Extracts of Thesium chinense inhibit SARS-CoV-2 and inflammation in vitro

Abstract

Context

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still spreading rapidly. Relevant research based on the antiviral effects of Thesium chinense Turcz (Santalaceae) was not found.

Objective

To investigate the antiviral and anti-inflammatory effects of extracts of T. chinense.

Materials and methods

To investigate the anti-entry and replication effect of the ethanol extract of T. chinense (drug concentration 80, 160, 320, 640, 960 μg/mL) against the SARS-CoV-2. Remdesivir (20.74 μM) was used as positive control, and Vero cells were used as host cells to detect the expression level of nucleocapsid protein (NP) in the virus by real-time quantitative polymerase chain reaction (RT-PCR) and Western blotting. RAW264.7 cells were used as an anti-inflammatory experimental model under lipopolysaccharide (LPS) induction, and the expression levels of tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) were detected by enzyme-linked immunosorbent assay (ELISA).

Results

The ethanol extract of T. chinense significantly inhibited the replication (half maximal effective concentration, EC₅₀: 259.3 μg/mL) and entry (EC₅₀: 359.1 μg/mL) of SARS-CoV-2 into Vero cells, and significantly reduced the levels of IL-6 and TNF-α produced by LPS-stimulated RAW264.7 cells. Petroleum ether (EC₅₀: 163.6 μg/mL), ethyl acetate (EC₅₀: 22.92 μg/mL) and n-butanol (EC₅₀: 56.8 μg/mL) extracts showed weak inhibition of SARS-CoV-2 replication in Vero cells, and reduced the levels of IL-6 and TNF-α produced by LPS-stimulated RAW264.7 cells.

Conclusion

T. chinense can be a potential candidate to fight SARS-CoV-2, and is becoming a traditional Chinese medicine candidate for treating COVID-19.

Keywords:

• Thesium chinense Turcz
• SARS-CoV-2
• COVID-19
• anti-inflammatory

Authors’ contributions

Juncheng Ma: Conceptualization, Methodology, Software, Data curation, Writing - Original draft preparation, Writing - Reviewing and Editing. Juanru Wei: Visualization, Formal analysis. Gang Chen and Hechun Sun: Supervision. Xiaowei Yan: Investigation. Ning Li: Supervision, Project administration, Writing - Reviewing and Editing.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This work was supported by the National Science Foundation of China (NO. 32270424), Major Project of Science and Technology of Anhui Province, China (202203a07020014), and Scientific Research Platform Improvement Project of Anhui Medical University (2022xkjT045).