Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters

Abstract

The biosynthetic machinery responsible for the production of bacterial specialised metabolites is encoded by physically clustered group of genes called biosynthetic gene clusters (BGCs). The experimental characterisation of numerous BGCs has led to the elucidation of subclusters of genes within BGCs, jointly responsible for the same biosynthetic function in different genetic contexts. We developed an unsupervised statistical method able to successfully detect a large number of modules (putative functional subclusters) within an extensive set of predicted BGCs in a systematic and automated manner. Multiple already known subclusters were confirmed by our method, proving its efficiency and sensitivity. In addition, the resulting large collection of newly defined modules provides new insights into the prevalence and putative biosynthetic role of these modular genetic entities. The automated and unbiased identification of hundreds of co-evolving group of genes is an essential breakthrough for the discovery and biosynthetic engineering of high-value compounds.

Fig. 1

Module detection algorithm. By processing the entire collection of microbial genomes considered in this study, antiSMASH predicted tens of thousands BGCs. OrthoMCL was then used for the smCOG annotation. Next, these smCOGs were organised into a network where two smCOGs are connected only if they share a significant number of adjacency or colocalization interactions. Fully connected sub-graphs (cliques) are considered as putative biosynthetic modules

Fig. 2

Deoxysugar, pyrrole ring, and aminocoumarin modules. Overview of the clorobiocin, coumermycin, novobiocin, simocyclinone, prodigiosin, pyluteorin, indanomycin, and calcimycin BGCs. When present, the genes covered by Module M142052 (blue), Module M113610 (green), and Module M2466 (orange) are highlighted in the clusters. The chemical moieties related to the modules are highlighted in the chemical structures. Clusters are not drawn to scale

Fig. 3

Schematic representation of the chemical moieties produced and the hierarchical organisation of the ACP-linked PKS extender modules. l-Serine and 1,3-biphosphoglycerate are the two possible substrates accepted by the modules. The galbonolides cluster has been chosen as a representative example of the known clusters containing the methoxymalonyl-ACP module. It is also noteworthy that all modules share a common intermediate