction of a PPI network of G. sinensis. This network contained one giant component and 91 smaller components (Figure 5 and Figure ). The giant component contained 1,897 nodes with 7078 links between nodes. The degree distribution of giant component conformed to , implying that the network was scale free and similar to other biological networks (Figure 6).

Figure 5: Illustration of the giant component of unigenes in G. sinensis.
Figure 6: The topological analysis of the giant component in G. sinensis with 1897 nodes and 7078 edges. Log-log plots of the node degree distribution with a power-law fit (red line).
3.7. Prediction of Resistance Genes in G. sinensis

Resistance genes and protein sequences from Arabidopsis thaliana relating to freezing, drought, and salinity tolerance were downloaded from STIFDB and compared with G. sinensis unigenes to locate sequence homology. 435 freezing tolerance genes, 284 drought tolerance genes, and 348 salinity tolerance genes were found (Additional File 4). Based on the constructed G. sinensis PPI network, new freezing, drought, and salinity tolerance genes were predicted with a protein considered a potential resistance gene if interactions with over four known resistance genes were noted. The results revealed 19 new freezing tolerance genes, 11 drought tolerance genes, and 18 salinity tolerance genes (Table 3). This provides a theoretical basis for future experimental studies on resistance genes and for culturing resistant G. sinensis varieties.

Table 3: Novel resistant genes relating to cold, drought, and salinity tolerance identified by network analysis.

4. Discussion

Gleditsia sinensis is a tree species of important economic and medicinal value. However, due to a lack of genomic research, molecularly based breeding of G. sinensis is hindered. Recently, RNA-seq technology has provided a new approach to obtaining rich sequence information to include successful applications in many plants, such as Youngia japonica [29], cabbage [30], tea plant [15], and citrus psyllid [31]. In the present study, RNA-seq was used for transcriptome sequencing of G. sinensis with 7.6 Gbp examined and 75.6 million clean reads obtained. The Trinity software was used for de novo assembly and a total of 58583 unigenes were obtained. Among these, 31385 unigenes were functionally annotated and shown to participate in a variety of biological processes, accounting for 53.6% of all obtained unigenes.

Besides, freezing, drought, and salinity tolerance genes in G. sinensis were predicted by searching for homologous genes linked to resistance and using PPI networks. Currently there are many available methods for gene function prediction based on PPI. For example, George et al. assumed that genes interacting with known disease genes were also disease genes and studied third-degree interactions, yet many false positives were discovered based on nondirect interactions [32]. Xu and Li reported five topological characteristics of disease-related PPI networks, yet these characteristics were not found in the yeast two-hybrid network. These characteristics were used to predict disease-related genes, yet they were unable to explain the biological significance of these characteristics, and their results still required experimental verifications [33]. Based on these previous studies and to ensure the lowest rate of false positive predictions, the present study applied more strict conditions to include direct connections with known resistance genes and interactions with over four known resistance genes. Whether the identified genes are indeed related to resistance still needs further experimental validation and this will be the focus of our future research.

5. Conclusion

In the present study, Illumina RNA-seq and de novo assembly methods were applied to study the transcriptome of G. sinensis for the first time. A total of approximately 75.6 million reads, assembled into 58583 unigenes with an average length of 900 bp, were identified. Among these unigenes, 31385 obtained annotation from NR, Swiss-prot, COG, and KEGG databases. The results of the present study confirm that RNA-seq technology is a fast, effective method for transcriptome analysis of nonmodel plants and provides a good resource for further gene expression analysis. The constructed PPI network for G. sinensis when compared to known resistance genes of Arabidopsis predicted 18 freezing tolerance genes, 11 drought tolerance genes, and 19 salinity tolerance genes. Thus these findings provide a theoretical basis for future culturing of stress-resistant G. sinensis varieties.


UT:Unique transcript
NR:NCBI nonredundant protein
COG:Cluster of Orthologous Groups
GO:Gene ontology
KEGG:Kyoto Encyclopedia of Genes and Genomes database
KAAS:KEGG automatic annotation server
BLAST:Basic local alignment search tool
PPI:Protein-protein network.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors’ Contribution

Liucun Zhu and Ying Zhang contributed equally to this work.


This work was supported by Grants from the Science and Technology Commission of Shanghai Municipality (12ZR1444200), Foundation for the Author of National Excellent Doctoral Dissertation of China (201134), Doctor Gathering Scheme of Jiangsu Province, and the High Performance Computing Platform of Shanghai University.


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