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Current IssueEditorial BoardOnline First ArticlesArchive

Full Research Article

Screening of Internal Reference Genes Analysis in Alfalfa under Different Abiotic Stress Conditions

W
W.N. Fan1,*
P
P.F. Shi1
M
M.Y. Zhang1
Y
Y.Q. Shi1
Y
Y.X. Yang1
1Animal Science and Technology College, Henan University of Science and Technology, Luoyang, Henan 471 003, China.

  • Submitted06-02-2025|

  • Accepted08-07-2025|

  • First Online 17-09-2025|

  • doi 10.18805/LRF-858

ABSTRACT

Background: Alfalfa is a kind of high-quality legume grass with high crude protein content, good palatability, high grass yield and good nitrogen fixation capacity. Abiotic stresses have become the key factors restricting the yield and quality in alfalfa and other crops, such as drought, extreme changes in ambient temperature, floods, land salinization and acidification, while under the different abiotic stress conditions, gene expression levels will also change accordingly. The selection and using of appropriate reference genes are crucial for the accuracy of gene expression quantification.

Methods: We chose the candidate internal reference genes of alfalfa from transcriptome sequence datasets (162 RNA-seq sequencing data) through comparative analysis. Finally, 10 candidate reference genes were selected. These candidate reference gene expressions were determined by RT-qPCR under three common abiotic stresses in production, such as alkali, drought and low temperature. The stability index of these candidate genes was calculated and evaluated correspondently using specific softwares and different algorithms, such as GeNorm, Normfinder, Bestkeeper, △Ct method and an online analysis tool RefFinder.

Result: The results showed that all 10 pair candidate reference genes could be used in gene expression quantification except for GAPDH and Ms.33066 under low-temperature stress based on screening criteria. Under alkaline stress, the optimal reference gene is UBL-2a and the optimal combination of reference genes is GAPDH and UBL-2a; Under drought stress, the optimal reference gene is Rer1 and the optimal combination of reference genes is Ms.33066 (some candidate reference genes haven’t been annotated yet, using gene ID abbreviation number of Medicago sativa L. instead) and Actin; Under low-temperature stress, the optimal reference gene is Ms.99505 and the optimal combination of reference genes is Ms.65463, UBL-2a, Ms.99505 and Actin. Among all the samples, the optimal reference gene is MS.99505 and the optimal combination of reference genes is MS.073307, Rer1, MS.99505 and UBL-2a. GAPDH and Actin aren’t the most appropriate reference genes of alfalfa under different abiotic stresses, the optimal reference gene and the optimal combination of reference genes under other abiotic stress need further validation. This paper provides scientific evidence for quantitative analysis of the genes of alfalfa.

INTRODUCTION

Alfalfa (Medicago sativa L.), is a kind of high-quality legume herbaceous with high crude protein content, good palatability, high grass yield and good nitrogen fixation capacity (Long and Zhang, 2024; Zhang and Long, 2024; Zhang et al., 2023). It is well known for its high crude protein content and named the "King of Forage” (Sun et al., 2019; Zhang et al., 2015). The abiotic stresses have become constraints on alfalfa and other crops, affecting crop yield and quality, including drought and extreme changes in environmental temperature, floods, severe land salinization and acidification and other abiotic stresses (Elgharably and Benes, 2021; Fang et al., 2022). Under abiotic stress conditions, gene expression levels also exhibit corresponding changes and appropriate or scientific reference genes are crucial for the scientificity and accuracy of gene expression quantification. Real-time fluorescence quantitative PCR can analyze the specific gene expression in alfalfa, such as changes in expression at different tissue sites, growth stages, or different environmental conditions (Gutierrez et al., 2008; Udvardi et al., 2008; Wan et al., 2010). Compared with the traditional gene expression techniques of quantitative analysis, such as Northern blots, Western blots and cDNA microarrays, RT-qPCR (reverse transcription-quantitative PCR) is most widely used because of its low cost, time-saving and accuracy in gene expression (Dekkers et al., 2012; Hao et al., 2014; Li et al., 2017; Ma et al., 2016; Maroufi et al., 2010; Tian et al., 2015; Wu et al., 2016; Zhu et al., 2013).
       
To obtain more accurate and reliable results, it is usually necessary to introduce internal reference genes as reference corrections in RT-qPCR experiments to reduce the RNA quality and reverse transcription efficiency of different samples, as well as errors in the sample loading process and standardize the measurement of the target gene. Reference genes, called housekeeping genes, described as ‘‘crucial for cellular existence” frequently, are often selected because of their high copy numbers and their stable expression level is rarely affected by the experimental conditions, or external environment (Joshi et al., 2022; Kozera and Rapacz, 2013). Under an ideal situation, the selected reference genes should be expressed consistently in different growth and development stages, tissues and organs and different environmental conditions, without being affected by experimental conditions (Die et al., 2010). The research results have indicated that there is no internal reference gene that fully meets the ideal criteria. Regardless of different developmental stages, environmental conditions, species, tissue sources, or experimental conditions, the expression of any reference gene will change and there is no universality between genes (Huggett et al., 2005; Jain et al., 2006; Winnepenninckx et al., 1996). This phenomenon makes the selection of reference genes more complex.
       
The selection of reference genes requires certain conditions to meet, including expression stability, moderate abundance, independence from the target gene and external environmental influences and consistent expression within the cell. Selecting inappropriate housekeeping genes as reference genes can lead to significant biases and errors, resulting in inaccurate data. The selection and use of reference genes are key factors in ensuring more accurate and reliable experimental results (Small et al., 1989; Wu et al., 2021; Zhou et al., 2016). A good reference gene for a qPCR experiment is one that is stably expressed in different conditions.
       
Currently, the study about the assembly of the genome of alfalfa is as follows: Chen et al. (2020) deciphered the genome of the cultivated alfalfa variety 'large leaf alfalfa, 'autotetraploid. Shen et al. (2020) deciphered the genome of the cultivated alfalfa variety 'Zhongmu No.1' autotetraploid. Long et al. (2022) deciphered the genome of the cultivated alfalfa variety 'Zhongmu No. 4' autotetraploid. Shi et al. (2024) provides a high-quality reference genome of an important diploid alfalfa germplasm. In production, the common alfalfa variety is tetraploid. We chose the large leaf alfalfa (cv.Xinjiang Daye) as a reference genome based on following justifications: Completeness and quality, large leaf alfalfa genome was the most complete and well-annotated genome available after comprehensive analysis. Utilizing a high-quality reference genome is critical for accurate mapping and subsequent analysis of RNA-seq data. Consistency across datasets: By mapping different RNA-seq data onto one single reference genome, we could get reliable gene expression atlas from multiple RNA-seq datasets. Conservation of reference genes: Reference genes which are the focus of our study are typically highly conserved across different cultivars of alfalfa.
       
In our study, the candidate reference genes of alfalfa from transcriptome sequence datasets (162 RNA-seq sequencing data through comparative analysis were chose, including different tissues of alfalfa). 10 candidate reference genes were selected. RT-qPCR was then adopted to determine the candidate reference genes under five abiotic stresses of drought, alkali and low temperature. The stability of these candidate genes was evaluated correspondently using specific software and algorithms, such as GeNorm (Vandesompele et al., 2002), Normfinder (Andersen et al., 2004), Bestkeeper (Pfaffl et al., 2004), △Ct method (Silver et al., 2006) and an online analysis tool RefFinder (Zsóri et al., 2013). This research systematically explored the appropriate reference genes of alfalfa under different abiotic stresses to provide scientific evidence for quantitative analysis of the genes of alfalfa.

MATERIALS AND METHODS

Plant material
 
Three abiotic stresses (alkali, drought and low temperature) experiment were conducted using hydroponics (Ma et al., 2021). Full and uniformly sized alfalfa seeds (Zhongmu NO. 3, from Beijing Institute of Animal Husbandry and Veterinary Medicine, Chinese Academy of Agricultural Sciences) were selected, soaked in HgCl2 solution for 8 minutes and then rinsed repeatedly with distilled water to ensure there was no residue on the surface of the seeds. After soaking in distilled water for 12 hours, the soaked seeds were evenly spread in a culture dish with two layers of high-temperature sterilized filter paper. 40 seeds were placed on each dish, setting 3 repetitions. Then the culture dishes were placed in an artificial climate incubator for cultivation. The incubator simulated natural light conditions with three light intensity stages: 4000 lx, 6000 lx and full light in a day (light: darkness= 14 h/25oC:10h/20oC). Water the seedlings to keep sufficient moisture and conduct the treatments after cultivation for 14 days.
 
Alkali stress treatment
 
Add the Mixture of NaHCO3:Na2CO3 (9:1) to the Hogland nutrient solution and set up four treatments: 0 mmol/L (CK), 30 mmol/L, 50 mmol/L and 70 mmol/L. Each dish is irrigated with 5ml of treatment solution every morning and evening, with 5 replicates per treatment for 3 days.
 
Drought stress treatment
 
PEG-6000 was used to simulate drought stress using the weighting method. Irrigated different water potentials to ensure consistent weight per petri dish and weighed once every morning and evening. Set up four groups: control 0% (water potential, 0MPa), mild drought 5% (water potential-0.10MPa), moderate drought 10% (water potential-0.20MPa) and severe drought 20% (water potential-0.40MPa), 5 replicates per treatment for 3 days.
 
Low-temperature (Low T) stress treatment
 
Put the petri dishes in the artificial incubator for low-temperature stress treatment (under normal light conditions), The control group was set at 25oC, with the low temperature set at 4oC and took samples every 4 hours (4h, 8h, 12h and 16 h), 5 replicates.
       
After the experimental treatment ended, we selected the 5 whole alfalfa normal seedlings randomly from each petri dish. Cleaned and dried with filter paper. Froze the samples with tin foil in liquid nitrogen immediately, then store them in a -80oC freezer.
 
RNA isolation and cDNA synthesis
 
Total RNA was extracted by RNAiso plus (Takara Co., LTD, Beijing, China) with RNA detection using NanoDrop 2000 Spectrophotometer and the reverse transcription reagent used the PrimeScript™ RT reagent Kit with gDNA Eraser (Takara Co., LTD, Beijing, China). All the reactions were conducted seriously according to the manufacturer’s instructions.
 
Selection of candidate reference genes
 
We chose the candidate reference genes of alfalfa in transcriptome datasets (162 RNA seq sequencing data, the datasets generated and/or analyzed during the current study are available in the NCBI repository. The accession number and project number are provided in Attachment Table 1) Identify reference genes that can be stably expressed under different conditions and tissues, using 162 RNA seq sequencing data (including different tissues of alfalfa, root, stem, leaf and flowers). A total of 162 RNA-seq data from different alfalfa tissues, development stages and treatments were retrieved from NCBI SRA database. Then downloaded SRA file was first converted to FASTQ format. Sequencing adapters were removed using trim_galore (version 0.6.6, https://www.bioinformatics. babraham.ac.uk /projects/trim_galore/). Then sequencing data was converted to FASTA format using seqkit (version 0.14.0) (Shen et al., 2016). All of the 162-sequencing data were then aligned to the alfalfa reference genome. The allele-aware chromosome-level genome assembly of this research was the most complete and well-annotated genome available, large leaf alfalfa (cv. Xinjiang Daye) using HISAT2 (version 2.2.1) (Kim et al., 2019). Resulting SAM files were converted to BAM files and gene counts were obtained using featureCounts (version 2.0.1) (Liao et al., 2014). Gene counts data was then fed into CustomSelection (version 1.0) R (version 4.1.2) package for candidate reference gene selection with the top genes cut-off of 0.05 (Santos et al., 2020).

Attachment Table 1: 162 RNA seq sequencing data.


 
Primer design and RT-qPCR
       
Primers were designed using Primer Premier 5.0 based on the sequence retrieved from the 162 RNA-Seq datasets of alfalfa. Reactions were conducted using TB Green® Premix Ex Taq™ II (Tli RnaseHPlus) according to the manufacturer’s instructions (Takara Co., LTD, Beijing, China) and amplified on a Bio-Rad Real-Time PCR system. The efficiency of 10 candidate reference gene primers was determined by the equation E= [10(1/slope) - 1] × 100% and the efficiency values should change between 90% and 110% (Radonić et al., 2004).

Data analysis
 
The main methods for the results of RT-qPCR analysis of reference gene stability include programs, such as the △Ct method, GeNorm, Normfinder, Bestkeeper and an online analysis tool RefFinder. The DCt method calculates the SD value of each pair of candidate genes and the average SD value of each candidate reference gene to make comparisons. The GeNorm method calculates the candidate reference genes expression stability value (M value) and the pairwise variation value (Vn/n+1). Higher M values mean lower stability of gene expression. When the value of Vn/n+1 is less than 0.15, the reference gene has no significant contribution. NormFinder algorithm reveals the variation of candidate gene expression using the stability value (SV) when the internal reference genes for normalization by the ANOVA model. Lower SV means higher stability. The Bestkeeper method calculates and analyzes various internal reference genes by importing raw Ct values into an Excel spreadsheet. The stability is mainly analyzed using standard coefficient of variation (SD) and coefficient of correlation with variation (CV). The smaller the two values, the more stable the gene is. The RefFinder tool is a collection that combines the analysis and calculation of all the above algorithms. The results of the above analysis methods can be comprehensively evaluated and the conclusions can be seen more intuitively and clearly, avoiding the one-sidedness of using a single evaluation method to analyze genes.

RESULTS AND DISCUSSION

Although alfalfa has a high yield and good quality, making it the preferred forage for production and planting in feed crops, the research on the internal reference genes has not been conducted sufficiently for the normalization of gene expression. The reference gene selection requires certain conditions, including expression stability, moderate abundance, independence under external environmental influences and consistent expression within the cell. Currently, commonly used internal reference genes in alfalfa are GAPDH and Actin (β-actin) (Cui et al., 2022; Li et al., 2017; Ma et al., 2016; Ma et al., 2024; Wang et al., 2023). In our study, the relatively more stable reference gene and reference gene combinations than GAPDH were found. With the development technique of RT-qPCR, reference genes’ selection and validation for expression normalization were carried out. The candidate genes in alfalfa were retrieved from different tissues of alfalfa, root, stem, leaf and flowers (162 the transcriptome data of alfalfa).
       
RT-qPCR was always used to detect the reference genes’ expression under the conditions of drought stress, alkaline stress and low-temperature stress and the stable reference genes were selected. To obtain the suitable reference genes, five calculation methods including GeNorm, Normfinder, Bestkeeper, △Ct and RefFinder were applied in analyzing the stability of these candidate reference genes in our work. The rankings in our results derived from the five methods were different slightly because using the different algorithms (Sabeh et al., 2018).
       
By comparing five analysis software, rCt method represents the genes’ by comparing the candidate genes’ average standard deviation values. The smaller the average standard deviation value of a candidate reference gene, the more stable the genes’ expression and the rule is the opposite conversely. GeNorm analyzes candidates based on their internal factors and sorts the similarity of expression in different samples. NormFinder calculates the stability value row sorting. BestKeeper directly pairs correlation analysis based on Ct values. If SD is more than 1, then this gene is directly excluded and it is considered to be the most unstable gene-expressed sample. It is usually used to enter preliminary screening, but GeNorm and NormFinder analysis methods are more effective. RefFinder is a comprehensive online tool, the analysis software combines the above four analysis methods (Andersen et al., 2004; Pfaffl et al., 2004; Silver et al., 2006; Vandesompele et al., 2002; Zsóri et al., 2013).
 
Expression of candidate reference genes of alfalfa
 
10 candidate reference genes of alfalfa from transcriptome sequence datasets (162 RNA-seq sequencing data through comparative analysis were chose, including different tissues of alfalfa, Table 1). Primers of 10 candidate reference genes were used to amplify the cDNA template by PCR and all target amplicons obtained single strips, which were consistent with the expected target genes stripe size. All candidate genes’ primers melting curves were plotted with a single peak, which showed the primers had no non-specific amplification with strong characters of specificity amplification. When the amplification length of the RT-qPCR product increased from 60bp to 118bp, these genes amplifi-cation efficiency changed from 90.55.3% to 105.07% (Table 1).

Table 1: The primer sequences of candidate reference genes and the amplicon efficiency.


 
The Ct value analysis
 
The expression level is usually represented by Ct values, with smaller Ct values indicating higher gene expression levels and larger Ct values indicating lower gene expression levels. The Ct values fluctuation range reflects the genes’ stability. The smaller the fluctuation range of Ct values, the more stable the genes are and the rule is opposite conversely. The analysis using the interquartile range of 10 candidate genes found that s, Rer1 gene has the highest expression abundance, while MS.00617 gene has the lowest expression abundance; By comparing the interquartile range of 10 candidate reference genes, it can be seen that the fluctuation range of Ct values is in ascending order, UBL-2a <MS.073307 <MS.65463 <Rer 1 <MS.99505 <MS.74923 <MS.00617 <Actin <MS.33066 <GAPDH (Fig 1). However, there are outliers and extreme values in the UBL-2a 0MS.073307and MS.65463 genes (Fig 1), only analysis of Ct values can’t fully demonstrate the stability of the genes and further analysis is needed.

Fig 1: Ct values for each reference genes in all samples.


 
△ Ct method analysis
 
The △Ct method represents the genes’ by comparing the candidate genes’ average standard deviation values. The smaller the average standard deviation value of a candidate reference gene, the more stable the genes’ expression and the rule is the opposite conversely. The result of △Ct analysis is shown in Table 2. Among the samples, Ms.99505 showed the most stable performance with an average standard deviation value of 0.99. Under alkaline stress, except for Ms.74923, the average standard deviation values of other candidate reference genes were all greater than 0.99 and Ms.99505 showed the most stable performance. Under drought stress, the average standard deviation values of the 10 candidate reference genes were all greater than 0.99 and Rer 1 showed the most stable performance. Under low temperature stress, the average standard deviation values of Actin and Ms.99505 were both greater than 0.99, indicating that Actin exhibited the most stable performance.

Table 2: The rank of RGs for normalization calculated by the DCt method.


 
Norm finder analysis
 
Norm finder analysis calculates the S-value of reference genes, with the rule the more stable the gene, the smaller the S-value. Table 3 shows that under alkaline stress, Ms.99505 exhibits the most stable performance; under drought stress, Rer1 showed relatively stable performance; under low-temperature stress, Actin shows the most stable performance. Among all the samples, Ms.99505 showed the most stable performance. The NormFinder analysis results in this study are basically consistent with those of △Ct method analysis.

Table 3: The rank of RGs for normalization calculated by the NormFinder program.


 
Best keeper analysis
 
Best keeper measures the gene expression stability of these candidate reference genes by the Ct values of standard deviation and variation coefficient. Using SD=1 as the standard, reference genes with an SD value less than 1 are considered stable expressed genes. The smaller the value of the SD and CV are, the more stable the internal reference is and the rule is the opposite conversely. From Table 4, it can be seen that under alkaline stress, the standard deviation of 10 candidate reference genes is less than 1 and UBL-2a shows the most stable performance; Under drought stress, the standard deviation of all 10 candidate reference genes is less than 1, with Ms.073307 showing the most stable performance; Under low temperature stress, except for GAPDH and Ms.33066, the standard deviations of the other 8 candidate reference genes were all less than 1, with Ms.99505 showing the most stable performance; Among all the samples, except for GAPDH and Ms.33066, the standard deviations of the other 8 candidate reference genes were all less than 1, with UBL-2a showing the most stable performance.

Table 4: The rank of RGs for normalization calculated by the BestKeeper program.


       
All 10 pair candidate reference genes could be used in gene expression quantification except for GAPDH and Ms.33066 under low-temperature stress based on screening criteria by the BestKeeper program.

GeNorm analysis
 
In the GeNorm method, the internal reference gene is determined by the sum of M values. The M value less than 1.5 indicates that it can be used as an internal reference gene and the lower the M value, the better the stability. The number of reference genes is determined by the relationship between the Vn/n+1 value and 0.15. Since the GeNorm method considers a single reference gene to be unstable, at least 2 reference genes are selected. Therefore, n in Vn/n+1 must be greater than or equal to 2. When V2/3<0.15, the 2 candidate reference genes with the smallest M value are selected as reference genes. If V2/3>0.15, a new reference gene is introduced. Compare the relationship between V3/4 and 0.15. If V3/4>0.15, another new reference gene is introduced until Vn/n+1<0.15 and the n candidate reference genes with the smallest M value are selected as reference genes.
       
From the GeNorm analysis results (Table 5 and Table 6), it can be seen that under alkaline, the M values of 10 candidate reference genes are all less than 1.5 and V2/3<0.15. GAPDH and UBL-2a with smaller M values are selected as reference genes; Under drought stress, the M values of 10 candidate internal reference genes are all less than 1.5 and V2/3<0.15. Ms.33066 and Actin with smaller M values were selected as internal reference genes; Under low temperature stress, the M values of the 10 candidate internal reference genes were all less than 1.5 and V4/5 was less than 0.15. Ms.65463, UBL-2a, Ms.99505 and Actin with smaller M values were selected as internal reference genes; Among all the samples, the M values of 10 candidate reference genes were less than 1.5 and V4/5 was less than 0.15. Ms.073307, Rer1, Ms.99505 and UBL-2a with smaller M values were selected as reference genes.

Table 5: Average expression stability values (M) and ranking of the candidate RGs calculated using GeNorm.



Table 6: Pairwise varation to determine the optimal number of control genes for accurate normalization.



RefFinder comprehensive analysis
 
RefFinder is a comprehensive analysis based on all the results of four methods: DCt, GeNorm, NormFinder and Best keeper. After integrating the ranking of candidate reference genes in different methods with certain weights, the ranking geometric mean is calculated. The lower the average value it is, the more stable it is; otherwise, it is unstable. From Table 7, it can be seen that under alkaline stress, the most stable internal reference gene is UBL-2a, has the highest un-stability index. Under drought stress, Rer 1has the highest stability. Under low-temperature stress, the most stable internal reference gene is Ms.99505; Among all the samples, the best stable reference gene is Ms.9950.

Table 7: The comprehensive ranking of RGs for normalization.


       
Under the full, complete consideration of the different ranks from the five algorithms, the rankings were calculated. Accordingly, the relative suitable reference genes or reference gene combinations were selected. GAPDH and Actin are taken as traditional reference genes commonly used in alfalfa. However, the results of our study showed that GAPDH were considered unstable reference genes. Though the GAPDH interquartile range is low, it has outliers. Based on the comprehensive validation of five methods analysis, the GAPDH doesn’t have the highest stability. The reason why GAPDH is not the most stable and suitable internal reference gene may be because it not only serves as a component of the glycolysis pathway but also participates in other processes. Mallona et al. (2010) found that GAPDH is also not suitable for petunias and Dai et al. (2016) also proved that GAPDH does not have high stability in the late stage of grape development. Under different stress conditions in our study, Actin did not show good stability either, which is consistent with research results in plants such as Arabidopsis (Czechowski et al., 2005), orchids (Zhang et al., 2023), dogtooth roots (Chen et al., 2015), soybeans (Luo et al., 2023) and bamboo (Wu et al., 2019).
       
There are certain differences in gene expression under different treatments, in different tissues, or also can be stably expressed under all changing conditions, including reference genes. Whether these optimal reference genes and the optimal combinations of reference genes are suitable under other abiotic stress is uncertainly. And these genes’ stability also may be affected slightly by the different algorithms of the five-analysis software and the principles of gene screening (Andersen et al., 2004; Hou, 2016; Kumar et al., 2011; Pfaffl et al., 2004; Silver et al., 2006; Vandesompele et al., 2002; Zsóri et al., 2013).

CONCLUSION

This study provides the optimal single reference gene and combination of reference genes for alfalfa under different stresses. All 10 pair candidate reference genes could be used in gene expression quantification except for GAPDH and Ms.33066 under low-temperature stress based on screening criteria. Under alkaline stress, the optimal reference gene is UBL-2a and the optimal combination of reference genes is GAPDH and UBL-2a; Under drought stress, the optimal reference gene is Rer1 and the optimal combination of reference genes is Ms.33066 and Actin; Under low-temperature stress, the optimal reference gene is Ms.99505 and the optimal combination of reference genes is Ms.65463, UBL-2a, Ms.99505 and Actin. Among all the samples, the optimal reference gene is MS.99505 and the optimal combination of reference genes is MS.073307, Rer1, MS.99505 and UBL-2a. GAPDH and Actin aren’t the most appropriate reference genes of alfalfa under different abiotic stresses.

ACKNOWLEDGEMENT

This work was supported by the National Natural Science Foundation of China (32102585).

CONFLICT OF INTEREST

All authors declared that there is no conflict of interest.

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    Screening of Internal Reference Genes Analysis in Alfalfa under Different Abiotic Stress Conditions

    W
    W.N. Fan1,*
    P
    P.F. Shi1
    M
    M.Y. Zhang1
    Y
    Y.Q. Shi1
    Y
    Y.X. Yang1
    1Animal Science and Technology College, Henan University of Science and Technology, Luoyang, Henan 471 003, China.

    • Submitted06-02-2025|

    • Accepted08-07-2025|

    • First Online 17-09-2025|

    • doi 10.18805/LRF-858

    ABSTRACT

    Background: Alfalfa is a kind of high-quality legume grass with high crude protein content, good palatability, high grass yield and good nitrogen fixation capacity. Abiotic stresses have become the key factors restricting the yield and quality in alfalfa and other crops, such as drought, extreme changes in ambient temperature, floods, land salinization and acidification, while under the different abiotic stress conditions, gene expression levels will also change accordingly. The selection and using of appropriate reference genes are crucial for the accuracy of gene expression quantification.

    Methods: We chose the candidate internal reference genes of alfalfa from transcriptome sequence datasets (162 RNA-seq sequencing data) through comparative analysis. Finally, 10 candidate reference genes were selected. These candidate reference gene expressions were determined by RT-qPCR under three common abiotic stresses in production, such as alkali, drought and low temperature. The stability index of these candidate genes was calculated and evaluated correspondently using specific softwares and different algorithms, such as GeNorm, Normfinder, Bestkeeper, △Ct method and an online analysis tool RefFinder.

    Result: The results showed that all 10 pair candidate reference genes could be used in gene expression quantification except for GAPDH and Ms.33066 under low-temperature stress based on screening criteria. Under alkaline stress, the optimal reference gene is UBL-2a and the optimal combination of reference genes is GAPDH and UBL-2a; Under drought stress, the optimal reference gene is Rer1 and the optimal combination of reference genes is Ms.33066 (some candidate reference genes haven’t been annotated yet, using gene ID abbreviation number of Medicago sativa L. instead) and Actin; Under low-temperature stress, the optimal reference gene is Ms.99505 and the optimal combination of reference genes is Ms.65463, UBL-2a, Ms.99505 and Actin. Among all the samples, the optimal reference gene is MS.99505 and the optimal combination of reference genes is MS.073307, Rer1, MS.99505 and UBL-2a. GAPDH and Actin aren’t the most appropriate reference genes of alfalfa under different abiotic stresses, the optimal reference gene and the optimal combination of reference genes under other abiotic stress need further validation. This paper provides scientific evidence for quantitative analysis of the genes of alfalfa.

    INTRODUCTION

    Alfalfa (Medicago sativa L.), is a kind of high-quality legume herbaceous with high crude protein content, good palatability, high grass yield and good nitrogen fixation capacity (Long and Zhang, 2024; Zhang and Long, 2024; Zhang et al., 2023). It is well known for its high crude protein content and named the "King of Forage” (Sun et al., 2019; Zhang et al., 2015). The abiotic stresses have become constraints on alfalfa and other crops, affecting crop yield and quality, including drought and extreme changes in environmental temperature, floods, severe land salinization and acidification and other abiotic stresses (Elgharably and Benes, 2021; Fang et al., 2022). Under abiotic stress conditions, gene expression levels also exhibit corresponding changes and appropriate or scientific reference genes are crucial for the scientificity and accuracy of gene expression quantification. Real-time fluorescence quantitative PCR can analyze the specific gene expression in alfalfa, such as changes in expression at different tissue sites, growth stages, or different environmental conditions (Gutierrez et al., 2008; Udvardi et al., 2008; Wan et al., 2010). Compared with the traditional gene expression techniques of quantitative analysis, such as Northern blots, Western blots and cDNA microarrays, RT-qPCR (reverse transcription-quantitative PCR) is most widely used because of its low cost, time-saving and accuracy in gene expression (Dekkers et al., 2012; Hao et al., 2014; Li et al., 2017; Ma et al., 2016; Maroufi et al., 2010; Tian et al., 2015; Wu et al., 2016; Zhu et al., 2013).
           
    To obtain more accurate and reliable results, it is usually necessary to introduce internal reference genes as reference corrections in RT-qPCR experiments to reduce the RNA quality and reverse transcription efficiency of different samples, as well as errors in the sample loading process and standardize the measurement of the target gene. Reference genes, called housekeeping genes, described as ‘‘crucial for cellular existence” frequently, are often selected because of their high copy numbers and their stable expression level is rarely affected by the experimental conditions, or external environment (Joshi et al., 2022; Kozera and Rapacz, 2013). Under an ideal situation, the selected reference genes should be expressed consistently in different growth and development stages, tissues and organs and different environmental conditions, without being affected by experimental conditions (Die et al., 2010). The research results have indicated that there is no internal reference gene that fully meets the ideal criteria. Regardless of different developmental stages, environmental conditions, species, tissue sources, or experimental conditions, the expression of any reference gene will change and there is no universality between genes (Huggett et al., 2005; Jain et al., 2006; Winnepenninckx et al., 1996). This phenomenon makes the selection of reference genes more complex.
           
    The selection of reference genes requires certain conditions to meet, including expression stability, moderate abundance, independence from the target gene and external environmental influences and consistent expression within the cell. Selecting inappropriate housekeeping genes as reference genes can lead to significant biases and errors, resulting in inaccurate data. The selection and use of reference genes are key factors in ensuring more accurate and reliable experimental results (Small et al., 1989; Wu et al., 2021; Zhou et al., 2016). A good reference gene for a qPCR experiment is one that is stably expressed in different conditions.
           
    Currently, the study about the assembly of the genome of alfalfa is as follows: Chen et al. (2020) deciphered the genome of the cultivated alfalfa variety 'large leaf alfalfa, 'autotetraploid. Shen et al. (2020) deciphered the genome of the cultivated alfalfa variety 'Zhongmu No.1' autotetraploid. Long et al. (2022) deciphered the genome of the cultivated alfalfa variety 'Zhongmu No. 4' autotetraploid. Shi et al. (2024) provides a high-quality reference genome of an important diploid alfalfa germplasm. In production, the common alfalfa variety is tetraploid. We chose the large leaf alfalfa (cv.Xinjiang Daye) as a reference genome based on following justifications: Completeness and quality, large leaf alfalfa genome was the most complete and well-annotated genome available after comprehensive analysis. Utilizing a high-quality reference genome is critical for accurate mapping and subsequent analysis of RNA-seq data. Consistency across datasets: By mapping different RNA-seq data onto one single reference genome, we could get reliable gene expression atlas from multiple RNA-seq datasets. Conservation of reference genes: Reference genes which are the focus of our study are typically highly conserved across different cultivars of alfalfa.
           
    In our study, the candidate reference genes of alfalfa from transcriptome sequence datasets (162 RNA-seq sequencing data through comparative analysis were chose, including different tissues of alfalfa). 10 candidate reference genes were selected. RT-qPCR was then adopted to determine the candidate reference genes under five abiotic stresses of drought, alkali and low temperature. The stability of these candidate genes was evaluated correspondently using specific software and algorithms, such as GeNorm (Vandesompele et al., 2002), Normfinder (Andersen et al., 2004), Bestkeeper (Pfaffl et al., 2004), △Ct method (Silver et al., 2006) and an online analysis tool RefFinder (Zsóri et al., 2013). This research systematically explored the appropriate reference genes of alfalfa under different abiotic stresses to provide scientific evidence for quantitative analysis of the genes of alfalfa.

    MATERIALS AND METHODS

    Plant material
     
    Three abiotic stresses (alkali, drought and low temperature) experiment were conducted using hydroponics (Ma et al., 2021). Full and uniformly sized alfalfa seeds (Zhongmu NO. 3, from Beijing Institute of Animal Husbandry and Veterinary Medicine, Chinese Academy of Agricultural Sciences) were selected, soaked in HgCl2 solution for 8 minutes and then rinsed repeatedly with distilled water to ensure there was no residue on the surface of the seeds. After soaking in distilled water for 12 hours, the soaked seeds were evenly spread in a culture dish with two layers of high-temperature sterilized filter paper. 40 seeds were placed on each dish, setting 3 repetitions. Then the culture dishes were placed in an artificial climate incubator for cultivation. The incubator simulated natural light conditions with three light intensity stages: 4000 lx, 6000 lx and full light in a day (light: darkness= 14 h/25oC:10h/20oC). Water the seedlings to keep sufficient moisture and conduct the treatments after cultivation for 14 days.
     
    Alkali stress treatment
     
    Add the Mixture of NaHCO3:Na2CO3 (9:1) to the Hogland nutrient solution and set up four treatments: 0 mmol/L (CK), 30 mmol/L, 50 mmol/L and 70 mmol/L. Each dish is irrigated with 5ml of treatment solution every morning and evening, with 5 replicates per treatment for 3 days.
     
    Drought stress treatment
     
    PEG-6000 was used to simulate drought stress using the weighting method. Irrigated different water potentials to ensure consistent weight per petri dish and weighed once every morning and evening. Set up four groups: control 0% (water potential, 0MPa), mild drought 5% (water potential-0.10MPa), moderate drought 10% (water potential-0.20MPa) and severe drought 20% (water potential-0.40MPa), 5 replicates per treatment for 3 days.
     
    Low-temperature (Low T) stress treatment
     
    Put the petri dishes in the artificial incubator for low-temperature stress treatment (under normal light conditions), The control group was set at 25oC, with the low temperature set at 4oC and took samples every 4 hours (4h, 8h, 12h and 16 h), 5 replicates.
           
    After the experimental treatment ended, we selected the 5 whole alfalfa normal seedlings randomly from each petri dish. Cleaned and dried with filter paper. Froze the samples with tin foil in liquid nitrogen immediately, then store them in a -80oC freezer.
     
    RNA isolation and cDNA synthesis
     
    Total RNA was extracted by RNAiso plus (Takara Co., LTD, Beijing, China) with RNA detection using NanoDrop 2000 Spectrophotometer and the reverse transcription reagent used the PrimeScript™ RT reagent Kit with gDNA Eraser (Takara Co., LTD, Beijing, China). All the reactions were conducted seriously according to the manufacturer’s instructions.
     
    Selection of candidate reference genes
     
    We chose the candidate reference genes of alfalfa in transcriptome datasets (162 RNA seq sequencing data, the datasets generated and/or analyzed during the current study are available in the NCBI repository. The accession number and project number are provided in Attachment Table 1) Identify reference genes that can be stably expressed under different conditions and tissues, using 162 RNA seq sequencing data (including different tissues of alfalfa, root, stem, leaf and flowers). A total of 162 RNA-seq data from different alfalfa tissues, development stages and treatments were retrieved from NCBI SRA database. Then downloaded SRA file was first converted to FASTQ format. Sequencing adapters were removed using trim_galore (version 0.6.6, https://www.bioinformatics. babraham.ac.uk /projects/trim_galore/). Then sequencing data was converted to FASTA format using seqkit (version 0.14.0) (Shen et al., 2016). All of the 162-sequencing data were then aligned to the alfalfa reference genome. The allele-aware chromosome-level genome assembly of this research was the most complete and well-annotated genome available, large leaf alfalfa (cv. Xinjiang Daye) using HISAT2 (version 2.2.1) (Kim et al., 2019). Resulting SAM files were converted to BAM files and gene counts were obtained using featureCounts (version 2.0.1) (Liao et al., 2014). Gene counts data was then fed into CustomSelection (version 1.0) R (version 4.1.2) package for candidate reference gene selection with the top genes cut-off of 0.05 (Santos et al., 2020).

    Attachment Table 1: 162 RNA seq sequencing data.


     
    Primer design and RT-qPCR
           
    Primers were designed using Primer Premier 5.0 based on the sequence retrieved from the 162 RNA-Seq datasets of alfalfa. Reactions were conducted using TB Green® Premix Ex Taq™ II (Tli RnaseHPlus) according to the manufacturer’s instructions (Takara Co., LTD, Beijing, China) and amplified on a Bio-Rad Real-Time PCR system. The efficiency of 10 candidate reference gene primers was determined by the equation E= [10(1/slope) - 1] × 100% and the efficiency values should change between 90% and 110% (Radonić et al., 2004).

    Data analysis
     
    The main methods for the results of RT-qPCR analysis of reference gene stability include programs, such as the △Ct method, GeNorm, Normfinder, Bestkeeper and an online analysis tool RefFinder. The DCt method calculates the SD value of each pair of candidate genes and the average SD value of each candidate reference gene to make comparisons. The GeNorm method calculates the candidate reference genes expression stability value (M value) and the pairwise variation value (Vn/n+1). Higher M values mean lower stability of gene expression. When the value of Vn/n+1 is less than 0.15, the reference gene has no significant contribution. NormFinder algorithm reveals the variation of candidate gene expression using the stability value (SV) when the internal reference genes for normalization by the ANOVA model. Lower SV means higher stability. The Bestkeeper method calculates and analyzes various internal reference genes by importing raw Ct values into an Excel spreadsheet. The stability is mainly analyzed using standard coefficient of variation (SD) and coefficient of correlation with variation (CV). The smaller the two values, the more stable the gene is. The RefFinder tool is a collection that combines the analysis and calculation of all the above algorithms. The results of the above analysis methods can be comprehensively evaluated and the conclusions can be seen more intuitively and clearly, avoiding the one-sidedness of using a single evaluation method to analyze genes.

    RESULTS AND DISCUSSION

    Although alfalfa has a high yield and good quality, making it the preferred forage for production and planting in feed crops, the research on the internal reference genes has not been conducted sufficiently for the normalization of gene expression. The reference gene selection requires certain conditions, including expression stability, moderate abundance, independence under external environmental influences and consistent expression within the cell. Currently, commonly used internal reference genes in alfalfa are GAPDH and Actin (β-actin) (Cui et al., 2022; Li et al., 2017; Ma et al., 2016; Ma et al., 2024; Wang et al., 2023). In our study, the relatively more stable reference gene and reference gene combinations than GAPDH were found. With the development technique of RT-qPCR, reference genes’ selection and validation for expression normalization were carried out. The candidate genes in alfalfa were retrieved from different tissues of alfalfa, root, stem, leaf and flowers (162 the transcriptome data of alfalfa).
           
    RT-qPCR was always used to detect the reference genes’ expression under the conditions of drought stress, alkaline stress and low-temperature stress and the stable reference genes were selected. To obtain the suitable reference genes, five calculation methods including GeNorm, Normfinder, Bestkeeper, △Ct and RefFinder were applied in analyzing the stability of these candidate reference genes in our work. The rankings in our results derived from the five methods were different slightly because using the different algorithms (Sabeh et al., 2018).
           
    By comparing five analysis software, rCt method represents the genes’ by comparing the candidate genes’ average standard deviation values. The smaller the average standard deviation value of a candidate reference gene, the more stable the genes’ expression and the rule is the opposite conversely. GeNorm analyzes candidates based on their internal factors and sorts the similarity of expression in different samples. NormFinder calculates the stability value row sorting. BestKeeper directly pairs correlation analysis based on Ct values. If SD is more than 1, then this gene is directly excluded and it is considered to be the most unstable gene-expressed sample. It is usually used to enter preliminary screening, but GeNorm and NormFinder analysis methods are more effective. RefFinder is a comprehensive online tool, the analysis software combines the above four analysis methods (Andersen et al., 2004; Pfaffl et al., 2004; Silver et al., 2006; Vandesompele et al., 2002; Zsóri et al., 2013).
     
    Expression of candidate reference genes of alfalfa
     
    10 candidate reference genes of alfalfa from transcriptome sequence datasets (162 RNA-seq sequencing data through comparative analysis were chose, including different tissues of alfalfa, Table 1). Primers of 10 candidate reference genes were used to amplify the cDNA template by PCR and all target amplicons obtained single strips, which were consistent with the expected target genes stripe size. All candidate genes’ primers melting curves were plotted with a single peak, which showed the primers had no non-specific amplification with strong characters of specificity amplification. When the amplification length of the RT-qPCR product increased from 60bp to 118bp, these genes amplifi-cation efficiency changed from 90.55.3% to 105.07% (Table 1).

    Table 1: The primer sequences of candidate reference genes and the amplicon efficiency.


     
    The Ct value analysis
     
    The expression level is usually represented by Ct values, with smaller Ct values indicating higher gene expression levels and larger Ct values indicating lower gene expression levels. The Ct values fluctuation range reflects the genes’ stability. The smaller the fluctuation range of Ct values, the more stable the genes are and the rule is opposite conversely. The analysis using the interquartile range of 10 candidate genes found that s, Rer1 gene has the highest expression abundance, while MS.00617 gene has the lowest expression abundance; By comparing the interquartile range of 10 candidate reference genes, it can be seen that the fluctuation range of Ct values is in ascending order, UBL-2a <MS.073307 <MS.65463 <Rer 1 <MS.99505 <MS.74923 <MS.00617 <Actin <MS.33066 <GAPDH (Fig 1). However, there are outliers and extreme values in the UBL-2a 0MS.073307and MS.65463 genes (Fig 1), only analysis of Ct values can’t fully demonstrate the stability of the genes and further analysis is needed.

    Fig 1: Ct values for each reference genes in all samples.


     
    △ Ct method analysis
     
    The △Ct method represents the genes’ by comparing the candidate genes’ average standard deviation values. The smaller the average standard deviation value of a candidate reference gene, the more stable the genes’ expression and the rule is the opposite conversely. The result of △Ct analysis is shown in Table 2. Among the samples, Ms.99505 showed the most stable performance with an average standard deviation value of 0.99. Under alkaline stress, except for Ms.74923, the average standard deviation values of other candidate reference genes were all greater than 0.99 and Ms.99505 showed the most stable performance. Under drought stress, the average standard deviation values of the 10 candidate reference genes were all greater than 0.99 and Rer 1 showed the most stable performance. Under low temperature stress, the average standard deviation values of Actin and Ms.99505 were both greater than 0.99, indicating that Actin exhibited the most stable performance.

    Table 2: The rank of RGs for normalization calculated by the DCt method.


     
    Norm finder analysis
     
    Norm finder analysis calculates the S-value of reference genes, with the rule the more stable the gene, the smaller the S-value. Table 3 shows that under alkaline stress, Ms.99505 exhibits the most stable performance; under drought stress, Rer1 showed relatively stable performance; under low-temperature stress, Actin shows the most stable performance. Among all the samples, Ms.99505 showed the most stable performance. The NormFinder analysis results in this study are basically consistent with those of △Ct method analysis.

    Table 3: The rank of RGs for normalization calculated by the NormFinder program.


     
    Best keeper analysis
     
    Best keeper measures the gene expression stability of these candidate reference genes by the Ct values of standard deviation and variation coefficient. Using SD=1 as the standard, reference genes with an SD value less than 1 are considered stable expressed genes. The smaller the value of the SD and CV are, the more stable the internal reference is and the rule is the opposite conversely. From Table 4, it can be seen that under alkaline stress, the standard deviation of 10 candidate reference genes is less than 1 and UBL-2a shows the most stable performance; Under drought stress, the standard deviation of all 10 candidate reference genes is less than 1, with Ms.073307 showing the most stable performance; Under low temperature stress, except for GAPDH and Ms.33066, the standard deviations of the other 8 candidate reference genes were all less than 1, with Ms.99505 showing the most stable performance; Among all the samples, except for GAPDH and Ms.33066, the standard deviations of the other 8 candidate reference genes were all less than 1, with UBL-2a showing the most stable performance.

    Table 4: The rank of RGs for normalization calculated by the BestKeeper program.


           
    All 10 pair candidate reference genes could be used in gene expression quantification except for GAPDH and Ms.33066 under low-temperature stress based on screening criteria by the BestKeeper program.

    GeNorm analysis
     
    In the GeNorm method, the internal reference gene is determined by the sum of M values. The M value less than 1.5 indicates that it can be used as an internal reference gene and the lower the M value, the better the stability. The number of reference genes is determined by the relationship between the Vn/n+1 value and 0.15. Since the GeNorm method considers a single reference gene to be unstable, at least 2 reference genes are selected. Therefore, n in Vn/n+1 must be greater than or equal to 2. When V2/3<0.15, the 2 candidate reference genes with the smallest M value are selected as reference genes. If V2/3>0.15, a new reference gene is introduced. Compare the relationship between V3/4 and 0.15. If V3/4>0.15, another new reference gene is introduced until Vn/n+1<0.15 and the n candidate reference genes with the smallest M value are selected as reference genes.
           
    From the GeNorm analysis results (Table 5 and Table 6), it can be seen that under alkaline, the M values of 10 candidate reference genes are all less than 1.5 and V2/3<0.15. GAPDH and UBL-2a with smaller M values are selected as reference genes; Under drought stress, the M values of 10 candidate internal reference genes are all less than 1.5 and V2/3<0.15. Ms.33066 and Actin with smaller M values were selected as internal reference genes; Under low temperature stress, the M values of the 10 candidate internal reference genes were all less than 1.5 and V4/5 was less than 0.15. Ms.65463, UBL-2a, Ms.99505 and Actin with smaller M values were selected as internal reference genes; Among all the samples, the M values of 10 candidate reference genes were less than 1.5 and V4/5 was less than 0.15. Ms.073307, Rer1, Ms.99505 and UBL-2a with smaller M values were selected as reference genes.

    Table 5: Average expression stability values (M) and ranking of the candidate RGs calculated using GeNorm.



    Table 6: Pairwise varation to determine the optimal number of control genes for accurate normalization.



    RefFinder comprehensive analysis
     
    RefFinder is a comprehensive analysis based on all the results of four methods: DCt, GeNorm, NormFinder and Best keeper. After integrating the ranking of candidate reference genes in different methods with certain weights, the ranking geometric mean is calculated. The lower the average value it is, the more stable it is; otherwise, it is unstable. From Table 7, it can be seen that under alkaline stress, the most stable internal reference gene is UBL-2a, has the highest un-stability index. Under drought stress, Rer 1has the highest stability. Under low-temperature stress, the most stable internal reference gene is Ms.99505; Among all the samples, the best stable reference gene is Ms.9950.

    Table 7: The comprehensive ranking of RGs for normalization.


           
    Under the full, complete consideration of the different ranks from the five algorithms, the rankings were calculated. Accordingly, the relative suitable reference genes or reference gene combinations were selected. GAPDH and Actin are taken as traditional reference genes commonly used in alfalfa. However, the results of our study showed that GAPDH were considered unstable reference genes. Though the GAPDH interquartile range is low, it has outliers. Based on the comprehensive validation of five methods analysis, the GAPDH doesn’t have the highest stability. The reason why GAPDH is not the most stable and suitable internal reference gene may be because it not only serves as a component of the glycolysis pathway but also participates in other processes. Mallona et al. (2010) found that GAPDH is also not suitable for petunias and Dai et al. (2016) also proved that GAPDH does not have high stability in the late stage of grape development. Under different stress conditions in our study, Actin did not show good stability either, which is consistent with research results in plants such as Arabidopsis (Czechowski et al., 2005), orchids (Zhang et al., 2023), dogtooth roots (Chen et al., 2015), soybeans (Luo et al., 2023) and bamboo (Wu et al., 2019).
           
    There are certain differences in gene expression under different treatments, in different tissues, or also can be stably expressed under all changing conditions, including reference genes. Whether these optimal reference genes and the optimal combinations of reference genes are suitable under other abiotic stress is uncertainly. And these genes’ stability also may be affected slightly by the different algorithms of the five-analysis software and the principles of gene screening (Andersen et al., 2004; Hou, 2016; Kumar et al., 2011; Pfaffl et al., 2004; Silver et al., 2006; Vandesompele et al., 2002; Zsóri et al., 2013).

    CONCLUSION

    This study provides the optimal single reference gene and combination of reference genes for alfalfa under different stresses. All 10 pair candidate reference genes could be used in gene expression quantification except for GAPDH and Ms.33066 under low-temperature stress based on screening criteria. Under alkaline stress, the optimal reference gene is UBL-2a and the optimal combination of reference genes is GAPDH and UBL-2a; Under drought stress, the optimal reference gene is Rer1 and the optimal combination of reference genes is Ms.33066 and Actin; Under low-temperature stress, the optimal reference gene is Ms.99505 and the optimal combination of reference genes is Ms.65463, UBL-2a, Ms.99505 and Actin. Among all the samples, the optimal reference gene is MS.99505 and the optimal combination of reference genes is MS.073307, Rer1, MS.99505 and UBL-2a. GAPDH and Actin aren’t the most appropriate reference genes of alfalfa under different abiotic stresses.

    ACKNOWLEDGEMENT

    This work was supported by the National Natural Science Foundation of China (32102585).

    CONFLICT OF INTEREST

    All authors declared that there is no conflict of interest.

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