Microbial contamination of commercial corn-based fuel ethanol fermentations

Food Webs and Feedbacks: The Untold Ecological Relevance of Antimicrobial Resistance as Seen in Harmful Algal Blooms

Antimicrobial resistance (AMR) has long been framed as an epidemiological and public health concern. Its impacts on the environment are unclear. Yet, the basis for AMR is altered cell physiology. Just as this affects how microbes interact with antimicrobials, it can also affect how they interact with their own species, other species, and their non-living environment. Moreover, if the microbes are globally notorious for causing landscape-level environmental issues, then these effects could alter biodiversity and ecosystem function on a grand scale. To investigate these possibilities, we compiled peer-reviewed literature from the past 20 years regarding AMR in toxic freshwater cyanobacterial harmful algal blooms (HABs). We examined it for evidence of AMR affecting HAB frequency, severity, or persistence. Although no study within our scope was explicitly designed to address the question, multiple studies reported AMR-associated changes in HAB-forming cyanobacteria (and co-occurring microbes) that pertained directly to HAB timing, toxicity, and phase, as well as to the dynamics of HAB-afflicted aquatic food webs. These findings highlight the potential for AMR to have far-reaching environmental impacts (including the loss of biodiversity and ecosystem function) and bring into focus the importance of confronting complex interrelated issues such as AMR and HABs in concert, with interdisciplinary tools and perspectives.

Strain dynamics of contaminating bacteria modulate the yield of ethanol biorefineries

Bioethanol is a sustainable energy alternative and can contribute to global greenhouse-gas emission reductions by over 60%. Its industrial production faces various bottlenecks, including sub-optimal efficiency resulting from bacteria. Broad-spectrum removal of these contaminants results in negligible gains, suggesting that the process is shaped by ecological interactions within the microbial community. Here, we survey the microbiome across all process steps at two biorefineries, over three timepoints in a production season. Leveraging shotgun metagenomics and cultivation-based approaches, we identify beneficial bacteria and find improved outcome when yeast-to-bacteria ratios increase during fermentation. We provide a microbial gene catalogue which reveals bacteria-specific pathways associated with performance. We also show that Limosilactobacillus fermentum overgrowth lowers production, with one strain reducing yield by ~5% in laboratory fermentations, potentially due to its metabolite profile. Temperature is found to be a major driver for strain-level dynamics. Improved microbial management strategies could unlock environmental and economic gains in this US $ 60 billion industry enabling its wider adoption.

Novel endolysin LysMP for control of Limosilactobacillus fermentum contamination in small-scale corn mash fermentation

BACKGROUND

Traditional bioethanol fermentation industries are not operated under strict sterile conditions and are prone to microbial contamination. Lactic acid bacteria (LAB) are often pervasive in fermentation tanks, competing for nutrients and producing inhibitory acids that have a negative impact on ethanol-producing yeast, resulting in decreased yields and stuck fermentations. Antibiotics are frequently used to combat contamination, but antibiotic stewardship has resulted in a shift to alternative antimicrobials.

RESULTS

We demonstrate that endolysin LysMP, a bacteriophage-encoded peptidoglycan hydrolase, is an effective method for controlling growth of LAB. The LysMP gene was synthesized based on the prophage sequence in the genome of Limosilactobacillus fermentum KGL7. Analysis of the recombinant enzyme expressed in E. coli and purified by immobilized metal chelate affinity chromatography (IMAC) showed an optimal lysis activity against various LAB species at pH 6, with stability from pH 4 to 8 and from 20 to 40 °C up to 48 h. Moreover, it retains more than 80% of its activity at 10% ethanol (v/v) for up to 48 h. When LysMP was added at 250 µg/mL to yeast corn mash fermentations containing L. fermentum, it reduced bacterial load by at least 4-log fold compared to the untreated controls and prevented stuck fermentation. In comparison, untreated controls with contamination increased from an initial bacterial load of 1.50 × 107 CFU/mL to 2.25 × 109 CFU/mL and 1.89 × 109 CFU/mL after 24 h and 48 h, respectively. Glucose in the treated samples was fully utilized, while untreated controls with contamination had more than 4% (w/v) remaining at 48 h. Furthermore, there was at least a fivefold reduction in lactic acid (0.085 M untreated contamination controls compared to 0.016 M treated), and a fourfold reduction in acetic acid (0.027 M untreated contamination controls vs. 0.007 M treated), when LysMP was used to treat contaminated corn mash fermentations. Most importantly, final ethanol yields increased from 6.3% (w/v) in untreated contamination samples to 9.3% (w/v) in treated contamination samples, an approximate 50% increase to levels comparable to uncontaminated controls 9.3% (w/v).

CONCLUSION

LysMP could be a good alternative to replace antibiotics for mitigation of LAB contamination in biofuel refineries.

A Snapshot Picture of the Fungal Composition of Bee Bread in Four Locations in Bulgaria, Differing in Anthropogenic Influence

Information about the fungal composition of bee bread, and the fermentation processes to which the fungi contribute significantly, is rather scarce or fragmentary. In this study, we performed an NGS-based metagenomics snapshot picture study of the fungal composition of bee bread in four locations in Bulgaria during the most active honeybee foraging period at the end of June 2020. The sampling locations were chosen to differ significantly in climatic conditions, landscape, and anthropogenic pressure, and the Illumina 2 × 250 paired-end reads platform was used for amplicon metagenomics study of the ITS2 region. We found that some of the already reported canonical beneficial core fungal species were present within the studied samples. However, some fungal genera such as Monilinia, Sclerotinia, Golovinomyces, Toxicocladosporium, Pseudopithomyces, Podosphaera and Septoriella were reported for the first time among the dominant genera for a honeybee related product. Anthropogenic pressure negatively influences the fungal composition of the bee bread in two different ways-urban/industrial pressure affects the presence of pathogenic species, while agricultural pressure is reflected in a decrease of the ratio of the beneficial fungi.

Directing carbohydrates toward ethanol using mesophilic microbial communities

Bioethanol production is an established biotechnological process. Margins are low which prevent a larger scale production of bioethanol. As a large part of the production cost is due to the feedstock, the use of low value unsterile feedstocks fermented by microbial communities will enable a more cost-competitive bioethanol production. To select for high yield ethanol producing communities, three selective conditions are proposed: acid washing of the cells after fermentation, a low pH (<5) during the fermentation and microaerobiosis at the start of the fermentation. Ethanol producers, such as Zymomonas species and yeasts, compete for carbohydrates with volatile fatty acid and lactic acid producing bacteria. Creating effective consortia of lactic acid bacteria and homo-ethanol producers at low pH will lead to robust and competitive ethanol yields and titres. A conceptual design of an ecology-based bioethanol production process is proposed using food waste to produce bioethanol, electricity, digestate and heat.

Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice

A hybrid neural model (HNM) and particle swarm optimization (PSO) was used to optimize ethanol production by a flocculating yeast, grown on cashew apple juice. HNM was obtained by combining artificial neural network (ANN), which predicted reaction specific rates, to mass balance equations for substrate (S), product and biomass (X) concentration, being an alternative method for predicting the behavior of complex systems. ANNs training was conducted using an experimental set of data of X and S, temperature and stirring speed. The HNM was statistically validated against a new dataset, being capable of representing the system behavior. The model was optimized based on a multiobjective function relating efficiency and productivity by applying the PSO. Optimal estimated conditions were: S₀ = 127 g L^-1, X₀ = 5.8 g L^-1, 35 °C and 111 rpm. In this condition, an efficiency of 91.5% with a productivity of 8.0 g L^-1 h^-1 was obtained at approximately 7 h of fermentation.