In Silico Prediction and Functional Characterization of Genes Related to Abiotic and Biotic Stresses in Chickpea

Corresponding Author: Dinesh Kumar Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-11012, India Email: dineshkumarbhu@gmail.com Abstract: Chickpea (Cicer arietinum L.) is second largest grown legumes worldwide contributing 75% of total pulse production. It is a cool season legume crop and grown in tropical and subtropical areas. Due to drastic climatic changes, chickpea suffers from many biotic (blight and wilt) and abiotic (salinity, drought, cold) stresses that directly impact the growth and yield. In our study, we predicted and annotated the genes related to biotic and abiotic stresses. Total 20162 ESTs for salinity, 34346 for drought and 191 for cold stress were downloaded. For biotic stresses, viz., wilt and blight disease, 7866 and 56 ESTs were collected, respectively from public domain. All these ESTs were assembled into contigs and blast against protein nonredundant database. Each blast results were mapped to get the corresponding GO terms. Total 1631, 3133 and 13 contigs for salinity, drought and cold stress showed 1333, 2693 and 7 GO terms respectively, while 1144 contigs for Fusarium wilt and 6 contigs for Ascochyta blight disease showed 955 and 4 GO terms. These GO terms describe biological process, molecular function and cellular components of corresponding stresses. Remaining 298 (salinity), 440 (drought), 6 (cold), 189 (wilt) and 2 (blight) contigs were mapped to reference genome and further used for annotation using gene prediction methods and promoter analysis. This study provide insight to novel gene related to abiotic and biotic stress mechanism that can be further analyzed in molecular biology studies for breeding programs.


Introduction
Chickpea, a member of Fabaceae family also known as legume family is of varied nutritional and economic values. Studies show that chickpea seed comprise of 3-6% oil content, 40% carbohydrate and 20-30% crude protein (Jha et al., 2014). The climate requirement of chickpea is very favorable as it can easily grow in tropical and sub-tropical regions during winters that make it third highest produced legume crop in world and highest in Asia . India is the largest producer of chickpea, comprising of 68% of global production as compared to America (3.8%), Africa (4.8%) and Europe (0.9%). Total production of 13.1 million tons from an area of 13.5 million ha and a productivity of 9676 Hg/ha was recorded in year 2013 (FAOSTAT, 2012).
Comparative statistics of the global versus Indian chickpea production, area harvested and yield shows that India has major impact and contribution on global chickpea requirement. Various unfavorable conditions during life cycle of chickpea, especially during growing season causes approximately 50% yield loss each year and are increasing every year Ahmad et al., 2005;Varshney et al., 2010). Among the abiotic stresses, drought and salt stress are the major reasons for loss in production. Reports depicted 6.4 million tons yield loss due to abiotic stresses, out of which more than 40% occurred from terminal drought (Garg et al., 2016). Abiotic stress (drought, cold and salinity) contributes economic loss of approximately1.3 billion, 186 million and 354 million US dollars, respectively (Ryan, 1997). Among the various abiotic stresses affecting chickpea production, drought stress, particularly at the end of the growing season is a major constraint to chickpea production and yield stability in arid and semi-arid regions of the world. World's 20% of cultivable land is unable to provide quality yield due to increased soil salinity and high salt concentrated water used for irrigation (Flowers et al., 2010;Selvakumar et al., 2014). Other than abiotic stress, various biotic stresses also affect yield loss of 4.8 million tons (Ryan 1997). Supplementary Table S1 shows many biotic diseases listed in literature with their causative age.
Many fungal diseases damage this crop, of which Ascochyta blight disease caused by Ascochyta rabiei is very important, leading to severe damage. Sometimes, it results into complete yield loss due to blight formation during flowering and podding stage (Nene, 1982;Nene et al., 1996). Fusarium oxysporum pathogen infected seedlings show leaf drop and collapse due to browning and blackening of xylem (Kraft et al., 1994) causing wilt disease. Another rust causing fungus, Uromyces cicerisarietini infects the plant seedlings with visible round, brown spots causing leaf drop to death of plant (Stuteville et al., 2010). Phytoplasma are specialized bacteria that causes phyllody (Pallavi et al., 2012). Due to adverse climate and pathogen scenario, there is a great necessity to develop varieties resistant to such biotic and abiotic stresses.After the draft genome of chickpea, which was sequenced in 2013 (Varshney et al., 2013;Jain et al., 2013) ways to explore the novel and unpredicted stress genes expressing in biotic and abiotic stresses has been created.
There is a gap between potential and produced yield due to these stresses and need to fill by regulating corresponding genes. Although there are few studies available for identification of abiotic/biotic stress responsive genes using allele diversity approach (Roorkiwal et al., 2014) and microarray analysis (Mantri et al., 2007), but there are many unexplored genes that control stress responses and triggered stress responsive pathway by regulating corresponding transcription factors (Chen and Zhu, 2004). Available information and techniques for chickpea crop improvement needs more candidate genes for breeding strategies viz., gene pyramiding, marker assisted recurrent selection, multiline strategy.
In our study, efforts are made to understand the biology behind the stress conditions and characterization of genes that are getting expressed. In addition to functional characterization, computational analysis has been done to predict the novel candidate genes and their mapping on chromosome which can provide a good insight into complex abiotic/biotic stress tolerance pathways.

Materials and Methods
ESTs sequences related to abiotic and biotic stresses in chickpea were downloaded from NCBI-EST (http://www.ncbi.nlm.nih.gov/). Boolean search has been performed with the keywords like chickpea with salinity, drought, cold, fusarium wilt, Ascochyta blight and Chickpea rust. Figure 1 describes the flow of analysis for gene prediction and functional characterization. These ESTs were aligned and merged to reconstruct the gene sequences. Pre-processing of these ESTs were performed for repeat masking and cleaning, which was followed by assembly using EGassembler (Masoudi-Nejad et al., 2006). The generated contigs from EGassembler were considered for further analysis.

Functional Characterization and Annotation
Contigs assembled by EGassembler were annotated using Blast2GO Pro (Conesa et al., 2005) pipeline. Blastx (Altschul et al., 1990) was used to annotate the contigs for each stress individually. This was followed by mapping and InterProscan (Quevillon et al., 2005) to retrieve complete information of Gene Ontology (GO) and domains of annotated contigs searched against protein database for all six reading frames. Each GO term describes its involvement in molecular function, biological process and cellular component. Unannotated or uncharacterized contigs from all stress datasets were filtered out for gene prediction.

Identification and Prediction of Unannotated Contig
Filtered unannotated contigs were stored in fasta file format and subjected to ab initio gene prediction tool, FGENESH (Salamov and Solovyev, 1998)

Linkage Map and Promoter Analysis
Predicted candidate genes were located on chromosomes and a linkage map has been created for disease/ stress findings using chickpea genomic web resource (http://www.nipgr.res.in/CGWR/home.php) (Kumari et al., 2014). All the predicted genes were searched in PLACE (Higo et al., 1999) database for identification of cis-elements, motifs, corresponding transcription factors and its description in other species.

Results and Discussion
In this study, chickpea ESTs related to abiotic stresses, like salinity, draught and cold downloaded were 20162, 34346 and 191, respectively. Among the biotic stresses in chickpea, wilt, blight, rust and phyllody diseases ESTs obtained were 7866, 56, 2 and 3 ESTs, respectively. A total of 1631, 3133 and 13 contigs were generated by EGassembler for salt, drought and cold stress, respectively. Similarly, 1144, 6, 1 and 1 for contigs were generated for wilt, blight, rust and phyllody, stress, respectively (Table 1).

Functional Annotation and Prediction of Candidate Genes
Blast2Go Pro annotated total 1333, 2693 and 7 contigs related to salinity, drought and cold stress, respectively. Annotation was not obtained for contigs from rust and phyllody ESTs, while 955 and 4 annotation were recorded for wilt and blight related contigs, respectively. All these annotation were mapped to different GO categories i.e., biological process, molecular function and cellular components. Distribution of GO terms showed that 45%-47% were related to biological process, 33-36% to molecular functions and 19-20% to cellular components for drought, salinity, blight and wilt related contigs, respectively (Fig. 2).
Out of these, few contigs remained uncharacterized and did not map to any annotation. Selected uncharacterized contigs listed in Table 1 were subjected to gene prediction using FGENESH. Total 79 genes (salinity), 145 genes (drought), 27 (wilt) and 1 gene (blight) were predicted while, there is no gene was predicted for cold stress, rust and phyllody disease.

Mapping of Candidate Genes and Identification of Cis-Regulatory Elements
All predicted candidate genes were mapped to chromosomes. Mapping of genes shows that all genes were distributed randomly over all 8 chromosomes while many are still unallocated and placed on UN chromosome. Major genes lie on chromosome 3, 5 for drought and salinity, while wilt genes are almost equally distributed on all chromosomes. Single gene predicted for blight disease is located on chromosome 4 ( Fig. 3-6).    Total candidate genes located on chromosome shows that 39 genes are activated during exposure of both stresses while 20 genes are unique for salinity stress and 80 genes for drought stress (Fig. 7). It shows that these can be defense genes and playing major role in stress pathways (Supplementary Table S2).   Study also includes identification of cis-regulatory DNA elements that regulate the biological process in stress or disease conditions. To analyze this, we used PLACE database with all predicted genes as query and identified cis-regulatory motifs. Analysis of cisregulatory elements describes the candidate gene expression and corresponding functional transcription factor. It can be suggested that genes which share common regulatory motifs are co-expressing and functioning in biological process in response to the corresponding biotic/abiotic stress. Cis-regulatory motifs and functioning transcription factor for each predicted candidate gene shows that in stress, transcription factors like DOF, bZIP, WRKY, RAV, ABRE and MYB are expressed majorly. These transcription factors are also reported in other crops viz., DOF in Chinese cabbage (Ma et al., 2015), bZIP in tepary bean (Phaseolus acutifolius) and common bean (P. vulgaris) (Rodriguez-Uribe and O'Connell, 2006) and other five legume genomes (Wang et al., 2015), ABRE and WRKY in soya bean (Li et al., 2005;Zhou et al., 2008). DOF, which is plant specific transcription factor and known as DNA binding with one finger, contains conserved C2-C2 zinc finger and plays a significant role in plant growth and transcriptional regulation during stress conditions by aid of mobile proteins (Le Hir and Bellini, 2013;Yanagisawa, 2004). ABRE transcription factor participates in drought and high salinity tolerance in various crops by ABA signaling (Hossain et al., 2010). ABRE-binding bZIP transcription factor shows its presence in biotic stress. bZIPis reported as defense transcription factor that works during pathogen attacks in various crops like maize, Arabidopsis, rice and cotton and can assume same functioning in chickpea crop for stress management (Alves et al., 2013). WRKY transcription factor often functions in many stress responses simultaneously and participate in common signaling pathways. This property of WRKY makes it a good candidate for stress tolerance mechanism (Chen et al., 2012). Many predicted genes that were expressed during stress condition contain cis elements that provide binding sites to RAV which is known as Related to ABA-insensitiveViviparaous1. RAV transcription factor found to controls drought and salinity responses by participating in ABA independent stress pathway (Fu et al., 2014). Similarly MYB Family transcription factor plays role in various biological processes for ABA associated biotic and abiotic stress responses. It regulates functional genes to regulate functions as Phenylpropanoid metabolism, hormone responses, formation of cyclin -type B during plant defense reactions (Ambawat et al., 2013). Contig wise transcription factor abundance are listed in Supplementary Table S3.

Conclusion
Chickpea is economically very important crop and suffers from various biotic and abiotic stresses during its life cycle. In the present study, genomics approach is applied to predict genes related to drought, salinity, cold and disease caused by pathogen infections from ESTs available in public domain. In this study 1333, 2693 and 7 genes related to salinity, drought and cold stress respectively, were predicted, while 955 and 4 annotations were found for genes related to wilt and blight, respectively. These genes were found to be functional for DOF, bZIP, WRKY, RAV, ABRE and MYB transcription factors. The reported genes can be further used for candidate gene discovery required for Marker Assisted Selection (MAS) or gene pyramiding in crop improvement programme. Cis-regulatory elements and transcription factors study provides insight of their role in corresponding stress condition, whose validation is further warranted in Endeavour of improving chickpea productivity.