The educational content in this post, elaborated in collaboration with Lesaffre, was independently developed and approved by the GMFH publishing team and editorial board.
What is the value of intestinal fermentation in vitro models in microbiome research?
The way towards determining causality and enabling therapeutic development of gut microbiome-targeted interventions involves: 1) compositional and functional characterization of a specific microbial niche; 2) data-driven hypothesis generation and 3) experimental validation of hypothesis.
Different tools and technologies available for experimental hypothesis validation encompass in silico (i.e., performed in a virtual setting), in vitro (e.g., cell lines, organoids, and organ-on-a-chip) and in vivo approaches (e.g., animal models). In particular, in vitro human gut microbiota fermentation models are a complement to animal and human studies for exploring the impact of dietary components, pathogens, drugs and toxic substances on gut microbiota composition and functions.
In vitro models are widely used to develop new pre- and probiotics under tightly controlled conditions in reactors that mimic different parts of the human gastrointestinal tract. For instance, these models enable not only isolating microorganisms and molecules in the microbiota that are potentially therapeutic, but also study the beneficial effects of probiotics on the gut microbiota, the fermentation properties of prebiotics and the survivability of specific probiotics across the entire digestive tract.
How different types of in vitro fermentation models work?
In vitro models have been used in nutrition and microbiology research for the cultivation of gut microbiome since the 1990s and include 3 main types that have been reviewed previously (here, here and here):
- Static or batch fermentation models
They are simple (one chamber), closed and inexpensive systems in which the substrate and microorganisms are added in anaerobic conditions at the beginning and are not removed until the fermentation is complete for a short-term period. An example of application of this kind of model includes the impact of a given dietary compound (e.g., selected prebiotic) on the composition and fermentation of the gut microbiota.
However, one of the limitations of this model is the decrease of nutrients supplied due to their utilization by microbes and the accumulation of toxic compounds as a result of microbial activity, which is overcome by dynamic fermentation models.
- Dynamic fermentation models
These are open systems that have the advantage of providing a semi-continuous or continuous substrate supply throughout the fermentation process using a single or multi chambers. A continuous supplementation of feed allows maintaining the microbes in the exponential phase of growth and removing excessive toxic metabolites, better mimicking the gastrointestinal tract’s conditions.
These models also integrate a software that allow a high control of physiological parameters (continuous feed supply, temperature, pH, and anaerobic atmosphere) while simulating the spatial, environmental, and temporary characteristics of a defined gut environment. These models are preferred to the batch models due to their high productivity, no batch-to-batch variation and high in vivo resemblance, while they are not always cost accessible for small labs.
Examples of applications where semi-continuous and continuous models are used include the spread of antibiotic resistance in the gut microbiome, the impact of dietary components such as emulsifiers and polyphenols on the gut microbiome and the protective role of probiotics against antibiotic-induced diarrhea. In certain applications, such as gut microbiome responses to Clostridioides difficile, the results are similar to those obtained with mice experiments.
- Advanced culture systems (complex multistage models)
Lately, organoids engineered with cellular and microbiome niches that are not integrated in traditional in vitro fermentation models have drawn great attention in the microbiome field and allow investigating mechanisms underlying with disease onset, progression, and response to microbiome-targeted interventions.
These models include tissue culture bioreactors and organ-on-chips that recreate long term structural and functional features of the gut, which allow a better studying of anaerobic bacteria-host interactions. Depending on the primary source of tissues, one can study such interactions related to health or disease. One of their main limitations is the formation of oxygen and pH gradients to provide sustained in vivo-like environments.
Pros and cons of in vitro cultivation of human gut microbiota
The rationale of in vitro models is to reproduce the human gut microbiome under defined conditions and study its composition and metabolic changes over time. Below is a summary of the main pros and cons of in vitro fermentation models, which can be reviewed elsewhere (here, here and here):
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The source of the microbiota inoculated to the models is one of the most critical factors affecting the results of in vitro research. Fresh fecal samples are preferred, but oxygen exposure and freezing during the transport of samples affect bacterial viability. Using microbiota samples from different donors may not be optimal as everyone’s baseline microbiota is unique and affects the efficacy of microbiota-targeted interventions in a different way. In order to overcome the low reproducibility of complex fecal suspensions, simplified microbial populations that include the main bacteria representative of the human gut have been used.
In contrast, in vivo research on the human gut microbiome is restricted due to ethical concerns and is mainly limited to pathological conditions. In addition, murine and human gut are different at the morphological level and physiological features, which influence the composition and diversity of the gut microbiota (e.g., in humans the colon is the main site for fermentation, whereas in mice it mainly takes places in the caecum, which is nearly absent in humans). Another caveat of in vivo research is that metabolites measured can have host and microbial origin. In at least part of the in vitro models discussed here, metabolites are strictly generated by microbes.
Concluding remarks
In vitro gut microbiota models may provide timely and cost-efficient solutions to study microbiome responses to dietary interventions and drugs and, when combined with in vivo approaches, they can strengthen the future development of human studies.
While continuous dynamic fermentation models are the closest ones to in vivo-like environments, major challenges of their application include the reproduction of anaerobic conditions and the study of host-microbiome interactions.
Tissue culture bioreactors and organoid-based models are emerging approaches for studying the role of host-microbiota interactions in health and disease that ultimately allow gaining better mechanistic insights into disease mechanisms and allow personalized nutrition.
References:
Young RB, Marcelino VR, Chonwerawong M, et al. Key technologies for progressing discovery of microbiome-based medicines. Front Microbiol. 2021; 12:685935. doi: 10.3389/fmicb.2021.685935.
Isenring J, Bircher L, Geirnaert A, et al. In vitro human gut microbiota fermentation models: opportunities, challenges, and pitfalls. Microbiome Res Rep. 2023; 2:2. doi: 10.20517/mrr.2022.15.
Pham VT, Mohajeri MH. The application of in vitro human intestinal models on the screening and development of pre- and probiotics. Benef Microbes. 2018; 9(5):725-742. doi: 10.3920/BM2017.0164.
Nissen L, Casciano F, Gianotti A. Intestinal fermentation in vitro models to study food-induced gut microbiota shift: an updated review. FEMS Microbiol Lett. 2020; 367(12):fnaa097. doi: 10.1093/femsle/fnaa097.
Singh V, Son H, Lee G, et al. Role, relevance, and possibilities of in vitro fermentation models in human dietary, and gut-microbial studies. Biotechnol Bioeng. 2022; 119(11):3044-3061. doi: 10.1002/bit.28206.
Rubert J, Schweiger PJ, Mattivi F, et al. Intestinal organoids: a tool for modelling diet-microbiome-host interactions. Trends Endocrinol Metab. 2020; 31(11):848-858. doi: 10.1016/j.tem.2020.02.004.
