International Research Journal of Biotechnology

De novo transcriptome of Taverniera cuneifolia (Roth) Ali root

Talib Ali Momin^1*, Apurva Punvar², Harshvardhan Zala³, Ayachit Garima², Madhvi Joshi² and Padamnabhi S. Nagar^{1 1}Department of Botany, Faculty of Science, The Maharaja Sayajirao University of Baroda, 390002, Gujarat, India
²Department of Science and Technology, Gujarat Biotechnology Research Center (GBRC), Govt. of Gujarat, Gandhinagar 382 011, India
³Department of Genetics and Plant Breeding, C. P. College of Agriculture, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar - 385 506, Gujarat, India
^*Corresponding Author:
Talib Ali Momin, Department of Botany, Faculty of Science, The Maharaja Sayajirao University of Baroda, 390002, Gujarat, India, Email: talib429@gmail.com

Received: 27-Oct-2020 Published: 30-Oct-2021

Introduction

The genus Taverniera has sixteen different species (Roskov et al., 2006). It is endemic to North-east Africa and South-west Asian countries (Naik, 1998). Taverniera cuneifolia (family Fabaceae) is an important traditional medicinal plant of India as mention in Charak Samita in Nigantu period. It is often referred to as Indian licorice having the same sweet taste as of Glycyrrhiza glabra (commercial Licorice) (Zore, 2008). Licorice is used as important traditional Chinese medicine with many clinical and industrial applications like Food, Herbal medicine, cosmetics etc. T.cuneifolia locally known as Jethimad is used by the tribal’s of Barda Hills of Jamnagar in Western India (Saurashtra, Gujarat) as a substitute for Licorice or in other words, the Plant itself is considered to be Glycyrrhiza glabra (Nagar, 2005). Many pharmacological benefits of the plants have been reported earlier like expectorant, blood purification, anti-inflammatory, wound healing, anti-ulcer and used in treating spleen tumors. At the biochemical level, T. cuneifolia has shown the presence of alkaloids, flavonoids,  Tannins,  proteins,  Reducing  sugar and Saponins. The presence of oil content in the seeds of T. cuneifolia showed polyunsaturated fatty acids, monounsaturated fatty acids and saturated fatty acids (Manglorkar, 2016).

T.cuneifolia has been assessed on phytochemical basis; there are no attempts to characterize this phytochemical basis at on molecular level. Based on the above references attempts have been made to identify the genes of various metabolic pathways in T.cuneifolia through Root transcriptome sequencing. The study will give scientific insight into the molecular network of Taverniera cuneifolia. The coding regions will assist in identifying the proteins involved in Glycyrrhizin biosynthesis and other pathways. SSR markers were identified and transcription factors from the data can help in further research in future.

Materials and Methods

Plant material and RNA isolation

Taverniera cuneifolia plant was collected from Kutch, Gujarat,  India  (23.7887°  N,  68.79580°  E)  on 16/12/2016 from its natural habitat near the area of Lakhpat. The tissue of the plant, i.e., roots were cleaned with water than with ethanol and stored in RNA later solution (Qiagen) for longer- term storage. It was then shifted to -20˚C in the refrigerator. The total RNA was isolated from the root tissues of the Plant using the RNeasy Plant Mini Kit (Qiagen) following the manufacturer’s instructions. The integrity of the RNA was assessed by formaldehyde agarose gel electrophoresis. Total RNA was quantified by using a Qiaxpert (Qiagen), Qubit 2.0 fluorometer (Life Technologies, Carlsbad, CA, USA) and Qiaxcel capillary electrophoresis (Qiagen). RNA Integrity Score (RIS) was higher than approx. 7.0 for the sample.

cDNA library preparation and ion torrent sequencing

Ribosomal RNA depletion was carried out using a Ribominus RNA plant kit for RNA- SEQ (Life Technologies, C.A). mRNA fragmentation and cDNA library was constructed using an Ion total RNA-seq kit v2 (Life Technologies, C.A), further purified using Ampure XP beads (Beckman coulter, Brea, CA, USA). The library was enriched on Ion sphere particles using my one C1 dynabeads using standard protocols for the Ion Proton sequencing. The raw transcriptome data have been deposited in the Sequence Read Archive (SRA) NCBI database with the accession number SRR5626167.

RNA-Seq data processing and de novo assembly

Quality control of raw sequence reads was filtered to obtain the high-quality clean reads using bioinformatics tools such as FASTQCv.0.11.5 using a minimum quality threshold Q20 (Andrews, 2010). The clean reads were subjected to de novo assembly using the Trinity v2.4.0 (Grabherr MG, Haas BJ et al.,2011) software to recover full-length transcripts. The redundancy of Trinity generated contigs were clustered for removing duplicate reads with 85% identity using CD-HIT v4.6.1 ( Li W., and Godzik, 2006).

Functional annotation of transcripts and classification

Functional characterization of assembled sequences was done by performing BlastX (Altschul et al.,1990) of contigs against the non-redundant (nr) database, (https://www.ncbi.nlm.nih.gov/) using an e-value cut-off of 1E-5 followed by further annotation was carried out using Blast2GO (Conesa and Gotz, 2005). GO (Gene Ontology) study was used to classify the functions of the predicted coding sequences. The gene ontology classified the functionally annotated coding sequences into three main domains: Biological Process (BP), Molecular function (MF) and Cellular component (CC). Using the  Kyoto  (Encyclopedia Of Genes and Genomes KEGG) (Kanehisa and Goto, 2000) pathway maps were determined. Further, KEGG Automated Annotation Server (KAAS) was used for pathway mapping in addition to Blast2GO (Moriya et al., 2007). for assignment and mapping of the Coding DNA Sequence (CDS) to the biological pathways. KAAS provides functional annotation of genes by BLAST comparison against the manually curated KEGG genes database. The identification of the transcription factor was done by Blastx using Plant TFDB (http://planttfdb.cbi.pku.edu.cn).

Identification of transcription factors families

Transcription Factors (TFs) were identified using genome- scale protein and nucleic acid sequences by analyzing InterProScan domain patterns in protein sequences with high coverage and sensitivity using PlantTFcat  analysis  tool (http://plantgrn.noble.org/PlantTFcat/) tool (Dai et al, 2013).

SSR prediction

Simple sequence repeats (SSRs) were identified using the MISA tool (Microsatellite; http://pgrc.ipk-gatersleben.de/misa/misa.html). (Beier et al., 2017).We searched for SSRs ranging from mono to hexanucleotide in size. The minimum repeats number 10 for mononucleotide, 6 for Dinucleotide and 5 for trinucleotide to hexanucleotide was set for SSR search. The maximal number of bases interrupting 2 SSRs in a compound microsatellite is 100 i.e. the minimum distance between two adjacent SSR markers was set 100 bases.

Results and Discussion

The total RNA of two root samples along with RIN value more than 7, converted to cDNA library using Ion Total RNA- seq kit v2 (Life Technologies, C.A), further purified using Ampure XP beads (Beckman coulter, Brea, CA, USA). The library was enriched on Ion sphere particles using my one C1 dynabeads. A total of 7.29 gb of raw data was generated using standard protocols for the Ion proton sequencing (Table 1).

The good quality roots of T.cuneifolia were used for the RNA sequencing, and a total of 55,991,233 reads containing 7,286,727,421 bases were generated. The raw reads were subjected to quality check by FastQC tool and the average base quality was above Q20. De novo transcriptome assembly resulted in 36,896 reads assembled and the final assembly of 35,590 unique high-quality reads was prepared using CD-HIT at 85% sequence similarity,  with N50 value  of 441 bp. The average GC content of 43% and average contig length of 419.45 bp was obtained. The statistics of transcriptome sequencing and assembly generated by Trinity assembler as given (Table 2).

Functional annotation

A total of 35,590 transcripts (contigs) assembled by trinity were subjected to functional annotation using different databases like the Nr Protein database, KEGG, UniProt, etc. GO terms were assigned to unigenes ( Figures 1 and 2). All transcripts were screened for similarity to a known organism based on the data of species-specific distribution, and it can be concluded that the transcript showed the highest blast hits with Cicer arietinum (6488) followed by Medicago truncatula (3498) and Trifolium subterraneum (2119). A total of 2103, 1843, 1754, 1644 contigs were found to be similar to Glycine max, Cajanus cajan, Trifolium pratense, Mucuna pruriens, respectively. (Table 3 and supplementary Figure 3).The functionally annotated transcripts (27,884) of Taverniera cuneifolia were classified using Blast 2GO into three main domains: Biological processes gene ontology, Cellular component gene ontology and Molecular function gene ontology (Table 4 and supplementary Figure 4). The annotated transcripts were subjected to the Kyoto encyclopedia genes and genomes (KEGG) pathway wherein the transcripts were linked to enzymes found in a large number of pathways available in KEGG. The maximum number of annotated transcripts assigned to hydrolases, followed by transferases and oxidoreductases class of enzymes (Supplementary Figures 5 and 6).

Gene ontology classification

The contigs were further annotated by Blast2Go software with assembled 27,884 transcripts GO terms and divided into three broad categories as Molecular Function (26,382[38%]), Biological Processes (25,890[37%]) and Cellular Component (17,099[25%])  category  (Supplementary Figures 7 and 8). The Molecular functions were the most abundant component of GO terms. Among the 26,382 Molecular functions, the maximum number of contigs i.e. represented “Nucleotide Binding,” followed by “Hydrolase activity” and “Transferase activity”.

In addition, Biological Processes a total of 25,890 transcripts were associated with the “Biosynthetic process” as the highest match followed by “cellular protein modification process” and “nucleo base-containing compound metabolic process” respectively.

A total of 17,099 transcripts were associated with the cellular component and a relatively large no of the transcript was associated with “Membrane” followed by “Nucleus” and “Cytoplasm”, respectively.

pathway annotation by KEGG

Kyoto Encyclopedia of Genes and Genomes (KEGG) serves as knowledge source to perform functional annotation of the genes. The KEGG represents various biochemical pathways for the genes associated with it. Approximately 279 pathways were annotated and among them, metabolic pathways (100), Biosynthesis of secondary metabolites (46), biosynthesis of antibiotics (24) showed the maximum hit with the database (Table 5).

Candidate genes involved in biosynthesis pathway

There were 11 unigenes specific that matched with Glycyrrhiza species which were associated with the Glycyrrhizin biosynthesis from this plant (Table 6). There were 4912 unigenes hypothetical protein predicted from this plant, of which 30 unigenes that had a hit length above 400 were noted (Table 7). 94 unigenes that predicted Cytochrome P450 family protein from this plant, of which 17 unigenes with a hit length above 150 were noted (Table 8).

Secondary metabolites have key role in providing the defense mechanism to plants against stresses and these metabolites have very important role in many economic important like industries, pharma sector etc (Pagare et al., 2015). There has been no molecular data recorded for this plants as such.

The new advancement in the field of omics technologies has led to high-throughtput sequencing data which lead us to prediction of genes, enzymes, complex pathways. (Metzker, 2010). De novo of many medicinally important plants such as Saussurea lappa (Bains, S et al, 2018), Vigna radiate L (Chen, H et al, 2015), Glycyrrhiza glabra (Chin,Y et al, 2007), pigeonpea Cajanus cajan (L.) Millspaugh (Dutta, S. et al, 2011), Dracocephalum tanguticum (Li, H., Fu, Y., Sun, H., Zhang, Y., & Lan, X., 2017) etc. have reported the trancripts involved in active metabolite production using NGS technology.

Transcriptome analysis has proved to be one of the advanced methods for the identification of gene expressing in different pathways of metabolism, growth, development, response towards stress, cell signaling etc. This has help in classifying and categorization different role in secondary metabolic compound. Glycyrrhizin, a well-known secondary metabolite that is found in roots of Licorice has same property that is been found in the roots Taverniera cuneifolia which has many uses as described above. A whole transcriptome analysis of root of Taverniera cuneifolia has opened the unique transcripts which are reported first time from this plant to be involved in the pathways of primary and secondary metabolism (P. Sharma, S. Kumar, S. Beriwal, et al, 2019).

The de novo assembled transcripts of T.cuneifolia were mapped to non-redundant protein database using blastx tool. A total of 35,590 transcripts annotated to the database showed the maximum similarity with Cicer arietinum (18.2%), Medicago truncatula (9.8%), Trifolium subterraneum (6%) and so on. Whi ch belong to same family Fabaceae, order fabales.

Main metabolism-related gene of Taverniera cuneifolia

Glycyrrhizin is triterpenoid-saponin produced in Licorice roots. It is synthesized via the cytosolic melvonic acid pathway for the production of 2,3-oxidosqualene, which is then cyclized to β-amyrin by β-amyrin synthase (bAS). Then, β-amyrin undergoes a two-step oxidation at the C-30 position followed by glycosylation reactions at the C-3 hydroxyl group to synthesise glycyrrhizin as shown in Figure 9 (Seki et al 2008, 2011). Taverniera cuneifolia also known as Indian Licorice can be used as substitute of Glycyrrhiza glabra as it has same features that of this plant. This plant contains varieties of different compound that can be used in future research like triterpenoids, flavonoids, polysaccharides etc. which have been reported first time from this plant. Among them Glycyrrhizin is a primary focus compound that has many economic importance use in different fields. In our experiment we have compared the enzymes and genes for the production of Glycyrrhizin with proposed pathway for biosynthesis of Glycyrrhizin by (Seki et al, 2011), In which Glycyrrhizin is produce by a series of chemical reaction i.e oxidation of different compound associated with Melvanoic Acid pathway. In this particular pathway there are series of chemical reaction by which Farnesyl diphosphate (FPP) molecule catalyzed by squalene synthase (SQS) originating Squalene. There are 6 different transcripts that we have found in our plants that are associated for the production of squalene given in (Table 6) and then by oxidation by squalene epoxidase (SQE) to 2,3 – oxidosqualene or cyclization catalyzed by bAS i.e β- Amyrin CYP88D6 given in (Table 6) and one gene catalyzed by CYP72A154 (11-oxo-β- amyrin) which show a single oxidation reaction a dotted arrow signify undefined oxidation and glycosylation steps in the pathways.

At this point Taverniera cuneifolia have not been intensively studied and there as such no any reports that showed the details about the enzymes associated in the Glycyrrhizin pathway we have associated with reference pathway proposed by (seki et al 2011 - Garg et al 2013). As there have been no proper investigation for the pathway of glycyrrhizin known till today.

We have extensively worked upon the proteins which we have opted from our datas of Taverniera cuneifolia. Approx. 4912 gene have been isolated that showed different proteins reported firstly from this plant among them the details have been provided in (Table 7) (we have approx. shown only those hypothetical protein whose hit length is above 400) (Maroufi et al 2016 - Rasool et al 2016) In our studies we also found that there were more than 90 transcripts that showed the function related to Cytochrome P450 family protein. This protein has an immense ability to synthesis many new molecules required in the system to function and cope up with.

Identification of SSR markers and Transcription factors

The potential SSR from mono to hexanucleotide were predicted using MISA perl script. A total of 35,590 unigene sequences were examined and 2912 SSR were obtained. It was found that only 2454 number of sequences were containing SSRs. Further, only 365 sequences contained >1 SSR marker and 265 were present in compound form (Li 2014 - Villa-Ruano 2015). Tri-nucleotide represented the maximum numbers of SSRs (1291), followed by Mono-nucleotide (832) and then Di-nucleotide (597). The analysis of the transcripts revealed 1557 unique transcripts belonging to 85 transcription factor families. Among the identified unigenes, the highest of them represented the WD40 family followed by C2H2, MYB-HB, AP2-EREBP, PHD etc. the top 15 have been shown in the (Tables 9 and 10).

Acknowledgments

We are grateful to GBRC (Gujarat Biotechnology Research Centre) for providing the platform for performing the experiment. All the facilities were provided by GBRC including Computational Analysis.

Author’s contribution

All authors have contributed to various aspects of this work. PSN and MJ conceived the idea and designed the experiments. TM and HZ performed the experiment. TM, HZ, AG and AP analyzed the data. TM analyzed the results and wrote the manuscript. PSN, HZ and MJ finalized the manuscript.

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