Strain dynamics of contaminating bacteria modulate the yield of ethanol biorefineries

Bioethanol, a sustainable energy alternative derived primarily from yeast fermentation of plant sugars, offers substantial potential for reducing greenhouse gas emissions. Global bioethanol production, however, faces significant challenges due to lower-than-optimal yields, partly attributed to bacterial contamination. Traditional approaches like broad-spectrum antibiotic treatments or acid washes have proven ineffective in fully addressing this issue, highlighting the need for a more nuanced understanding of microbial community dynamics within the biorefinery environment. Previous research has characterized contaminating microbes with limited genomic resolution using culture-based methods and meta-barcoding, but these studies often lacked comprehensive microbiome surveys across all process steps and failed to fully elucidate the functional consequences of microbial community shifts. Laboratory studies have indicated species-specific effects of lactic acid bacteria on yeast, with some species even showing potential benefits. However, accurately assessing bioconversion performance in industrial settings is complicated by the fluctuating nature of the process. Current methods focusing solely on ethanol yield are considered oversimplified and should incorporate factors like yeast cell viability and other resident microbiota. The need for a more comprehensive approach, considering the complex interactions within the microbial community and their impact on bioethanol production efficiency, forms the basis of this research. The global transition to sustainable energy sources underscores the importance of optimizing bioethanol production to improve efficiency, reduce costs, and promote wider adoption of this crucial renewable energy resource.

Existing literature on bacterial contamination in bioethanol production has primarily focused on culture-dependent methods and targeted meta-barcoding approaches, yielding limited insights into the true diversity and functional roles of the microbial community. Studies have shown that lactic acid bacteria can have both detrimental and beneficial impacts on yeast fermentation, but their effects are often species-specific. The fluctuating conditions within industrial bioreactors (oxygen levels, temperature, pH, sugar and ethanol concentrations) further complicate the analysis of microbial community dynamics and their impact on ethanol yield. Current approaches centered on ethanol yield as the sole performance indicator are inadequate, neglecting important factors such as yeast viability and the composition of the resident microbiota. This research addresses the need for high-resolution metagenomic analysis across all process steps of industrial bioethanol production, aiming to provide a comprehensive understanding of the ecological interactions shaping the process efficiency and identify targeted strategies for optimizing yields.

This study involved a comprehensive survey of the microbiome across all process steps at two independent sugarcane ethanol biorefineries in Brazil (Mills A and B). Samples were collected at three different time points during a single production season. The study used a combination of shotgun metagenomics and cultivation-based methods to identify and characterize the microbial communities. Shotgun metagenomic sequencing generated over 2.8 × 10⁶ Gbp of high-quality data. These data were assembled into contiguous sequences, and a non-redundant catalogue of 296,257 genes was created. A composite metric, incorporating ethanol yield, quality of biological catalysts, and potential fermentation inhibitors, was developed to rank fermentation batches by performance. Taxonomic profiling was performed using small and large subunit rRNA genes detected in the metagenomic samples to assess the relative abundance of archaea, bacteria, and eukaryotes across different process steps and performance levels. Functional profiling utilized the gene catalogue to identify KEGG modules associated with changes in fermentation performance. High-resolution genome-based approaches were used to profile bacterial populations, identifying dominant species and their relationship to fermentation performance. Laboratory-scale fermentations were conducted using the industrial *S. cerevisiae* strain PE-2 and several bacterial strains isolated from the biorefinery samples to investigate the strain-specific effects of bacteria on ethanol yield. Metabolite profiling and co-cultivation experiments were performed to elucidate the underlying mechanisms of these effects. Statistical analyses, including PERMANOVA, Spearman’s correlation, and Wilcoxon rank-sum tests, were used to identify significant correlations between microbial community composition, environmental factors, and fermentation performance.

The study's metagenomic analysis revealed that the microbiome of the bioethanol production process was primarily composed of eukaryotic and bacterial populations with fluctuating abundances throughout the process. High-performing batches were characterized by an increasing eukaryote-to-bacteria ratio during fermentation, irrespective of initial microbial dominance. Functional profiling identified 16 KEGG modules consistently linked to changes in fermentation performance across both biorefineries, including pathways involved in amino acid biosynthesis, carbohydrate metabolism, lipopolysaccharide biosynthesis, and the phosphotransferase system. *Lactobacillaceae* species were the most abundant bacteria, with *L. amylovorus* and *L. fermentum* being particularly prevalent. High-performing batches showed increased *L. amylovorus* and *Weissella* species and decreased *L. fermentum*, *L. buchneri*, and *L. plantarum*. A strong inverse relationship was observed between *L. amylovorus* and *L. fermentum* abundances during fermentation, particularly in lower-performing batches, suggesting ecological interplay between these two species. Lower acidity titres were associated with higher ethanol yields, and increased bacterial cell counts were linked to lower yeast viability. *L. fermentum* correlated most strongly with increased acidity and bacterial cell counts. Laboratory-scale fermentations revealed strain-specific effects of *L. fermentum* on ethanol yield, with one strain reducing yield by approximately 5%, potentially due to its distinct metabolite profile (higher lactate production). Metabolite profiling and co-cultivation experiments suggested that the inhibition of yeast ethanol production by lactic acid bacteria is mediated at the strain level and is driven by differences in organic acid production and yeast growth inhibition. Finally, higher temperatures were found to favor the growth of *L. fermentum* over *L. amylovorus*, indicating temperature as a potential factor in controlling the relative abundance of these key species.

This study provides a detailed characterization of the complex microbial ecology underlying industrial bioethanol production. The findings demonstrate that the interplay between yeast and bacterial strains significantly impacts bioconversion efficiency. The identification of both beneficial and detrimental bacterial species highlights the limitations of broad-spectrum microbial control strategies and emphasizes the need for a more targeted approach. The strain-level variation observed within species such as *L. fermentum* underscores the importance of high-resolution metagenomic analysis in understanding microbial community dynamics. Temperature control emerges as a potentially valuable tool for managing microbial community composition and enhancing bioethanol production, as it significantly impacts the growth rates of key bacterial species. The study's findings have substantial economic and environmental implications, suggesting that targeted strategies aimed at minimizing detrimental bacterial strains while preserving beneficial populations could lead to significant gains in ethanol yield and carbon emissions reduction.

This research provides a comprehensive understanding of the microbial ecology driving industrial bioethanol production, revealing the importance of strain-level interactions and the potential for targeted microbial management strategies. The findings suggest that controlling temperature and focusing on specific bacterial strains, rather than employing broad-spectrum removal methods, can significantly enhance ethanol yield and reduce environmental impact. Further research could focus on developing more precise microbial control techniques, exploring the mechanisms of strain-specific effects on yeast fermentation, and investigating the potential of using beneficial bacterial strains to improve bioethanol production.

The study's analysis is limited to two biorefineries in Brazil, potentially limiting the generalizability of the findings to other geographic locations or production processes. Confidentiality constraints limited the availability of some process data, which might introduce confounders. The laboratory-scale experiments, while providing insights into strain-specific effects, may not fully capture the complexity of industrial-scale fermentation. Further research with more diverse biorefineries and broader environmental conditions will validate the findings.