MIDDAS-M: Motif-Independent De Novo Detection of Secondary Metabolite Gene Clusters through the Integration of Genome Sequencing and Transcriptome Data

Abstract

Many bioactive natural products are produced as “secondary metabolites” by plants, bacteria, and fungi. During the middle of the 20th century, several secondary metabolites from fungi revolutionized the pharmaceutical industry, for example, penicillin, lovastatin, and cyclosporine. They are generally biosynthesized by enzymes encoded by clusters of coordinately regulated genes, and several motif-based methods have been developed to detect secondary metabolite biosynthetic (SMB) gene clusters using the sequence information of typical SMB core genes such as polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS). However, no detection method exists for SMB gene clusters that are functional and do not include core SMB genes at present. To advance the exploration of SMB gene clusters, especially those without known core genes, we developed MIDDAS-M, a motif-independent de novo detection algorithm for SMB gene clusters. We integrated virtual gene cluster generation in an annotated genome sequence with highly sensitive scoring of the cooperative transcriptional regulation of cluster member genes. MIDDAS-M accurately predicted 38 SMB gene clusters that have been experimentally confirmed and/or predicted by other motif-based methods in 3 fungal strains. MIDDAS-M further identified a new SMB gene cluster for ustiloxin B, which was experimentally validated. Sequence analysis of the cluster genes indicated a novel mechanism for peptide biosynthesis independent of NRPS. Because it is fully computational and independent of empirical knowledge about SMB core genes, MIDDAS-M allows a large-scale, comprehensive analysis of SMB gene clusters, including those with novel biosynthetic mechanisms that do not contain any functionally characterized genes.

<i>Aspergillus flavus</i> Secondary Metabolites: More than Just Aflatoxins

“…The influence of temperature or water activity combined with RNA-seq has been used to examine small non-coding RNAs 62) , development 63) and SM production 64,65) . Yu et al 65) showed 10 of the 55 gene clusters had higher expression at 30°C as compared to 37°C (cluster 1,10,11,19,20,21,24,45,54, and 55) and cluster 3 decreasing at lower temperatures. Medina et al 64) found genome-wide, a decreased transcriptome response to changing environmental conditions correlating with low water activity or high temperature, with water being the primary predictor.…”

“…Finally, MIDDAS-M does not utilize characterized motifs as a basis for prediction 10) . Rather, it utilizes both genomic and transcriptomic data to calculate expression ratios of neighboring genes, and large induction ratios to indicate cluster activity.…”

“…However, these prediction algorithms are based in large part on the presence of typical SM cluster backbone genes such as PKSs and NRPSs. A novel algorithm created by Umemura et al 10) , designated MIDDAS-M was able to predict fungal SM gene clusters lacking a backbone gene based on integration of annotated genome and transcriptomic data. Using this approach, they were able to identify a gene cluster in A. flavus predicted to consist of 18 genes, none of which were identified as a typical cluster backbone gene.…”

“…The total number of SM clusters in A. flavus can be increased to 56 when including the kojic acid (KA) gene cluster identified originally in A. oryzae by Marui et al 9) . However this may be an underestimation, as other SM gene cluster prediction programs have identified even greater numbers of gene clusters in A. flavus 10) . Although about 40 SMs have been identified from A. flavus cultures [11][12][13] (Table 1), metabolites have only been experimentally assigned to twelve of the predicted 56 A. flavus SM clusters (see section 5).…”

Fungal Chemotaxonomy

“…It has been a very clear result obtained after the bioinformatic studies of genomes sequenced from filamentous fungi that they have many apparently silent gene clusters potentially coding the production of "new" secondary metabolites [59, 123,125,[132][133][134][135][136][137][138][139][140]. This is, of course, of great interest for the area of chemotaxonomy, as an expression of a more diverse sampling of secondary metabolites will help classifying the different species even better.…”