Uncovering the anti-proliferation mechanism and bioactive compounds in red kidney bean coat against B16-F10 melanoma cells by metabolomics and network pharmacology analysis

Nie, Jiajia; Huang, Jianxiang; Wu, Qing-Rong; Qin, Xuemei; Li, Zhenyu

doi:10.1039/c8fo01738g

Cited by 11 publications

(5 citation statements)

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“…A database search revealed a total of 256 previously reported compounds related to the B16F10 cell line [ [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] ]. In four of these studies, LC-MS was used for instrumental analysis [ [22] , [23] , [24] , [25] ] while NMR was employed in nine cases [ [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] ]. In vivo metabolomics was considered in eight cases [ 22 , [29] , [30] , [31] , [32] , [33] , [34] , [35] ].…”

Section: Resultsmentioning

confidence: 99%

“…In the current investigation, 42 biologically relevant compounds were putatively determined ( Table 1 ). Among them, 21 metabolites were found in the literature as described above ( Table S1 ) [ [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] ]. The different numbers of detected and identified compounds as compared to the literature might be due to the different sample preparation techniques and/or different instrumental platforms used in the particular experiments, and/or different matrices employed.…”

Section: Resultsmentioning

confidence: 99%

“…Other identified compounds included amino acids, vitamins (both groups were cell culture medium components), small peptides, and derivatives of the above listed medium components ( Table S1, Fig. S1 ) [ [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] ].

Fig.…”

Section: Resultsmentioning

confidence: 99%

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One extraction tool for in vitro-in vivo extrapolation? SPME-based metabolomics of in vitro 2D, 3D, and in vivo mouse melanoma models

Jaroch

Taczyńska

Czechowska

et al. 2021

Journal of Pharmaceutical Analysis

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Solid phase microextraction (SPME) in combination with high-resolution mass spectrometry was employed for the determination of metabolomic profile of mouse melanoma growth within in vitro 2D, in vitro 3D, and in vivo models. Such multi-model approach had never been investigated before. Due to the low-invasiveness of SPME, it was possible to perform time-course analysis, which allowed building time profile of biochemical reactions in the studied material. Such approach does not require the multiplication of samples as subsequent analyses are performed from the very same cell culture or from the same individual. SPME already reduces the number of animals required for experiment; therefore, it is with good concordance with the 3Rs rule (replacement, reduction, and refinement). Among tested models, the largest number of compounds was found within the in vitro 2D cell culture model, while in vivo and in vitro 3D models had the lowest number of detected compounds. These results may be connected with a higher metabolic rate, as well as lower integrity of the in vitro 2D model compared to the in vitro 3D model resulting in a lower number of compounds released into medium in the latter model. In terms of in vitro-in vivo extrapolation, the in vitro 2D model performed more similar to in vivo model compared to in vitro 3D model; however, it might have been due to the fact that only compounds secreted to medium were investigated. Thus, in further experiments to obtain full metabolome information, the intraspheroidal assessment or spheroid dissociation would be necessary.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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One extraction tool for in vitro-in vivo extrapolation? SPME-based metabolomics of in vitro 2D, 3D, and in vivo mouse melanoma models

Jaroch

Taczyńska

Czechowska

et al. 2021

Journal of Pharmaceutical Analysis

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“…15 Several studies have discovered and demonstrated the potential mechanism of drug treatment of CM or inhibition of CM metastasis through network pharmacology, which also provides a new idea for this study. [16][17][18] Therefore, this study aims to combine network pharmacology and basic experiments to initially explore the potential mechanism of PN intervention in CM metastasis through network pharmacology and to demonstrate our network pharmacology results through cellular experiments. Providing theoretical and experimental support for clinical application of traditional Chinese medicine and drug research and development.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the rise of network pharmacology, has provided a scientific and inexpensive method for exploring the underlying mechanism of drug action on disease 15 . Several studies have discovered and demonstrated the potential mechanism of drug treatment of CM or inhibition of CM metastasis through network pharmacology, which also provides a new idea for this study 16–18 …”

Section: Introductionmentioning

confidence: 99%

Exploring and validating the metastasis mechanism of pathenolide interfering with cutaneous melanoma through ER stress‐dependent apoptosis based on the network pharmacology

Liu

Zhang

et al. 2022

Phytochemical Analysis

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BackgroundThis study aimed to explore the mechanism of parthenolide in inhibiting melanoma metastasis through network pharmacology and cell experiment.Materials and MethodsThis research obtained the targets of the drug from the HERB database and PubChem database, the differential expression gene of metastatic cutaneous melanoma was obtained by differentially expression gene analysis of four Gene Expression Omnibus (GEO) datasets. The intersection of drug targets and differentially expression genes were considered to be related to drugs that inhibit metastasis of cutaneous melanoma. The STRING database was used to construct the protein–protein interaction (PPI) network, and cytohubba package in Cytoscape software was used to rank the PPI network targets. The enrichment analysis was used to screen out the relevance Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology to explain the underlying mechanism of drug inhibiting the cutaneous melanoma metastatic; cell viability, apoptosis, cell migration and protein levels were assessed using cell counting kit‐8 (CCK‐8) assay, Annexin V‐FITC/PI assay, wound healing assay, respectively. Finally, combining pathway maps and literature, we detected ATF4 and proteins upstream and downstream of ATF4 through Western blot.ResultsA total of 87 targets were screened out from the drug databases, and a total of 1635 differentially expression genes was obtained from the differentially expression genes analysis of GEO datasets, a total of nine targets (VEGFA, ANXA5, ICAM1, SELE, NFKBIA, ATF4, CTNNB1, SELP and HPGDS) were considered to be related to drugs that inhibit metastasis of cutaneous melanoma. The result of enrichment analysis showed that the drug inhibits the metastatic of cutaneous melanoma through multiple pathways such as TNF signalling pathway, lipid and atherosclerosis and fluid shear stress and atherosclerosis, relevance multiple biological processes, cellular components and molecular function; cell experiments showed that parthenolide could inhibit tumour cell migration and induce a decrease of cell viability. Flow cytometry results showed that parthenolide induced tumour cell apoptosis. Western blot results suggested that parthenolide exerted therapeutic effects by regulating ATF4 protein and its upstream and downstream proteins, namely endoplasmic reticulum (ER) stress signalling pathway.ConclusionParthenolide induces ER stress‐dependent apoptosis in melanoma cells.

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Exploring the nutritional and health benefits of pulses from the Indian Himalayan region: A glimpse into the region’s rich agricultural heritage

et al. 2023

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Uncovering the anti-proliferation mechanism and bioactive compounds in red kidney bean coat against B16-F10 melanoma cells by metabolomics and network pharmacology analysis

Abstract: In this study, coat (RKBC) and kernel (RKBK) extracts of red kidney bean were prepared, and their chemical compositions and potential anti-cancer activity against B16-F10 cells were evaluated.

Cited by 11 publications

References 45 publications

One extraction tool for in vitro-in vivo extrapolation? SPME-based metabolomics of in vitro 2D, 3D, and in vivo mouse melanoma models

One extraction tool for in vitro-in vivo extrapolation? SPME-based metabolomics of in vitro 2D, 3D, and in vivo mouse melanoma models

Exploring and validating the metastasis mechanism of pathenolide interfering with cutaneous melanoma through ER stress‐dependent apoptosis based on the network pharmacology

Exploring the nutritional and health benefits of pulses from the Indian Himalayan region: A glimpse into the region’s rich agricultural heritage

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