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核磁共振氢谱引导下从某属物种中分离生物活性化合物

H-NMR Guided Isolation of Bioactive Compounds from Species of the Genus .

作者信息

Oliveira Celso R, Burroughs Megan J, Richards Lora A, Dyer Lee A, Urbano-Muñoz Federico, Lee Camryn, Warner Megan, Dodson Craig D, Wallace Ian S, Jeffrey Christopher S

机构信息

Department of Forestry and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA.

Department of Chemistry and Biochemistry, University of Missouri-St. Louis, 1 University Blvd. 316 Benton Hall, St. Louis, MO 63121, USA.

出版信息

Molecules. 2025 Apr 30;30(9):2020. doi: 10.3390/molecules30092020.

DOI:10.3390/molecules30092020
PMID:40363825
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12073215/
Abstract

The discovery of bioactive natural products is often challenged by the complexity of isolating and characterizing active compounds within diverse mixtures. Previously, we introduced a H NMR-based weighted gene correlation network analysis (WGCNA) approach to identify spectral features linked to growth inhibitory activity of () leaf extracts against model plant, fungal, and bacterial organisms. This method enabled us to prioritize specific spectral features linked to bioactivity, offering a targeted approach to natural product discovery. In this study, we validate the predictive capacity of the WGCNA by isolating the compounds responsible for the bioactivity-associated resonances and confirming their antifungal efficacy. Using growth inhibition assays, we verified that the isolated compounds, including three novel antifungal agents, exhibited significant bioactivity. Notably, one of these compounds contains a rare imidazolium heterocyclic motif, marking a new structural class in . These findings substantiate the H NMR-based WGCNA as a reliable tool for identifying structural types associated with biological activity, streamlining the process of discovering bioactive natural products in complex extracts.

摘要

生物活性天然产物的发现常常面临挑战,因为要从各种混合物中分离和鉴定活性化合物非常复杂。此前,我们引入了一种基于¹H NMR的加权基因共表达网络分析(WGCNA)方法,以识别与()叶提取物对模式植物、真菌和细菌生物体的生长抑制活性相关的光谱特征。该方法使我们能够对与生物活性相关的特定光谱特征进行优先排序,为天然产物的发现提供了一种有针对性的方法。在本研究中,我们通过分离负责生物活性相关共振的化合物并确认其抗真菌功效,验证了WGCNA的预测能力。通过生长抑制试验,我们证实分离出的化合物,包括三种新型抗真菌剂,具有显著的生物活性。值得注意的是,其中一种化合物含有罕见的咪唑鎓杂环基序,这在()中代表了一种新的结构类别。这些发现证实了基于¹H NMR的WGCNA是一种可靠的工具,可用于识别与生物活性相关的结构类型,简化了在复杂提取物中发现生物活性天然产物的过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/1a8e4e8ce72e/molecules-30-02020-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/77adaeb8dd8a/molecules-30-02020-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/64b4cb64661b/molecules-30-02020-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/d1746f39162a/molecules-30-02020-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/ab55d421166f/molecules-30-02020-sch001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/c862a017a7a4/molecules-30-02020-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/1a8e4e8ce72e/molecules-30-02020-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/77adaeb8dd8a/molecules-30-02020-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/64b4cb64661b/molecules-30-02020-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/d1746f39162a/molecules-30-02020-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/ab55d421166f/molecules-30-02020-sch001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/c862a017a7a4/molecules-30-02020-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16e8/12073215/1a8e4e8ce72e/molecules-30-02020-g005.jpg

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