Identification of Suitable Reference Genes for Studying Expression of Cell Wall-Related and Developmental Associated Genes in Mungbean (Vigna radiata (L.) Wilczek)

Identification of Suitable Reference Genes for Studying Expression of Cell Wall-Related and Developmental Associated Genes in Mungbean (Vigna radiata (L.) 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Wilczek) Shouvik Das, Anant Mohan Sharma, Apurva Gangal, Vikrant Bhati, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7154578/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Spatio-temporal changes in gene expression are associated with certain biological activities. Therefore, identifying reference genes is paramount to elucidate the gene expression using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Therefore, to identify suitable reference genes, we initially screened 15 putative reference genes by studying their gene expression in different tissues of mungbean. Further, ten candidate reference genes were ranked based on stability using algorithms such as GeNorm, NormFinder, Bestkeeper, and RefFinder. The EF-1 ALPHA , CYP1 and GAPDH showed the best stable expression across different tissues as compared to other reference genes. The EF-1ALPHA exhibited the lowest Ct value, the highest expression and most stable (using different algorithms) by the comparative analysis among 10 reference genes after analysing its expression in leaf and stem. Further, the suitability of EF-1 ALPHA as reference genes was validated by checking the expression of PHENYLALANINE AMMONIA LYASE (PAL) , CINNAMOYL ALCOHOL DEHYDROGENASE (CAD), IRREGULAR XYLEM 14 (IRX14) and RIBULOSE-1,5-BISPHOSPHATE CARBOXYLASE/OXYGENASE (RuBisCO ) in three different mungbean genotypes and tissue types. We further tested the expression of RuBisCO in seedlings under salt and osmotic stress and found its expression was lower as compared to control plants, which correlated with the phenotype of seedlings. Therefore, we propose EF1-ALPHA is the best reference gene to analyse the expression of genes in different tissues and development stages with and without stress conditions. Gene expression studies Real time PCR Reference genes EF-1 ALPHA CYP1 GAPDH Mungbean Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Gene expression profiling is an important tool to understand the complex gene regulatory network functioning at the molecular level. Differential expression of genes in different tissues and developmental stages may determine their function and biological role in the cell. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and accurate method which allows the monitoring of gene amplification in real-time and relates it to the amount of gene expression level product present in the sample (Bustin 2000 ). However, there are several factors that determine the accuracy of gene expression. These includes quality and quantity of tissue used for RNA extraction, presence of inhibitors, primer and retro-transcription efficiencies (Wang et al. 2023 ). Therefore, the normalization of the gene expression against certain reference or housekeeping genes becomes crucial to adjust sample-to-sample variation and to assess differential expression in different tissue types and genotypes depending on its application (Schmittgen and Zakrajsek 2000 ). Ideally, a standard reference gene should have a constant level of expression pattern among different cells, different tissues and developmental stages which should be irrespective of experimental parameters (Kozera and Rapacz 2013 ). There are many reports where different housekeeping genes such as ELONGATION FACTOR ( EF-1 ALPHA ), 18S ribosomal RNA (18S) , POLYUBIQUITIN (UBQ ), ACTIN (ACT ), Β-TUBULIN (TUB) , and GLYCERALDEHYDE-3-PHOSPHATE DEHYDROGENASE ( GAPDH ) and others have been used to determine target gene expression (Kozera and Rapacz 2013 ; Ling et al. 2014a ; Joseph et al. 2018 ). However, studies suggest several internal control genes for normalization of target gene expression (Hong et al. 2008 ; Artico et al. 2010 ; Gu et al. 2011 ; Manoli et al. 2012a ). A specific genes or gene combinations is not found suitable for different experimental conditions or tissue types (Die et al. 2010 ). Therefore, it is highly imperative to define specific reference genes or gene combinations to normalize gene expression in different experimental conditions. Several reports identified suitable reference genes for specific experimental conditions in cereals and legumes ((Jarošová and Kundu 2010 ; Ferdous et al. 2015 ; Shivhare and Lata 2016 ; Reddy et al. 2016 ; Dudziak et al. 2020 ). Specifically, suitable reference genes are reported in Medicago (Kakar et al. 2008 ; Sun YaLi 2014) chickpea (Garg et al. 2010 ; Reddy et al. 2016 ), Pigeon pea (Sinha et al. 2015b , a ) and soybean (Libault et al. 2008 ; Hu et al. 2009 ). However, less efforts have been made to identify suitable reference genes in Vigna radiata . (Kundu et al. 2013 ; Ke et al. 2020 ). Mungbean ( Vigna radiata (L.) Wilczek ) is third most important legume in India (Singh et al. 2021 ). It is cultivated extensively in South-Asia, East-Asia, and South-East Asia from ancient time and native to Central Asia and India ((Ha and Lee 2019 ). Mungbean is an underutilized legume that is a rich source of dietary protein, vitamin and micronutrients (Manjunatha et al. 2023 ). It also plays a crucial role in maintaining soil fertility fitting with different crop rotational systems. Considering the huge population growth and hidden hunger, it is highly imperative to improve mungbean production. However, most of the yield-contributing traits, like seed size or seed weight, seed per pod are complex and quantitative in nature. Moreover, several factors including biotic or abiotic stresses can affect mungbean yield ((Nair et al. 2019 ). These factors may be regulated by the development and re-modulation of cell wall components. The plant cell wall is highly complex structure that is mainly composed of cellulose, hemicellulose, lignin and pectin and varies across plant species and tissue types. The diversity in plant cell shapes and sizes is because of the distinct physicochemical properties of the cell wall. (Farrokhi et al. 2006 )Also, in the last couple of decades, tremendous effort has been put forward to understand the genetic regulation of the cell walls which may impact on growth and development, resistance to abiotic or biotic stress and mechanical resistance (Cosgrove 2005 ; Caffall and Mohnen 2009 ). Thus, fine-tuning of cell wall composition can lead to improved yield (Ha et al. 2021 ). The cellulose and lignin content of leaf and stem tissues were significantly different in mungbean. The cellulose content of mungbean leaf and stem was 6.5% and 29%, respectively. Whereas, the lignin content of mung leaf and stem was found to be 8.5% and 8%, respectively (Das et al. 2024 ). Considering the variation in cell wall composition, gene expression analysis is crucial to infer the gene regulatory function for cell wall biosynthesis and remodelling. In mungbean, less efforts have been made to define reference genes in different experimental conditions and tissue types (Ke et al. 2020 ; Singh et al. 2021 ). There is scarce information available for suitable reference gene which can be used for normalization of gene which are differentially expressed under different stress conditions. Moreover, to the best of our knowledge, there is no report to define suitable reference genes to analyse expression of cell wall related genes in mungbean under normal and abiotic stress conditions. Therefore, the aim of this study was to identify suitable reference genes to normalize the expression of genes regulating cell wall biogenesis. Thus, we employed RT - qPCR to check the expression levels of ten housekeeping genes ( ACTIN, EF-1 ALPHA, UBQ , GAPDH, LECTIN, CYP1, TIP41, IF4A, HSP90 and CYP450 ) in leaf and stem tissue of mungbean under normal conditions. Using different algorithm based on GeNorm and RefFinder, the ten candidate reference genes were ranked as per their stability. Three genes - EF-1 ALPHA , CYP1 and GAPDH showed stable expression across different tissues and EF1 was most stable among all tested genes. Also, the transcript level was higher for EF-1 ALPHA as compared to other reference genes in normal and stress condition in different tissue types. In conclusion, this study has identified several reference genes using robust experimental design which can be used to normalize the expression of gene of interest using RT-qPCR in mungbean. Material and Method Plant material The seeds of mungbean ( Vigna radiata (L.) Wilczek ) were procured from the Division of Genetics, Indian Agriculture Research Institute, New Delhi, India. The seeds were thoroughly washed and soaked overnight in reverse osmosis (RO) purified water. The seedlings were grown in autoclaved mixture (1:1) of agropeat and vermiculite in plastic pots at 32°C ± 1°C in a culture room under 16 h light/8 h dark cycle. The leaf and stem of five weeks old mature plant was harvested. At least three independent biological replicates of each tissue sample were harvested and immediately frozen in liquid nitrogen. In this study, three commonly grown genotypes developed by Indian Agricultural Research Institute, New Delhi Pusa i.e., Pusa Vishal, Pusa 1641 and Pusa 1431 were used for analyses. Stress related experiments were performed on above mentioned three genotypes. For stress related experiments, seedlings were initially grown on wet filter paper for 2-day. These seedlings were then subjected to 100 mM NaCl and 20% PEG stress for 3 days. The pictures were recorded, samples were harvested after total 5-day and crushed in liquid nitrogen and stored in -80°C for RNA extraction. RNA extraction Total RNA was extracted from seedlings (stress and non-stress conditions), leaf and stem tissues using Ambion Trizol reagent (Sigma Life Science, United States of America) according to the manufacturer’s instructions. The DNA contamination was removed using DNase I (Cat no: EN0521, Thermo Fisher Scientific, United States of America). The RNA was quantified using a NanoDrop micro volume spectrophotometer (Thermo Fisher Scientific Inc., United States of America) and all the RNA samples were adjusted to the same concentration. Only the RNA samples with 260/280 ratio from 1.9 to 2.1, 260/230 ratio from 2.0 to 2.5 and RIN (RNA integrity number) more than of 8.0, were used for the analysis. The RIN value was determined using the Bioanalyzer (Agilent, Santa Clara, United States of America). The integrity of RNA samples was also assessed by agarose gel (1.3%) electrophoresis. cDNA synthesis Complementary DNA (cDNA) was then synthesized by using the iScript™ cDNA Synthesis Kit (1708891, Bio-Rad, United States of America). In brief, a total of 10 µl reaction mixture was prepared by mixing 1 µl of 500 ng of RNA samples, 2 µl of 5x iScript Reaction mix, 1 µl iScript reverse transcriptase and 6 µl nuclease-free water. Then the reaction mixture was incubated in a thermal cycler following a protocol of priming for 5 min at 25 o C, reverse transcription for 20 min at 46 o C and RT inactivation for 1 min at 95 o C. The PCR product was confirmed using Melt Curve analysis and by running on 1.3% agarose gel. qRT-PCR Analysis The sequences of commonly used putative reference genes were retrieved from mungbean genome database; the legume information system ( https://www.legumeinfo.org/ ). These genes are regularly used in different plant species for the selection of suitable reference genes. The details of gene IDs and NCBI IDs are represented in Table 1 . The primers for real-time PCR analysis were designed using PrimerQuest™ Tool (Integrated DNA Technologies, Inc., United States of America) under the default parameters; except for minimum primer length was set to 20. The specificity of primer pairs was confirmed by Primer-BLAST with all the nucleotide sequences available for mungbean at National Centre for Biotechnology Information (NCBI). The RT-qPCR was conducted with Quant-Studio 6 Flex Real-Time PCR Systems (Thermo Fisher Scientific Inc., United States of America) using the HOT FIREPol® EvaGreen® qPCR Mix Plus (ROX), 5x. The following reaction set up was done for the preparation of cDNA from all types of tissues under non-stress and stress conditions. The reaction was set up in ten µl reaction containing 2 µl HOT FIREPol® EvaGreen® qPCR Mix Plus, 0.3 µL of each primer (10 µM), 1 µl of cDNA (100 ng/µl) and 6.4 µL ddH 2 O. The PCR reaction conditions were as follows − 95°C for 10 min; 95°C for 15 s; 60°C for 1 min; 40 cycles. The PCR amplification specificity was determined from melting curve analysis and semi-quantitative PCR. The melting curve was analysed by heating the amplicon from 60°C to 95°C to confirm primer specificity. Each reaction was performed with three technical and three biological replicates. Relative fold changes in gene expression were calculated using the comparative 2 − ΔΔ CT method (Bubner and Baldwin 2004 ). First the delta Ct value was calculated by subtracting the Ct value of reference gene from the Ct value of the target gene. Then the 2 − ΔΔ CT formula is used to obtain the relative fold change. Table 1 List of all the primers used in this study Name of primer Gene ID Forward Primer Reverse Primer Expected band size Annealing temp Efficiency (%) Amplification factor R^2 Actin XM_014638363.1 AGGCTGTTCTGTCCTTGTA GAAGAGCGTAGCCCTCAT 105 60°C 104.1 2.0 0.99 Alpha tubulin XM_014646147.1 CTGGTATGTGGGTGAAGGT GCCAACCTCCTCGTAATCT 91 60°C 190.3 2.9 0.97 Beta tubulin XM_014634642.1 CTGGTGAGGGAATGGATG TCATCAGCAGTAGCATCT 100 58.6°C 168.1 3.6 0.84 Elongation factor 1 XM_014656474.1 CTAACTTCACCTCCCAGG CAGCGAACTTGACAGCAA 108 60°C 106.6 2 0.99 Polyubiquitin XM_022783996 CGTGAAAGCTAAGATCCAGG CCTCGGAGACGGAGAACTAAG 150 60 131.2 2.3 0.99 Ribosomal protein L7-2 XM_014634336 GGATCCAGAGGCTAAGCTC CAGCATGTTCATGGTGGCC 151 55°C 81.1 1.8 0.98 CYP1 AB020612.1 GGGCTCGATCTTCCACCGTG GTTCCAGGACCGGCGTTCGC 201 60°C 101.3 2.0 0.99 Histone H2A XM_014641486 GGCTGGAAATGCTGCTCGTG CCTTGGAAGCCTTCTCAGTC 182 60°C 110.6 2.1 0.99 Lectin XM_014639113 CCCGGACACACGAAAATTCTTGA CTACTCCTTGTTTCATGGGAGC 173 60°C 100.8 2.0 0.99 TIP41 XM_014655703.1 CCTTTGGGAAGACTGCAAGG CCAAGAGCTTGGCATTACTCTC 157 60°C 110.5 2.1 0.99 GAPDH XM_014634520.1 GAGGGTTTGATGACCACAGTTC CCATTCCAGTCAACTTTCC 181 60°C 98.6 1.9 0.99 Initiation factor 4a XM_014646146 GCTGCGCAGACAGTCACTTAGAC CAAGTGCCTCAGGAGGCATCG 185 60°C 134.4 2.3 0.98 HSP90 XM_014665349 GGTGTTGGATTCTACTCTGCTT ACCCTGAATCCTGGTTGGCTC 195 60°C 102.2 2.0 0.99 CYP450 XM_014637189 GGGGAACCCCGTTAACGTTGGG CCGAGACAAGGCACAAAGTC 190 60°C 124.4 2.1 0.98 18 s RMT XM_014653246 GGTGACATGGGCCAGGGTTTAG CTTGAAACACTGCTCTGGCTCC 185 60°C 112.2 2.1 0.99 PAL LOC106778742 GCCACTTAGGGGAACAATC AGGTTGCAACTCAAAGAAC 195 60°C 110.6 2.0 0.98 CAD LOC106775051 GGTGCGCCACAAGATGAATC GTCTGCACCAAGCAAGGTC 196 60°C 111.2 2.1 0.98 RuBisCO KU519326.1 GAGGAACAGCTGGGTAAGGAAG CACATGGTCCAGTACCTTCCAT 188 60°C 121.8 2.0 0.99 IRX 14 Vradi11g04510 GCCAAGTCTGGCATCAGAACAATC CATCAGCAAACACCACAACTCCG 190 60°C 120.3 2.1 0.99 Primer efficiency calculations To estimate the efficiency of the qPCR primers, a cDNA pool was created using equal volumes of leaf and stem cDNA. This pool was then diluted 10 times. From this diluted sample, dilution series of 4X, 16X and 64X were prepared. A RT-qPCR reaction was set up using these dilutions, and a graph of Ct value versus log of cDNA concentration was used to calculate the primer efficiency (in percentage) was then calculated using the following formula- Efficiency (%) = [10 ^ (-1/The Slope Value) -1] * 100 The primer efficiency for all the candidate reference genes has been determined (Table 1 ). Gene expression stability analysis According to the principle of RT-qPCR analysis, the detection of the amount of fluorescently labelled amplicon is directly proportional to the amount of amplified DNA (Bubner and Baldwin 2004 ). The threshold cycle (Ct) is adjusted to the cycle number at which the fluorescence generated within a reaction exceeds the fluorescence threshold. The fluorescence threshold is the signal which is above the background fluorescence. The Ct values of each sample were determined using QuantStudio™ Real-Time PCR Software. Several algorithms are recommended for the analysis of stability according to gene expression. In our study, different tools based on different algorithms, including RefFinder ( https://blooge.cn/RefFinder/ ), BestKeeper, NormFinder andgeNorm were used to identify the most stable gene among these selected reference genes. The RefFinder tools provides a comprehensive ranking of all the reference genes according to their stability. However, different algorithm follows different type of calculations (comprehensive gene stability and delta CT method). The geNorm determines the expression stability (M-value) for each candidate genes. The M-value was calculated, and the gene with the lowest M value was recommended as the defined reference gene (Vandesompele et al. 2002 ). In our study, the M value cut-off of less than 1.5 was set up. The GeNorm algorithm also employed a method to determine the optimal number of reference genes in a given set of samples. The BestKeeper algorithm uses Ct values to estimate the coefficient of variation (CV) and standard deviation (SD) for each reference gene (Pfaffl et al. 2004 ). The gene with the lowest SD value was considered as the most stable reference gene. The NormFinder algorithm was used to determine the stability value (SV) by measuring the variance in gene expression within and between groups. The genes with the lowest SV were considered the most stable genes. Validation of reference genes Phenylalanine ammonia lyase ( PAL ) (LOC106778742), cinnamyl alcohol dehydrogenase ( CAD ) (LOC106775051), IRREGULAR XYLEM8 (IRX8) (Vradi11g04510), RuBisCO (gene bank ID KU519326.1) were selected for normalization of their expression using EF-1 ALPHA as reference gene. To evaluate the validity of the selection of reference genes, the expression levels of PAL , CAD and RuBisCO were analysed in stem and leaf tissues of mungbean. The expression of these genes was normalized using EF-1 ALPHA , GAPDH and HSP90 . The RT-qPCR was performed, and the average Ct value was calculated from three biological and technical replicates. The relative fold change of these genes was subsequently calculated and normalized according to Huis et al. 2010 . Results Selection and amplification of putative reference genes using RT-qPCR The PCR was performed on mungbean cDNA for 15 candidate genes to assess the specificity of the primers by semi-quantitative PCR (Table 1 ). The PCR products amplified from each primer pair was assessed using agarose gel electrophoresis (Fig. 1 a, Fig. S3 ). The desired size band was visible for the following ten genes: EF-1 ALPHA, GAPDH, CYP1, HISTONE H2A, IF-4A, HSP90, LECTIN, TIP41, ACTIN and POLYUBQ. To further validate, we analysed these primer pairs using RT-qPCR. The melting-curve analysis also revealed a single peak corresponding to the expected size of the amplicon (Fig. 1 b, Fig. S2 ). These results indicated that primer pairs of these ten genes produce specific amplicons of the target genes. The details of these primers regarding primer sequence, amplicon length, melting temperature, efficiency and correlation coefficient have been provided in Table 1 . The RT-qPCR amplification efficiency varied from 98% for GAPDH to 134% for IF-4a , whereas the correlation coefficients varied from 0.9821 for IF-4A to 0.997 for HSP90 . EF-1 ALPHA showed the lowest Ct value Further, we wanted to identify and validate the suitable reference gene to normalise gene expression of cell wall and biomass-related genes in mungbean. Therefore, the Ct value of all the candidate reference genes was analysed independently across stem, leaf and both the tissues (Fig. 2 ). The Ct value ranged from 19 to 35 across stem and leaf tissues of mungbean (Fig. 2 a, 2 b). The EF-1 ALPHA exhibited the lowest mean Ct value (20) and LECTIN had the highest mean Ct value (31), suggesting that EF-1 ALPHA has the highest expression and LECTIN has the lowest expression level. The IF4a (24), GAPDH (23), POLYUBIQUITIN (22.33), HSP90 (24.) and CYP1 (21) showed slightly higher Ct value as compared to EF-1 ALPHA suggesting their transcript abundance with moderate expression. The ACTIN (27.00), TIP41 (26) and HISTONE H2A (25) exhibited comparatively less transcript abundance as compared to the above genes suggesting moderately lower expression. Interestingly, all the genes followed same Ct value pattern for stem & leaf combined and separately as well. This suggested that most of them with low and moderately high Ct values can be used as reference genes. Gene expression stability analysis of candidate reference genes To further understand the gene expression stability of all the candidate genes, and ΔCt was used in GeNorm, NormFinder, BestKeeper and RefFinder statistical tools. GeNorm analysis was performed to define the most suitable reference genes (Vandesompele et al. 2002 ). The analysis suggested that GAPDH and EF-1 ALPHA are the most stable genes followed CYP1 . The HSP90 was the least stable gene ( Fig. 3 , Table S1 ). In stem and leaf both tissues, HSP90 showed the lowest stability (M-value 0.210) and GAPDH had the highest stability (M value 0.102) followed by EF-1 ALPHA (M value 0.105) and CYP1 (0.106) ( Fig. 3 a, Table S1 ). According to the GeNorm principle, lower gene expression corresponds to a high M value, and the lowM value corresponds to stable gene. In our study, a total of ten genes were analysed in stem and leaf tissues using GeNorm. In stem, HSP90 exhibited the lowest stability with the highest M value (0.185), whereas EF-1 ALPHA and GAPDH both exhibited the most stability with the lowest M value (0.106) ( Fig. 3 b, Table S1 ). In the case of leaf tissues, HSP90 exhibited the highest M value (0.267), with the lowest stability, whereas EF-1 ALPHA exhibited the highest stability with the lowest M value (0.080) ( Fig. 3 c, Table S1 ). The NormFinder analysis depends on Analysis of Variance (ANOVA), which determines the intra- and inter-group variation of gene expression stability for each reference gene (Anderson et al. 2004). The NormFinder algorithm determines the stability value, which is the lowest for the highest stable gene. GAPDH showed the highest stability (SV 0.005) followed by EF-1 ALPHA (SV 0.015) and HSP90 has the lowest stability (SV 0.112) in stem tissue (Fig. 4 a, Table S2 ). Whereas, LECTIN was most stable (SV 0.006) followed by IF4a (SV 0.008), and EF-1 ALPHA (SV 0.013) and HSP90 (SV 0.183) in leaf tissue (Fig. 4 b, Table S2 ). Whereas BestKeeper algorithm determines the standard deviation (SD) across samples to check the expression stability. A gene with SD more than one is not preferred as a reference gene. The gene with the lowest SD value is considered as the most favourable reference gene. In stem, IF4a was the most stable (0.57) followed by H2A (0.76) and GAPDH (1.12) (Fig. 5 a). In case of leaf, IF4a was the most stable (SD 0.27) followed by LECTIN (0.41) (Fig. 5 b). RefFinder that is an integrated tool of all the algorithms, was also used to define the suitable reference gene. The analysis suggests that GAPDH followed by CYP1 and EF-1 ALPHA are comprehensively the most favourable reference gene ( Fig. 6 a ) . Delta Ct method also suggests that GAPDH followed by CYP1 and EF-1 ALPHA are the most suitable reference gene ( Fig. 6 b ) . RefFinder also employed BestKeeper, NormFinder and GeNorm algorithm. According to the analysis BestKeeper algorithm suggested IF4a , EF-1 ALPHA and GAPDH were the most stable gene whereas, Polyubiquitin is the least stable gene, which is in line with independent BestKeeper analysis ( Fig. 6 c ) . The NormFinder algorithm indicated GAPDH as the most stable followed by EF-1 ALPHA and CYP1 ( Fig. 6 d ) . The GeNorm algorithm referred CYP1 and GAPDH as the most stable followed by EF-1 ALPHA ( Fig. 6 e ) . In summary, EF-1 ALPHA and GAPDH were identified common reference genes by all the algorithm analysis. All the reference genes showed optimal pairwise variation value (V) Generally, a single gene with high expression stability is used for normalization of gene expression data. However, it is suggested to use two or more genes as reference for accurate and reliable results (Die et al. 2010 ). The GeNorm algorithm determines the optimal number of reference genes in a given set of samples (Vandesompele et al. 2002 ). The algorithm estimates normalization factor (NF) for the two genes with highest expression stability (NFn and NFn + 1) and then for other genes by stepwise addition of one after the other (Vn/Vn + 1), towards lower expression stability. According to the principle, pairwise variation (V) values for the reference genes in which the value is lower than 0.15 as to be optimized. In our study, no combination exhibited V value of more than 0.15, rather it was very less than 0.15. Therefore, any of two genes can be used for normalization of gene expression. However, based the GeNorm analysis, either GAPDH with EF-1 ALPHA or GAPDH with CYP1 can be used in combination for normalization of gene expression analysis in mungbean (Fig S4). EF-1 ALPHA is the best reference gene for expression analysis of RuBisCO, PAL, CAD and IXR14 To validate the reference gene efficacy, we performed gene expression studies in young leaf (2-week-old), old leaf (4-week-old) and stem (4-week-old) in mungbean genotype (Pusa Vishal, Pusa 1641 and Pusa 1451) (Figure S5a). The RNA integrity was tested on agarose gel and 28S or 18S RNA band was intact and this RNA was used for further analysis (Figure S5b). We first tested expression of RuBisCO which is abundant in leaf tissue and its expression will vary depending on development stage and tissue types (SUZUKI et al. 2009 ). We normalised the expression of RuBisCO to EF-1 ALPHA (most stable), GAPDH (medium stable) and HSP90 (less stable) (Fig. 4 ). Each panel figure relative fold change expression was normalised with one of the genotypes’ tissues that is represented by N. The relative fold change expression for N is approximately 1 for all panel figures. The values are calculated based on this formula - ddCt = ((dCtGene of Interest - dCt Reference gene) – (dCt of normalized gene)). Relative fold change = 2^(-ddCt). Based on this formula, we found that relative fold change expression of RuBisCO was higher in 2-week -old leaf and 4-week-old leaf when normalized to EF-1 ALPHA and GAPDH as compared to 4-week-old stem (Fig. 7 ). However, the gene expression was similar in 4-week-old leaf and stem when normalized to HSP90 . This further suggested that HSP90 may not be an appropriate reference gene for normalization. The expression of lignin and xylan biosynthetic genes can vary in different tissues. Phenylalanine ammonia lyase (PAL) converts phenylalanine to cinnamic acid which is the first step in lignin biosynthesis (Huang et al. 2010 ). The expression of PAL was higher in the stems of all genotypes as compared to 2-week-old leaf and 4-week-old leaf (Fig. 7 d- 7 f). However, average expression of PAL when normalized with EF-1 ALPHA and GAPDH was similar in all the tissues and genotypes. However, relative fold change expression was comparatively higher in Pusa (P) Vishal and variation was also observed after normalizing with HSP90 (Fig. 2 f). Cinnamyl alcohol dehydrogenase converts phenylalanine derived aldehyde to monolignol which is last step in lignin biosynthesis (Wang et al. 2015 ). Like PAL expression pattern, similar pattern of expression was observed for CAD gene expression. Expression was higher in stem tissue as compared to leaf tissue (Fig. 7 g-i). In P.Vishal stem tissue, relative fold expression of CAD was more than 400 after normalizing with HSP90 which was very different from other genotypes and as compared to data normalized with EF-1 ALPHA and GAPDH . Xylan is one of main component of secondary cell wall which abundant generally in stem tissue of angiosperm plants ((Wierzbicki et al. 2019 ). Irregular Xylem 14 ( IRX14 ) belongs glycosyl transferase 43 (GT43) family and is involved in xylan chain elongation. Therefore, we checked the expression of mungbean IRX14 . We found that expression in stem tissue was approximately 40–50 times higher than leaf tissue (Fig. 7 j-l) after normalizing with all the reference genes ( EF-1 ALPHA, GAPDH and HSP90 ). However, P.Vishal showed approximately 2000 relative fold change expression after normalization with HSPP90 which was different from other genotypes and compared to after normalization with EF-1 ALPHA and GAPDH . All these data further confirmed that EF-1 ALPHA and GAPDH are better reference genes than HSP90 which again correlated with our previous data. Further, we wanted to check the expression of RuBisCO under salt and osmotic stress after normalisation with different reference genes. RuBisCO activity is generally reduced under different stress. Therefore, we grew mungbean seedling under salt (100 mM, NaCl) and osmotic (20% PEG4000) for 3 days after growing 2 days on wet filter paper and we found reduction in root length after salt and osmotic stress as compared to normal condition grown seedlings (Fig. 8 b, Figure S5a). Also, the cotyledonary leaf turned green to greenish white, suggesting a stress phenotype. Further, RNA samples were intact in all seedlings grown under normal and stress conditions (Figure S6b). The Ct values for all references genes were higher as compared to leaf and stem tissue for all reference genes. Single melt curve and PCR product were observed for EF-1 ALPHA (Figure S6c). However, multiple size products were observed after amplification with GAPDH (Figure S6d). In fact, we did not see expression of HSP90 in normal as well as stress condition. Therefore, we normalized the expression of RuBisCO with EF-1 ALPHA . The expression of RuBisCO was significantly lower in Pusa Vishal, Pusa 1641 and Pus1431 for salt-treated as compared to control seedlings (Fig. 8 b). Also, the expression of RuBisCo was lower in osmotic-treated as compared to control seedlings of Pusa 1641 (Fig. 8 b). This further confirmed that EF-1 ALPHA can be used for normalizing expression of RuBisCO under normal and salt or osmotic stress conditions. Discussion The reliability and accuracy of RT-qPCR results are primarily dependent on the normalization of the target gene using suitable reference gene. Therefore, the selection of the suitable reference gene is the prime factor for analysis of target gene expression (Die et al. 2010 ) ; Ling et al. 2014b ). An ideal reference gene should have a constant level of expression pattern among cells of different tissues and experimental conditions such as biotic or abiotic stress and irrespective of experimental parameters. However, very limited number of reference genes have been identified in mungbean which are either in Vigna mungo and Chinese mungbean species (Kundu et al. 2013 ; Ke et al. 2020 ; Zhou et al. 2023 ). Zhou et al. 2023 proposed Tubulin (TUA) as the most stable reference gene under biotic stress and hormone treatment in Vigna radiata by RT-qPCR only in one genotype. Ke et al. 2021 identified that ubiquitin-conjugating enzyme was suitable as reference under drought and pathogen infection stress; elongation factor 1 was the most stable gene under waterlogging; and actin performed the best under saline stress. And this study was performed in Chinese LvFeng 5 (LF5) cultivar. Also, ACT and EF-1 ALPHA was identified as the most suitable reference during MYMIV stress, while H2A , EF-1 ALPHA and ACT were found to be most suitable in salinity stress experiments and TUB and 18S during drought treatments (Kundu et al. 213). The above study was in Vigna mungo which has different phenotypic characteristics than Vigna radiata. Also, in all these studies, cell wall-related or biomass-related genes were not tested. Therefore, in this study, we systematically tested several reference genes in different tissue types and under stress or non-stress conditions in different Indian genotypes of mungbean ( Vigna radiata ). Initially, we screened a total of 15 potential reference genes that had been reported in other crops for gene expression analysis in leaf and stem tissues of mungbean. Several algorithm including, GeNorm, NormFinder, BestKeeper and RefFinder has been used to define the most suitable reference gene. The computed result using geNorm and Normfiner algorithm suggested that GAPDH and EF-1 ALPHA are the most stable genes. These observations were further corroborated with following studies. The GAPDH have been reported as the most suitable reference gene in peanut and chickpea (Garg et al. 2010 ; Reddy et al. 2016 ). The EF-1 ALPHA was also reported as the suitable reference gene in many legumes (Garg et al. 2010 ; Gutierrez et al. 2011 ). BestKeeper analysis revealed that IF4a and EF-1 ALPHA are the most suitable candidate reference gene. RefFinder analysis defined GAPDH, CYP1 and EF-1 ALPHA as the most stable reference genes. The Cytochromes P450 (P450s or CYP s) are heme-cofactor containing enzyme that, function as monooxygenases. There is no report of using CYP as reference gene in legume, but in cereal CYP have been used as reference gene (Jaiswal et al. 2019). Therefore, in this study, CYP is newly identified as a suitable reference gene in mungbean and the homolog of CYP can be tested for suitability in other legume species. Based on the computed algorithms and analyses, EF-1 ALPHA exhibited the lowest Ct value across the tissues, hence the highest expression. Therefore, we chose EF-1 ALPHA along with GAPDH and HSP90 for comparison to normalize and validate the gene expression of cell wall and biomass-related genes in mungbean using different tissues and developmental stages under stress and non-stress conditions. PAL ;, CAD , IRX14 and RuBisCO were selected for validation. These genes are involved in cell wall biosynthesis and biomass improvement. The gene expression studies were performed using young leaf (2-week-old), old leaf (4-week-old) and stem (4-week-old) in mungbean genotypes (Pusa Vishal, Pusa 1641 and Pusa 1451). The PAL is involved in the first step of the phenylpropanoid pathway. Thus, it regulates the biosynthesis of polyphenol compounds such as flavonoids, phenylpropanoids, and lignin in plants (Wakabayashi et al. 2012 ). The expression of PAL was much higher in the stems of all genotypes as compared to the 2-week-old leaves and 4-week-old leaves. The relative fold change expression was comparatively higher in the stem of Pusa Vishal, suggesting more lignin deposition in the stem secondary cell wall of Pusa Vishal as compared to other genotypes. The CAD is the key enzyme involved in the final step of the phenylpropanoid and lignin pathway. Mungbean leaves are abundant with phenolic compounds as compared to other legumes (Bai et al. 2016 ; Wang et al. 2021 ). In our previous study, we found that the lignin content was higher in mungbean leaf as compared to Populus stem, Arabidopsis stem and rice leaf (Das 2024). However, in this study, we observed that the expression pattern of CAD gene was similar to the PAL gene expression. The expression of CAD gene was higher in stem tissue as compared to leaf tissue (Fig. 7 g-i). The CAD gene expression was also higher in P. Vishal stem as compared to other genotypes; this may indicate higher lignin deposition in P. Vishal stem as compared to other genotypes. The PAL and CAD expression was upregulated in stem as compared to leaf. IRX14 is involved in xylan chain elongation (Qaseem et al., 2024). In this study, IRX14 expression was much higher in stem tissue as compared to leaf tissue (Fig. 7 j-l). Also its expression was higher in stem of Pusa Vishal as compared to other genotypes, suggesting more developed stem of Pusa Vishal as compared to other genotypes at this stage. RuBisCO is the carboxylase of the C3 cycle, where it fixes CO 2 onto a ribulose bisphosphate (RuBP) sugar. This is the central carbon cycle reaction which converts around 100 gigatons of carbon from CO 2 into biomass annually (Prywes et al. 2023 ). It is a major enzyme, abundant in leaf, contributing towards higher plant biomass production. The carbon fixed through RuBisCO is used for cell wall polysaccharide synthesis (Verbančič et al. 2018). The RuBisCO is the prime enzyme that plays the most crucial role in C3 cycle of photosynthesis. The level of expression of this enzyme is higher in leaf as compared to stem. We observed much higher expression of RuBisCO in leaf as compared to stem of mungbean (Fig. 7 a, 7 b, 7 c). The expression of RuBisCO was also checked under salt and osmotic stress after normalizing with EF1-APLHA . The expression of RuBisCO was significantly lower in Pusa Vishal, Pusa 1641 and Pusa 1431 in salt-treated seedlings as compared to control seedlings Fig. 8 b). The expression of RuBisCo was also lower in osmotic-treated seedlings as compared to control seedlings of Pusa 1641 (Fig. 8 b). Thus, RuBisCO activity may be reduced under different stress. Conclusion For the first time, the CYP1 is reported as unique reference gene for analysis of gene expression in mungbean and its homolog may be tested in other mungbean species. Extensive analysis of selected reference genes in different genotypes of mungbean under stress and non-stress conditions revealed EF-1 ALPHA as the best reference gene, followed by GAPDH . Also, HSP90 should be avoided in normalizing expression of genes tested under normal or stress conditions. as well as. Based on this, we propose EF-1 ALPHA as the best-suited and robust reference candidate gene to normalize differential gene expression in different tissue types under stress and non-stress conditions. Declarations Data availability No datasets were generated or analysed during the current study. Acknowledgements The RT-qPCR was performed using QuantStudio 6 Pro Real-Time PCR systems at Central Instrumentation Facility (CIF), Regional Centre for Biotechnology, Faridabad. Funding This work is supported by DBT-MKB fellowship (102/IFD/SAN/2570/2021-22) and RCB core funding mechanism. Author information Authors and Affiliations 1 Laboratory of Plant Cell Wall Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana India. 2 Division of Genetics, Indian Agricultural Research Institute, New Delhi-110012, India. 3 Division of Seed Science and Technology, Indian Agricultural Research Institute, New Delhi-110012, India. Contributions SD and PM-AP designed and conceptualized the research. AMS performed experiments in revised version. AG performed most of the experiments. VB assisted in some experiments. AG, SD, and PM-AP performed data analysis. SD and PM-AP wrote the manuscript. VB, HKD, and GPM provided mungbean accession and suggestions during manuscript preparation. All authors have read and agreed to publish the manuscript. Corresponding author Shouvik Das and Prashant Anupama-Mohan Pawar Ethics declarations Ethics approval and consent to participate No specific permit was required for the samples analyzed in this study. The authors comply with relevant institutional, national, and international guidelines and legislation for plant studies. Consent for publication Not applicable. Conflict of statement Authors declare no conflict of interest References Artico S, Nardeli SM, Brilhante O et al (2010) Identification and evaluation of new reference genes in Gossypium hirsutumfor accurate normalization of real-time quantitative RT-PCR data. BMC Plant Biol 10:49. https://doi.org/10.1186/1471-2229-10-49 Bai Y, Xu Y, Chang J et al (2016) Bioactives from stems and leaves of mung beans (Vigna radiata L). 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Figure S3: Primer efficiency determination. The plot denotes Plot log cDNA concentration versus the Ct value. The slope of this plot was used to predict the primer efficiencies for the given pair of primers and the amplification factor. Figure S4: Determination of the optimal number of reference genes calculated by geNorm Figure S5: Pictures, RNA gel images and RT-qPCR amplified products from three mungbean genotypes. Figure S6: Figure S6. Seedlings pictures, RNA gel images and amplified PCR products from mungbean genotypes grown under normal and stress conditions Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7154578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488402054,"identity":"5610305d-39e9-4208-b05f-c4a71a2d8eb8","order_by":0,"name":"Shouvik Das","email":"","orcid":"","institution":"Regional Centre for Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Shouvik","middleName":"","lastName":"Das","suffix":""},{"id":488402055,"identity":"441a0911-bf17-4dbe-b262-94aff35074af","order_by":1,"name":"Anant Mohan Sharma","email":"","orcid":"","institution":"Regional Centre for Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Anant","middleName":"Mohan","lastName":"Sharma","suffix":""},{"id":488402056,"identity":"ab520626-50aa-45d7-a106-f1a8a38b7f7e","order_by":2,"name":"Apurva Gangal","email":"","orcid":"","institution":"Regional Centre for Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Apurva","middleName":"","lastName":"Gangal","suffix":""},{"id":488402057,"identity":"3a528544-3a33-4e1c-ac61-eafaf24502cc","order_by":3,"name":"Vikrant Bhati","email":"","orcid":"","institution":"Regional Centre for Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Vikrant","middleName":"","lastName":"Bhati","suffix":""},{"id":488402058,"identity":"ede78d00-c02d-4aae-9f11-a192035f0702","order_by":4,"name":"Gyan Prakash Mishra","email":"","orcid":"","institution":"Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Gyan","middleName":"Prakash","lastName":"Mishra","suffix":""},{"id":488402060,"identity":"d660d864-17d9-4a0e-a0a5-fa222396d249","order_by":5,"name":"Harsh Kumar Dikshit","email":"","orcid":"","institution":"Indian Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Harsh","middleName":"Kumar","lastName":"Dikshit","suffix":""},{"id":488402063,"identity":"09be1fa3-c1cd-4809-86ba-80045c8220f0","order_by":6,"name":"Prashant Anupama","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBAC+wYGBmYkvg0DgwSExYNLi8EBiBaoOoY00rUcRjBxarl9+PHngpo7dQzSzc8kfu45n88/u4Htw4c/DDLmuPzSl2YmPePYMwkGmWNmkj3PblvOuHOAeebMNgYeywYctvAwmDHzsB2WYJBIMLvBc+C2gYFEAjMzbwMDD8jN2LWwf/7M8w+kJf3bzT8HzkG0/PmDTwuPgTRvG0hLjtltngMHIFoY2PBqKZPm7Tss2SZzpvy3zIFkA4k7B5sZe9sk8Dls82eeb4f5+aXbNxu+OWBnwD+7+TDDjz829ri0wAEbIjoYGxgIxw4DkWpGwSgYBaNgZAIABFdS+vzcTiIAAAAASUVORK5CYII=","orcid":"","institution":"Regional Centre for Biotechnology","correspondingAuthor":true,"prefix":"","firstName":"Prashant","middleName":"","lastName":"Anupama","suffix":""}],"badges":[],"createdAt":"2025-07-18 06:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7154578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7154578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88394494,"identity":"e54da23e-3e7c-4dd4-ac7e-0b64a949966b","added_by":"auto","created_at":"2025-08-06 05:39:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":213391,"visible":true,"origin":"","legend":"\u003cp\u003ePCR product and melting curve analysis of ten candidate genes (a) PCR product of semi-quantitative PCR for all the ten candidate genes in leaf and stem cDNA (b) melting curve analysis of all the ten candidate genes using RT-qPCR.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/4dab9e483fc65672c2d84c46.png"},{"id":88394463,"identity":"8f7db2d0-7963-411b-b7e8-c6bb2e435cb4","added_by":"auto","created_at":"2025-08-06 05:39:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48539,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of candidate reference genes in mungbean tissue samples. The Bar graphs represent Ct values for each reference gene in (a) stem and (b) in leaf (b). The average represents Mean ± SD, n = 3 biological replicates.\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/7fa59b0f4ec49f01ade137bb.png"},{"id":88394467,"identity":"0795741b-8980-4ffd-96c6-ff1513403172","added_by":"auto","created_at":"2025-08-06 05:39:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31052,"visible":true,"origin":"","legend":"\u003cp\u003eAverage expression stability values (M) of Mungbean candidate reference genes. M values of the reference genes were calculated with the geNorm algorithm. Ranking of the stability was performed on (a) stem tissue (b) leaf tissues. The lowest average expression stability value indicates the most stably expressed gene.\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/4995989822dbd0224e386229.png"},{"id":88394492,"identity":"ba51b513-cf2a-4e6a-aeb7-925a5d7425b2","added_by":"auto","created_at":"2025-08-06 05:39:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24610,"visible":true,"origin":"","legend":"\u003cp\u003eAverage expression stability values of Mungbean candidate reference genes. Stability values of the reference genes were calculated with the NormFinder algorithm. Ranking of the stability was performed on (a) stem tissue (b) leaf tissues. The lowest average expression stability value indicates the most stably expressed gene.\u003c/p\u003e","description":"","filename":"Slide4.png","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/28d5abf558f3bdb01bc79e80.png"},{"id":88394518,"identity":"6b7f90f8-d36b-4ace-9cf3-0e1ca0d3935a","added_by":"auto","created_at":"2025-08-06 05:39:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":26285,"visible":true,"origin":"","legend":"\u003cp\u003eAverage expression stability values of Mungbean candidate reference genes. Standard deviation of the reference genes was calculated with the BestKeeper algorithm. Ranking of the stability was performed on (a) stem tissue (b) leaf tissues. The lowest SD value indicates the most stably expressed gene.\u003c/p\u003e","description":"","filename":"Slide5.png","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/2dd803f9c4e98711b23515b6.png"},{"id":88394474,"identity":"ba5740de-b202-4fa0-9f2e-540c3a120033","added_by":"auto","created_at":"2025-08-06 05:39:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":107101,"visible":true,"origin":"","legend":"\u003cp\u003eGene stability analysis of candidate reference genes. The stability value of reference genes was calculated with the RefFinder algorithm. Ranking of the stability was performed on leaf and stem tissues (a) Comprehensive gene stability (b) delta CT method (c) Best Keeper (d) Norm Finder (e) GeNorm. The lowest SD value indicates the most stably expressed gene.\u003c/p\u003e","description":"","filename":"Slide6.png","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/9ead2593a46d2d139f53a3cf.png"},{"id":88394489,"identity":"ca81a364-1239-4f30-8fdb-3141c0dd4a9e","added_by":"auto","created_at":"2025-08-06 05:39:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":171724,"visible":true,"origin":"","legend":"\u003cp\u003eComparative gene expression analysis (relative fold change) among different mungbean genotypes (Pusa Vishal, Pusa 1641 and Pusa 1431) and tissue types (2-week-old leaf, 4-week-old leaf and 4-week-old stem). P in genotypes corresponds to Pusa. (a), (b) and (c) represent relative gene expression of \u003cem\u003eRubisco \u003c/em\u003e(d), (e) and (f) \u003cem\u003ePAL\u003c/em\u003e (g), (h) and (i) CAD1 (j), (k), (l) IRX8) normalised using\u003cem\u003eEF-1 ALPHA\u003c/em\u003e, GAPDH and HSP90, respectively as represented on Y-axis. The relative fold change was calculated in each graph by normalising expression using one of genotypes and tissue represented by N. The error bar represents mean ± SD from n =3-4 biological replicates.\u003c/p\u003e","description":"","filename":"Slide7.png","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/da2dac86a506e53a3ea25cdf.png"},{"id":88394468,"identity":"cea1380d-56e1-420f-87b5-9d393951f585","added_by":"auto","created_at":"2025-08-06 05:39:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":147918,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Seedlings picture of 5-day-old mungbean genotypes, i.e., Pusa Vishal, Pusa 1641 and Pusa 1431, grown under control (without treatment), salt (100 mM NaCl) and osmotic (20% PEG4000) stress on germination paper. (b) The relative fold change gene expression of \u003cem\u003eRuBisCO\u003c/em\u003e using \u003cem\u003eEF-1 ALPHA\u003c/em\u003e as reference gene and expression normalized to respective control in Pusa Vishal, Pusa 1641 and Pusa 1431 mungbean genotypes. The asterisk represents significant differences as compared to the control using Student’s t-test at *p \u0026lt; 0.1, ** p \u0026lt; 0.05, mean ±SD, n =3-4 biological replicates.\u003c/p\u003e","description":"","filename":"Slide8.png","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/488a5437b13a0615ac151240.png"},{"id":90269023,"identity":"0a306702-fd30-49cd-9bcf-cf56a2e3479f","added_by":"auto","created_at":"2025-08-31 20:16:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1784451,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/eca634b3-d7c2-4918-8962-307277aa6a0f.pdf"},{"id":88394450,"identity":"d307c597-4ff8-4afb-b055-b8669d6b877b","added_by":"auto","created_at":"2025-08-06 05:39:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e: Ranking of candidate genes based on expression stability using GeNorm algorithm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2\u003c/strong\u003e: Ranking of candidate genes based on expression stability using NormFinder algorithm.\u003c/p\u003e","description":"","filename":"SupplementarytablePMR.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/4c58c3e0ad5bbb57c3f57f16.xlsx"},{"id":88394465,"identity":"5aba393a-3d34-4193-a60d-3418bf1dd8b2","added_by":"auto","created_at":"2025-08-06 05:39:46","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3210429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1\u003c/strong\u003e: Uncropped gel image of PCR product of semi-quantitative PCR for all the ten candidate genes in leaf and stem cDNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2\u003c/strong\u003e: Melting curve analysis of candidate genes using RT-qPCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3\u003c/strong\u003e: Primer efficiency determination. The plot denotes Plot log cDNA concentration versus the Ct value. The slope of this plot was used to predict the primer efficiencies for the given pair of primers and the amplification factor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S4\u003c/strong\u003e: Determination of the optimal number of reference genes calculated by geNorm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S5\u003c/strong\u003e: Pictures, RNA gel images and RT-qPCR amplified products from three mungbean genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S6\u003c/strong\u003e: Figure S6. Seedlings pictures, RNA gel images and amplified PCR products from mungbean genotypes grown under normal and stress conditions\u003c/p\u003e","description":"","filename":"SupfigurePMR.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7154578/v1/8bf8a60ccd356e5f5a7f7ea2.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Suitable Reference Genes for Studying Expression of Cell Wall-Related and Developmental Associated Genes in Mungbean (Vigna radiata (L.) Wilczek)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGene expression profiling is an important tool to understand the complex gene regulatory network functioning at the molecular level. Differential expression of genes in different tissues and developmental stages may determine their function and biological role in the cell. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and accurate method which allows the monitoring of gene amplification in real-time and relates it to the amount of gene expression level product present in the sample (Bustin \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). However, there are several factors that determine the accuracy of gene expression. These includes quality and quantity of tissue used for RNA extraction, presence of inhibitors, primer and retro-transcription efficiencies (Wang et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the normalization of the gene expression against certain reference or housekeeping genes becomes crucial to adjust sample-to-sample variation and to assess differential expression in different tissue types and genotypes depending on its application (Schmittgen and Zakrajsek \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Ideally, a standard reference gene should have a constant level of expression pattern among different cells, different tissues and developmental stages which should be irrespective of experimental parameters (Kozera and Rapacz \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). There are many reports where different housekeeping genes such as \u003cem\u003eELONGATION FACTOR\u003c/em\u003e (\u003cem\u003eEF-1 ALPHA\u003c/em\u003e), \u003cem\u003e18S ribosomal RNA (18S)\u003c/em\u003e, \u003cem\u003ePOLYUBIQUITIN (UBQ\u003c/em\u003e), \u003cem\u003eACTIN (ACT\u003c/em\u003e), \u003cem\u003eΒ-TUBULIN (TUB)\u003c/em\u003e, and \u003cem\u003eGLYCERALDEHYDE-3-PHOSPHATE DEHYDROGENASE\u003c/em\u003e (\u003cem\u003eGAPDH\u003c/em\u003e) and others have been used to determine target gene expression (Kozera and Rapacz \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ling et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e; Joseph et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, studies suggest several internal control genes for normalization of target gene expression (Hong et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Artico et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Manoli et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e). A specific genes or gene combinations is not found suitable for different experimental conditions or tissue types (Die et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, it is highly imperative to define specific reference genes or gene combinations to normalize gene expression in different experimental conditions. Several reports identified suitable reference genes for specific experimental conditions in cereals and legumes ((Jarošov\u0026aacute; and Kundu \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ferdous et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shivhare and Lata \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Reddy et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dudziak et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, suitable reference genes are reported in Medicago (Kakar et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sun YaLi 2014) chickpea (Garg et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Reddy et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Pigeon pea (Sinha et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003ea\u003c/span\u003e) and soybean (Libault et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, less efforts have been made to identify suitable reference genes in \u003cem\u003eVigna radiata\u003c/em\u003e. (Kundu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ke et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMungbean (\u003cem\u003eVigna radiata\u003c/em\u003e (L.) \u003cem\u003eWilczek\u003c/em\u003e) is third most important legume in India (Singh et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is cultivated extensively in South-Asia, East-Asia, and South-East Asia from ancient time and native to Central Asia and India ((Ha and Lee \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Mungbean is an underutilized legume that is a rich source of dietary protein, vitamin and micronutrients (Manjunatha et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It also plays a crucial role in maintaining soil fertility fitting with different crop rotational systems. Considering the huge population growth and hidden hunger, it is highly imperative to improve mungbean production. However, most of the yield-contributing traits, like seed size or seed weight, seed per pod are complex and quantitative in nature. Moreover, several factors including biotic or abiotic stresses can affect mungbean yield ((Nair et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These factors may be regulated by the development and re-modulation of cell wall components. The plant cell wall is highly complex structure that is mainly composed of cellulose, hemicellulose, lignin and pectin and varies across plant species and tissue types. The diversity in plant cell shapes and sizes is because of the distinct physicochemical properties of the cell wall. (Farrokhi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)Also, in the last couple of decades, tremendous effort has been put forward to understand the genetic regulation of the cell walls which may impact on growth and development, resistance to abiotic or biotic stress and mechanical resistance (Cosgrove \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Caffall and Mohnen \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, fine-tuning of cell wall composition can lead to improved yield (Ha et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The cellulose and lignin content of leaf and stem tissues were significantly different in mungbean. The cellulose content of mungbean leaf and stem was 6.5% and 29%, respectively. Whereas, the lignin content of mung leaf and stem was found to be 8.5% and 8%, respectively (Das et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Considering the variation in cell wall composition, gene expression analysis is crucial to infer the gene regulatory function for cell wall biosynthesis and remodelling. In mungbean, less efforts have been made to define reference genes in different experimental conditions and tissue types (Ke et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There is scarce information available for suitable reference gene which can be used for normalization of gene which are differentially expressed under different stress conditions. Moreover, to the best of our knowledge, there is no report to define suitable reference genes to analyse expression of cell wall related genes in mungbean under normal and abiotic stress conditions.\u003c/p\u003e\u003cp\u003eTherefore, the aim of this study was to identify suitable reference genes to normalize the expression of genes regulating cell wall biogenesis. Thus, we employed RT - qPCR to check the expression levels of ten housekeeping genes (\u003cem\u003eACTIN, EF-1 ALPHA, UBQ\u003c/em\u003e, \u003cem\u003eGAPDH, LECTIN, CYP1, TIP41, IF4A, HSP90\u003c/em\u003e and \u003cem\u003eCYP450\u003c/em\u003e) in leaf and stem tissue of mungbean under normal conditions. Using different algorithm based on GeNorm and RefFinder, the ten candidate reference genes were ranked as per their stability. Three genes - \u003cem\u003eEF-1 ALPHA\u003c/em\u003e, \u003cem\u003eCYP1\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e showed stable expression across different tissues and EF1 was most stable among all tested genes. Also, the transcript level was higher for \u003cem\u003eEF-1 ALPHA\u003c/em\u003e as compared to other reference genes in normal and stress condition in different tissue types. In conclusion, this study has identified several reference genes using robust experimental design which can be used to normalize the expression of gene of interest using RT-qPCR in mungbean.\u003c/p\u003e"},{"header":"Material and Method","content":"\u003cp\u003e\u003cb\u003ePlant material\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe seeds of mungbean (\u003cem\u003eVigna radiata\u003c/em\u003e (L.) \u003cem\u003eWilczek\u003c/em\u003e) were procured from the Division of Genetics, Indian Agriculture Research Institute, New Delhi, India. The seeds were thoroughly washed and soaked overnight in reverse osmosis (RO) purified water. The seedlings were grown in autoclaved mixture (1:1) of agropeat and vermiculite in plastic pots at 32\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C in a culture room under 16 h light/8 h dark cycle. The leaf and stem of five weeks old mature plant was harvested. At least three independent biological replicates of each tissue sample were harvested and immediately frozen in liquid nitrogen. In this study, three commonly grown genotypes developed by Indian Agricultural Research Institute, New Delhi Pusa i.e., Pusa Vishal, Pusa 1641 and Pusa 1431 were used for analyses. Stress related experiments were performed on above mentioned three genotypes. For stress related experiments, seedlings were initially grown on wet filter paper for 2-day. These seedlings were then subjected to 100 mM NaCl and 20% PEG stress for 3 days. The pictures were recorded, samples were harvested after total 5-day and crushed in liquid nitrogen and stored in -80\u0026deg;C for RNA extraction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA extraction\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Total RNA was extracted from seedlings (stress and non-stress conditions), leaf and stem tissues using Ambion Trizol reagent (Sigma Life Science, United States of America) according to the manufacturer\u0026rsquo;s instructions. The DNA contamination was removed using DNase I (Cat no: EN0521, Thermo Fisher Scientific, United States of America). The RNA was quantified using a NanoDrop micro volume spectrophotometer (Thermo Fisher Scientific Inc., United States of America) and all the RNA samples were adjusted to the same concentration. Only the RNA samples with 260/280 ratio from 1.9 to 2.1, 260/230 ratio from 2.0 to 2.5 and RIN (RNA integrity number) more than of 8.0, were used for the analysis. The RIN value was determined using the Bioanalyzer (Agilent, Santa Clara, United States of America). The integrity of RNA samples was also assessed by agarose gel (1.3%) electrophoresis.\u003c/p\u003e\u003cp\u003e\u003cb\u003ecDNA synthesis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComplementary DNA (cDNA) was then synthesized by using the iScript\u0026trade; cDNA Synthesis Kit (1708891, Bio-Rad, United States of America). In brief, a total of 10 \u0026micro;l reaction mixture was prepared by mixing 1 \u0026micro;l of 500 ng of RNA samples, 2 \u0026micro;l of 5x iScript Reaction mix, 1 \u0026micro;l iScript reverse transcriptase and 6 \u0026micro;l nuclease-free water. Then the reaction mixture was incubated in a thermal cycler following a protocol of priming for 5 min at 25\u003csup\u003eo\u003c/sup\u003eC, reverse transcription for 20 min at 46\u003csup\u003eo\u003c/sup\u003eC and RT inactivation for 1 min at 95\u003csup\u003eo\u003c/sup\u003eC. The PCR product was confirmed using Melt Curve analysis and by running on 1.3% agarose gel.\u003c/p\u003e\u003cp\u003e\u003cb\u003eqRT-PCR Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe sequences of commonly used putative reference genes were retrieved from mungbean genome database; the legume information system (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.legumeinfo.org/\u003c/span\u003e\u003cspan address=\"https://www.legumeinfo.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These genes are regularly used in different plant species for the selection of suitable reference genes. The details of gene IDs and NCBI IDs are represented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The primers for real-time PCR analysis were designed using PrimerQuest\u0026trade; Tool (Integrated DNA Technologies, Inc., United States of America) under the default parameters; except for minimum primer length was set to 20. The specificity of primer pairs was confirmed by Primer-BLAST with all the nucleotide sequences available for mungbean at National Centre for Biotechnology Information (NCBI). The RT-qPCR was conducted with Quant-Studio 6 Flex Real-Time PCR Systems (Thermo Fisher Scientific Inc., United States of America) using the HOT FIREPol\u0026reg; EvaGreen\u0026reg; qPCR Mix Plus (ROX), 5x. The following reaction set up was done for the preparation of cDNA from all types of tissues under non-stress and stress conditions. The reaction was set up in ten \u0026micro;l reaction containing 2 \u0026micro;l HOT FIREPol\u0026reg; EvaGreen\u0026reg; qPCR Mix Plus, 0.3 \u0026micro;L of each primer (10 \u0026micro;M), 1 \u0026micro;l of cDNA (100 ng/\u0026micro;l) and 6.4 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003eO. The PCR reaction conditions were as follows \u0026minus;\u0026thinsp;95\u0026deg;C for 10 min; 95\u0026deg;C for 15 s; 60\u0026deg;C for 1 min; 40 cycles. The PCR amplification specificity was determined from melting curve analysis and semi-quantitative PCR. The melting curve was analysed by heating the amplicon from 60\u0026deg;C to 95\u0026deg;C to confirm primer specificity. Each reaction was performed with three technical and three biological replicates. Relative fold changes in gene expression were calculated using the comparative 2\u0026thinsp;\u0026minus;\u0026thinsp;\u003csup\u003eΔΔ\u003c/sup\u003e CT method (Bubner and Baldwin \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). First the delta Ct value was calculated by subtracting the Ct value of reference gene from the Ct value of the target gene. Then the 2\u0026thinsp;\u0026minus;\u0026thinsp;\u003csup\u003eΔΔ\u003c/sup\u003e CT formula is used to obtain the relative fold change.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of all the primers used in this study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName of primer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eForward Primer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReverse Primer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExpected band size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnnealing temp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEfficiency (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAmplification factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014638363.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGGCTGTTCTGTCCTTGTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGAAGAGCGTAGCCCTCAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e104.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlpha tubulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014646147.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTGGTATGTGGGTGAAGGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGCCAACCTCCTCGTAATCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e190.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta tubulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014634642.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTGGTGAGGGAATGGATG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTCATCAGCAGTAGCATCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58.6\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e168.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElongation factor 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014656474.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTAACTTCACCTCCCAGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCAGCGAACTTGACAGCAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e106.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolyubiquitin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_022783996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCGTGAAAGCTAAGATCCAGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCCTCGGAGACGGAGAACTAAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e131.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRibosomal protein L7-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014634336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGATCCAGAGGCTAAGCTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCAGCATGTTCATGGTGGCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e81.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAB020612.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGGCTCGATCTTCCACCGTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGTTCCAGGACCGGCGTTCGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e101.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistone H2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014641486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGCTGGAAATGCTGCTCGTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCCTTGGAAGCCTTCTCAGTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e110.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLectin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014639113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCCCGGACACACGAAAATTCTTGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCTACTCCTTGTTTCATGGGAGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e100.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIP41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014655703.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCCTTTGGGAAGACTGCAAGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCCAAGAGCTTGGCATTACTCTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e110.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGAPDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014634520.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGAGGGTTTGATGACCACAGTTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCCATTCCAGTCAACTTTCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e98.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitiation factor 4a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014646146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCTGCGCAGACAGTCACTTAGAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCAAGTGCCTCAGGAGGCATCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e134.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSP90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014665349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGTGTTGGATTCTACTCTGCTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eACCCTGAATCCTGGTTGGCTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e102.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014637189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGGGAACCCCGTTAACGTTGGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCCGAGACAAGGCACAAAGTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e124.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18 s RMT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXM_014653246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGTGACATGGGCCAGGGTTTAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCTTGAAACACTGCTCTGGCTCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e112.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOC106778742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCCACTTAGGGGAACAATC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAGGTTGCAACTCAAAGAAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e110.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOC106775051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGTGCGCCACAAGATGAATC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGTCTGCACCAAGCAAGGTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e111.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRuBisCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKU519326.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGAGGAACAGCTGGGTAAGGAAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCACATGGTCCAGTACCTTCCAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e121.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRX 14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVradi11g04510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCCAAGTCTGGCATCAGAACAATC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCATCAGCAAACACCACAACTCCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e120.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrimer efficiency calculations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo estimate the efficiency of the qPCR primers, a cDNA pool was created using equal volumes of leaf and stem cDNA. This pool was then diluted 10 times. From this diluted sample, dilution series of 4X, 16X and 64X were prepared. A RT-qPCR reaction was set up using these dilutions, and a graph of Ct value versus log of cDNA concentration was used to calculate the primer efficiency (in percentage) was then calculated using the following formula-\u003c/p\u003e\u003cp\u003eEfficiency (%) = [10 ^ (-1/The Slope Value) -1] * 100\u003c/p\u003e\u003cp\u003eThe primer efficiency for all the candidate reference genes has been determined (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene expression stability analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAccording to the principle of RT-qPCR analysis, the detection of the amount of fluorescently labelled amplicon is directly proportional to the amount of amplified DNA (Bubner and Baldwin \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The threshold cycle (Ct) is adjusted to the cycle number at which the fluorescence generated within a reaction exceeds the fluorescence threshold. The fluorescence threshold is the signal which is above the background fluorescence. The Ct values of each sample were determined using QuantStudio\u0026trade; Real-Time PCR Software. Several algorithms are recommended for the analysis of stability according to gene expression. In our study, different tools based on different algorithms, including RefFinder (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blooge.cn/RefFinder/\u003c/span\u003e\u003cspan address=\"https://blooge.cn/RefFinder/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), BestKeeper, NormFinder andgeNorm were used to identify the most stable gene among these selected reference genes. The RefFinder tools provides a comprehensive ranking of all the reference genes according to their stability. However, different algorithm follows different type of calculations (comprehensive gene stability and delta CT method). The geNorm determines the expression stability (M-value) for each candidate genes. The M-value was calculated, and the gene with the lowest M value was recommended as the defined reference gene (Vandesompele et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In our study, the M value cut-off of less than 1.5 was set up. The GeNorm algorithm also employed a method to determine the optimal number of reference genes in a given set of samples. The BestKeeper algorithm uses Ct values to estimate the coefficient of variation (CV) and standard deviation (SD) for each reference gene (Pfaffl et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The gene with the lowest SD value was considered as the most stable reference gene. The NormFinder algorithm was used to determine the stability value (SV) by measuring the variance in gene expression within and between groups. The genes with the lowest SV were considered the most stable genes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eValidation of reference genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePhenylalanine ammonia lyase (\u003cem\u003ePAL\u003c/em\u003e) (LOC106778742), cinnamyl alcohol dehydrogenase (\u003cem\u003eCAD\u003c/em\u003e) (LOC106775051), \u003cem\u003eIRREGULAR XYLEM8 (IRX8) (Vradi11g04510), RuBisCO\u003c/em\u003e (gene bank ID KU519326.1) were selected for normalization of their expression using \u003cem\u003eEF-1 ALPHA\u003c/em\u003e as reference gene. To evaluate the validity of the selection of reference genes, the expression levels of \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eCAD\u003c/em\u003e and \u003cem\u003eRuBisCO\u003c/em\u003e were analysed in stem and leaf tissues of mungbean. The expression of these genes was normalized using \u003cem\u003eEF-1 ALPHA\u003c/em\u003e, \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eHSP90\u003c/em\u003e. The RT-qPCR was performed, and the average Ct value was calculated from three biological and technical replicates. The relative fold change of these genes was subsequently calculated and normalized according to Huis et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSelection and amplification of putative reference genes using RT-qPCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe PCR was performed on mungbean cDNA for 15 candidate genes to assess the specificity of the primers by semi-quantitative PCR (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The PCR products amplified from each primer pair was assessed using agarose gel electrophoresis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cb\u003eFig. S3\u003c/b\u003e). The desired size band was visible for the following ten genes: \u003cem\u003eEF-1 ALPHA, GAPDH, CYP1, HISTONE H2A, IF-4A, HSP90, LECTIN, TIP41, ACTIN\u003c/em\u003e and \u003cem\u003ePOLYUBQ.\u003c/em\u003e To further validate, we analysed these primer pairs using RT-qPCR. The melting-curve analysis also revealed a single peak corresponding to the expected size of the amplicon (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). These results indicated that primer pairs of these ten genes produce specific amplicons of the target genes. The details of these primers regarding primer sequence, amplicon length, melting temperature, efficiency and correlation coefficient have been provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The RT-qPCR amplification efficiency varied from 98% for \u003cem\u003eGAPDH\u003c/em\u003e to 134% for \u003cem\u003eIF-4a\u003c/em\u003e, whereas the correlation coefficients varied from 0.9821 for \u003cem\u003eIF-4A\u003c/em\u003e to 0.997 for \u003cem\u003eHSP90\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEF-1 ALPHA\u003c/b\u003e \u003cb\u003eshowed the lowest Ct value\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFurther, we wanted to identify and validate the suitable reference gene to normalise gene expression of cell wall and biomass-related genes in mungbean. Therefore, the Ct value of all the candidate reference genes was analysed independently across stem, leaf and both the tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Ct value ranged from 19 to 35 across stem and leaf tissues of mungbean (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The \u003cem\u003eEF-1 ALPHA\u003c/em\u003e exhibited the lowest mean Ct value (20) and \u003cem\u003eLECTIN\u003c/em\u003e had the highest mean Ct value (31), suggesting that \u003cem\u003eEF-1 ALPHA\u003c/em\u003e has the highest expression and \u003cem\u003eLECTIN\u003c/em\u003e has the lowest expression level. The \u003cem\u003eIF4a\u003c/em\u003e (24), \u003cem\u003eGAPDH\u003c/em\u003e (23), \u003cem\u003ePOLYUBIQUITIN\u003c/em\u003e (22.33), \u003cem\u003eHSP90\u003c/em\u003e (24.) and \u003cem\u003eCYP1\u003c/em\u003e (21) showed slightly higher Ct value as compared to \u003cem\u003eEF-1 ALPHA\u003c/em\u003e suggesting their transcript abundance with moderate expression. The \u003cem\u003eACTIN\u003c/em\u003e (27.00), \u003cem\u003eTIP41\u003c/em\u003e (26) and \u003cem\u003eHISTONE H2A\u003c/em\u003e (25) exhibited comparatively less transcript abundance as compared to the above genes suggesting moderately lower expression. Interestingly, all the genes followed same Ct value pattern for stem \u0026amp; leaf combined and separately as well. This suggested that most of them with low and moderately high Ct values can be used as reference genes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene expression stability analysis of candidate reference genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further understand the gene expression stability of all the candidate genes, and ΔCt was used in GeNorm, NormFinder, BestKeeper and RefFinder statistical tools. GeNorm analysis was performed to define the most suitable reference genes (Vandesompele et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The analysis suggested that \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eEF-1 ALPHA\u003c/em\u003e are the most stable genes followed \u003cem\u003eCYP1\u003c/em\u003e. The \u003cem\u003eHSP90\u003c/em\u003e was the least stable gene \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). In stem and leaf both tissues, \u003cem\u003eHSP90\u003c/em\u003e showed the lowest stability (M-value 0.210) and \u003cem\u003eGAPDH\u003c/em\u003e had the highest stability (M value 0.102) followed by \u003cem\u003eEF-1 ALPHA\u003c/em\u003e (M value 0.105) and \u003cem\u003eCYP1\u003c/em\u003e (0.106) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). According to the GeNorm principle, lower gene expression corresponds to a high M value, and the lowM value corresponds to stable gene. In our study, a total of ten genes were analysed in stem and leaf tissues using GeNorm. In stem, \u003cem\u003eHSP90\u003c/em\u003e exhibited the lowest stability with the highest M value (0.185), whereas \u003cem\u003eEF-1 ALPHA\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e both exhibited the most stability with the lowest M value (0.106) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). In the case of leaf tissues, \u003cem\u003eHSP90\u003c/em\u003e exhibited the highest M value (0.267), with the lowest stability, whereas \u003cem\u003eEF-1 ALPHA\u003c/em\u003e exhibited the highest stability with the lowest M value (0.080) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe NormFinder analysis depends on Analysis of Variance (ANOVA), which determines the intra- and inter-group variation of gene expression stability for each reference gene (Anderson et al. 2004). The NormFinder algorithm determines the stability value, which is the lowest for the highest stable gene. \u003cem\u003eGAPDH\u003c/em\u003e showed the highest stability (SV 0.005) followed by \u003cem\u003eEF-1 ALPHA\u003c/em\u003e (SV 0.015) and \u003cem\u003eHSP90\u003c/em\u003e has the lowest stability (SV 0.112) in stem tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Whereas, \u003cem\u003eLECTIN\u003c/em\u003e was most stable (SV 0.006) followed by IF4a (SV 0.008), and \u003cem\u003eEF-1 ALPHA\u003c/em\u003e (SV 0.013) and \u003cem\u003eHSP90\u003c/em\u003e (SV 0.183) in leaf tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Whereas BestKeeper algorithm determines the standard deviation (SD) across samples to check the expression stability. A gene with SD more than one is not preferred as a reference gene. The gene with the lowest SD value is considered as the most favourable reference gene. In stem, \u003cem\u003eIF4a\u003c/em\u003e was the most stable (0.57) followed by \u003cem\u003eH2A\u003c/em\u003e (0.76) and \u003cem\u003eGAPDH\u003c/em\u003e (1.12) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In case of leaf, \u003cem\u003eIF4a\u003c/em\u003e was the most stable (SD 0.27) followed by \u003cem\u003eLECTIN\u003c/em\u003e (0.41) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRefFinder that is an integrated tool of all the algorithms, was also used to define the suitable reference gene. The analysis suggests that \u003cem\u003eGAPDH\u003c/em\u003e followed by \u003cem\u003eCYP1\u003c/em\u003e and \u003cem\u003eEF-1 ALPHA\u003c/em\u003e are comprehensively the most favourable reference gene \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Delta Ct method also suggests that \u003cem\u003eGAPDH\u003c/em\u003e followed by \u003cem\u003eCYP1\u003c/em\u003e and \u003cem\u003eEF-1 ALPHA\u003c/em\u003e are the most suitable reference gene \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. RefFinder also employed BestKeeper, NormFinder and GeNorm algorithm. According to the analysis BestKeeper algorithm suggested \u003cem\u003eIF4a\u003c/em\u003e, \u003cem\u003eEF-1 ALPHA\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e were the most stable gene whereas, Polyubiquitin is the least stable gene, which is in line with independent BestKeeper analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. The NormFinder algorithm indicated \u003cem\u003eGAPDH\u003c/em\u003e as the most stable followed by \u003cem\u003eEF-1 ALPHA\u003c/em\u003e and \u003cem\u003eCYP1\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. The GeNorm algorithm referred \u003cem\u003eCYP1\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e as the most stable followed by \u003cem\u003eEF-1 ALPHA\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. In summary, \u003cem\u003eEF-1 ALPHA and GAPDH\u003c/em\u003e were identified common reference genes by all the algorithm analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAll the reference genes showed optimal pairwise variation value (V)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenerally, a single gene with high expression stability is used for normalization of gene expression data. However, it is suggested to use two or more genes as reference for accurate and reliable results (Die et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The GeNorm algorithm determines the optimal number of reference genes in a given set of samples (Vandesompele et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The algorithm estimates normalization factor (NF) for the two genes with highest expression stability (NFn and NFn\u0026thinsp;+\u0026thinsp;1) and then for other genes by stepwise addition of one after the other (Vn/Vn\u0026thinsp;+\u0026thinsp;1), towards lower expression stability. According to the principle, pairwise variation (V) values for the reference genes in which the value is lower than 0.15 as to be optimized. In our study, no combination exhibited V value of more than 0.15, rather it was very less than 0.15. Therefore, any of two genes can be used for normalization of gene expression. However, based the GeNorm analysis, either \u003cem\u003eGAPDH\u003c/em\u003e with \u003cem\u003eEF-1 ALPHA\u003c/em\u003e or \u003cem\u003eGAPDH\u003c/em\u003e with \u003cem\u003eCYP1\u003c/em\u003e can be used in combination for normalization of gene expression analysis in mungbean (Fig S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEF-1 ALPHA\u003c/b\u003e \u003cb\u003eis the best reference gene for expression analysis of\u003c/b\u003e \u003cb\u003eRuBisCO, PAL, CAD\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eIXR14\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo validate the reference gene efficacy, we performed gene expression studies in young leaf (2-week-old), old leaf (4-week-old) and stem (4-week-old) in mungbean genotype (Pusa Vishal, Pusa 1641 and Pusa 1451) (Figure S5a). The RNA integrity was tested on agarose gel and 28S or 18S RNA band was intact and this RNA was used for further analysis (Figure S5b).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe first tested expression of \u003cem\u003eRuBisCO\u003c/em\u003e which is abundant in leaf tissue and its expression will vary depending on development stage and tissue types (SUZUKI et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). We normalised the expression of \u003cem\u003eRuBisCO to EF-1 ALPHA\u003c/em\u003e (most stable), \u003cem\u003eGAPDH\u003c/em\u003e (medium stable) and \u003cem\u003eHSP90\u003c/em\u003e (less stable) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Each panel figure relative fold change expression was normalised with one of the genotypes\u0026rsquo; tissues that is represented by N. The relative fold change expression for N is approximately 1 for all panel figures. The values are calculated based on this formula - ddCt = ((dCtGene of Interest - dCt Reference gene) \u0026ndash; (dCt of normalized gene)). Relative fold change\u0026thinsp;=\u0026thinsp;2^(-ddCt). Based on this formula, we found that relative fold change expression of \u003cem\u003eRuBisCO\u003c/em\u003e was higher in 2-week -old leaf and 4-week-old leaf when normalized to \u003cem\u003eEF-1 ALPHA\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e as compared to 4-week-old stem (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, the gene expression was similar in 4-week-old leaf and stem when normalized to \u003cem\u003eHSP90\u003c/em\u003e. This further suggested that \u003cem\u003eHSP90\u003c/em\u003e may not be an appropriate reference gene for normalization.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe expression of lignin and xylan biosynthetic genes can vary in different tissues. Phenylalanine ammonia lyase (PAL) converts phenylalanine to cinnamic acid which is the first step in lignin biosynthesis (Huang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The expression of \u003cem\u003ePAL\u003c/em\u003e was higher in the stems of all genotypes as compared to 2-week-old leaf and 4-week-old leaf (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003ed-\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). However, average expression of \u003cem\u003ePAL\u003c/em\u003e when normalized with \u003cem\u003eEF-1 ALPHA\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e was similar in all the tissues and genotypes. However, relative fold change expression was comparatively higher in Pusa (P) Vishal and variation was also observed after normalizing with \u003cem\u003eHSP90\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Cinnamyl alcohol dehydrogenase converts phenylalanine derived aldehyde to monolignol which is last step in lignin biosynthesis (Wang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Like \u003cem\u003ePAL\u003c/em\u003e expression pattern, similar pattern of expression was observed for \u003cem\u003eCAD\u003c/em\u003e gene expression. Expression was higher in stem tissue as compared to leaf tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003eg-i). In P.Vishal stem tissue, relative fold expression of CAD was more than 400 after normalizing with \u003cem\u003eHSP90\u003c/em\u003e which was very different from other genotypes and as compared to data normalized with \u003cem\u003eEF-1 ALPHA\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e. Xylan is one of main component of secondary cell wall which abundant generally in stem tissue of angiosperm plants ((Wierzbicki et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Irregular Xylem 14 (\u003cem\u003eIRX14\u003c/em\u003e) belongs glycosyl transferase 43 (GT43) family and is involved in xylan chain elongation. Therefore, we checked the expression of mungbean \u003cem\u003eIRX14\u003c/em\u003e. We found that expression in stem tissue was approximately 40\u0026ndash;50 times higher than leaf tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003ej-l) after normalizing with all the reference genes (\u003cem\u003eEF-1 ALPHA, GAPDH\u003c/em\u003e and \u003cem\u003eHSP90\u003c/em\u003e). However, P.Vishal showed approximately 2000 relative fold change expression after normalization with \u003cem\u003eHSPP90\u003c/em\u003e which was different from other genotypes and compared to after normalization with \u003cem\u003eEF-1 ALPHA\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e. All these data further confirmed that \u003cem\u003eEF-1 ALPHA\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e are better reference genes than \u003cem\u003eHSP90\u003c/em\u003e which again correlated with our previous data.\u003c/p\u003e\u003cp\u003eFurther, we wanted to check the expression of \u003cem\u003eRuBisCO\u003c/em\u003e under salt and osmotic stress after normalisation with different reference genes. RuBisCO activity is generally reduced under different stress. Therefore, we grew mungbean seedling under salt (100 mM, NaCl) and osmotic (20% PEG4000) for 3 days after growing 2 days on wet filter paper and we found reduction in root length after salt and osmotic stress as compared to normal condition grown seedlings (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, Figure S5a). Also, the cotyledonary leaf turned green to greenish white, suggesting a stress phenotype. Further, RNA samples were intact in all seedlings grown under normal and stress conditions (Figure S6b). The Ct values for all references genes were higher as compared to leaf and stem tissue for all reference genes. Single melt curve and PCR product were observed for \u003cem\u003eEF-1 ALPHA\u003c/em\u003e (Figure S6c). However, multiple size products were observed after amplification with \u003cem\u003eGAPDH\u003c/em\u003e (Figure S6d). In fact, we did not see expression of \u003cem\u003eHSP90\u003c/em\u003e in normal as well as stress condition. Therefore, we normalized the expression of \u003cem\u003eRuBisCO\u003c/em\u003e with \u003cem\u003eEF-1 ALPHA\u003c/em\u003e. The expression of \u003cem\u003eRuBisCO\u003c/em\u003e was significantly lower in Pusa Vishal, Pusa 1641 and Pus1431 for salt-treated as compared to control seedlings (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Also, the expression of \u003cem\u003eRuBisCo\u003c/em\u003e was lower in osmotic-treated as compared to control seedlings of Pusa 1641 (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). This further confirmed that \u003cem\u003eEF-1 ALPHA\u003c/em\u003e can be used for normalizing expression of \u003cem\u003eRuBisCO\u003c/em\u003e under normal and salt or osmotic stress conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe reliability and accuracy of RT-qPCR results are primarily dependent on the normalization of the target gene using suitable reference gene. Therefore, the selection of the suitable reference gene is the prime factor for analysis of target gene expression (Die et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) ; Ling et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e). An ideal reference gene should have a constant level of expression pattern among cells of different tissues and experimental conditions such as biotic or abiotic stress and irrespective of experimental parameters.\u003c/p\u003e\u003cp\u003eHowever, very limited number of reference genes have been identified in mungbean which are either in \u003cem\u003eVigna mungo\u003c/em\u003e and Chinese mungbean species (Kundu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ke et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Zhou et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e proposed Tubulin (TUA) as the most stable reference gene under biotic stress and hormone treatment in \u003cem\u003eVigna radiata\u003c/em\u003e by RT-qPCR only in one genotype. Ke et al. 2021 identified that ubiquitin-conjugating enzyme was suitable as reference under drought and pathogen infection stress; \u003cem\u003eelongation factor 1\u003c/em\u003e was the most stable gene under waterlogging; and actin performed the best under saline stress. And this study was performed in Chinese LvFeng 5 (LF5) cultivar. Also, \u003cem\u003eACT\u003c/em\u003e and \u003cem\u003eEF-1 ALPHA\u003c/em\u003e was identified as the most suitable reference during MYMIV stress, while \u003cem\u003eH2A\u003c/em\u003e, \u003cem\u003eEF-1 ALPHA\u003c/em\u003e and \u003cem\u003eACT\u003c/em\u003e were found to be most suitable in salinity stress experiments and \u003cem\u003eTUB\u003c/em\u003e and \u003cem\u003e18S\u003c/em\u003e during drought treatments (Kundu et al. 213). The above study was in \u003cem\u003eVigna mungo\u003c/em\u003e which has different phenotypic characteristics than \u003cem\u003eVigna radiata.\u003c/em\u003e Also, in all these studies, cell wall-related or biomass-related genes were not tested. Therefore, in this study, we systematically tested several reference genes in different tissue types and under stress or non-stress conditions in different Indian genotypes of mungbean (\u003cem\u003eVigna radiata\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eInitially, we screened a total of 15 potential reference genes that had been reported in other crops for gene expression analysis in leaf and stem tissues of mungbean. Several algorithm including, GeNorm, NormFinder, BestKeeper and RefFinder has been used to define the most suitable reference gene. The computed result using geNorm and Normfiner algorithm suggested that \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eEF-1 ALPHA\u003c/em\u003e are the most stable genes. These observations were further corroborated with following studies. The GAPDH have been reported as the most suitable reference gene in peanut and chickpea (Garg et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Reddy et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The \u003cem\u003eEF-1 ALPHA\u003c/em\u003e was also reported as the suitable reference gene in many legumes (Garg et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gutierrez et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). BestKeeper analysis revealed that \u003cem\u003eIF4a\u003c/em\u003e and \u003cem\u003eEF-1 ALPHA\u003c/em\u003e are the most suitable candidate reference gene. RefFinder analysis defined \u003cem\u003eGAPDH, CYP1\u003c/em\u003e and \u003cem\u003eEF-1 ALPHA\u003c/em\u003e as the most stable reference genes. The Cytochromes P450 (P450s or \u003cem\u003eCYP\u003c/em\u003es) are heme-cofactor containing enzyme that, function as monooxygenases. There is no report of using \u003cem\u003eCYP\u003c/em\u003e as reference gene in legume, but in cereal \u003cem\u003eCYP\u003c/em\u003e have been used as reference gene (Jaiswal et al. 2019). Therefore, in this study, \u003cem\u003eCYP\u003c/em\u003e is newly identified as a suitable reference gene in mungbean and the homolog of \u003cem\u003eCYP\u003c/em\u003e can be tested for suitability in other legume species.\u003c/p\u003e\u003cp\u003eBased on the computed algorithms and analyses, \u003cem\u003eEF-1 ALPHA\u003c/em\u003e exhibited the lowest Ct value across the tissues, hence the highest expression. Therefore, we chose \u003cem\u003eEF-1 ALPHA\u003c/em\u003e along with \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eHSP90\u003c/em\u003e for comparison to normalize and validate the gene expression of cell wall and biomass-related genes in mungbean using different tissues and developmental stages under stress and non-stress conditions. \u003cem\u003ePAL\u003c/em\u003e;, \u003cem\u003eCAD\u003c/em\u003e, \u003cem\u003eIRX14\u003c/em\u003e and \u003cem\u003eRuBisCO\u003c/em\u003e were selected for validation. These genes are involved in cell wall biosynthesis and biomass improvement. The gene expression studies were performed using young leaf (2-week-old), old leaf (4-week-old) and stem (4-week-old) in mungbean genotypes (Pusa Vishal, Pusa 1641 and Pusa 1451). The PAL is involved in the first step of the phenylpropanoid pathway. Thus, it regulates the biosynthesis of polyphenol compounds such as flavonoids, phenylpropanoids, and lignin in plants (Wakabayashi et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The expression of \u003cem\u003ePAL\u003c/em\u003e was much higher in the stems of all genotypes as compared to the 2-week-old leaves and 4-week-old leaves. The relative fold change expression was comparatively higher in the stem of Pusa Vishal, suggesting more lignin deposition in the stem secondary cell wall of Pusa Vishal as compared to other genotypes. The \u003cem\u003eCAD\u003c/em\u003e is the key enzyme involved in the final step of the phenylpropanoid and lignin pathway. Mungbean leaves are abundant with phenolic compounds as compared to other legumes (Bai et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our previous study, we found that the lignin content was higher in mungbean leaf as compared to \u003cem\u003ePopulus\u003c/em\u003e stem, Arabidopsis stem and rice leaf (Das 2024). However, in this study, we observed that the expression pattern of \u003cem\u003eCAD\u003c/em\u003e gene was similar to the \u003cem\u003ePAL\u003c/em\u003e gene expression. The expression of \u003cem\u003eCAD\u003c/em\u003e gene was higher in stem tissue as compared to leaf tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003eg-i). The \u003cem\u003eCAD\u003c/em\u003e gene expression was also higher in P. Vishal stem as compared to other genotypes; this may indicate higher lignin deposition in P. Vishal stem as compared to other genotypes. The \u003cem\u003ePAL\u003c/em\u003e and \u003cem\u003eCAD\u003c/em\u003e expression was upregulated in stem as compared to leaf. \u003cem\u003eIRX14\u003c/em\u003e is involved in xylan chain elongation (Qaseem et al., 2024). In this study, \u003cem\u003eIRX14\u003c/em\u003e expression was much higher in stem tissue as compared to leaf tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003ej-l). Also its expression was higher in stem of Pusa Vishal as compared to other genotypes, suggesting more developed stem of Pusa Vishal as compared to other genotypes at this stage. \u003cem\u003eRuBisCO\u003c/em\u003e is the carboxylase of the C3 cycle, where it fixes CO\u003csub\u003e2\u003c/sub\u003e onto a ribulose bisphosphate (RuBP) sugar. This is the central carbon cycle reaction which converts around 100 gigatons of carbon from CO\u003csub\u003e2\u003c/sub\u003e into biomass annually (Prywes et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is a major enzyme, abundant in leaf, contributing towards higher plant biomass production. The carbon fixed through RuBisCO is used for cell wall polysaccharide synthesis (Verbančič et al. 2018). The \u003cem\u003eRuBisCO\u003c/em\u003e is the prime enzyme that plays the most crucial role in C3 cycle of photosynthesis. The level of expression of this enzyme is higher in leaf as compared to stem. We observed much higher expression of \u003cem\u003eRuBisCO\u003c/em\u003e in leaf as compared to stem of mungbean (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). The expression of \u003cem\u003eRuBisCO\u003c/em\u003e was also checked under salt and osmotic stress after normalizing with \u003cem\u003eEF1-APLHA\u003c/em\u003e. The expression of \u003cem\u003eRuBisCO\u003c/em\u003e was significantly lower in Pusa Vishal, Pusa 1641 and Pusa 1431 in salt-treated seedlings as compared to control seedlings Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). The expression of \u003cem\u003eRuBisCo\u003c/em\u003e was also lower in osmotic-treated seedlings as compared to control seedlings of Pusa 1641 (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Thus, RuBisCO activity may be reduced under different stress.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFor the first time, the \u003cem\u003eCYP1\u003c/em\u003e is reported as unique reference gene for analysis of gene expression in mungbean and its homolog may be tested in other mungbean species. Extensive analysis of selected reference genes in different genotypes of mungbean under stress and non-stress conditions revealed \u003cem\u003eEF-1 ALPHA\u003c/em\u003e as the best reference gene, followed by \u003cem\u003eGAPDH\u003c/em\u003e. Also, \u003cem\u003eHSP90\u003c/em\u003e should be avoided in normalizing expression of genes tested under normal or stress conditions. as well as. Based on this, we propose \u003cem\u003eEF-1 ALPHA\u003c/em\u003e as the best-suited and robust reference candidate gene to normalize differential gene expression in different tissue types under stress and non-stress conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RT-qPCR was performed using QuantStudio 6 Pro Real-Time PCR systems at Central Instrumentation Facility (CIF), Regional Centre for Biotechnology, Faridabad.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by DBT-MKB fellowship (102/IFD/SAN/2570/2021-22) and RCB core funding mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eLaboratory of Plant Cell Wall Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDivision of Genetics, Indian Agricultural Research Institute, New Delhi-110012, India.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDivision of Seed Science and Technology, Indian Agricultural Research Institute, New Delhi-110012, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSD and PM-AP designed and conceptualized the research. AMS performed experiments in revised version. AG performed most of the experiments. VB assisted in some experiments. AG, SD, and PM-AP performed data analysis. SD and PM-AP wrote the manuscript. VB, HKD, and GPM provided mungbean accession and suggestions during manuscript preparation. All authors have read and agreed to publish the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShouvik Das and Prashant Anupama-Mohan Pawar\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo specific permit was required for the samples analyzed in this study. The authors comply with relevant institutional, national, and international guidelines and legislation for plant studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no conflict of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArtico S, Nardeli SM, Brilhante O et al (2010) Identification and evaluation of new reference genes in Gossypium hirsutumfor accurate normalization of real-time quantitative RT-PCR data. 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Genes (Basel) 14:1739. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/genes14091739\u003c/span\u003e\u003cspan address=\"10.3390/genes14091739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gene expression studies, Real time PCR, Reference genes, EF-1 ALPHA, CYP1, GAPDH, Mungbean","lastPublishedDoi":"10.21203/rs.3.rs-7154578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7154578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpatio-temporal changes in gene expression are associated with certain biological activities. Therefore, identifying reference genes is paramount to elucidate the gene expression using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Therefore, to identify suitable reference genes, we initially screened 15 putative reference genes by studying their gene expression in different tissues of mungbean. Further, ten candidate reference genes were ranked based on stability using algorithms such as GeNorm, NormFinder, Bestkeeper, and RefFinder. The \u003cem\u003eEF-1 ALPHA\u003c/em\u003e, \u003cem\u003eCYP1\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e showed the best stable expression across different tissues as compared to other reference genes. The \u003cem\u003eEF-1ALPHA\u003c/em\u003e exhibited the lowest Ct value, the highest expression and most stable (using different algorithms) by the comparative analysis among 10 reference genes after analysing its expression in leaf and stem. Further, the suitability of \u003cem\u003eEF-1 ALPHA\u003c/em\u003e as reference genes was validated by checking the expression of \u003cem\u003ePHENYLALANINE AMMONIA LYASE (PAL)\u003c/em\u003e, \u003cem\u003eCINNAMOYL ALCOHOL DEHYDROGENASE (CAD), IRREGULAR XYLEM 14 (IRX14)\u003c/em\u003e and \u003cem\u003eRIBULOSE-1,5-BISPHOSPHATE CARBOXYLASE/OXYGENASE (RuBisCO\u003c/em\u003e) in three different mungbean genotypes and tissue types. We further tested the expression of \u003cem\u003eRuBisCO\u003c/em\u003e in seedlings under salt and osmotic stress and found its expression was lower as compared to control plants, which correlated with the phenotype of seedlings. Therefore, we propose \u003cem\u003eEF1-ALPHA\u003c/em\u003e is the best reference gene to analyse the expression of genes in different tissues and development stages with and without stress conditions.\u003c/p\u003e","manuscriptTitle":"Identification of Suitable Reference Genes for Studying Expression of Cell Wall-Related and Developmental Associated Genes in Mungbean (Vigna radiata (L.) Wilczek)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 05:38:16","doi":"10.21203/rs.3.rs-7154578/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"437261f4-24e7-4b7c-9001-a6ebac843f62","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-31T20:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 05:38:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7154578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7154578","identity":"rs-7154578","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}