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. 2023 Jul 4;12(7):1383.
doi: 10.3390/antiox12071383.

Antioxidant and Wound Healing Bioactive Potential of Extracts Obtained from Bark and Needles of Softwood Species

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Antioxidant and Wound Healing Bioactive Potential of Extracts Obtained from Bark and Needles of Softwood Species

Elisabeta-Irina Geana et al. Antioxidants (Basel). .

Abstract

Interest in the extraction of phytochemical bioactive compounds, especially polyphenols from biomass, has recently increased due to their valuable biological potential as natural sources of antioxidants, which could be used in a wide range of applications, from foods and pharmaceuticals to green polymers and bio-based materials. The present research study aimed to provide a comprehensive chemical characterization of the phytochemical composition of forest biomass (bark and needles) of softwood species (Picea abies L., H. Karst., and Abies alba Mill.) and to investigate their in vitro antioxidant and antimicrobial activities to assess their potential in treating and healing infected chronic wounds. The DPPH radical-scavenging method and P-LD were used for a mechanistic explanation of the biomolecular effects of the investigated bioactive compounds. (+)-Catechin, epicatechin, rutin, myricetin, 4 hydroxybenzoic and p-cumaric acids, kaempherol, and apigenin were the main quantified polyphenols in coniferous biomass (in quantities around 100 µg/g). Also, numerous phenolic acids, flavonoids, stilbenes, terpenes, lignans, secoiridoids, and indanes with antioxidant, antimicrobial, anti-inflammatory, antihemolytic, and anti-carcinogenic potential were identified. The Abies alba needle extract was more toxic to microbial strains than the eukaryotic cells that provide its active wound healing principles. In this context, developing industrial upscaling strategies is imperative for the long-term success of biorefineries and incorporating them as part of a circular bio-economy.

Keywords: PI3Kγ; anti-hemolytic activity; antimicrobial activity; antioxidant activity; coniferous biomass; phytochemical bioactive compounds; polyphenols; protein-ligand docking.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Bioactive characteristics of coniferous biomass (spruce needles—SN (n = 13), spruce bark—SB (n = 13), fir needles—FN (n = 4), and fir bark—FB (n = 4)): (a) Total polyphenols (TP), (b) Total flavonoids (TF), and (c) Antioxidant activity (AA).
Figure 2
Figure 2
A total ion current (TIC) chromatogram showing the identification of the main bioactive compounds in spruce bark extract using UHPLC–MS/MS detection in negative ionization mode.
Figure 3
Figure 3
PCA results (scores and loading biplots) of different coniferous extracts (spruce bark—SB, spruce needles—SN, fir bark—FB, and fir needles—FN) based on quantified phenolic compound biomarkers (a) and non-targeted HRMS screening analysis (b).
Figure 4
Figure 4
A heat map of discriminant features according to the different types of coniferous biomass (spruce bark—SB, spruce needles—SN, fir bark—FB, and fir needles—FN) based on quantified phenolic compounds biomarkers (a) and non-targeted HRMS screening analysis (b). Red and green cells correspond to low and high compound levels, respectively.
Figure 5
Figure 5
Antimicrobial activity of bark and needles extracts: (a) Mean of DIZ exhibited by hydroalcoholic extracts of P. abies and A. alba (needles and barks) vs. solvent control (ethanol 50%) and (b) IC50 related to microbial cell viability in the presence of P. abies and A. alba extracts (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).
Figure 6
Figure 6
Biocompatibility of the extracts: (a) IC50 (µL/mL) and (b) LDH release (%) of HaCaT cells exposed to fir and spruce bark and needle extracts; significant differences (p < 0.05) in all comparisons are shown (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).
Figure 7
Figure 7
Anti-hemolytic activity of bark and needles extracts: (a) Hemolysis (%) induced by Picea abies and Abies alba extracts and (b) Antihemolytic activity of fir and spruce extracts against the AAPH-induced oxidative hemolysis of ram erythrocytes (* p < 0.05, *** p < 0.001, **** p < 0.0001).
Figure 8
Figure 8
The superimposing of the structural files (3D) for the two RCSB PDB entries for PI3Kγ with the co-crystalized ligands (1E8W’s structural components are colored in blue—PI3Kγ and quercetin; 1E90’s structural components are colored in red—PI3Kγ and myricetin; macromolecules are depicted as wireframes, with their secondary structures being drawn as cartoons, while ligands are figured as sticks; to ensure a clear image, only the amino acid residues around a distance of 6 Å from ligands are shown). (a) General view of the molecular surface (80% transparency) of the ATP-binding pocket (with the two co-crystalized ligands inside), colored by hydrophobicity (residue atoms are colored according to the hydropathy index of amino acids). To ensure a clear image, the molecular surface is shown only for the structure chosen for docking: 1E8W. (b) Detailed view of the ATP-binding pocket with the two co-crystalized ligands inside, with molecular surface (80% transparency) colored according to the electrostatic partial charges of residues (blue corresponds to the positive charge, red corresponds to the negative charge). To ensure a clear image, the molecular surface is shown only for the structure chosen for docking: 1E8W. (c) Simplified detailed view of the two co-crystalized ligands and their targets (the region of the ATP binding pocket).
Figure 9
Figure 9
Comparative docking: graphical depiction of data for the best-ranked poses of the ligands (with blue showing the PyRx w/Autodock Vina results; red showing the SwissDock w/EADock DSS results). The vertical y-axis is negative. A lower value indicates a better binder. The blue color represents the values of BA (kcal/mol). The red color represents values of ΔG (kcal/mol). The values of BA and ΔG of quercetin (control/reference ligand) are depicted as horizontal dashed lines.
Figure 10
Figure 10
Venn diagram: comparative graphic depiction of the results generated by the two P–LD runs for the best-ranked poses of the ligands. Venny 2.1 “https://bioinfogp.cnb.csic.es/tools/venny/index.html (accessed on 25 May 2023)” was used to analyze the output of the P–LD runs (matchings are shown as numbers and percentage).
Figure 11
Figure 11
Graphical depiction of the eight best poses of the strongest binders based on results of the PyRx w/Autodock Vina P–LD run against PI3Kγ (PI3Kγ is depicted in the secondary structures drawn as cartoons, while ligands are depicted as sticks; to ensure a clear image, the distant amino acid residues have been hidden/cropped). (a) Detailed view of the ATP-binding pocket with docked ligands, with molecular surface (80% transparency) colored according to the parental color of the residues. (b) Simplified detailed view of binding modes of ligands in the region of the ATP binding pocket of PI3Kγ.

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