Transcriptomic analysis reveals hub genes and pathways in response to acetic acid stress in Kluyveromyces marxianus during high-temperature ethanol fermentation.

The thermotolerant yeast Kluyveromyces marxianus is known for its potential in high-temperature ethanol fermentation, yet it suffers from excess acetic acid production at elevated temperatures, which hinders ethanol production. To better understand how the yeast responds to acetic acid stress during high-temperature ethanol fermentation, this study investigated its transcriptomic changes under this condition. RNA sequencing (RNA-seq) was used to identify differentially expressed genes (DEGs) and enriched gene ontology (GO) terms and pathways under acetic acid stress. The results showed that 611 genes were differentially expressed, and GO and pathway enrichment analysis revealed that acetic acid stress promoted protein catabolism but repressed protein synthesis during high-temperature fermentation. Protein-protein interaction (PPI) networks were also constructed based on the interactions between proteins coded by the DEGs. Hub genes and key modules in the PPI networks were identified, providing insight into the mechanisms of this yeast's response to acetic acid stress. The findings suggest that the decrease in ethanol production is caused by the imbalance between protein catabolism and protein synthesis. Overall, this study provides valuable insights into the mechanisms of K. marxianus's response to acetic acid stress and highlights the importance of maintaining a proper balance between protein catabolism and protein synthesis for high-temperature ethanol fermentation.

The microbial diversity in industrial effluents makes high-throughput sequencing-based source tracking of the effluents possible.

In order to explore the microbial diversity in industrial effluents, and on this basis, to verify the feasibility of tracking industrial effluents in sewer networks based on sequencing data, we collected 28 sewage samples from the industrial effluents relative to four factories in Shenzhen, China, and sequenced the 16S rRNA genes to profile the microbial compositions. We identified 5413 operational taxonomic units (OTUs) in total, and found that microbial compositions were highly diverse among samples from different locations in the sewer system, with only 107 OTUs shared by 90% of the samples. These shared OTUs were enriched in the phylum of Proteobacteria, the families of Comamonadaceae and Pseudomonadaceae, as well as the genus of Pseudomonas, with both degradation related and pathogenic bacteria. More importantly, we found differences in microbial composition among samples relevant to different factories, and identified microbial markers differentiating effluents from these factories, which can be used to track the sources of the effluents. This study improved our understanding of microbial diversity in industrial effluents, proved the feasibility of industrial effluent source tracking based on sequencing data, and provided an alternative technique solution for environmental surveillance and management.