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\begin{document}

\title{A novel, scalable, and modular bioreactor design for dynamic simulation
of the digestive tract}



\author[1]{Salam Habib}%
\author[1]{Anikka Swaby}%
\author[1]{Stan Kubow}%
\author[1]{Luis Agellon}%
\affil[1]{McGill University - MacDonald Campus}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
In vitro gut model systems permit the growth of gut microbes outside
their natural habitat and are essential to the study of gut microbiota.
Systems available today are limited by lack of scalability and
flexibility in mode of operation. Here we describe the development of a
versatile bioreactor module capable of sensing and controlling of
environmental parameters such as pH control of culture medium, rate of
influx and efflux of the culture medium, and aerobic/anaerobic
atmosphere. Modules can be linked in series to construct a model of a
digestive tract to allow the growth of microbiota in vitro. We tested
the growth of a model bacterial community in a simulated mammalian gut
model. The model attained and maintained a stable bacterial community
that metabolized bile acids. The findings illustrate the utility of the
model to grow to culture a mixed bacterial community and recapitulate
biological activities such as bile acid metabolism in vitro.%
\end{abstract}%



\sloppy


\textbf{A novel, scalable, and modular bioreactor design for dynamic
simulation of the digestive tract}

Salam Habib \textbar{} Anikka M. Swaby \textbar{} Stan Kubow \textbar{}
Luis B. Agellon

School of Human Nutrition, McGill University, Ste. Anne de Bellevue, QC,
H9X 3V9 Canada

Running title: Modular bioreactor for culture of gut bacteria

\textbf{Correspondence}

Luis B. Agellon, School of Human Nutrition, McGill University, 21111
Lakeshore Road, Ste. Anne de Bellevue, QC H9X 3V9 Canada.

E-mail: luis.agellon@mcgill.ca

\textbf{Funding information}

Natural Sciences and Engineering Council of Canada, Discovery Grants
RGPIN-2015-04390 and RGPIN-2020-04901.

\textbf{Abstract}

In vitro gut model systems permit the growth of gut microbes outside
their natural habitat and are essential to the study of gut microbiota.
Systems available today are limited by lack of scalability and
flexibility in mode of operation. Here we describe the development of a
versatile bioreactor module capable of sensing and controlling of
environmental parameters such as pH control of culture medium, rate of
influx and efflux of the culture medium, and aerobic/anaerobic
atmosphere. Modules can be linked in series to construct a model of a
digestive tract to allow the growth of microbiota in vitro. We tested
the growth of a model bacterial community in a simulated mammalian gut
model. The model attained and maintained a stable bacterial community
that metabolized bile acids. The findings illustrate the utility of the
model to grow to culture a mixed bacterial community and recapitulate
biological activities such as bile acid metabolism in vitro.

\textbf{KEYWORDS}

In vitro gut model, artificial gut, microbiota, continuous culture

\textbf{1 \textbar{} INTRODUCTION}

The gut microbiota is a diverse and complex community of bacteria,
archaea, fungi and viruses. The characteristics of its species
composition and function have garnered great interest of researchers in
recent years. The microbial community is an integral part of the
gastrointestinal tract as it provides the host with an extended set of
metabolic, structural and protective functions (O'Hara \& Shanahan,
2006). In recent times, dysbiosis of the gut microbiota has been
associated with a variety of diseases, such as inflammatory bowel
diseases, diabetes, obesity, cancers, and even some behavioral disorders
(Musso et al., 2011; Manichanh et al., 2012; Zhu et al., 2013; Vuong et
al., 2017). It is now accepted that the gut microbiota has a strong
influence on host health.

Due to the complex nature of the host-microbial interaction, it is
difficult to infer specific mechanistic aspects of this interaction
using conventional in vivo models and thus highlights the need for
facile in vitro systems to help understand the complex interactions
between the host and its gut microbiota (de Vos \& de Vos, 2017). The
currently available in vitro gut models vary in their complexity (Molly
et al, 1993; Minekus et al., 1999; Makivuokko \& Nurminen, 2006; Payne
et al., 2012; Tanner et al., 2014; Moon et al., 2016; Card et al., 2017;
von Martels et al., 2017; Dupont et al., 2019). Batch culture models are
simple closed fermentation systems, that usually operate for less than
two days. In continuous culture models, which might be single or
multi-stage in design, fresh culture medium, supplemental growth factors
or any other additives, are added continuously, while wastes and toxic
byproducts are prevented from accumulating excessively by continuous
dilution of the spent medium with fresh culture medium (Moon et al.,
2016). The addition of fresh medium can be scheduled at specified time
intervals as in the semi-continuous model EnteroMix (Makivuokko \&
Nurminen, 2006), or continuously supplied as in the Simulator of the
Human Intestinal Microbial Ecosystem (SHIME) model (Molly et al, 1993).
Some models, such as the TNO Gastro-Intestinal Model TIM1 and TIM2
systems (Minekus et al., 1999), permit simulation of intestinal tract
processes like digestion and absorption. Other models permit
incorporation of intestinal tissues by employing co-culturing techniques
(von Martels et al., 2017). Different gut models have been developed for
in vitro study of complex microbial communities associated with various
physiological and pathological states, or from the digestive tracts of
humans and animals (Tanner et al., 2014; Card et al., 2017).

Since most of the in vitro gut models in existence were designed to
simulate a specific digestive tract or condition, their designs
inherently make it difficult to study different conditions or
applications using the one device. Here we describe the design and
implementation of a modular bioreactor. The novel bioreactor design
serves as a new platform that features unparalleled functional
flexibility for the culturing of mono- and mixed bacterial cultures in
vitro. In particular, the bioreactors can operate either as single
static batch cultures or connected in series to dynamically simulate the
environments of the various segments of the gastrointestinal tract.

\textbf{2 \textbar{} MATERIALS AND METHODS}

\textbf{2.1 \textbar{} System design}

The basic component of the gut model is a standardized bioreactor module
(Figure 1). The growth chamber (clear or brown media bottles with GL45
caps; Corning Incorporated) is placed in a jacketed reaction beaker
(Chemglass) that serves as the temperature control vessel and is set on
top of a stir plate. The temperature control vessel is attached to a
recirculating heater/chiller with
Prestone\selectlanguage{ngerman}\textsuperscript{®}antifreeze/engine coolant as the heat
transfer fluid. The growth chamber is equipped with a stir bar and the
caps are drilled to accommodate dedicated ports for a pH sensor (Atlas
Scientific, Long Island City, NY), inlet and outlet ports for transfer
of culture medium, inlet ports for acid and base, inlet port for
nitrogen gas, outlet port for venting excess gas, and a valved outlet
port for culture sampling. Brown bottles offer protection of light
sensitive metabolites from photodamage. The space between the growth
chamber and the temperature control vessel is filled with
Prestone\textsuperscript{®} antifreeze/coolant to ensure efficient heat
transfer. The nitrogen gas inlet can be adapted to permit either an
aerobic (port open to the room atmosphere) or anaerobic (constant flow
of nitrogen gas) culture environment as required. Mini-peristaltic pumps
(Kamoer KPP-DH-B03W), which convey acid (HCl) or base (NaOH) to the
growth chamber, and larger peristaltic pumps (New Era NE-9000), which
drive the transfer of culture medium from one growth chamber to the
next, are all controlled by a Raspberry Pi computer version B-R2
(Raspberry Pi Foundation) running the system control software (Figure
S1) written in Python programming language (Python Software Foundation).
All the computers controlling each of the modules are connected via an
ethernet router. The pH probe is connected to the Raspberry Pi computer
through an EZO\textsuperscript{TM} pH circuit stamp (Atlas Scientific,
Long Island City, NY). A time server running on a dedicated Raspberry Pi
computer fitted with a real-time clock module synchronizes the clocks
across all network devices. The number of bioreactor modules operated at
a time is flexible (Figure S2). Batch culture mode (growth medium In/Out
ports sealed), continuous mode (single bioreactor with growth medium
with In/Out ports operational) and series mode can be selected at the
start of the experiment and the system parameters are supplied to each
control computer through a configuration file.

\textbf{2.2 \textbar{} Starter culture and culture conditions}

The model gut microbiota in this study was prepared from dehydrated
bacterial cultures of \emph{Lactobacillus acidophilus, Lactobacillus
delbrueckii} subspecies \emph{bulgaricus, Lactobacillus casei,
Lactobacillus rhamnosus, Bifidobacterium bifidum, Bifidobacterium breve,
Bifidobacterium longum and Streptococcus salivarius}
subspecies\emph{thermophilus} . Some experiments used monocultures
of\emph{Escherichia coli} BL21 or \emph{Bifidobacterium longum}
subspecies\emph{infantis} 35624 (Bifantis\selectlanguage{english}, Align). To establish working
bacterial cultures, dehydrated bacteria were first reconstituted in
sterile peptone water (Oxoid). A 1 mL aliquot of the reconstituted
bacteria was then added to 9 mL of de Man Rogosa and Sharpe (MRS) medium
(Oxoid) supplemented with 0.05 \% w/v L-cysteine (Amresco). The starter
culture was grown at 37 degC anaerobically until the optical density at
600 nm (OD\textsubscript{600}) reached \selectlanguage{english}[?] 1.5 before being used in
experiments.

\textbf{2.3 \textbar{} DNA extraction and quantification}

Bacterial cells were sedimented from 1 mL cultures by centrifugation.
The pellet was resuspended in buffer containing 0.2 M NaCl, 0.1 M
ethylenediaminetetraacetic acid (EDTA) and 15 mg/mL lysozyme, and then
incubated at 37 degC for 60 min. SST solution (20 \% sarkosyl, 0.2 M NaCl,
0.5 M Tris-HCl pH 8.0, 1 mg/mL proteinase K and 4 mM dithiothreitol) was
then added and the samples were further incubated at 55 degC for 60 min.
Nucleic acids were extracted with an equal volume of phenol:chloroform
(1:1) solvent. The aqueous phase was recovered, and nucleic acids were
precipitated with 2 volumes of cold absolute ethanol in the presence of
0.3 M sodium acetate, washed with cold 70 \% ethanol, dried and then
finally dissolved in buffer containing 5 mM Tris-HCl (pH 8.0) and 0.1 mM
EDTA. The concentration of DNA was determined using the
Qubit\textsuperscript{(r)} dsDNA BR Assay Kit (Invitrogen). The integrity
of the isolated DNA was assessed by agarose gel electrophoresis.

\textbf{2.4 \textbar{} Biochemical analyses}

Quantitative real-time PCR (qPCR) reactions were carried out using a
Bio-Rad CFX96 real-time system operated by CFX Manager software (version
3.1). The standard reaction mixture consisted of 80 mM Tris-HCl pH 8.3,
100 mM KCl, 6 mM MgCl\textsubscript{2}, 300 mM trehalose, 200 mM
betaine-HCl, 0.2 mg/mL bovine serum albumin, 0.2 \% (v/v) Tween-20, 1 X
SYBR Green I, 2 mM dNTP mix, 0.4 U/\selectlanguage{greek}µ\selectlanguage{english}L Taq polymerase, 5 \selectlanguage{greek}μ\selectlanguage{english}M each of
forward and reverse primers and 1 \selectlanguage{greek}µ\selectlanguage{english}g/mL DNA template in a total volume
reaction volume of 10 \selectlanguage{greek}µ\selectlanguage{english}L. The sequences of the primers used in this
study is shown in Table S1. The standard PCR reaction included an
initial denaturation step of 95 \selectlanguage{ngerman}°C for 5 min and 40 cycles of 95 °C for
10 s and annealing and extension at 65 °C for 30 s, followed by melt
curve analysis from 65 °C to 95 °C in 0.5 °C increments. Results were
expressed as proportions of the copy number determined for each target
within the sample, and each sample was represented as a percentage of
the culture with the highest bacterial density which was assigned an
arbitrary value of 100 \%.

Bile acids were separated by high performance liquid chromatography
(HPLC) as described previously (Torchia et al., 2001). Bile acid species
were identified based on retention times of certified bile acid
standards (Steraloids) and quantified against known amounts of the bile
acid standards using area under the curve method.

\textbf{2.5 \textbar{} Statistical analyses}

Differences between means were evaluated by Student's \emph{t} -tests
using GraphPad Prism software version 6.01 for Windows (GraphPad
Software). Differences were considered significant when
\emph{P}\textless{} 0.05.

\textbf{3 \textbar{} RESULTS AND DISCUSSION}

\textbf{3.1 \textbar{} Aerobic and anaerobic environment}

To evaluate the ability of the bioreactors to maintain aerobic and
anaerobic conditions, two bioreactor modules were inoculated with a
monoculture of the anaerobe \emph{Bifidobacterium longum}
subspecies\emph{infantis (B. infantis)} and operated in the batch
culture mode. This bacterium was able to grow and thrive as indicated by
the increase in the OD\textsubscript{600} of the culture after 24 h
(Figure 2A). Analysis of the composition of the culture by qPCR
confirmed that the increase in bacterial density over the 24 h of
culture was due to the growth of \emph{B. infantis} (Figure 2B). In a
separate experiment, a set of bioreactor modules were inoculated with a
mixed bacterial culture containing different species of bifidobacteria
along with other bacterial species from two of the major phyla
(Firmicutes and Actinobacteria) of the human gut microbiota (Kostic et
al., 2013) to enable us to assess the growth dynamics of bifidobacteria
within a mixed bacterial community. In this experiment, the MRS culture
medium (Lee \& Lee, 2008; Süle et al., 2014) was used and the bioreactor
modules were operated in the continuous culture mode. As shown in Figure
2C, the mixed culture maintained under anaerobic condition showed higher
growth rates compared to that maintained under aerobic condition.
Moreover, the bacterial growth under anaerobic culture condition was
attributable to the growth of \emph{Bifidobacterium} \emph{spp} . and
\emph{Streptococcus spp.} at the expense of the \emph{Lactobacillus
spp.} (Figure 2D). These results demonstrated the ability of the
bioreactor modules to maintain anaerobic culture conditions and support
the growth of anaerobic bacteria such as bifidobacteria.

\textbf{3.2 \textbar{} pH control}

To assess the efficacy of pH control, six bioreactor modules were
operated independently in batch culture mode. All bioreactor modules
were inoculated with an active \emph{E. coli} BL21 culture. One group
had no pH control (Figure 3A) and the other group was set to maintain pH
7 (Figure 3B). The pH of the cultures was monitored for 24 h and samples
of the cultures were collected for assessment of optical density at
regular intervals over the course of the experiment. The cultures
of\emph{E. coli} maintained at pH 7 showed enhanced growth during the
exponential growth phase compared to the cultures without pH control.
The volume of acid and base added to the growth medium to maintain pH 7
are shown in Figure 3C. These results demonstrated the ability of the
bioreactor modules to maintain a steady pH of the culture at a
predetermined value.

\textbf{3.3 \textbar{} Genetic stability of a model mixed bacterial
community in culture}

To assess the genetic stability of the bacterial community in long-term
culture, the bioreactors were connected in series to create a dynamic
model of the human digestive tract. In this experiment, six bioreactor
modules were connected in series, each set to maintain a specific pH and
held under anaerobic conditions to simulate a segment of the human
gastrointestinal tract (Figure 4A). The pH values used in the stomach,
small intestine, and large intestine compartments were based on the pH
values reported for these regions of the human digestive tract
(Khutoryanskiy, 2015). At the start of the experiment, Module 1
(representing the stomach) was inoculated with the model microbiota as
described earlier (Figure 4B) and the system was operated continuously
for 120 h. As shown in Figure 4C-4H, bacterial density in Module 1
decreased (Figure 4C), while the bacterial density in Modules 2 and 3
(representing the proximal and distal small intestine, respectively;
Figures 4D and 4E) as well as Modules 4-6 (representing the ascending
colon, transverse colon and descending colon of the large intestine,
respectively; Figures 4F-4H) increased over the duration of the
experiment. Modules 4-6 exhibited the greatest bacterial density. At the
end of the experiment, the genetic composition of the bacterial
community in Module 6, which simulates the most distal large intestine
compartment (Figure 4H), closely resembled that of the initial inoculum
(Figure 4B) at the genus level. The specific bacterial species and their
abundance in each of the bioreactor modules in the series were also
determined. The composition of the bacterial community was distinct at
different pH represented by the different segments of the digestive
tract (Figure 4A). \emph{B. bifidum} was not detectable after 24 h of
culture, suggesting that specific nutritional or structural substrate
requirements of this particular species that may have not been met by
the culture conditions. In contrast, \emph{L. casei} ,\emph{L.
rhamnosus} , \emph{B. longum} , and \emph{S. thermophilus} remained
easily detectable throughout the entire duration of the experiment
(Figure S3).

\textbf{3.4 \textbar{} Assessment of biological activity of the model
mixed bacterial community}

To test if the model gut microbial community grown in the in vitro gut
model exhibited selected biological activities associated with gut
microbiota in vivo, the ability of the culture to metabolize bile acids
was assayed. In the first experiment, six bioreactor modules operating
in the batch culture mode with pH control were inoculated with the model
microbiota. Taurocholic acid (TCA), a common conjugated bile acid, was
added to the culture medium at 0, 10, or 100 mM concentrations. The
growth of the model microbiota treated with TCA was substantially less
compared to the untreated model microbiota. Nevertheless, bacterial
growth was observed even at the highest TCA concentration used in the
experiment (Figure 5A). At 10 mM TCA treatment, the proportion
of\emph{Streptobacteria spp.} in the community was reduced while that
of\emph{Lactobacillus spp.} was increased (Figure 5B), which is likely
due to its ability to metabolize TCA (Gilliland \& Speck, 1977). HPLC
analysis of the bile acid species in the culture medium showed the
appearance of cholic acid, a product of TCA deconjugation by bacteria,
after 24 h of culture (Figure 5C). In the second experiment, the model
microbiota was treated with chenodeoxycholic acid (CDCA), an
unconjugated bile acid. CDCA was more potent than TCA in inhibiting the
growth of the model microbiota (Figure 5D). As shown in Figure 5E,
ursodeoxycholic acid, the 7\selectlanguage{greek}β-\selectlanguage{english}hydroxyl group epimer of the 7\selectlanguage{greek}α-\selectlanguage{english}hydroxyl
group-containing CDCA produced by bacterial metabolism, was present in
the culture after 24 h. These results demonstrated that the model
microbiota grown in the in vitro digestive tract model could exhibit
biological activities normally associated with healthy gut microbiota in
vivo.

\textbf{4 \textbar{} CONCLUSIONS}

We described here the design and validation of a modular bioreactor
module that can be operated under various configurations. Each module
can sense and control the pH of its culture medium and can be set to
operate in static or dynamic culturing mode to serve specific
applications. The modular bioreactor design offers flexibility in terms
of culture size, aerobic/anaerobic atmosphere, and static/dynamic
culture. Specifically, the bioreactor design enables the creation of
simple or complex models of digestive tracts of desired culture sizes.
The resulting systems can be used to study the growth and evolution of
cultured microbes, such as gut microbes, independent of host immune
surveillance as well as to determine the response of the microbial
community to controlled availability of specific metabolites that are
normally secreted by the host into the digestive tract.

\textbf{ACKNOWLEDGMENTS}

This work was supported by the Discovery Grants from the Natural
Sciences and Engineering Council of Canada (RGPIN-2015-04390 to LBA;
RGPIN-2020-04901 to SK).

\textbf{CONFLICT OF INTERESTS}

None declared.

\textbf{AUTHOR CONTRIBUTIONS}

SH and AMS assembled hardware components, wrote software, performed
experiments, analyzed the data and wrote the manuscript. SK and LBA
developed the bioreactor design, designed experiments, analyzed the
data, wrote and edited the manuscript.

\textbf{ORCID}

\emph{Luis B. Agellon} (ID) https://orcid.org/0000-0002-2209-8702

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\textbf{SUPPORTING INFORMATION}

This manuscript has additional supporting information.

\textbf{FIGURE LEGENDS}

\textbf{FIGURE 1} Schematic representation of a bioreactor module.

\textbf{FIGURE 2} Aerobic and anaerobic growth of a gut bacteria. (A)
Growth of \emph{Bifidobacterium longum} subspecies \emph{infantis (B.
infantis)} monocultures under anaerobic conditions at pH 5.8. Data shown
is the average of two trials with n=4 replicates. (B) Abundance of
bacteria detected by the bacterial 16S rDNA universal primers and 16S
rDNA primers specific for \emph{Bifidobacterium longum} in equivalent
masses of input template DNA extracted from the monocultures in (A).
Data shown is the average of two trials with n=4 replicates. (C) Growth
of a model gut microbiota (mixed bacterial cultures) over time in
aerobic and anaerobic conditions maintained at pH 5.8. Data shown is the
average of two trials with n=2 replicates. (D) Proportions of bacterial
genera present in the starter culture (0 h) and after 24 h of growth in
aerobic and anaerobic cultures (C). *\emph{P} \textless{} 0.05.

\textbf{FIGURE 3} Growth of a bacterial monocultures with and without pH
control. Growth of an \emph{E. coli} monocultures over time (A) without
pH control and (B) at pH 7.0. (C) Total volume of acid and base added to
the pH-controlled culture (B) over time. Data shown is the average of 2
trials with n=3 replicates.

\textbf{FIGURE 4} Stability of the genotype of a model gut microbiota in
a dynamic simulation of the human digestive tract. (A) The pH setpoint
of the culture medium representing segments of the simulated digestive
tract. The rate of influx and efflux of culture medium into and out of
each bioreactor was set to 0.1 mL/min. (B) Proportions of bacterial
genera of the starter culture. The growth of the model gut microbiota
over 5 days and the proportions of bacterial genera assayed at 24, 72,
120 h in modules representing (C) Stomach (Module 1), (D) Proximal small
intestine (Module 2), (E) Distal small intestine (Module 3), (F)
Ascending colon (Module 4), (G) Transverse colon (Module 5) and (H)
Descending colon (Module 6). The areas of the circles depict the density
of the bacterial cultures at the time of sampling with the density of
the inoculum (B) set to 100 \%. Data shown is the average of 2 trials
with n=2 replicates.

\textbf{FIGURE 5} Functionality of a model gut microbiota in response to
bile acid treatment. (A) Growth of a model gut microbiota in the
anaerobic conditions at pH 5.8 in the absence or presence of taurocholic
acid (TCA). Data shown is the average of two trials with n=2 replicates.
(B) Proportions of bacterial genera present at inoculation (0 h) and
after 24 h of culture (A). (C) Representative chromatogram showing the
detection of TCA and cholic acid (CA) in the growth medium of inoculated
cultures at 0 h and 24 h, and uninoculated culture (no bacteria) at 24
h, in the presence of 10 mM TCA. (D) Growth of a model gut microbiota in
the presence of chenodeoxycholic acid (CDCA). Data shown is the mean of
2 trials with n=4 replicates. (E) Detection of CDCA and ursodeoxycholic
acid (UDCA) in the growth medium at inoculation (0 h) and after 24 h of
culture. ND, not detected.

\textbf{FIGURE S1} Algorithm of the bioreactor control software.

\textbf{FIGURE S2} Example configurations of simulated gut models.

\textbf{FIGURE S3} Species composition of the bacterial community
present in specific bioreactor modules of the dynamic simulation of the
human digestive tract as described in the legend of FIGURE 4.\selectlanguage{english}
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