Selection of Reference Genes for Gene Expression Normalization in Peucedanum praeruptorum Dunn under Abiotic Stresses, Hormone Treatments and Different Tissues

Abstract

Peucedanum praeruptorum Dunn is one of the main traditional Chinese medicines producing coumarins and plenty of literatures are focused on the biosynthesis of coumarins. Quantitative real-time reverse transcription PCR (qRT-PCR) is a widely used method in studying the biosynthesis pathway and the selection of reference genes plays a crucial role in accurate normalization. To facilitate biosynthesis study of coumarins, twelve candidate reference genes were selected from the transcriptome database of P. praeruptorum according to previous studies. Then, BestKeeper, geNoFrm and NormFinder were used for selecting stably expressed reference genes in different tissues and under various stress treatments. The results indicated that, among the twelve candidate reference genes, the SAND family protein (SAND), actin 2 (ACT2), ubiquitin-conjugating enzyme 9 (UBC9), protein phosphatase 2A gene (PP2A) and polypyrimidine tract-binding protein (PTBP1) were the most stable reference genes under different experimental treatments, while glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and tubulin beta-6 (TUB6) were the least stable genes. In addition, the suitability of SAND, TIP41-like protein (TIP41), UBC9, ACT2, TUB6 and their combination as reference genes were confirmed by normalizing the expression of 1-aminocyclopropane-1-carboxylate oxidase (ACO) in different treatments. This work is the first survey of the stability of reference genes in P. praeruptorum and provides guidelines to obtain more accurate qRT-PCR results in P. praeruptorum and other plant species.

Selection and Validation of Appropriate Reference Genes for Real-Time Quantitative PCR Analysis in Needles of Larix olgensis under Abiotic Stresses

“…This result is in agreement with previous research on Caragana korshinskii under heat stress [46] and Coffea arabica leaves under drought stress and GA, SA, and MeJA treatments [52]. However, GAPDH was ranked as the least stable RG in various tissues and under abiotic stresses in Sorghum bicolor and in various tissues and under PEG6000 and MeJA treatments in Peucedanum praeruptorum [53,54]. These results demonstrate that there is no universal RG that is stably expressed in all treatments and tissues.…”

“…ACT2 is a traditional housekeeping gene widely used in many species, such as Betula platyphylla under salt stress and Panicum virgatum L. leaves and roots [58,59]. In this study, ACT2 was an appropriate RG under heat stress, consistent with results from P. praeruptorum under heat stress [54]. Nonetheless, geNorm, NormFinder, and Bestkeeper all showed that ADP was the least stable RG under different experimental conditions.…”

“…In the present study, TIP41 and ACT2 showed highly stable expression under heat stress. In a previous work, TIP41 was a stable RG in M. charantia under UV and CuSO 4 treatments [51] and in P. praeruptorum under NaCl stress [54]. No previous research has shown that TIP41 is the most stable RG under heat stress.…”

Selection of the Reference Gene for Expression Normalization in Papaver somniferum L. under Abiotic Stress and Hormone Treatment

“…Overall, according to the validation, there is no doubt that NCBP2 and PP2A can be selected as the best reference genes for P. somniferum under the tested conditions. In addition, the expression pattern of a target gene, 1-aminocyclopropane-1-carboxylate oxidase (ACO) was analyzed using the most stable and least stable reference genes according to our previous reports [13]. As depicted in Figure 5, the expression of ACO was steady in different treatments with NCBP2 and PP2A as reference.…”

“…With the development of sequencing technology, clarifying the biosynthetic pathway of this plant has become increasingly simple and convenient [11,12]. Additionally, another popular technology, RT-qPCR, is inclusively used for gene expression research due to its quantitative accuracy, high sensitivity, and high-throughput capabilities [13][14][15][16]. However, the results of RT-qPCR are unavoidably influenced by plenty of factors, including cDNA quality, RNA integrity, PCR efficiency, sample amount, and primer design [16][17][18].…”