Abstract
Secondary metabolites (SMs) are a vast group of compounds with different structures and properties that have been utilized as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs, but they have problems with distinguishing essential genes from gap genes. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or transcriptome analyses. In this study, we developed a method that allows semi-automated identification of essential genes in a BGC based on co-evolution analysis. To this end, the protein sequences of a BGC are blasted against a suitable proteome database. For each protein, a phylogenetic tree is created. The trees are compared by treeKO to detect co-evolution. The results of this comparison are visualized in different output formats, which are compared visually. Our results suggest that co-evolution is commonly occurring within BGCs, albeit not all, and that especially those genes that encode for enzymes of the biosynthetic pathway are co-evolutionary linked and can be identified with FunOrder. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs.
Highlights
Secondary metabolites (SMs) are a diverse group of compounds with a plethora of different chemical structures and properties which are found in all domains of life, but are predominantly studied in bacteria, fungi, and plants [1]
Our results suggest that co-evolution is commonly occurring within biosynthetic gene clusters (BGCs), albeit not all, and that especially those genes that encode for enzymes of the biosynthetic pathway are co-evolutionary linked and can be identified with FunOrder
A way to identify novel secondary metabolites is to express the corresponding genes in a suitable expression host
Summary
Secondary metabolites (SMs) are a diverse group of compounds with a plethora of different chemical structures and properties which are found in all domains of life, but are predominantly studied in bacteria, fungi, and plants [1]. Untargeted approaches aim to induce the expression of any SM To this end, biotic and abiotic stresses are applied, or global regulators and regulatory mechanisms are manipulated [8]. Biotic and abiotic stresses are applied, or global regulators and regulatory mechanisms are manipulated [8] These strategies may lead to the discovery of novel compounds, whose corresponding genes have to be identified subsequently by time-consuming and expensive methods [7]. Targeted SM discovery approaches aim to induce the production of specific SMs by either overexpressing genes in the native host or by heterologous expression in another organism [12]. The bottom-up approach is depending on modern genomics and accurate gene prediction tools [13]
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