Plants are rich in a variety of metabolites with diverse bioactivities, whose contents are extremely low in plant tissues. Studies on the biosynthetic pathways of these metabolites are very important for producing them using heterologous expression system. Scientists mainly use multi-omics methods to analyze the biological pathways of metabolites. Although genomic and transcriptional information of numerous medicine plants have been generated and made available to public, the progress of candidate gene mining and whole pathway dissection of specialized plant metabolites remains slow due to the following factors1, 2, 3, 4, 5. The biosynthesis of several specified metabolites lacks tissue specificity, which hinders the prediction of the metabolic biosynthetic genes by simple differentially expression analysis6. The divergent evolution of metabolic gene families generated numerous individual members that are phylogenetically close and can decorate diverse type of natural products. For this reason, predicting the related pathway gene solely based on phylogenetic analysis is impossible7. In addition, the biosynthetic genes of specific plant metabolites are scattered in different regions of the genome, further increasing the difficulty in identifying candidates precisely by physical distance of metabolic pathway-related genes8, 9, even if the generation and good assembly of whole-genome sequences10, 11. Therefore, an efficient strategy needs to be developed and improved urgently to accurately predict the key genes in the complex none-clustered biosynthetic pathway of specialized plant metabolites.
The co-expression network analysis combining the distributions of specific metabolites, different gene expressions, and phylogenetic analysis was used to predict the key genes involved in the biosynthetic pathway of steroidal saponins in P. polyphylla, a medicine plant with more 50 Gb genome size. This study provides a universal method to elucidate the complex pathway of other specialized plant metabolites.
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Journal: Communications Biology.
DOI : 10.1038/s42003-022-03000-z
Title : Effective prediction of biosynthetic pathway genes involved in bioactive polyphyllins in Paris polyphylla .