ArticleChemistry & biodiversity2026
Genome-Wide Identification and Structural Characterization of Secondary Metabolite Biosynthetic Gene Clusters in African Baobab (Adansonia digitata).
Article in Chemistry & biodiversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Adansonia digitata L., known as the African Baobab, is renowned for its extraordinary longevity and its long-standing use in traditional African medicine. However, the genomic basis underlying its specialized secondary metabolism remains largely unexplored. This study aimed to computationally identify and characterize putative biosynthetic gene clusters (BGCs) within the publicly available A. digitata genome assembly (GenBank accession GCA_029448705.1) using an integrated bioinformatics pipeline. Ab initio gene structure prediction was performed with AUGUSTUS (Arabidopsis thaliana species model), functional protein domains were annotated with InterProScan, and candidate BGCs were mined using the plantiSMASH 2.0 webserver. Gene prediction yielded 52 721 protein-coding gene models genome-wide, of which 45 641 (86.6%) received at least one InterProScan domain annotation. The analysis identified 50 putative BGCs distributed across 13 scaffolds, ranging from 15.3 to 492.8 kb in size and provisionally classified into eight chemical categories, most frequently saccharide-type (n = 20) and cyclopeptide-type (n = 9) clusters. Notably, nine clusters carried a BURP-domain core gene consistent with a cyclopeptide/defense-peptide-associated pathway, including the single largest cluster identified (492.8 kb); other clusters carried core domains consistent with terpene synthase, squalene-hopene cyclase, chalcone/stilbene synthase (Chal_sti_synt_N), and dirigent-protein/lignan-associated biosynthesis. None of the 50 clusters showed a significant match to a characterized reference cluster in the MIBiG database, suggesting these represent largely uncharacterized biosynthetic loci. These results represent in silico predictions rather than experimentally confirmed metabolic functions, since no transcriptomic, metabolomic, or biochemical validation was performed as part of this study. Nonetheless, this work provides one of the first genome-wide catalogues of putative BGCs in A. digitata and offers a preliminary resource to guide future comparative genomics, expression profiling, and experimental validation of specialized metabolism in this species.
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