This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Researchers have developed a practical strategy for recovering biosynthetic information from fragmented metagenomic data. Through this work, the researchers identified and prioritized promising natural-product candidates with potential antibacterial or anticancer activity.
The study was published in Microbiology Spectrum. Microorganisms produce many natural products that have become important medicines, including antibiotics and anticancer drugs. "The biosynthesis of these natural products is encoded by biosynthetic gene clusters (BGCs), which are groups of neighboring genes that work together to produce a specific compound," said corresponding study author Lei Zhang, Ph.D., a pharmaceutical engineering professor at Jining Medical University in China.
"However, most microorganisms in the environment cannot yet be cultured in the laboratory, and BGCs recovered from metagenomic assemblies are often fragmented across contigs (a series of overlapping DNA sequences). This makes it difficult to reconstruct complete biosynthetic pathways and determine which clusters are most likely to produce medically useful compounds." To develop their new strategy for recovering biosynthetic information from fragmented metagenomic data, the researchers started with biosynthetic gene clusters whose products had already been experimentally characterized and used them as reference "maps." They searched large metagenomic data sets for related pieces of biosynthetic information. When these pieces were fragmented, they used the known clusters as guides to help reconstruct the missing pathway and predict what kinds of molecules it might produce.
They then chemically synthesized selected predicted compounds and tested their biological activity. "We connected computational mining of metagenomic data with experimental validation," Zhang said. The researchers found that fragmented metagenomic data can contain valuable biosynthetic information that would be missed if each fragment were analyzed separately.
By using known biosynthetic gene clusters as guides, they were able to piece together candidate pathways and identify potential bioactive products. "Importantly, we went beyond computational prediction. We chemically synthesized six candidate compounds and tested them across seven cancer cell lines.
The compounds showed different patterns of cytotoxic activity, with compounds D and E showing the most notable activity and clear differences among cancer cell lines," Zhang said. The main significance of this work is that it offers a route for turning incomplete or fragmented BGCs into testable natural-product hypotheses. Instead of treating partial BGCs as unusable, the approach uses experimentally characterized BGCs as guides for reconstruction and then focuses experimental resources on candidates with a stronger basis for further study.
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