Secondary Metabolite Transcriptomic Pipeline (SeMa-Trap), an expression-based exploration tool for increased secondary metabolite production in bacteria

Author:

Mungan Mehmet Direnç123ORCID,Harbig Theresa Anisja2,Perez Naybel Hernandez1,Edenhart Simone1,Stegmann Evi13,Nieselt Kay2,Ziemert Nadine123ORCID

Affiliation:

1. Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), University of Tübingen , Auf der Morgenstelle 28, 72076 Tübingen, Germany

2. Interfaculty Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen , 72076 Tübingen, Germany

3. German Center for Infection Research (DZIF) , Partnersite Tübingen, 72076 Tübingen, Germany

Abstract

Abstract For decades, natural products have been used as a primary resource in drug discovery pipelines to find new antibiotics, which are mainly produced as secondary metabolites by bacteria. The biosynthesis of these compounds is encoded in co-localized genes termed biosynthetic gene clusters (BGCs). However, BGCs are often not expressed under laboratory conditions. Several genetic manipulation strategies have been developed in order to activate or overexpress silent BGCs. Significant increases in production levels of secondary metabolites were indeed achieved by modifying the expression of genes encoding regulators and transporters, as well as genes involved in resistance or precursor biosynthesis. However, the abundance of genes encoding such functions within bacterial genomes requires prioritization of the most promising ones for genetic manipulation strategies. Here, we introduce the ‘Secondary Metabolite Transcriptomic Pipeline’ (SeMa-Trap), a user-friendly web-server, available at https://sema-trap.ziemertlab.com. SeMa-Trap facilitates RNA-Seq based transcriptome analyses, finds co-expression patterns between certain genes and BGCs of interest, and helps optimize the design of comparative transcriptomic analyses. Finally, SeMa-Trap provides interactive result pages for each BGC, allowing the easy exploration and comparison of expression patterns. In summary, SeMa-Trap allows a straightforward prioritization of genes that could be targeted via genetic engineering approaches to (over)express BGCs of interest.

Funder

German Center for Infection Research

Germany’s Excellence Strategy

German Research Foundation

Zentrum für Datenverarbeitung of the University of Tübingen

Federal Ministry of Education and Research

Publisher

Oxford University Press (OUP)

Subject

Genetics

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