S-Adenosyl-L-Methionine and Cu(II) Impact Green Plant Regeneration Efficiency

Abstract

The biological improvement of triticale, a cereal of increasing importance in agriculture, may be accelerated via the production of doubled haploid lines using in vitro culture. Among the relevant factors affecting the culture efficiency are Cu(II) or Ag(I) acting, e.g., as cofactors of enzymes. The copper ions are known to positively affect green plant regeneration efficiency. However, the biochemical basis, mainly its role in the generation of in vitro-induced genetic and epigenetic variation and green plant regeneration efficiency, is not well understood. Here, we employed structural equation modeling to evaluate the relationship between de novo DNA methylation affecting the asymmetric context of CHH sequences, the methylation-sensitive Amplified Fragment Length Polymorphism related sequence variation, and the concentration of Cu(II) and Ag(I) ions in induction media, as well as their effect on S-adenosyl-L-methionine perturbations, observed using FTIR spectroscopy, and the green plant regeneration efficiency. Our results allowed the construction of a theory-based model reflecting the biological phenomena associated with green plant regeneration efficiency. Furthermore, it is shown that Cu(II) ions in induction media affect plant regeneration, and by manipulating their concentration, the regeneration efficiency can be altered. Additionally, S-adenosyl-L-methionine is involved in the efficiency of green plant regeneration through methylation of the asymmetric CHH sequence related to de novo methylation. This shows that the Yang cycle may impact the production of green regenerants.

An insight into tissue culture-induced variation origin shared between anther culture-derived triticale regenerants

“…The FTIR analysis delivered spectra previously assigned to metabolites (GSH: 2550 − 2540 cm −1 [ 26 ], SAM: 1630…1470 cm −1 [ 25 ]), representing biochemical pathways and cycles, and proved to be essential for partial structural equation models. The mean absorbance values of low methylated pectins (LMP), SAM, and GSH across all samples encompassing all trials were 0.5512, 3.9617, and 0.00515, respectively.…”

“…Data from previously published partial structural models of the relationships between CHH_SV, CHH_DNMV, Cu(II), Ag(I), time of anther cultures, β -glucans/pectins, GSH, and GPRE were employed (Ag + [ 30 ], GSH [ 26 ], SAM [ 25 ], GSH and SAM [ 28 ]). Five endogenous variables (described by the model; CHH_DNMV, CHH_SV, FTIR absorbance in the region assigned to SAM, FTIR absorbance in the region assigned to GSH and GPRE) and two exogenous (not described within the model) (FTIR absorbance in the region assigned to LMP, Cu(II)) variables make up the proposed model.…”

“…Our previous studies have demonstrated that SAM and GSH impact GPRE. SAM achieves this by methylating the asymmetric CHH sequence, which is related to de novo methylation [ 25 ]. At the same time, GSH does so through an epigenetic process that is not fully understood [ 26 ].…”

“…The current study was based on former experiments involving specially designed biological materials [ 13 ], the metAFLP approach [ 19 ], FTIR data concerning β -glucans/pectins [ 27 , 28 ], SAM [ 25 ], and GSH [ 26 ]. A structural equation model [ 30 ] was used to frame the data.…”

“…Therefore, it was hypothesized that it might have a biochemical background. A series of studies conducted on barley and triticale demonstrated that S-adenosyl-L-methionine (SAM) [ 25 ], glutathione (GSH) [ 26 ], b -glucans [ 27 ]/pectins [ 28 ], and Cu(II) ions [ 22 ] are involved in GPRE. The metabolites, via structural equation models [ 28 ], were linked to the Krebs and Yang cycles and biochemical pathways, including glycolysis or transsulfuration.…”