Abstract
BACKGROUND: Industrial fermentations can generally be described as dynamic biotransformation processes in which microorganisms convert energy rich substrates into a desired product. The knowledge of active physiological pathways, reflected by corresponding gene activities, allows the identification of beneficial or disadvantageous performances of the microbial host. Whole transcriptome RNA-Seq is a powerful tool to accomplish in-depth quantification of these gene activities, since the low background noise and the absence of an upper limit of quantification allow the detection of transcripts with high dynamic ranges. Such data enable the identification of potential bottlenecks and futile energetic cycles, which in turn can lead to targets for rational approaches to productivity improvement. Here we present an overview of the dynamics of gene activity during an industrial-oriented fermentation process with Bacillus licheniformis, an important industrial enzyme producer. Thereby, valuable insights which help to understand the complex interactions during such processes are provided. RESULTS: Whole transcriptome RNA-Seq has been performed to study the gene expression at five selected growth stages of an industrial-oriented protease production process employing a germination deficient derivative of B. licheniformis DSM13. Since a significant amount of genes in Bacillus strains are regulated posttranscriptionally, the generated data have been confirmed by 2D gel-based proteomics. Regulatory events affecting the coordinated activity of hundreds of genes have been analyzed. The data enabled the identification of genes involved in the adaptations to changing environmental conditions during the fermentation process. A special focus of the analyses was on genes contributing to central carbon metabolism, amino acid transport and metabolism, starvation and stress responses and protein secretion. Genes contributing to lantibiotics production and Tat-dependent protein secretion have been pointed out as potential optimization targets. CONCLUSIONS: The presented data give unprecedented insights into the complex adaptations of bacterial production strains to the changing physiological demands during an industrial-oriented fermentation. These are, to our knowledge, the first publicly available data that document quantifiable transcriptional responses of the commonly employed production strain B. licheniformis to changing conditions over the course of a typical fermentation process in such extensive depth.