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. 2020 Feb 12;10:2429. doi: 10.1038/s41598-020-59168-z

Identification and evaluation of reliable reference genes for quantitative real-time PCR analysis in tea plants under differential biotic stresses

Wei Xu 1,#, Yanan Dong 1,2,#, Yongchen Yu 2,3, Yuxian Xing 2,3, Xiwang Li 2,3, Xin Zhang 2,3, Xiangjie Hou 2,3, Xiaoling Sun 2,3,✉
PMCID: PMC7015943  PMID: 32051495

Abstract

The selection of reliable reference genes (RGs) for normalization under given experimental conditions is necessary to develop an accurate qRT-PCR assay. To the best of our knowledge, only a small number of RGs have been rigorously identified and used in tea plants (Camellia sinensis (L.) O. Kuntze) under abiotic stresses, but no critical RG identification has been performed for tea plants under any biotic stresses till now. In the present study, we measured the mRNA transcriptional levels of ten candidate RGs under five experimental conditions; these genes have been identified as stable RGs in tea plants. By using the ΔCt method, geNorm, NormFinder and BestKeeper, CLATHRIN1 and UBC1, TUA1 and SAND1, or SAND1 and UBC1 were identified as the best combination for normalizing diurnal gene expression in leaves, stems and roots individually; CLATHRIN1 and GAPDH1 were identified as the best combination for jasmonic acid treatment; ACTIN1 and UBC1 were identified as the best combination for Toxoptera aurantii-infested leaves; UBC1 and GAPDH1 were identified as the best combination for Empoasca onukii-infested leaves; and SAND1 and TBP1 were identified as the best combination for Ectropis obliqua regurgitant-treated leaves. Furthermore, our results suggest that if the processing time of the treatment was long, the best RGs for normalization should be recommended according to the stability of the proposed RGs in different time intervals when intragroup differences were compared, which would strongly increase the accuracy and sensitivity of target gene expression in tea plants under biotic stresses. However, when the differences of intergroup were compared, the RGs for normalization should keep consistent across different time points. The results of this study provide a technical guidance for further study of the molecular mechanisms of tea plants under different biotic stresses.

Subject terms: Plant stress responses, Biotic

Introduction

With the increasing popularity of gene expression analysis in biological research, quantitative real-time polymerase chain reaction (qRT-PCR) has become a critical and powerful tool for rapid and reliable quantification of mRNA transcriptional expression levels of target genes due to its high-throughput screening, sensitivity, simplicity, specificity and accuracy1,2. Relative quantification of target gene expression under certain stresses has been widely studied since the beginning of this century3. An accurate assay of gene expression through qRT-PCR relies on every step of sample preparation and processing, e.g., the integrity of purified RNA, the efficiency of reverse transcription, and the overall transcriptional activity of the tissues or cells analysed4; each step needs to be accurately normalized by stably expressed reference genes (RGs)5,6. Therefore, the selection of reliable RGs for normalization under given experimental conditions is a requirement for developing an accurate qPCR assay.

Housekeeping genes, such as the glyceraldehyde 3-phosphate (GAPDH), the actin gene (ACTIN), translation elongation factor EF-1 alpha (EF-1α), 18 s rRNA, 25 S rRNA and poly-ubiquitin (UBQ), have been commonly used as the normalization scalar in studies of relative quantification of plant target genes, some of which (EF-1α, GAPDH, ACTIN) have been identified as reliable RGs in certain plants under given experimental conditions7–10. However, to date, no RG has been found to exhibit perfectly stable expression in all plant species, even in the same tissue from the same plant species, but under different experimental conditions11–13. For instance, DcACTIN and DcUBQ have been identified as the top two stable RGs in carrot (Daucus carota L.) under abiotic stresses, but eIF-4α and GAPDH have been ranked in the top two RGs in carrots under hormone stimuli7; in tea plants (Camellia sinensis (L.) O. Kuntze), CsTIP41 was identified as the most stable RG for leaf development, but CsTBP was identified as the most stable RG for tea leaves under hormone stimuli14. Therefore, to avoid missing or overemphasizing potential biological changes of target gene expression, it is essential to identify optimum stable RGs for the proposed research object, for different tissues of the same species, for the same tissue of the same species under different biotic or abiotic stresses and their processing time.

Tea is one of the most important leaf-type woody cash crops in China, and the tender buds and leaves of this plant are the raw material for commercial tea. Since the publication of the draft genome sequence of C. sinensis var. sinensis15, the molecular mechanisms of aroma components biosynthesis, cold spells or resistance, drought resistance, barren tolerance, and other interactions of tea plants with environmental factors or with other organisms around them have been elucidated16–20. During the development of tea plant, it usually suffers serious damage from the infestation of insect herbivores all year round. Therefore, the chemical and molecular mechanisms under interactions between tea plants and their herbivorous pests need to be widely excavated to offer theoretical foundations for utilizing chemical signals between them to control tea pests or breeding new insect-resistant tea varieties. The RGs used previously in the studies of herbivores (Ectropis obliqua, Empoasca onukii) induced tea plant defensive responses at the gene transcriptional level, such as CsGAPDH and 18SrRNA21–23, were roughly selected from previously reported RGs without critical identification under given experimental conditions, which may lead to the deviation of the results to some extent and may also lead to the neglect of some important experimental phenomena. Therefore, it is important to define the RG for qRT-PCR analysis in tea plants under infestations of different pests and their related biotic stresses.

According to previous reports, CsACTIN1, Clathrin adaptor complex subunit (CsCLATHRIN1), CsEF1, CsGAPDH1, SAND family protein gene (CsSAND1), Tap42-interacting protein of 41 kDa (CsTIP41), Ubiquitin-conjugating enzyme (CsUBC1), Polypyrimidine tract-binding protein (CsPTB1), alpha-1 tubulin (CsTUA1) and TATA-box binding protein gene (CsTBP1) are frequently used as stable RGs in the process of mRNA expression analysis (Tables 1 and 2)20,24–29. In the present study, we measured mRNA transcriptional levels of the above mentioned ten RGs in different tissues of tea plants in circadian rhythms, jasmonic acid-treated tea leaves, T. aurantii infested tea leaves, E. onukii infested tea leaves, and tea leaves treated with mechanical damage plus E. obliqua regurgitant. The results were evaluated by BestKeeper, geNorm, NormFinder and the ΔCt method to identify the most stably expressed RGs firstly; secondly, RefFinder was used to integrate the results to determine the most stable RG for each treatment. Finally, to demonstrate the importance of stable RGs in the normalization process of tea plants under infestations of different pests or their related biotic stresses, CsMYC2, CsOPR3, CsPAL and CsPALc were chosen as the target genes for validation. As we all know, MYC2 was a key transcription factor of JA signaling pathway30; OPR3 is the isoenzyme relevant for JA biosynthesis22 and PAL were closely associated with the accumulation of endogenous SA31. The aim of this study was to select the most appropriate RGs for the gene expression analysis of tea plants under different biotic stresses.

Table 1.

Ten housekeeping genes frequently used for qRT-PCR of tea plant.

NO. Abbreviation Given conditions Ref.
1 CsACTIN1

Different organs

Nitrogen stress

Fe stress

Sun et al.29;

Liu et al.20;

Wang et al.24
2 CsCLATHRIN1

Different organs

Leaves with Cold and short photoperiod treatments

Shoots after auxin antagonist auxinole treatments

 Hao et al.28
3 CsEF1 Diurnal expression in leaves Hao et al.28
4 CsGAPDH1

Different maturity of leaves

Leaves with Cold and drought treatments

Nitrogen stress

Drought, cold, Al, and NaCl stresses

Sun et al.29;

Ma et al.25;

Liu et al.20
5 CsSAND1 Different organs Hao et al.28
6 CsTIP41 In various tea leaf developmental stages Wu et al.26
7 CsUBC1

Shoots with cold and short photoperiod treatments

Mn stress

Hao et al.28;

Wang et al.24
8 CsPTB1 Shoots after auxin antagonist auxinole treatment Hao et al.28
9 CsTUA1 Physical damages Ma et al.25
10 CsTBP

In various tea leaf developmental stages

Leaves with hormone treatments

Mn stress

Post-harvest leaves

Posharvest

Wu et al.26;

Wang et al.24;

Zhou et al.27
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Table 2.

Sequence Information of the Candidate Reference Genes and Target Genes.

Name GeneBank Accession Number Primer sequence (5′–3′) forward/reverse Amplicon Length (bp) qRT-PCR Efficiency (%)
CsEF1 KA280301.1 TTGGACAAGCTCAAGGCTGAACG 110 98
ATGGCCAGGAGCATCAATGACAGT
CsCLATHRIN1 KA291473.1 TAGAGCGGGTAGTGGAGACCTCGTT 129 102
TACCAAAGCCGGCTCGTATGAGATT
CsACTIN1 KA280216.1 TGGGCCAGAAAGATGCTTATGTAGG 118 103
ATGCCAGATCTTTTCCATGTCATCC
CsGAPDH1 KA295375.1 TTTTTGGCCTTAGGAACCCAGAGG 107 93
GGGCAGCAGCCTTATCCTTATCAGT
CsSAND1 KM057790 TCCAATTGCCCCCTTAATGACTCA 109 106
GTAAGGGCAGGCAAACACCAGGTA
CsTIP41 AT4G34270 TGGAGTTGGAAGTGGACGAGACCGA 176 103.6
CTCTGGAAAGTGGGATGTTTGAAGC
CsUBC1 KA281185.1 TGCTGGTGGGGTTTTTCTTGTTACC 124 92
AAGGCATATGCTCCCATTGCTGTTT
CsPTB1 GAAC01052498.1 TGACCAAGCACACTCCACACTATCG 107 95
TGCCCCCTTATCATCATCCACAA
CsTUA1 JN399223.1 TCACTGTTTACCCATCTCCC 167 106.1
GTAGGTGGGTCGCTCAATAT
CsTBP AT1G55520 GGCGGATCAAGTGTTGGAAGGGAG 166 107.0
ACGCTTGGGATTGTATTCGGCATTA
CsMYC2 EF645810 TAGCGGTTGTGGCGGAGATT
TGAGCTTCTCTCGCCTCTGC
CsOPR3 XM_028243785.1 CGATCAACAGCCGGTGGATTT
GCGTGGACAGCATCAACCAC
CsPAL D26596.1 CCAATTCCTTGCCAATCCTGTAAC
CAACTGCCTCGGCTGTCTTTCT
CsPALc KY615671 CGGAACAAGGCCTTACATGG
TGGGCAAACATGAGCTTTCC
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Results

Expression profiles of candidate reference genes

The expression level of RGs in all treatments is performed in terms of the cycle threshold number (Ct value). As shown in Fig. 1, the raw Ct values of all candidate RGs ranged from 13.90 (EF1) to 28.29 (TBP). EF1 (18.44), ACTIN1 (18.91), GAPDH1 (18.97) and TUA1 (19.23) were the most abundant transcripts, reaching the threshold fluorescence peak after 18 cycles. PTB1 (23.65), CLATHRIN1 (23.71), SAND1 (24.04) and TBP (24.08) were expressed at the lowest levels. The raw Ct values of the four target genes ranged from 18.72 (PALc) to 27.26 (MYC2). More details were shown in Fig. S8.

Figure 1.

Figure 1

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Expression Profiles of Ten Candidate Reference Genes and Four Target Genes in C. sinensis. The expression level of RGs in all samples is performed in terms of the cycle threshold number (Ct value). The data are expressed as box-whisker plots; the short bar in the box refers to the Ct mean value; the box represents the 25th–75th percentiles; the median is indicated by a bar across the box; the whiskers on each box represent the distribution of the Ct values; and the dark spots refer to extreme outliers.

Diurnal expression in different tissues

Leaf

The gene expression stability of ten candidate RGs for leaves with circadian rhythm was analyzed by using geNorm, NormFinder, BestKeeper and the ΔCt method. The results showed that the gene stability ranking as analyzed by BestKeeper differed from the ranking as analyzed by the other three methods. For example, geNorm, NormFinder and the ΔCt method identified UBC1 and CLATHRIN1 as the most stable 2 of the 10 RGs in all test periods (from 0:00 am to 22:00 pm), whereas BestKeeper identified GAPDH1 and CLATHRIN1 as the most stable 2 of the 10 RGs for diurnal expression in leaves. However, all four methods identified PTB1 as the most variable RG. According to the results from RefFinder, the stability ranking of RGs from the most to the least was as follows: UBC1 > CLATHRIN1 > GAPDH1 > TBP > EF1 > SAND1 > TUA1 > ACTIN1 > TIP41 > PTB1 (Table 3). With GeNorm (Fig. 2), all pairwise variation (Vn/n + 1) was below 0.15 (the recommended cut-off), indicating that the inclusion of an additional RG was unnecessary. Based on the ranking of the RGs by RefFinder, CLATHRIN1 and UBC1 were identified as the best combination for normalizing the diurnal expression in leaves (Tables 4 and 5).

Table 3.

Ranking of 10 Reference Genes Expression under Different Experimental Manipulations.

Group Rank geNorm NormFinder BestKeeper ΔCt RefFinder
Reference Gene Stability Reference Gene Stability Reference Gene Standard Deviation r Reference Gene Standard Deviation
Circadian rhythm of leaf 1 UBC 0.243 UBC1 0.160 GAPDH1 0.366 0.885 UBC1 0.333 UBC1
2 CLATHRIN1 0.243 CLATHRIN1 0.201 CLATHRIN1 0.367 0.894 CLATHRIN1 0.353 CLATHRIN1
3 TBP 0.267 GAPDH1 0.225 ACTIN1 0.383 0.726 GAPDH1 0.362 GAPDH1
4 GAPDH1 0.284 TBP 0.256 UBC1 0.391 0.933 TBP 0.379 TBP
5 EF1 0.308 SAND1 0.274 TBP 0.396 0.858 SAND1 0.395 EF1
6 TUA1 0.320 TUA1 0.288 EF1 0.417 0.863 EF1 0.401 SAND1
7 SAND1 0.343 EF1 0.289 TUA1 0.442 0.868 TUA1 0.402 TUA
8 TIP41 0.357 TIP41 0.296 SAND1 0.469 0.891 TIP41 0.408 ACTIN1
9 ACTIN1 0.373 ACTIN1 0.373 TIP41 0.486 0.871 ACTIN1 0.455 TIP41
10 PTB1 0.399 PTB1 0.434 PTB1 0.583 0.858 PTB1 0.503 PTB1
Circadian rhythm of stem 1 SAND1 0.208 TUA1 0.184 UBC1 0.241 0.559 TUA1 0.492 TUA1
2 TIP41 0.208 CLATHRIN1 0.253 TUA1 0.264 0.819 CLATHRIN1 0.525 SAND1
3 PTB1 0.246 SAND1 0.315 SAND1 0.270 0.547 SAND1 0.532 CLATHRIN1
4 UBC1 0.323 ACTIN1 0.33 CLATHRIN1 0.328 0.792 TIP41 0.548 UBC1
5 TUA1 0.347 UBC1 0.334 TIP41 0.331 0.577 UBC1 0.552 TIP41
6 CLATHRIN1 0.368 TIP41 0.342 PTB1 0.342 0.530 PTB1 0.574 PTB1
7 ACTIN1 0.410 PTB1 0.375 TBP 0.377 0.786 ACTIN1 0.591 ACTIN1
8 TBP 0.443 TBP 0.376 ACTIN1 0.467 0.869 TBP 0.604 TBP
9 EF1 0.490 EF1 0.599 EF1 0.520 0.615 EF1 0.733 EF1
10 GAPDH1 0.639 GAPDH1 1.182 GAPDH1 0.768 0.719 GAPDH1 1.234 GAPDH1
Circadian rhythm of root 1 SAND1 0.308 UBC1 0.211 TIP41 0.431 0.833 UBC1 0.581 SAND1
2 TBP 0.308 SAND1 0.287 CLATHRIN1 0.433 0.851 SAND1 0.594 UBC1
3 TIP41 0.367 CLATHRIN1 0.323 SAND1 0.454 0.878 TBP 0.609 TBP
4 CLATHRIN1 0.421 TBP 0.327 PTB1 0.471 0.738 CLATHRIN1 0.617 TIP41
5 UBC1 0.429 TIP41 0.349 UBC1 0.492 0.931 TIP41 0.618 CLATHRIN1
6 PTB1 0.451 PTB1 0.459 TBP 0.520 0.909 PTB1 0.680 PTB1
7 GAPDH1 0.502 GAPDH1 0.496 EF1 0.616 0.800 GAPDH1 0.710 GAPDH1
8 EF1 0.549 EF1 0.584 GAPDH1 0.660 0.939 EF1 0.780 EF1
9 TUA1 0.638 TUA1 0.885 ACTIN1 0.814 0.387 TUA1 0.995 TUA1
10 ACTIN1 0.727 ACTIN1 0.987 TUA1 0.992 0.857 ACTIN1 1.085 ACTIN1
JA treatment 1 CLATHRIN1 0.209 CLATHRIN1 0.132 SAND1 0.194 0.604 CLATHRIN1 0.290 CLATHRIN1
2 GAPDH1 0.209 GAPDH1 0.166 PTB1 0.194 0.42 GAPDH1 0.303 GAPDH1
3 UBC1 0.221 UBC1 0.213 TIP41 0.196 0.625 UBC1 0.325 UBC1
4 SAND1 0.250 TIP41 0.228 GAPDH1 0.223 0.815 TIP41 0.333 TIP41
5 TIP41 0.269 TBP 0.231 UBC1 0.227 0.716 TBP 0.340 PTB1
6 PTB1 0.281 ACTIN1 0.234 CLATHRIN1 0.269 0.893 ACTIN1 0.342 SAND1
7 ACTIN1 0.297 SAND1 0.243 ACTIN1 0.322 0.876 SAND1 0.346 TBP
8 TBP 0.309 PTB1 0.313 TBP 0.332 0.864 PTB1 0.384 ACTIN1
9 EF1 0.329 EF1 0.325 EF1 0.379 0.868 EF1 0.400 EF1
10 TUA1 0.349 TUA1 0.363 TUA1 0.421 0.796 TUA1 0.432 TUA1
T. aurantii infestation 1 ACTIN1 0.490 ACTIN1 0.336 ACTIN1 0.32 0.501 ACTIN1 0.709 ACTIN1
2 TBP 0.490 UBC1 0.515 EF1 0.412 0.184 UBC1 0.777 UBC1
3 CLATHRIN1 0.507 GAPDH1 0.563 GAPDH1 0.458 0.553 GAPDH1 0.812 GAPDH1
4 GAPDH1 0.531 CLATHRIN1 0.592 UBC1 0.464 0.510 CLATHRIN1 0.820 CLATHRIN1
5 TIP41 0.541 EF1 0.617 CLATHRIN1 0.465 0.453 PTB1 0.848 TBP
6 UBC1 0.688 PTB1 0.639 TBP 0.533 0.456 EF1 0.855 EF1
7 SAND1 0.758 SAND1 0.643 PTB 0.560 0.617 SAND1 0.869 PTB1
8 PTB1 0.792 TBP 0.682 SAND1 0.571 0.558 TBP 0.872 SAND1
9 EF1 0.815 TIP41 0.756 TIP41 0.638 0.508 TIP41 0.914 TIP41
10 TUA1 0.843 TUA1 0.792 TUA1 0.65 0.441 TUA 0.954 TUA1
E. onukii infestation 1 GAPDH1 0.275 UBC1 0.201 EF1 0.560 0.892 UBC1 0.574 UBC1
2 UBC1 0.275 GAPDH1 0.230 GAPDH1 0.590 0.941 GAPDH1 0.585 GAPDH1
3 EF1 0.334 TIP41 0.338 CLATHRIN1 0.620 0.761 EF1 0.628 EF1
4 TIP41 0.420 EF1 0.347 TIP41 0.630 0.891 TIP41 0.643 TIP41
5 SAND1 0.461 SAND1 0.439 SAND1 0.660 0.868 SAND1 0.688 SAND1
6 TBP 0.491 TBP 0.466 UBC1 0.660 0.957 TBP 0.701 CLATHRIN1
7 TUA1 0.542 TUA1 0.566 PTB1 0.700 0.494 TUA1 0.773 TBP
8 CLATHRIN1 0.583 CLATHRIN1 0.589 ACTIN1 0.730 0.715 CLATHRIN1 0.784 TUA1
9 ACTIN1 0.664 ACTIN1 0.868 TBP 0.800 0.924 ACTIN1 0.995 ACTIN1
10 PTB1 0.743 PTB1 0.947 TUA1 0.860 0.894 PTB1 1.058 PTB1
Mechanical damage and E.obliqua regurgitant treatment 1 SAND1 0.261 SAND1 0.194 ACTIN1 0.344 0.806 SAND1 0.422 SAND1
2 TBP 0.322 TBP 0.216 CLATHRIN1 0.372 0.799 TBP 0.435 TBP
3 CLATHRIN1 0.337 PTB1 0.240 TBP 0.381 0.897 PTB1 0.451 CLATHRIN1
4 TIP41 0.343 CLATHRIN1 0.279 PTB1 0.382 0.862 CLATHRIN1 0.460 PTB1
5 PTB1 0.363 ACTIN1 0.292 SAND1 0.429 0.915 TIP41 0.477 ACTIN1
6 UBC1 0.388 TIP41 0.328 TIP41 0.436 0.810 ACTIN1 0.482 TIP41
7 ACTIN1 0.420 UBC1 0.374 UBC1 0.447 0.801 UBC1 0.513 UBC1
8 EF1 0.453 EF1 0.451 EF1 0.494 0.698 EF1 0.576 EF1
9 GAPDH1 0.518 GAPDH1 0.460 GAPDH1 0.520 0.779 GAPDH1 0.583 GAPDH1
10 TUA1 0.261 TUA1 0.709 TUA1 0.616 0.537 TUA1 0.775 TUA1
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Figure 2.

Figure 2

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Optimal Number of Reference Genes for the Normalization of C. sinensis under Different Experimental Manipulations. The pairwise variation (Vn/n + 1) was analysed by geNorm software to determine the optimal number of RGs included in the qPCR analysis. Values less than 0.15 indicate that another RG will not significantly improve normalization.

Table 4.

Ranking of 10 Reference Genes Expression in Different Processing Time under Different Experimental Manipulations.

Analysis Tool Ranking Order (from the most stable to the least stable)
1 2 3 4 5 6 7 8 9 10
JA treatment in the time interval from 0.5 h to 1.5 h
ΔCT CLATHRIN1 UBC1 ACTIN1 TIP41 TBP GAPDH1 PTB1 EF1 SAND1 TUA1
BestKeeper TIP41 PTB1 CLATHRIN1 UBC1 SAND1 GAPDH1 TBP ACTIN1 EF1 TUA1
Normfinder CLATHRIN1 UBC1 ACTIN1 TIP41 TBP SAND1 GAPDH1 PTB1 EF1 TUA1
Genorm CLATHRIN1 | UBC1 ACTIN1 GAPDH1 EF1 TIP41 TBP PTB1 SAND1 TUA1
Recommended comprehensive ranking CLATHRIN1 UBC1 TIP41 ACTIN1 PTB1 GAPDH1 TBP SAND1 EF1 TUA1
JA treatment in the time interval from 3 h to 6 h
ΔCT GAPDH1 UBC1 TIP41 CLATHRIN1 TBP PTB1 SAND1 EF1 TUA1 ACTIN1
BestKeeper TBP SAND1 GAPDH1 PTB1 UBC1 TIP41 CLATHRIN1 EF1 TUA1 ACTIN1
Normfinder GAPDH1 UBC1 TIP41 CLATHRIN1 TBP PTB1 SAND1 EF1 TUA1 ACTIN1
Genorm TIP41 | PTB1 CLATHRIN1 UBC1 GAPDH1 TBP SAND1 EF1 TUA1 ACTIN1
Recommended comprehensive ranking GAPDH1 TIP41 UBC1 PTB1 TBP CLATHRIN1 SAND1 EF1 TUA1 ACTIN1
JA treatment in the time interval from 12 h to 48 h
ΔCT CLATHRIN1 TBP GAPDH1 ACTIN1 SAND1 TIP41 EF1 UBC1 TUA1 PTB1
BestKeeper CLATHRIN1 SAND1 GAPDH1 UBC1 TIP41 PTB1 TBP ACTIN1 TUA1 EF1
Normfinder CLATHRIN1 TBP GAPDH1 ACTIN1 TIP41 SAND1 EF1 UBC1 TUA1 PTB1
Genorm CLATHRIN1 | GAPDH1 TBP ACTIN1 SAND1 EF1 UBC1 TIP41 TUA1 PTB1
Recommended comprehensive ranking CLATHRIN1 GAPDH1 TBP SAND1 ACTIN1 TIP41 UBC1 EF1 PTB1 TUA1
T. aurantii infestation in the time interval from 6 h to 24 h
ΔCT ACTIN1 UBC1 GAPDH1 CLATHRIN1 TBP SAND1 PTB1 EF1 TIP41 TUA1
BestKeeper ACTIN1 CLATHRIN1 UBC1 GAPDH1 EF1 TBP SAND1 PTB1 TIP41 TUA1
Normfinder ACTIN1 UBC1 GAPDH1 CLATHRIN1 SAND1 EF1 TBP PTB1 TIP41 TUA1
Genorm ACTIN1 | TBP CLATHRIN1 TIP41 GAPDH1 UBC1 SAND1 PTB1 EF1 TUA1
Recommended comprehensive ranking ACTIN1 UBC1 CLATHRIN1 GAPDH1 TBP SAND1 EF1 TIP41 PTB1 TUA1
T. aurantii infestation at 48 h
ΔCT ACTIN1 EF1 PTB1 TUA1 SAND1 UBC1 CLATHRIN1 TIP41 TBP GAPDH1
BestKeeper ACTIN1 EF1 PTB1 TUA1 UBC1 SAND1 TBP CLATHRIN1 GAPDH1 TIP41
Normfinder ACTIN1 PTB1 EF1 TUA1 SAND1 CLATHRIN1 UBC1 TIP41 TBP GAPDH1
Genorm EF1 | TUA1 PTB1 SAND1 UBC1 ACTIN1 CLATHRIN1 TIP41 TBP GAPDH1
Recommended comprehensive ranking ACTIN1 EF1 PTB1 TUA1 SAND1 UBC1 CLATHRIN1 TBP TIP41 GAPDH1
E. onukii infestation in the time interval from 12 h to 72 h
ΔCT UBC1 GAPDH1 EF1 TIP41 SAND1 TBP TUA1 CLATHRIN1 PTB1 ACTIN1
BestKeeper SAND1 EF1 TIP41 GAPDH1 CLATHRIN1 UBC1 PTB1 TBP ACTIN1 TUA1
Normfinder GAPDH1 UBC1 EF1 TIP41 SAND1 TBP TUA1 CLATHRIN1 PTB1 ACTIN1
Genorm GAPDH1 | UBC1 EF1 TIP41 SAND1 TBP TUA1 CLATHRIN1 PTB1 ACTIN1
Recommended comprehensive ranking GAPDH1 UBC1 EF1 SAND1 TIP41 TBP CLATHRIN1 TUA1 PTB1 ACTIN1
E. onukii infestation at 96 h
ΔCT PTB1 TBP GAPDH1 UBC1 ACTIN1 SAND1 CLATHRIN1 TIP41 EF1 TUA1
BestKeeper EF1 GAPDH1 ACTIN1 SAND1 UBC1 PTB1 CLATHRIN1 TBP TUA1 TIP41
Normfinder PTB1 TBP GAPDH1 UBC1 ACTIN1 SAND1 CLATHRIN1 TIP41 EF1 TUA1
Genorm PTB1 | TBP GAPDH1 UBC1 ACTIN1 CLATHRIN1 SAND1 EF1 TIP41 TUA1
Recommended comprehensive ranking PTB1 TBP GAPDH1 UBC1 ACTIN1 EF1 SAND1 CLATHRIN1 TIP41 TUA1
E. onukii infestation in the time interval from 120 h to 144 h
ΔCT TIP41 EF1 TBP UBC1 GAPDH1 SAND1 CLATHRIN1 ACTIN1 TUA1 PTB1
BestKeeper UBC1 GAPDH1 EF1 CLATHRIN1 TIP41 ACTIN1 TBP SAND1 PTB1 TUA1
Normfinder TIP41 EF1 UBC1 TBP GAPDH1 SAND1 CLATHRIN1 ACTIN1 TUA1 PTB1
Genorm TIP41 | TBP EF1 UBC1 GAPDH1 SAND1 CLATHRIN1 ACTIN1 TUA1 PTB1
Recommended comprehensive ranking TIP41 EF1 UBC1 TBP GAPDH1 CLATHRIN1 SAND1 ACTIN1 TUA1 PTB1
E. obliqua regurgitant treatment in the time interval from 1.5 h to 3 h
ΔCT TIP41 SAND1 ACTIN1 CLATHRIN1 TBP PTB1 UBC1 EF1 TUA1 GAPDH1
BestKeeper TBP ACTIN1 PTB1 UBC1 TIP41 CLATHRIN1 SAND1 EF1 TUA1 GAPDH1
Normfinder ACTIN1 TIP41 SAND1 PTB1 TBP CLATHRIN1 UBC1 EF1 TUA1 GAPDH1
Genorm TIP41 | TBP SAND1 CLATHRIN1 EF1 ACTIN1 PTB1 UBC1 TUA1 GAPDH1
Recommended comprehensive ranking TIP41 TBP ACTIN1 SAND1 PTB1 CLATHRIN1 UBC1 EF1 TUA1 GAPDH1
E. obliqua regurgitant treatment at 6 h
ΔCT TBP CLATHRIN1 SAND1 UBC1 TIP41 ACTIN1 PTB1 GAPDH1 EF1 TUA1
BestKeeper GAPDH1 UBC1 TIP41 ACTIN1 SAND1 CLATHRIN1 PTB1 EF1 TBP TUA1
Normfinder TBP SAND1 UBC1 CLATHRIN1 ACTIN1 TIP41 PTB1 GAPDH1 EF1 TUA1
Genorm CLATHRIN1 | TIP41 UBC1 TBP SAND1 ACTIN1 EF1 PTB1 GAPDH1 TUA1
Recommended comprehensive ranking TBP CLATHRIN1 UBC1 TIP41 SAND1 GAPDH1 ACTIN1 PTB1 EF1 TUA1
E. obliqua regurgitant treatment in the time interval from 12 h to 48 h
ΔCT SAND1 CLATHRIN1 TBP PTB1 GAPDH1 ACTIN1 TIP41 UBC1 EF1 TUA1
BestKeeper SAND1 ACTIN1 TBP CLATHRIN1 PTB1 GAPDH1 TIP41 UBC1 EF1 TUA1
Normfinder SAND1 TBP CLATHRIN1 PTB1 GAPDH1 ACTIN1 TIP41 UBC1 EF1 TUA1
Genorm SAND1 | TBP CLATHRIN1 PTB1 TIP41 UBC1 GAPDH1 ACTIN1 EF1 TUA1
Recommended comprehensive ranking SAND1 TBP CLATHRIN1 PTB1 ACTIN1 GAPDH1 TIP41 UBC1 EF1 TUA1
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Table 5.

Summary of treatments and results.

No. Treatments Recommended RGs for each treatment
Names Organs Conditions
1 Circadian rhythm of different tissues Leaf All test period CsUBC1, CsCLATHRIN1
Stem All test period CsTUA1, CsSAND1
Root All test period CsSAND1, CsUBC1
2 JA treatment 2nd leaves 0.5–1.5 h CsCLATHRIN1, CsUBC1
3–6 h CsGAPDH1, CsTIP41
12–48 h CsCLATHRIN1, CsGAPDH1
All test period CsCLATHRIN1, CsGAPDH1
3 T. aurantii infestation 2nd leaves 6–24 h CsACTIN1, CsUBC1
48 h CsACTIN1, CsEF1
All test period CsACTIN1, CsUBC1
4 E. onukii infestation 2nd leaves 12–72 h CsGAPDH1, CsUBC1
96 h CsPTB1, CsTBP
120–144 h CsTIP41, CsEF1
All test period CsGAPDH1, CsUBC1
5 Mechanical damage and E.obliqua regurgitant treatment 2nd leaves 1.5–3 h CsTIP1, CsTBP1
6 h CsTBP, CsCLATHRIN
12–48 h CsSAND1, CsTBP
All test period CsSAND1, CsTBP
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Stem

GeNorm identified SAND1 and TIP41 as the most stable RGs in all test periods (from 0:00 am to 22:00 pm) (Table 4). NormFinder and the ΔCt method identified TUA1 and CLATHRIN1 as the most stable RGs. BestKeeper identified TUA1, CLATHRIN1 and SAND1 as the top three RGs. However, all four methods identified GAPDH1 as the most unstable RG (Table 3). According to the results from RefFinder, the stability ranking of RGs from the most to the least was as follows: TUA1 > SAND1 > CLATHRIN1 > UBC1 > TIP41 > PTB1 > ACTIN1 > TBP > EF1 > GAPDH1. Based on the ranking of the RGs by RefFinder, TUA1 and SAND1 were identified as the best combination for normalizing the diurnal expression in the stem (Table 5).

Root

NormFinder and the ΔCt method identified UBC1 and SAND1 as the most stable RGs, and ACTIN1 as the least stable RG in all test period (from 0:00 am to 22:00 pm) (Table 3). GeNorm identified SAND1 as the most stable RG. BestKeeper identified TIP41 as the most stable RG. According to the results of RefFinder, the stability ranking of RGs from the most to the least was as follows: SAND1 > UBC1 > TBP > TIP41 > CLATHRIN1 > PTB1 > GAPDH1 > EF1 > TUA1 > ACTIN1. The results of the geNorm analysis revealed that all V values were below 0.15 (Fig. 2). Thus, SAND1 and UBC1 were identified as the best combination for normalizing the gene diurnal expression in roots (Table 5).

JA treatment

GeNorm, NormFinder and the ΔCt method identified CLATHRIN1, GAPDH1 and UBC1 as the top three stable RGs in all test periods (from 0.5 h to 48 h) (Table 3). BestKeeper identified SAND1, PTB1 and TIP41 as the top three stable RGs. All four methods identified TUA1 as the most unstable RG (Table 3). According to the results of RefFinder, the stability ranking of RGs from the most to the least was as follows: CLATHRIN1 > GAPDH1 > UBC1 > TIP41 > PTB1 > SAND1 > TBP > ACTIN1 > EF1 > TUA1. The results of the geNorm analysis revealed that all V values were below 0.15 (Fig. 2). Thus, CLATHRIN1 and GAPDH1 were identified as the best combination for normalizing JA-treated leaves. With further analysis, RefFinder identified CLATHRIN1 and UBC1 as the best combination for JA treatment in the time interval from 0.5 h to 1.5 h, GAPDH1 and TIP41 as the best combination in the time interval from 3 h to 6 h, and CLATHRIN1 and GAPDH1 as the best combination in the time interval from 12 h to 48 h (Tables 4 and 5).

T. aurantii infestation

NormFinder and ΔCt identified ACTIN1 and UBC as the most stable 2 of the 10 RGs in all test periods (from 6 h to 48 h) (Table 4). BestKeeper ranked ACTIN1 and EF1 as the top two stable RGs. GeNorm ranked ACTIN1 and TBP as the top two RGs. According to the results of RefFinder, the stability ranking of RGs from the most to the least was as follows: ACTIN1 > UBC1 > GAPDH1 > CLATHRIN1 > TBP > EF1 > PTB1 > SAND1 > TIP41 > TUA1 (Table 3). The results of the geNorm analysis revealed that almost all V values were below 0.15 (Fig. 2). Thus, ACTIN1 and UBC1 were identified as the best combination for normalizing T. aurantii-infested leaves. With further analysis, RefFinder identified ACTIN1 and UBC1 as the best combination in the time interval from 6 h to 24 h, ACTIN1 and EF1 as the best combination at 48 h (Tables 4 and 5).

E. onukii infestation

The GeNorm, NormFinder and ΔCt methods identified GAPDH1 and UBC1 as the most stable 2 of the 10 RGs, while PTB1 was the least stable RG in all test periods (from 12 h to 144 h) (Table 3). BestKeeper identified EF1, GAPDH1 and CLATHRIN1 as the top three stable RGs. According to the results of RefFinder, the stability ranking of RGs from the most to the least was as follows: UBC1 > GAPDH1 > EF1 > TIP41 > SAND1 > CLATHRIN1 > TBP > TUA1 > ACTIN > PTB1. The results of the geNorm analysis revealed that all V values were below 0.15 (Fig. 2). Thus, UBC1 and GAPDH1 were identified as the best combination for normalizing E. onukii-infested leaves. With further analysis, RefFinder identified GAPDH1 and UBC1 as the best combination in the time interval from 12 h to 72 h, PTB1 and TBP as the best combination at 96 h, TIP41 and EF1 as the best combination in the time interval from 120 h to 144 h (Tables 4 and 5).

Mechanical damage and E. obliqua regurgitant treatment

GeNorm, NormFinder and the ΔCt method identified SAND1 and TBP1 as the most stable 2 of the 10 RGs, while TUA1 was the least stable RG in all test periods (from 1.5 h to 48 h) (Table 3). BestKeeper identified ACTIN1, CLATHRIN1 and TBP as the top three stable RGs. According to the results of RefFinder, the stability ranking of RGs from the most to the least was as follows: SAND1 > TBP > CLATHRIN1 > PTB1 > ACTIN1 > TIP41 > UBC1 > EF1 > GAPDH1 > TUA1. The results of geNorm revealed that all V values were below 0.15 (Fig. 2). Thus, SAND1 and TBP1 were identified as the best combination for normalizing regurgitant-treated leaves. With further analysis, RefFinder identified TIP41 and TBP as the best combination in the time interval from 1.5 h to 3 h, TBP and CLATHRIN1 as the best combination at 6 h, and SAND1 and TBP as the best combination in the time interval from 12 h to 48 h (Tables 4 and 5).

Validation of proposed RGs

CsMYC2 was chosen as the target gene to validate the rationality of the recommended RGs used in diurnal expression analysis (Fig. 3A–C). The expression level of CsMYC2 in leaves at 14:00 pm was significantly higher than that in the time period from 0:00 am to 12:00 am (NF 9–10, F = 14.098, P = 0.000; P = 0.000; P = 0.000; P = 0.000; P = 0.000; P = 0.000) and that at 16:00 pm, 20:00 pm and 22:00 pm (NF 9–10, F = 14.098, P = 0.000; P = 0.000; P = 0.000) when normalized with the two unstable RGs, TIP41 and PTB1 (NF 9–10); these expression level trends were quite similar to that normalized with the combination of UBC1 and CLATHRIN1 (NF 1–2, F = 10.169, P = 0.000; P = 0.000; P = 0.003; P = 0.003; P = 0.005; P = 0.000), except for 10:00 am (NF 1–2, F = 10.169, P = 0.138) (Fig. 3A); the expression level of CsMYC2 in leaves at 4:00 am was significantly higher than that at 0:00 am and 2:00 am when normalized with the combination of UBC1 and CLATHRIN1 (NF 1–2, F = 10.169, P = 0.000; P = 0.002), but no significant differences were detected when normalized with the combination of TIP41 and PTB1 (NF 9–10, F = 14.098, P = 0.141; P = 0.485) (Fig. 3A). The expression level of CsMYC2 in stem at 10:00 am was significantly higher than that at the time period from 0:00 am to 6:00 am and from 12:00 am to 22:00 pm when normalized either with the combination of TUA1 and SAND1 (NF 1–2, F = 3.743, P = 0.000; P = 0.003; P = 0.019; P = 0.000; P = 0.003; P = 0.008; P = 0.002; P = 0.030; P = 0.001) or with the combination of EF1 and GAPDH1 (NF 9–10, F = 6.969, P = 0.000; P = 0.001; P = 0.005; P = 0.000; P = 0.000; P = 0.005; P = 0.000; P = 0.005; P = 0.006), except for 16:00 pm (NF 1–2, F = 3.734, P = 0.383; NF 9–10, F = 6.969, P = 0.000); however, the expression level of CsMYC2 in stem at 16:00 pm was significantly higher than that at 12:00 am and 18:00 pm when normalized with the combination of TUA1 and SAND1 (NF 1–2, F = 3.734, P = 0.030; P = 0.023), and no significant differences were detected when normalized with the combination of EF1 and GAPDH1 (NF 9–10, F = 6.969, P = 0.145; P = 0.256) (Fig. 3B). The expression level of CsMYC2 at 16:00 pm in root was significantly higher than that at4:00 am, 12:00 am, 14:00 pm, 20:00 pm and 22:00 pm when normalized with the most stable combination of SAND1 and UBC1 (NF 1–2, F = 3.610, P = 0.013; P = 0.000; P = 0.000; P = 0.002; P = 0.003;), but the expression level of CsMYC2 at 16:00 pm has no significant differences with that at all the time points (NF 9–10, F = 3.972, P = 0.521; P = 0.080; P = 0.464; P = 0.179; P = 0.604; P = 0.173; P = 0.360; P = 0.789; P = 0.525; P = 0.200), except for 10:00 am(NF 9–10, F = 3.972, P = 0.001), when normalized with the most unstable combination of TUA1 and ACTIN1 (NF 9–10) (Fig. 3C).

Figure 3.

Figure 3

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Validation of the gene stability measure. Expression profiles of CsMYC2, CsOPR3, CsPAL and CsPALc under different experimental conditions using different RGs. (A) Diurnal expression profile of CsMYC2 in leaves, NF (1–2) were UBC1 and CLATHRIN1, NF (9–10) were TIP41 and PTB1; (B) Diurnal expression profile of CsMYC2 in stems, NF (1–2) were TUA1 and SAND1, NF (9–10) were EF1 and GAPDH1; (C) Diurnal expression profile of CsMYC2 in roots, NF(1–2) were SAND1 and UBC1, NF (9–10) were TUA1 and ACTIN1; (D) Expression profile of CsOPR3 at 3 h normalized with the best combination (GAPDH1 and TIP41) at 3 h, the best combination (CLATHRIN1 and UBC1) at 0.5–1.5 h, and the best combination (CLATHRIN1 and GAPDH1) at 12–48 h RGs under JA treatment; (E) Expression profile of CsPAL at 48 h normalized with the best combination (ACTIN1 and EF1) at 48 h, and the best combination (ACTIN1 and UBC1) at 6–24 h RGs under T. aurantii infestation; (F) Expression profile of CsPALc at 96 h normalized with the best combination (PTB1 and TBP) at 96 h, the best combination (GAPDH1 and UBC1) at 12–72 h, and the best combination (TIP41 and EF1) at 120–144 h under E. onukii infestation; (G) Expression profile of CsOPR3 at 6 h normalized with the best combination (TBP1 and CLATHRIN1) at 6 h, the best combination (TIP41 and TBP) at 1.5–3 h, and the best combination (SAND1 and TBP) at 12–48 h RGs under E. obliqua infestation; (H) Expression profile of CsOPR3 normalized with the stable and unstable RGs at 3 h under JA treatment. NF1 was GAPDH1, NF (1–2) were GAPDH1 and TIP41, NF10 was ACTIN1, NF (9–10) were TUA1 and ACTIN1; (I) Expression profiles of CsPAL normalized with the stable and unstable RGs at 6 h under T. aurantii infestation. NF1 was ACTIN1, NF (1–2) were ACTIN1 and UBC1, NF10 was TUA1, NF (9–10) were PTB1 and TUA1; (J) Expression profile of CsPALc normalized with the stable and unstable RGs at 96 h under E. onukii infestation. NF1 was PTB1, NF (1–2) were PTB1 and TBP, NF10 was TUA1, NF (9–10) were TIP41 and TUA1; (K) Expression profile of CsOPR3 normalized with the stable and unstable RGs at 6 h under E. obliqua infestation. NF1 was TBP, NF (1–2) were TBP and CLATHRIN1, NF10 was TUA1, NF (9–10) were EF1 and TUA1; Data are means ± SE. One-way ANOVA (Tukey’s test) was used to analyze significant difference among treatments (A~C,F,G,J,K); different letters indicate significant differences among treatments (lowercase letters, P < 0.05; uppercase letters, P < 0.01). Two samples were compared by using Student’s t-test (D, E, H, I); **P < 0.01.

CsOPR3 was chosen as the target gene to validate the rationality of the recommended RGs used in exogenous application of JA (Fig. 3D,H). When the best combination of the time interval from 3 h to 6 h, GAPDH1 and TIP41 (NF 1–2, F = 1.426, P = 0.028) was used for normalization, the expression level of CsOPR3 in JA-treated leaves was significantly higher than that in the control at 3 h, but no significant difference was found when normalized with the best combination of the time interval from 0.5 h to 1.5 h, CLATHRIN1 and UBC1 (NF 1–2, F = 0.163, P = 0.091) or 12 h to 48 h, CLATHRIN1 and GAPDH1 (NF 1–2, F = 0.599, P = 0.126) (Fig. 3D). When the most appropriate RG–GAPDH1 (NF 1, F = 0.023, P = 0.037) or the best combination of GAPDH1 and TIP41 (NF 1–2, F = 1.426, P = 0.028) of the time interval from 3 h to 6 h was used for normalization, the expression level of CsOPR3 in JA-treated leaves at 3 h was significantly higher than that in the control, but no significant difference was found when normalized with the combination of the two unstable RGs, TUA1 and ACTIN1 (NF 9–10, F = 0.138, P = 0.204), or with the most unstable RG (NF 10, F = 3.888, P = 0.259) (Fig. 3H).

CsPAL was chosen as the target gene to validate the rationality of the recommended RGs used in T. aurantii infestation (Fig. 3E,I). When the best combination at 48 h, ACTIN1 and EF1 (NF 1–2, F = 2.458, P = 0.047), was used for normalization, the expression level of CsPAL in treated leaves at 48 h was significantly higher than that in control, but no significant difference was found when normalized with the most stable combination of the time interval from 6 h to 24 h, ACTIN1 and UBC1 (NF 1–2, F = 2.921, P = 0.063) (Fig. 3E). When the most appropriate RG–ACTIN (NF 1, F = 0.116, P = 0.041) or the best combination of ACTIN1 and UBC1 (NF 1–2, F = 0.245, P = 0.030) of the time interval from 6 h to 24 h was used for normalization, the expression level of CsPAL in treated leaves at 6 h was significantly higher than that in control, but no significant difference was found when normalized with the most unstable combination of PTB1 and TUA1 (NF 9–10, F = 0.820, P = 0.141) or with the most unstable RG (NF 10, F = 2.355, P = 0.120) (Fig. 3I).

CsPALc was chosen as the target gene to validate the rationality of the recommended RGs used in E. onukii infestation (Fig. 3F,J). When the best combination of PTB1 and TBP at 96 h was used for normalization, the expression level of CsPALc at 96 h in pre-pregnant female-infested leaves was significantly higher than that of pregnant female-infested leaves (NF 1–2, F = 13.471, P = 0.002) and control leaves (F = 13.471, P = 0.008), but a relatively slight difference between pre-pregnant female-infested leaves and pregnant female-infested leaves was found when normalized with the combination of the two stable RGs in 12–72 h, GAPDH1 and UBC1 (NF 1–2, F = 4.838, P = 0.040) or in 120–144 h, TIP41 and EF1 (NF 1–2, F = 5.934, P = 0.018) (Fig. 3F). When the most appropriate RG–PTB1, or the most stable combination of PTB1 and TBP at 96 h was used for normalization, the expression level of CsPALc at 96 h in pre-pregnant female-infested leaves was significantly higher than that of pregnant female-infested leaves (NF 1, F = 10.566, P = 0.005; NF 1–2, F = 13.471, P = 0.002) and control leaves (NF 1, F = 10.566, P = 0.017; NF 1–2, F = 13.471, P = 0.008), but a relatively slight difference between pregnant female-infested leaves and pre-pregnant female-infested leaves was found when normalized with the most unstable combination, TIP41 and TUA1 (NF 9–10, F = 4.938, P = 0.037), and no significant difference was found when normalized with the most unstable RG (NF 10, F = 4.769, P = 0.072) (Fig. 3J).

CsOPR3 was chosen as the target gene to validate the rationality of the recommended RGs used in E. obliqua regurgitant treatment (Fig. 3G,K). When the best combination at 6 h, TBP and CLATHRIN1 was used for normalization, the expression level of CsOPR3 at 6 h in wounding leaves was significantly higher than that of regurgitant-treated leaves (NF 1–2, F = 32.921, P = 0.015) and intact leaves ((NF 1–2, F = 32.921, P = 0.000), but no significant difference between regurgitant-treated leaves and wounding leaves was found when normalized with the combination of the most two stable RGs in 1.5–3 h, TIP41 and TBP ((NF 1–2, F = 23.023, P = 0.051) or in 12–48 h, SAND1 and TBP (NF 1–2, F = 14.784, P = 0.176) (Fig. 3G). When the most appropriate RG–TBP (NF 1), or the most stable combination of TBP and CLATHRIN1 (NF 1–2) at 6 h was used for normalization, the expression level of CsOPR3 at 6 h in wounding leaves was significantly higher than that of regurgitant-treated leaves (NF 1, F = 26.647, P = 0.023; NF 1–2, F = 32.921, P = 0.015) and intact leaves (NF 1, F = 26.647, P = 0.001; NF 1–2, F = 32.921, P = 0.000), but no significant difference between regurgitant-treated leaves and wounding leaves was found when normalized with the most unstable combination, EF1 and TUA1 (NF 9–10, F = 7.557, P = 0.277) or with the most unstable RG (NF 10, F = 10.295, P = 0.117) (Fig. 3K).

Discussion

Normalizing results with one or more appropriate internal RGs is a simple and popular method for controlling error in qRT-PCR assays. To date, a few housekeeping genes have been rigorously identified and used as RGs in tea plants under abiotic stresses, such as cold, barrenness, drought, photoperiod and exogenous application of plant hormones (auxin, ABA, GA, IAA, MeJA and SA)25,26,28,32–34, leaf developmental stages and even different organs26,35. These results demonstrate that identifying appropriate RGs for target gene expression analysis under different experimental conditions is an essential prerequisite for developing a qPCR assay of tea plants. To the best of our knowledge, the present study is the first to define the proper RGs for qRT-PCR analysis in tea plants under infestations of different herbivorous pests and their related biotic stresses.

In the present study, ten candidate RGs were selected from those already identified as stably expressed RGs with high efficiency in tea molecular studies (Table 1). Previously, CsACINT1 was identified as one of the most unstable RGs under different experimental manipulations, such as different organs, cold or photoperiod treatment of leaves and shoots, diurnal expression in leaves, auxinole and lanolin treatment28. In the current study, our results showed that CsACINT1 was ranked as one of the five most unstable RGs for diurnal variation of different organs, JA-treated leaves, infestation of E. onukii, and mechanical damage plus E. obliqua regurgitant; however, this gene was determined as the best RG in T. aurantii infested leaves (Table 4). Similarly, CsACINT1 was found to be the most stably expressed RG in tea plants under Fe stress and in different organs33. CsUBC1 was identified as the most stable RG in almost all treatments, except for E. obliqua regurgitant treatment, while CsUBC1 was identified as the suitable RG when tea plants were under Mn stress24. CsTUA1 was ranked as the most unstable RG for tea plants across most of our experimental conditions, except for diurnal expression in stems (Table 4), while previous results revealed that CsTUA1 was the most stable RG for damage stresses of tea shoots. CsTBP was identified as one of the top two appropriate RGs for qRT-PCR analysis in hormonal stimuli tea leaf samples by GeNorm and NormFinder26, which includes ABA, GA, IAA, MeJA and SA. However, among the 10 RGs tested in this study, CsTBP was recommended as the seventh stable RG in JA stimuli samples, and CsGAPDH1 and CsCLATHRIN1 were recommended as the best RG combination for JA treatment (Table 4). The main reason for the difference is probably because different proposed RGs were adopted to rank the order. The results described above indicate, unsurprisingly, that no RG has been found to exhibit perfectly stable transcript accumulation in tea plants across different experimental conditions, even the already identified stable RGs.

The stability of the same RG varies with different plant species under diverse experimental conditions. TIP41-like protein (TIP41) was appraised as the best RG in different stages during development of bamboo (Phyllostachys edulis), reproductive stages of rapeseed (Brassica napus)36, and cucumber (Cucumis sativus) subjected to abiotic stresses and growth regulators37. Our results verified that TIP41 was the second most stable RG in JA-treated leaves in the time interval from 3 h to 6 h and the most stable RG in tea leaves infested by E. onukii in the time interval from 120 h to 144 h (Table 5). EF1 has been proven to be an appropriate RG for normalization of flower buds at different stages of female flower bud differentiation in the English walnut (Juglans regia)38, and EF1 was the second stable RG in tea leaves infested by E. onukii in the time interval from 120 h to 144 h or infested by T. aurantii at 48 h as well (Table 5). Similarly, EF1-a gene was found to perform well for aphid-infested chrysanthemum39, and EF1A 2a, EF1A 1a1 and EF1A 2b were also identified as the best RG in JA-treated leaves of soybean40. GAPDH, ACTIN and UBC are the commonly used RGs for qRT-PCR analysis in varied plant, whose function is maintaining cell survival irrespective of physiological conditions41–43. In this study, we found that ACTIN, UBC and GAPDH were the top three appropriate RGs for the whole samples of T. aurantii-infested leaves (Table 4), but GAPDH and ACTIN were less stable in peach44. CsUBC1 was also identified as an appropriate RG in almost all treatments, except for E. obliqua regurgitant treatment. HbUBC2a and HbUBC4 were identified as the most stable RGs in Brazilian rubber trees (Hevea brasiliensis) when all samples were analysed together45, but the UBC2 genes were not the proper RGs in soybean (Glycine max) and watermelon (Citrullus lanatus) exposed to cadmium or under abiotic stress46,47. Consequently, our results emphasize that the selection of reliable RGs for normalization under any given experimental design is a requirement for developing a proper qPCR assay.

Multiple RGs have been suggested for normalizing target gene expression, which will reduce the probability of biased normalization13,48. In the current study, our results demonstrated using multiple RGs simultaneously in qRT-PCR analysis would increase the sensitivity of gene expression in E. onukii infested leaves (Fig. 3J) or E. obliqua regurgitant treatment (Fig. 3K). Furthermore, our results suggest that if the processing time of treatment was long, the best RGs for normalization should be recommended according to the stability of the proposed RGs in different time intervals when intragroup differences were compared (Table 5; Fig. 3D–G), which would strongly increase the accuracy and sensitivity of target gene expression in tea plants under biotic stresses. However, when the differences of intergroup were compared, the RGs for normalization should keep consistent across different time points.

In summary, we screened a series of RGs to study the gene expression profile of different organs of tea plants with circadian rhythm, JA-treated tea leaves, tea leaves attacked by T. aurantii or E. onukii, and tea leaves treated with mechanical damage plus E. obliqua regurgitant. Our results provide a technical guidance for further study of the molecular mechanisms of tea plants under different biotic stresses.

Methods

Insects

The tea aphid (Toxoptera aurantii), the tea leafhooper (Empoasca onukii) and the tea looper (Ectropis obliqua) were caught from the experimental tea garden of the Tea Research Institute of the Chinese Academy of Agricultural Sciences (TRI, CAAS, N 30°10′, E 120°5′), Hangzhou, China. The insects were reared on the potted tea shoots in the controlled climate room at 26 ± 2 °C, 70 ± 5% rh, and a photoperiod of 14:10 h (L:D). Newly hatched larvae/nymphs were fed on tender tea shoots that were enclosed in net cages (75 × 75 × 75 cm) and kept in the room. After one generation, mixed age nymphs of T. aurantii were used for plant treatment. The 4th-instar E. onukii nymphs were collected individually and maintained in separate plastic tubes (1.5 cm wide × 9 cm high) with fresh tea stems, and then the newly molted adults were separated by sex according to morphological characteristics. One newly molted adult female and two males were kept in a plastic container (12 cm high × 7 cm diameter) with fresh tea shoots for 5 days to obtain a fully mated female. One-day-old virgin female adults were used as feeding adults, and 6-day-old fully mated females were used as pregnant females. Our biological bioassay results showed that the pre-oviposition period is 5 d, and 6-day-old fully mated females have similar food consumption to that of 1-day-old virgin females (unpublished data). Forth-instar larvae of E. obliqua were used for collecting regurgitants.

Regurgitant collection

As the method proposed by Yang et al.49, regurgitant was absorbed from E. obliqua oral cavity with a P200 Pipetteman (Gilson, Middleton, WI, USA). The collected regurgitant was homogenized at first. The homogeneous regurgitant was centrifuged for 5 min (10,000 × g), then the supernatant was collected and stored at −80 °C until use.

Tea plants and treatments

Longjing 43 tea plants (three-year-old) were used for experiments, which were planted individually in a plastic pot (14 cm diameter × 15 cm high), incubated in the greenhouse programmed at12-h photophase, 26 ± 2 °C, and 70–80% relative humidity. All materials were incubated under such conditions unless otherwise stated. Plants were fertilized with fertilizer once a month and irrigated once every other day. Day before processing, tea leaves were washed under the running water. Leaves in the same position but in different branches of the same tea plant were selected for each time points. Treatments were prepared as follows.

Different tissues in circadian rhythm

The second leaves (numbered sequentially from the most apically unfolded leaf down the stem), stems (tender internodes between the first and the second) and fibrous roots of tea plants were harvested every 2 h of a day in the autumn of 2018. Four replications were carried out.

Exogeneous application of JA

JA (Sigma Chemical Co., St. Louis, MO, USA) was dissolved in a small amount of ethylalcohol and made up to a concentration of 0.15 mg/mL in 50 mM sodium phosphate buffer (titrated with 1 M citric acid until pH 8). Treatments were individually sprayed with 8 mL of JA solution. Tea plants were individually sprayed with 8 mL of the buffer were used as control. Plants were treated at 10:00 am in the climate chamber. The second leaves were harvested at 0.5, 1.5, 3, 6, 12, 24 and 48 h after the start of treatment. Each treatment was replicated five times.

T. aurantii infestation

Fifty aphids were inoculated on the tender bud and the 1st leaves. A fine-mesh sleeve was used to cover the 2nd leaf to prevent aphid infestation and honeydew pollution. The second leaves that covered with mesh sleeves only were used as controls. The 2nd leaves were harvested at 6, 12, 24, 48 h after the start of treatment. Each treatment was replicated five times.

E. onukii infestation

The 2nd tender leaf was covered with a mesh sleeve into which 4 one-day-old virgin adult females or 4 six-day-old fully mated adult females that had been starved for 2 h were introduced at 9:00 pm. Plants with only their 2nd leaves covered with mesh sleeves were used as controls. Seventy-two hours after the start of treatments, E. onukii adults were carefully removed. Then, the 2nd leaves were harvested at 12, 24, 48, 72, 96, 120 and 144 h after the start of removal. Each treatment was replicated six times.

Mechanical damage plus E. obliqua regurgitant treatment

A fabric pattern wheel was used to damage tea leaves following the method described previously (2004)50. Each leaf was rolled 6 times, and 15 μL regurgitant was immediately painted to the puncture wounds. Deionized water in equal amounts was painted to the wounds for wounding treatment. The intact 2nd leaf was used as control. The treated and control 2nd leaves were harvested at 1.5, 3, 6, 12, 24 and 48 h after the start of treatment. Each treatment was replicated five times.

All treatments are briefly summarized below (Table 5).

Total RNA isolation, cDNA synthesis and qPCR analysis

The TRIzol™ kit (TIANGEN, Beijing, China) was used to isolate plant total RNA according to the protocol. The ratios of A260/280 and A260/230 of isolated RNA were examined by a spectrophotometer (Nanodrop ND 1000, Wilmington, DE, USA), and their ratios ranging from 2.0 to 2.2 and 2.0 to 2.3 individually suggested a high purity. One µg of total RNA was used to synthesize the first-strand cDNA by using a PrimerScript® RT Reagent Kit (Takara, Dalian, China) according to the protocol. A five gradient dilutions of cDNA was used as a template for each treatment to create the standard curves. After reverse transcription, the synthesized cDNA was stored at −20 °C until use.

Ten candidate RGs, including CsACTIN1, CsCLATHRIN1, CsEF1, CsGAPDH1, CsSAND1, CsTIP41, CsUBC1, CsPTB1, CsTUA1 and CsTBP, were chosen from previous reports for their high stability under different stresses of tea plant (Table 2). The qPCR reactions were carried out on a LightCycle® 480 Real-Time PCR System (Roche Diagnostics, Mannheim, Germany) with a 10-μl reaction system, which contains 0.5 μl forward and reverse primers (10 μM), 5 μl FastStart Essential DNA Green Master and 25 ng first-strand complementary DNA. The programs for all genes included a preliminary step at 95 °C for 10 min, 45 cycles of denaturation amplification at 95 °C for 15 s, at 60 °C for 15 s and at 70 °C for 12 s. Finally, a melting curve analysis from 60 °C to 95 °C was carried out to confirm the specificity of the PCR products. The standard curve method was used to calculate the gene relative expression level. Each sample was analyzed in triplicate.

Validation of selected reference genes

JA and SA signaling pathways play key roles in plant defense against herbivorous insects51,52, and JA and SA responsive genes could be expressed upon herbivore attack or hormone stimuli51,53. A key transcription factor of JA signaling–CsMYC2, a key enzyme in the biosynthesis of JA–CsOPR3, two enzyme involved in the biosynthesis of SA–CsPAL and CsPALc were selected as target genes to validate the rationality of diurnal expression in different tissues, JA treatment and E. onukii infestation, T. aurantii infestation or E. obliqua regurgitant treatment individually. RefFinder is a comprehensive tool, which was used to determine the geometric mean of genes. Based on the geometric mean of the genes, two different normalization factors (NFs) were the lowest and the highest mean values, and a single RG was the lowest or the highest mean value. Raw Ct values were transferred to relative quantities by the ΔΔCt method.

Data analysis

BestKeeper, geNorm, NormFinder, the ΔCt method and RefFinder were used to evaluate the stability of the candidate RGs. All the above methods can recommend the most stable RGs. While NormFinder, geNorm and the ΔCt method rely on transforming Ct values of (1 + E) ± ΔCt, original Ct values were used in RefFinder and BestKeeper. GeNorm software was used to identify the optimum number of RGs through the cut-off value. The Vn/n + 1 value means the pair-wise variation between two sequential NFs and the optimal number of RGs required for a perfect normalization. One-way ANOVA (Tukey’s test) was used to compare the differences among more than two treatments. The difference between two samples was analyzed by Student’s t-test.

Supplementary information

Supplementary Information. (1.7MB, pdf)
Dataset 1. (54KB, xlsx)

Acknowledgements

We gratefully acknowledge AJE team (www.aje.com) for editorial assistance. The study was sponsored by National Natural Science Foundation of China (31772180, 31471784) and Department of Science and Technology, Jilin Province, China (20180201015NY).

Author contributions

X.L.S., W.X. and Y.N.D. designed the research; X.W.L., X.Z. and X.J.H. collected the samples; Y.N.D. and Y.X.X. performed the experiment; Y.C.Y. and Y.N.D. analyzed the results. X.L.S. and Y.N.D. wrote the paper. All authors reviewed the manuscript.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Wei Xu and Yanan Dong.

Supplementary information

is available for this paper at 10.1038/s41598-020-59168-z.

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Associated Data

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Supplementary Materials

Supplementary Information. (1.7MB, pdf)
Dataset 1. (54KB, xlsx)

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