Skip to main content

Transcriptome analysis of activated charcoal-induced growth promotion of wheat seedlings in tissue culture



Activated charcoal (AC) is highly adsorbent and is often used to promote seedling growth in plant tissue culture; however, the underlying molecular mechanism remains unclear. In this study, root and leaf tissues of 10-day-old seedlings grown via immature embryo culture in the presence or absence of AC in the culture medium were subjected to global transcriptome analysis by RNA sequencing to provide insights into the effects of AC on seedling growth.


In total, we identified 18,555 differentially expressed genes (DEGs). Of these, 11,182 were detected in the roots and 7373 in the leaves. In seedlings grown in the presence of AC, 9460 DEGs were upregulated and 7483 DEGs were downregulated in the presence of AC as compared to the control. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed 254 DEG-enriched pathways, 226 of which were common between roots and leaves. Further analysis of the major metabolic pathways revealed that AC stimulated the expression of nine genes in the phenylpropanoid biosynthesis pathway, including PLA, CYP73A, COMT, CYP84A, and 4CL, the protein products of which promote cell differentiation and seedling growth. Further, AC upregulated genes involved in plant hormone signaling related to stress resistance and disease resistance, including EIN3, BZR1, JAR1, JAZ, and PR1, and downregulated genes related to plant growth inhibition, including BKI1, ARR-B, DELLA, and ABF.


Growth medium containing AC promotes seedling growth by increasing the expression of certain genes in the phenylpropanoid biosynthesis pathway, which are related to cell differentiation and seedling growth, as well as genes involved in plant hormone signaling, which is related to resistance.


Bread wheat (Triticum aestivum L.) is the most widely grown crop globally, with a cropping area of more than 220 million hectares. It is the staple food for 30% of the global population. However, wheat yields have been affected by global climate change, and new resistant varieties are urgently needed, which is a challenge to be addressed through wheat breeding. As a conventional technique, embryo culture has been widely used in distant hybridization, rapid crop development, and haploid breeding and has promoted the development of new wheat breeds.

Activated charcoal (AC) is a porous carbonized substance with a large inner surface area on which many substances can be adsorbed. AC is often used in plant tissue culture to improve growth and development [1]. It can adsorb harmful substances present in culture media, including impurities in agar, 5-hydroxymethylfurfural produced by sucrose during high-pressure sterilization, and phenoquinones secreted by explants during culture, as well as beneficial substances available in culture media, such as growth regulators, vitamin B6, folic acid, and nicotinic acid [2]. There are many reports on its effects such as anti-browning, improvement of primary culture survival rates, promotion of bud proliferation and seedling growth in the dark, and promotion of rooting [3,4,5,6,7]. However, the mechanism of action of AC in promoting plant growth has been rarely reported.

In recent years, high-throughput sequencing technologies have been widely used in plant research, and their efficiency has dramatically improved [8,9,10,11]. In this study, gene expression in 10-day-old wheat seedlings cultured in the presence or absence of AC was compared through transcriptome sequencing. With this study, we aimed to lay a foundation for further study of the mechanisms by which AC promotes the development of immature wheat embryos. Genes that promote wheat growth were thoroughly analyzed to provide a theoretical basis for breeding high-yield wheat varieties.


Effect of AC on physiological and biochemical indices of wheat seedlings

Briefly, we grew seedlings from scutella in base medium (N6 supplemented with 0.02 mg/L NAA and 0.05 mg/L 6-BA) or NAC (base medium supplemented with 4 g/L AC) in vitro, and seedlings were collected after 5 and/or 10 days for physiological and biochemical analyses as described below in the Methods section. The leaf area was not determined in 5-day-old seedlings, as the leaves are not unfolded at this stage. For 5- as well as 10-day-old seedlings, the growth rate was significantly higher (P < 0.05) on NAC than on base medium (Figs. 1 and 2). The results of biochemical analyses of 10-day-old seedlings revealed that NAC promoted root activity and significantly increased the soluble protein content in wheat seedlings compared to N6 medium, whereas the total phenol and soluble sugar contents were lower than on N6 medium (Fig. 3) (P < 0.05).

Fig. 1

Images showing wheat seedlings grown for 5 days (a) or 10 days (b) in NAC (basal medium supplemented with 4 g/LAC) or N6 (basal medium). Bar = 1 cm

Fig. 2

Comparison of growth indices for 5- and 10-day-old seedlings grown on NAC or N6. Data are shown separately for leaves and roots. a Fresh seedling weight (leaves plus roots). b Dry weight. c First-leaf area for 10-day-old seedlings. d Leaf length. e Root length. f Root number. *P < 0.05, ** P < 0.01, t-test

Fig. 3

Comparison of biochemical indices for 10-day-old seedlings grown on NAC or N6. Data are shown separately for leaves and roots. a Soluble protein content. b Total phenol content. c Root dehydrogenase activity. d Soluble carbohydrate content in the seedlings (mg/g FW). *P < 0.05, **P < 0.01, t-test

RNA sequencing analysis of seedlings grown on N6 and NAC

Two biological replicates were set up for each treatment. For each treatment, 10 roots and 10 leaves from 10-day-old seedlings were collected separately and used to prepare cDNA libraries. The sequencing results showed that the correlation between the biological replicates was high, indicating that the sequencing data were repeatable and reliable (Fig. 4). After joining overlapping reads and removing low-quality sequences from the raw reads, high-quality, clean reads of Q > 20 were retained: 255,820,114 reads for the leaf samples and 283,192,836 reads for the root samples. In total, 461,062,200 filtered clean reads were compared to wheat reference genomes using Tophat2. In total, 452,832,933 reads (85.6%) were mapped to gene regions, 97.7% (442,365,747) of which were mapped to exon regions.

Fig. 4

Correlation analysis of the samples used for sequencing. The sample numbers are indicated, and the values in the squares are the Pearson correlation coefficients calculated by R Studio

To validate the RNA sequencing data, 15 DEGs were randomly selected and assessed by quantitative reverse-transcription PCR (qRT-PCR). Gene expression was determined relative to a control (seedlings grown on N6), which was set as 1.0. The qRT-PCR results showed that the relative expressions of four root genes and three leaf genes were lower in seedlings grown on NAC than those of the seedlings grown in the control. Three root genes and five leaf genes were expressed at significantly higher levels in seedlings grown on NAC than in the control (Fig. 5). Correlation between differential gene expression levels in RNA-seq and qRT-PCR was analyzed after log2 transformation. The Pearson correlation coefficient was 0.992, which indicated significant correlation at the 0.01 level. Linear correlation analysis showed that the coefficient of correlation between RNA-seq and qRT-PCR data was 0.643, the R2 value was 0.860, which is higher than 0.85 (Fig. 6), indicating that RNA-seq and qRT-PCR data were consistent.

Fig. 5

Validation of the RNA sequencing data by RT-qPCR. Comparison of log2-transformed relative expression of genes in seedling grown on NAC versus N6

Fig. 6

Correlation analysis of DEGs between RNA-seq and qRT-PCR data. The scatter plot indicates the log2-transformed gene expression values in RNA-seq and qRT-PCR

Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DEGs

In total, 18,555 DEGs were identified using DESeq (version 1.18.0), including 1182 DEGs in the roots and 7373 DEGs in the leaves, and 1612 DEGs in common between the roots and leaves. Among the DEGs, 9460 were upregulated in seedlings grown on NAC compared to N6 medium, and 7483 were downregulated (Fig. 7). To identify the functional pathways the DEGs are involved in, we used KEGG pathway analysis, including 254 KEGG functional pathways. In total, 226 KEGG pathways were commonly differentially regulated by AC in the roots as well as leaves. Among these, “metabolic pathways” (105, 39.10%) represented the largest group, followed by “organismal systems” (58, 25.66%), “environmental information processing” (24, 10.62%), “genetic information processing” (21, 9.29%), and “cellular processes” (18, 7.96%). P < 0.05 was considered as a threshold for screening. Further, 37 KEGG pathways were enriched for AC-regulated genes in the roots, and 30 KEGG pathways in the leaves (Fig. 8). In the roots, the three most gene-enriched pathways were “phenylpropanoid biosynthesis”, “starch and sucrose metabolism”, and “biosynthesis of amino acids”. In the leaves, the three most enriched pathways were “plant hormone signal transduction”, “phenylpropanoid biosynthesis”, and “glyoxylate and dicarboxylate metabolism”. By comparison, we found that “phenylpropanoid biosynthesis”, “plant hormone signal transduction”, “starch and sucrose metabolism”, “biosynthesis of amino acids”, and other metabolic pathways were the main gene-enriched pathways in wheat seedlings (Fig. 9). We analyzed three major metabolic pathways, i.e., “phenylpropanoid biosynthesis”, “plant hormone signal transduction”, and “starch and sucrose metabolism” in more detail. In these pathways, there were 29 DEGs between the NAC and N6 groups. Twenty-one of these genes were upregulated, including genes related to cell differentiation, seedling growth, and enhanced stress and disease resistance (e.g., PLA, HCT, ZIM, and JAC), and eight of them were downregulated, and were mainly related to the inhibition of plant growth (e.g., BKI1, ARR-B, DELLA, and ABF) (Table 1).

Fig. 7

DEGs in wheat roots and leaves from seedlings grown on NAC or N6 (control condition)

Fig. 8

Major pathways differentially regulated by AC in roots and leaves as revealed by KEGG enrichment analysis. KEGG pathways are shown in the ordinate. Dot size indicates how many DEGs are annotated to the pathway. The 20 most significant pathways identified in roots and leaves are shown

Fig. 9

Major pathways differentially regulated by AC in wheat seedings as indicated by KEGG enrichment analysis

Table 1 Three pathways and major related genes differentially expressed in wheat seedlings grown on medium containing CA, as indicated by KEGG enrichment analysis


AC stimulates phenylpropane metabolism

The phenylpropane metabolic pathway is of high physiological significance in plants, as it directly and indirectly generates all substances in the phenylpropane skeleton [12]. Nine classes of genes were upregulated in seedlings grown in the presence of AC in the medium, including PAL, CYP73A, COMT, CYP84A, and 4CL. The phenylalanine ammonia-lyase (PAL) gene family was actively expressed. PAL catalyzes the nonoxidative deamination of L-phenylalanine to form trans-cinnamic acid and a free ammonium ion [13]. The conversion of the amino acid phenylalanine to trans-cinnamic acid is the entry step for the channeling of carbon from primary metabolism into phenylpropanoid secondary metabolism in plants. The phenylpropane pathway can produce intermediate products such as trans-cinnamic acid, coumaric acid, ferulic acid, and sinapic acid. These intermediate products can be converted into coumarin, chlorogenic acid, and trans-coumaric coenzyme A ester, which can be further converted into secondary metabolites such as lignin, flavonoids, isoflavones, alkaloids, and benzoate glycosides. These products play vital roles in plant growth and development, and the contents of these substances are closely related to PAL activity, which is of great importance in plant physiology [14, 15]. One of the physiological roles of PAL is to promote cell differentiation and plant growth [16]. This study revealed that addition of AC to the growth medium can accelerate seedling growth, at least in part, by promoting PAL expression.

AC affects plant hormone signal transduction

Using KEGG enrichment analysis, 169 DEGs were mapped to plant hormone signal transduction pathways, which represented the second largest group among the mapped functional pathways. Ninety-six DEGs mapped to this pathway were upregulated, and 73 DEGs were downregulated in the NAC compared to the N6 group. Addition of AC to the seedling culture medium increased the expression of EIN3, BZR1, JAR1, JAZ, and PR1. These genes are known to be involved in plant hormone signal transduction pathways, which directly or indirectly play an important role in regulating stress resistance or disease resistance [17,18,19,20,21]. For example, PR1 is a water-soluble protein that is produced by plants in response to infection by pathogens or stimulation by biotic factors. Its main functions include attacking pathogens, degrading cell wall macromolecules, degrading pathogen toxins, and binding viral coat protein to plant receptor molecules [22]. Inversely, the expression of genes involved in the regulation of plant growth inhibition (BKI1, ARR-B, DELLA, and ABF) was reduced (Table 1). For example, DELLA proteins are transcription factors that negatively regulate gibberellin signaling [23]. Our study showed that the addition of AC to the culture medium stimulated the expression of plant hormone signaling-related genes involved in resistance in wheat seedlings.


AC can significantly promote wheat seedling growth, and this study revealed it likely did so, at least in part, by promoting the expression of certain genes in the phenylpropanoid biosynthesis pathway related to cell differentiation and seedling growth and that of hormone signal transduction-related genes involved in resistance. Our transcriptome data provide new insights into gene expression influenced by AC. AC stimulated gene expression related to phenylpropanoid biosynthesis to promote cell differentiation and seedling growth as well as gene expression related to stress and disease resistance, and suppressed the expression of growth-inhibiting genes through the regulation of plant hormone signaling. Results of this study preliminarily show that AC can significantly promote the molecular mechanisms underlying wheat seedling growth, which will be helpful for further studies on wheat growth.


Plant materials and growth conditions

Winter wheat Liangxing 99 (Triticum aestivum) from Dezhou liangxing seed research institute, a popular cultivar cultivated in the Huang-huai winter wheat region of China, was used. In May 2016, a young ear at 15 days post blooming was adopted in the field. The middle part of young spikes of wheat was peeled, sterilized with 1.5% NaClO for 15 min, and rinsed thoroughly with distilled water. Then, immature embryos were peeled off and the scutella were inoculated downward in base medium (N6 supplemented with 0.02 mg/L NAA and 0.05 mg/L 6-BA) or NAC (base medium supplemented with 4 g/L AC). Ten biological replicates were prepared for each group, with 10 immature embryos in each replicate. Ten 5-day-old and 10 10-day-old seedlings were taken to determine dry weight, leaf and root fresh weights, leaf length, leaf number, first-leaf area, root length, and root number. Biochemical indices related to growth were measured in 10-day-old seedlings. Root activity was determined by naphthylamine TCC colorimetry [24]. Soluble sugars were determined by anthrone colorimetry [25], soluble protein content was determined by Coomassie bright blue G-250 staining [26], and total phenol was determined by the tannin method [27]. Trait differences were analyzed by statistical analysis using SPSS 18.0 software (IBM, USA).

From 40 10-day-cultured seedlings grown on N6 and NAC media, roots and leaves were collected separately. Each sample comprised 20 independent leaves or 20 independent roots; two biological replicates were paired for each sample, immediately frozen in liquid nitrogen, and stored at − 80 °C.

RNA isolation and cDNA library construction and sequencing

Total RNA was isolated using a TRIzol total RNA extraction kit (Invitrogen, USA), which yielded ~ 10 μg of total RNA per sample. RNA quality was examined by 0.8% agarose gel electrophoresis and spectrophotometry. High-quality RNA with 28S:18S > 1.5 and a 260/280 absorbance ratio of 1.8–2.2 was used for library construction and sequencing. Illumina HiSeq library construction was performed according to the manufacturer’s instructions (Illumina, USA). Magnetic beads with poly-T oligos attached were used to purify mRNA from total RNA. mRNA was broken into 200–300 bp fragments using ion interruption. Using mRNA as the template, 6-base random primers and reverse transcriptase were used to synthesize the first cDNA chain, which was used as a template for the synthesis of the second chain of cDNA, where the base T was replaced with the base U. After library construction, library fragments were enriched by PCR amplification and selected according to a fragment size of 300–400 bp. The library was quality-assessed using an Agilent 2100 Bioanalyzer (Agilent, USA). The library was sequenced using the Illumina HiSeq sequencing platform, using paired-end sequencing to generate raw reads (Shanghai Personal Biotechnology Co., Ltd., China).

RNA-sequencing data analysis

Raw reads were filtered before data analysis; high-quality reads with Q > 20 were retained for subsequent analysis. Reference genome data were collected from the Ensembl database ( The reference genome index was created using Bowtie2 software [28]. The reads were filtered by Tophat2 ( and compared to the reference index The read count for each gene was determined using HTSeq0.6.1p2 ( as the original gene expression level. Expression levels were normalized using reads per kilo bases per million reads (RPKM), with RPKM values > 1 considered as the gene expression standard [29]. Differential gene expression was determined using DESeq, and genes with a more than a two-fold change in expression (log2 fold change > 1) and P < 0.05 were considered as DEGs [30]. KEGG pathway analysis was used to analyze the metabolic pathways and signaling pathways the DEGs were primarily involved in.

RT-qPCR analysis

To validate the DEGs identified by RNA sequencing, 15 candidate DEGs were randomly selected for RT-qPCR analysis. The gene names and primer information are listed in Table 2. The wheat housekeeping gene, TaRP15, was used as an internal control for normalization [31]. Three biological replicates were paired for each sample. cDNA was transcribed from 1 μg RNA using a PrimeScript™ RT reagent Kit with gDNA Eraser (TakaRa, Japan). qPCRs were run using a SYBR Premix Ex Taq kit (TakaRa) in an ABI ViiATM7 instrument (Applied Biosystems, USA). The 2–ΔΔCT method was used to quantify relative target gene expression [32].

Table 2 Primers used for qRT-PCR

Availability of data and materials

Supplementary data to this article can be found online at



Activated charcoal


Differentially expressed genes


Kyoto Encyclopedia of Genes and Genomes


Base medium with 4 g/L AC


Phenylalanine ammonia-lyase


Quantitative real-time polymerase chain reaction

4CL :

4-Coumarate--CoA ligase gene


Two-component response regulator ARR-B family gene

BKI1 :

BRI1 kinase inhibitor 1 gene

BZR1 :

Brassinosteroid resistant 1/2 gene


Caffeic acid 3-O-methyltransferase gene

CYP73A :

Trans-cinnamate 4-monooxygenase gene

EIN3 :

Ethylene-insensitive protein 3 gene

JAR1 :

Jasmonic acid-amino synthetase gene


Jasmonate ZIM domain-containing protein gene


N6 base medium (supplemented with 0.02 mg/L NAA, 0.05 mg/L 6-BA)


Phenylalanine ammonia-lyase gene

PR1 :

Pathogenesis-related protein 1 gene


  1. 1.

    Thomas TD. The role of activated charcoal in plant tissue culture. Biotechnol Adv. 2008;26(6):618–31.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Fridborg G, Eriksson T. Effects of activated charcoal on growth and morphogenesis in cell cultures. Physiol Plant. 1975;34(4):306–8.

    Article  Google Scholar 

  3. 3.

    Pan MJ, Van Staden J. The use of charcoal in in vitro culture–a review. Plant Growth Regul. 1998;26(3):155–63.

    CAS  Article  Google Scholar 

  4. 4.

    Susanthi S, Babu DS, Ramajayam D. Effect of activated charcoal, culture media and plant growth regulators on in vitro germination and development of elite dura oil palm (Elaeis guineensis Jacq.) zygotic embryos. PCBMB. 2019;20:314–23.

    Google Scholar 

  5. 5.

    Koene FM, Amano É, Ribas LLF. Asymbiotic seed germination and in vitro seedling development of Acianthera prolifera (Orchidaceae). S Afr J Bot. 2019;121:83–91.

    CAS  Article  Google Scholar 

  6. 6.

    Chutipaijit S, Sutjaritvorakul T. Application of activated charcoal and nanocarbon to callus induction and plant regeneration in aromatic rice (Oryza sativa L.). Chem Spec Bioavailab. 2018;30(1):1–8.

    CAS  Article  Google Scholar 

  7. 7.

    Mojarabi M, Nasr SMH, Jalilvand H, Kooch Y. Effect of activated charcoal, growth supplements and storage on removing dormancy, germination indices and vigour of Ash (Fraxinus excelsior L.). Ann Biol Res. 2011;2(5):203.

    CAS  Google Scholar 

  8. 8.

    Iquebal MA, Sharma P, Jasrotia RS, Jaiswal S, Kaur A, Saroha M, Angadi UB, Sheoran S, Singh R, Singh GP, Rai A. RNAseq analysis reveals drought-responsive molecular pathways with candidate genes and putative molecular markers in root tissue of wheat. Sci Rep. 2019;9(1):1–18.

    Article  Google Scholar 

  9. 9.

    Li C, Zhang W, Yuan M, Jiang L, Sun B, Zhang D, Shao Y, Liu A, Liu X, Ma J. Transcriptome analysis of osmotic-responsive genes in ABA-dependent and-independent pathways in wheat (Triticum aestivum L.) roots. PeerJ. 2019;7:e6519.

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Han L, Li JL, Jin M, Su YH. Transcriptome analysis of Arabidopsis seedlings responses to high concentrations of glucose. Genet Mol Res. 2015;14(2):4784–801.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Zhao JL, Pan JS, Guan Y, Nie JT, Yang JJ, Qu ML, He HL, Cai R. Transcriptome analysis in Cucumis sativus identifies genes involved in multicellular trichome development. Genomics. 2015;105(5–6):296–303.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Hyun MW, Yun YH, Kim JY, Kim SH. Fungal and plant phenylalanine ammonia-lyase. Mycobiology. 2011;39(4):257–65.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Cui JD, Qiu JQ, Fan XW, Jia SR, Tan ZL. Biotechnological production and applications of microbial phenylalanine ammonia lyase: a recent review. Crit Rev Biotechnol. 2014;34(3):258–68.

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Minami E, Ozeki Y, Matsuoka M, Koizuka N, Tanaka Y. Structure and some characterization of the gene for phenylalanine ammonia-lyase from rice plants. Eur J Biochem. 1989;185(1):19–25.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Liu F, Xie L, Yao Z, Zhou Y, Zhou W, Wang J, Sun Y, Gong C. Caragana korshinskii phenylalanine ammonialyase is up-regulated in the phenylpropanoid biosynthesis pathway in response to drought stress. Biotechnol Biotechnological Equip. 2019;33(1):842–54.

    CAS  Article  Google Scholar 

  16. 16.

    Hong YY, Sun KJ, Hwan KS, Sik WK. Cloning and expression analysis of phenylalanine Ammonia-Lyase gene in the mycelium and fruit body of the edible mushroom Flammulina velutipes. Mycobiology. 2015;43(3):327–32.

    Article  Google Scholar 

  17. 17.

    Konishi M, Yanagisawa S. Ethylene signaling in Arabidopsis involves feedback regulation via the elaborate control of EBF2 expression by EIN3. Plant J. 2008;55(5):821–31.

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Oh E, Zhu JY, Wang ZY. Interaction between BZR1 and PIF4 integrates brassinosteroid and environmental responses. Nat Cell Biol. 2012;14(8):802.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Suza WP, Staswick PE. The role of JAR1 in jasmonoyl-L-isoleucine production during Arabidopsis wound response. Planta. 2008;227(6):1221–32.

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Major IT, Yoshida Y, Campos ML, Kapali G, Xin XF, Sugimoto K, et al. Regulation of growth–defense balance by the JASMONATE ZIM-DOMAIN (JAZ)-MYC transcriptional module. New Phytol. 2017;215(4):1533–47.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Hong JK, Hwang BK. Induction of enhanced disease resistance and oxidative stress tolerance by overexpression of pepper basic PR-1 gene in Arabidopsis. Physiol Plant. 2005;124(2):267–77.

    CAS  Article  Google Scholar 

  22. 22.

    Mitsuhara I, Iwai T, Seo S, Yanagawa Y, Kawahigasi H, Hirose S, et al. Characteristic expression of twelve rice PR1 family genes in response to pathogen infection, wounding, and defense-related signal compounds (121/180). Mol Gen Genomics. 2008;279(4):415–27.

    CAS  Article  Google Scholar 

  23. 23.

    Djakovic-Petrovic T, Wit M, Voesenek LACJ, Pierik R. DELLA protein function in growth responses to canopy signals. Plant J. 2007;51(1):117–26.

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Pawar RK, Shivani S, Singh KC, Sharma RK. HPTLC method for the determination of plumbagin from Plumbago zeylanica Linn. (root). Int J Pharm Pharm Sci. 2010;2:219–23.

    CAS  Google Scholar 

  25. 25.

    Xiao-Xu LI, Jia-Zheng LI. Determination of the content of soluble sugar in sweet corn with optimized Anthrone colorimetric method. Storage Process. 2013;4:29–32.

    Google Scholar 

  26. 26.

    Blakesley RW, Boezi JA. A new staining technique for proteins in polyacrylamide gels using Coomassie brilliant blue G250. Anal Biochem. 1977;82(2):580–2.

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Scalbert A. Quantitative methods for the estimation of tannins in plant tissues. In: Hemingway RW, Laks PE, editors. Plant polyphenols. Basic Life Sci, vol. 59; 1992. p. 259–80.

    Google Scholar 

  28. 28.

    Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9(4):357–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 2012;131(4):281–5.

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Anders S, Huber W. Differential expression of RNA-Seq data at the gene level–the DESeq package. Heidelberg: European Molecular Biology Laboratory (EMBL); 2012.

    Google Scholar 

  31. 31.

    Xue GP, Sadat S, Drenth J, McIntyre CL. The heat shock factor family from Triticum aestivum in response to heat and other major abiotic stresses and their role in regulation of heat shock protein genes. J Exp Bot. 2013;65(2):539–57.

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta DeltaC(T)) method. Methods. 2001;25:402–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references


Not applicable.


This work was supported by the National Key Research and Development Program of China (2016YFD0101802), the Key Research and Development Program of Hebei (19226322D), and the Innovation Project of Hebei Academy of Agriculture and Forestry Sciences (2019–4-1A-4).

Author information




SZ and FSD designed the experiments and wrote the manuscript. MYL, FSD, and JPW conducted the immature embryo culture and tissue sampling. XPS, YWL, and FY performed RNA extraction and qRT-PCR. FSD, HZ, and JFC analyzed the data. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shuo Zhou.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dong, Fs., lv, My., Wang, Jp. et al. Transcriptome analysis of activated charcoal-induced growth promotion of wheat seedlings in tissue culture. BMC Genet 21, 69 (2020).

Download citation


  • RNA sequencing
  • Wheat
  • Immature embryo culture
  • Phenylpropanoid biosynthesis
  • Plant hormone signaling