bioRxiv - a low volume in vitro model of the small …1 The Smallest Intestine (TSI) – a low volume in vitro model of the small intestine with 2 increased throughput 3 4 T. Cieplak1,

The Smallest Intestine (TSI) – a low volume in vitro model of the small intestine with 1

increased throughput 2

3

T. Cieplak1, M. Wiese1, S. Nielsen2, T. Van de Wiele3, F. van den Berg1, D.S. Nielsen1 4

1 Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, 5

Denmark 6

2 Department of Plant and Environmental Sciences, Precision Engineering Workshop, University 7

of Copenhagen Frederiksberg C, Denmark 8

3 Faculty of Bioscience Engineering, Ghent University, Gent, Belgium 9

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Work has been done at University of Copenhagen, Faculty of Science 11

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Abbreviated running headline: Small intestine in vitro model 13

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Corresponding Author: 15

Tomasz Cieplak 1 16

Rolighedsvej 26, Frederiksberg C, 1958, Denmark 17

Email address: [email protected] 18

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certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was notthis version posted January 8, 2018. . https://doi.org/10.1101/244764doi: bioRxiv preprint

https://doi.org/10.1101/244764

Abstract 24

Aims. To develop a low volume in vitro model with increased throughput simulating the human 25

small intestine, incorporating the presence of ileal microbiota, to study microbial behaviour in the 26

small intestine. 27

Methods and Results. The Smallest Intestine in vitro model (TSI) was developed. During the 28

simulated passage of the small intestine bile is absorbed by dialysis and pH adjusted to 29

physiologically relevant level. A consortium of seven representative bacterial members of the 30

ileum microbiota is included in the ileal stage of the model. It was found that the model can be 31

applied for the testing of survival of probiotic bacteria in upper intestine. Moreover, it was found, 32

that the presence of the ileal microbiota significantly impacted probiotic survival. 33

Conclusions. TSI allows testing a substantial number of samples, at low cost and short time, and 34

is thus suitable as an in vitro screening platform. Multiples of five reactors can be added to 35

increase the number of biological and technical replicates. 36

Significance and Impact of the Study. TSI serves as an alternative for costly and low 37

throughput in vitro models currently existing on the market. Above that, significance of small 38

intestine bacteria presence on survival of probiotic bacteria in the upper intestine was proved. 39

40

Keywords 41

In vitro model; probiotics; small intestine; intestinal microbiology; Lactobacillus. 42

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Introduction 44

There is a strong interest in finding efficient ways to investigate the behaviour of drugs, 45

foodstuffs, bioactives and probiotics in the gastrointestinal tract. Probiotics are live 46


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microorganisms that when ingested in appropriate amounts have beneficial effects on host-health 47

(Hill et al., 2014a). Recent findings suggest that certain strains have a positive impact on e.g. 48

lactose intolerance, enteritis, gastritis and antibiotic-induced diarrhoea (Hill et al., 2014b; Iannitti 49

& Palmieri, 2010; Szajewska et al., 2016; Pakdaman et al., 2016). 50

The small intestine is generally defined as consisting of three distinct sections: duodenum, 51

jejunum and ileum (Schneeman, 2002). The duodenum is the shortest part of the SI where bile 52

salts and enzymes from the pancreas are secreted to breakdown and solubilize fats, proteins and 53

carbohydrates. The duodenal concentration of bile varies from around 4 mM (fasted state) to 10 54

mM (fed conditions) (Kalantzi et al., 2006; Riethorst et al., 2016; Minekus et al., 2014). The 55

jejunum and ileum are mainly responsible for the absorption of small nutrients, minerals and 56

active reabsorption of bile salts. The latter part of the small intestine harbours a rather densely 57

populated microbial community reaching 106 – 108 cells g-1 in the ileum (Booijink et al., 2007). 58

At present our knowledge of the composition of the small intestinal microbiota is rather scarce, 59

but recent studies indicate that in healthy subjects Streptococcus, Veillonella, Haemophilus, 60

Escherichia spp. among others are commonly found commensals (Dlugosz et al., 2015; Chung et 61

al., 2015). 62

Human intervention studies remain the golden standard for testing the effect of probiotics, but 63

they are also tedious and mechanistic studies are virtually impossible. There are furthermore 64

many ethical considerations in relation to human testing. As an alternative for studies on humans, 65

animal in vivo models have been developed. In terms of anatomy, physiology, immunology and 66

microbiota, pigs closely represent human conditions (Saif et al., 1996; Leser et al., 2002; Graham 67

& Åman, 2009), while rodents are also widely used even though their GIT is less similar to the 68

human GIT than the pig. Despite the advantages of having a live organism that can mimic all 69


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physiological aspects of the GIT, in vivo models have several disadvantages. To tackle them, a 70

variety of in vitro models of the upper as well as the lower gastrointestinal tract (GIT) have been 71

developed. These models chiefly fall in two groups according to sample movement, namely static 72

models and dynamic models (Venema & Van den Abbeele, 2013; Guerra et al., 2012; Hur et al., 73

2011). 74

Static models are based on a single reactor where the pH is usually fixed during the simulation 75

(pH 1-3 for stomach and pH 6-7 in the small intestinal phase). There are no dynamic changes of 76

pH along the process and gastric emptying or absorption are not mimicked. Static models are 77

useful for screening large number of samples, but they are also very much simplified and are 78

targeted to specific applications. Many different protocols are also in use, making comparison of 79

results between research groups challenging. To tackle this problem an INFOGEST working 80

group recently published a standardised static in vitro digestion protocol (Minekus et al., 2014), 81

which is now widely used. 82

The most complex in vitro models reproduce processes occurring in the small intestine such as 83

bile salts and small nutrient absorption, dynamic changes of pH, reproduction and simulation of 84

enzymes secretion rates and realistic transit times (Table 1). All existing models operate with 85

relatively large volumes, and thus require large amounts of sample material for testing, which 86

can lead to high experimental costs. A relatively low throughput (up to two replicates per 87

experiment) makes it challenging to obtain enough repetitions to infer statistical differences 88

between treatments. None of the existing models mimic the small intestinal microbiota, even 89

though recent findings indicate that especially the ileal microbiota plays an important role in the 90

metabolism of nutrients and influences absorption of bioactives (El-Aidy et al., 2015) as well as 91

in short oligosaccharide and plant cell wall polysaccharide degradation, carbohydrate uptake and 92


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energy metabolism (Patrascu et al., 2017; Zoetendal et al., 2012). Further, SI microbiota 93

dysbiosis is associated with diseases like small bowel bacteria overgrowth (SIBO) (Bures et al., 94

2010), celiac disease (Collado et al., 2009; Ou et al., 2009) and inflammatory bowel disease 95

(IBD) (Booijink et al., 2010; Willing et al., 2009). Probiotics and prebiotics might be an 96

attractive way to lower the risk of some SI diseases and could be used as an alternative for 97

antibiotic treatment with their acute side effects by restoring a predominance of beneficial 98

microbes (Ducatelle et al., 2015; Sartor, 2004). Therefore the addition of a small intestine 99

microbiota to an in vitro SI model might offer a valuable tool for studying the interplay between 100

probiotics, prebiotics, SI microbiota and bioavailability of bioactives. 101

The aim of the present study was to develop a small volume in vitro model of the human SI 102

(duodenum, jejunum and ileum) with increased throughput as a screening platform for studying 103

microbial behaviour during small intestinal passage. 104

105

Materials and methods 106

Microorganisms and growth condition: 107

Seven bacterial strains representing prominent members of the ileal microbiota were obtained 108

from the German Collection of Microorganisms and Cell Cultures (DSMZ) (Table 2). All strains 109

were cultured anaerobically at 37oC in Gifu Anaerobic Medium (GAM Broth, NISSUI) and 110

stored at -80 °C until use. 111

Three strains with putative probiotic properties were also included (Table 2). Lactobacillus 112

rhamnosus GG (DSM 20021) was purchased from DSMZ, Lactobacillus plantarum (LP80 113

pCBH1 GZH+) encoding a bile salts hydrolase enzyme (EC 3.5.1.24) was obtained from Ghent 114

University, Belgium. Lactobacillus casei Z11 isolated from an adult human intestinal biopsy 115


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(Larsen et al., 2009) was obtained from the culture collection at Department of Food Science, 116

University of Copenhagen, Denmark. All three strains were cultured anaerobically at 37oC in 117

deMan, Rogosa and Sharpe broth (MRS, Oxoid) for 24 h and stored at -80 °C until use. 118

Prior to the experiments each strain from the small intestine microbiota was inoculated in 10 ml 119

GAM broth and cultured in 37oC for the time appropriate for each strain (Table 2). After this 120

tubes were centrifuged (5,000 g for 2 min), the supernatant was discarded and the pellet was re-121

suspended in the volume of phosphate buffered saline (pH 7.4) appropriate to obtain a suspension 122

of 108 CFU ml-1 according to growth curves developed for each strain (OD vs. CFU ml-1). All 123

strains were mixed together before addition to the reactor. 124

125

The Smallest Intestine in vitro model 126

The Smallest Intestine in vitro model (TSI) simulates the passage through the human small 127

intestine by an adjustment of pH and concentration of bile salts, pancreatic enzymes and dialysis 128

to mimic absorption. Each TSI unit consist of 5 reactors, each with a working volume of 12 ml. 129

Fig 1 depicts the experimental flow, simulating passage of duodenum, jejunum and ileum. Parts 130

of the model (outer cabinet, temperature and pH control) are adapted from the design of the 131

recently developed CoMiniGut in vitro colon model (Wiese et al., 2018). 132

During simulated small intestinal passage, temperature, pH levels and bile salt concentrations are 133

controlled at physiologically relevant levels. The main unit of TSI is a composite climate box 134

(Fig 2) where temperature is controlled (37oC) by flow of water from a circulating water bath 135

(A10/AC150, ThermoScentific) through a copper/aluminium heat exchanger placed inside of the 136

box. An external temperature sensor connected with the water bath and placed inside of the main 137

unit constantly monitors the temperature. During the whole experiment temperature is recorded 138


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by an independent temperature data logger (Temp 101A MadgeTech). Each box contains five 139

stirred fused quartz glass reactors (AdValue Technology, USA), where each one represents the 140

passage through the small intestine of one individual (see Fig 1). Reactors are separately closed 141

in PVC chambers and can be constantly flushed with nitrogen to provide anaerobic conditions. 142

Alternatively, anaerobic conditions can be achieved by using anaerobic sachets (Oxoid 143

AnaeroGen, ThermoScientific). Anaerobic conditions are verified by colour change of Oxoid 144

Anaerobic Indicator (BR0055, Oxoid), which is impregnated with a resazurin redox indicator 145

solution. Chambers are placed on a 5-unit magnetic stirrer plate (R05, IKA), which assures equal 146

mixing of samples inside of the reactors. The reactors are closed with a septa lid (GR-2 rubber, 147

Sigma-Aldrich) containing a pH probe inlet and needle inlets for the supply of enzymes and bile 148

salts, as well as sampling, input and output for the dialysis chamber. Bile is absorbed by a 149

dialysis process, which takes place in dialysis cassettes (Slide-A-Lyser G2, ThermoScientific). 150

They are placed in a chamber made of PVC with a septa lid that ensures anaerobic conditions 151

inside. The dialysis chamber is filled with dialysis fluid (Milli-q water). A multichannel 152

peristaltic pump (205S, Watson-Marlow) ensures constant flow of the chyme between the reactor 153

and the dialysis cassettes (2.64 ml min-1). Beakers are stirred continuously (170 rpm) to facilitate 154

effective dialysis. The dialysis process simulates also absorption of small nutrients and 155

electrolytes from the chyme with a size smaller than the cut-off of the dialysis cassette specified 156

as 10 kDa. 157

The pH in each module is measured every 20 seconds using SP28X (Consort) pH electrodes. The 158

readout is sent to the computer that according to the pre-set value decides on the dosing. In the 159

TSI model pH is controlled with 0.1M NaOH for each reactor according to the pre-programmed 160

experimental setup using syringe pumps (NE-500, WPI). The syringe pumps are automatically 161


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controlled by a computer using in-house made scripts in the Matlab software (ver. R2015a, 162

TheMathWorks, Inc.) 163

To simulate the electrolyte composition and osmotic pressure occurring in the human GIT - 164

Simulated Gastric Fluid (SGF) and Simulated Intestinal Fluid (SIF) were used (Minekus et al., 165

2014). The SIF composition was modified by removal of sodium bicarbonate to allow more 166

precise pH control during experiments. The TSI reactor was prepared by adding 4.1 ml of SGF, 3 167

ml or 4.5 ml of SIF depending if the experiment was conducted as simulating fed or fasted state, 168

and 10 µl of 0.6M CaCl2. To simulate the duodenum, pancreatic juice, bile solution and 169

feed/water were added. Pancreatic juice was added as a mixture (100 mg ml-1) of SIF and 8xUSP 170

porcine pancreatine from porcine pancreas (Sigma-Aldrich). This provides an enzyme 171

composition close to what is found in humans (Minekus et al., 2014). The amount of pancreatic 172

juice added was established according to the activity of trypsin which was at a level of 100 U ml-173

1 in fed conditions (McConnell et al., 2008) and 40 U ml-1 in fasted state. 174

A stock of 80 g l-1 was prepared and the exact concentration of bile salts was determined using a 175

commercially available kit (Total Bile Acids Assay, Diazyme). To simulate conditions after 176

ingestion of a meal, “fed state” was mimicked by the presence of 10 mM bile salts in the reactor 177

and with addition of 1.4 ml of food replacement (Nutrison Energy Multi Fibre, Nutricia) as a 178

source of nutrients. Small intestinal passage without the presence of food components was 179

simulated by a “fasted state” by presence of 4 mM bile acids and 1.4 ml of water. 180

Finally, pH was adjusted (by syringe) to 6.5 by titrating with 1 M NaOH. During the duodenal 181

passage (2 hours) pH was elevated from 6.5 to 6.8 in steady increments (Fig 3). Next, the 182

absorption of small nutrients and bile salts in the jejunum was simulated by continuously 183

pumping the samples through the dialysis chambers with a 10 kDa membrane cut-off at a 184


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pumping rate of 2.64 ml min-1 for 4 hours, as specified above. During the 4 hour simulation of 185

the jejunal passage pH was increased from 6.8 to 7.2. To simulate ileum, fresh SIF with a pH of 186

7.2 was added to the reactor to obtain a chyme to SIF ratio of 50:40 (v/v). Moreover 1 ml of 187

small intestine microbiota inoculum (Table 2) was added. The inoculum was adjusted to obtain 188

107 CFU ml-1 in the reactor. During simulated ileal passage (2 hours) pH was kept constant at 7.2. 189

The amount of base used to control pH was constantly recorded in order to compare inter and 190

intra-variability between samples and experimental conditions. After 8 hours of small intestine 191

passage samples were taken to check the concentration of bile salts. 192

193

Flow cytometry analysis of probiotic resistance towards bile salts 194

The response of the probiotic strains (Table 2) to bile was first assessed using flow cytometry 195

(FCM). Each bacterium was inoculated in 10 ml MRS broth medium and cultured for 24 hours. 196

Cells were harvested by centrifugation (10,000 g for 2 min) after which the supernatant was 197

discarded and the pellet re-suspended in 10 ml phosphate buffered saline (pH 7.4). The microbes 198

were then exposed to three different concentrations of the bile salts (10 mM, 4 mM and 1 mM) 199

by mixing 0.1 ml of inoculum with 0.9 ml of bile solution. 200

A live/dead staining working solution was prepared by mixing 10 µl of SYBR green (10,000x 201

concentrated in DSMO, Sigma-Aldrich) and 20 µl of propidium iodide (PI, FisherScientific; 20 202

mM in DMSO) with 970 µl of filtered DMSO (Barbesti et al., 2000). After this, samples were 203

diluted 100 and 1000 times and stained with live/dead stain (2% in final solution), followed by 204

15 min incubation in 37oC prior to analysis (De Roy et al., 2012). Flow cytometry was carried 205

out using a FACSJazz flow cytometer (BD) collecting green (530 to 540 nm) and red (640 to 692 206


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nm) fluorescence in order to assess the percentage of live/dead cells. All measurements were 207

taken in duplicates. Results were analysed in FlowJo software (FlowJo X 10.0.7r2, LLC). 208

209

Survival of probiotic strains during simulated small intestinal passage 210

Each probiotic strain (Table 2) was inoculated in 10 ml MRS broth medium and cultured for 24 211

hours. Cells were harvested by centrifugation (10,000 g for 2 min) after which the supernatant 212

was discarded and the pellets re-suspended in 10 ml of phosphate buffered saline (pH 7.4). Four 213

reactors were inoculated with 0.5 ml of bacterial solution, while the fifth was kept as a control to 214

check if there is a contamination in any of the compartments used along the experiment. Survival 215

was evaluated after 8 hours of simulated digestion via colony plate counts on MRS agar medium 216

(37°C, 24 hours incubations at anaerobic conditions) after serial dilution using a saline solution 217

(0.9% NaCl in water). Experiments were conducted separately for each probiotic strain and two 218

different feeding conditions in triplicates. Moreover the influence of ileal microbiota presence on 219

survival of probiotic strains in the small intestine was investigated by comparison of probiotic 220

survival with and without the presence of a small intestine microbial consortium. 221

222

Simplified stomach compartment for simulation of gastric conditions and its application for 223

testing survival of probiotic bacteria 224

Even though primarily developed to simulate the small intestine, the TSI model can be adjusted 225

to simulate human stomach conditions in fed and fasted state if desired. Simulated Gastric (SGF) 226

and saliva (SSF) fluids were prepared as described by Minekus et al. (2014). To mimic stomach 227

conditions, TSI reactors were prepared by mixing 0.5 ml of sample with 0.9 ml of food 228

replacement (fed conditions, Nutrison Energy Multi Fibre, Nutricia) or water (fasted conditions), 229


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and 1.4 ml of SSF (pH 7). Then a mixture of 2.3 ml of SGF (pH 3) and 0.5 ml of pepsin solution 230

was added into the reactor in order to reach 2000 U ml-1 of pepsin in the final volume (Minekus 231

et al., 2014). The pH throughout the experiment was controlled according to in vivo data 232

measured on human volunteers. (Dressman et al., 1990; Tyssandier et al., 2003; Kalantzi et al., 233

2006). By using 0.5 M HCl the pH was adjusted and kept during the whole simulation pH 2 (in 234

fasted stage) or pH 4 (in fed stage). Simulation took 2 hours in fed state, which is half the 235

emptying time of semi-solid foods, and 1 hour in fasted conditions, which is the time for water to 236

transit through the stomach (Malagelada et al., 1979; Lin et al., 2005; Goetze et al., 2007). After 237

this time 1 M NaOH was titrated to reach pH 6 in order to stop activity of pepsin. Survival was 238

measured via colony plating on MRS agar medium (37°C, 24 hours incubations at anaerobic 239

conditions) after serial dilution using a saline solution (0.9% NaCl in water). Each experiment 240

was performed in triplicates (n = 3). 241

242

Statistical analysis 243

All statistical analyses were performed using the Prism 7 v 7.0b software (GraphPad). 244

Differences between groups were calculated using paired/unpaired t-test, and one-way ANOVA 245

with Tukey’s multiple comparisons test, with a single pooled variance. Significance was 246

determined at the P < 0.05 level. 247

248

Results 249

Basic parameters during a TSI run 250

During all experiments pH was measured and controlled. The pH profile during simulated small 251

intestinal passage was based on the results obtained from sampling with the IntelliCap® system 252


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(Koziolek et al., 2015). Fig 3 shows the pH profile during the entire simulated small intestinal 253

passage (8 hours) with an average standard deviation of ± 0.02 between all 36 runs. Along the 254

entire experiment sodium hydroxide was pumped to control the pH in each of the reactors. The 255

amount of alkali used to maintain pH during the experiment, according to probiotic strains and 256

feeding conditions, was monitored. There was no relationship between volume of sodium 257

hydroxide used and probiotic strain tested (data not shown). On the other hand, base 258

consumption correlated with feeding conditions. On average during fed state 717.44 ± 35.00 µl 259

of base was used versus 564.44 ± 22.90 µl in fasted state (p = 0.05). The bile salts concentration 260

was measured at the beginning and at the end of the experiment in each of the reactors. On 261

average the initial amount of bile salts was lowered by 61.98 ± 1.58 % (n = 36; data not shown). 262

There was no significant influence of feeding conditions and probiotic bacteria presence on bile 263

salts absorption (p = 0.34). 264

265

Bile salts resistance of probiotic strains 266

Bile salt resistance of the included probiotic strains was initially assessed using flow cytometry 267

(Table 3). Three different concentrations of bile salts were tested: 10 mM equivalent to fed 268

condition is TSI, 4 mM as the fasted state and 1 mM as a minimal dosage of bile salts. Control 269

samples only contained probiotic bacteria in PBS (pH 7.4). Lactobacillus plantarum LP80 270

survived well, even at a high concentration of bile salts. The lowest survival rates were shown by 271

Lactobacillus rhamnosus GG. Only 10 % of Lactobacillus GG cells survived the lowest 272

concentration of bile. Interestingly, Lactobacillus GG showed similar survival rates at bile salt 273

concentrations of 4 and 10 mM. Lactobacillus casei Z11 showed good survival, but still 274


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depending on the bile salts concentration (L. casei Z11 survival, 84% in 1 mM; 36% in 10 mM, P 275

< 0.001, n = 4). 276

277

Survival of probiotic bacteria in simplified gastric compartment 278

Gastric conditions can be simulated in TSI model in both “fasted” (i.e. after ingestion of a glass 279

of water) and “fed” state (after ingestion of food). Persistence of the three probiotic strains was 280

inspected during the passage through the simplified gastric compartment (Table 4). In fed 281

conditions (pH 4, t = 120 min) all strains showed good survival, without any significant changes 282

in number of live cells. To elucidate dynamics of probiotic survival in fasted conditions (pH 2, t 283

= 60 min), samples were taken every 20 min for 1 hour. L. plantarum LP80 was the most 284

susceptible bacterium towards fasted gastric conditions. The number of cultivable cells went 285

below detection limit already after 20 minutes. Survival of L. casei Z11 was good during the first 286

20 min of experiment where the number of cultivable cells did not change significantly (Table 4, 287

P = 0.07). At the second sampling point after 40 min, the number of cultivable cells went below 288

detection limit. The number of cultivable L. rhamnosus GG cells went down with time more 289

gradually (Table 4). 290

291

Probiotic bacteria survival during the small intestine simulation 292

The survival of the three probiotic strains was tested in TSI model in two different feeding 293

conditions: conditions mimicking a “fasted” small intestine (i.e. before food ingestion; bile salts 294

= 4mM; pancreatic juice = 40 U ml-1) and a “fed” small intestine (i.e. after a meal; bile salts = 10 295

mM; pancreatic juice = 100 U ml-1). The influence of the small intestinal microbiota consortium 296

was tested in both feeding states (Fig 4). Lactobacillus plantarum LP80, which is bile salt 297


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hydrolase positive, showed good survival in both feeding conditions without the presence of the 298

small intestine microbiota (Fig 4; Fasted: P = 0.38, Fed: P = 0.18). Interestingly, the addition of 299

an ileal microbiota resulted in a significantly decreased survival of L. plantarum LP80 in both 300

feeding states (Fig 4; Fasted: P = 0.02, Fed: P = 0.03). Lactobacillus rhamnosus GG survived 301

well in the fasted state without ileal microbiota and moderately in fasted conditions with ileal 302

microbiota. It showed poor survival when facing high concentrations of bile salts in the “fed” 303

state (Fig 4; Without microbiota: P < 0.001, With microbiota: P < 0.001), which is in agreement 304

with susceptibility to bile salts as determined by flow cytometry. Among the three tested strains, 305

the most susceptible one towards bile salts was Lactobacillus casei Z11. It showed moderate 306

survival in fasted conditions without ileal microbiota, but during fed conditions and when 307

including the ileal microbiota survival was significantly reduced (Fig 4. P < 0.001 in all 308

experiments). 309

310

Discussion 311

Development of the TSI in vitro model 312

Our low volume and high-throughput in vitro model of the human small intestine consists of 5 313

units, which can be multiplied with additional modules if large numbers of samples need to be 314

tested in parallel. All parameters of the model were set based on in vitro data from various 315

human small intestine samplings and in vitro digestion protocols. Values for pH in each of the 316

three stages were based on values from human volunteers (Dressman et al., 1990; Fallingborg et 317

al., 1990; Fallingborg et al., 1998; Koziolek et al., 2015). Concentrations of bile salts and 318

pancreatic juice were chosen based on in vivo data (Gullo et al., 1988; Kalantzi et al., 2006; 319

McConnell et al., 2008), complemented with values used in other static and dynamic in vitro 320


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models (Molly et al., 1993; Minekus et al., 1995; 2014). The incorporation of a dialysis module 321

allows simulation of bile absorption of bile salts and smaller nutrients, though with the limitation, 322

that in the TSI model molecule size is the main factor influencing absorption, whereas in vivo the 323

small intestinal epithelium is more permeable to some molecules than others due to e.g. the 324

presence of specific transporters (Burant et al., 1992; Suzuki & Sugiyama, 2000; Cragg et al., 325

2005). The pH control unit allowed us to precisely regulate the pH at any point of the experiment 326

and change the pH slope according to the experimental design. As seen from Fig 3, the pH 327

deviates somewhat from the target value at the beginning of the jejunum stage. Along with 328

diffusion of small nutrients and bile salts there was a small volume of dialysis fluid getting into 329

the cassette, caused by osmotic pressure. The fluid had a pH higher than the sample in the 330

chamber, therefore the pH slightly increased initially due to the asymmetric pH control. 331

However, pH was rapidly normalised back to the target value (Fig 3). Elevated values of base 332

consumption occurring in the fed condition might be related to the higher dosage of bile salts 333

applied at the beginning of experiment, but further biochemical analysis are required to prove 334

this effect. 335

336

Ileum microbial consortium 337

Based on data obtained via both culture-dependent and culture-independent studies an 338

appropriate bacterial consortium was selected and a conglomerate of 7 strains created in order to 339

produce a simple, but representative microbiota of the small intestine (Thadepalli et al., 1979; X. 340

Wang et al., 2003; M. Wang et al., 2005; Hayashi et al., 2005; Booijink et al., 2010; van den 341

Bogert et al., 2013). This is a unique characteristic of TSI, being the first in vitro model to 342

incorporate or mimic small intestine microbiota. The chosen strains were all of human origin and 343


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had to be resistant towards bile acids as the bile acid concentration at the beginning of the ileal 344

stage is still relatively high. An advantage of the defined consortium based on culture collection 345

strains, compared to e.g. a faecal inoculum, is that while fecal inoculum sooner or later will be 346

exhausted, the defined inoculum can always be reconstituted. Moreover, faecal samples are not 347

necessarily a good representation of small intestine microbiota (Chung et al., 2015). The small 348

intestine is the place where food components during digestion encounter microbes in higher 349

numbers for the first time after ingestion. It also characterised by high transit speed (Yu et al., 350

1996; Yu & Amidon, 1998). Therefore the ileum microbiota is specialised in fast uptake and 351

conversion of relatively simple carbohydrates, in contrast to the colonic microbiota which is 352

efficient in the degradation of complex carbohydrates (Zoetendal et al., 2012). 353

354

Probiotic bacteria survival in TSI model 355

Probiotics as a dietary supplement are administrated orally. Thus, to exert their positive effect on 356

the host they have to survive the passage through the harsh environment of the upper GIT, with 357

the main limiting factor being the low pH in the stomach (Morelli, 2000; Kailasapathy, 2006). 358

The persistence of probiotic bacteria depends strongly on feeding conditions and the pH. In 359

experiments simulating the stomach in fed state with elevated pH and presence of nutrients all 360

three strains survive perfectly. These results are in line with previous studies showing that 361

addition of nutrients significantly improves survival of L. rhamnosus GG (Corcoran et al., 2005). 362

In the fasted state, the tested bacteria showed diverse survival, but none of them manages to 363

thrive 60 minutes at pH 2. These results are comparable to previously published studies, where 364

number of cultivable cells goes below detection level within the first 40 min of the incubation 365

(Corcoran et al., 2005; Doherty et al., 2012). 366


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Detailed studies on probiotics survival in the human small intestine are scarce due to difficulties 367

with material sampling and ethical constrains. To address this issue, the TSI in vitro model was 368

developed and applied to determine probiotic survival by simulating conditions resembling 369

various sections of the human small intestine (duodenum, jejunum and ileum), in both “fed” and 370

“fasted” conditions, with and without the presence of simulated ileal microbiota. The three tested 371

probiotics strains exhibit diverse resistance towards SI conditions. Lactobacillus plantarum LP80 372

survived well, even at a high concentration of bile salts (Fig 4). This was expected as this strain 373

produces bile salt hydrolase enzyme (Begley et al., 2005; 2006; Jason M Ridlon, 2014), and due 374

to this ability we used this strain as a positive control in our experiments. The overall 375

performance of Lb. casei Z11 in the static in vitro experiment against bile salts was contradictory 376

to the results from the TSI model, where this probiotic showed poor survival (3.5 log reduction 377

on average). This is possibly due to a longer incubation time in the presence of bile salts, and 378

pancreatic juice containing protease enzymes or a mixture of both. Lactobacillus casei Shirota 379

has previously been found to show good survival in the small intestine, indicating that different 380

strains of L. casei show divergent resistance towards the harsh conditions in the gut (Oozeer et 381

al., 2006; R. Wang et al., 2015). 382

To date no other in vitro setup includes a simulated small intestinal microbiota. Here we show, 383

that probiotic survival during simulated small intestinal passages indeed was influenced by the 384

presence of the ileal microbiota. In five out of six experiments we observed a significant 385

difference in probiotic survival between experiments performed with and without the inoculum. 386

This effect is more pronounced in “fasted” reactors, due to lower concentration of bile salts and 387

higher probiotic survival rate. 388


https://doi.org/10.1101/244764

The in vitro model of the small intestine presented in this study has a potential to become a 389

screening platform for modelling microbial survival during the passage of the human small 390

intestine. Thanks to affordable and exchangeable reactors and peripheries the TSI model can be 391

applied for testing pathogenic and spore forming bacteria. The TSI model can also potentially be 392

used in the future for studying behaviour of bacteriophages and effectiveness of probiotics and 393

enzymes microencapsulation formulations. 394

All in all the TSI represents a novel small intestinal in vitro model, that will be particularly useful 395

for screening purposes, where it is desirable to work with relatively small volumes and/or when 396

the small intestinal microbiota needs to be taken into the equation, and/or when assessing small 397

intestinal passage of microbes or enzymes. 398

399

Acknowledgments 400

The research leading to these results has received funding from the People Programme (Marie 401

Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under 402

REA grant agreement no 606713. 403

404

Conflict of interest 405

No conflict of interest declared. 406

407

References 408

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619

Figure legends 620

Fig 1. Flowchart describing main processes occurring during simulated small intestinal passage 621

using the The Smallest Intestine (TSI) model. 622

Fig 2. General overview of the model: A) TSI basic module B) scheme of the one reactor with 623

all periphery instruments (marked on picture A), 1. Base vessel, 2. Bile vessel, 3. Pancreatic juice 624

vessel, 4. Reaction vial, 5. Dialysis cassette. 625

Fig 3. The average pH profile during simulated small intestinal passage. Each of the experiments 626

was divided into three stages: duodenum (1) which lasted 2 hours, jejunum (2) and ileum (3) that 627


https://doi.org/10.1101/244764

took 4 and 2 hours respectively (n = 36). The microbial consortium simulating the ilium 628

microbiota (Table 2) was added after 6 hours (i.e. when “entering” the ilium). Average standard 629

deviation of pH between all 36 reactors was 0.02. 630

Fig 4. Survival of three probiotic strains during simulated small intestinal passage of the TSI 631

model simulating fed/fasted conditions and with (with) or without (W/O) addition of ileal 632

microbiota: (a) Lactobacillus plantarum LP 80, (b) Lactobacillus rhamnosus GG and (c) 633

Lactobacillus casei Z11. Significant differences in survival of bacteria before and after each 634

experiment were marked. Moreover, significant impact of ileal microbiota on probiotic survival 635

was marked above bars for each experiment. All experiments were performed in triplicates (n = 636

3) 637

638

Table 1. Dynamic in vitro models mimicking the small intestine available on the market and 639 their properties. 640

Name of

the

system

Volume

[ml]

pH

control

Maximal

number of

replicates

Intestine

absorption

simulation

Small

intestine

microbiota

presence

Reference

SHIME 300 –

1200

+ 1 - 2 - -

(Molly et al., 1993)

TIM-1 300 + 1 + - (Minekus et al., 1995)

Tiny-

TIM

~70 + 2 + - (Havenaar et al.,

2013)

ESIN ~150 + 1 + - (Guerra et al., 2016)

IViDiS No data + 1 - - (Mainville et al.,

2005)

DIDGI 200 -

300

+ 1 + - (Ménard et al.,

2014)


https://doi.org/10.1101/244764

Abbreviations: SHIME, Simulator of the Human intestinal microbial ecosystem; TIM -1, TNO 641

gastro-intestinal tract model; ESIN, Engineered stomach and small intestine; IViDiS, In Vitro 642

Digestive System. 643

644 Table 2. Bacterial strains, their source, culturing time and optimal medium. 645

Species Strain source Origin Culture

time [h]

Culture

media

Small intestine consortium

Escherichia coli DSM 1058 Human origin 24 GAM

Streptococcus salivarius DSM 20560 Blood 6 GAM

Streptococcus luteinensis DSM 15350 Human origin 24 GAM

Enterococcus faecalis DSM 20478 Human faeces 24 GAM

Bacteroides fragilis DSM 2151 Appendix abscess 24 GAM

Veillonella parvula DSM 2008 Human intestine 48 GAM

Flavonifractor plautii DSM 6740 Human faeces 48 GAM

Probiotic strains

Lactobacillus plantarum (Christiaens et al.,

1992)

Grass silage 24 MRS

Lactobacillus rhamnosus GG DSM 20021 Oral cavity 24 MRS

Lactobacillus casei (Larsen et al.,

2009)

Human intestine 24 MRS

GAM - Gifu Anaerobic Medium, MRS - deMan, Rogosa and Sharpe medium. 646

647 Table 3. Survival of Lactobacillus plantarum LP 80, (b) Lactobacillus rhamnosus GG and (c) 648

Lactobacillus casei Z11 after 2 hours exposed to different concentrations of bile salts. The 649

fraction of live and dead cells was determined by staining cells with SYBR green/propidium 650

iodide mixture. 651

652 L. plantarum LP 80 L. rhamnosus GG L. casei Z11

Control 99.11 ± 0.08 98.66 ± 0.93 92.19 ± 0.41


https://doi.org/10.1101/244764

1 mM 92.40 ± 0.52* 9.60 ± 0.52* 83.45 ± 1.12 4 mM 90.31 ± 0.64* 13.47 ± 0.94* 49.24 ± 2.43* 10 mM 88.57 ± 2.41* 6.99 ± 0.68* 34.69 ± 4.49*

653 * Values within same treatment that are significantly different from control (p<0.05) 654 655 Table 4. Survival of Lactobacillus plantarum LP 80, Lactobacillus rhamnosus GG and 656

Lactobacillus casei Z11 in a simplified gastric compartment of the TSI model. Bacteria were 657

tested under two different feeding conditions, fed state leasting two hours and fasted state lasting 658

60 min. Survival was assessed via determining CFU counts on MRS agar plates. 659

660 Time

[min] L. plantarum LP80

(log CFU g-1) L. rhamnosus GG

(log CFU g-1) L. casei Z11 (log CFU g-1)

Fed 0 9.69 ± 0.00a 9.65 ± 0.00d 8.70 ± 0.00f

120 9.74 ± 0.04a 9.51 ± 0.16d 8.98 ± 0.35f

Fasted 0 8.00 ± 0.00b 9.30 ± 0.00e 8.90 ± 0.00g

20 BDLc 9.18 ± 0.28e 8.66 ± 0.11g

40 BDLc 2.33 ± 0.58e BDLb

60 BDLc BDLc BDLb

BDL: Below Detection Limit (2 log CFU/ml) 661

a, b, c, d, e, f, g Values within same treatment with different letters are significantly different (p<0.05). 662

663 664 665


https://doi.org/10.1101/244764


https://doi.org/10.1101/244764

1

2 3 4 5

A Bcertified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

The copyright holder for this preprint (which was notthis version posted January 8, 2018. . https://doi.org/10.1101/244764doi: bioRxiv preprint

https://doi.org/10.1101/244764


https://doi.org/10.1101/244764

W/O With W/O With 0123456789

10

L. casei

Log1

0 C

FU/m

l

Before

Fasted Fed

After


10

L. plantarum LP80

Log1

0 C

FU/m

l

Before

Fasted Fed

After


10

L. rhamnosus GG

Log1

0 C

FU/m

l

Before

Fasted Fed

After

a b

c

* *

* *

*

** *

* * *

* *

Log1

0 CF

U m

l-1

Log1

0 CF

U m

l-1

Log1

0 CF

U m

l-1


https://doi.org/10.1101/244764

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