scholarly journals Complex yeast-bacteria interactions affect the yield of industrial ethanol fermentations

2020 ◽  
Author(s):  
Felipe Lino ◽  
Djordje Bajic ◽  
Jean Vila ◽  
Alvaro Sanchez ◽  
Morten Sommer
Keyword(s):  
Microbial Communities ◽  
Human Health ◽  
Ethanol Fermentation ◽  
Ethanol Yield ◽  
Microbial Interactions ◽  
Natural Processes ◽  
Lactobacillus Amylovorus ◽  
Negative Effect ◽  
Balance Analysis ◽  
Microbial Systems

Abstract Microbial communities affect several natural processes, from nutrient cycling to human health. Nevertheless, the mechanisms of interaction between microorganisms and their influence on community functions are not well understood. Sugarcane ethanol fermentations represent a simple microbial community dominated by S. cerevisiae and co-occurring bacteria with a clearly defined functionality. In this study, we dissected the microbial interactions in sugarcane ethanol fermentation by combinatorically reconstituting every possible combination of species, comprising approximately 80% of the biodiversity in terms of relative abundance. Functional landscape analysis showed that higher-order interactions counterbalance the negative effect of pairwise interactions on ethanol yield. In addition, we found that Lactobacillus amylovorus improves the yeast growth rate and ethanol yield by cross-feeding acetaldehyde, as shown by flux balance analysis and laboratory experiments. Our results suggest that Lactobacillus amylovorus could be considered an industrial probiotic with the potential to improve sugarcane ethanol fermentation yields by more than 10%. These data highlight the biotechnological importance of comprehensively studying microbial communities and could be extended to other microbial systems with relevance to human health and the environment.

scholarly journals Complex yeast–bacteria interactions affect the yield of industrial ethanol fermentation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Felipe Senne de Oliveira Lino ◽  
Djordje Bajic ◽  
Jean Celestin Charles Vila ◽  
Alvaro Sánchez ◽  
Morten Otto Alexander Sommer
Keyword(s):  
Ethanol Fermentation ◽  
Ethanol Yield ◽  
Microbial Interactions ◽  
Flux Balance ◽  
Pairwise Interactions ◽  
Beneficial Bacterium ◽  
Lactobacillus Amylovorus ◽  
Negative Effect ◽  
Balance Analysis ◽  
Microbial Systems

AbstractSugarcane ethanol fermentation represents a simple microbial community dominated by S. cerevisiae and co-occurring bacteria with a clearly defined functionality. In this study, we dissect the microbial interactions in sugarcane ethanol fermentation by combinatorically reconstituting every possible combination of species, comprising approximately 80% of the biodiversity in terms of relative abundance. Functional landscape analysis shows that higher-order interactions counterbalance the negative effect of pairwise interactions on ethanol yield. In addition, we find that Lactobacillus amylovorus improves the yeast growth rate and ethanol yield by cross-feeding acetaldehyde, as shown by flux balance analysis and laboratory experiments. Our results suggest that Lactobacillus amylovorus could be considered a beneficial bacterium with the potential to improve sugarcane ethanol fermentation yields by almost 3%. These data highlight the biotechnological importance of comprehensively studying microbial communities and could be extended to other microbial systems with relevance to human health and the environment.

scholarly journals A multidimensional perspective on microbial interactions

2019 ◽  
Vol 366 (11) ◽  
Author(s):  
Alan R Pacheco ◽  
Daniel Segrè
Keyword(s):  
Quorum Sensing ◽  
Microbial Communities ◽  
Human Health ◽  
Rational Design ◽  
Microbial Interactions ◽  
Interaction Mechanisms ◽  
Potential Value ◽  
Quantitative Measurements ◽  
Dynamical Properties

ABSTRACTBeyond being simply positive or negative, beneficial or inhibitory, microbial interactions can involve a diverse set of mechanisms, dependencies and dynamical properties. These more nuanced features have been described in great detail for some specific types of interactions, (e.g. pairwise metabolic cross-feeding, quorum sensing or antibiotic killing), often with the use of quantitative measurements and insight derived from modeling. With a growing understanding of the composition and dynamics of complex microbial communities for human health and other applications, we face the challenge of integrating information about these different interactions into comprehensive quantitative frameworks. Here, we review the literature on a wide set of microbial interactions, and explore the potential value of a formal categorization based on multidimensional vectors of attributes. We propose that such an encoding can facilitate systematic, direct comparisons of interaction mechanisms and dependencies, and we discuss the relevance of an atlas of interactions for future modeling and rational design efforts.

scholarly journals The impact of interactions on invasion and colonization resistance in microbial communities

2020 ◽  
Author(s):  
Helen Kurkjian ◽  
M. Javad Akbari ◽  
Babak Momeni
Keyword(s):  
Microbial Communities ◽  
Human Health ◽  
Microbial Interactions ◽  
Community Resident ◽  
Colonization Resistance ◽  
Community Or ◽  
Microbial Species ◽  
Model Based ◽  
Human Microbiota ◽  
The Impact

AbstractIn human microbiota, the prevention or promotion of invasions can be crucial to human health. Invasion outcomes, in turn, are impacted by the composition of resident communities and interactions among resident microbes. Microbial communities differ from communities composed of other types of organisms in that many microbial interactions are mediated by chemicals that are released into or consumed from the environment. We ask what determines invasion outcomes in such microbial communities. Here, we use a model based on chemical-mediated interactions among microbial species to assess the impact of positive and negative interactions on invasion outcomes. We classified invasion outcomes as resistance, augmentation, displacement, or disruption depending on whether the richness of the resident community was maintained or dropped and whether the invader was maintained in the community or went extinct. We found that as the number of invaders increased relative to size of the resident community, resident communities were increasingly disrupted. As facilitation of the invader by the resident community increased, resistance outcomes were replaced by displacement and augmentation. By contrast, as facilitation increased among residents, displacement outcomes shifted to resistance. When facilitation of the resident community by the invader was eliminated, augmentation outcomes were replaced by displacement outcomes, while when inhibition of residents by invaders was eliminated, there was little change in the frequency of invasion outcomes. These results suggest that a better understanding of the chemical-mediated interactions within resident communities and between residents and invaders is crucial to predicting the success of invasions into microbial communities.

scholarly journals Influence of 1-butyl-3-methylimidazolium Chloride on the Ethanol Fermentation Process of Pichia pastoris GS115

2015 ◽  
Vol 9 (1) ◽  
pp. 109-112
Author(s):  
Wenjing Huang ◽  
Yanjie Tong ◽  
Wangxiang Huang ◽  
Ke Wang ◽  
Qiming Chen ◽  
...  
Keyword(s):  
Pichia Pastoris ◽  
Ethanol Fermentation ◽  
Fermentation Process ◽  
Ethanol Concentration ◽  
Ethanol Yield ◽  
Fermentable Sugars ◽  
Yeast Growth ◽  
Ethanol Formation ◽  
Negative Effect ◽  
Final Ethanol Concentration

To evaluate the influence of 1-butyl-3-methylimidazolium chloride ([Bmim]Cl) on the ethanol fermentation process of Pichia pastoris GS115, this paper investigated the yeast growth, ethanol formation and the fermentable sugars consumption during the ethanol fermentation process of Pichia pastoris GS115 at different [Bmim]Cl concentrations in the medium. The results indicated that the [Bmim]Cl had no influence on the ethanol fermentation process at its concentration less than 0.0001 g.L-1. The [Bmim]Cl inhibited the yeast growth and had a negative effect on ethanol formation at its concentration higher than 0.0001 g.L-1. The final biomass and ethanol concentration, and the overall ethanol yield from the fermentable sugars all decreased with its concentration increasing. The yeast growth was very slow and nearly no ethanol formed when its concentration reached 5 g.L-1. Compared to Saccharomyces cerevisiae, the growth of Pichia pastoris GS115 was more sensitive to the [Bmim]Cl, and its ethanol fermentation had lower final ethanol concentration and overall ethanol yield from fermentable sugars at the same [Bmim]Cl concentration. This work provides useful information on selecting suitable strains for ethanol fermentation containing the [Bmim]Cl in the medium.

A peek in the micro-sized world: a review of design principles, engineering tools, and applications of engineered microbial community

2020 ◽  
Vol 48 (2) ◽  
pp. 399-409
Author(s):  
Baizhen Gao ◽  
Rushant Sabnis ◽  
Tommaso Costantini ◽  
Robert Jinkerson ◽  
Qing Sun
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Environmental Remediation ◽  
Real Life ◽  
Design Principles ◽  
Microbial Interactions ◽  
Potential Applications ◽  
New Gene ◽  
Global Biogeochemical Cycles ◽  
Synthetic Microbial Communities

Microbial communities drive diverse processes that impact nearly everything on this planet, from global biogeochemical cycles to human health. Harnessing the power of these microorganisms could provide solutions to many of the challenges that face society. However, naturally occurring microbial communities are not optimized for anthropogenic use. An emerging area of research is focusing on engineering synthetic microbial communities to carry out predefined functions. Microbial community engineers are applying design principles like top-down and bottom-up approaches to create synthetic microbial communities having a myriad of real-life applications in health care, disease prevention, and environmental remediation. Multiple genetic engineering tools and delivery approaches can be used to ‘knock-in' new gene functions into microbial communities. A systematic study of the microbial interactions, community assembling principles, and engineering tools are necessary for us to understand the microbial community and to better utilize them. Continued analysis and effort are required to further the current and potential applications of synthetic microbial communities.

scholarly journals Understanding the host-microbe interactions using metabolic modeling

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jack Jansma ◽  
Sahar El Aidy
Keyword(s):  
Human Health ◽  
Flux Balance Analysis ◽  
Bacterial Species ◽  
Flux Balance ◽  
Interaction Field ◽  
In Silico Simulation ◽  
Balance Analysis ◽  
Microbe Interactions ◽  
Host Microbe Interactions ◽  
Genome Scale

AbstractThe human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health.

scholarly journals The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

2018 ◽  
Vol 35 (13) ◽  
pp. 2332-2334 ◽  
Author(s):  
Federico Baldini ◽  
Almut Heinken ◽  
Laurent Heirendt ◽  
Stefania Magnusdottir ◽  
Ronan M T Fleming ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Microbial Interactions ◽  
Microbial Genome ◽  
Metagenomic Data ◽  
Metabolic Interactions ◽  
Constraint Based Modeling ◽  
Genome Scale

Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

scholarly journals Deciphering the evolution of microbial interactions: in silico studies of two-member microbial communities

2022 ◽  
Author(s):  
Gayathri Sambamoorthy ◽  
Karthik Raman
Keyword(s):  
Microbial Communities ◽  
In Silico ◽  
Microbial Interactions ◽  
Interaction Patterns ◽  
Wild Type ◽  
Community Function ◽  
Evolutionary Forces ◽  
In Silico Studies ◽  
Wide Range ◽  
In The Wild

Microbes thrive in communities, embedded in a complex web of interactions. These interactions, particularly metabolic interactions, play a crucial role in maintaining the community structure and function. As the organisms thrive and evolve, a variety of evolutionary processes alter the interactions among the organisms in the community, although the community function remains intact. In this work, we simulate the evolution of two-member microbial communities in silico to study how evolutionary forces can shape the interactions between organisms. We employ genomescale metabolic models of organisms from the human gut, which exhibit a range of interaction patterns, from mutualism to parasitism. We observe that the evolution of microbial interactions varies depending upon the starting interaction and also on the metabolic capabilities of the organisms in the community. We find that evolutionary constraints play a significant role in shaping the dependencies of organisms in the community. Evolution of microbial communities yields fitness benefits in only a small fraction of the communities, and is also dependent on the interaction type of the wild-type communities. The metabolites cross-fed in the wild-type communities appear in only less than 50% of the evolved communities. A wide range of new metabolites are cross-fed as the communities evolve. Further, the dynamics of microbial interactions are not specific to the interaction of the wild-type community but vary depending on the organisms present in the community. Our approach of evolving microbial communities in silico provides an exciting glimpse of the dynamics of microbial interactions and offers several avenues for future investigations.

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