The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
Christophe Lacroix - One of the best experts on this subject based on the ideXlab platform.
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Stepwise Development of an in vitro Continuous Fermentation Model for the Murine Caecal Microbiota.
Frontiers in Microbiology, 2019Co-Authors: Sophie A. Poeker, Marianne R. Spalinger, Michael Scharl, Tomas De Wouters, Christophe Lacroix, Annelies GeirnaertAbstract:Murine models are valuable tools to study the role of gut microbiota in health or disease. However, murine and human microbiota differ in species composition, so further investigation of the murine gut microbiota is important to gain a better mechanistic understanding. Continuous in vitro Fermentation models are powerful tools to investigate microbe-microbe interactions while circumventing animal testing and host confounding factors, but are lacking for murine gut microbiota. We therefore developed a novel Continuous Fermentation model based on the PolyFermS platform adapted to the murine caecum and inoculated with immobilized caecal microbiota. We followed a stepwise model development approach by adjusting parameters [pH, retention time (RT), growth medium] to reach Fermentation metabolite profiles and marker bacterial levels similar to the inoculum. The final model had a stable and inoculum-alike Fermentation profile during Continuous operation. A lower pH during startup and Continuous operation stimulated bacterial Fermentation (115 mM short-chain fatty acids at pH 7 to 159 mM at pH 6.5). Adjustments to nutritive medium, a decreased pH and increased RT helped control the in vitro Enterobacteriaceae levels, which often bloom in Fermentation models, to 6.6 log gene copies/mL in final model. In parallel, the Lactobacillus, Lachnospiraceae, and Ruminococcaceae levels were better maintained in vitro with concentrations of 8.5 log gene copies/mL, 8.8 log gene copies/mL and 7.5 log gene copies/mL, respectively, in the final model. An independent repetition with final model parameters showed reproducible results in maintaining the inoculum Fermentation metabolite profile and its marker bacterial levels. Microbiota community analysis of the final model showed a decreased bacterial diversity and compositional differences compared to caecal inoculum microbiota. Most of the caecal bacterial families were represented in vitro, but taxa of the Muribaculaceae family were not maintained. Functional metagenomics prediction showed conserved metabolic and functional KEGG pathways between in vitro and caecal inoculum microbiota. To conclude, we showed that a rational and stepwise approach allowed us to model in vitro the murine caecal microbiota and functions. Our model is a first step to develop murine microbiota model systems and offers the potential to study microbiota functionality and structure ex vivo.
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in vitro Continuous Fermentation model polyferms of the swine proximal colon for simultaneous testing on the same gut microbiota
PLOS ONE, 2014Co-Authors: Sabine A Tanner, Annina Zihler Berner, Eugenia Rigozzi, Franck Grattepanche, Christophe Chassard, Christophe LacroixAbstract:In vitro gut modeling provides a useful platform for a fast and reproducible assessment of treatment-related changes. Currently, pig intestinal Fermentation models are mainly batch models with important inherent limitations. In this study we developed a novel in vitro Continuous Fermentation model, mimicking the porcine proximal colon, which we validated during 54 days of Fermentation. This model, based on our recent PolyFermS design, allows comparing different treatment effects on the same microbiota. It is composed of a first-stage inoculum reactor seeded with immobilized fecal swine microbiota and used to constantly inoculate (10% v/v) five second-stage reactors, with all reactors fed with fresh nutritive chyme medium and set to mimic the swine proximal colon. Reactor effluents were analyzed for metabolite concentrations and bacterial composition by HPLC and quantitative PCR, and microbial diversity was assessed by 454 pyrosequencing. The novel PolyFermS featured stable microbial composition, diversity and metabolite production, consistent with bacterial activity reported for swine proximal colon in vivo. The constant inoculation provided by the inoculum reactor generated reproducible microbial ecosystems in all second-stage reactors, allowing the simultaneous investigation and direct comparison of different treatments on the same porcine gut microbiota. Our data demonstrate the unique features of this novel PolyFermS design for the swine proximal colon. The model provides a tool for efficient, reproducible and cost-effective screening of environmental factors, such as dietary additives, on pig colonic Fermentation.
Jamie A. Hestekin - One of the best experts on this subject based on the ideXlab platform.
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Modeling of Clostridium t yrobutyricum for Butyric Acid Selectivity in Continuous Fermentation
Energies, 2014Co-Authors: Jianjun Du, Amy Mcgraw, Jamie A. HestekinAbstract:A mathematical model was developed to describe batch and Continuous Fermentation of glucose to organic acids with Clostridium tyrobutyricum . A modified Monod equation was used to describe cell growth, and a Luedeking-Piret equation was used to describe the production of butyric and acetic acids. Using the batch Fermentation equations, models predicting butyric acid selectivity for Continuous Fermentation were also developed. The model showed that butyric acid production was a strong function of cell mass, while acetic acid production was a function of cell growth rate. Further, it was found that at high acetic acid concentrations, acetic acid was metabolized to butyric acid and that this conversion could be modeled. In batch Fermentation, high butyric acid selectivity occurred at high initial cell or glucose concentrations. In Continuous Fermentation, decreased dilution rate improved selectivity; at a dilution rate of 0.028 h −1 , the selectivity reached 95.8%. The model and experimental data showed that at total cell recycle, the butyric acid selectivity could reach 97.3%. This model could be used to optimize butyric acid production using C. tyrobutyricum in a Continuous Fermentation scheme. This is the first study that mathematically describes batch, steady state, and dynamic behavior of C. tyrobutyricum for butyric acid production.
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Modeling of clostridium tyrobutyricum for butyric acid selectivity in Continuous Fermentation
Energies, 2014Co-Authors: Jianjun Du, Amy Mcgraw, Jamie A. HestekinAbstract:A mathematical model was developed to describe batch and Continuous Fermentation of glucose to organic acids with Clostridium tyrobutyricum. A modified Monod equation was used to describe cell growth, and a Luedeking-Piret equation was used to describe the production of butyric and acetic acids. Using the batch Fermentation equations, models predicting butyric acid selectivity for Continuous Fermentation were also developed. The model showed that butyric acid production was a strong function of cell mass, while acetic acid production was a function of cell growth rate. Further, it was found that at high acetic acid concentrations, acetic acid was metabolized to butyric acid and that this conversion could be modeled. In batch Fermentation, high butyric acid selectivity occurred at high initial cell or glucose concentrations. In Continuous Fermentation, decreased dilution rate improved selectivity; at a dilution rate of 0.028 h−1, the selectivity reached 95.8%. The model and experimental data showed that at total cell recycle, the butyric acid selectivity could reach 97.3%. This model could be used to optimize butyric acid production using C. tyrobutyricum in a Continuous Fermentation scheme. This is the first study that mathematically describes batch, steady state, and dynamic behavior of C. tyrobutyricum for butyric acid production.
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Continuous Fermentation of Clostridium tyrobutyricum with partial cell recycle as a long-term strategy for butyric acid production
Energies, 2012Co-Authors: Jianjun Du, Amy Mcgraw, Nicole Lorenz, Edgar C. Clausen, Robert R. Beitle, Jamie A. HestekinAbstract:In making alternative fuels from biomass feedstocks, the production of butyric acid is a key intermediate in the two-step production of butanol. The Fermentation of glucose via Clostridium tyrobutyricum to butyric acid produces undesirable byproducts, including lactic acid and acetic acid, which significantly affect the butyric acid yield \r\nand productivity. This paper focuses on the production of butyric acid using \r\nClostridium tyrobutyricum in a partial cell recycle mode to improve fermenter yield and productivity. Experiments with Fermentation in batch, Continuous culture and Continuous culture with partial cell recycle by ultrafiltration were conducted. The results show that a Continuous Fermentation can be sustained for more than 120 days, which is the first reported long-term production of butyric acid in a Continuous operation. Further, the results also show that partial cell recycle via membrane ultrafiltration has a great influence on the selectivity and productivity of butyric acid, with an increase in selectivity from ≈9% to 95% butyric acid with productivities as high as 1.13 g/Lh. Continuous Fermentation with low dilution rate and high cell recycle ratio has been found to be desirable for optimum productivity and selectivity toward butyric acid and a comprehensive model explaining this phenomenon is given.
Jianjun Du - One of the best experts on this subject based on the ideXlab platform.
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Modeling of Clostridium t yrobutyricum for Butyric Acid Selectivity in Continuous Fermentation
Energies, 2014Co-Authors: Jianjun Du, Amy Mcgraw, Jamie A. HestekinAbstract:A mathematical model was developed to describe batch and Continuous Fermentation of glucose to organic acids with Clostridium tyrobutyricum . A modified Monod equation was used to describe cell growth, and a Luedeking-Piret equation was used to describe the production of butyric and acetic acids. Using the batch Fermentation equations, models predicting butyric acid selectivity for Continuous Fermentation were also developed. The model showed that butyric acid production was a strong function of cell mass, while acetic acid production was a function of cell growth rate. Further, it was found that at high acetic acid concentrations, acetic acid was metabolized to butyric acid and that this conversion could be modeled. In batch Fermentation, high butyric acid selectivity occurred at high initial cell or glucose concentrations. In Continuous Fermentation, decreased dilution rate improved selectivity; at a dilution rate of 0.028 h −1 , the selectivity reached 95.8%. The model and experimental data showed that at total cell recycle, the butyric acid selectivity could reach 97.3%. This model could be used to optimize butyric acid production using C. tyrobutyricum in a Continuous Fermentation scheme. This is the first study that mathematically describes batch, steady state, and dynamic behavior of C. tyrobutyricum for butyric acid production.
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Modeling of clostridium tyrobutyricum for butyric acid selectivity in Continuous Fermentation
Energies, 2014Co-Authors: Jianjun Du, Amy Mcgraw, Jamie A. HestekinAbstract:A mathematical model was developed to describe batch and Continuous Fermentation of glucose to organic acids with Clostridium tyrobutyricum. A modified Monod equation was used to describe cell growth, and a Luedeking-Piret equation was used to describe the production of butyric and acetic acids. Using the batch Fermentation equations, models predicting butyric acid selectivity for Continuous Fermentation were also developed. The model showed that butyric acid production was a strong function of cell mass, while acetic acid production was a function of cell growth rate. Further, it was found that at high acetic acid concentrations, acetic acid was metabolized to butyric acid and that this conversion could be modeled. In batch Fermentation, high butyric acid selectivity occurred at high initial cell or glucose concentrations. In Continuous Fermentation, decreased dilution rate improved selectivity; at a dilution rate of 0.028 h−1, the selectivity reached 95.8%. The model and experimental data showed that at total cell recycle, the butyric acid selectivity could reach 97.3%. This model could be used to optimize butyric acid production using C. tyrobutyricum in a Continuous Fermentation scheme. This is the first study that mathematically describes batch, steady state, and dynamic behavior of C. tyrobutyricum for butyric acid production.
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Continuous Fermentation of Clostridium tyrobutyricum with partial cell recycle as a long-term strategy for butyric acid production
Energies, 2012Co-Authors: Jianjun Du, Amy Mcgraw, Nicole Lorenz, Edgar C. Clausen, Robert R. Beitle, Jamie A. HestekinAbstract:In making alternative fuels from biomass feedstocks, the production of butyric acid is a key intermediate in the two-step production of butanol. The Fermentation of glucose via Clostridium tyrobutyricum to butyric acid produces undesirable byproducts, including lactic acid and acetic acid, which significantly affect the butyric acid yield \r\nand productivity. This paper focuses on the production of butyric acid using \r\nClostridium tyrobutyricum in a partial cell recycle mode to improve fermenter yield and productivity. Experiments with Fermentation in batch, Continuous culture and Continuous culture with partial cell recycle by ultrafiltration were conducted. The results show that a Continuous Fermentation can be sustained for more than 120 days, which is the first reported long-term production of butyric acid in a Continuous operation. Further, the results also show that partial cell recycle via membrane ultrafiltration has a great influence on the selectivity and productivity of butyric acid, with an increase in selectivity from ≈9% to 95% butyric acid with productivities as high as 1.13 g/Lh. Continuous Fermentation with low dilution rate and high cell recycle ratio has been found to be desirable for optimum productivity and selectivity toward butyric acid and a comprehensive model explaining this phenomenon is given.
Sabine A Tanner - One of the best experts on this subject based on the ideXlab platform.
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in vitro Continuous Fermentation model polyferms of the swine proximal colon for simultaneous testing on the same gut microbiota
PLOS ONE, 2014Co-Authors: Sabine A Tanner, Annina Zihler Berner, Eugenia Rigozzi, Franck Grattepanche, Christophe Chassard, Christophe LacroixAbstract:In vitro gut modeling provides a useful platform for a fast and reproducible assessment of treatment-related changes. Currently, pig intestinal Fermentation models are mainly batch models with important inherent limitations. In this study we developed a novel in vitro Continuous Fermentation model, mimicking the porcine proximal colon, which we validated during 54 days of Fermentation. This model, based on our recent PolyFermS design, allows comparing different treatment effects on the same microbiota. It is composed of a first-stage inoculum reactor seeded with immobilized fecal swine microbiota and used to constantly inoculate (10% v/v) five second-stage reactors, with all reactors fed with fresh nutritive chyme medium and set to mimic the swine proximal colon. Reactor effluents were analyzed for metabolite concentrations and bacterial composition by HPLC and quantitative PCR, and microbial diversity was assessed by 454 pyrosequencing. The novel PolyFermS featured stable microbial composition, diversity and metabolite production, consistent with bacterial activity reported for swine proximal colon in vivo. The constant inoculation provided by the inoculum reactor generated reproducible microbial ecosystems in all second-stage reactors, allowing the simultaneous investigation and direct comparison of different treatments on the same porcine gut microbiota. Our data demonstrate the unique features of this novel PolyFermS design for the swine proximal colon. The model provides a tool for efficient, reproducible and cost-effective screening of environmental factors, such as dietary additives, on pig colonic Fermentation.
R. Chmúrny - One of the best experts on this subject based on the ideXlab platform.
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Simulation and control of Fermentation complex systems
Bioprocess and Biosystems Engineering, 2000Co-Authors: D. Chmúrny, R. ChmúrnyAbstract:The paper deals with biotechnological complex systems with focus onto Fermentation processes. DisContinuous, semiContinuous and Continuous Fermentation processes are discussed. Mathematical model of Continuous biotechnological process for ethanol production is introduced and verified.