Characterizing reference genes for high-fidelity gene expression analysis under different abiotic stresses and elicitor treatments in fenugreek leaves

Abstract

Background: Quantifying gene expression is a critical aspect of applied genomics research across all organisms, and real-time PCR has emerged as a powerful tool for this purpose. However, selecting appropriate internal control genes for data normalization presents specific challenges. This study aimed to identify suitable reference genes for gene expression analysis under various conditions, encompassing salinity, low and high-temperature stresses, and different elicitor treatments. These treatments included titanium dioxide, cold plasma, 24-epibrassinolide, and melatonin, resulting in a total of 13 unique treatments and 148 treatment combinations applied to fenugreek plants.

Results: As per the analysis performed with the BestKeeper tool, EEF-1α, and GAPDH were recognized as the most stable reference genes under the majority of conditions. Furthermore, the GeNorm and NormFinder tools identified β-tubulin and EEF-1α as the most stable reference genes. The findings of this research demonstrated that, although the stability of three reference genes expression was acceptable in almost all evaluated treatments, fluctuations in their expression were observed under the treatments of cold stress with TiO₂ NPs application, cold plasma application with salinity stress, and cold plasma application with high-temperature stress compared to others. Simultaneously, the GeNorm analysis results demonstrated that in the mentioned treatments, relying on only one reference gene is inadequate. To corroborate the results, we examined the expression profile of the SSR gene, a pivotal gene in diosgenin biosynthesis, under all investigated treatments and treatment combinations. The outcomes suggested that employing stable reference genes yielded highly consistent results.

Conclusions: The varying expression patterns of the target genes emphasize the crucial need for precise optimization of experimental conditions and selecting stable reference genes to achieve accurate results in gene expression studies utilizing real-time PCR. These findings offer valuable insights into the selection of appropriate reference genes for gene expression analysis under diverse conditions using real-time PCR.

Keywords: Abiotic stress; Elicitors; Fenugreek; Housekeeping gene; Real-time PCR.

Fig. 1

Quantification cycle (Cq) values of ten candidate reference genes under all experimental treatments

Fig. 2

Gene expression stability values (M) and ranking of ten reference genes under all treatments altogether as assayed by GeNorm

Fig. 3

Gene expression stability values (M) and ranking of ten reference genes under salinity stress as assayed by GeNorm. A Control conditions, B Melatonin + salinity stress, C Melatonin + cold stress, D Melatonin + high-temperature stress

Fig. 4

Gene expression stability values (M) and ranking of ten reference genes under cold stress as assayed by GeNorm. A EBR + salinity stress, B EBR + cold stress, C EBR + high-temperature

Fig. 5

Gene expression stability values (M) and ranking of ten reference genes under high-temperature stress as assayed by GeNorm. A TiO₂ NPs + salinity stress, B TiO₂ NPs + cold stress, C TiO₂ NPs + high-temperature stress

Fig. 6

Gene expression stability values (M) and ranking of ten reference genes under high-temperature stress as assayed by GeNorm. A Cold plasma + salinity stress, B Cold plasma + cold stress, C Cold plasma + high-temperature stress

Fig. 7

The pairwise variation values of ten reference genes under all treatment together obtained using GeNorm analysis

Fig. 8

The pairwise variation values of ten reference genes under salinity stress obtained using GeNorm analysis. A TiO₂ NPs + salinity stress, B TiO₂ NPs + cold stress, C TiO₂ NPs + high-temperature stress.

Fig. 9

The pairwise variation values of ten reference genes under cold stress obtained using GeNorm analysis. A Cold plasma + salinity stress, B Cold plasma + cold stress, C Cold plasma + high-temperature stress.

Fig. 10

The pairwise variation values of ten reference genes under high-temperature stress obtained using GeNorm analysis. A EBR + salinity stress, B EBR + cold stress, C EBR + high-temperature

Fig. 11

The pairwise variation values of ten reference genes under high-temperature stress obtained using GeNorm analysis. A Melatonin + salinity stress, B Melatonin + cold stress, C Melatonin + high-temperature

Fig. 12

Illustrates the effects of various melatonin levels (M30, 60, and 90 ppm), temperature treatments (10, 23, and 42 °C), and salinity stress (200 mM) on the SSR expression. Duncan’s method was employed to compare the means at a 1% probability level, and columns with the same letters are not significantly different from each other

Fig. 13

Illustrates the effects of various EBR levels (EBR0, 4, 8 and 16 µM), temperature treatments (10, 23, and 42 °C), and salinity stress (200 mM) on the SSR expression. Duncan’s method was employed to compare the means at a 1% probability level, and columns with the same letters are not significantly different from each other

Fig. 14

Illustrates the effects of various TiO₂ NPs levels (0, 2, 5 and 10 ppm), temperature treatments (10, 23, and 42 °C), and salinity stress (200 mM) on the SSR expression. Duncan’s method was employed to compare the means at a 1% probability level, and columns with the same letters are not significantly different from each other

Fig. 15

Illustrates the effects of exposure times to plasma (h 0, 1, 2, and 4 min), temperature treatments (10, 23, and 42 °C), and salinity stress (200 mM) on the SSR expression. Duncan’s method was employed to compare the means at a 1% probability level, and columns with the same letters are not significantly different from each other