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. 2023 May 25;29(5):613–627. doi: 10.1007/s12298-023-01315-7

Identification and validation of reference genes in vetiver (Chrysopogon zizanioides) root transcriptome

Abhishek Singh Chauhan 1,3, Madhu Tiwari 3, Yuvraj Indoliya 3, Shashank Kumar Mishra 4, Umesh Chandra Lavania 2, Puneet Singh Chauhan 1,4, Debasis Chakrabarty 1,3,✉, Rudra Deo Tripathi 1,5,✉
PMCID: PMC10284770  PMID: 37363421

Abstract

Vetiver [Vetiveria zizanioides (L.) Roberty] is a perennial C-4 grass traditionally valued for its aromatic roots/root essential oil. Owing to its deep penetrating web-forming roots, the grass is now widely used across the globe for phytoremediation and the conservation of soil and water. This study has used the transcriptome data of vetiver roots in its two distinct geographic morphotypes (North Indian type A and South Indian type B) for reference gene(s) identification. Further, validation of reference genes using various abiotic stresses such as heat, cold, salt, and drought was carried out. The de novo assembly based on differential genes analysis gave 1,36,824 genes (PRJNA292937). Statistical tests like RefFinder, NormFinder, BestKeeper, geNorm, and Delta-Ct software were applied on 346 selected contigs. Eleven selected genes viz., GAPs, UBE2W, RP, OSCam2, MUB, RPS, Core histone 1, Core histone 2, SAMS, GRCWSP, PLDCP along with Actin were used for qRT-PCR analysis. Finally, the study identified the five best reference genes GAPs, OsCam2, MUB, Core histone 1, and SAMS along with Actin. The two optimal reference genes SAMS and Core histone 1 were identified with the help of qbase + software. The findings of the present analyses have value in the identification of suitable reference gene(s) in transcriptomic and molecular data analysis concerning various phenotypes related to abiotic stress and developmental aspects, as well as a quality control measure in gene expression experiments. Identifying reference genes in vetiver appears important as it allows for accurate normalization of gene expression data in qRT-PCR experiments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01315-7.

Keywords: Vetiver, Abiotic stress, Reference genes, qRT-PCR, Differential genes, Normalization

Introduction

Chrysopogon zizanioides, commonly known as vetiver, is a perennial C4 grass that produces essential oil in its roots. This oil has a wide range of applications in the perfumery and healthcare industries (Gavira et al. 2022). Vetiver essential oil is known to possess anti-inflammatory, nervine, sedative, tonic, and vulnerary properties (Lunz and Stappen 2021). It is also known for its ability to exclude cadmium (Cd) metal and has been utilized for the phytostabilization of heavy metal pollution (Danh et al. 2009). Cd contamination adversely affects plant photosynthesis, growth, and mineral nutrition (Zhang et al. 2014). In addition to its phytostabilization properties, Vetiver grass is also utilized for the phytoremediation of iron (Fe) contaminated and iron overburden soil (Vimala et al. 2022), reduction in soil erosion, and its salt tolerance properties (Liu et al. 2016).

In this study, two geographically distinct morphotypes of vetiver were used the North Indian (A-type) and South Indian (B-type). These morphotypes are differentiated by their root-shoot morphology, oil quality, and reproductive behavior (Chakrabarty et al. 2015). The North Indian type is characterized by tall plants with thick roots, fast growth habits, and a good seed set, producing low concentrations of superior quality oil. In contrast, the South Indian type has thin roots and produces higher concentrations of low perfumery quality essential oil, along with profuse tillering and low seed set. Exo-morphologically, the two types can be broadly distinguished by their leaf size, plant height, and flowering behavior, with the North Indian type having broad leaves, tall plants, and profuse flowering, while the South Indian type has thinner leaves, shorter plants, and late and low flowering.

Transcript abundance analysis is advantageous to unravel complex biological processes such as plant development, metabolic pathways, and signal transduction (Dussert et al. 2013; Tranbarger et al. 2011). Microarray, Northern blotting and qRT-PCR tools are widely used to analyze relative gene expression patterns (Ding et al. 2007; Tarca et al. 2006). qRT-PCR uses reference gene concepts with high accuracy, high specificity, and high sensitivity for the validation of next-generation sequencing data (Andersen et al. 2004; Garson et al. 2005; Ginzinger 2002; Pombo et al. 2017; Tranbarger et al. 2011). RNA sequencing is an alternative to microarray for comprehensive expression analysis. RNA-Seq is a high-throughput sequencing method used for gene expression analysis (Marioni et al. 2008). Reference genes can also be used as a quality control measure in gene expression experiments. By monitoring the expression of reference genes, researchers can ensure that the samples are of good quality and experimental conditions are consistent. Reference genes are expressed in all cells of an individual and, these genes are constitutively expressed under normal and treatment conditions. Reference genes are stably expressed under abiotic stress treatment conditions. TUB, ACT, and GAPDH are the housekeeping genes that were selected as reference genes in other plants also (Kozera and Rapacz 2013).

Identification and validation of reference genes are analyzed through statistical methods including BestKeeper (https://www.gene-quantification.de/bestkeeper.html), NormFinder (https://moma.dk/normfinder-software), and geNorm (https://genorm.cmgg.be). NormFinder, Bestkepper, and RefFinder (https://heartcure.com.au) are used to identify reference genes according to stability and score value (Andersen et al. 2004; Pfaffl et al. 2004; Vandesompele et al. 2002). The stability value and standard deviation is the main criterion of geNorm, NormFinder and BestKeeper algorithms to find out stable reference genes. The lower the stability value higher will be the chance of acceptance of that gene. RefFinder tool has a comprehensive ranking method associated with NormFinder, BestKepper, geNorm and Delta-Ct as in-built software.

The comprehensive ranking method uses the concept of geomean of ranking value. The lower the geomean ranking values, the higher will be the chances of acceptance. Further, NormFinder uses the concept of stability value. A stability value < 1.5 considers the most stable gene (Zhuang et al. 2015). geNorm follows the concept of average expression stability value (M). geNorm uses the threshold value 1.5 for the selection of stable genes. The Delta-Ct method uses the average standard deviation calculation for the selection of stable genes. SD < 1 provides a stable gene (Xia et al. 2014). BestKeeper uses the concept of standard deviation [± CP] < 0.5 (Xia et al. 2014).

Pairwise variation analysis is also required to examine the optimal reference genes for different conditions. Pairwise variation analysis is a statistical method used to evaluate the stability of reference genes across different conditions or experimental treatments. It involves calculating the variation between two candidate reference genes to determine which one is more stable and therefore better suited as a reference gene (Xie et al. 2021; Hossain et al. 2019). The optimal reference genes identification was performed with qbase + software.

Previously, our group validate selected differential genes of vetiver using Actin as a reference gene (Chakrabarty et al. 2015). Besides Actin, several other key reference genes have been reported to express ubiquitously in given treatments (Pabuayon et al. 2016). The reproducibility of qRT-PCR depends on an appropriate selection of reference gene(s) and their normalization. Furthermore, the identification of reference genes in vetiver can help to improve the accuracy and reproducibility of gene expression studies in related grass species. Many types of grasses, such as cereal crops and turf grass, share similar gene expression patterns and developmental stages with vetiver, and thus, the identified reference genes can be used in comparative studies across different grass species. Overall, identifying reference genes in Vetiver can greatly facilitate the investigation of the molecular and biochemical mechanisms underlying the diverse functions of this plant and can help to optimize its applications in allied research fields.

This study identified and validated key reference genes, along with Actin, for Vetiver using a software-driven pipeline and specific parameters. As little information is available on Vetiver in biological databases, this study aids to standardize a set of reference gene/s that enables researchers to compare gene expression data across different experiments and labs, ensuring reproducible and comparable results.

Material and methods

Raw data collection

Raw sequence data were collected using the NCBI database (https://www.ncbi.nlm.nih.gov/sra). The Vetiver NCBI bioproject PRJNA292937 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA292937) has an SRP062463 accession number. North Indian (Sample A) biosample SRR2167610, SAMN03996104, SRS1036550, and South Indian (Sample B) biosample SRR2167619, SAMN03996105, SRS1036563 were used to download the files for further analysis. DDBJ (https://ddbj.nig.ac.jp/search?query=%22PRJNA292937%22) and EMBL-EBI (ENA) (https://www.ebi.ac.uk/ena/browser/text-search?query=PRJNA292937) are the other resources to download fastq files. For RNA-Seq analysis, an equal amount of total RNA from three different biological replicates was pooled and used for further processing (Chakrabarty et al. 2015).

Quality trimming and filtering

The sequencer generates paired-end sequences of 101 bp without the adaptor. The NGSQCToolKit (http://www.nipgr.res.in/ngsqctoolkit.html) software performed for quality trimming with default parameters like q ≥ 20, p (Processor) = 12, z and t. The output of this software is fastq paired files (Patel and Jain 2012).

de novo assembly and differential gene expression analysis

Transcriptome-based analysis was done for vetiver grass. Trinity software (https://github.com/trinityrnaseq/trinityrnaseq) was used for de novo assembly of reads. Contigs were used further for downstream analysis. The output of the NGSQCToolKit is a fastq file that is input in Trinity assembler software. Trinity software was run with default parameters like left, right, maximum memory of 56 GB and CPU 12 (Grabherr et al. 2011). Due to the unavailability of the vetiver genome; we use de novo assembly for sequences. These contigs were further used in differential gene identification and annotation. The RSEM software was used for gene count. A total of 1,36,824 contigs were obtained as differential genes (Supplementary File S1; Supplementary File S2). Sample A and B have 97,205 and 1,11,854 contigs respectively (FPKM > 0). These transcript counts were further used in the egdeR script with common dispersion. Q-value identified with statistical Python script. The edger and q-value packages give differential gene files with all values related to analysis. P-value (≤ 0.05) and log2fold change (-2 to + 2) based study carried out for further analysis.

Reference genes selection criteria for contigs

The statistically significant transcriptome data was used for reference gene selection. Contigs have expression value in the form of FPKM value. Lower the coefficient of variation (CV) value which indicates that the expression of a gene (FPKM value) has less variation; therefore, the genes are similar and most stable (Liang et al. 2020). These steps are applied to reduce the number of contigs. The differential file contains 1,36,824 contigs for analysis. The initial selection of the contigs was done with FPKM ≥ 1.0 value. Further, the log2fold change (-2 to + 2) and p-value (≤ 0.05) criteria were applied for the elimination of more contigs. The FPKM of sample A contigs were subtracted from sample B contigs. If the expression of contigs was the same in both samples then the integer place has 0 values from subtraction. The difference was present only in decimal places. Other values were discarded from the final result. Then p and q value ~ 1 parameter was applied for the reduction of more contigs. 5181 contigs were obtained after applying the p and q value criteria (Supplementary File S3). Further, the FPKM ≥ 6.0 criteria were applied for the selection of contigs. Finally, 346 contigs were obtained from reference gene analysis (Supplementary File S4).

MS office excel software was used to perform the analysis. Annotation of contigs was done with rice protein sequences. The Blastx tool was used for alignment purposes. Reference genes have the same expression in both samples, but differences were present only in decimal places. RefFinder, NormFinder, BestKeeper, Delta-Ct and geNorm software were applied on the 346 contigs to calculate the stability value of the reference gene. The final selection of the gene was done based on annotation and stability value. RefFinder run with 346 contigs for cross-checking and validation purposes. 346 genes showed good stability values; therefore, annotation and statistical parameters were applied for the selection of eleven genes out of 346 genes as reference genes.

Stability value calculation for reference genes selection

RefFinder

RefFinder (https://www.heartcure.com.au/reffinder/) is a web-based statistical tool developed for reference gene evaluation (Xie et al. 2012, 2023). It is an integrated tool that includes comprehensive ranking, BestKeeper, Delta-Ct, NormFinder, and geNorm. RefFinder used the concept of comprehensive ranking to rank the reference genes, but the other four software used their algorithm finding reference genes. RefFinder tool ranks the gene according to the calculation of geomean ranking value. This calculation is a part of the comprehensive ranking method (Ren et al. 2021). The outcome of RefFinder is statistically significant genes.

NormFinder

NormFinder (https://moma.dk/normfinder-software) is a software or Python script. The Python script is available on the internet (https://moma.dk/files/r.NormOldStab5.txt). NormFinder is used to identify reference genes according to stability value. The Python script of NormFinder runs on a Python terminal. Python script run on the Python 2.4 software terminal. Two samples A and B (North Indian and South Indian) run with FPKM value in the script. The zero ‘0’ and lower stability value was used for the selection of contigs as reference genes. Software-based analysis was further used for the confirmation of the reference gene. The stability value was calculated for the selection of reference genes (Andersen et al. 2004). NormFinder uses the concept of stability value. A stability value < 1.5 considers the most stable gene (Zhuang et al. 2015). The most stable genes have minimum stability value. The outcome of NormFinder is statistically significant genes.

geNorm

The geNorm software was used for the identification of the most stable gene. The minimum stability value indicates that the gene is the most stable. The normalization method was required for the identification of the reference genes (Vandesompele et al. 2002). geNorm uses a gene stability value < 1.5 which considers a stable gene (Gopalam et al. 2017). The outcome of geNorm is statistically significant genes.

Delta-Ct

The Delta-Ct value of reference genes was calculated by the comparative Delta-Ct method (Silver et al. 2006). The Delta-Ct method uses the average standard deviation calculation for the selection of stable genes. The SD < 1 provides a stable gene for the reference genes selection (Xia et al. 2014). The outcome of Delta-Ct is statistically significant genes.

BestKeeper

The coefficient of variation and standard deviation method was used in the BestKeeper algorithm. If the coefficient of variation and standard deviation has minimum value then the stability of the gene is higher, among candidate reference genes (Pfaffl et al. 2004). BestKeeper uses the concept of standard deviation [± CP] < 0.5 for the selection of reference genes (Xia et al. 2014). The outcome of BestKeeper is statistically significant genes.

All 346 contigs were qualified for the reference genes selection, but 11 reference genes were selected for the qRT-PCR analysis. NormFinder, BestKeeper and geNorm are the most popular statistical techniques used for the identification of the reference genes. The outcome of these software was statistically significant genes. The eleven candidate reference genes were ranked with expression stability value (M).

Plant material and exposure to abiotic stress

In the present study, two distinct Indian morphotypes of Chrysopogon zizaniodes (L.) Roberty (vetiver) i.e., North and South Indian vetiver (Chakrabarty et al. 2015) were targeted. The vetiver morphotypes initially collected from natural habitats in India (North Indian type from Lakhimpur-Khiri, U.P., and South Indian type from Chennai, Tamilnadu), were procured from the CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, India. Initially, the intact plant (vetiver grass) was grown in soil, and then the vetiver slips were transferred to a hydroponic medium that contained a half-strength Hewitt medium. Roots were carefully protected to prevent damage. For acclimatization, the vetiver slips were kept in Hewitt medium for two weeks. The stress treatments were applied to acclimated grass slips. For salt stress; 150 mM sodium chloride (NaCl) was used, and for mimicking drought stress, 20% polyethylene glycol (PEG-6000) was used. For temperature stress, the slips were kept at 42 °C and 4 °C, respectively. In each case, the treatment was given for 48 h in a growth chamber with a photoperiod of 16 h light/8 h darkness. After 48 h, each sample proceeded to be grounded in liquid N2 and proceeded for RNA extraction.

Primer designing

Trinity software gives the full-length cDNA sequence of candidate reference genes. Trinity is giving contigs sequences from the reads. These contigs were generated from mRNA sequences. These contigs are the CDS sequences of samples. Trinity uses the de-Bruijn graph algorithm to construct the contigs. The primer pairs for the candidate reference gene were synthesized manually and evaluation of each potential primer was done with Primer stat software (https://www.bioinformatics.org/sms2/pcr_primer_stats.html). The list of primers utilized during this study has been provided in (Supplementary File S5).

RNA extraction and cDNA synthesis

Total RNA was isolated from stress-treated plant samples and controlled using RNeasy plant Mini Kit (QIAGEN, MD), following the manufacturer’s instructions. The isolated RNA samples were treated with RNase-free DNaseI (Fermentas, USA) to remove the DNA contamination. The quantity and quality of isolated RNA were examined spectrophotometrically (NanoDrop, Wilmington, DE) and by agarose gel electrophoresis. High-quality total RNA (OD260 and OD280 ratio from 2 to 2.1; OD260 and OD230 ratio from 2.0 to 2.5) was used for cDNA synthesis. 1 μg of total RNA was utilized in RevertAid First-strand cDNA Synthesis Kit (Fermentas, USA) for cDNA synthesis. Briefly, 1 μg of total RNA was mixed with 20 pmol of oligo dT primers. The reaction was kept at 65  °C for 5 min in a thermocycler and kept on ice. Further, 4 μl of 5 × reaction buffers, 20 units of RiboLock, 1 μl of 10 mM dNTP mix, and 200 units of Reverse transcriptase (RevertAid H minus M-MuLV) were added to the reaction, the final volume was maintained at 20 μl with deionized nuclease-free water (Fermentas, USA). The final reaction was performed by placing the reaction mixture in a thermocycler at 42 °C for 1 h. The termination of the reaction was performed at 70 °C for 5 min. The final product was utilized as a template during qRT- PCR.

Quantitative real-time PCR (qRT-PCR)

Fast SYBR Green Master Mix (Applied Biosystem, USA) and gene-specific primers were used in qRT-PCR. The reaction was run with an initial setup of 95 °C for 20 s, followed by 40 cycles of 95 °C for 3 s, and 60 °C for 30 s in 96 well plates. The delta-delta CT method (Schmittgen and Livak 2008) was used to calculate the relative expression of different genes. For qRT- PCR analysis, we used three biological replicates and three technical replicates individually. Additionally, 400 ng template concentrations were used for all samples throughout the analysis.

To ensure comparability between the selected reference genes, we first determined the Ct value-based PCR efficiency of each sample by measuring serial dilutions of 400 ng cDNA from different samples to 300 ng–200 ng–100 ng–50 ng in triplicate (Radonic et al. 2004). 400 ng of cDNA was used as a template to carry out further qRT-PCR analysis. The column plot was constructed with the help of MS Excel software. Further, Duncan’s multiple range test (DMRT) was used for the statistical significance of Ct values using SPSS software.

Identification of optimal reference genes

The statistical test comprehensive ranking, BestKeeper, Delta-Ct, NormFinder, and geNorm were applied on 346 contigs. The 11 reference genes were selected based on annotation and stability values. The qRT-PCR analysis gave 5 reference genes along with Actin from 11 reference genes. The Ct value of 5 reference genes from 10 abiotic stress-treated samples was used in qbase + software. The optimal or best reference gene identification was performed with the qbase + (https://www.qbaseplus.com/) software. The qbase + software used the geNorm tool to normalize the Ct values. The Ct-value of reference genes was normalized with the calculation of geNorm pairwise variation (V) and geNorm stability value (M) (Hellemans et al. 2007). The pairwise variation (Vn / Vn + 1) method was used to calculate the optimal reference gene. geNorm analysis was initiated on 10 samples and 6 reference targets. 60 sample-target combinations were used to calculate geNorm M and geNorm V values. The geNorm V < 0.15 and average geNorm M ≤ 0.5 criteria were used in qbase + software to rank the reference gene (Giri and Sundar 2022).

Results

Identification of reference genes using transcriptome dataset

To date, whole information about vetiver genomic data is not available. However, RNA-Seq-mediated de novo assemblies have been carried out using different samples of vetiver genotypes. de novo transcriptome assembly was performed by using two vetiver genotypes, North Indian and South Indian. Transcriptome analysis revealed 1,36,824 contigs using trinity software followed by the edgeR tool for differential gene expression analysis. Further, reference gene selection criteria provide 5181 contigs. Subsequently, contigs with FPKM ≥ 6.0 were used for further downstream analysis. This analysis provides 346 contigs that may act as reference genes. Pearson correlation result indicates that sample A and sample B have a positive relationship (correlated) or less variation between the expression values (Supplementary Figure S1). The coefficient of variation (https://www.statskingdom.com/correlation-calculator.html) was applied to 346 reference genes. Out of 346, 11 genes were selected through statistical tests and annotation.

Selected genes were GTPases activating proteins (GAPs), Ubiquitin conjugating enzyme E2W (UBE2W), Ribosomal protein (RP), OSCam2 Calmodulin, Membrane anchored ubiquitin fold protein (MUB), Ribosomal protein S13p/ S18e (RPS), Core histone H2A/H2B/H3/H4 domain containing protein, putative, expressed (Core histone 1), Core histone H2A/H2B/H3/H4 domain containing protein, putative, expressed (Core histone 2), S-adenosylmethionine synthetase, putative expressed (SAMS), Glycine-rich cell wall structural protein 2 precursor, putative expressed (GRCWSP), Plastocyanin-like domain containing protein, putative expressed (PLDCP) and Actin. Reference genes have approximately similar expression values. FPKM values of the genes were almost the same among Sample A and Sample B (Fig. 1). OsCam2 has less FPKM among all genes, whereas the core histone 1 gene has maximum FPKM. All 346 genes have approximately similar FPKM in both samples. The statistical tests were applied to 346 genes only for the confirmation of reference genes.

Fig. 1.

Fig. 1

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Expression value of eleven selected reference genes. FPKM values represent sample A and sample B

The outcome of comprehensive ranking

The comprehensive ranking is a statistical test for reference gene identification. The statistical test gave 346 genes with good geomean ranking values (Supplementary File S6; Fig. 2A). The selection of eleven genes was done with the help of annotation of contigs. GAPs gene has a minimum value (26.5). RP gene has a maximum value (314.5). RPS gene, Core histone 1, Core histone 2, MUB, GRCWSP, SAMS, OsCam2, UBE2W and PLDCP had 37.15, 64.18, 136.74, 181.45, 217.23, 237.69, 272.45, 283.99 and 286.99 values respectively. This test use geomean of ranking value for the stability of the genes. The order of stability was calculated based on the geomean of ranking values. Descending order of value of genes is RP > PLDCP > UBE2W > OsCam2 > SAMS > GRCWSP > MUB > Core histone 2 > Core histone 1 > RPS > GAPs (Fig. 2B). The least geomean ranking value indicates that the GAPs gene is the most stable (Xie et al. 2020).

Fig. 2.

Fig. 2

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The ranking of 346 genes was comprehensively evaluated and presented in two ways. A A bar plot was generated to display the geomean ranking values of all 346 genes. B The geomean ranking value of the eleven selected genes was also determined and presented. Among these eleven genes, GAPs reference gene was found to have the lowest geomean ranking value. The outcome of the comprehensive ranking method is statistically significant genes

Outcome of NormFinder

The stability values of 346 genes were plotted using a barplot (Supplementary File S7; Fig. 3A). Ribosomal protein (RP) has a maximum stability value (0.486) whereas the RPS gene has a minimum stability value (0.02). GAPS gene, Core histone 1, Core histone 2, MUB, GRCWSP, SAMS, OsCam2, UBE2W, and PLDCP depicted stability values of 0.051, 0.094, 0.166, 0.195, 0.258, 0.294, 0.365, 0.386 and 0.393 respectively. The decreasing order of stability value for eleven selected contigs was RP > PLDCP > UBE2W > OsCam2 > SAMS > GRCWSP > MUB > Core histone 2 > Core histone 1 > GAPs > RPS (Fig. 3B). Based on the data, the least stability value indicates that the RPS gene is the most stable (Boff et al. 2018; Yu et al. 2020).

Fig. 3.

Fig. 3

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The NormFinder analysis was performed on a set of 346 genes, and the stability value was used as a criterion for the selection of reference genes. The results were presented in two parts. A A bar plot was created to display the stability values of all 346 genes. B The stability value of the eleven selected genes was calculated using NormFinder. Among these eleven genes, the RPS gene was found to have the minimum stability value. The outcome of the NormFinder is statistically significant genes

Outcome of geNorm

A barplot was generated to display the stability values of 346 genes (Supplementary File S8; Fig. 4A). The GAPs gene has minimum stability of 0, while ribosomal protein (RP) showed a maximum stability value. The order of contigs with stability value was RP (0.263) > PLDCP (0.231) > UBE2W (0.227) > OsCam2 (0.219) > SAMS (0.184) > GRCWSP (0.16) > MUB (0.124) > RPS (0.063) > Core histone 2 (0.054) > Core histone 1 (0.016) > GAPs (0) (Fig. 4B).

Fig. 4.

Fig. 4

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A The stability value for 346 genes was calculated using geNorm, which showed their relative stability in expression levels. B Among the eleven selected genes, the GAPs reference gene had the minimum stability value, indicating its high stability in expression. On the other hand, the RP gene had the maximum stability value among the eleven genes when analyzed with the Genorm method. The outcome of the geNorm is statistically significant genes

Outcome of Delta-Ct

Barplot plotted with a standard deviation of 346 genes (Supplementary File S9; Fig. 5A). Reference gene selection was done with an average standard deviation. SD < 1 was considered for the stability of the gene. GAPs and RPS have a minimum value (0.23). These genes are stable gene due to stability value. RP has a maximum value (0.49). The order of stability was found to be RP (0.49) > UBE2W (0.41) = PLDCP (0.41) > OsCam2 (0.39) > SAMS (0.34) > GRCWSP (0.31) > MUB (0.28) > Core histone 2 (0.26) > Core histone 1 (0.24) > GAPs (0.23) = RPS (0.23) (Fig. 5B).

Fig. 5.

Fig. 5

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The Delta-Ct analysis was conducted for 346 genes. A The average of standard deviations was calculated for all the 346 genes, with the gene having the minimum value being considered the most stable. B The average standard deviation value was calculated for the eleven selected reference genes using Delta-Ct analysis. The outcome of the Delta-Ct is statistically significant genes

Outcome of BestKeeper

A barplot (Supplementary File S10; Fig. 6A) was used to display the Standard deviation (S.D.) [± CP] values of 346 genes. These values were utilized in the BestKeeper to determine reference gene stability, with a lower SD[± CP] indicating higher stability. The RPS gene was found to be the most stable with a minimum value of 0.02. The reference gene order based on SD[± CP] was RPS (0.02) > GAPs (0.03) > Core histone 1 (0.06) > Core histone 2 (0.11) > MUB (0.15) > GRCWSP (0.17) > SAMS (0.2) > OsCam2 (0.25) > PLDCP (0.27) = UBE2W (0.27) > RP (0.35) (Fig. 6B).

Fig. 6.

Fig. 6

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A A bar plot was created to display the standard deviation values of the 346 genes analyzed with BestKeeper. B The result of BestKeeper analysis for the eleven reference genes was obtained, which showed the reference gene RPS with the lowest standard deviation value among the eleven genes. The outcome of the BestKeeper is statistically significant genes

Expression analysis for candidate reference genes

The reference genes were manually selected using MS Office Excel software based on the stability value from the different tools mentioned above. Stresses including heat, cold, salt, and PEG were applied to slips of samples A and B, and reference gene selection was done using the stability value. Although Ct values may differ with different genes under different treatment conditions, minimal change in Ct values was identified in an average range from 21.09 to 32.38 under different treatments (Supplementary File S11). The differential genes obtained from transcriptome data with de novo assembly were normalized during differential gene analysis and used for the search of reference genes. After reference gene selection, qRT-PCR Ct value had variation among sample A and sample B. Melting curve analysis showed high melting temperature due to high GC content. The melting curve of GAPs, OsCam2, MUB, Core histone 1 and SAMS along with the Actin gene has also been shown in Supplementary Figure S2. Two reference genes (Core histone 1, SAMS) were selected for expression analysis using the serial dilution method to ensure qRT-PCR efficiency among different samples (Supplementary Figure S3). Validation of the reference genes was done using Actin and eleven selected reference genes, with GAPs, OsCam2, MUB, Core histone 1, and SAMS showing almost similar gene expression in terms of Ct value (Fig. 7; Supplementary Figure S4; Supplementary Figure S5).

Fig. 7.

Fig. 7

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The expression levels of reference genes (Core histone 1, SAMS, Actin, MUB, GAPs and OsCam2)in response to different abiotic stress treatments (drought, salinity, heat, and cold) were measured using qRT-PCR in Chrysopogon zizaniodes (L.) Roberty (vetiver) for both sample A (North Indian) and sample B (South Indian), with a control group included. The Y-axis indicates the cycle threshold (Ct) values obtained from qRT-PCR, while the X-axis represents the type of treatment. The column chart represents the Ct values obtained from all experimental samples. The average values marked with similar letters are not significantly different (Duncan's multiple Range Test: p ≤ 0.05) using SPSS software

Optimal reference genes

The qbase + software run for all possible pairs of average Ct values of 5 reference genes along with Actin from abiotic stress-treated samples and it was possible to determine the most stable combination of genes that can be used as a reference for the normalization of gene expression data (Supplementary File S12). In qbase + software, geNorm (V) determines the optimal number of reference targets. The criteria of geNorm V < 0.15 gave 2 or 3 the most stable targets (Supplementary Figure S6). The comparison was performed with the help of the normalization factor. A maximum of two reference targets (SAMS and Core histone 1) was optimal in this experiment based on geometric mean. Based on average geNorm M ≤ 0.5, the reference target SAMS and Core histone 1 were the most stable (Supplementary Figure S7). The SAMS gene showed a Ct value variation range from 22.12 to 26.26. The Core histone 1 showed a Ct value variation range from 23.04 to 27.38.

Discussion

Vetiver is a perennial C4-type grass, widely occurring in diverse habitats across India which is closely related to Sorghum bicolor and many other monocot organisms. Previously, we have reported that North Indian (Sample A) and South Indian (Sample B) varieties have morphological and transcriptomic level differences. Transcriptome data from both varieties were used for the selection of reference genes (Chakrabarty et al. 2015). The vetiver reference genome is not sequenced till this study, therefore de novo assembly was performed with Trinity software.

In our retrieved transcriptome data, 1,36,824 differential genes were obtained. The selection of reference genes was done based on annotation and FPKM ≥ 6.0. These criteria were filtered 346 genes as reference genes. A large data set was used for reference gene identification and validation. Normalization methods were used for the reduction of reference genes (Reddy et al. 2013). Reference based statistical software was used to determine the stable reference gene. The result of one software is not identical to another software due to its inbuilt internal algorithm, so five software were used to select the stable reference gene(s). Our methodology is based on the same expression value (FPKM) of transcriptome data. A similar expression value of contigs may be used for the selection and validation of reference genes. Statistical software like RefFinder, NormFinder, BestKeeper, geNorm, and Delta-Ct was used for the validation of our assumption. All 346 contigs were qualified as reference genes with these statistical softwares. All 346 contigs have similar FPKM values, but 11 reference genes were selected for the qRT-PCR analysis based on annotation. The stresses like heat, cold, salt, and PEG were given to slips of sample A and sample B. The 5 reference genes along with Actin have less variation in Ct values. The SAMS and Core histone 1 was identified as optimal reference genes based on qbase + software analysis.

These statistical softwares follow the stability value and standard deviation criteria for the selection of the most stable reference genes. A minimum or low stability value gives the most stable gene. These statistical software results are similar to our assumption. The stability value and SD value is the main parameter for the selection of reference genes to maintain highly efficient and minimal error-containing gene (Wang and Bhullar 2021; Li et al. 2021). The Pearson correlation coefficient was also used to check the variation between the expression values. Similar value has less variation between the values. The further selection of reference genes was carried out based on annotation, so it gives us a preliminary idea about their occurrences in other species, too.

The evaluation and screening of reference genes in the plant, and animals is a very critical step (Santos et al. 2018). All 346 contigs were used in the RefFinder tool for the validation of reference genes. These eleven reference genes were used because they were validated through RefFinder, Normfinfer, BestKeeper, Delta-Ct, and geNorm (Garrido et al. 2020). GTPases activating proteins (GAPs), Ubiquitin conjugating enzyme E2W (UBE2W), Ribosomal protein (RP), OSCam2 Calmodulin, Membrane anchored ubiquitin fold protein (MUB) and Ribosomal protein S13p/ S18e (RPS), Core histone H2A/H2B/H3/H4 domain containing protein, putative expressed (Core histone 1), Core histone H2A/H2B/H3/H4 domain containing protein, putative expressed (Core histone 2), S-adenosylmethionine synthetase, putative expressed (SAMS), Glycine-rich cell wall structural protein 2 precursor, putative expressed (GRCWSP), Plastocyanin-like domain containing protein, putative expressed (PLDCP) and Actin were selected for reference gene validation.

To assess the stability of the selected reference genes under abiotic stress, sample A and sample B were exposed to heat, cold, salt, and PEG. Reference genes are known to maintain a consistent expression level, irrespective of the experimental conditions. Reference gene selection was also carried out using the stability value provided by the RefFinder tool. However, it should be noted that Ct values may vary for different genes under different treatment conditions. In fact, previous studies have shown significant variation in the Ct values of selected reference genes within the average range of 14.7 to 34.49 (Chen et al. 2020) and 9.6 to 37.9 (Yu et al. 2019). Furthermore, in the present study, minimal changes in the Ct values were identified within the average range of 21.09 to 32.38 under different treatments. Differential gene analysis of the transcriptome data generated a set of normalized genes, which were then used to search for reference genes. The abiotic stress treatment was administered to vetiver slips after the reference genes selection process was completed. As a result, qRT-PCR Ct values showed variations between sample A and sample B.

The melting curve analysis of the reference genes, including GAPs, OsCam2, MUB, Core histone 1, and SAMS, along with the Actin gene shows the specificity of the selected primers with the respective genes. The high GC content of sample A and sample B resulted in a high melting temperature for the reference genes. To ensure qRT-PCR efficiency across different samples, two reference genes (Core histone 1 and SAMS) were selected for expression analysis using the serial dilution method. As the sample dilution increased, the Ct value also increased, indicating a lower expression pattern with increasing dilutions (Radonic et al. 2004). The transcript or copy number of genes varies with the dilution rate, resulting in Ct value variation. Finally, qRT-PCR was used to validate the selected reference genes. The analysis was carried out using Actin and the eleven selected reference genes of vetiver. Primers for GAPs, OsCam2, MUB, Core histone 1, and SAMS showed similar gene expression levels in terms of Ct value. The two optimal reference gene Core histone 1 and SAMS were identified from the 5 reference genes. The results were based on the defined primer of OsCam2 (Calmodulin, expressed), MUB (Membrane anchored ubiquitin fold protein), GAPs (GTPase-activating protein, putative, expressed), Core histone H2A/H2B/H3/H4 domain containing protein, putative expressed (Core histone 1), and S-adenosylmethionine synthetase, putative expressed (SAMS) may be used as a reference gene.

Calmodulin (CaM) known as a calcium-modulated protein, is expressed in all eukaryotic cells and requires Ca+2 for activation. GTPases are the hydrolase group of enzymes and they interact with GDP. G protein plays a major role in the interaction with the molecule (Sprang 2016). Ubiquitin is a small regulatory protein found in all eukaryotic organisms. Some ubiquitin proteins do not interact with other proteins and these proteins just attach to the cell membrane. The attachment of protein was associated with phospholipids and isoprene. Rice UBE has a role in vegetative and reproductive, and many abiotic and biotic stresses such as plant disease resistance (E et al. 2015). This protein sequence is highly conserved from protozoan to higher organisms. Histone proteins protect DNA from damage. It plays a major role in DNA replication and gene expression (Prado et al. 2017; Mei et al. 2017). Histone protein stabilized the negative charge of DNA. SAMS has a role in stress tolerance and epigenetic gene regulation (Ezaki et al. 2016).

Actin, RPS and GADPH are the traditional reference genes. TUB, EF1a, and UBQ are also the basic reference genes that have been previously used in qRT-PCR experiments (Wang et al. 2018). The Actin gene is widely used for reference gene validation. The research community gives more attention to housekeeping genes as these genes are used as reference genes. GAPs, MUB, OsCam2, Core histone 1, and SAMS reference genes give constant expression in terms of qRT-PCR Ct value. However, Ct values are not constant for RP, RPs, UBE2W, PLDCP, GRCWSP, and Core histone 2. Based on our result, five reference genes viz., GAPs, OsCam2, MUB, Core histone 1, and SAMS could be selected. GAPs, OsCam2, SAMS, MUB, Core histone 1 have minimum stability values and lower standard deviation. The SAMS and Core histone 1 are the optimal reference genes based on qbase + software analysis. The SAMS gene showed a Ct value variation range from 22.12 to 26.26. The Core histone 1 showed a Ct value variation range from 23.04 to 27.38. The factors such as species and tissue type are required for the selection of the best reference gene (Zhao et al. 2018). The expression of the gene is mainly affected by various stress conditions as well as different environmental stimuli. In the present study, abiotic stresses are used for studying reference genes which may help to identify stress-responsive reference genes, compare the stability of reference gene expression under different stress conditions, and provide insight into the effects of stress on gene expression in organisms.

Conclusion

In this study, vetiver, a perennial grass closely related to Sorghum bicolor, was selected for reference gene identification due to its plantation value for industrial and environmental applications. Trinity-based transcriptome analysis identified 1,36,824 contigs, which were further filtered to 346 contigs based on FPKM ≥ 6.0 filter criteria. Eleven reference genes were selected using stability value parameters and validated through the treatment of vetiver slips with various abiotic stresses. The study identified five reference genes, GAPs, OsCam2, MUB, Core histone 1, and SAMS, which can be used as quality control measures for gene expression experiments. The SAMS and Core histone 1 were identified as the optimal reference gene from 5 reference genes. These genes can also be used as reference genes for subsequent analysis of stress-related parameters and other experimental datasets using different plant species. The selected reference genes will help to ensure the accuracy, reliability, and comparability of gene expression data in genetics, molecular biology, and allied research fields.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (FASTA 112635 kb) (110MB, fasta)
Supplementary file2 (XLSX 105991 kb) (103.5MB, xlsx)
Supplementary file3 (XLSX 542 kb) (542.6KB, xlsx)
Supplementary file4 (XLSX 49 kb) (49.2KB, xlsx)
Supplementary file5 (XLSX 17 kb) (17.3KB, xlsx)
Supplementary file6 (XLSX 16 kb) (16.9KB, xlsx)
Supplementary file7 (XLSX 19 kb) (19.9KB, xlsx)
Supplementary file8 (XLSX 16 kb) (16.3KB, xlsx)
Supplementary file9 (XLSX 33 kb) (33KB, xlsx)
Supplementary file10 (XLSX 9 kb) (9.6KB, xlsx)
Supplementary file11 (XLSX 12 kb) (12.8KB, xlsx)
Supplementary file12 (XLSX 17 kb) (17KB, xlsx)
Supplementary file13 (DOCX 2209 kb) (2.2MB, docx)

Acknowledgements

The authors acknowledge the logistic support received from Dr. A. K. Shasany, Director CSIR – National Botanical Research Institute, Lucknow India, and AcSIR for Ph. D. degree registration. DC is grateful for funding from project OLP-110. R. D. Tripathi is grateful to NASI Prayagraj for the award of the NASI-Senior Scientist Platinum Jubilee Fellowship (GAP-3495). UCL is supported by the INSA Senior Scientist scheme. Y. Indoliya is grateful to CSIR, New Delhi for the award of RAship. This work is a part of the AcSIR Ph.D. program of ASC. CSIR-NBRI allotted the manuscript number CSIR-NBRI_MS/2023/04/16.

Author contributions

DC, RDT, PSC, UCL and ASC designed and drafted the work. ASC carried out the bioinformatics analysis. MT and YI performed quantitative expression analysis (qRT-PCR). RDT, SKM, DC and PSC participated in the supervision of the study. YI helped in the writing of the manuscript. UCL facilitated to arrange the live material of the two contrasting Vetiver morphotypes used in this study. SKM corporated in the functional execution of the experiments. RDT and DC helped in the editing of the manuscript. All the authors read and approved the final manuscript.

Availability of the material

The target vetiver genotypes used in this study could be availed through UCL.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Publisher's Note

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Change history

7/10/2023

A Correction to this paper has been published: 10.1007/s12298-023-01330-8

Contributor Information

Debasis Chakrabarty, Email: chakrabartyd@nbri.res.in.

Rudra Deo Tripathi, Email: tripathird@gmail.com.

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Associated Data

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Supplementary Materials

Supplementary file1 (FASTA 112635 kb) (110MB, fasta)
Supplementary file2 (XLSX 105991 kb) (103.5MB, xlsx)
Supplementary file3 (XLSX 542 kb) (542.6KB, xlsx)
Supplementary file4 (XLSX 49 kb) (49.2KB, xlsx)
Supplementary file5 (XLSX 17 kb) (17.3KB, xlsx)
Supplementary file6 (XLSX 16 kb) (16.9KB, xlsx)
Supplementary file7 (XLSX 19 kb) (19.9KB, xlsx)
Supplementary file8 (XLSX 16 kb) (16.3KB, xlsx)
Supplementary file9 (XLSX 33 kb) (33KB, xlsx)
Supplementary file10 (XLSX 9 kb) (9.6KB, xlsx)
Supplementary file11 (XLSX 12 kb) (12.8KB, xlsx)
Supplementary file12 (XLSX 17 kb) (17KB, xlsx)
Supplementary file13 (DOCX 2209 kb) (2.2MB, docx)

Data Availability Statement

The target vetiver genotypes used in this study could be availed through UCL.

Articles from Physiology and Molecular Biology of Plants are provided here courtesy of Springer

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