Selection of reference genes for normalization of microRNA expression in sugarcane stalks during its interaction with Colletotrichum falcatum

Abstract

The microRNAs role in various cellular and metabolic functions is gaining more limelight in line with second-generation NGS technology. For the validation of candidate miRNA genes, the quantitative real-time PCR is the widely trusted and efficient method to follow. Sugarcane miRNAs are less explored in sugarcane defense response during their interaction with Colletotrichum falcatum inciting red rot. Further, for RT-qPCR experiments involving sugarcane miRNA expression studies, a stable internal reference gene is required. Hence, we have taken a study involving 20 candidate genes to identify stable expressing reference genes using NormFinder, geNorm, BestKeeper, and deltaCt statistical algorithms. The candidate reference genes included miRNAs and protein-coding genes. The results indicated that there is a variation in ranking among the algorithms. We found miR1862c as the stably expressed miRNA reference gene among the candidates and miR444b.2 along miR1862c formed the best reference gene pair combination, which can be used in the experiments aiming to explore sugarcane miRNAs in the defense mechanism against C. falcatum. The stable miRNA reference gene was further validated with other lesser stable reference gene candidates to assess the effect of stable reference genes during normalization. The present study evaluating the sugarcane miRNAs as reference genes for normalizing RT-qPCR expression data involving miRNAs during sugarcane × C. falcatum interaction is the first of its kind. Further, this systematic approach can be followed to assess the reference gene in various experimental conditions involving sugarcane miRNAs.

Introduction

MicroRNAs are endogenous small RNAs with 19–24 nt that originate from pri-miRNAs obtained from precursor stem-loop pre-miRNAs (Bartel 2004). In plants, miRNAs play a crucial role in cellular functions, development, and stress responses by regulating the target genes post-transcriptionally by repression or target cleavage (Rhoades et al. 2002; Brodersen and Voinnet 2009; Bartel 2009). The rapid advancements in second-generation high-throughput sequencing system studies exploring the miRNA role in various biotic and abiotic stresses keep on increasing to understand miRNAs and their targets in particular cellular or metabolic functions. These advanced sequencing technologies open up a more in-depth insight about an organism at the molecular level in greater detail by producing large reliable datasets. For validation of the targets obtained from NGS experiments, different expression analysis methods are available, and among them, real-time quantitative RT-PCR is the most widely used. It is because the experimental setup of RT-qPCR is cost efficient, easily accessible, and widely accepted method for gene expression studies. It gives a quick turnaround time in understanding gene expression in an organism. The quantitative gene expression analysis by RT-qPCR analysis is affected by various factors such as sample purity, nucleic acid concentration, RNA integrity, and cDNA synthesis efficiency, along with the type of samples (Anderson et al. 2004). To produce effective and reliable gene expression results from the RT-qPCR analysis, a stable reference gene as an internal control is needed to normalize the variations among the samples. These reference genes; expression should not hinder by the experimental or the organisms’ biological conditions (Vandesompele et al. 2002; Die et al. 2010). Traditionally, protein-coding genes are mostly the ‘Housekeeping genes’ involved in essential cellular processes and are widely used as reference genes (Kulcheski et al. 2010).

The sugarcane plant is a major source of sugar, ethanol and its by-products are being cultivated worldwide for more than a century. The crop cultivation expands due to its economic value, and this expansion favors attacks by various threats such as diseases caused by fungi, bacteria, viruses, and phytoplasma along with abiotic factors such as drought, cold, and salinity. Among the diseases, red rot caused by Colletotrichum falcatum severely impacts sugarcane since the pathogen attacks cane stalks, the storehouse of sugar. Hence, the affected canes become unsuitable for sugar extraction, and this leads to huge losses and causes an economic impact on the sugar industry and farmers (Viswanathan 2010, 2020). This severe disease prevails in most of the South and Southeast Asian countries (Viswanathan et al. 2018). The sugarcane varieties recommended for cultivation possess desirable agronomic traits of cane yield and sugar along with resistance to red rot. However, in due course of time, new pathotypes adapt to the varieties and make them susceptible (Viswanathan et al. 2020; Viswanathan and Selvakumar 2020). Though sugarcane breeders have succeeded in developing red rot-resistant varieties, the resistance mechanism is poorly understood due to the crop’s complex polyploidy (Viswanathan, 2020). However, good progress has been made to understand the molecular basis of interaction between sugarcane and C. falcatum using different molecular approaches (Ganesh et al. 2020; Rahul et al. 2015, 2016; Sathyabhama et al. 2015, 2016; Viswanathan et al. 2009, 2016).

Recently, we have initiated studies to assess the roles played by miRNAs in this host–pathogen interaction. Recent research involving high-throughput sequencing to study sugarcane miRNAs in host defense mechanism against C. falcatum highlighted the crucial regulatory role of miRNAs during compatible and incompatible interactions. Studies to evaluate suitable and stable reference genes for miRNA RT-qPCR experiments have advanced steadfastly in recent years. In sugarcane, 25s rRNA, GAPDH, and eEF1α were the most commonly used reference genes in the studies during its interaction with C. falcatum (Ashwin et al. 2017). However, for miRNA RT-qPCR quantification studies, U6 small nuclear RNA (U6 snRNA), 5.8S ribosomal RNA (5.8S rRNA), 5S ribosomal RNA (5S rRNA), and GAPDH were used (Yang et al. 2016; Thiebaut et al. 2012; Song et al. 2010). Studies related to selecting the reference genes for miRNA RT-qPCR are increasing in recent years. In sugarcane, miRNA studies were carried out mostly with high-throughput sequencing technologies to establish their role in biotic and abiotic stress interactions (Ferreira et al. 2012; Bottino et al. 2013; Thiebaut et al. 2012). However, miRNA reference gene evaluation for sugarcane × C. falcatum interaction has not been done earlier. Hence, we made a detailed study to identify a suitable reference gene that expresses stably in sugarcane stalk tissue during compatible and incompatible sugarcane interaction with C. falcatum. Accordingly, 20 candidate reference genes, including miRNAs and protein-coding genes, were evaluated by RT-qPCR (Busk 2014; Bustin et al. 2009). The identified efficient reference gene is validated along with lesser stable reference genes in normalizing the miRNA RT-qPCR expression analysis during the host–pathogen interaction with a set of resistant and susceptible cultivars.

Materials and methods

Plant material and pathogen inoculation

Sugarcane cultivars varying in resistance to red rot pathogen C. falcatum Co 93009 (resistant cultivar) and CoC 671 (susceptible cultivar) were used in the study. The cultivars were grown in Plant Pathology Research Farm, ICAR-Sugarcane Breeding Institute, Coimbatore, following suitable agriculture practices required for tropical sugarcane (Sundara 1998). During the grand growth stage, the canes were inoculated with C. falcatum inoculum of virulent pathotypes Cf671 grown in oatmeal agar medium (OMA) at 25 °C in a biological incubator (pHCBi) 12-h/12-h light and dark cycle. The conidial suspension (1 × 10⁶ mL⁻¹) was prepared with MilliQ^® water from 7-day-old sporulated culture. The canes from the above cultivars were inoculated with C. falcatum inoculum by following the standard plug method (Mohanraj et al. 2012). An incision of 6 mm diameter in the internode tissue was made using red rot inoculator, 50 µL of suspension was delivered onto the bore-hole. The tissue core was replaced, and the injured portion was sealed with Parafilm^®. The stalk tissue samples were collected at 0, 6, 12, 24, 48, 72, 120, and 600 h post-inoculation (HPI). The same samples were snap-frozen in liquid nitrogen and stored at − 80 °C till further processing.

RNA isolation and cDNA synthesis

Total RNA was extracted using TRI reagent (Sigma Aldrich, USA) following the in-house lab protocol, and the extracted RNA was stored at − 80 °C. The extracted RNA was treated with DNase using DNA-Free (Amnion, USA). The quality of RNA was assessed by 1.5% agarose gel electrophoresis and then analyzed spectrometrically with Nanodrop^TM Spectrophotometer (Thermo Fisher, USA) using A260/A280 and A260/A230. For miRNA cDNA synthesis, Universal RT-miRNA PCR protocol was followed, in which reverse transcription using anchored Poly(T) primers after Poly(A) tailing was carried out (Busk 2014).

Selection of candidate miRNA reference genes and primer designing

In total, 18 putative candidate reference miRNA genes and two protein-coding reference genes reported earlier viz. 25S rRNA and Sc-EF1α were evaluated in the current study. The miRNA primers were designed using the mirPrimer tool (Busk, 2014; Mentzel et al. 2014). The other primers were designed using Primer 3 software (https://primer3plus.com/cgi-bin/dev/primer3plus.cgi) (Untergasser et al. 2012) (Table 1).

Table 1.

Details of the candidate genes and their primer sequences used in the study

Gene name	Accession no	Primer sequence (5′–3′)	TA (°C)
miR1862c	MIMAT0007812	ACGAGGTTGGTTTATTTTGGG GTCCAGTTTTTTTTTTTTTTTCGTC	60
miR319a.3p	MIMAT0020915	CTGGATGACGCGGGA GGTCCAGTTTTTTTTTTTTTTTAGC	60
miR5538	MIMAT0022174	CGCAGACTGAACTCAATCAC AGTTTTTTTTTTTTTTTGCAGCAAG	60
miR6227.3p	MIMAT0026429	CAGCTCACAACACTTGCT GTCCAGTTTTTTTTTTTTTTTCCCA	60
miR167b	MIMAT0001664	GTGAAGCTGCCAGCA GGTCCAGTTTTTTTTTTTTTTTCAGA	60
miR162b	MIMAT0001032	GTCGATAAGCCTCTGCATC GGTCCAGTTTTTTTTTTTTTTTCTG	60
miIR168a	MIMAT0020294	CGCTTGGTGCAGATCG GGTCCAGTTTTTTTTTTTTTTTGTC	60
miR164c	MIMAT0015139	CAGCATGTGCCCTTCTTC CCAGTTTTTTTTTTTTTTTGATGGAG	60
miR396e.5p	MIMAT0001601	CAGTCCACAGGCTTTCTTG GTCCAGTTTTTTTTTTTTTTTCAGTTC	60
miR159a	MIMAT0001658	GCGCAGTTTGGATTGAAG GGTCCAGTTTTTTTTTTTTTTTCAG	60
miR444b.2	MIMAT0020285	TGCAGTTGTTGCCTCAAG GGTCCAGTTTTTTTTTTTTTTTAAGC	60
miR162a	MIMAT0000632	CAGTCGATAAACCTCTGCATC GGTCCAGTTTTTTTTTTTTTTTCTG	60
miR167a	MIMAT0001663	GTGAAGCTGCCAGCA GGTCCAGTTTTTTTTTTTTTTTCAGA	60
miR444a	MIMAT0020283	TGCAGTTGTTGCCTCAAG GGTCCAGTTTTTTTTTTTTTTTAAGC	60
miR319a.5p	MIMAT0020914	CAGAGCTGCCGAATCATC GTCCAGTTTTTTTTTTTTTTTGAATGG	60
ScEF1α	KU575115.1	GCTCTCCTTGCTTTCACCCT TTGTCACCCTCAAAACCAGAG	60
miR166d.5p	MIMAT0022858	AGGGAATGTTGTCTGGCT GTCCAGTTTTTTTTTTTTTTTCCTC	60
miR166g.3p	MIMAT0001072	CGGACCAGGCTTCATTC GGTCCAGTTTTTTTTTTTTTTTGAG	60
miR169b.3p	MIMAT0015141	GCAGGGCAAGTTGTTCT CCAGTTTTTTTTTTTTTTTGTAGCCA	60
Sc25SrRNA	BQ536525.1	GCAGCCAAGCGTTCATAGC CGCGGCACGGTCATCAGTAG	60

RT-qPCR conditions

The qRT-PCR expression analysis of the putative candidate reference miRNA genes was performed in the real-time PCR machine (StepOnePlus™, Applied Biosystems, CA, USA). Reactions were prepared using the Power SYBR Green PCR Master Mix (Applied Biosystems). The 20-µL reaction mix consisted of 10-µL SYBR Green PCR Master Mix, 10 µm each primer, 1.5-µL cDNA, and 8.3-µL MilliQ^© water. The RT-qPCR experiment conditions were as follows: 95 °C for 10 min followed by 40 amplification cycles 95 °C for 15 s and 60 °C for 60 s followed by melting curve analysis to detect the non-specific amplification. Three technical replicates of the samples were used in the study. The Ct values were determined to carry out further analysis.

Data analysis

To determine the expression stability of the 20 putative reference genes during sugarcane × C. falcatum interaction, we have used NormFinder (Andersen et al. 2004) (https://moma.dk/normfinder-software), an excel add-in statistical algorithm along with the web-based software tool RefFinder (Silver et al. 2006) (https://www.heartcure.com.au/reffinder/). The RefFinder tool incorporates the different statistical algorithms viz., NormFinder (Andersen et al. 2004), geNorm (Vandesompele et al. 2002), BestKeeper (Pfaffl et al. 2004), and deltaCt (Silver et al. 2006) to analyze the results. For the statistical analysis, raw Ct values were used to assess the candidate reference genes’ stability.

Validation of reference gene expression stability

To validate the efficiency of the selected stable reference genes for normalizing the RT-qPCR dataset, we have used sugarcane stalk tissue samples collected from the resistant and susceptible cultivars at different time intervals, viz., 6, 12, 24, 48, 72, 120, and 600 HPI after pathogen inoculation as mentioned above. The miRNA miR167b was selected as the target miRNA gene for the expression analyses. The miR167b gene expression was normalized using the most stable reference gene candidate, miR1862c, followed by miR319a.3p and miR5538. Along with it, moderate stable genes miR167a and miR159a and the least stable genes miR169b.3p and Sc25SrRNA, which were selected according to NormFinder and RefFinder software analyses also used. To calculate the target gene expression, 2^−ΔΔCT method was used (Livak and Schmittgen 2001).

Results and discussions

In this study, sugarcane stalk tissue samples collected at definite time intervals after pathogen inoculation were processed following the standard protocol. The RNA quality was good as determined by the Nanodrop™1000 Spectrophotometer, and the integrity of RNA samples was also good for further experiments to be carried out. Currently, real-time quantitative RT-PCR for analyzing candidate gene expression in plants becomes the most popular method. However, the RT-qPCR expression data from the experiments must be normalized to overcome the variations from biological and non-biological factors. The chances of doubt arise in gene expression results when a reference gene is not being used (Bustin et al. 2009; Derveaux et al. 2010). The normalization helps control the variations such as RNA concentration, cDNA yield, and amplification efficiency during comparison with different samples (Bustin et al. 2009). A suitable reference gene needs to be identified for each species during individual experimental conditions to normalize the results to overcome these variations.

In sugarcane, evaluation of miRNA as reference genes was carried out earlier in various experimental conditions. During Sorghum mosaic virus (SrMV) interaction, miR159 and PP2A were reported as the best single reference gene and either miR171 + miR1520 and CUL + CAC as reference gene combination (Ling et al. 2019). In cold stress study of sugarcane buds, miR171 + miR5059 and miR171 + 18SrRNA were identified as suitable reference genes along with miR171 and 18S rRNA as an individual candidate for miRNA RT-qPCR normalization (Yang et al. 2016). In addition, protein-coding reference genes were evaluated for normalizing RT-qPCR expression datasets obtained in drought, salinity, different varieties, stalk, and leaf. For which glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 25S ribosomal RNA (25S rRNA), eukaryotic elongation factor 1-alpha (eEF-1α), and ubiquitin (UBQ) were identified as stable reference genes (de Andrade et al. 2017; Silva et al. 2014; Guo et al. 2014; Iskandar et al. 2004; Ling et al. 2014). In view of this, 18 miRNA candidate genes and two protein-coding genes were tested to identify a suitable candidate reference gene in the study involving sugarcane miRNA expression during C. falcatum interaction. We found differential expression of 20 candidate reference genes during C. falcatum interaction between the cultivars (Fig. 1). Among the candidate reference genes, 25S rRNA was highly expressed with a Cq value of 10.33, while miR5538 showed minimal expression with a Cq value of 32.12 during the pathogen interaction. Among the candidate genes, miR169b.3p, miR166g.3p, miR166d.5p, and 25S rRNA showed higher variations as the whisker caps were higher as compared to other candidate reference genes (Fig. 1). These results show a selection of the right reference gene to normalize the gene expression under specific experimental conditions is of paramount importance.

Fig. 1.

Expression levels of 20 candidate reference genes used in the study. Expression data RT-qPCR quantification cycle (Cq) values for each reference gene in sugarcane cultivars Co 93009 (R) and CoC 671(S) stalk tissue during C. falcatum interaction are displayed. The box plot indicates the 25th and 75th percentiles, and whisker caps represent the maximum and minimum values

In the study, the RT-qPCR stability of the candidate reference genes was assessed by quantifying the miRNA levels. Here, we calculated each PCR cycle Cq value representing the significant increase in the product amplification. The stability of the candidate genes is generally the center point of the exponential phase (Bustin 2000). The important criteria in finding the suitable reference gene to normalize the obtained results are finding the most stable expressing reference gene with minimum biological variations. As there is no single method followed in selecting the reference gene, we applied the most frequently used statistical algorithms NormFinder and geNorm, BestKeeper, and deltaCt in the RefFinder tool. NormFinder software is a Microsoft Excel add-in statistical algorithm that performs based on the ANOVA model to estimate the expression variations among the candidates. It thus estimates the overall variations among the candidates and also the intergroup and intragroup variations among the sample sets. It ranks the candidates based on the inter- and intragroup variations where a lower stability value indicates the stable expression, and it ranked first (Andersen et al. 2004).

In our study, the NormFinder algorithm ranked the reference genes (RG) with low stability value indicating minimal inter and intragroup variations in the top list. Here, miR1862c is identified as the stable reference gene, and it was ranked first. In addition, the combination of miR1862c and miR444b.2 pair was found to be the best combination for normalization. Further, the results of NormFinder align with the RefFinder tool as it also ranked the same RG miR1862c as first in its incorporated NormFinder algorithm (Fig. 2; Table 2). These analyses showed a seeking variation among the miRNA and mRNA reference genes in sugarcane, as reported earlier (Kulcheski et al. 2011).

Fig. 2.

Gene stability analysis and comprehensive gene rankings by four different algorithms incorporated in RefFinder. The stable genes are on the right side while the least stable genes on the left side

Table 2.

Comparison of rankings of 20 candidate reference genes by NormFinder and RefFinder tools

Ranking	RefFinder		NormFinder
	Gene name	Geomean of ranking values	Gene name	Stability value
1	miR1862c	1.57	miR1862c	2.06
2	miR319a.3p	2.11	miR167a	2.24
3	miR5538	2.71	miR444b.2	2.96
4	miR6227.3p	3.46	miR396e.5p	3.00
5	miR167b	4.47	miR162b	3.10
6	miR162b	6.16	miR319a.3p	3.30
7	miIR168a	8.18	miR5538	3.67
8	miR164c	8.24	miR162a	4.38
9	miR396e.5p	8.76	miR6227.3p	4.67
10	miR159a	10.03	miR444a	4.91
11	miR444b.2	10.49	miR159a	5.08
12	miR162a	10.69	miR164c	5.32
13	miR167a	12.08	miR168a	5.73
14	miR444a	12.67	ScEF1α	5.96
15	miR319a.5p	14.21	miR319a.5p	6.43
16	ScEF1α	14.23	miR166d.5p	7.11
17	miR166d.5p	17	miR167b	7.22
18	miR166g.3p	18.73	Sc25S rRNA	7.60
19	miR169b.3p	19.22	miR169b.3p	8.85
20	Sc25S rRNA	20.21	miR166g.3p	12.12

The RefFinder tool encompasses four algorithms, viz., NormFinder, geNorm, BestKeeper, and deltaCt (Silver et al. 2006). Among them, the BestKeeper algorithm analyses variations among the candidate reference genes using standard deviations (SD) and Pearson correlation coefficient calculations (Pfaffl et al. 2004). Here, the RG with the lowest standard deviation (SD) value is the most stably expressed gene. Like miR6227.3p, which is ranked first, followed by miR5538 and miR159a with low SD values (Fig. 2). The results of BestKeeper contradict the NormFinder algorithm as it was common due to the differences in the statistical analysis used for predicting the stable reference genes.

The deltaCt method uses comparatives of relative expression levels of the genes in the given sample for the treatment and considering the SD of the samples (Silver et al. 2006; Fausto et al. 2017). The deltaCt method suggests that miR1862c is a stable reference gene among the other candidate reference genes since it had a high stability value compared to others (Fig. 2). The stability of the candidate reference genes was evaluated by statistical algorithm NormFinder (Andersen et al. 2004) and web-based software tool RefFinder (Silver et al. 2006), which integrates other most used statistical algorithms for stability evaluation, viz., geNorm (Vandesompele et al. 2002), BestKeeper (Pfaffl et al. 2004) and deltaCt (Silver et al. 2006).

Further, the geNorm algorithm in RefFinder gave the miRNA reference genes miR319 and miR1862c combination as the most stable reference genes. These results correlate with other algorithms such as NormFinder and deltaCt. The RefFinder tool gives a comprehensive ranking on comparing all the four algorithms, and miR1862c is ranked the first among the miRNA reference candidate genes and protein-coding genes. In general, there are some variations among the rankings found between different algorithms. Among 20 reference gene candidates tested during C. falcatum interaction in resistant and susceptible cultivars, the miRNA miR1862c is the best RG. In addition, the miRNAs are better than the protein-coding genes amongst the tested.

Further, to validate the identified candidate reference miRNA, the stable miRNA RG miR1862c is used to normalize the expression data of miR167b. Along with it, moderately stable RGs miR167a and miR159a and the least stable RGs miR319a.5p and 25s rRNA were compared. The results revealed that the comparison of miR1862c with other candidate RGs showed good normalization of its expression in both the cultivars CoC 671 (susceptible) and Co 93009 (resistant) during the interaction of the pathogen C. falcatum. The least stable miRNA RG miR169b.3p showed higher variations while the moderate stable miRNA RG miR167a and miR159a showed differences in normalization at all the intervals in both the resistant and susceptible cultivars (Fig. 3).

Fig. 3.

Normalization of miR167b with candidate reference genes. Here, most stable—miR1862c then miR319a.3p and miR5538. Moderate stable—miR167a and miR159a. miR169b.3p and Sc25SrRNA were least stable in the assay. Samples drawn from 0 to 600 HPI were used to assess expression of candidate reference genes and miRNA

Conclusion

In the present study, 20 candidate reference genes encompassing miRNAs and protein-coding genes studied for their expression by applying different statistical algorithms encompassing diverse approaches to identify the reference genes stability. Our main aim was to select suitable reference genes for normalizing the miRNA expression studies involving miRNAs during sugarcane × C. falcatum interaction. The stability analysis during this biotic stress involving the NormFinder algorithm and RefFinder tool encompassing geNorm, BestKeeper, and deltaCt algorithms identified miR1862c as the most stable reference gene across resistant and susceptible cultivars. The study is the first systematic approach for validation of miRNA reference gene for quantification miRNAs in sugarcane × red rot pathogen interaction. The study also suggested the use of a combination of reference genes for reliable normalization of sugarcane tissue samples in different cultivars. Further, the study also suggested a systematic approach to identify miRNA reference genes in sugarcane for qRT-PCR expression analysis, which can be adopted for other experiments in sugarcane. Therefore, this study will lead to future gene expression studies involving sugarcane miRNAs in various other biotic and abiotic stresses.

Author contributions

RV conceptualized, designed, initiated, and supervised the project. MN prepared materials and performed whole genome and transcriptome data analysis. MN performed the lab experiments and contributed to data analysis and data interpretations. RV, PM and ARS analyzed the interpreted whole information retrieved from genome and transcriptome. MN and RV wrote the manuscript. All the authors read and approved the final manuscript.

Funding

The part of the study received financial support from Sugarcane Development Fund (No. 6-7/2006-R&D/SDF/885), Ministry of Consumer Affairs, Food & Public, New Delhi.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

The present research did not involve human participants and/or animals.

Informed consent

Informed consent was obtained from all individual participants included in the study.