Untargeted metabolomics using UHPLC-Q-Orbitrap HRMS for identifying cytotoxic compounds on MCF-7 breast cancer cells from Annona muricata Linn leaf extracts as potential anticancer agents.

INTRODUCTION: The leaves of Annona muricata L., known as "soursop" or "sirsak" in Indonesia, are used traditionally for cancer treatment. However, the bioactive components remain largely unidentified. OBJECTIVE: This study used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics to identify potential cytotoxic compounds in A. muricata leaf extracts on MCF-7 breast cancer cells in vitro. METHODS: A. muricata leaves were macerated with water, 99% ethanol, and aqueous mixtures containing 30%, 50%, and 80% ethanol. Cytotoxic activity of the extracts against MCF-7 breast cancer cells was determined using the MTT assay. Ultra-high-performance liquid chromatography-Q-Orbitrap high-resolution mass spectroscopy (UHPLC-Q-Orbitrap-HRMS) was used to characterize the metabolite composition of each extract. The correlations between metabolite profile and cytotoxic activities were evaluated using orthogonal partial least square discriminant analysis (OPLS-DA). The binding of these bioactive compounds to the tumorigenic alpha-estrogen receptor (3ERT) was then evaluated by in silico docking simulations. RESULTS: Ninety-nine percent ethanol extracts demonstrated the greatest potency for reducing MCF-7 cell viability (IC50 = 22 µg/ml). We detected 35 metabolites in ethanol extracts, including alkaloids, flavonoids, and acetogenins. OPLS-DA predicted that annoreticuin, squadiolin C, and xylopine, and six unknown acetogenin metabolites, might reduce MCF-7 cell viability. In silico analysis predicted that annoreticuin, squadiolin C, and xylopine bind to 3ERT with an affinity comparable to doxorubicin. CONCLUSION: Untargeted metabolomics and in silico modeling identified cytotoxic compounds on MCF-7 cells and binding affinity to 3ERT in A. muricata leaf extracts. The findings need to be further verified to prove the screening results.

Collecting wood core samples from Macassar ebony (Diospyros celebica Bakh.) for multi-purpose analysis using pickering punch.

Sample collection activities for a study of population genetics across the natural distribution of targeted tree species require a lot of resources, mainly if repeated field visits are necessary. Conventionally, population genetic studies use good sample material like leaves. In addition, cambium or small pieces of fresh wood can be used to replace leaf samples. Currently, restrictions from the permit regulation have caused only a limited number of samples that can be collected. Therefore, efficient use of samples must be designed to maximize their uses for research. Due to the small amount of successfully sampled materials, hence there are limitations to extend their uses for other analyses and are often sufficient only for genetic analysis. Therefore, innovation in sampling methods using pickering punch (https://www.agroisolab.com/pickering-punch) to collect ebony wood cores in this study is required to cover multi-analyses not only limited to genetics but also for other analyses such as isotopes, near-infrared spectroscopy (NIRs), anatomy, and chemical compounds.•Pickering punch is recommended for efficient wood core sample collection from ebony standing trees.•323 wood core samples were successfully collected from 16 natural populations across Celebes (Sulawesi).•Multi-analyses studies on sampled wood cores are possible for ebony wood identification (e.g., species and origin/provenance).

Metabolite profiling of Andrographis paniculata leaves and stem extract using UHPLC-Orbitrap-MS/MS.

Andrographis paniculata is known as the king of bitter and it has been widely used as a medicinal plant. The properties of A. paniculata are generally determined by the metabolite composition, which may be influenced by several factors, one of which is the part of the plant extracted. The objectives of this research are to identify putatively the metabolite composition of the stem and the leaves extracts using LC-MS/MS and classify them using PCA. The stem and the leaves samples were separated and extracted using sonication with 70% ethanol. A total of 31 metabolite compounds has been putatively identified. All compounds were identified in the stem and the leaves extracts, which only differed in their intensity. These metabolite compounds were divided into diterpene lactones, flavonoids, and phenolic acid groups. By using the peak intensities of the 18 compounds identified, the leaves and stem extracts were grouped using PCA.