Microbial interactions during sugar cane must fermentation for bioethanol production: does quorum sensing play a role?

Brexó, Ramon Peres; Sant’Ana, Anderson de Souza

doi:10.1080/07388551.2017.1332570

Cited by 27 publications

(13 citation statements)

References 118 publications

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“…This increased susceptibility may be explained by the interaction between yeast and contaminant LAB (Brexó and Sant'Ana ). In the absence of nutrients, high‐density yeast can utilize glucose metabolism enzymes to generate both antimicrobial and bioactive peptides, which may harm the present bacteria (Brexó and Sant'Ana ). However, this highlights the need to evaluate proposed antimicrobials in the dynamic environmental conditions that they will be employed in.…”

Section: Resultsmentioning

confidence: 99%

Nisin incorporation enhances the inactivation of lactic acid bacteria during the acid wash step of bioethanol production from sugarcane juice

Zhang

Holle

Kim

et al. 2019

Lett Appl Microbiol

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Bioethanol fermentation is continuously exposed to contamination by Lactic Acid Bacteria (LAB). In this study, the effect of adding nisin (250 mg l−1) to the acid wash on the viability of five bacterial contaminants were evaluated both alone and in co‐incubation with Saccharomyces cerevisiae. Additionally, fed‐batch fermentation was performed using an acid or acid/nisin wash for S. cerevisiae alone and cocultured with the LAB strains. Parameters such as ethanol production, sugar consumption and lactic acid production were monitored. Four model LAB were more susceptible to the acid/nisin wash than the acid wash, and were most susceptible when incubated with yeast. A fifth model LAB was very sensitive to both treatments regardless of the presence of yeast. The addition of nisin to the acid wash lowered the required time for adequate washing and resulted in a higher ethanol production (54·5 ± 0·1 g l−1) than the acid wash alone (52·6 ± 0·1 g l−1) in a subsequent fermentation. These results indicate the potential benefits of supplementing with nisin to improve the acid wash step of bioethanol fermentations. Significance and Impact of the Study Acid washing by the bioethanol fermentation industry reduces yeast efficiency and selects for contaminant bacteria that are resistant to acid treatments. This study demonstrates that the incorporation of nisin into the acid wash step results in a more potent removal of lactic acid bacteria while significantly shortening the length of time needed for the acid wash.

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Section: Resultsmentioning

confidence: 99%

Nisin incorporation enhances the inactivation of lactic acid bacteria during the acid wash step of bioethanol production from sugarcane juice

Zhang

Holle

Kim

et al. 2019

Lett Appl Microbiol

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“…At 1.5 and 6 days, the formation rate of TYR reached the maximum (Fig. 3C), which is mainly ascribed to resource availability (31), and the microbial consortium composition and function related to the BA pathway exhibited a steady pattern. The level of tyrDC transcription at 1.5 days was relatively low, while the specific production rate of TYR was high, indicating a high efficiency of tyrDC.…”

Section: Microbial Functional Analysis With Respect To Decarboxylase mentioning

confidence: 94%

Primary and Secondary Succession Mediate the Accumulation of Biogenic Amines during Industrial Semidry Chinese Rice Wine Fermentation

Luo

Huang

et al. 2020

Appl Environ Microbiol

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The exogenous functional microorganisms to regulate the biogenic amines (BAs) content is a common approach in fermentation systems. Here, to better understand the microbial traits of succession trajectories in resource-based and biotic interference system, the BAs-related primary and secondary succession were tracked during industrial semidry Chinese rice wine (CRW) fermentation. Dominant abundance and BAs-associated microbial functionality based on PICRUSt indicated that Citrobacter, Acinetobacter, Lactobacillus, Exiguobacterium, Bacillus, Pseudomonas and Enterobacter were prominently contributed to decarboxylase gene family in CRW. The expression level of tyrosine decarboxylase (tyrDC), ornithine decarboxylase (odc) and agmatine deiminase gene (aguA) were assessed by q-PCR. The transcription levels of these genes are unmatched with BAs formation rate during post fermentation, indicating that acidification and carbon source depletion upregulated the expression and microbes launch the dormancy strategy to respond to unfavorable conditions. Furthermore, microbial interference of CRW fermentation by co-inoculated Lactobacillus plantarum (ACBC271) and Staphylococcus xylosus (CGMCC1.8382) at a ratio of 1:2 exhibited the best synergetic control of BAs. Spearman correlations revealed that Lactobacillus and Staphylococcus exhibited influence on BAs-associated microbiota (|ρ|>0), Exiguobacterium and Pseudomonas were strongly suppress by Lactobacillus (P < 0.05, ρ = -0.867, ρ = -0.782), Staphylococcus showed the strongest inhibitory effect toward Lactobacillus (ρ = -0.115) and Citrobacter (ρ = -0.188) in co-inoculated 1:2 group. The high inhibitory effect of exogenous added strains on specific bacteria presented the evidence for the obtained BAs-associated contributors. Overall, this work provides important insight into the microbial traits rely on resource usage and functional microbiota within food microbial ecology. Importance Understanding the shifting patterns of substance usage and microbial interactions is a fundamental objective within microbiology and ecology. Analyses of primary and secondary microbial succession allows for determinations of taxonomic diversity, community traits and functional transformations over time or after a disturbance. The kinetics of BA generation, the patterns of resource consumption, functional metagenome prediction and microbial interactions were profiled to elucidate the equilibrium mechanism of microbial systems. Secondary succession after a disturbance triggers a change in resource usage, which in turn affects primary succession and metabolism. In the study, the functional potential of exogenous microorganisms under disturbance synergized with secondary succession strategies, including rebalancing and dormancy, which ultimately lowered BA accumulation. Thus, this succession system could facilitate to settle essential issues with respect to microbial traits rely on resource usage and microbial interactions that occur in natural ecosystems.

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“…It also was verified that the homofermentative Lactobacillus plantarum was more detrimental to industrial yeast strain (CAT-1) when compared with heterofermentative Lactobacillus fermentum due to reduced yeast viability and higher amount of lactic acid in the growth medium (Basso et al, 2014). Brexó and Sant'Ana (2018) reported strategies for survival by certain microorganisms, one of them is associated with increasing the concentration of ethanol, organic acids, and other extracellular metabolites. These strategies already are scientifically well established, however, there is still a gap about the physiological and molecular mechanisms governing these interactions.…”

Section: Introductionmentioning

confidence: 96%

“…In addition, native yeasts of cane sugar and contaminant bacteria are reported frequently. Consequently, the microflora associated with fermentative ecosystem is highly complex and composed for many interactions between yeast-yeast and yeast-bacteria (Brexó & Sant'Ana, 2018). All this diversity microbial in fermentation reflect in different behaviour the of microorganisms.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the existence of reports on the consequences of microbial interactions during bioethanol production, there is still little knowledge of the molecular mechanisms involved in the process between yeast-yeast, as well as, the relation the of effects of yeast-yeast interactions above fermentation kinetic parameters (Albergaria & Arneborg, 2016;Brexó & Sant'Ana, 2018). Therefore, understanding the modulations metabolic in the eco-physiological process fermentation may constitute a very useful tool to ensure the process is better controlled.…”

Section: Introductionmentioning

confidence: 99%

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Interaction between S. cerevisiae MONA, PE-2, CAT-1 and ATCC in fermentation sugar cane broth

Santos

Freitas

Souza

et al. 2021

RSD

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The diversity microbial in ethanolic fermentation generate different behavior metabolic that depended on the microorganisms present. Some kinetic parameters can tell how interactions between microorganisms are occurring in fermentation and can also predict your metabolic behaviors. However, there are little studys about the influence of interactions microbial on kinetic parameters in fermentation sugar cane. Therefore, this work aimed to understand the influence of the yeast strain Saccharomyces cerevisiae CAT-1, MONA, PE-2 and ATCC in the production of biomass, ethanol, glycerol and sugar consumption, as well as to evaluate the kinetic parameters by means of response surface methodology for mixing. From the biomass models generated, it was observed that the yeasts ATCC and MONA when in the presence of CAT-1 and PE-2 showed antagonisms. For the ethanol, the synergistic effect was verified for the mixture MONA/ATCC and CAT-1/PE-2 being that CAT-1 and PE-2 were the yeasts that strongly favored the ethanol production. It stands out yeast MONA due to having lower glycerol production, character desirable in the sugar and alcohol industry. Thus, it is clear that from the analysis employed it was possible to infer about the kinetic behavior of the yeasts in pure cultures as well as the effect of the interaction between them during the cultivation.

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Microbial interactions during sugar cane must fermentation for bioethanol production: does quorum sensing play a role?

Cited by 27 publications

References 118 publications

Nisin incorporation enhances the inactivation of lactic acid bacteria during the acid wash step of bioethanol production from sugarcane juice

Nisin incorporation enhances the inactivation of lactic acid bacteria during the acid wash step of bioethanol production from sugarcane juice

Primary and Secondary Succession Mediate the Accumulation of Biogenic Amines during Industrial Semidry Chinese Rice Wine Fermentation

Interaction between S. cerevisiae MONA, PE-2, CAT-1 and ATCC in fermentation sugar cane broth

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