Skip to main content

Barley somatic embryogenesis-an attempt to modify variation induced in tissue culture



Somatic embryogenesis is a phenomenon carried out in an environment that generates abiotic stress. Thus, regenerants may differ from the source of explants at the morphological, genetic, and epigenetic levels. The DNA changes may be the outcome of induction media ingredients (i.e., copper and silver ions) and their concentrations and time of in vitro cultures.


This study optimised the level of copper and silver ion concentration in culture media parallel with the induction medium longevity step towards obtaining barley regenerants via somatic embryogenesis with a minimum or maximum level of tissue culture-induced differences between the donor plant and its regenerants. The optimisation process is based on tissue culture-induced variation evaluated via the metAFLP approach for regenerants derived under varying in vitro tissue culture conditions and exploited by the Taguchi method. In the optimisation and verification experiments, various copper and silver ion concentrations and the different number of days differentiated the tested trials concerning the tissue culture-induced variation level, DNA demethylation, and de novo methylation, including symmetric (CG, CHG) and asymmetric (CHH) DNA sequence contexts. Verification of optimised conditions towards obtaining regenerants with minimum and maximum variability compared to donor plants proved useful. The main changes that discriminate optimised conditions belonged to DNA demethylation events with particular stress on CHG context.


The combination of tissue culture-induced variation evaluated for eight experimental trials and implementation of the Taguchi method allowed the optimisation of the in vitro tissue culture conditions towards the minimum and maximum differences between a source of tissue explants (donor plant) and its regenerants from somatic embryos. The tissue culture-induced variation characteristic is mostly affected by demethylation with preferences towards CHG sequence context.


Genetic or epigenetic purity of regenerants can be perturbed by e.g. chromosomal changes [1], point mutations [2], movement of transposable elements [3], or changes in methylation status of DNA [4] originating due to numerous stresses (sterilization factors [5], media components [6], light conditions [7] or humidity [8]). The extent of changes may depend on a type of explants [9], genotype [10], or time of culture on induction medium [11, 12].

Many reports pointed out media components as the factors inducing genetic [13] or epigenetic [14] changes shared among in vitro derived plant. Among media components, copper and silver ions are indispensable for plant growth and development. Copper ions at the physiological level are crucial for growth and plant survival [15]. A somewhat elevated level of copper ions in induction or regeneration media may positively influence somatic embryogenesis [16, 17] or androgenesis [18]. However, overstepping bounds of toxic doses may imbalance the chloroplast redox state altering photosynthesis [19,20,21], and inducing oxidative stress [22]. Another ion utilized in tissue culture media is silver [23]. The positive effect of AgNO3 on somatic embryogenesis, shoot growth, and direct organogenesis is well established [24]. However, at increased concentration silver ions may be toxic for plant tissues inducing reactive oxygen species [25] followed by oxidative DNA damages [26, 27]. Silver ions may also influence retrograde (plastid-to-nucleus) signaling [28], epigenetic processes [29] and activate reparation mechanisms [30].

The other factors that may affect (epi)genetic purity of regenerants are, i.e., time of in vitro induction medium [31, 32] and the presence of callus stage [33]. In contrast to direct embryogenesis [34], indirect one may induce somaclonal variation at the DNA sequence level due to callus phase [35]. Studies devoted to the mutation rate in plant tissue cultures pointed at the prevalence of transition over transversion. However, with increasing tissue culture time, the transversion rate also rises [36]. Apart from DNA mutations, changes in ploidy were observed when tissue cultures kept for a prolonged time [37].

Although rare, the influence of different factors affecting tissue culture plant regeneration can be seen at the morphological level of regenerants [10]. However, the lack of such changes does not mean they are not present at the genetic or DNA methylation levels [38, 39]. DNA-based marker techniques, including Methylation Sensitive Amplification Polymorphism (MSAP) [40, 41] or methylation-sensitive Amplified Fragment Length Polymorphism (metAFLP) [42,43,44] could be appropriate for their identification.

The metAFLP is dedicated to studying DNA methylation and sequence variation [43, 45]. In contrast, the MSAP identifies DNA methylation variation exclusively. Both techniques identify changes in the DNA methylation pattern, but distinct DNA sequences are being analyzed. The advantages of the metAFLP approach [44], namely quantification of sequence variation (SV), DNA demethylation (DMV) and de novo methylation (DNMV) including symmetric CG, CHG, and asymmetric CHH contexts (where H = A, T, or C) and the fact the approach was successfully used in the case of in vitro tissue culture studies of many species [39, 46,47,48], predisposes that method to research. The metAFLP is a choice for studies involving different aspects of tissue cultures. One such issue is the optimisation of plant regeneration via somatic embryogenesis [49, 50], which could be performed if an adequate statistical approach is used to simultaneously cope with many factors and minimize experimental efforts [51]. The Taguchi approach [52] of experimental design optimisation could be considered for such purposes. The method has not been widely exploited in the biological studies [53] except for identifying factors affecting germination in barley [54], Miscanthus sinensis [55] and in the optimisation of androgenesis in barley [44, 56].

The study aimed to optimise Cu2+ and Ag+ concentration in tissue culture medium in parallel to the longevity of plant regeneration step at the induction medium using the Taguchi approach involving the metAFLP quantitative characteristics in the case of barley regenerants derived via somatic embryogenesis.


Source of explant tissue-donor plants

All donors that were the progeny of double haploid plants derived via anther cultures were morphologically uniform and in the type of source plant materials. The molecular analysis utilized 13 primer pairs (Additional file 1: Table S1), amplifying 335 Acc65I/MseI and 326 KpnI/MseI markers. After virtual ‘extraction’ of markers related to DNA methylation pattern change, forty-eight Acc65I/MseI-KpnI/MseI markers were evaluated.

Cluster analysis based on KpnI/MseI markers employing Jaccard’s coefficients of similarity showed uniformity of donor plants ranging from 98.6 to 100%, with the majority being identical (Additional file 2: Fig. S1).

However, DNA methylation related markers Acc65I/MseI-KpnI/MseI (Additional file 3: Fig. S2) showed that at this level, donor plants were similar in the range from 76 to 100%.

The metAFLP marker characteristics for donor and regenerants in M1–M9 trials

All regenerants were fertile, and in the shape of donor plants in terms of plant height, leaf shape, color, anther color, plant vigor, spike number, and method of tillering. The metAFLP analyses of donor and regenerants with eight primer pairs (Additional file 1: Table S1) resulted in 192 markers evaluated for all trials (M1–M9) and shared between KpnI/MseI and Acc65I/MseI platforms. There were 19 and 14 polymorphic markers amplified in the Acc65I/MseI and KpnI/MseI platforms. The polymorphic loci value (P %) ranged from 0% (M9) to 3.13% (M7) for KpnI/MseI and from 1.04 (M9) to 4.69% (M7) for Acc65I/MseI-KpnI/MseI digests. The mean value of P % was lower for KpnI/MseI (1.16) platform than for the Acc65I/MseI-KpnI/MseI (2.78). Similarly, a mean value of Shannon’s information index (I) was lower in the case of KpnI/MseI (0.005) than in Acc65I/MseI-KpnI/MseI (0.016) derived markers. Also, expected heterozygosity (He) and unbiased expected heterozygosity (uHe) values were lower for the sequence data (0.003 and 0.004) than for methylation (0.011 and 0.012) (Additional file 1: Table S2).

Analysis of sequence variation and DNA methylation alternations for donor and regenerants in M1–M9 trials

Regenerants derived via somatic embryogenesis were either identical or differed by 2% as indicated by cluster analysis performed on KpnI/MseI markers. Most of the regenerants derived in the M1, M3, M4, M5, M8, and M9 trials were identical in sequence variation. The highest level of sequence differences (4%) was demonstrated for the donor plant (JDHK) used as a source of tissues for the in vitro plant regeneration and a single M7 regenerant (Additional file 4: Fig. S3).

Agglomeration of regenerants based on the Acc65I/MseI-KpnI/MseI markers showed that DNA methylation changes demonstrated that the analyzed plant similarity ranged from ca. 77 to 97%. However, based on bootstrap values, the differences were insignificant except for a single regenerant and the donor plant. The latter differed from regenerants in more than 40%. In contrast to the KpnI/MseI based grouping, the regenerants derived in the M1–M9 trials failed to form close subclusters except for the M8 and M9 ones (Additional file 5: Fig. S4).

Analysis of molecular variance (AMOVA) performed on the metAFLPs amplified on DNAs extracted from regenerants obtained via somatic embryogenesis in the optimisation experiment showed that explained variance among trials (M1–M9) equaled to 0.576 and 0.529 in the case of KpnI/MseI and Acc65I/MseI-KpnI/MseI markers, respectively.

Quantifying the metAFLP-derived variation and ANOVA for M1–M9 trials

Quantitative the metAFLP characteristics, namely TCIV, SV, DMV and DNMV (Table 1) evaluated for the regenerants derived via the M1–M9 trials indicated that in general, SV was higher than DMV and DMV was higher than DNMV. The values of all metAFLP characteristics increased in the M8 and M9 regenerants that grew on the in vitro medium with the highest concentration of copper ions (Table 1). The highest concentration of silver ions and short time of in vitro cultures in the M8 acted the same way as lack of the ions and long-propagated cultures. In general, the regenerants derived under control conditions (M1) exhibited the lowest values of variation except for DNMV.

Table 1 MetAFLP quantitative characteristics of the regenerants derived via somatic embryogenesis using tissue culture conditions of the M1–M9 trials and ANOVA results, including Tukey’s HSD tests

Analysis of variance indicated that differences among mean values of all variation types were significant (Table 1). Tukey’s HSD grouping revealed an intricate pattern of grouping regenerants dependent on the metAFLP quantitative characteristic. The TCIV demonstrated that the M1–M6 trials formed the first, M7–M8 the second, and M8–M9 the third group of regenerants. Sequence variation showed that the regenerants of the trials could be grouped into four partly overlapping groups. Also, three somewhat coinciding groups were distinguished with DNMV. Only DMV could evaluate three separate groups of trials with the M8 and M9 forming the first, M4–M7 the second, and M1–M3 the last one (Table 1).

Taguchi based optimisation

Based on the metAFLPs amplified on DNAs of regenerants extracted from leaves of the M2–M9, using TCIV characteristic subjected to the Taguchi approach, minimum differences between the donor plant and its regenerants should be expected under 10 µM CuSO4 and 60 µM AgNO3 (the M12 trial) concentrations of the induction medium. When the induction medium was 2.95 µM CuSO4, and 15 µM AgNO3 (M13), the highest values of differences were predicted. The longevity of in vitro tissue culture evaluated by the Taguchi method was 21 and 28 days for the M12 and M13 optimised trials, respectively.

Regenerants derived under optimised conditions in M10–M13 trails

The M12 and M13 trials resulting from Taguchi-based optimisation of tissue culture conditions of plant regeneration via somatic embryogenesis resulted in regenerants that were identical with the donor plant and control regenerants (M10). Regenerants from trials M12 and M13 exhibited any morphological differences in assessed traits (plant height and vigor, leaf shape and color, anther color, plant vigor, spike number, and method of tillering).

Analysis of sequence variation and DNA methylation alternations for donor and regenerants in M10–M13 trails

The metAFLP analyses based on DNAs of donor plant and regenerants were performed on eight primers pair (Additional file 1: Table S2) resulted in 195 markers shared between KpnI/MseI and Acc65I/MseI platforms among the M10, M12, and M13 trials. The Acc65I/MseI amplified 49 and KpnI/MseI 26 polymorphic DNA fragments.

Agglomeration analysis of regenerants derived via the M10, M12, and M13 trials and based on the KpnI/MseI markers (Additional file 6: Fig. S5) demonstrated that the donor plant was separated from all regenerants that formed two major clusters. The first one consisted of five regenerants from the M10 trial, whereas the second subcluster encompassed all other regenerants. Although further subclustering of the data was insignificant, the regenerants of the M13 trial (optimised towards maximum differences relative to the donor plant) exhibited the highest level of sequence variation with a similarity of about 97%. The M12 (towards minimum differences relative to the donor plant) regenerants were nearly identical at the sequence variation level (~ 99% of similarity) and were similar to the remaining M10 regenerants.

Grouping with Acc65I/MseI-KpnI/MseI markers (Additional file 7: Fig. S6) revealed higher than KpnI/MseI markers level of differences among regenerants with the donor plant being apart of them. No significant clustering was evaluated. However, the M12 regenerants, in contrast to the M13, were much more similar to each other. Furthermore, the M10 plants seemed to be the most uniform (except in two cases) and close to the M12 regenerants.

Analysis of molecular variance (AMOVA) performed on the KpnI/MseI (K) markers revealed that the explained variance among regenerants of the trials equaled to 49.8% (ΦPT value = 0.498, p = 0.001) whereas in the case of Acc65I/MseI-KpnI/MseI (M) markers the respective variance equaled to 34.6% (ΦPT value = 0.346, p = 0.001).

Quantifying the metAFLP-derived variation and ANOVA for M10–M13 trials

Based on the TCIV, the Grubbs test showed that regenerant number two (M10) and seven (M13) were outliers and were removed from the analysis (data not shown).

Quantification of the metAFLP characteristics (TCIV, SV, DMV, DNMV) obtained for regenerants derived under optimised conditions showed that the SV was slightly higher than the DMV (Table 2). In contrast, methylation was more than three times lower than SV. The mean value for DNMV and DMV demonstrates that demethylation prevailed over de novo methylation in all trials. The highest values of the TCIV were in the M13 trial (under the highest concentration of CuSO4-maximum differences conditions). The lowest values of differences in TCIV were in the M12 (minimum differences between donor and regenerants). TCIV characteristics for the M10 (control) trial were in between those for the M12 and M13. The DNMV was the highest for the M13 and the lowest for the M12. Although DMV for M13 was higher than for the M12, under M10 control conditions, DMV was the highest. Interestingly, SV was the highest for the M10 and the lowest for M13.

Table 2 The arrangement of ANOVA statistics and Tukey’s HSD tests for trials in the verification process

Based on ANOVA, all but the SV metAFLP characteristics showed significant differences between tested trials M10, M12, and M13 (Table 2). According to Tukey’s HSD test, the M10 and M13 formed a single group, whereas the M12 remained an individual based on the TCIV. Grouping arranged for DMV divided trials into three distinct groups. The DNMV based grouping creates two groups share for M10, M13, and M10, M12 trials. ANOVA underlines the most variabilities for comparing M12 and M13 trials.

Methylation contexts in M10–M13 trials

The comparison made for regenerants from trials in a verification experiment concerning demethylation and de novo methylation indicated that DMV was most often observed in the context of CHG. DNMV in the context of CHG was four times less frequent. De novo methylation and demethylation in the CG methylation context were lower than in the CHG context. The changes in DNA methylation in the asymmetric CHH context concerned only de novo methylation. In all tested contexts, the highest values of either DMV or DNMV were in the M13 trial (Table 3).

Table 3 The ANOVA and Tukey’s HSD test for different statistical models by methylation contexts and within DNMV and DMV

ANOVA was significant in the case of CG and CHH contexts for de novo methylation. In DNA demethylation, only the CHG context was significant, while the CG one was close to significance. Tukey’s HSD test differentiated between the M10, M12, and M13 trials so that either the M10 or M13 formed separate groups or were one of the trials located in the same group with M12 in the case of significant ANOVA results (Table 3).


Efforts to optimise plant regeneration via in vitro cultures began almost with the initiation of plant tissue culture techniques [57]. Initially, these works led to the compilation of appropriate ingredients forming the growing media. In this way, even commonly used media such as MS [58] or Gamborg B5 medium [59] have been created. Although many species responded positively to MS medium, many of them required more sophisticated compounds. Thus, from the beginning of plant tissue cultures to nowadays, the regeneration conditions are being improved. With the development of knowledge on tissue cultures, it has been proven that the conditions used in vitro may lead to changes in regenerated plants. Apart from morphological [60] also genetic [61] and epigenetic [39, 42, 62] alterations between regenerants and donor plants are observed and quantified by the metAFLP approach [42]. The metAFLP approach showed that it is sufficiently informative for analysis of even closely related species as well as tissue culture-derived regenerants [63, 64]. Shannon’s Indices were low in the given study, ranging from 0.000 to 0.18 and 0.007 to 0.023 in KpnI/MseI and Acc65I/MseI-KpnI/MseI data sets, respectively. Such a result was expected as all regenerants originated from a single, double haploid donor plant. A low level of variation was confirmed by percentages of polymorphic loci (P %), hetero- (He), and expected heterozygosity (uHe) with increased for the Acc65I/MseI-KpnI/MseI data. Presented results are congruent with those published by the others where metAFLP model was successfully applied in studies devoted to cereals [42, 44, 64,65,66] and other plants: Armeria maritima s.l. [67], Arabidopsis thaliana [48], Gentiana pannonica Scop [46], and Gentiana kurroo [68].

To fit the metAFLP analysis model, donor plants need to be uniform at genetic and epigenetic levels. The donor plants were the generative progeny of regenerants obtained via androgenesis as generative cycles may stabilize genetic and epigenetic alterations induced during in vitro plant regeneration [39, 44]. Cluster analysis demonstrated the genetic and epigenetic uniformity of the donors reached up to 100%. However, DNA methylation-related variation was higher than genetic one what may reflect demethylation and de novo methylation processes required for plant regeneration via tissue cultures during the cell reprogramming stage [69, 70]. The cell reprogramming process is error-prone and not necessarily leads to a donor plant’s identity and its regenerants. Thus, a randomly chosen donor double haploid plant after a single generative cycle was used as a source of explants for the optimisation step and another plant that originated from the same progeny for the verification experiments. The approach should minimize the donor’s effect as it may impact regenerated plants [71, 72].

It was shown that under optimisation experimental conditions, DNA methylation alternations were usually higher than sequence changes in the case of barley genotypes. On the other hand, in triticale, genetic changes prevailed [73]. Thus, the preference may depend on species. Also, medium ingredients, such as copper and silver ions, may influence (epi)genetic variation. In barley anther culture, the two ions may act as mediators of sequence variation that involves different methylation contexts [74]. They may also affect green plant regeneration [75] and somewhat differently act in somatic embryogenesis [76]. Thus, playing with the concentration of copper and silver ions in the induction medium may influence the variation level. That is what was precisely observed in an optimisation experiment. Depending on the trials’ experimental conditions, the fluctuations in SV, DNMV, and DMV were demonstrated underlined by cluster analysis and supported by AMOVA, suggesting that more than 50% of the explained variance was due to trial differences. In general, with the increased concentration of copper ions, the upsurge of all metAFLP characteristics was observed. The role of silver ions was related to the time of tissue cultures. The ions higher concentration was significant only when a short time of cultures was used (M8); however, when the time increased, the lack of silver ions resulted in comparable levels of TCIV variation detected in the M8–M9 trials. The observed differences in TCIV in optimisation experiments may be related to some problems with the electron transport chain (complex I and III) that involves Cu/Zn dismutase. In the presence of copper ions, superoxide dismutase functioning might be disturbed, influencing the Yang cycle [77,78,79]. The notion is confirmed by the fact that TCIV is composed of SV, DNMV, and DMV. Furthermore, DNMV and DMV were predictors leading to SV using copper ions as mediators in andro- [74] and embryogenesis [76]. Besides, copper ions participate in oxidative stress and may modify methylated cytosines leading to SV [80]. In this context optimisation of tissue culture conditions towards maximum and minimum variation is of the highest priority.

Using the Taguchi method and TCIV evaluated for the M2–M9 trials, the in vitro conditions leading towards either minimum or maximum differences between the donor plant and its regenerants were proposed. The differences were mainly due to the concentration of CuSO4. The differences between the trials are also highlighted in the cluster analysis. Explained molecular variance related to sequence changes for the regenerants of the M10–M13 trials was close to 50% (as in the optimisation experiment). In contrast, the variation due to methylation pattern equaled 35%-it suggested that sequence changes in the optimisation and verification experiments were nearly at the same level. When the respective regenerants were tested using the metAFLP approach, it was shown that the trials differed among each other and that in contexts of TCIV, the optimisation resulted in expected results. The M13 regenerants exhibited higher than the M12 level of DMV and DNMV. Furthermore, the M12 and M13 trials were grouped in opposite groups based on Tukey’s HSD test. While in all cases DNMV methylation related changes exceeded those for DMV, the Taguchi method suggested in vitro culture conditions under which the level of TCIV was optimised in the expected direction compared to the control conditions. It was also demonstrated that the within trial variation was higher for the regenerants derived under the M13 conditions compared to the M12, whereas the M12 were closer to the M10 or M10 was in between M12 and M13. The presented data is coherent with earlier results devoted to optimising barley anther culture where differences between optimised towards maximum and minimum differences trials were evaluated [44].

A remarkable aspect of the study is that the metAFLP approach can identify DNA methylation changes affecting all sequence methylation contexts [44]. Differences in DNA methylation context were illustrated using regenerants derived on optimised tissue culture conditions. Depending on the sequence context, either DNMV or DMV were responsible for the grouping of optimised trials. ANOVA showed that trials (M10–M13) differed considering the DNA de novo methylation in CG and CHH methylation contexts. A similar comparison between trials (M10–M13) carried for DNA demethylation showed significant differences only for CHG methylation context. Thus, problems with S-adenosyl-l-methionine compound, which is responsible for DNMV, were crucial in differentiating trials. Remarkably, DNMV of the CG contexts was significant with the highest value for the M13 compared to the other trials, whereas DNMV of the CHH sequence was the highest for the M10 control trial. The CG context is hardly stable in terms of methylation [81], and the methylation pattern is reestablished via replication mechanisms [82]. On the other hand, the CHH context is regulated epigenetically via, e.g., RNAi mechanisms [83]. Assuming that the in vitro tissue cultures lead to many DNA methylation changes due to the in vitro-induced stresses, it is entirely reasonable to assume that the CG and CHH contexts should be less affected by changes than the CHG as the methylation of the latter is regulated by replication and epigenetic mechanisms [84], as it was demonstrated in presented data. Possibly higher DMV than DNMV of the CHG contexts reflects the demethylation stage followed by only partial DNA methylation pattern recovery [69, 70]. On the other hand, slightly increased de novo methylation of the CHH sequences may reflect either experimental issues to detect subtle changes or real phenomena related to methylation contexts. Assuming that many metAFLP markers were analyzed, experimental artifacts could be excluded. What was observed reflects not fully recognized mechanisms affecting in vitro tissue culture plant regeneration at the DNA methylation level.

The presented data shows that DNA demethylation (both in optimisation and verification experiments) dominates over de novo methylation independently of the DNA methylation sequence context analyzed (except CHH where DMV was not detected). Similar data were obtained in previous studies on barley [42] and triticale regenerants [39] from anther cultures. Interestingly, the CHG context is the most affected one by DMV. Possibly, the cell reprogramming step [85] involving active [86, 87] and passive [88, 89] DNA demethylation followed by different mechanisms of de novo methylation (depending on sequence contexts) [90, 91] and variations in the preferences towards methylation of specific cytosines compared to methylation density of some genomic regions [92] and correctness of pattern reestablishment due to (epi)genetic mechanisms [93] may be the most probable explanation both for the increased values of DMV and preferential demethylation of the CHG sequences.


Presented research showed that by manipulating the concentration of the in vitro tissue culture medium ingredients such as copper and silver ions and the time of tissue cultures, it is possible to regenerate plants that are either close to the source of explant or vice versa at the level of the tissue culture-induced variation. Furthermore, using the metAFLP approach combined with the Taguchi method, the in vitro tissue culture plant regeneration could be optimised in the expected direction. Moreover, the metAFLP approach may quantify methylation changes affecting symmetric and asymmetric sites. Most of the methylation changes evaluated in the verification experiment were related to the DMV of the CHG contexts; however, the reason for such a preference is not apparent.


Donor plant derivation

Spring barley cultivar NAD2 accessible from Poznan Plant Breeders LTD-Nagradowice, (Poland) served as source material to prepare donor plants via in vitro anther culture [44]. The diploidisation of haploid regenerants carried spontaneously. Doubled haploid regenerants were used to obtain generative offspring, which served as donor plants for regenerant production via somatic embryogenesis from immature zygotic embryo culture for the optimisation and verification process based on the Taguchi method. Thirty-six donor plants were derived for optimisation and verification experiments.


Plant material and sterilization

Twenty-four donor plants were cultivated in controlled conditions in the growth chamber. Collection of spikes with unripe caryopses were done after 12-16 days of self-pollination. After dissection, the caryopses were surface-sterilized in 70% ethanol for 1 min and transferred to 10% sodium hypochlorite solution (NaOCl) for 20 min with stirring. Next, caryopses were thoroughly rinsed four times with sterile water. From disinfected caryopses, immature zygotic embryos were extracted and plated on solid N6L medium containing macro- and micronutrients [94] with 2 mg l−1 2,4-dichlorophenoxyacetic acid (2,4-D), 0,5 mg l−1 6-naphthalene acetic acid (NAA), and 0.5 mg l−1 kinetin.

Callus induction

Induction media based on N6L were prepared in nine combinations (M1–M9) according to the Taguchi approach, supplemented with CuSO4 and AgNO3 at varying concentrations (Table 1). The time of induction step was counted from plating immature embryos on induction media to transferring calli, first somatic embryos and embryo-like structures on regeneration media. Immature zygotic embryos were incubated for callus and somatic embryos induction at 26 °C under photoperiod 16 h light/8 h dark. Primary explant derived calluses, somatic embryos, and embryo-like structures were observed several days later.

Somatic embryogenesis conditions

The regeneration step was conducted on solid medium K4NB with 0.225 mg l−1 6-benzylaminopurine (BAP) [95]. The calli and embryo-like structures were incubated at 26 °C under a 16 h light/8 h dark photoperiod. Regenerated plantlets were put into flasks with induction N6I rooting medium [94] supplemented with 2 mg l−1 indole-3-acetic acid (IAA). Next, regenerants were potted to the soil-sand mixture (3:1) and were grown to maturity under controlled conditions in a greenhouse on the abovementioned day/night regime. Forty-five regenerants derived via somatic embryogenesis prepared in nine trials (M1–M9) derived from one donor plant were ready for optimisation experiment.


After establishing optimised conditions, the selected levels of used factors (CuSO4, AgNO3, length of induction step) were verified by new regeneration plants from immature zygotic embryo cultures. The somatic embryogenesis was established on explants from 12 donor plants. The essential components of induction media were prepared for the optimisation process with changed levels of tested factors (Table 2). A total of three trials were performed: M10-control, M12-verification towards the lowest percentage of differences based on Tissue Culture Induced Variation (TCIV) between donor and regenerants, and M13 verification towards the highest percentage of differences between donor and regenerants (Table 2). The regeneration media in the verification step were the same as for the optimisation step. All regenerants obtained in three trials (M10, M12, M13) were derived from a single donor plant in the verification process.

DNA preparation and metAFLP analysis

The genomic DNA was isolated from 100 mg of fresh leaves of donor plants and regenerants obtained via somatic embryogenesis from optimisation and verification processes using the DNasy MiniPrep kit (Qiagen, Hilden, Germany). The metAFLP was used as described earlier [42] with slight modifications [43]. The genomic DNA was subjected to cutting with two pairs of enzymes Acc65I/MseI and KpnI/MseI. The subsequent metAFLP steps (adaptor ligation, pre-selective, and selective amplification steps) were performed using dedicated adaptors and primers (Additional file 1: Table S3). The completion of all stages of the metAFLP technique resulted in obtaining a band profile of DNA fragments. The bands’ profile obtained with cutting DNA with the Acc65I/MseI and KpnI/MseI platforms, expressed as 0–1 matrices (absence-presence), were contrasted. This combination of data, where the platform Acc65I/MseI, which concerns sequence changes and DNA methylation with data from KpnI/MseI platform, results in information about the DNA sequence, allows for the extraction of only methylation data. Simply, markers that were present in the first and missed in the second (or vice versa) metAFLP platform were related to DNA methylation [63]. Thus, the data from the Acc65I/MseI-KpnI/MseI were used to evaluate the “methylation markers”, whereas those from the KpnI/MseI one were the source of information concerning sequence variation. Data obtained from both metAFLP platforms were used to assess the quantitative metAFLP characteristics described elsewhere [42, 43]. Among others, the tissue-culture induced variation (TCIV), sequence variation (SV), demethylation (DMV), and de novo methylation (DNMV), were assessed. Based on specially designed selective primers to metAFLP, it was possible to estimate changes related to CG and CHG methylation (symmetric contexts) and CHH (asymmetric context). Thus, events identified using markers amplified by selective primers directed towards symmetric and asymmetric contexts could be evaluated as described earlier [44].


The molecular markers obtained in both metAFLP platforms were subjected to GenAlEx6.501 (Excel add-in software) [96] to estimate a percentage of polymorphic loci (%P), Shannon’s information index (I), expected heterozygosity (He), and unbiased expected heterozygosity (uHe).

UPGMA based on the Jaccard index was conducted in PAST software [97]. The significance of grouping was evaluated using 999 bootstrap replicates.

The analysis of molecular variance (AMOVA) conducted using the sequence and methylation metAFLP markers evaluated based on KpnI/MseI and Acc65I/MseI-KpnI/MseI AFLP platforms in GenAlEx6.501 software allowed assessing ΦPT index values.

Qualitative and quantitative metAFLP characteristics were evaluated as described earlier [42,43,44].

The outliers were identified using metAFLP TCIV in XLSTAT 2018.1.49, 205 software (Addinsoft, Paris, France). The Grubbs’ test at a significance level of 5% was performed.

The Taguchi approach towards the maximum and the minimum number of differences between donor plants and regenerants was conducted using TCIV characteristics evaluated based on the M2-M9 trials in QI Macros230T (KnowWare International, Inc, Denver, USA).

ANOVA and Tukey’s HSD groupings at p < 0.05 were used to evaluate differences among trials using the TCIV, SV, DMV, DNMV, and symmetric and asymmetric metAFLP characteristics in XLSTAT 2018.1.49205 Excel add-in software.

Availability of data and materials

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



2,4-dichloropchenoxyacetic acid




Murashige and Skoog basal salt mixture


Methylation sensitive amplified polymorphism


Methylation sensitive amplified fragment length polymorphism


6-naphthalene acetic acid


  1. Devi SP, Kumaria S, Rao SR, Tandon P. Genetic fidelity assessment in micropropagated plants using cytogenetical analysis and heterochromatin distribution: a case study with Nepenthes khasiana Hook f. Protoplasma. 2015;252:1305–12.

    Article  Google Scholar 

  2. Qin Y, Shin K-S, Woo H-J, Lim M-H. Genomic variations of rice regenerants from tissue culture revealed by whole genome re-sequencing. Plant Breed Biotech. 2018;6:426–33.

    Article  Google Scholar 

  3. Gao D-Y, Vallejo VA, He B, Gai Y-C, Sun L-H. Detection of DNA changes in somaclonal mutants of rice using SSR markers and transposon display. Plant Cell Tissue Organ Cult. 2009;98:187–96.

    Article  CAS  Google Scholar 

  4. Han Z, Crisp PA, Stelpflug S, Kaeppler SM, Li Q, Springer NM. Heritable epigenomic changes to the maize methylome resulting from tissue culture. Genetics. 2018;209:983–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kataky A, Handique PJ. Standardization of sterilization techniques prior to in vitro propagation of Andrographis paniculata (Burm. f) Nees. Asian J Sci Technol. 2010;6:119–22.

    Google Scholar 

  6. Martin KP, Pachathundikandi SK, Zhang CL, Slater A, Madassery J. RAPD analysis of a variant of banana (Musa sp.) cv. grande naine and its propagation via shoot tip culture. In Vitro Cell Dev Biol Plant. 2006;42:188–92.

    Article  CAS  Google Scholar 

  7. Cassells AC, Roche TD. The influence of the gas permeability of the vessel lid and growth-room light intensity on the characteristics of Dianthus microplants in vitro and ex vitrum. In: Lumsden PJ, Nicholas JR, Davies WJ, editors. Physiology, Growth and Development of Plants in Culture. Dordrecht: Springer Netherlands; 1994. p. 204-14.

  8. Isah T. Adjustments to in vitro culture conditions and associated anomalies in plants. Acta Biol Crac Ser Bot. 2015;57:9–28.

    Article  Google Scholar 

  9. Sahijram L, Soneji JR, Bollamma KT. Analyzing somaclonal variation in micropropagated bananas (Musa spp.). In Vitro Cell Dev Biol Plant. 2003;39:551–6.

    Article  Google Scholar 

  10. Bradaï F, Pliego-Alfaro F, Sánchez-Romero C. Long-term somatic embryogenesis in olive (Olea europaea L.): Influence on regeneration capability and quality of regenerated plants. Sci Hortic. 2016;199:23–31.

    Article  Google Scholar 

  11. Bartoszewski G, Havey MJ, Ziółowska A, Długosz M, Malepszy S. The selection of mosaic (MSC) phenotype after passage of cucumber (Cucumis sativus L.) through cell culture - a method to obtain plant mitochondrial mutants. J Appl Genet. 2007;48:1–9.

    Article  PubMed  Google Scholar 

  12. Rodríguez López CM, Wetten AC, Wilkinson MJ. Progressive erosion of genetic and epigenetic variation in callus-derived cocoa (Theobroma cacao) plants. New Phytol. 2010;186:856–68.

    Article  CAS  PubMed  Google Scholar 

  13. Abass MH, Al-Utbi SD, Al-Samir EARH. Genotoxicity assessment of high concentrations of 2,4-D, NAA and Dicamba on date palm callus (Phoenix dactylifera L.) using protein profile and RAPD markers. J Genet Eng Biotechnol. 2017;15:287–95.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Fraga HPF, Vieira LN, Caprestano CA, Steinmacher DA, Micke GA, Spudeit DA, et al. 5-Azacytidine combined with 2,4-D improves somatic embryogenesis of Acca sellowiana (O. Berg) Burret by means of changes in global DNA methylation levels. Plant Cell Rep. 2012;31:2165–76.

    Article  CAS  PubMed  Google Scholar 

  15. Festa RA, Thiele DJ. Copper: an Essential Metal in Biology. Curr Biol. 2011;8:R877–83.

    Article  CAS  Google Scholar 

  16. Ðorđević B, Neděla V, Tihlaříková E, Trojan V, Havel L. Effects of copper and arsenic stress on the development of Norway spruce somatic embryos and their visualization with the environmental scanning electron microscope. New Biotechnol. 2019;48:35–43.

    Article  CAS  Google Scholar 

  17. Ibrahim AS, Fahmy AH, Ahmed SS. Copper nanoparticles elevate regeneration capacity of (Ocimum basilicum L.) plant via somatic embryogenesis. Plant Cell Tissue Organ Cult. 2019;136:41–50.

    Article  CAS  Google Scholar 

  18. Makowska K, Kałużniak M, Oleszczuk S, Zimny J, Czaplicki A, Konieczny R. Arabinogalactan proteins improve plant regeneration in barley (Hordeum vulgare L.) anther culture. Plant Cell Tissue Organ Cult. 2017;131:247–57.

    Article  CAS  Google Scholar 

  19. Maksymiec W. Effect of copper on cellular processes in higher plants. Photosynthetica. 1998;34:321–42.

    Article  Google Scholar 

  20. Marschner H. Mineral Nutrition of Higher Plants: Academic press; 1995.

  21. Yruela I. Copper in plants. Braz J Plant Physiol. 2005;17:145–56.

    Article  CAS  Google Scholar 

  22. Chen J, Shafi M, Li S, Wang Y, Wu J, Ye Z, et al. Copper induced oxidative stresses, antioxidant responses and phytoremediation potential of Moso bamboo (Phyllostachys pubescens). Sci Rep. 2015;5:13554.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Tahoori F, Majd A, Nejadsattari T, Ofoghi H, Iranbakhsh A. Effects of silver nitrate (AgNO3) on growth and anatomical structure of vegetative organs of liquorice (Glycyrrhiza glabra L.) under in vitro condition. Plant OMICS. 2018;11:153–60.

    Article  CAS  Google Scholar 

  24. Sridevi V, Giridhar P. In vitro shoot growth, direct organogenesis and somatic embryogenesis promoted by silver nitrate in Coffea dewevrei. J Plant Biochem Biotechnol. 2014;23:112–8.

    Article  CAS  Google Scholar 

  25. Georgiadou EC, Kowalska E, Patla K, Kulbat K, Smolińska B, Leszczyńska J, et al. Influence of Heavy Metals (Ni, Cu, and Zn) on Nitro-Oxidative Stress Responses, Proteome Regulation and Allergen Production in Basil (Ocimum basilicum L.) Plants. Front Plant Sci. 2018;9:862.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kumari M, Mukherjee A, Chandrasekaran N. Genotoxicity of silver nanoparticles in Allium cepa. Sci Total Environm. 2009;407:5243–6.

    Article  CAS  Google Scholar 

  27. Dutta S, Mitra M, Agarwal P, Mahapatra K, De S, Sett U, et al. Oxidative and genotoxic damages in plants in response to heavy metal stress and maintenance of genome stability. Plant Signal Behav. 2018;13:e1460048.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shapiguzov A, Vainonen JP, Wrzaczek M, Kangasjärvi J. ROS-talk - how the apoplast, the chloroplast, and the nucleus get the message through. Front Plant Sci. 2012;3:292.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Banerjee AK, Guo W, Huang Y. Genetic and epigenetic regulation of phenotypic variation in invasive plants – linking research trends towards a unified framework. NeoBiota. 2019;49:77–103.

    Article  Google Scholar 

  30. Van Houten B, Santa-Gonzalez GA, Camargo M. DNA repair after oxidative stress: current challenges. Curr Opin Toxicol. 2018;7:9–16.

    Article  PubMed  Google Scholar 

  31. Sun SL, Zhong JQ, Li SH, Wang XJ. Tissue culture-induced somaclonal variation of decreased pollen viability in torenia (Torenia fournieri Lind.). Bot Stud. 2013;54:36.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Rival A, Ilbert P, Labeyrie A, Torres E, Doulbeau S, Personne A, et al. Variations in genomic DNA methylation during the long-term in vitro proliferation of oil palm embryogenic suspension cultures. Plant Cell Rep. 2013;32:359–68.

    Article  CAS  PubMed  Google Scholar 

  33. Zayova E, Vassilevska IR, Kraptchev B, D. S. Somaclonal variations through indirect organogenesis in eggplant (Solanum melongena L.). Biol Diver Conserv. 2010;3:1-5;

  34. Vázquez AM. Insight into somaclonal variation. Plant Biosystems. 2001;135:57–62.

    Article  Google Scholar 

  35. Bouman H, De Klerk G-J. Measurement of the extent of somaclonal variation in begonia plants regenerated under various conditions. Comparison of three assays. Theor Appl Genet. 2001;102:111-7.

  36. Kiselev KV, Dubrovina AS, Shumakova OA. DNA mutagenesis in 2- and 20-yr-old Panax ginseng cell cultures. Vitro Cell Dev Biol Plant. 2013;49:175–82.

    Article  CAS  Google Scholar 

  37. Yaacob JS, Mat Taha R. Genetic stability of in vitro propagated African blue lily (Agapanthus praecox ssp minimus). Caryologia. 2014;67:227–33.

    Article  Google Scholar 

  38. Orłowska R, Machczyńska J, Oleszczuk S, Zimny J, Bednarek PT. DNA methylation changes and TE activity induced in tissue cultures of barley (Hordeum vulgare L.). J Bio Res (Thessalon). 2016;23:19.

    Article  CAS  Google Scholar 

  39. Machczyńska J, Zimny J, Bednarek PT. Tissue culture-induced genetic and epigenetic variation in triticale ((× Triticosecale spp. Wittmack ex A. Camus 1927) regenerants. Plant Mol Biol. 2015;89:279–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hao YJ, Liu QL, Deng XX. Effect of cryopreservation on apple genetic resources at morphological, chromosomal, and molecular levels. Cryobiology. 2001;43:46–53.

    Article  CAS  PubMed  Google Scholar 

  41. Bednarek PT, Orłowska R, Niedziela A. A relative quantitative Methylation-Sensitive Amplified Polymorphism (MSAP) method for the analysis of abiotic stress. BMC Plant Biol. 2017;17:79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bednarek PT, Orłowska R, Koebner RMD, Zimny J. Quantification of the tissue-culture induced variation in barley (Hordeum vulgare L.). BMC Plant Biol. 2007;7:10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Machczyńska J, Orłowska R, Zimny J, Bednarek PT. Extended metAFLP approach in studies of the tissue culture induced variation (TCIV) in triticale. Mol Breed. 2014;34:845–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Orłowska R, Bednarek PT. Precise evaluation of tissue culture-induced variation during optimisation of in vitro regeneration regime in barley. Plant Mol Biol. 2020;103:33–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Peraza-Echeverria S, Herrera-Valencia VA, Kay AJ. Detection of DNA methylation changes in micropropagated banana plants using methylation-sensitive amplification polymorphism (MSAP). Plant Sci. 2001;161:359–67.

    Article  CAS  PubMed  Google Scholar 

  46. Fiuk A, Bednarek PT, Rybczyński JJ. Flow Cytometry, HPLC-RP, and metAFLP Analyses to Assess Genetic Variability in Somatic Embryo-Derived Plantlets of Gentiana pannonica Scop. Plant Mol Biol Rep. 2010;28:413–20.

    Article  CAS  Google Scholar 

  47. Mikuła A, Tomiczak K, Rybczyński JJ. Cryopreservation enhances embryogenic capacity of Gentiana cruciata (L.) suspension culture and maintains (epi)genetic uniformity of regenerants. Plant Cell Rep. 2011;30:565–74.

    Article  CAS  PubMed  Google Scholar 

  48. Coronel CJ, González AI, Ruiz ML, Polanco C. Analysis of somaclonal variation in transgenic and regenerated plants of Arabidopsis thaliana using methylation related metAFLP and TMD markers. Plant Cell Rep. 2018;37:137–52.

    Article  CAS  PubMed  Google Scholar 

  49. Arrillaga I, Morcillo M, Zanón I, Lario F, Segura J, Sales E. New Approaches to Optimize Somatic Embryogenesis in Maritime Pine. Front Plant Sci. 2019;10:138.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Marimuthu K, Subbaraya U, Suthanthiram B, Marimuthu SS. Molecular analysis of somatic embryogenesis through proteomic approach and optimization of protocol in recalcitrant Musa spp. Physiol Plant. 2019;167:282–301.

    Article  CAS  PubMed  Google Scholar 

  51. Compton ME. Statistical methods suitable for the analysis of plant tissue culture data. Plant Cell Tissue Organ Cul. 1994;3:217–42.

    Article  Google Scholar 

  52. Taguchi G. Introduction to Quality Engineering: Designing Quality into Products and Processes. NY: White Plains, UNIPUB/Quality Resources; Dearborn, Mich., American Supplier Institute; 1986. 181 p.

  53. Rao RS, Kumar CG, Prakasham RS, Hobbs PJ. The Taguchi methodology as a statistical tool for biotechnological applications: a critical appraisal. Biotechnol J. 2008;3:510–23.

    Article  CAS  PubMed  Google Scholar 

  54. Yaldagard M, Mortazavi SA, Tabatabaie F. Application of ultrasonic waves as a priming technique for accelerating and enhancing the germination of barley seed: optimization of method by the taguchi approach. J Inst Brew. 2008;114:14–21.

    Article  Google Scholar 

  55. Awty-Carroll D, Ravella S, Clifton-Brown J, Robson P. Using a Taguchi DOE to investigate factors and interactions affecting germination in Miscanthus sinensis. Sci Rep. 2020;10:1602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Orłowska R, Pachota KA, Machczyńska J, Niedziela A, Makowska K, Zimny J, et al. Improvement of anther cultures conditions using the Taguchi method in three cereal crops. Electron J Biotechnol. 2020;43:8–15.

    Article  CAS  Google Scholar 

  57. Hildebrandt AC, Riker AJ, Duggar BM. The influence of the composition of the medium on growth in vitro of excised tobacco and sunflower tissue cultures. Am J Bot. 1946;33:591–7.

    Article  CAS  Google Scholar 

  58. Murashige T, Skoog F. A revised medium for rapid growth and bio assays with tobacco tissue culture. Physiol Plant. 1962;15:473–97.

    Article  CAS  Google Scholar 

  59. Gamborg OL, Murashige T, Thorpe TA, Vasil IK. Plant Tissue Culture Media. Vitro. 1976;12:473–8.

    Article  CAS  Google Scholar 

  60. Jekayinoluwa T, Gueye B, Bhattacharjee R, Osibanjo O, Shah T, Abberton M. Agromorphologic, genetic and methylation profiling of Dioscorea and Musa species multiplied under three micropropagation systems. PLoS ONE. 2019;14:e0216717.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Samarina L, Gvasaliya M, Koninskaya N, Rakhmangulov R, Efremov A, Kiselyova N, et al. A comparison of genetic stability in tea [Camellia sinensis (L.) Kuntze] plantlets derived from callus with plantlets from long-term in vitro propagation. Plant Cell Tissue Organ Cult. 2019;138:467–74.

    Article  CAS  Google Scholar 

  62. Li J, Wang M, Li Y, Zhang Q, Lindsey K, Daniell H, et al. Multi-omics analyses reveal epigenomics basis for cotton somatic embryogenesis through successive regeneration acclimation process. Plant Biotechnol J. 2019;17:435–50.

    Article  CAS  PubMed  Google Scholar 

  63. Chwedorzewska KJ, Bednarek PT. Genetic and epigenetic variation in a cosmopolitan grass Poa annua from Antarctic and Polish populations. Polish Polar Res. 2012;33:63–80.

    Article  Google Scholar 

  64. Machczyńska J, Orłowska R, Ogórek KA, Bednarek PT. Comparative study on tissue culture induced variation identified with metAFLP and RP–HPLC in barley and triticale regenerants. BioTechnologia. 2015;96:1.

    Google Scholar 

  65. Orłowska R, Pachota KA, Machczynska J, Niedziela A, Zimny J, Bednarek PT. Application of the Taguchi method in cereal tissue cultures process: maximization of green regenerants. In vitro cellular & developmental biology-plant. 2018. Springer; New York.

  66. Lu J, Greene S, Reid S, Cruz VMV, Dierig DA, Byrne P. Phenotypic changes and DNA methylation status in cryopreserved seeds of rye (Secale cereale L.). Cryobiology. 2018;82:8–14.

    Article  CAS  PubMed  Google Scholar 

  67. Abratowska A, Wąsowicz P, Bednarek PT, Telka J, Wierzbicka M. Morphological and genetic distinctiveness of metallicolous and non-metallicolous populations of Armeria maritima s.l. (Plumbaginaceae) in Poland. Plant Biol. 2012;14:586–95.

    Article  CAS  PubMed  Google Scholar 

  68. Mikula A, Tomiczak K, Wójcik A, Rybczynski JJ. Encapsulation-dehydration method elevates embryogenic abilities of Gentiana kurroo cell suspension and carrying on genetic stability of its regenerants after cryopreservation. Acta Hortic. 2011;908:143–54.

    Article  CAS  Google Scholar 

  69. Gao Y, Ran L, Kong Y, Jiang J, Sokolov V, Wang Y. Assessment of DNA methylation changes in tissue culture of Brassica napus. Genetika. 2014;50:1338–44.

    Article  CAS  PubMed  Google Scholar 

  70. Stroud H, Ding B, Simon SA, Feng S, Bellizzi M, Pellegrini M, et al. Plants regenerated from tissue culture contain stable epigenome changes in rice. eLife. 2013;2:e00354;

  71. Etienne H, Bertrand B. Somaclonal variation in Coffea arabica: effects of genotype and embryogenic cell suspension age on frequency and phenotype of variants. Tree Physiol. 2003;23:419–26.

    Article  CAS  PubMed  Google Scholar 

  72. Bradaï F, Sánchez-Romero C, Martín C. Somaclonal variation in olive (Olea europaea L.) plants regenerated via somatic embryogenesis: Influence of genotype and culture age on genetic stability. Sci Hortic. 2019;251:260–6.

    Article  CAS  Google Scholar 

  73. Machczyńska J, Orłowska R, Mańkowski DR, Zimny J, Bednarek PT. DNA methylation changes in triticale due to in vitro culture plant regeneration and consecutive reproduction. Plant Cell Tiss Organ Cult. 2014;119:289–99.

    Article  CAS  Google Scholar 

  74. Bednarek PT, Orłowska R. CG demethylation leads to sequence mutations in an anther culture of barley due to the presence of Cu, Ag Ions in the Medium and Culture Time. Int J Mol Sci. 2020;21:4401.

    Article  CAS  PubMed Central  Google Scholar 

  75. Bednarek PT, Orłowska R. Time of in vitro anther culture may moderate action of copper and silver ions that affect the relationship between dna methylation change and the yield of barley green regenerants. Plants. 2020;9:1064.

    Article  CAS  PubMed Central  Google Scholar 

  76. Orłowska R, Zimny J, Bednarek PT. Copper ions induce DNA sequence variation in zygotic embryo culture-derived barley regenerants. Front Plant Sci. 2021;11:2272.

    Article  Google Scholar 

  77. Yang SF, Hoffman NE. Ethylene biosynthesis and its regulation in higher plants. Annu Rev Plant Physiol Plant Mol Biol. 1984;35:155–89.

    Article  CAS  Google Scholar 

  78. Sauter M, Moffatt B, Saechao Maye C, Hell R, Wirtz M. Methionine salvage and S-adenosylmethionine: essential links between sulfur, ethylene and polyamine biosynthesis. Biochem J. 2013;451:145–54.

    Article  CAS  PubMed  Google Scholar 

  79. Kende H. Ethylene Biosynthesis. Annu Rev Plant Physiol Plant Mol Biol. 1993;44:283–307.

    Article  CAS  Google Scholar 

  80. Lee DH, O’Connor TR, Pfeifer GP. Oxidative DNA damage induced by copper and hydrogen peroxide promotes CG– > TT tandem mutations at methylated CpG dinucleotides in nucleotide excision repair-deficient cells. Nucleic Acids Res. 2002;30:3566–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD, et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature. 2008;452(7184):215–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Matzke MA, Mosher RA. RNA-directed DNA methylation: an epigenetic pathway of increasing complexity. Nat Rev Genet. 2014;15:394–408.

    Article  CAS  Google Scholar 

  83. Huettel B, Kanno T, Daxinger L, Bucher E, van der Winden J, Matzke AJM, et al. RNA-directed DNA methylation mediated by DRD1 and Pol IVb: a versatile pathway for transcriptional gene silencing in plants. Biochim Biophys Acta. 2007;1769:358–74.

    Article  CAS  PubMed  Google Scholar 

  84. Lindroth AM, Cao X, Jackson JP, Zilberman D, McCallum CM, Henikoff S, et al. Requirement of CHROMOMETHYLASE3 for Maintenance of CpXpG Methylation. Science. 2001;292:2077–80.

    Article  CAS  PubMed  Google Scholar 

  85. Lee K, Seo PJ. Dynamic Epigenetic Changes during Plant Regeneration. Trends Plant Sci. 2018;23:235–47.

    Article  CAS  PubMed  Google Scholar 

  86. Liu R, Lang Z. The mechanism and function of active DNA demethylation in plants. J Integr Plant Biol. 2020;62:148–59.

    Article  CAS  PubMed  Google Scholar 

  87. Parrilla-Doblas JT, Roldán-Arjona T, Ariza RR, Córdoba-Cañero D. Active DNA Demethylation in Plants. Int J Mol Sci. 2019;20:4683.

    Article  CAS  PubMed Central  Google Scholar 

  88. Zhu JK. Active DNA demethylation mediated by DNA glycosylases. Annu Rev Genet. 2009;43:143–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Slotkin RK, Vaughn M, Borges F, Tanurdzić M, Becker JD, Feijó JA, et al. Epigenetic reprogramming and small RNA silencing of transposable elements in pollen. Cell. 2009;136:461–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Law JA, Jacobsen SE. Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat Rev Genet. 2010;11:204–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Přibylová A, Čermák V, Tyč D, Fischer L. Detailed insight into the dynamics of the initial phases of de novo RNA-directed DNA methylation in plant cells. Epigenetics Chromatin. 2019;12:54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. To TK, Saze H, Kakutani T. DNA Methylation within Transcribed Regions. Plant Physiol. 2015;168:1219–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Iglesias FM, Cerdán PD. Maintaining Epigenetic Inheritance During DNA Replication in Plants. Front Plant Sci. 2016;7:38.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Chu CC. The N6 medium and its applications to anther culture of cereal crops. In: Hu H, editor. Proc Symp Plant Tissue Culture. Peking: Science Press; 1978. p. 45–50.

    Google Scholar 

  95. Kumlehn J, Serazetdinova L, Hensel G, Becker D, Loerz H. Genetic transformation of barley (Hordeum vulgare L.) via infection of androgenetic pollen cultures with Agrobacterium tumefaciens. Plant Biotechnol J. 2006;4:251–61.

    Article  CAS  PubMed  Google Scholar 

  96. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel Population genetic software for teaching and research-an update. Bioinformatics. 2012;28:2537–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Schönswetter P, Tribsch A. Vicariance and dispersal in the alpine perennial Bupleurum stellatum L. (Apiaceae). Taxon. 2005;54:725–32.

    Article  Google Scholar 

Download references


I would like to express my gratitude to professor Piotr T. Bednarek for reading and commenting on the manuscript draft. I would also like to acknowledge the technical support provided by Katarzyna Anna Pachota for her patient, and contribution to the laboratory work.


This study was funded by the Ministry of Agriculture and Rural Development (Poland) (HORhn-801-PB-22/15-18).

Author information

Authors and Affiliations



This research was conducted by a single author. The help provided in the lab work and preparing the manuscript was recognized in the acknowledgments section. The author read and approved the final manuscript.

Corresponding author

Correspondence to Renata Orłowska.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The author declare that have no competing interests.

Additional information

Publisher's Note

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

Supplementary information

Additional file 1: Table S1.

Primers used in metAFLP for donors and regenerants in optimisation and verification experiments. Table S2. The indices characterizing KpnI/MseI and Acc65I/MseI-KpnI/MseI metAFLP marker systems evaluated for regenerants derived via somatic embryogenesis. Table S3. Oligonucleotides applied for metAFLP in barley studies.

Additional file 2: Figure S1.

Agglomeration analysis based on the sequence markers KpnI/MseI for donor plants: JDH1-JDH24.The bootstrap values are indicated at the nodes.

Additional file 3: Figure S2.

Agglomeration analysis based on virtually derived markers (Acc65I/MseI-KpnI/MseI) reflecting DNA methylation difference between JDH1–JDH24 donor plants. The bootstrap values are indicated at the nodes.

Additional file 4: Figure S3.

Agglomeration analysis (UPGMA, Jaccard) based on KpnI/MseI markers for regenerants derived via somatic embryogenesis via M1–M9 trials and their JDHK donor plant. The bootstrap values are marked at the nodes.

Additional file 5: Figure S4.

Agglomeration analysis (UPGMA, Jaccard) based on Acc65I/MseI-KpnI/MseI virtual markers related to DNA methylation differences evaluated for regenerants derived via somatic embryogenesis in the M1–M9 trials and JDHM-donor plant. The bootstrap values are marked at the nodes.

Additional file 6: Figure S5.

Agglomeration analysis (UPGMA, Jaccard) conducted on the metAFLPs related to the DNA mutations KpnI/MseI for the M10-M13 trials. The M10 reflects control conditions, whereas the M12 and M13 regenerants derived according to the optimised conditions directed towards the minimum and maximum differences between donor and regenerants. Bootstrap value is indicated on the nodes.

Additional file 7: Figure S6.

Agglomeration analysis (UPGMA, Jaccard) conducted on the metAFLPs related to the DNA methylation changes (Acc65I/MseI-KpnI/MseI) for the M10-M13 trials. The M10 reflects control conditions, whereas the M12 and M13 regenerants derived according to the optimised conditions directed towards the minimum and maximum differences between donor and regenerants. Bootstrap value is indicated on the nodes.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Orłowska, R. Barley somatic embryogenesis-an attempt to modify variation induced in tissue culture. J of Biol Res-Thessaloniki 28, 9 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: