Saccharomyces cerevisiae transcriptional reprograming due to bacterial contamination during industrial scale bioethanol production

TLDR

Brazilian bioethanol production relies on sucrose fermentation by Saccharomyces cerevisiae, but bacterial contaminants form yeast‑bacteria aggregates that slow fermentation and lower yield. The study examined the molecular physiology of the PE‑2 strain under typical and co‑aggregated fermentation conditions. Transcriptional profiling by RNA‑seq during industrial‑scale fed‑batch fermentation was performed to assess these conditions. Co‑aggregation induced widespread gene repression, was associated with Lactobacillus fermentum, and the resulting high‑resolution transcriptome dataset provides a resource for further development of the PE‑2 biocatalyst.

Abstract

The bioethanol production system used in Brazil is based on the fermentation of sucrose from sugarcane feedstock by highly adapted strains of the yeast Saccharomyces cerevisiae. Bacterial contaminants present in the distillery environment often produce yeast-bacteria cellular co-aggregation particles that resemble yeast-yeast cell adhesion (flocculation). The formation of such particles is undesirable because it slows the fermentation kinetics and reduces the overall bioethanol yield. In this study, we investigated the molecular physiology of one of the main S. cerevisiae strains used in Brazilian bioethanol production, PE-2, under two contrasting conditions: typical fermentation, when most yeast cells are in suspension, and co-aggregated fermentation. The transcriptional profile of PE-2 was assessed by RNA-seq during industrial scale fed-batch fermentation. Comparative analysis between the two conditions revealed transcriptional profiles that were differentiated primarily by a deep gene repression in the co-aggregated samples. The data also indicated that Lactobacillus fermentum was likely the main bacterial species responsible for cellular co-aggregation and for the high levels of organic acids detected in the samples. Here, we report the high-resolution gene expression profiling of strain PE-2 during industrial-scale fermentations and the transcriptional reprograming observed under co-aggregation conditions. This dataset constitutes an important resource that can provide support for further development of this key yeast biocatalyst.