Internal reference genes for normalizing quantitative real-time PCR in different tissues of <i>Gelsemium elegans</i> or under low temperature, MeJA, and SA stresses

Chuihuai You; Shoujian Zang; Tianzhen Cui; Xinlu Sun; Yachun Su; Qing Lin; Hailing Lin; Youxiong Que; Wancai Que; Chuihuai You; Shoujian Zang; Tianzhen Cui; Xinlu Sun; Yachun Su; Qing Lin; Hailing Lin; Youxiong Que; Wancai Que

doi:10.48130/mpb-0024-0014

2024 Volume 3

Article Contents

Abstract

References

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ARTICLE Open Access

Internal reference genes for normalizing quantitative real-time PCR in different tissues of Gelsemium elegans or under low temperature, MeJA, and SA stresses

1.
College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2.
Key Laboratory of Sugarcane Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou 350002, China
3.
The Second People’s Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou 350003, China
4.
Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou 350001, China
^# Authors contributed equally: Chuihuai You, Shoujian Zang

More Information

Corresponding authors: queyouxiong@126.com; wancaique@fjmu.edu.cn

Received: 22 December 2023
Revised: 10 March 2024
Accepted: 10 April 2024
Published online: 23 July 2024
Medicinal Plant Biology 3, Article number: e014 (2024) | Cite this article

Abstract

Gelsemium elegans is a traditional Chinese medicinal plant, with indole alkaloids as its main active ingredient. The plant can be used as medicine, mainly for pain relief, anti-inflammation and anti-tumor. Exogenous stress, such as low temperature, methyl jasmonate (MeJA), and salicylic acid (SA), can affect the growth of G. elegans and the synthesis of secondary metabolites. Under these conditions, there are only a few reports on the selection and validation of internal reference genes in G. elegans. In this study, seven candidate internal reference genes that showed stable expression abundance in the transcriptome database of G. elegans were selected. The stability and reliability of these genes were analyzed in different G. elegans tissues and under low temperature, MeJA, and SA stresses. The results showed that CUL was the optimal reference gene for expression analysis in different G. elegans tissues and under cold stress, SA stress, followed by eEF-1α. Under MeJA stress, the best one was eEF-1α, and the next was CUL. Furthermore, the expression patterns of three different G. elegans genes, including GPPS, ERF, and 60S, were carried out to confirm that single reference gene (CUL or eEF-1α) or double reference genes (CUL + eEF-1α) can be selected as the best or perfect combination of reference genes for gene expression analysis in G. elegans. This study lays a foundation for the accurate normalization and quantification of gene expression in G. elegans.
- Gelsemium elegans,
- Quantitative real-time PCR,
- Reference gene,
- Methyl jasmonate,
- Salicylic acid,
- Cold stress

Introduction

Starting in the early 2000s, China has experienced rapid growth as an emerging wine market. It has now established itself as the world's second-largest grape-growing country in terms of vineyard surface area. Furthermore, China has also secured its position as the sixth-biggest wine producer globally and the fifth-most significant wine consumer in terms of volume^[1]. The Ningxia Hui autonomous region, known for its reputation as the highest quality wine-producing area in China, is considered one of the country's most promising wine regions. The region's arid or semiarid climate, combined with ample sunlight and warmth, thanks to the Yellow River, provides ideal conditions for grape cultivation. Wineries in the Ningxia Hui autonomous region are renowned as the foremost representatives of elite Chinese wineries. All wines produced in this region originate from grapes grown in their vineyards, adhering to strict quality requirements, and have gained a well-deserved international reputation for excellence. Notably, in 2011, Helan Mountain's East Foothill in the Ningxia Hui Autonomous Region received protected geographic indication status in China. Subsequently, in 2012, it became the first provincial wine region in China to be accepted as an official observer by the International Organisation of Vine and Wine (OIV)^[2]. The wine produced in the Helan Mountain East Region of Ningxia, China, is one of the first Agricultural and Food Geographical Indications. Starting in 2020, this wine will be protected in the European Union^[3].

Marselan, a hybrid variety of Cabernet Sauvignon and Grenache was introduced to China in 2001 by the French National Institute for Agricultural Research (INRA). Over the last 15 years, Marselan has spread widely across China, in contrast to its lesser cultivation in France. The wines produced from Marselan grapes possess a strong and elegant structure, making them highly suitable for the preferences of Chinese consumers. As a result, many wineries in the Ningxia Hui Autonomous Region have made Marselan wines their main product^[4]. Wine is a complex beverage that is influenced by various natural and anthropogenic factors throughout the wine-making process. These factors include soil, climate, agrochemicals, and human intervention. While there is an abundance of research available on wine production, limited research has been conducted specifically on local wines in the Eastern Foot of Helan Mountain. This research gap is of significant importance for the management and quality improvement of Chinese local wines.

Ion mobility spectrometry (IMS) is a rapid analytical technique used to detect trace gases and characterize chemical ionic substances. It achieves this through the gas-phase separation of ionized molecules under an electric field at ambient pressure. In recent years, IMS has gained increasing popularity in the field of food-omics due to its numerous advantages. These advantages include ultra-high analytical speed, simplicity, easy operation, time efficiency, relatively low cost, and the absence of sample preparation steps. As a result, IMS is now being applied more frequently in various areas of food analysis, such as food composition and nutrition, food authentication, detection of food adulteration, food process control, and chemical food safety^[5,6]. The orthogonal hyphenation of gas chromatography (GC) and IMS has greatly improved the resolution of complex food matrices when using GC-IMS, particularly in the analysis of wines^[7].

The objective of this study was to investigate the changes in the physicochemical properties of Marselan wine during the winemaking process, with a focus on the total phenolic and flavonoids content, antioxidant activity, and volatile profile using the GC-IMS method. The findings of this research are anticipated to make a valuable contribution to the theoretical framework for evaluating the authenticity and characterizing Ningxia Marselan wine. Moreover, it is expected that these results will aid in the formulation of regulations and legislation pertaining to Ningxia Marselan wine in China.

Materials and methods

Plant materials

All the grapes used to produce Marselan wines, grow in the Xiban vineyard (106.31463° E and 38.509541° N) situated in Helan Mountain's East Foothill of Ningxia Hui Autonomous Region in China.

Chemicals and reagents

Folin-Ciocalteau reagent, (±)-6-Hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), 2,20-azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS), 2,4,6-tris (2-pyridyl)-s-triazine (TPTZ), anhydrous methanol, sodium nitrite, and sodium carbonate anhydrous were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Reference standards of (+)-catechin, gallic acid, and the internal standard (IS) 4-methyl-2-pentanol were supplied by Shanghai Yuanye Bio-Technology Co., Ltd (Shanghai, China). The purity of the above references was higher than 98%. Ultrapure water (18.2 MΩ cm) was prepared by a Milli-Q system (Millipore, Bedford, MA, USA).

Preparation of Marselan juices and wines

Stage 1−Juice processing: Grapes at the fully mature stage are harvested and crushed, and potassium metabisulfite (5 mg/L of SO₂) was evenly spread during the crushing process. The obtained must is transferred into stainless steel tanks. Stage 2−Alcoholic fermentation: Propagated Saccharomyces cerevisiae ES488 (Enartis, Italy) are added to the fresh must, and alcoholic fermentation takes place, after the process is finished, it is kept in the tanks for 7 d for traditional maceration to improve color properties and phenolics content. Stage 3−Malolactic fermentation: When the pomace is fully concentrated at the bottom of the tanks, the wine is transferred to another tank for separation from these residues. Oenococcus oeni VP41 (Lallemand Inc., France) is inoculated and malic acid begins to convert into lactic acid. Stage 4−Wine stabilization: After malolactic fermentation, potassium metabisulfite is re-added (35 mg/L of SO₂), and then transferred to oak barrels for stabilization, this process usually takes 6-24 months. A total of four batches of samples during the production process of Marselan wine were collected in this study.

Total polyphenols and flavonoid content determination

Total polyphenols were determined on 0.5 mL diluted wine sample using the Folin-Ciocalteu method^[8], using gallic acid as a reference compound, and expressed as milligrams of gallic acid equivalents per liter of wine. The total flavonoid content was measured on 0.05 mL of wine sample by a colorimetric method previously described^[9]. Results are calculated from the calibration curve obtained with catechin, as milligrams of catechin equivalents per liter of wine.

ABTS free-radical scavenging assay

The antioxidative activity was determined using the ABTS^·+ assay^[10]. Briefly, the ABTS^·+ radical was prepared from a mixture of 88 μL of potassium persulfate (140 mmol/L) with 5 mL of the ABTS^·+ solution (7 mmol/L). The reaction was kept at room temperature under the absence of light for 16 h. Sixty μL samples were mixed with 3 mL of ABTS^·+ solution with measured absorption of 0.700 ± 0.200 at 734 nm. After 6 min reaction, the absorbance of samples were measured with a spectrophotometer at 734 nm. Each sample was tested in triplicate. The data were expressed as mmol Trolox equivalent of antioxidative capacity per liter of the wine sample (mmol TE/L). Calibration curves, in the range 64.16−1,020.20 μmol TE/L, showed good linearity (R² ≥ 0.99).

FRAP free-radical scavenging assay

The FRAP assay was conducted according to a previous study^[11]. The FRAP reagent was freshly prepared and mixed with 10 mM/L TPTZ solution prepared in 20 mM/L FeCl₃·6H₂O solution, 40 mM/L HCl, and 300 mM/L acetate buffer (pH 3.6) (1:1:10; v:v:v). Ten ml of diluted sample was mixed with 1.8 ml of FRAP reagent and incubated at 37 °C for 30 min. The absorbance was determined at 593 nm and the results were reported as mM Fe (II) equivalent per liter of the wine sample. The samples were analyzed and calculated by a calibration curve of ferrous sulphate (0.15−2.00 mM/mL) for quantification.

HS-GC-IMS analysis

The volatile compounds were analyzed on a GC-IMS instrument (FlavourSpec, GAS, Dortmund, Germany) equipped with an autosampler (Hanon Auto SPE 100, Shandong, China) for headspace analysis. One mL of each wine was sampled in 20 mL headspace vials (CNW Technologies, Germany) with 20 μL of 4-methyl-2-pentanol (20 mg/L) ppm as internal standard, incubated at 60 °C and continuously shaken at 500 rpm for 10 min. One hundred μL of headspace sample was automatically loaded into the injector in splitless mode through a syringe heated to 65 °C. The analytes were separated on a MxtWAX capillary column (30 m × 0.53 mm, 1.0 μm) from Restek (Bellefonte, Pennsylvania, USA) at a constant temperature of 60 °C and then ionized in the IMS instrument (FlavourSpec®, Gesellschaft für Analytische Sensorsysteme mbH, Dortmund, Germany) at 45 °C. High purity nitrogen gas (99.999%) was used as the carrier gas at 150 mL/min, and drift gas at 2 ml/min for 0−2.0 min, then increased to 100 mL/min from 2.0 to 20 min, and kept at 100 mL/min for 10 min. Ketones C₄−C₉ (Sigma Aldrich, St. Louis, MO, USA) were used as an external standard to determine the retention index (RI) of volatile compounds. Analyte identification was performed using a Laboratory Analytical Viewer (LAV) 2.2.1 (GAS, Dortmund, Germany) by comparing RI and the drift time of the standard in the GC-IMS Library.

Statistical data analysis

All samples were prepared in duplicate and tested at least six times, and the results were expressed as mean ± standard error (n = 4) and the level of statistical significance (p < 0.05) was analyzed by using Tukey's range test using SPSS 18.0 software (SPSS Inc., IL, USA). The principal component analysis (PCA) was performed using the LAV software in-built 'Dynamic PCA' plug-in to model patterns of aroma volatiles. Orthogonal partial least-square discriminant analysis (OPLS-DA) in SIMCA-P 14.1 software (Umetrics, Umeă, Sweden) was used to analyze the different volatile organic compounds in the different fermentation stages.

Results

Changes in total polyphenol and flavonoid contents and antioxidant activity during fermentation

The results of the changes in the antioxidant activity of Marselan wines during the entire brewing process are listed in Table 1. It can be seen that the contents of flavonoids and polyphenols showed an increasing trend during the brewing process of Marselan wine, which range from 315.71−1,498 mg CE/L and 1,083.93−3,370.92 mg GAE/L, respectively. It was observed that the content increased rapidly in the alcoholic fermentation stage, but slowly in the subsequent fermentation stage. This indicated that the formation of flavonoid and phenolic substances in wine mainly concentrated in the alcoholic fermentation stage, which is consistent with previous reports. This is mainly because during the alcoholic fermentation of grapes, impregnation occurred to extract these compounds^[12]. The antioxidant activities of Marselan wine samples at different fermentation stages were detected by FRAP and ABTS methods^[11]. The results showed that the ferric reduction capacity and ABST^·+ free radical scavenging capacity of the fermented Marselan wines were 2.4 and 1.5 times higher than the sample from the juice processing stage, respectively, indicating that the fermented Marselan wine had higher antioxidant activity. A large number of previous studies have suggested that there is a close correlation between antioxidant activity and the content of polyphenols and flavonoids^[13−15]. Previous studies have reported that Marselan wine has the highest total phenol and anthocyanin content compared to the wine of Tannat, Cabernet Sauvignon, Merlot, Cabernet Franc, and Syrah^[13]. Polyphenols and flavonoids play an important role in improving human immunity. Therefore, Marselan wines are popular because of their high phenolic and flavonoid content and high antioxidant capacity.

Table 1. GC-IMS integration parameters of volatile compounds in Marselan wine at different fermentation stages.

No.	Compounds	Formula	RI^*	Rt [sec]^**	Dt [RIPrel]^***	Identification approach	Concentration (μg/mL) (n = 4)
No.	Compounds	Formula	RI^*	Rt [sec]^**	Dt [RIPrel]^***	Identification approach	Stage 1	Stage 2	Stage 3	Stage 4
Aldehydes
5	Furfural	C₅H₄O₂	1513.1	941.943	1.08702	RI, DT, IS	89.10 ± 4.05^c	69.98 ± 3.22^c	352.16 ± 39.06^b	706.30 ± 58.22^a
6	Furfural dimer	C₅H₄O₂	1516.6	948.77	1.33299	RI, DT, IS	22.08 ± 0.69^b	18.68 ± 2.59^c	23.73 ± 2.69^b	53.39 ± 9.42^a
12	(E)-2-hexenal	C₆H₁₀O	1223.1	426.758	1.18076	RI, DT, IS	158.17 ± 7.26^a	47.57 ± 2.51^b	39.00 ± 2.06^c	43.52 ± 4.63^bc
17	(E)-2-pentenal	C₅H₈O	1129.2	333.392	1.1074	RI, DT, IS	23.00 ± 4.56^a	16.42 ± 1.69^c	18.82 ± 0.27^b	18.81 ± 0.55^b
19	Heptanal	C₇H₁₄O	1194.2	390.299	1.33002	RI, DT, IS	17.28 ± 2.25^a	10.22 ± 0.59^c	14.50 ± 8.84^b	9.11 ± 1.06^c
22	Hexanal	C₆H₁₂O	1094.6	304.324	1.25538	RI, DT, IS	803.11 ± 7.47^c	1631.34 ± 19.63^a	1511.11 ± 26.91^b	1526.53 ± 8.12^b
23	Hexanal dimer	C₆H₁₂O	1093.9	303.915	1.56442	RI, DT, IS	588.85 ± 7.96^a	93.75 ± 4.67^b	92.93 ± 3.13^b	95.49 ± 2.50^b
29	3-Methylbutanal	C₅H₁₀O	914.1	226.776	1.40351	RI, DT, IS	227.86 ± 6.39^a	33.32 ± 2.59^b	22.36 ± 1.18^c	21.94 ± 1.73^c
33	Dimethyl sulfide	C₂H₆S	797.1	193.431	0.95905	RI, DT, IS	120.07 ± 4.40^c	87.a02 ± 3.82^d	246.81 ± 5.62^b	257.18 ± 3.04^a
49	2-Methylpropanal	C₄H₈O	828.3	202.324	1.28294	RI, DT, IS	150.49 ± 7.13^a	27.08 ± 1.48^b	19.36 ± 1.10^c	19.69 ± 0.92^c
Ketones
45	3-Hydroxy-2-butanone	C₄H₈O₂	1293.5	515.501	1.20934	RI, DT, IS	33.20 ± 3.83^c	97.93 ± 8.72^b	163.20 ± 21.62^a	143.51 ± 21.48^a
46	Acetone	C₃H₆O	836.4	204.638	1.11191	RI, DT, IS	185.75 ± 8.16^c	320.43 ± 12.32^b	430.74 ± 3.98^a	446.58 ± 10.41^a
Organic acid
3	Acetic acid	C₂H₄O₂	1527.2	969.252	1.05013	RI, DT, IS	674.66 ± 46.30^d	3602.39 ± 30.87^c	4536.02 ± 138.86^a	4092.30 ± 40.33^b
4	Acetic acid dimer	C₂H₄O₂	1527.2	969.252	1.15554	RI, DT, IS	45.25 ± 3.89^c	312.16 ± 19.39^b	625.79 ± 78.12^a	538.35 ± 56.38^a
Alcohols
8	1-Hexanol	C₆H₁₄O	1365.1	653.825	1.32772	RI, DT, IS	1647.65 ± 28.94^a	886.33 ± 32.96^b	740.73 ± 44.25^c	730.80 ± 21.58^c
9	1-Hexanol dimer	C₆H₁₄O	1365.8	655.191	1.64044	RI, DT, IS	378.42 ± 20.44^a	332.65 ± 25.76^a	215.78 ± 21.04^b	200.14 ± 28.34^b
13	3-Methyl-1-butanol	C₅H₁₂O	1213.3	414.364	1.24294	RI, DT, IS	691.86 ± 9.95^c	870.41 ± 22.63^b	912.80 ± 23.94^a	939.49 ± 12.44^a
14	3-Methyl-1-butanol dimer	C₅H₁₂O	1213.3	414.364	1.49166	RI, DT, IS	439.90 ± 29.40^c	8572.27 ± 60.56^b	9083.14 ± 193.19^a	9152.25 ± 137.80^a
15	1-Butanol	C₄H₁₀O	1147.2	348.949	1.18073	RI, DT, IS	157.33 ± 9.44^b	198.92 ± 3.92^a	152.78 ± 10.85^b	156.02 ± 9.80^b
16	1-Butanol dimer	C₄H₁₀O	1146.8	348.54	1.38109	RI, DT, IS	24.14 ± 2.15^c	274.75 ± 12.60^a	183.02 ± 17.72^b	176.80 ± 19.80^b
24	1-Propanol	C₃H₈O	1040.9	274.803	1.11042	RI, DT, IS	173.73 ± 4.75^a	55.84 ± 2.16^c	80.80 ± 4.99^b	83.57 ± 2.34^b
25	1-Propanol dimer	C₃H₈O	1040.4	274.554	1.24784	RI, DT, IS	58.20 ± 1.30^b	541.37 ± 11.94^a	541.33 ± 15.57^a	538.84 ± 9.74^a
28	Ethanol	C₂H₆O	930.6	231.504	1.11901	RI, DT, IS	5337.84 ± 84.16^c	11324.05 ± 66.18^a	9910.20 ± 100.76^b	9936.10 ± 101.24^b
34	Methanol	CH₄O	903.6	223.79	0.98374	RI, DT, IS	662.08 ± 13.87^a	76.94 ± 2.15^b	61.92 ± 1.96^c	62.89 ± 0.81^c
37	2-Methyl-1-propanol	C₄H₁₀O	1098.5	306.889	1.35839	RI, DT, IS	306.91 ± 4.09^c	3478.35 ± 25.95^a	3308.79 ± 61.75^b	3313.85 ± 60.88^b
48	1-Pentanol	C₅H₁₂O	1257.6	470.317	1.25222	RI, DT, IS	26.13 ± 2.52^c	116.50 ± 3.71^ab	112.37 ± 6.26^b	124.17 ± 7.04^a
Esters
1	Methyl salicylate	C₈H₈O₃	1859.6	1616.201	1.20489	RI, DT, IS	615.00 ± 66.68^a	485.08 ± 31.30^b	470.14 ± 23.02^b	429.12 ± 33.74^b
7	Butyl hexanoate	C₁₀H₂₀O₂	1403.0	727.561	1.47354	RI, DT, IS	95.83 ± 17.04^a	62.87 ± 3.62^a	92.59 ± 11.88^b	82.13 ± 3.61^c
10	Hexyl acetate	C₈H₁₆O₂	1298.6	524.366	1.40405	RI, DT, IS	44.72 ± 8.21^a	33.18 ± 2.17^d	41.50 ± 4.38^c	40.89 ± 4.33^b
11	Propyl hexanoate	C₉H₁₈O₂	1280.9	499.577	1.39274	RI, DT, IS	34.65 ± 3.90^d	70.43 ± 5.95^a	43.97 ± 4.39^b	40.12 ± 4.05^c
18	Ethyl hexanoate	C₈H₁₆O₂	1237.4	444.749	1.80014	RI, DT, IS	55.55 ± 5.62^c	1606.16 ± 25.63^a	787.24 ± 16.95^b	788.91 ± 28.50^b
20	Isoamyl acetate	C₇H₁₄O₂	1127.8	332.164	1.30514	RI, DT, IS	164.22 ± 1.00^d	243.69 ± 8.37^c	343.51 ± 13.98^b	365.46 ± 1.60^a
21	Isoamyl acetate dimer	C₇H₁₄O₂	1126.8	331.345	1.75038	RI, DT, IS	53.61 ± 4.79^d	4072.20 ± 11.94^a	2416.70 ± 49.84^b	2360.46 ± 43.29^c
26	Isobutyl acetate	C₆H₁₂O₂	1020.5	263.605	1.23281	RI, DT, IS	101.65 ± 1.81^a	15.52 ± 0.67^c	44.87 ± 3.21^b	45.96 ± 1.41^b
27	Isobutyl acetate dimer	C₆H₁₂O₂	1019.6	263.107	1.61607	RI, DT, IS	34.60 ± 1.05^d	540.84 ± 5.64^a	265.54 ± 8.31^c	287.06 ± 3.66^b
30	Ethyl acetate dimer	C₄H₈O₂	885.2	218.564	1.33587	RI, DT, IS	1020.75 ± 6.86^d	5432.71 ± 6.55^a	5052.99 ± 9.65^b	5084.47 ± 7.30^c
31	Ethyl acetate	C₄H₈O₂	878.3	216.574	1.09754	RI, DT, IS	215.65 ± 3.58^a	38.29 ± 2.37^c	71.59 ± 2.99^b	69.32 ± 2.85^b
32	Ethyl formate	C₃H₆O₂	838.1	205.127	1.19738	RI, DT, IS	175.48 ± 3.79^d	1603.20 ± 13.72^a	1472.10 ± 5.95^c	1509.08 ± 13.26^b
35	Ethyl octanoate	C₁₀H₂₀O₂	1467.0	852.127	1.47312	RI, DT, IS	198.86 ± 36.71^b	1853.06 ± 17.60^a	1555.51 ± 24.21^a	1478.05 ± 33.63^a
36	Ethyl octanoate dimer	C₁₀H₂₀O₂	1467.0	852.127	2.03169	RI, DT, IS	135.50 ± 13.02^d	503.63 ± 15.86^a	342.89 ± 11.62^b	297.28 ± 14.40^c
38	Ethyl butanoate	C₆H₁₂O₂	1042.1	275.479	1.5664	RI, DT, IS	21.29 ± 2.68^c	1384.67 ± 8.97^a	1236.52 ± 20.21^b	1228.09 ± 5.09^b
39	Ethyl 3-methylbutanoate	C₇H₁₄O₂	1066.3	288.754	1.26081	RI, DT, IS	9.70 ± 1.85^d	200.29 ± 4.21^a	146.87 ± 8.70^b	127.13 ± 12.54^c
40	Propyl acetate	C₅H₁₀O₂	984.7	246.908	1.48651	RI, DT, IS	4.57 ± 1.07c	128.63 ± 4.28a	87.75 ± 3.26b	88.49 ± 1.99b
41	Ethyl propanoate	C₅H₁₀O₂	962.1	240.47	1.46051	RI, DT, IS	10.11 ± 0.34^d	107.08 ± 3.50^a	149.60 ± 5.39^c	167.15 ± 12.90^b
42	Ethyl isobutyrate	C₆H₁₂O₂	971.7	243.229	1.56687	RI, DT, IS	18.29 ± 2.61^d	55.22 ± 1.07^c	98.81 ± 4.67^b	104.71 ± 4.73^a
43	Ethyl lactate	C₅H₁₀O₃	1352.2	628.782	1.14736	RI, DT, IS	31.81 ± 2.91^c	158.03 ± 2.80^b	548.14 ± 74.21^a	527.01 ± 39.06^a
44	Ethyl lactate dimer	C₅H₁₀O₃	1351.9	628.056	1.53618	RI, DT, IS	44.55 ± 2.03^c	47.56 ± 4.02^c	412.23 ± 50.96^a	185.87 ± 31.25^b
47	Ethyl heptanoate	C₉H₁₈O₂	1339.7	604.482	1.40822	RI, DT, IS	39.55 ± 6.37^a	38.52 ± 2.47^a	28.44 ± 1.52^c	30.77 ± 2.79^b
Unknown
1						RI, DT, IS	15.53 ± 0.18	35.69 ± 0.80	12.70 ± 0.80	10.57 ± 0.86
2						RI, DT, IS	36.71 ± 1.51	120.41 ± 3.44	198.12 ± 6.01	201.19 ± 3.70
3						RI, DT, IS	44.35 ± 0.88	514.12 ± 4.28	224.78 ± 6.56	228.32 ± 4.62
4						RI, DT, IS	857.64 ± 8.63	33.22 ± 1.99	35.05 ± 5.99	35.17 ± 3.97
* Represents the retention index calculated using n-ketones C4−C9 as external standard on MAX-WAX column. Represents the retention time in the capillary GC column. * Represents the migration time in the drift tube.

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Analysis of the change in volatile organic compounds (VOCs) during fermentation by GS-IMS

This study adopted the GC-IMS method to test the volatile organic compounds (VOCs) in the samples from the different fermentation stages of Marselan wine. Figure 1 shows the gas phase ion migration spectrum obtained, in which the ordinate represents the retention time of the gas chromatographic peaks and the abscissa represents the ion migration time (normalized)^[16]. The entire spectrum represents the aroma fingerprints of Marselan wine at different fermentation stages, with each signal point on the right of the relative reactant ion peak (RIP) representing a volatile organic compound detected from the sample^[17]. Here, the sample in stage 1 (juice processing) was used as a reference and the characteristic peaks in the spectrum of samples in other fermentation stages were compared and analyzed after deducting the reference. The colors of the same component with the same concentration cancel each other to form a white background. In the topographic map of other fermentation stages, darker indicates higher concentration compared to the white background. In the 2D spectra of different fermentation stages, the position and number of peaks indicated that peak intensities are basically the same, and there is no obvious difference. However, it is known that fermentation is an extremely complex chemical process, and the content and types of volatile organic compounds change with the extension of fermentation time, so other detection and characterization methods are needed to make the distinction.

Figure 1. 2D-topographic plots of volatile organic compounds in Marselan wine at different fermentation stages.

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Fingerprint analysis of VOCs at different fermentation stages of Marselan wine

To visually display the dynamic changes of various substances in the fermentation process of Marselan wine, peaks with obvious differences were extracted to form the characteristic fingerprints for comparison (Fig. 2). Each row represents all signal peaks selected from samples at the same stage, and each column means the signal peaks of the same volatile compound in samples from different fermentation stages. Figure 2 shows the volatile organic compounds (VOCs) information for each sample and the differences between samples, where the numbers represent the undetermined substances in the migration spectrum library. The changes of volatile substances in the process of Marselan winemaking is observed by the fingerprint. As shown in Fig. 2 and Table 2, a total of 40 volatile chemical components were detected by qualitative analysis according to their retention time and ion migration time in the HS-GC-IMS spectrum, including 17 esters, eight alcohols, eight aldehydes, two ketones, one organic acid, and four unanalyzed flavor substances. The 12 volatile organic compounds presented dimer due to ionization of the protonated neutral components before entering the drift tube^[18]. As can be seen from Table 2, the VOCs in the winemaking process of Marselan wine are mainly composed of esters, alcohols, and aldehydes, which play an important role in the construction of aroma characteristics.

Figure 2. Fingerprints of volatile organic compounds in Marselan wine at different fermentation stages.

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Table 2. Antioxidant activity, total polyphenols, and flavonoids content of Marselan wine at different fermentation stages.

Winemaking stage	TFC (mg CE/L)	TPC (mg GAE/L)	FRAP (mM FeSO₄/mL)	ABTs (mM Trolox/L)
Stage 1	315.71 ± 0.00^d	1,083.93 ± 7.79^d	34.82^c	38.92 ± 2.12^c
Stage 2	1,490.00 ± 7.51^c	3,225.51 ± 53.27^c	77.32^b	52.17 ± 0.95^b
Stage 3	1,510.00 ± 8.88^a	3,307.143 ± 41.76^b	77.56^b	53.04 ± 0.76^b
Stage 4	1,498.57 ± 6.34^b	3,370.92 ± 38.29^a	85.07^a	57.46 ± 2.55^a
Means in the same column with different letters are significantly different (p < 0.05).

| Show Table

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Esters are produced by the reaction of acids and alcohols in wine, mainly due to the activity of yeast during fermentation^[19], and are the main components of fruit juices and wines that produce fruit flavors^[20,21]. In this study, it was found that they were the largest detected volatile compound group in Marselan wine samples, which is consistent with previous reports^[22]. It can be observed from Table 2 that the contents of most esters increased gradually with the extension of fermentation time, and they mainly began to accumulate in large quantities during the stage of alcohol fermentation. The contents of ethyl hexanoate (fruity), isoamyl acetate (banana, pear), ethyl octanoate (fruity, pineapple, apple, brandy), ethyl acetate (fruity), ethyl formate (spicy, pineapple), and ethyl butanoate (sweet, pineapple, banana, apple) significantly increased at the stage of alcoholic fermentation and maintained a high level in the subsequent fermentation stage (accounting for 86% of the total detected esters). These esters can endow a typical fruity aroma of Marselan wine, and played a positive role in the aroma profiles of Marselan wine. Among them, the content of ethyl acetate is the highest, which is 5,153.79 μg/mL in the final fermentation stage, accounting for 33.6% of the total ester. However, the content of ethyl acetate was relatively high before fermentation, which may be from the metabolic activity of autochthonous microorganisms present in the raw materials. Isobutyl acetate, ethyl 3-methyl butanoate, propyl acetate, ethyl propanoate, ethyl isobutyrate, and ethyl lactate were identified and quantified in all fermentation samples. The total contents of these esters in stage 1 and 4 were 255.28 and 1,533.38 μg/mL, respectively, indicating that they may also have a potential effect on the aroma quality of Marselan wine. The results indicate that esters are an important factor in the formation of flavor during the brewing process of Marselan wine.

Alcohols were the second important aromatic compound in Marselan wine, which were mainly synthesized by glucose and amino acid decomposition during alcoholic fermentation^[23,24]. According to Table 2, eight alcohols including methanol, ethanol, propanol, butanol, hexanol, amyl alcohol, 3-methyl-1-butanol, and 2-methyl-1-propanol were detected in the four brewing stages of Marselan wine. The contents of ethanol (slightly sweet), 3-methyl-1-butanol (apple, brandy, spicy), and 2-methyl-1-propanol (whiskey) increased gradually during the fermentation process. The sum of these alcohols account for 91%−92% of the total alcohol content, which is the highest content of three alcohols in Marselan wine, and may be contributing to the aromatic and clean-tasting wines. On the contrary, the contents of 1-hexanol and methanol decreased gradually in the process of fermentation. Notably, the content of these rapidly decreased at the stage of alcoholic fermentation, from 2,026.07 to 1,218.98 μg/mL and 662.08 to 76.94 μg/mL, respectively, which may be ascribed to volatiles changed from alcohols to esters throughout fermentation. The reduction of the concentration of some alcohols also alleviates the strong odor during wine fermentation, which plays an important role in the improvement of aroma characteristics.

Acids are mainly produced by yeast and lactic acid bacteria metabolism at the fermentation stage and are considered to be an important part of the aroma of wine^[22]. Only one type of acid (acetic acid) was detected in this experiment, which was less than previously reported, which may be related to different brewing processes. Acetic acid content is an important factor in the balance of aroma and taste of wine. Low contents of volatile acids can provide a mild acidic smell in wine, which is widely considered to be ideal for producing high-quality wines. However, levels above 700 μg/mL can produce a pungent odor and weaken the wine's distinctive flavor^[25]. The content of acetic acid increased first and then decreased during the whole fermentation process. The content of acetic acid increased rapidly in the second stage, from 719.91 to 3,914.55 μg/mL reached a peak in the third stage (5,161.81 μg/mL), and decreased to 4,630.65 μg/mL in the last stage of fermentation. Excessive acetic acid in Marselan wine may have a negative impact on its aroma quality.

It was also found that the composition and content of aldehydes produced mainly through the catabolism of amino acids or decarboxylation of ketoacid were constantly changing during the fermentation of Marselan wines. Eight aldehydes, including furfural, hexanal, heptanal, 2-methylpropanal, 3-methylbutanal, dimethyl sulfide, (E)-2-hexenal, and (E)-2-pentenal were identified in all stage samples. Among them, furfural (caramel bread flavor) and hexanal (grass flavor) are the main aldehydes in Marselan wine, and the content increases slightly with the winemaking process. While other aldehydes such as (E)-2-hexenal (green and fruity), 3-methylbutanol (fresh and malt), and 2-methylpropanal (fresh and malt) were decomposed during brewing, reducing the total content from 536.52 to 85.15 μg/mL, which might potently affect the final flavor of the wine. Only two ketones, acetone, and 3-hydroxy-2-butanone, were detected in the wine samples, and their contents had no significant difference in the fermentation process, which might not affect the flavor of the wine.

Multivariate statistical analysis

To more intuitively analyze the differences of volatile organic compounds in different brewing stages of Marselan wine samples, principal component analysis was performed^[26−28]. As presented in Fig. 3, the points corresponding to one sample group were clustered closely on the score plot, while samples at different fermentation stages were well separated in the plot. PC1 (79%) and PC2 (18%) together explain 97% of the total variance between Marselan wine samples, indicating significant changes in volatile compounds during the brewing process. As can be seen from the results in Fig. 3, samples of stages 1, 2, and 3 can be distinguished directly by PCA, suggesting that there are significant differences in aroma components in these three fermentation stages. Nevertheless, the separation of stage 3 and stage 4 samples is not very obvious and both presented in the same quadrant, which means that their volatile characteristics were highly similar, indicating that the volatile components of Marselan wine are formed in stage 3 during fermentation (Fig. S1). The above results prove that the unique aroma fingerprints of the samples from the distinct brewing stages of Marselan wine were successfully constructed using the HS-GC-IMS method.

Figure 3. PCA based on the signal intensity obtained with different fermentation stages of Marselan wine.

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Based on the results of the PCA, OPLS-DA was used to eliminate the influence of uncontrollable variables on the data through permutation test, and to quantify the differences between samples caused by characteristic flavors^[28]. Figure 4 revealed that the point of flavor substances were colored according to their density and the samples obtained at different fermentation stages of wine have obvious regional characteristics and good spatial distribution. In addition, the reliability of the OPLS-DA model was verified by the permutation method of 'Y-scrambling'' validation. In this method, the values of the Y variable were randomly arranged 200 times to re-establish and analyze the OPLS-DA model. In general, the values of R² (y) and Q² were analyzed to assess the predictability and applicability of the model. The results of the reconstructed model illustrate that the slopes of R² and Q² regression lines were both greater than 0, and the intercept of the Q² regression line was −0.535 which is less than 0 (Fig. 5). These results indicate that the OPLS-DA model is reliable and there is no fitting phenomenon, and this model can be used to distinguish the four brewing stages of Marselan wine.

Figure 4. Scores plot of OPLS-DA model of volatile components in Marselan wine at different fermentation stages.

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Figure 5. Permutation test of OPLS-DA model of volatile components in Marselan wine at different fermentation stages (n = 200).

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VIP is the weight value of OPLS-DA model variables, which was used to measure the influence intensity and explanatory ability of accumulation difference of each component on classification and discrimination of each group of samples. In previous studies, VIP > 1 is usually used as a screening criterion for differential volatile substances^[28−30]. In this study, a total of 22 volatile substances had VIP values above 1, indicating that these volatiles could function as indicators of Marselan wine maturity during fermentation (see Fig. 6). These volatile compounds included furfural, ethyl lactate, heptanal, dimethyl sulfide, 1-propanol, ethyl isobutyrate, propyl acetate, isobutyl acetate, ethanol, ethyl hexanoate, acetic acid, methanol, ethyl formate, ethyl 3-methylbutanoate, ethyl acetate, hexanal, isoamyl acetate, 2-methylpropanal, 2-methyl-1-propanol, and three unknown compounds.

Figure 6. VIP plot of OPLS-DA model of volatile components in Marselan wine at different fermentation stages.

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Conclusions

This study focuses on the change of volatile flavor compounds and antioxidant activity in Marselan wine during different brewing stages. A total of 40 volatile aroma compounds were identified and collected at different stages of Marselan winemaking. The contents of volatile aroma substances varied greatly at different stages, among which alcohols and esters were the main odors in the fermentation stage. The proportion of furfural was small, but it has a big influence on the wine flavor, which can be used as one of the standards to measure wine flavor. Flavonoids and phenols were not only factors of flavor formation, but also important factors to improve the antioxidant capacity of Marselan wine. In this study, the aroma of Marselan wines in different fermentation stages was analyzed, and its unique aroma fingerprint was established, which can provide accurate and scientific judgment for the control of the fermentation process endpoint, and has certain guiding significance for improving the quality of Marselan wines (Table S1). In addition, this work will provide a new approach for the production management of Ningxia's special wine as well as the development of the native Chinese wine industry.

Author contributions

The authors confirm contribution to the paper as follows: study conception and design: Gong X, Fang L; data collection: Fang L, Li Y; analysis and interpretation of results: Qi N, Chen T; draft manuscript preparation: Fang L. All authors reviewed the results and approved the final version of the manuscript.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

This work were supported by the project of Hainan Province Science and Technology Special Fund (ZDYF2023XDNY031) and the Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences in China (Grant No. 1630122022003).

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary information

Supplemental Table S1 FPKM of seven candidate genes of Gelsemium elegans in RNA-seq data.
Supplemental Table S2 Quality assessment of total RNA of Gelsemium elegans.
Supplemental Table S3 Stability of reference genes by Ct value in Gelsemium elegans.
Supplemental Fig. S1 Gel electrophoresis for the total RNA samples from different Gelsemium elegans tissues and under different stress treatments.
Supplemental Fig. S2 Gel electrophoresis of the first strand cDNA reversed from the RNA samples of different Gelsemium elegans tissues and under different stresses.
Supplemental Fig. S3 Melting curves of seven internal reference genes in Gelsemium elegans.

Rights and permissions
Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.

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You C, Zang S, Cui T, Sun X, Su Y, et al. 2024. Internal reference genes for normalizing quantitative real-time PCR in different tissues of Gelsemium elegans or under low temperature, MeJA, and SA stresses. Medicinal Plant Biology 3: e014 doi: 10.48130/mpb-0024-0014

You C, Zang S, Cui T, Sun X, Su Y, et al. 2024. Internal reference genes for normalizing quantitative real-time PCR in different tissues of Gelsemium elegans or under low temperature, MeJA, and SA stresses. Medicinal Plant Biology 3: e014 doi: 10.48130/mpb-0024-0014

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Introduction

Quantitative real-time PCR (RT-qPCR) is a technique for analyzing gene expression levels^[1−3], which is widely used due to its advantages of sensitivity, high efficiency, and throughput. In RT-qPCR experiments, there are differences in the quality and quantity of initial RNA, cDNA synthesis efficiency, PCR amplification efficiency, and other factors among samples, resulting in the deviation of target genes in relative expression analysis^[4]. It is necessary to use one or more internal reference genes to correct and standardize the target gene expression data^[5]. Secondary metabolites play a major role in medicinal plants, and it is very important for mining functional genes in their synthetic pathways^[6]. Therefore, appropriate reference genes are the prerequisite for the analysis of key gene expression in medicinal plants.

Generally, housekeeping genes have stable expression and are often used as internal reference genes for RT-qPCR analysis. There are various internal reference genes commonly used in plants, such as the actin gene (Actin), β-tubulin gene (TUB), eukaryotic elongation factor 1-alpha (eEF-1α), polyubiquitin gene (UBQ), etc^[7,8]. However, the expression of these genes has a certain specificity. Actin was the most stable reference gene in different tissues and in leaves treated with methyl jasmonate (MeJA) in Artemisia argyi^[9]. Serinethreonine-protein phosphatase (PP2A) can be used as an optimal internal reference gene for analyzing gene expression in different tissues of Polygonum multiflorum Thunb^[10]. eEF-1α was suitable to be used as the internal reference gene under low temperature (4 °C) treatment in Chenopodium quinoa Willd^[11]. Even in the same species, the expression of housekeeping genes in different tissues, at various developmental stages, or under different biotic and abiotic stresses is variable. Not all common housekeeping genes can be applied to all species or all experimental environments^[12]. We thus reasonably deduce that selecting appropriate internal reference genes for calibration always plays an important role in the expression pattern analysis of target genes.

Gelsemium elegans (Gardner and Champ.) Benth. (G. elegans) (National Center for Biotechnology Information Taxonomy ID: 427660), an evergreen woody vine of Gelsemium in Loganiaceae, is also known as Gou Wen, Hu Man Teng, Da Cha Yao, and Duan Chang Cao, etc^[13]. It was first recorded in 'Shen Nong's Materia Medica' and used as a traditional Chinese medicinal plant with a pungent and bitter taste^[14]. G. elegans has the functions of immune regulation, anti-inflammatory, anti-anxiety, anti-tumor, and neuropathic pain^[15−19]. As a short-day medicinal plant, G. elegans is not resistant to low temperature. When G. elegans suffers from cold stress, its young leaves turn from green to red, and thus affect its normal growth and metabolism. The root, stem, and leaf of G. elegans can be used as medicines, and its main medicinal components alkaloids are distributed in different tissues^[20]. As is well known, hormones such as JA and SA affect the synthesis and accumulation of secondary metabolites in plants. At present, most of the research on the medicinal chemistry of G. elegans focuses on its main active component indole alkaloids^[21,22]. There are no previous reports on functional gene mining in the synthesis pathway and molecular biology related to the gene expression level in G. elegans. So far, the internal reference genes of G. elegans have not yet been reported under different stress conditions.

In the present study, seven commonly used housekeeping genes in plants (Actin, TUB, eEF-1α, UBQ, cullin (CUL), adenine phosphoribosyl transferase (APRT), and pseudo response regulator (PRR) which showed stable expression abundances in our previous transcriptome database of G. elegans under cold stress and in different tissues were selected and validated. GeNorm^[23,24], NormFinder^[25], BestKeeper^[26], Delta CT^[27] and RefFinder^[28] was used to analyze the expression level and expression stability of these candidate internal reference genes in different tissues (root, stem, and leaf) of G. elegans and under 4 °C stress. Moreover, the expression patterns of three candidate genes in G. elegans, including a key gene (geranyl diphosphate synthase, GPPS) in the alkaloid synthesis pathway, an ethylene-responsive transcription factor (ERF), and a structural gene 60S ribosomal protein (60S), were investigated by RT-qPCR with the selected reference genes or their combinations. This study aims to lay a foundation for more accurate quantification of gene expression in G. elegans.

Materials and methods

Plant materials

G. elegans used in this study were collected from Yongding District, Longyan City, Fujian Province (altitude 602 m, 116.9° E, 24.4° N). The G. elegans plants were identified to be an evergreen woody vine of Gelsemium in Loganiaceae by Professor Zhongyi Zhang of Fujian Agriculture and Forestry University. This species is widely distributed in China, and no permits are required to collect it. The present work did not contain any studies with human participants or animals and did not involve any endangered or protected species. The experimental research on plants performed in this study complied with relevant institutional, national, and international guidelines and legislation. There were two experimental groups in this study. The first group was the three different tissue samples (root, stem, and young leaf) of G. elegans, which was referred to as DTS. The second group of G. elegans under low-temperature (4 °C) stress was referred to as LTS. Firstly, the G. elegans seedlings in Yongding District were transplanted into the greenhouse at the research institute of Fujian Agriculture and Forestry University. After 60 d of transplantation, the healthy seedlings with consistent growth were selected as experimental materials. The young leaves under low-temperature stress for 0 h (as a control), 12 h, and 24 h were collected. The leaves of G. elegans (60-day-old seedlings) were sprayed with 100 μM Methyl jasmonate (MeJA) and 5 mM salicylic acid (SA) in the greenhouse, respectively. They were the third MeJA-treated group and the fourth SA-treated group. The leaf samples were collected at 0, 6, 12, and 24 h. Each sample contained three biological replicates, and each replicate consisted of two mixed plants. All the collected samples were frozen in liquid nitrogen and stored in a −80 °C refrigerator.

Total RNA extraction and cDNA synthesis
Total RNA was extracted from different tissues and low-temperature treatment samples using TRIzol reagent (Yeasen, Shanghai, China). The concentration of RNA samples was detected by a UV spectrophotometer (BioTek, Vermont, USA). Agilent 2100 BioAnalyzer and 1% gel electrophoresis (Supplemental Fig. S1) were used to detect the purity, concentration, and integrity of the RNA samples. The first strand cDNA was reversed according to the instructions of Hifair® III 1st Strand cDNA Synthesis Kit (gDNA digester plus) (Yeasen, Shanghai, China) and detected by 1% gel electrophoresis (Supplemental Fig. S2).

Primer design for candidate internal reference genes and three G. elegans genes

Seven candidate internal reference genes (CUL, eEF-1α, Actin, TUB, UBQ, APRT, and PRR), which showed stable expression abundances in our previous transcriptome database of G. elegans under both cold and MeJA stress (unpublished) (Supplemental Table S1), were selected. In addition, the key alkaloid synthesis gene GPPS, a transcription factor ERF, and a structural gene 60S were also screened from the above transcriptome database. RT-qPCR primers were designed by Primer Premier 5.0 software (Table 1). Primer specificity was analyzed using Primer-BLAST (www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome). All primers were synthesized by Fuzhou Shangya Biotechnology Co., Ltd.

Table 1. Sequences of RT-qPCR primers.

Gene name	Gene description	Gene ID	Primer name	Primer sequence (5'-3')	Product length (bp)
Actin	Actin	Ge.c133917	Actin-F	TGCGGCGATCATCTACTCCG	127
Actin	Actin	Ge.c133917	Actin-R	AGCGAGGCTGGAAATCCGAA
APRT	Adenine phosphoribosyl transferase	Ge.c138712	APRT-F	AGACAACGGTCCCAAGAAGCA	90
APRT	Adenine phosphoribosyl transferase	Ge.c138712	APRT-R	ACCATGGGATTGGTCGGTCC
CUL	Cullin	Ge.c140024	CUL-F	GTTCTTACAGGCACGACACAA	115
CUL	Cullin	Ge.c140024	CUL-R	CCAAGCACCTTCAGCATCAT
eEF-1α	Eukaryotic elongation factor 1-alpha	Ge.c81268	eEF-1α-F	GCGATGTTCCCCATGTCACC	117
eEF-1α	Eukaryotic elongation factor 1-alpha	Ge.c81268	eEF-1α-R	CGGTTGGAAGCCTCAGGTCAT
PRR	Pseudo response regulator	Ge.c146065	PRR-F	ACGCATCAATCACAGCCCAC	210
PRR	Pseudo response regulator	Ge.c146065	PRR-R	GTACGTGGCTCATACACGGC
TUB	β-tubulin	Ge.c147995	TUB-F	AGGTGTCCGCAGACTTGACA	291
TUB	β-tubulin	Ge.c147995	TUB-R	GCTGCGGCATATTGAAGGCA
UBQ	Polyubiquitin	Ge.c122802	UBQ-F	CTCCGTCTCCGTGGTGGATT	80
UBQ	Polyubiquitin	Ge.c122802	UBQ-R	TGGCCAAACTTCGGTGTAACCT
GPPS	Geranyl diphosphate synthase	Ge.c151190	GPPS-F	GTGAGTTTGTTGGTGGTGAGA	93
GPPS	Geranyl diphosphate synthase	Ge.c151190	GPPS-R	GGAGATGTTGGTGAGTGTATGTAG
ERF	Ethylene-responsive transcription factor	Ge.c141467	ERF-F	AGGAAGTGGTAGAAGACATTATCG	157
ERF	Ethylene-responsive transcription factor	Ge.c141467	ERF-R	CTTGAGAGCTGCTTCATCGTAT
60S	60S ribosomal protein	Ge.c131257	60S-F	CACCTGAGACCTGCTGAATATAAG	84
60S	60S ribosomal protein	Ge.c131257	60S-R	AGACAACACGCCACCATAAG

Primer-specific detection and amplification efficiency analysis of candidate internal reference genes

The RT-qPCR test was carried out using QuanStudio 3 real-time PCR instrument (ThermoFisher, Waltham, USA). Its reaction system (20 μL) included 10 μL SYBR Green Master Mix (Vazyme, Nanjing, China), 0.4 μL 10 μmol·L⁻¹ upstream primer, 0.4 μL 10 μmol·L⁻¹ downstream primer, 1.0 μL cDNA template, and 8.2 μL ddH₂O. RT-qPCR with distilled water as a template was performed as controls. The RT-qPCR reaction program was 50 °C for 2 min, 95 °C for 30 s, 40 cycles of 95 °C for 10 s, and 60 °C for 30 s, and the melting curve was collected at 95 °C for 15 s, 60 °C for 1 min and 95 °C for 1 s. Each sample was repeated three times. The amplification specificity of the ten candidate genes with a 5 × cDNA template of G. elegans leaves was analyzed according to the melting curve.

The cDNA template of G. elegans leaves was diluted five times successively, and five concentration gradients were set, which were 5⁻¹, 5⁻², 5⁻³, 5⁻⁴, and 5⁻⁵ times of the cDNA template, respectively. The CT values of each candidate gene under different gradients were obtained through RT-qPCR analysis, and the standard curve of the seven candidate internal reference genes (CUL, eEF-1α, Actin, TUB, UBQ, APRT, and PRR) was manufactured by Microsoft Excel 2016. Then the slope (k) and linear correlation coefficient (R²) were obtained, and the amplification efficiency was calculated following a formula of E = (10^−1/k − 1) × 100%.

Stability evaluation and validation of candidate internal reference genes
The expression stability of candidate internal reference genes was evaluated by BestKeeper, geNorm, NormFinder, Delta CT, and RefFinder software^[23−28]. For BestKeeper, the input data was the original CT value and the internal reference genes were evaluated by calculating the standard deviation (SD) of CT values of all samples and the variation coefficient (CV) of candidate genes. The input data for geNorm and NormFinder were the relative quantities converted from CT values with a formula of 2^−ΔCT, where ΔCT meant each corresponding CT value minus the minimum CT value. The data in geNorm and NormFinder were evaluated by the M value, and the lower the M value, the more stable the gene. Relative stability values for gene expression were generated in NormFinder, with smaller values indicating higher stability. The CT values of candidate internal reference genes were directly analyzed by BestKeeper and Delta CT. According to the analysis results of the four programs and the RefFinder online website (http://blooge.cn/RefFinder/?type=reference#), the stability of all seven candidates were evaluated by comprehensive comparison, and the optimal internal reference genes or their combinations of G. elegans for tissues specific expression and cold stress response was screened.

According to the optimal internal reference genes, the expression patterns of the three selected genes, including GPPS, ERF, and 60S were analyzed in different tissues of G. elegans and at different time points after low-temperature treatment. The relative expression of the gene was calculated using the 2^−ΔΔCT method^[29]. Quantitative data for internal reference genes validation were sorted to obtain the CT value of the gene and the CT value distribution map was drawn using GraphPad 6.0 software. The significance level of the data was analyzed using DPS 7.05 software, and graphs were plotted using GraphPad 6.0 and Adobe Illustrator CS6.

Discussion

RT-qPCR is an indispensable experimental technique in the research on the synthesis and regulation of secondary metabolites in medicinal plants, which is an important method to detect gene expression^[32]. Selecting an appropriate internal reference gene for calibration can improve the accuracy of RT-qPCR, and the appropriate internal reference gene should be selected according to specific experimental conditions^[33,34]. geNorm, NormFinder, BestKeeper, and Delta CT are commonly used methods for assessing the stability of reference genes. geNorm can identify the most stable combination of genes, but it incurs higher computational costs for a large number of samples^[23]. NormFinder considers both within-sample and between-sample variations, suitable for small samples but with higher computational cost^[35]. BestKeeper provides a comprehensive stability index, applicable to small samples but sensitive to outliers^[36]. The Delta CT method is simple and practical but overlooks potential mutual influences among genes^[37]. The choice of method should consider experimental conditions, and integrating results from multiple methods enhances the reliability of the assessment.

In this study, seven candidate internal reference genes were screened from the G. elegans transcriptome database. Through geNorm, NormFinder, BestKeeper, and Delta CT analysis (Figs 2−4; Table 3), the expression stability of these seven candidates in the root, stem, and leaf of G. elegans and the leaf at different time points after low temperature, MeJA, and SA stresses was comprehensively evaluated. The ranking of candidate internal reference genes varied slightly among algorithms^[38]. In general, the results of this study showed that the stability of CUL and eEF-1α was essentially better with the above four analyzed platforms in the four experiments, which were in accordance with the previous reports that both CUL and eEF-1α can be used as internal reference genes for gene expression normalization in different plant tissues and under low temperature, MeJA, and SA stresses^[39−41]. To synthesize the results of the four algorithms, RefFinder was utilized to rank the identified candidate genes in the G. elegans, and the platform played an important role in integrating the results of the other algorithms for the screening of internal reference genes^[42,43]. Fortunately, it was found that RefFinder's results were similar to those of the different algorithms in the four experiments (Fig. 5; Table 3).

GPPS is a key enzyme in the synthesis pathway of iridoid compounds^[44] and in the generation of indole alkaloid precursors. ERF is a key hub for regulating hormone and stress signals^[45] and a downstream regulator in the ethylene signal transduction pathway^[46], which plays an important role in plant stress response^[44]. Ribosomal proteins are important structural components of ribosomes^[47]. In this study, the single (CUL or eEF-1α), and double (CUL + eEF-1α) internal reference genes were used as internal reference genes to detect the relative expression level of GPPS, ERF, and 60S genes in leaf, root, and stem of G. elegans and under low temperature, MeJA, and SA stresses. Our data indicated that GPPS, ERF, and 60S genes were constitutively expressed in G. elegans tissues and significantly enriched in the root (unpublished). The content of the important medicinal ingredient koumine (indole alkaloid) of G. elegans in the root is significantly higher than that in other tissues^[20]. It is thus speculated that GPPS may play an important role in the synthesis of indole alkaloids in G. elegans. Prolonged periods of sustained cold temperatures have serious effects on G. elegans growth^[48]. The expression level of ERF and 60S genes could be induced by low-temperature (Fig. 6). A previous study found that ERF plays an active role in the cold resistance of Citrus reticulata Blanco^[49], and it is inferred that ERF can be used as a candidate gene for G. elegans in response to low-temperature stress. MeJA is a volatile methyl ester of jasmonic acid, which has been identified as the main signaling molecule in abiotic and biological stress^[50]. SA is a well-known inducer of systemic acquired resistance and could also promote the synthesis of secondary metabolites in plants. MeJA and SA as exciters were able to promote the synthesis of different active compounds in a variety of medicinal plants in different cultures^[51]. GPPS, ERF, and 60S genes were induced to be expressed under MeJA and SA treatments, respectively. Active participation of ERF in hormone signaling pathways enhances the synthesis of medicinally active plant compounds in various medicinal plants^[52]. In addition, the expression trends of GPPS, ERF, and 60S in the DTS, LTS, MeJA, and SA groups with the internal reference gene of CUL or eEF-1α were consistent with that using double reference genes (Fig. 6). For economic reasons, the best quantitative results can be obtained by using fewer reference genes. Therefore, single reference genes (CUL or eEF-1α) or double reference genes (CUL + eEF-1α) can be selected as the best or perfect combination of reference genes in subsequent studies of G. elegans.

Conclusions

In the present study, CUL and eEF-1α were selected to be the optimal internal reference gene for RT-qPCR analysis in different G. elegans tissues and under low temperature, MeJA, and SA stresses according to the combination analysis of geNorm, NormFinder, BestKeeper, Delta CT, and RefFinder programs. Moreover, single reference gene (CUL or eEF-1α) or double reference genes (CUL + eEF-1α) can be selected as the best or perfect combination of reference genes for gene expression analysis in G. elegans. This study not only lays a foundation for the expression analysis of related genes in the growth and development of G. elegans under different conditions, but also provides a reference for the subsequent studies on the expression and functional verification of other genes in G. elegans.

Author contributions

The authors confirm contribution to the paper as follows: study conception and design: You C, Zang S, Que Y, Que W; data collection: You C, Cui T, Sun X; analysis and interpretation of results: Su Y, Lin Q, Lin H; draft manuscript preparation: You C, Que Y, Que W. All authors reviewed the results and approved the final version of the manuscript.

Gene	Slope (k)	Amplification efficiency (%)	Correlation coefficient (R²)
CUL	−3.023	1.142	0.999
eEF-1α	−3.099	1.102	0.999
APRT	−3.013	1.147	0.999
TUB	−3.154	1.075	0.996
Actin	−2.992	1.159	0.993
PRR	−3.117	1.093	0.996
UBQ	−3.314	1.003	0.999

Group	Gene	geo mean	AR mean	Min	Max	SD	Stability rank
DTS	APRT	24.89	24.89	23.66	25.79	0.56	1
	TUB	24.14	24.15	23.00	25.54	0.73	2
	eEF-1α	20.25	20.28	18.40	22.10	0.79	3
	CUL	23.75	23.77	22.44	25.76	0.80	4
	Actin	23.63	23.65	22.44	25.68	0.84	5
	UBQ	18.80	18.84	17.13	20.97	0.99	6
	PRR	23.15	23.25	20.90	26.89	1.98	7
LTS	CUL	24.38	24.39	23.95	25.55	0.27	1
	eEF-1α	20.99	20.99	20.53	21.98	0.32	2
	TUB	26.15	26.16	25.35	27.28	0.34	3
	Actin	26.46	26.46	26.01	27.60	0.36	4
	APRT	26.17	26.17	25.67	27.56	0.41	5
	PRR	24.32	24.32	23.59	25.18	0.42	6
	UBQ	18.03	18.06	16.19	20.01	0.82	7
MeJA	CUL	24.13	24.13	23.28	24.99	0.31	1
	eEF-1α	22.34	22.35	21.54	23.08	0.41	2
	TUB	25.01	25.02	24.00	26.55	0.58	3
	UBQ	18.03	18.04	17.02	19.30	0.63	4
	APRT	25.35	25.36	23.97	27.00	0.74	5
	Actin	24.38	24.39	23.37	25.94	0.77	6
	PRR	21.88	21.89	20.14	23.13	0.79	7
SA	CUL	26.54	26.54	25.95	27.46	0.35	1
	eEF-1α	22.21	22.22	20.98	23.00	0.49	2
	PRR	25.59	25.60	24.29	27.28	0.67	3
	UBQ	19.42	19.44	18.21	20.97	0.70	4
	TUB	27.69	27.72	25.58	29.39	0.79	5
	APRT	27.50	27.52	25.11	29.74	0.83	6
	Actin	27.73	27.75	26.12	29.28	0.84	7
Notes: geo mean, geometric mean; AR mean, average mean; Min, minimum mean; Max, max mean; SD, standard deviation. DTS, different G. elegans tissue samples; LTS, G. elegans leaves under low temperature stress; CUL, cullin; eEF-1α, eukaryotic elongation factor 1-alpha; APRT, adenine phosphoribosyl transferase; TUB, β-tubulin; Actin, actin; PRR, pseudo response regulator; UBQ, polyubiquitin.

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Internal reference genes for normalizing quantitative real-time PCR in different tissues of Gelsemium elegans or under low temperature, MeJA, and SA stresses

Abstract

Introduction

Materials and methods

Plant materials

Chemicals and reagents

Preparation of Marselan juices and wines

Total polyphenols and flavonoid content determination

ABTS free-radical scavenging assay

FRAP free-radical scavenging assay

HS-GC-IMS analysis

Statistical data analysis

Results

Changes in total polyphenol and flavonoid contents and antioxidant activity during fermentation

Analysis of the change in volatile organic compounds (VOCs) during fermentation by GS-IMS

Fingerprint analysis of VOCs at different fermentation stages of Marselan wine

Multivariate statistical analysis

Conclusions

Author contributions

Data availability

Acknowledgments

Conflict of interest

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Internal reference genes for normalizing quantitative real-time PCR in different tissues of Gelsemium elegans or under low temperature, MeJA, and SA stresses

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Plant materials

Total RNA extraction and cDNA synthesis

Primer design for candidate internal reference genes and three G. elegans genes

Primer-specific detection and amplification efficiency analysis of candidate internal reference genes

Stability evaluation and validation of candidate internal reference genes

Extraction and quality of total RNA from G. elegans

Primer design and amplification specificity of candidate internal reference genes

Expression abundance of candidate internal reference genes

Stability of candidate internal reference genes by geNorm, NormFinder, BestKeeper, and Delta CT

Comprehensive stability of candidate internal reference genes

Stability verification for internal reference genes by the expression pattern of GPPS, ERF, and 60S genes

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Plant materials

Total RNA extraction and cDNA synthesis

Primer design for candidate internal reference genes and three G. elegans genes

Primer-specific detection and amplification efficiency analysis of candidate internal reference genes

Stability evaluation and validation of candidate internal reference genes

Extraction and quality of total RNA from G. elegans

Primer design and amplification specificity of candidate internal reference genes

Expression abundance of candidate internal reference genes

Stability of candidate internal reference genes by geNorm, NormFinder, BestKeeper, and Delta CT

Comprehensive stability of candidate internal reference genes

Stability verification for internal reference genes by the expression pattern of GPPS, ERF, and 60S genes

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