Phytochemical characterization and multi-biological potentials of Inula viscosa: new in vitro evidence
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Yasemin Celik Altunoglu
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Mustafa Ocal
Abstract
Inula viscosa is a common medicinal plant and is mostly spread in Mediterranean countries. The current study analyses I. viscosa extracts for antimicrobial capacity, phenolic content, antioxidant, enzyme inhibition, and molecular modeling. The extracts’ anticancer potential in prostate and breast cancer cells was evaluated, and their anti-metastatic and molecular activities were analyzed through gene expression. The most prevalent substances in the methanol extract of I. viscosa are phenolic acids, which include derivatives of caffeic acid and mono-, di-, and tri-caffeoylquinic acids. Dicaffeoylquinic acids represented 88 % of the quantified phenolics. The methanol extract showed a 50 % inhibitor concentration (IC50) of 101.4 μg/mL at 48 h in PC3 cells. Autophagy-related genes were found to be down-regulated in all three cancer cells. Regarding antimetastatic activity, the wound width remained ∼96 % at 48 h after ethyl acetate treatment in MCF-7 cells. The water extract had the highest total phenolic content and the strongest antioxidant capacity, followed by the methanol extract. Modest binding potential to B-cell leukemia-2 (Bcl-2) was observed with caffeoylquinic acid and caffeic acid derivatives, which may be related to pro-apoptotic/anticancer effects. Our results suggested that I. viscosa can be considered as an effective raw material for designing health-promoting applications.
Acknowledgment
Technical and human support provided by CICT of Universidad de Jaén (UJA, MINECO, Junta de Andalucía, FEDER) is gratefully acknowledged.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Yasemin Celik Altunoglu: Writing – review & editing, Writing – original draft, Project administration, Investigation. Mustafa Ocal: Writing – review & editing, Methodology, Investigation. Mehmet Cengiz Baloglu: Writing – review & editing, Validation, Investigation. Alsideeq Abdulsalam Ahmed Aboujanah: Methodology, Investigation. Erdoğan Horuz: Methodology, Investigation. E.J. Llorent-Martínez: Methodology, Investigation. Abdullahi Ibrahim Uba: Methodology, Investigation. Irina Lazarova: Methodology, Investigation. Gökhan Zengin: Writing – review & editing, Writing – original draft, Investigation. Kerim Güney: Investigation, Validation.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This research has been supported by Kastamonu University Scientific Research Projects Coordination Department (grant number: KÜ-BAP01/2021-29 and KÜ-HIZDES 2018-09).
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Data availability: The raw data can be obtained on request from the corresponding author.
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