Identifying Active Compounds and Mechanism of Camellia nitidissima Chi on Anti-Colon Cancer by Network Pharmacology and Experimental Validation

Chen, Yiwei; Hao, Erhong; Zhang, Fan; Du, Zhenhong; Xie, Jinling; Chen, Feng; Yu, Chunlin; Hou, Xin; Deng, Jiagang

doi:10.1155/2021/7169211

Cited by 4 publications

(2 citation statements)

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“…Notably, Kaempferide has shown superior pharmacokinetic properties compared to various other flavonoids, including Kaempferol, as demonstrated in a study by Jiang et al, 2018. Researchers have investigated the anticancer potential of Kaempferide against a range of cancer types in vitro. These include cervical cancer (Nath et al, 2015), breast cancer (Yusuf et al, 2021), lung cancer (Li et al, 2020), and colon cancer (Chen et al, 2021). Morin is a polyphenolic flavonol compound primarily found in plant families like Moraceae, Rosaceae, and Fagaceae, as indicated by Solairaja et al, in 2021.…”

Section: Discussionmentioning

confidence: 99%

Computer-aided drug design to discover DNMT inhibitors from phytochemicals

Kumari,

Wijesinghe

2024

Ceylon J. Sci.

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Inhibitors of DNA methyltransferase (DNMTs) are now a major family of epigenetic targets with therapeutic interest. However, only two cytosine analogues 5-azacytosine (azacytidine) and 20-deoxy-5-azacytidine (decitabine), have been approved as the most cutting-edge medications for treating epigenetic cancer with some restrictions. In this context, computational methods that rely on quantitative structure-activity relationship (QSAR) play a crucial role allowing us to predict the biological activity of potential molecules based on the theoretically calculated physicochemical properties of these compounds. When coupled with machine learning (ML), QSAR approaches create an ideal platform for discovering potential drug candidates. In this study, three Machine Learning (ML) models; Random Forest, Support Vector Machine, and Artificial Neural Network, were trained using modified TeachOpenCADD KNIME workflows and applied it to the identification of plant molecules that are structurally similar to the active pharmaceuticals of current DNMT inhibitors. Then molecular docking simulations were performed using AutoDock Vina, employing two human DNMT structures (PDB codes: 4WXX and 2QRV) as target proteins and the predicted phytochemicals as ligands. Additionally, we focused on the R882H mutation hotspot in the catalytic domain of DNMT3A, which is associated with aberrant DNA methylation in acute myeloid leukemia (AML). Consequently, the structure of R882H DNMT3A (PDB code: 6W8J) was docked with the identified novel ligands. As a result of our computational analysis, eight phytochemicals were predicted as potential DNMT inhibitors through the ML approaches from KNIME. Subsequently, three of these phytochemicals, namely Herbacetin, Kaempferide, and Morin were identified as virtual hits against DNMTs following the molecular docking simulations. Overall, our study demonstrates the effectiveness of this computational strategy in identifying DNMT inhibitors. These findings hold promise for the discovery of potent and selective anticancer drugs targeting DNMTs.

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Section: Discussionmentioning

confidence: 99%

Computer-aided drug design to discover DNMT inhibitors from phytochemicals

Kumari,

Wijesinghe

2024

Ceylon J. Sci.

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“…Chrysantha Chang of the Camellia genus, is predominantly endemic to southern China [ 1 ]. It is esteemed in traditional Chinese medicine for its diverse therapeutic properties, including anti-inflammatory, diuretic, anti-neoplastic, lipid-modulating, and glycemic regulatory effects [ 2 , 3 ]. Additionally, its distinct golden-yellow flowers confer ornamental value and render it a valuable genetic resource for breeding.…”

Section: Introductionmentioning

confidence: 99%

Selection and Validation of qRT-PCR Internal Reference Genes to Study Flower Color Formation in Camellia impressinervis

Zhang,

Chen,

Chen

et al. 2024

IJMS

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Real-time quantitative PCR (qRT-PCR) is a pivotal technique for gene expression analysis. To ensure reliable and accurate results, the internal reference genes must exhibit stable expression across varied experimental conditions. Currently, no internal reference genes for Camellia impressinervis have been established. This study aimed to identify stable internal reference genes from eight candidates derived from different developmental stages of C. impressinervis flowers. We employed geNorm, NormFinder, and BestKeeper to evaluate the expression stability of these candidates, which was followed by a comprehensive stability analysis. The results indicated that CiTUB, a tubulin gene, exhibited the most stable expression among the eight reference gene candidates in the petals. Subsequently, CiTUB was utilized as an internal reference for the qRT-PCR analysis of six genes implicated in the petal pigment synthesis pathway of C. impressinervis. The qRT-PCR results were corroborated by transcriptome sequencing data, affirming the stability and suitability of CiTUB as a reference gene. This study marks the first identification of stable internal reference genes within the entire genome of C. impressinervis, establishing a foundation for future gene expression and functional studies. Identifying such stable reference genes is crucial for advancing molecular research on C. impressinervis.

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