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Survival of probiotic lactobacilli in the upper gastrointestinal tract using an in vitro gastric model of digestion
Food Microbiology 28 (2011) 1359e1366
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Survival of probiotic lactobacilli in the upper gastrointestinal tract using an in vitro gastric model of digestion Alberto Lo Curto a, Iole Pitino b, Giuseppina Mandalari c, d, *, Jack Richard Dainty e, Richard Martin Faulks d, Martin Sean John Wickham d a
Department of Food and Environmental Sciences, Faculty of Sciences MM.FF.NN, University of Messina, Salita Sperone 31, 98166 S. Agata di Messina, Messina, Italy Department of Orto-Floro-Arboricoltura e Tecnologie Agro-alimentari (DOFATA), Faculty of Agriculture, University of Catania, Via Santa Sofia 98, 95123 Catania, Italy Pharmaco-Biological Department, Faculty of Pharmacy, University of Messina, Viale Annunziata 98168 Messina, Italy d Model Gut Platform, Institute of Food Research, Norwich Research Park, Colney Lane, Norwich NR4 7UA, UK e Bioinformatics and Statistics, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 March 2010 Received in revised form 19 May 2011 Accepted 1 June 2011 Available online 12 June 2011
The aim of this study was to investigate survival of three commercial probiotic strains (Lactobacillus casei subsp. shirota, L. casei subsp. immunitas, Lactobacillus acidophilus subsp. johnsonii) in the human upper gastrointestinal (GI) tract using a dynamic gastric model (DGM) of digestion followed by incubation under duodenal conditions. Water and milk were used as food matrices and survival was evaluated in both logarithmic and stationary phase. The % of recovery in logarithmic phase ranged from 1.0% to 43.8% in water for all tested strains, and from 80.5% to 197% in milk. Higher survival was observed in stationary phase for all strains. L. acidophilus subsp. johnsonii showed the highest survival rate in both water (93.9%) and milk (202.4%). Lactic acid production was higher in stationary phase, L. casei subsp. shirota producing the highest concentration (98.2 mM) after in vitro gastric plus duodenal digestion. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Probiotics In vitro digestion Dynamic Gastric Model
1. Introduction Commercial interest in functional food containing probiotic strains has consistently increased due to the awareness of the benefits for gut health, disease prevention and therapy (Chapman et al., 2011; Cimperman et al., 2011; Rook and Brunet, 2005). In order to exert their beneficial effect, probiotic bacteria need, firstly, to survive during the manufacturing food-process and then in the upper gastrointestinal (GI) ecosystem. The ability of probiotic strains to survive passage through the GI tract can be mainly attributed to their acid and bile tolerance. These are intrinsic characteristics of the strain, which can be improved by the protective action of carrier foods (Charalampopoulos et al., 2003) and/or by the presence of nutrients such as metabolisable sugars (Corcoran et al., 2005). The most common food matrices used as probiotic vehicles are dairy products, which are able to enhance the transit tolerance of bacteria. Some strains of Lactobacillus and Bifidobacterium have been shown to tolerate acidic stress when ingested with milk products (Mater et al., 2005). A number of studies evaluating how well probiotics fare * Corresponding author. Model Gut Platform, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK. Tel.: þ44 1603 251405; fax: þ44 1603 251413. E-mail address: [email protected] (G. Mandalari). 0740-0020/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.fm.2011.06.007
through the GI tract showed that about 10e30% generally survive, the variability depending on a number of factors, such the type of probiotic and the physiologic growth state (Marteau et al., 1997; Pochart et al., 1992). This state very much depends on the time of harvesting of the culture, on the processing of the probiotic during and after harvesting, and, finally, on the composition of the growth medium of the strain in relation to the composition of the food matrix (Heller, 2001). Several investigations showed that probiotic bacteria are more susceptible to environmental stresses in logarithmic phase compared to bacteria in stationary phase (Hartke et al., 1994; Rallu et al., 1996). A range of in vitro static models of digestion have been developed for the evaluation of probiotic survival in the GI tract (Tao et al., 2009; Ortega et al., 2009), their major limitations being the digestion products are not removed during the incubation, and they may have a potential inhibitory effect on enzyme activities and probiotic survival. Also, the key GI physical processes, including temporal nature of gastric and duodenal processing, structure of food, pattern of mixing, particle size reduction and shear, which all affect the digestion rate, are ignored. In the present study we evaluated the survival of three commercial probiotic strains (Lactobacillus casei subsp. shirota, L. casei subsp. immunitas, Lactobacillus acidophilus subsp. johnsonii) in the upper GI tract. In order to provide a realistic and predictive simulation of human gastric and duodenal processing a Dynamic
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Gastric Model (DGM), developed at the Institute of Food Research (IFR, Norwich, UK) was used (Pitino et al., 2010). The DGM is a computer controlled gastric model which incorporates the chemical, biochemical, physical environment and processes of the human stomach, based on the kinetic data derived from Echo planar -MRI and data on the rates of GI digestion obtained from human studies (Marciani et al., 2001, 2003, 2005, 2006). The DGM can not only process a food or meal ‘as eaten’ but it also replicates the additions, residence time, mixing and shear experienced by the digesta in the upper part of the stomach and the antrum. The results obtained provided information on principal factors affecting the viability of the tested strains, such as the initial pH of the stomach and its decrease during gastric digestion. Water and milk were used as food vehicles and survival of each strain was evaluated in both logarithmic and stationary phase.
equilibrated in an orbital shaking incubator (170 rpm) at 37 C. Simulated bile was prepared fresh daily. 2.5. Simulated pancreatic juice
2. Materials and methods
Pancreatic juice solution contained NaCl (125.0 mM), CaCl2 (0.6 mM), MgCl2 (0.3 mM), and ZnSO4 7H2O (4.1 mM). Porcine pancreatic lipase (activity 25,600 U/mg protein), porcine colipase, porcine trypsin (activity 13,800 U/mg of protein using N-benzoyl-Larginine ethyl ester (BAEE) as substrate), bovine a-chymotrypsin (activity 40 U/mg of protein using benzoyl-L-tyrosine ethyl ester (BTEE) as substrate), and porcine a-amylase (activity 26 U/mg of protein using starch as substrate) were added to pancreatic juice so that the final enzyme concentrations were the following: pancreatic lipase (590 U/mL), porcine colipase (3.2 mg/mL), porcine trypsin (11 U/mL), bovine a-chymotrypsin (24 U/mL) and porcine aamylase (300 U/mL).
2.1. Strains and culture conditions
2.6. Simulated human digestion
The three commercial probiotic strains used were L. casei subsp. shirota, L. casei subsp. immunitas and L. acidophilus subsp. johnsonii, isolated from YakultÒ, ActimelÒ and LC1Ò, respectively. All strains were grown in MRS medium (Oxoid, UK) under aerobic conditions and stored in 20% glycerol at 80 C until use.
The DGM was used to simulate human gastric digestion and serial samples obtained from the DGM were incubated under duodenal conditions as previously reported (Pitino et al., 2010). The DGM works in real time to produce a number (variable) of samples simulating the digesta that would be emptied from the antrum into the duodenum (Vardakou et al., 2011). The conditions (adult fasted) were such as to provide 6 samples using water as food and 7 samples using milk as food over the total gastric retention time of 44 min for water and 72 min for milk and a gastric emptying rate of approximately 3.4 mL/min. Each gastric sample was weighed, the pH measured and adjusted to 7.0 with saturated NaHCO3 in order to inhibit gastric enzyme activities. A subsample (1 mL) was taken for determination of viable counts on MRS agar plates. An aliquot (10 mL) of each gastric sample was then used to simulate duodenal digestion and 0.70 mL of hepatic mix and 2.00 mL of pancreatic enzyme solution were added. The pH was set to 6.8 by addition of 0.01 N HCl and semi-anaerobic conditions were obtained by sparging oxygen-free nitrogen. Samples were incubated at 37 C for a total of 2 h and aliquots (1 mL) taken after 1 h and 2 h for microbial counting, as described above. All determinations were performed in triplicate.
2.2. Preparation of vehicle foods for digestion Sterile water and UHT whole milk were used as food vehicles. Each strain was grown in MRS at 37 C for 16 h, centrifuged at 4000 g for 20 min and the pellet washed three times in phosphatebuffer saline (PBS: 0.020% KCl; 0.144% Na2HPO4; 0.8% NaCl; and 0.024% KH2PO4, pH 6.80). The washed pellet was then suspended in 10 mL of PBS and inoculated into 400 mL of either water or milk to give a final concentration of 109 CFU/mL. To evaluate the survival ability in log phase, strains from an overnight culture were initially added to either water or milk and then fed to the DGM. Survival in stationary phase was evaluated using cultures previously maintained at 4e6 C for 6 days in water or milk. 2.3. Simulated gastric juice The simulated gastric acid solution contained HCl (0.2 M), NaCl (0.08 M), CaCl2 (0.03 mM), and NaH2PO4 (0.9 mM). The simulated gastric enzyme solution was prepared by dissolving porcine gastric mucosa pepsin (activity 3300 U/mg of protein calculated using haemoglobin as substrate) and a gastric lipase analogue from Rhizopus oryzae (F-AP15, activity 150 U/mg, Amano Enzyme Inc. Nagoya, Japan) in the above described salt mixture (no acid) at the final concentration of 9000 U/mL and 60 U/mL for pepsin and lipase, respectively. A solution of single shelled lecithin liposomes prepared as previously described (Mandalari et al., 2008) was added to the gastric enzyme solution at a final concentration of 0.127 mM.
2.7. Lactic acid production during gastric and duodenal digestion Each sample (20 mL) post in vitro gastric and in vitro gastric plus duodenal digestion was injected into High-Pressure Liquid Chromatography (HPLC) system equipped with a refractive index detector (ion exclusion, Aminex HpX-87H column; 7.8 mm 300 mm, BioRad, Watford, UK), maintained at 50 C, and 6 mM H2SO4 as eluent at a flow rate of 0.6 mL/min. Quantification of lactic acid was carried out using a calibration curve at concentrations between 0.5 and 100 mM, and the results expressed in mM. 2.8. Statistical analysis
2.4. Simulated bile Simulated bile contained lecithin (6.5 mM), cholesterol (4 mM), sodium taurocholate (12.5 mM), and sodium glycodeoxycholate (12.5 mM) in a salt solution made of NaCl (146.0 mM), CaCl2 (2.6 mM), and KCl (4.8 mM). The suspension was covered with a blanket of nitrogen and sonicated at 5 C in a coolant-jacketed vessel using a sonication probe (Status US 200, Avestin) with a pulsed cycle of 30% full power on for 0.9 s and off for 0.1 s (Mandalari et al., 2008). The single shelled liposomes suspension was filtered through a 0.2 mm nylon syringe filter (Nalgene, UK) and
Statistical analyses were performed using the R data analysis software (R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www. R-project.org). Standard Linear Regression and ANOVA models were employed for all analyses. For all models, regression diagnostics were checked to determine if data transformations, outlier omissions or alternative, non-parametric models, were required. Results were considered significant if p < 0.05 for all tests. Area under the curve (AUC) was used as a summary statistic for the
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relationship between raw counts and time. It was estimated using the trapezoidal method.
using both food vehicles, survival in stationary phase was not affected by pH decrease when using milk as food matrix (Fig. 1D).
3. Results
3.2. Survival under duodenal conditions
3.1. Survival under gastric conditions
During simulated dynamic gastric digestion plus duodenal digestion after 1 h and 2 h in logarithmic phase L. acidophilus johnsonii (CFU/mL) decreased from 1.0$109 to 7.7$103 and from 5.3$108 to 2.0$103 after 1 h and 2 h duodenal digestion (Table 1A), respectively, when using water as food vehicle. L. casei shirota (CFU/mL) decreased from 6.6$108 to 5.3$103 and from 5.4$108 to 1.4$104 after 1 h and 2 h duodenal digestion (Table 1A), respectively, when using water as food vehicle. L. casei immunitas (CFU/mL) decreased from 6.0$108 to 4.8$101 and from 3.7$107 to 2.2$101 after 1 h and 2 h duodenal digestion (Table 1A), respectively, when using water as food vehicle.
Results obtained using water as food matrix (Fig. 1A) indicated that survival in logarithmic phase was significantly related to the pH decrease (p < 0.001) and the tested strain (p ¼ 0.032). Survival in water was also significantly related to the pH values (p ¼ 0.006) and the type of organism (p < 0.001) in stationary phase (Fig. 1B). Survival of the same probiotic strains was also evaluated in milk where no effect of pH or tested strain was observed on data count (Fig. 1C). Although survival in logarithmic phase appeared to be related to pH decrease under human simulated gastric digestion
A
C
B
D
Fig. 1. Relationship between pH and bacterial count in the log phase with water as the food vehicle (A); in the stationary phase with water as the food vehicle (B); in the log phase with milk as the food vehicle (C); in the stationary phase with milk as the food vehicle (D). All figures have data points represented by “o” for immunitas strain, “Δ” for johnsonii strain and “þ” for shirota strain. The regression lines represent the fit to the linear model, bacterial count w pH þ strain. Each level in the factor “strain” is represented by a different immunitas; “e e” johnsonii; “- - -” shirota). The slope of the lines is the regression coefficient for the “pH” variable. line (
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Table 1 Survival of Lactobacillus strains during simulated gastric plus duodenal digestion after 1 h (A) and 2 h (B) in water and after 1 h (C) and 2 h (D) in milk. Time (min)
L. acidophilus johnsonii log
L. casei shirota stat
log
L. casei immunitas stat
log
stat
A) Water 5 10 15 20 25 30
1.0 4.8 2.2 6.8 5.2 7.7
109 108 107 105 104 103
0.6 3.4 1.6 9.5 8.1 12
8.8 7.5 8.3 8.0 4.7 1.5
3.0 0.1 0.1 4.2 6.6 2.1
6.6 6.1 1.8 2.6 1.5 5.3
108 108 108 106 104 103
5.8 2.9 0.6 1.6 0.1 0.5
1.0 1.1 1.1 7.7 6.4 1.2
109 109 109 108 108 108
0.0 0.1 0.1 0.4 0.5 0.1
6.0 1.5 1.2 1.4 5.8 4.8
108 108 107 104 101 101
2.8 1.7 1.2 1.4 8.1 6.7
5.4 1.5 5.2 2.0 1.0 5.0
108 109 108 108 108 107
0.1 1.2 5.4 2.8 1.4 7.1
B) Water 5 10 15 20 25 30
5.3 3.4 6.4 3.5 3.3 2.0
108 108 106 105 104 103
2.7 2.8 6.6 4.5 4.7 3.4
1.1 108 7.8 9.6 109 2.0 7.9 108 1.5 4.7 108 1.3 6 108 8.5 8.5 107 12
5.4 4.1 7.7 1.6 9.2 1.4
108 108 107 106 104 104
2.8 1.0 4.7 1.7 3.3 0.8
7.4 8.6 5.9 4.6 2.7 1.2
108 108 108 108 108 108
0.0 0.6 0.6 0.8 0.3 0.3
3.7 1.1 4.7 1.9 3.6 2.2
107 107 105 103 101 101
2.4 1.3 6.1 2.6 5.0 3.0
1.5 3.5 3.2 2.0 1.0 5.0
108 108 108 108 108 105
1.9 2.7 0.0 2.8 1.4 7.0
C) Milk 9 18 27 36 45 54 63
9.3 1.2 1.1 6.7 5.4 1.6 8.9
108 109 109 108 108 107 104
3.1 0.3 0.1 1.1 2.9 1.9 3.9
1.3 1.0 8.1 8.4 9.5 3.3 1.3
109 109 108 108 108 109 109
0.6 0.1 2.6 0.9 0.7 0.2 0.6
6.7 9.6 8.9 2.0 1.9 2.6 5.7
108 108 108 109 109 109 109
5.2 3.8 4.6 0.6 1.8 3.6 9.9
1.8 9.8 1.4 1.1 2.7 6.4 1.8
109 108 109 109 109 109 109
0.1 1.6 0.8 0.2 0.2 0.0 0.1
6.9 7.6 7.6 1.3 1.6 5.6 1.5
108 108 108 109 109 109 108
2.8 1.9 4.8 0.3 0.7 1.8 1.4
5.4 7.2 7.1 9.1 1.3 6.9 5.4
108 108 108 108 109 109 108
0.2 1.6 2.5 2.7 0.1 0.2 0.2
D) Milk 9 18 27 36 45 54 63
7.4 9.4 9.5 4.8 7.1 2.6 4.4
108 108 108 108 108 107 104
3.0 2.6 0.5 2.0 5.7 3.6 3.9
4.0 4.3 4.3 5.5 7.8 3.6 3.8
108 108 108 108 108 109 104
2.6 0.3 0.3 4.2 1.2 2.1 4.0
5.4 6.0 1.3 1.3 9.5 1.8 3.7
108 108 109 109 108 109 109
4.9 4.3 0.3 0.1 6.8 2.3 6.3
2.3 1.4 1.2 1.1 1.3 1.1 5.6
109 2.7 109 1.3 109 0.8 109 0.4 109 0.1 1010 0.3 105 4.3
4.0 5.6 9.1 1.2 1.4 6.2 1.4
108 108 108 109 109 109 108
2.8 2.7 6.8 0.4 0.4 0.5 1.5
1.6 2.6 4.6 5.7 1.3 7.1 1.2
108 108 108 108 109 109 105
0.4 0.4 0.6 0.5 0.5 0.9 1.0
108 108 108 108 108 108
The behaviour of the three probiotic strains in water was different in stationary phase: L. acidophilus johnsonii (CFU/mL) decreased from 8.8$108 to 1.5$108 and from 1.1$108 to 8.5$107 after 1 h and 2 h duodenal digestion (Table 1B), respectively. L. casei shirota (CFU/mL) decreased from 1.0$109 to 1.2$108 and from 7.4$108 to 1.2$108 after 1 h and 2 h duodenal digestion (Table 1B), respectively. L. casei immunitas (CFU/mL) decreased from 5.4$108 to 5.0$107 and from 1.5$108 to 5.0$105 after 1 h and 2 h duodenal digestion (Table 1B), respectively. L. acidophilus johnsonii (CFU/mL) decreased from 9.3$108 to 8.9$104 and from 7.4$108 to 4.4$104 after 1 h and 2 h (Table 1C), respectively, in logarithmic phase during simulated dynamic gastric digestion plus duodenal digestion when using milk as food vehicle. L. casei shirota (CFU/mL) decreased from 6.7$108 to 5.7$109 and from 5.4$108 to 3.7$109 after 1 h and 2 h duodenal digestion (Table 1C), respectively, and L. casei immunitas (CFU/mL) decreased from 6.9$108 to 1.5$108 and from 4.0$108 to 1.4$108 after 1 h and 2 h duodenal digestion (Table 1C), respectively when using milk as food vehicle. L. acidophilus johnsonii (CFU/mL) ranged from 1.3$109 to 1.3$109 and from 4.0$108 to 3.8$104 after 1 h and 2 h (Table 1D), respectively when milk was used as food matrix in stationary phase during simulated dynamic gastric digestion plus duodenal digestion. L. casei shirota (CFU/mL) ranged from 1.8$109 to 1.8$109 and from 2.3$109 to 5.6$105 after 1 h and 2 h duodenal digestion (Table 1D), respectively, and L. casei immunitas (CFU/mL) ranged from 5.4$108 to 5.4$108 after 1 h and from 1.6$108 to 1.2$105 after 2 h duodenal digestion (Table 1D). Each strain was examined separately for differences in the area under the curve (AUC) as a summary statistic for the relationship between counts and time. There was no significant difference between the logarithmic and stationary phases for either the water or milk matrix when in gastric digestion conditions, gastric plus duodenal conditions for 1 h or gastric plus duodenal conditions for
2 h. The effect of strain on AUC was also examined. There was no significant difference of strain in logarithmic or stationary phase for either the water or milk matrix when in gastric digestion conditions, gastric plus duodenal conditions for 1 h or gastric plus duodenal conditions for 2 h. 3.3. Recovery (%) during gastric and gastric plus duodenal digestion The recovery (%) of the three strains during simulated gastric and gastric plus duodenal digestion in water and milk is shown in Fig. 2. The recovery for L. acidophilus johnsonii strain was 39.2% and 82.2% in water under gastric digestions in logarithmic and stationary phase, respectively. Higher recovery was observed with L. casei shirota in water, with 43.8% and 93.3% in logarithmic and stationary phase, respectively. Lower recoveries were observed with this strain after gastric plus duodenal in logarithmic phase (17.9% and 12.6% after 1 h and 2 h incubation, respectively), whereas an increase (97.4%) was found after 1 h gastric plus duodenal digestion in stationary phase. The recovery for L. casei immunitas strain was 34.2% under gastric digestion in logarithmic phase, with a slight increase (44.7%) in stationary phase. Similarly to L. casei shirota, higher recovery (160%) was observed after 1h gastric plus duodenal digestion in stationary phase. The statistical analysis of recovery (%) of Lactobacillus strains in logarithmic phase showed a significant higher recovery (p ¼ 0.012) for L. acidophilus johnsonii than L. casei immunitas and for L. casei shirota (p ¼ 0.033) than L. casei immunitas in duodenal digestion after 2 h incubation (Fig. 2A). No other significant differences were observed in the water matrix among the three strains in gastric and in duodenal conditions. In contrast, there were statistically significant differences in the milk matrix (logarithmic phase) among the three strains under
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Fig. 2. Recovery (%) of Lactobacillus strains during simulated gastric and gastric plus duodenal digestion in water (A) and milk (B). (black bars gastric digestion; white bars gastric plus duodenal digestion after 1 h; grey bars gastric plus duodenal digestion after 2 h in logarithmic phase). (dotted black bars gastric digestion; dotted white bars gastric plus duodenal digestion after 1 h; dotted grey bars gastric plus duodenal digestion after 2 h in stationary phase).
gastric and duodenal conditions (p < 0.05), except for L. casei shirota vs L. casei immunitas (p ¼ 0.39) during duodenal digestion after 1 h incubation (Fig. 2B). Higher recoveries were observed using milk as food matrix for all the tested strains, L. casei shirota showing the highest recovery (197%) after gastric digestion in logarithmic phase and L. acidophilus johnsonii in stationary phase (176%). Significant difference in milk among the three strains were observed in duodenal digestion after 1 h incubation for L. casei shirota vs L. casei immunitas (p < 0.001) and L. casei shirota vs L. acidophilus johnsonii (p < 0.001) and in duodenal digestion after 2 h incubation with L. casei shirota vs L. acidophilus johnsonii (p ¼ 0.036) (Fig. 2B). Finally, considering Lactobacillus strains in stationary phase, the statistical analysis of recovery (%) in both matrices showed that there was significant difference (p < 0.05) in water among the three strains under gastric conditions except for L. casei shirota vs L. acidophilus johnsonii (p ¼ 0.07), and during duodenal digestion after 2 h for L. acidophilus johnsonii vs L. casei immunitas (p ¼ 0.30) and L. casei shirota vs L. casei immunitas (p ¼ 0.12) (Fig. 2A). 3.4. Lactic acid production The lactic acid analysis of samples in logarithmic phase using milk as vehicle showed that the initial concentration of lactic acid (3.4 mM) with L. acidophilus johnsonii cultures detected in the first gastric sample increased up to 36.7 mM and 40.6 mM after 1 and 2 h duodenal digestion, respectively. The total lactic acid production was 27.3 mM in the gastric samples, 98.7 mM and 111.2 mM after 1 and 2 h duodenal digestion, respectively (Table 2). The starting concentration of lactic acid produced by L. casei shirota was 3.8 mM in the first gastric sample and increased up to
13.0 mM after 1 h duodenal digestion and up to 23.6 mM after 2 h duodenal digestion. Thus, the total lactic acid produced was 44.1 mM in the gastric samples, 81.7 mM and 135.7 mM after 1 h and 2 h duodenal digestion, respectively (Table 2).
Table 2 Concentration (mM) of lactic acid produced during gastric digestion plus duodenal digestion after 1 h and 2 h incubation in log phase. Time (min)
L. acidophilus johnsonii
Gastric digestion 9 3.4 1.2 18 2.7 1.1 27 2.9 0.7 36 3.2 0.1 45 3.4 0 54 8.1 2.2 63 3.7 2.5 Gastric plus duodenal digestion (1 h) 69 3.2 0.2 78 4.5 1.5 87 6.1 2.6 96 9.0 4.9 105 11.7 3.8 114 27.5 2.0 123 36.7 0.1 Gastric plus duodenal digestion (2 h) 129 4.1 1.7 138 5.4 1.9 147 6.5 1.7 156 8.7 2.0 165 12.5 4.1 174 33.6 0.5 183 40.6 1
L. casei shirota
L. casei immunitas
3.8 4.5 5.9 6.5 7.5 9.5 6.3
1.9 2.3 3.8 3.4 4.2 4.2 2.3
3.1 3.2 3.8 4.0 4.5 10.3 3.5
6.2 8.5 9.8 12.0 15.8 16.3 13.0
3.2 0.7 1.3 2.9 2.4 0.4 0.1
4.8 8.0 8.5 11.5 14.1 46.8 26.9
2.3 0.6 0.6 2.1 1.3 0.5 1.3
7.5 11.7 11.7 13.4 22.7 45.1 23.6
1.5 3.1 3.7 3.4 0.5 3.9 4.9
7.2 11.0 13.9 17.0 24.6 57.6 38.5
2.0 0.8 0.8 0.5 0.9 3.7 1.1
1.7 1.2 2.8 6.0 5.9 5.8 2.2
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The lactic acid concentration in L. casei immunitas cultures was 3.1 mM in the first gastric sample, which increased up to 26.9 mM after 1 h duodenal digestion and up to 38.5 mM after 2 h, with a total production of 32.4 mM during gastric digestion, 120.7 mM and 169.8 mM after 1 h and 2 h duodenal digestion, respectively (Table 2). There was no significant effect of count or strain on lactic acid production at the time of gastric digestion (Fig. 3A) or gastric plus duodenal conditions for 1 h (Fig. 3B) or 2 h (Fig. 3C). The lactic acid analysis was also performed in stationary phase using milk as food vehicle. L. acidophilus johnsonii cultures showed a lactic acid concentration of 6.9 mM in the first gastric sample which increased up to 35.7 mM after 1 h duodenal digestion and up to 24.0 mM after 2 h. The total lactic acid production in the gastric samples was 66.7 mM, 154.3 mM and 150.6 mM after 1 h and 2 h duodenal digestion, respectively (Table 3). The initial concentration of lactic acid produced by L. casei shirota was 7.4 mM in the first gastric sample which increased up to 65.9 mM and 98.2 mM after 1 and 2 h duodenal digestion, respectively (Table 3). The total lactic acid concentration produced was 76.8 mM in gastric samples, 194.0 mM after 1 h and 236.5 mM after 2 h duodenal digestion.
A
The lactic acid concentration produced by L. casei immunitas was 4.3 mM in the first gastric sample and increased up to 5.2 mM after 1 h duodenal incubation and up to 5.6 mM after 2 h. The total lactic acid production was 49.6 mM in gastric samples, 93.7 mM and 101.2 mM after 1 and 2 h duodenal digestion, respectively (Table 3). There was no significant effect of count or strain on lactic acid production at the time of gastric digestion (Fig. 4A). There was a significant effect of count (p ¼ 0.009) and strain (p ¼ 0.008) on lactic acid production at the time of gastric plus duodenal conditions for 1 h (Fig. 4B). There was a significant effect of strain (p ¼ 0.009) but not of strain on lactic acid production at the time of gastric plus duodenal conditions for 2 h (Fig. 4C). 4. Discussion The results obtained in this research have shown a great survival of all three probiotic strains after in vitro gastric and gastric plus duodenal digestion. We have recently observed good survival rates of six Lactobacillus rhamnosus strains during simulated in vivo digestion using MRS as delivery vehicle (Pitino et al.,
B
C
Fig. 3. Relationship between lactic acid concentration and bacterial count in the log phase following gastric digestion (A); following gastric and 1 h duodenal digestion (B); following gastric and 2 h duodenal digestion (C). All figures have data points represented by “o” for immunitas strain, “Δ” for johnsonii strain and “þ” for shirota strain. The regression immunitas; “e lines represent the fit to the linear model, lactic acid concentration w bacterial count þ strain. Each level in the factor “strain” is represented by a different line ( e” johnsonii; “- - -” shirota). The slope of the lines is the regression coefficient for the “bacterial count” variable.
A. Lo Curto et al. / Food Microbiology 28 (2011) 1359e1366 Table 3 Concentration (mM) of lactic acid produced during gastric digestion plus duodenal digestion at 1 h and 2 h incubation in stat phase. Time (min)
L. acidophilus johnsonii
Gastric digestion 9 6.9 3.5 18 7.8 4.3 27 7.2 3.2 36 7.8 4.0 45 8.3 3.3 54 13.2 2.8 63 15.4 1.9 Gastric plus duodenal digestion (1 h) 69 11.2 3.3 78 12.3 4.3 87 11.9 3.1 96 12.9 4.2 105 17.8 4.0 114 52.5 1.1 123 35.7 0.5 Gastric plus duodenal digestion (2 h) 129 15,7 0.1 138 10.4 1.8 147 9.3 1.8 156 12.4 2.2 165 14.8 3.3 174 64.0 4.9 183 24.0 4.0
L. casei shirota
L. casei immunitas
7.4 8.9 9.4 9.9 11.1 22.6 7.5
2.1 2.9 3.5 4.2 4.7 4.0 0.1
4.3 4.9 5.4 5.0 6.4 16.0 7.5
0.3 0.2 0.0 0.2 0 0.7 2.0
10.8 11.0 12.0 12.4 13.7 68.0 65.9
1.9 2.5 2.8 3.4 2.7 3.6 1.3
6.2 8.7 9.4 9.9 11.1 43.1 5.2
0.5 4.1 4.6 5.3 4.0 0.6 0.3
9.1 10.4 11.0 13.0 16.0 78.6 98.2
2.9 4.4 4.1 5.3 3.0 3.5 1.3
6.2 8.0 8.3 9.3 9.8 53.8 5.6
1.0 2.6 3.5 3.7 3.4 0.0 0.6
1365
2010). Since the lactic acid production is an important index of adaptation of bacteria which secrete lactic acid as end-product of lactose fermentation, the survival of probiotic strains was confirmed by data obtained measuring the production of lactic acid. Our experimental data showed that lactic acid levels secreted by all three strains confirmed a good fit and growth of these bacteria during digestion. These results were further supported by an accurate statistical analysis, which confirmed their validity. The results suggested some differences among the three strains used, with generally higher survival in milk compared with water. This observation could be related to the lower buffering capacity of the water compared to milk (Holzapfel et al., 2001). The milk buffering effect can protect strains against the harmful action of gastric and duodenal environment (Sirò et al., 2008). Moreover, the probiotic strains harvested during stationary growth phase, which better represent the conditions in which the commercial products are sold, survived better compared to the strains harvested in logarithmic phase growth (Pochart et al., 1992; Marteau et al., 1997). L. acidophilus subsp. johnsonii showed good survival in water in both logarithmic phase and stationary phase. The 100% of recovery rate in water in stationary phase showed that adaptation to gastric and duodenal conditions makes possible to reach the intestine live and vital (Makelainen et al., 2009). The high survival shown in the
Fig. 4. Relationship between lactic acid concentration and bacterial count in the stationary phase following gastric digestion (A); following gastric and 1 h duodenal digestion (B); following gastric and 2 h duodenal digestion (C). All figures have data points represented by “o” for immunitas strain, “Δ” for johnsonii strain and “þ” for shirota strain. The regression lines represent the fit to the linear model, lactic acid concentration w bacterial count þ strain. Each level in the factor “strain” is represented by a different line immunitas; “e e” johnsonii; “- - -” shirota). The slope of the lines is the regression coefficient for the “bacterial count” variable. (
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stationary phase is probably due to the ability of the strain to better adapt to changing pH in the gastric compartment. The same strain in milk showed a good survival both in logarithmic and stationary growth phase. L. casei subsp. shirota strain showed a similar behaviour to L. acidophilus subsp. johnsonii in water, with good survival in both logarithmic and stationary phase. The difference between the two phases could be related to the more susceptibility to environmental stresses by bacteria in log phase as previously reported (Heller, 2001). L. casei subsp. shirota showed excellent survival in milk matrix in both phases; in logarithmic phase the recovery rate was higher than L. acidophilus subsp. johnsonii. L. casei subsp. immunitas showed a different trend in water compared to other studied strains. In water matrix, the survival rate was very low both in log phase and stationary phase. Instead, the % of recovery in milk matrix was similar to L. casei subsp. shirota. The results showed that the best probiotic strain was L. acidophilus subsp. johnsonii for its highest survival in both tested foods. Moreover the other two probiotic tested strains showed good enough survival in upper gastrointestinal tract. This research confirms some previous reports on the survival of L. casei strains found in some commercial products (Spandhaak et al., 1998; Oozeer et al., 2006; Tormo Carnicer et al., 2006; Tuohy et al., 2007). Acknowledgements This research has been funded by the Biotechnology and Biological Research Sciences Council (BBSRC, UK). Appendix. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.fm.2011.06.007. References Chapman, C.M., Gibson, G.R., Rowland, I., 2011. Health benefits of probiotics: are mixtures more effective than single strains. Eur. J. Nutr. 50, 1e17. Charalampopoulos, D., Pandiella, S.S., Webb, C., 2003. Evaluation of the effect of malt, wheat and barley extracts on the viability of potentially probiotic lactic acid bacteria under acidic conditions. Int. J. Food Microbiol. 82, 133e141. Cimperman, L., Bayless, G., Best, K., Diligente, A., Mordarski, B., Oster, M., Smith, M., Vatakis, F., Wiese, D., Steiber, A., Katz, J., 2011. A Randomised, double-blind, placebo-controlled pilot study of Lactobacillus reuteri ATCC 55730 for the prevention of antibiotic-associated diarrhea in hospitalized adults. J. Clin. Gastroenterol. doi:10.1097/MCG.0b013e3182166a42. Corcoran, B.M., Stanton, C., Fitzgerald, G.F., Ross, R.P., 2005. Survival of probiotic lactobacilli in acidic environments is enhanced in the presence of metabolizable sugars. Appl. Environ. Microbiol. 71, 3060e3067. Hartke, A., Bouche, S., Gansel, X., Boutibonnes, P., Auffray, Y., 1994. Starvationinduced stress resistance in Lactococcus lactis subsp. lactis IL1403. Appl. Environ. Microbiol. 60, 3474e3478. Heller, K.J., 2001. Probiotic bacteria in fermented foods: product characteristics and starter organisms. Am. J. Clin. Nutr. 73, 374Se379S.
Holzapfel, W.H., Haberer, P., Geisen, R., Bjorkroth, J., Schillinger, U., 2001. Taxonomy and important features of probiotic microorganisms in food and nutrition. Am. J. Clin. Nutr. 73, 365Se373S. Makelainen, H., Forssten, S., Olli, K., Granlund, L., Rautonen, N., Ouwehand, A.C., 2009. Probiotic lactobacilli in semi-soft cheese survive in the simulated human gastrointestinal tract. Int. Dairy J. 19, 675e683. Mandalari, G., Faulks, R.M., Rich, G.T., Lo Turco, V., Picout, D.R., Lo Curto, R.B., Bisignano, G., Dugo, P., Dugo, G., Waldron, K.W., Ellis, P.R., Wickham, M.S.J., 2008. Release of protein, lipid and vitamin E from almond seeds during digestion. J. Agric. Food Chem. 56, 3409e3416. Marciani, L., Wickham, M.S.J., Bush, D., Faulks, R.M., Wright, J., Fillery-Travis, A., Spiller, R., Gowland, P., 2006. Magnetic resonance imaging of the behaviour of oil-in-water emulsions in the gastric lumen of man. Brit. J. Nutr. 95, 331e339. Marciani, L., Bush, D., Wright, P., Wickham, M.S.J., Pick, B., Wright, J., Faulks, R.M., Fillery-Travis, A.J., Spiller, R.C., Gowland, P.A., 2005. Monitoring of gallbladder and gastric coordination by EPI. J. Mag. Res. Imag 21, 82e85. Marciani, L., Ramanathan, C., Tyler, D.J., Young, P., Manoj, P., Wickham, M.S.J., FilleryTravis, A., Spiller, R.C., Gowland, P.A., 2001. Fat emulsification measured using NMR transverse relaxation. J. Mag. Res. Imag 153, 1e6. Marciani, L., Wickham, M.S.J., Wright, J., Bush, D., Faulks, R.M., Fillery-Travis, A., Gowland, P.A., Spiller, R.C., 2003. Magnetic resonance imaging (MRI) insights into how fat emulsion stability alters gastric emptying. Gastroent 124, A581. Marteau, P., Minekus, M., Havenaar, R., Huis in’t Veld, J.H., 1997. Survival of lactic acid bacteria in a dynamic model of the stomach and small intestine: validation and the effects of bile. J. Dairy Sci. 80, 1031e1037. Mater, D.D.G., Bretigny, L., Firmesse, O., Flores, M.J., Mogenet, A., Bresson, J.L., Corthier, G., 2005. Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus survive gastrointestinal transit of healthy volunteers consuming yogurt. FEMS Microbiol. Lett. 250, 185e187. Oozeer, R., Leplingard, A., Mater, D.D., Mogenet, A., Michelin, R., Seksek, I., Marteau, P., Dore, J., Bresson, J.L., Corthier, G., 2006. Survival of Lactobacillus casei in the human digestive tract after consumption of fermented milk. Appl. Environ. Microbiol. 72, 5615e5617. Ortega, N., Reguant, J., Romero, M.P., Macià, A., Motilva, M.J., 2009. Effect of fat content on the digestibility and bioaccessibility of cocoa polyphenol by an in vitro digestion model. J. Agric. Food Chem. 57, 5743e5749. Pitino, I., Randazzo, C.L., Mandalari, G., Lo Curto, A., Faulks, R.M., Le Marc, Y., Bisignano, C., Caggia, C., Wickham, M.S.J., 2010. Survival of Lactobacillus rhamnosus strains in the upper gastrointestinal tract. Food Microbiol. 27, 1121e1127. Pochart, P., Marteau, P., Bouhnik, Y., Goderel, I., Bourlioux, P., Rambaud, J.C., 1992. Survival of bifidobacteria ingested via fermented milk during their passage through the human small intestine: an in vivo study using intestinal perfusion. Am. J. Clin. Nutr. 55, 78e80. Rallu, F., Gruss, A., Maguin, E., 1996. Lactococcus lactis and stress. Antonie Leeuwenhoek 70, 243e251. Rook, G.A., Brunet, L.R., 2005. Microbes, immunoregulation, and the gut. Gut 54, 317e320. Sirò, I., Kàpolna, E., Kapolna, B., Lugasi, A., 2008. Functional food. Product development, marketing and consumer acceptance. Appetite 51, 456e467. Spandhaak, S., Havenaar, R., Schaafsma, G., 1998. The effect of consumption of milk fermented by Lactobacillus casei strain Shirota on the intestinal microflora and immune parameters in humans. Eur. J. Clin. Nutr. 52, 899e907. Tao, S., Lu, Y., Zhang, D., Yang, Y., Yang, Y., Lu, X., Sai, D., 2009. Assessment of oral bioaccessibility of organochlorine pesticides in soil using an in vitro gastrointestinal model. Environ. Sci. Technol. 43, 4524e4529. Tormo Carnicer, R., Infante Pina, D., Rosello Mayans, E., Bartolome Comas, R., 2006. Intake of fermented milk containing Lactobacillus casei DN-114 001 and its effect on gut flora. An Pediatr 65, 448e453. Tuohy, K.M., Pinart-Gilberga, M., Jones, M., Hoyles, L., McCartney, A.L., Gibson, G.R., 2007. Survivability of a probiotic Lactobacillus casei in the gastrointestinal tract of healthy human volunteers and its impact on the faecal microflora. J. Appl. Microbiol. 102, 1026e1032. Vardakou, M., Mercuri, A., Barker, S.A., Craig, D.Q., Faulks, R.M., Wickham, M.S.J., 2011. Achieving antral grinding forces in biorelevant in vitro models: comparing the USP dissolution apparatus II and the dynamic gastric model with human in vivo data. AAPS PharmSciTech. doi:10.1208/s12249-011-9616-z.
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Food Microbiology journal homepage: www.elsevier.com/locate/fm
Survival of probiotic lactobacilli in the upper gastrointestinal tract using an in vitro gastric model of digestion Alberto Lo Curto a, Iole Pitino b, Giuseppina Mandalari c, d, *, Jack Richard Dainty e, Richard Martin Faulks d, Martin Sean John Wickham d a
Department of Food and Environmental Sciences, Faculty of Sciences MM.FF.NN, University of Messina, Salita Sperone 31, 98166 S. Agata di Messina, Messina, Italy Department of Orto-Floro-Arboricoltura e Tecnologie Agro-alimentari (DOFATA), Faculty of Agriculture, University of Catania, Via Santa Sofia 98, 95123 Catania, Italy Pharmaco-Biological Department, Faculty of Pharmacy, University of Messina, Viale Annunziata 98168 Messina, Italy d Model Gut Platform, Institute of Food Research, Norwich Research Park, Colney Lane, Norwich NR4 7UA, UK e Bioinformatics and Statistics, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 25 March 2010 Received in revised form 19 May 2011 Accepted 1 June 2011 Available online 12 June 2011
The aim of this study was to investigate survival of three commercial probiotic strains (Lactobacillus casei subsp. shirota, L. casei subsp. immunitas, Lactobacillus acidophilus subsp. johnsonii) in the human upper gastrointestinal (GI) tract using a dynamic gastric model (DGM) of digestion followed by incubation under duodenal conditions. Water and milk were used as food matrices and survival was evaluated in both logarithmic and stationary phase. The % of recovery in logarithmic phase ranged from 1.0% to 43.8% in water for all tested strains, and from 80.5% to 197% in milk. Higher survival was observed in stationary phase for all strains. L. acidophilus subsp. johnsonii showed the highest survival rate in both water (93.9%) and milk (202.4%). Lactic acid production was higher in stationary phase, L. casei subsp. shirota producing the highest concentration (98.2 mM) after in vitro gastric plus duodenal digestion. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Probiotics In vitro digestion Dynamic Gastric Model
1. Introduction Commercial interest in functional food containing probiotic strains has consistently increased due to the awareness of the benefits for gut health, disease prevention and therapy (Chapman et al., 2011; Cimperman et al., 2011; Rook and Brunet, 2005). In order to exert their beneficial effect, probiotic bacteria need, firstly, to survive during the manufacturing food-process and then in the upper gastrointestinal (GI) ecosystem. The ability of probiotic strains to survive passage through the GI tract can be mainly attributed to their acid and bile tolerance. These are intrinsic characteristics of the strain, which can be improved by the protective action of carrier foods (Charalampopoulos et al., 2003) and/or by the presence of nutrients such as metabolisable sugars (Corcoran et al., 2005). The most common food matrices used as probiotic vehicles are dairy products, which are able to enhance the transit tolerance of bacteria. Some strains of Lactobacillus and Bifidobacterium have been shown to tolerate acidic stress when ingested with milk products (Mater et al., 2005). A number of studies evaluating how well probiotics fare * Corresponding author. Model Gut Platform, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK. Tel.: þ44 1603 251405; fax: þ44 1603 251413. E-mail address: [email protected] (G. Mandalari). 0740-0020/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.fm.2011.06.007
through the GI tract showed that about 10e30% generally survive, the variability depending on a number of factors, such the type of probiotic and the physiologic growth state (Marteau et al., 1997; Pochart et al., 1992). This state very much depends on the time of harvesting of the culture, on the processing of the probiotic during and after harvesting, and, finally, on the composition of the growth medium of the strain in relation to the composition of the food matrix (Heller, 2001). Several investigations showed that probiotic bacteria are more susceptible to environmental stresses in logarithmic phase compared to bacteria in stationary phase (Hartke et al., 1994; Rallu et al., 1996). A range of in vitro static models of digestion have been developed for the evaluation of probiotic survival in the GI tract (Tao et al., 2009; Ortega et al., 2009), their major limitations being the digestion products are not removed during the incubation, and they may have a potential inhibitory effect on enzyme activities and probiotic survival. Also, the key GI physical processes, including temporal nature of gastric and duodenal processing, structure of food, pattern of mixing, particle size reduction and shear, which all affect the digestion rate, are ignored. In the present study we evaluated the survival of three commercial probiotic strains (Lactobacillus casei subsp. shirota, L. casei subsp. immunitas, Lactobacillus acidophilus subsp. johnsonii) in the upper GI tract. In order to provide a realistic and predictive simulation of human gastric and duodenal processing a Dynamic
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Gastric Model (DGM), developed at the Institute of Food Research (IFR, Norwich, UK) was used (Pitino et al., 2010). The DGM is a computer controlled gastric model which incorporates the chemical, biochemical, physical environment and processes of the human stomach, based on the kinetic data derived from Echo planar -MRI and data on the rates of GI digestion obtained from human studies (Marciani et al., 2001, 2003, 2005, 2006). The DGM can not only process a food or meal ‘as eaten’ but it also replicates the additions, residence time, mixing and shear experienced by the digesta in the upper part of the stomach and the antrum. The results obtained provided information on principal factors affecting the viability of the tested strains, such as the initial pH of the stomach and its decrease during gastric digestion. Water and milk were used as food vehicles and survival of each strain was evaluated in both logarithmic and stationary phase.
equilibrated in an orbital shaking incubator (170 rpm) at 37 C. Simulated bile was prepared fresh daily. 2.5. Simulated pancreatic juice
2. Materials and methods
Pancreatic juice solution contained NaCl (125.0 mM), CaCl2 (0.6 mM), MgCl2 (0.3 mM), and ZnSO4 7H2O (4.1 mM). Porcine pancreatic lipase (activity 25,600 U/mg protein), porcine colipase, porcine trypsin (activity 13,800 U/mg of protein using N-benzoyl-Larginine ethyl ester (BAEE) as substrate), bovine a-chymotrypsin (activity 40 U/mg of protein using benzoyl-L-tyrosine ethyl ester (BTEE) as substrate), and porcine a-amylase (activity 26 U/mg of protein using starch as substrate) were added to pancreatic juice so that the final enzyme concentrations were the following: pancreatic lipase (590 U/mL), porcine colipase (3.2 mg/mL), porcine trypsin (11 U/mL), bovine a-chymotrypsin (24 U/mL) and porcine aamylase (300 U/mL).
2.1. Strains and culture conditions
2.6. Simulated human digestion
The three commercial probiotic strains used were L. casei subsp. shirota, L. casei subsp. immunitas and L. acidophilus subsp. johnsonii, isolated from YakultÒ, ActimelÒ and LC1Ò, respectively. All strains were grown in MRS medium (Oxoid, UK) under aerobic conditions and stored in 20% glycerol at 80 C until use.
The DGM was used to simulate human gastric digestion and serial samples obtained from the DGM were incubated under duodenal conditions as previously reported (Pitino et al., 2010). The DGM works in real time to produce a number (variable) of samples simulating the digesta that would be emptied from the antrum into the duodenum (Vardakou et al., 2011). The conditions (adult fasted) were such as to provide 6 samples using water as food and 7 samples using milk as food over the total gastric retention time of 44 min for water and 72 min for milk and a gastric emptying rate of approximately 3.4 mL/min. Each gastric sample was weighed, the pH measured and adjusted to 7.0 with saturated NaHCO3 in order to inhibit gastric enzyme activities. A subsample (1 mL) was taken for determination of viable counts on MRS agar plates. An aliquot (10 mL) of each gastric sample was then used to simulate duodenal digestion and 0.70 mL of hepatic mix and 2.00 mL of pancreatic enzyme solution were added. The pH was set to 6.8 by addition of 0.01 N HCl and semi-anaerobic conditions were obtained by sparging oxygen-free nitrogen. Samples were incubated at 37 C for a total of 2 h and aliquots (1 mL) taken after 1 h and 2 h for microbial counting, as described above. All determinations were performed in triplicate.
2.2. Preparation of vehicle foods for digestion Sterile water and UHT whole milk were used as food vehicles. Each strain was grown in MRS at 37 C for 16 h, centrifuged at 4000 g for 20 min and the pellet washed three times in phosphatebuffer saline (PBS: 0.020% KCl; 0.144% Na2HPO4; 0.8% NaCl; and 0.024% KH2PO4, pH 6.80). The washed pellet was then suspended in 10 mL of PBS and inoculated into 400 mL of either water or milk to give a final concentration of 109 CFU/mL. To evaluate the survival ability in log phase, strains from an overnight culture were initially added to either water or milk and then fed to the DGM. Survival in stationary phase was evaluated using cultures previously maintained at 4e6 C for 6 days in water or milk. 2.3. Simulated gastric juice The simulated gastric acid solution contained HCl (0.2 M), NaCl (0.08 M), CaCl2 (0.03 mM), and NaH2PO4 (0.9 mM). The simulated gastric enzyme solution was prepared by dissolving porcine gastric mucosa pepsin (activity 3300 U/mg of protein calculated using haemoglobin as substrate) and a gastric lipase analogue from Rhizopus oryzae (F-AP15, activity 150 U/mg, Amano Enzyme Inc. Nagoya, Japan) in the above described salt mixture (no acid) at the final concentration of 9000 U/mL and 60 U/mL for pepsin and lipase, respectively. A solution of single shelled lecithin liposomes prepared as previously described (Mandalari et al., 2008) was added to the gastric enzyme solution at a final concentration of 0.127 mM.
2.7. Lactic acid production during gastric and duodenal digestion Each sample (20 mL) post in vitro gastric and in vitro gastric plus duodenal digestion was injected into High-Pressure Liquid Chromatography (HPLC) system equipped with a refractive index detector (ion exclusion, Aminex HpX-87H column; 7.8 mm 300 mm, BioRad, Watford, UK), maintained at 50 C, and 6 mM H2SO4 as eluent at a flow rate of 0.6 mL/min. Quantification of lactic acid was carried out using a calibration curve at concentrations between 0.5 and 100 mM, and the results expressed in mM. 2.8. Statistical analysis
2.4. Simulated bile Simulated bile contained lecithin (6.5 mM), cholesterol (4 mM), sodium taurocholate (12.5 mM), and sodium glycodeoxycholate (12.5 mM) in a salt solution made of NaCl (146.0 mM), CaCl2 (2.6 mM), and KCl (4.8 mM). The suspension was covered with a blanket of nitrogen and sonicated at 5 C in a coolant-jacketed vessel using a sonication probe (Status US 200, Avestin) with a pulsed cycle of 30% full power on for 0.9 s and off for 0.1 s (Mandalari et al., 2008). The single shelled liposomes suspension was filtered through a 0.2 mm nylon syringe filter (Nalgene, UK) and
Statistical analyses were performed using the R data analysis software (R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www. R-project.org). Standard Linear Regression and ANOVA models were employed for all analyses. For all models, regression diagnostics were checked to determine if data transformations, outlier omissions or alternative, non-parametric models, were required. Results were considered significant if p < 0.05 for all tests. Area under the curve (AUC) was used as a summary statistic for the
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relationship between raw counts and time. It was estimated using the trapezoidal method.
using both food vehicles, survival in stationary phase was not affected by pH decrease when using milk as food matrix (Fig. 1D).
3. Results
3.2. Survival under duodenal conditions
3.1. Survival under gastric conditions
During simulated dynamic gastric digestion plus duodenal digestion after 1 h and 2 h in logarithmic phase L. acidophilus johnsonii (CFU/mL) decreased from 1.0$109 to 7.7$103 and from 5.3$108 to 2.0$103 after 1 h and 2 h duodenal digestion (Table 1A), respectively, when using water as food vehicle. L. casei shirota (CFU/mL) decreased from 6.6$108 to 5.3$103 and from 5.4$108 to 1.4$104 after 1 h and 2 h duodenal digestion (Table 1A), respectively, when using water as food vehicle. L. casei immunitas (CFU/mL) decreased from 6.0$108 to 4.8$101 and from 3.7$107 to 2.2$101 after 1 h and 2 h duodenal digestion (Table 1A), respectively, when using water as food vehicle.
Results obtained using water as food matrix (Fig. 1A) indicated that survival in logarithmic phase was significantly related to the pH decrease (p < 0.001) and the tested strain (p ¼ 0.032). Survival in water was also significantly related to the pH values (p ¼ 0.006) and the type of organism (p < 0.001) in stationary phase (Fig. 1B). Survival of the same probiotic strains was also evaluated in milk where no effect of pH or tested strain was observed on data count (Fig. 1C). Although survival in logarithmic phase appeared to be related to pH decrease under human simulated gastric digestion
A
C
B
D
Fig. 1. Relationship between pH and bacterial count in the log phase with water as the food vehicle (A); in the stationary phase with water as the food vehicle (B); in the log phase with milk as the food vehicle (C); in the stationary phase with milk as the food vehicle (D). All figures have data points represented by “o” for immunitas strain, “Δ” for johnsonii strain and “þ” for shirota strain. The regression lines represent the fit to the linear model, bacterial count w pH þ strain. Each level in the factor “strain” is represented by a different immunitas; “e e” johnsonii; “- - -” shirota). The slope of the lines is the regression coefficient for the “pH” variable. line (
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Table 1 Survival of Lactobacillus strains during simulated gastric plus duodenal digestion after 1 h (A) and 2 h (B) in water and after 1 h (C) and 2 h (D) in milk. Time (min)
L. acidophilus johnsonii log
L. casei shirota stat
log
L. casei immunitas stat
log
stat
A) Water 5 10 15 20 25 30
1.0 4.8 2.2 6.8 5.2 7.7
109 108 107 105 104 103
0.6 3.4 1.6 9.5 8.1 12
8.8 7.5 8.3 8.0 4.7 1.5
3.0 0.1 0.1 4.2 6.6 2.1
6.6 6.1 1.8 2.6 1.5 5.3
108 108 108 106 104 103
5.8 2.9 0.6 1.6 0.1 0.5
1.0 1.1 1.1 7.7 6.4 1.2
109 109 109 108 108 108
0.0 0.1 0.1 0.4 0.5 0.1
6.0 1.5 1.2 1.4 5.8 4.8
108 108 107 104 101 101
2.8 1.7 1.2 1.4 8.1 6.7
5.4 1.5 5.2 2.0 1.0 5.0
108 109 108 108 108 107
0.1 1.2 5.4 2.8 1.4 7.1
B) Water 5 10 15 20 25 30
5.3 3.4 6.4 3.5 3.3 2.0
108 108 106 105 104 103
2.7 2.8 6.6 4.5 4.7 3.4
1.1 108 7.8 9.6 109 2.0 7.9 108 1.5 4.7 108 1.3 6 108 8.5 8.5 107 12
5.4 4.1 7.7 1.6 9.2 1.4
108 108 107 106 104 104
2.8 1.0 4.7 1.7 3.3 0.8
7.4 8.6 5.9 4.6 2.7 1.2
108 108 108 108 108 108
0.0 0.6 0.6 0.8 0.3 0.3
3.7 1.1 4.7 1.9 3.6 2.2
107 107 105 103 101 101
2.4 1.3 6.1 2.6 5.0 3.0
1.5 3.5 3.2 2.0 1.0 5.0
108 108 108 108 108 105
1.9 2.7 0.0 2.8 1.4 7.0
C) Milk 9 18 27 36 45 54 63
9.3 1.2 1.1 6.7 5.4 1.6 8.9
108 109 109 108 108 107 104
3.1 0.3 0.1 1.1 2.9 1.9 3.9
1.3 1.0 8.1 8.4 9.5 3.3 1.3
109 109 108 108 108 109 109
0.6 0.1 2.6 0.9 0.7 0.2 0.6
6.7 9.6 8.9 2.0 1.9 2.6 5.7
108 108 108 109 109 109 109
5.2 3.8 4.6 0.6 1.8 3.6 9.9
1.8 9.8 1.4 1.1 2.7 6.4 1.8
109 108 109 109 109 109 109
0.1 1.6 0.8 0.2 0.2 0.0 0.1
6.9 7.6 7.6 1.3 1.6 5.6 1.5
108 108 108 109 109 109 108
2.8 1.9 4.8 0.3 0.7 1.8 1.4
5.4 7.2 7.1 9.1 1.3 6.9 5.4
108 108 108 108 109 109 108
0.2 1.6 2.5 2.7 0.1 0.2 0.2
D) Milk 9 18 27 36 45 54 63
7.4 9.4 9.5 4.8 7.1 2.6 4.4
108 108 108 108 108 107 104
3.0 2.6 0.5 2.0 5.7 3.6 3.9
4.0 4.3 4.3 5.5 7.8 3.6 3.8
108 108 108 108 108 109 104
2.6 0.3 0.3 4.2 1.2 2.1 4.0
5.4 6.0 1.3 1.3 9.5 1.8 3.7
108 108 109 109 108 109 109
4.9 4.3 0.3 0.1 6.8 2.3 6.3
2.3 1.4 1.2 1.1 1.3 1.1 5.6
109 2.7 109 1.3 109 0.8 109 0.4 109 0.1 1010 0.3 105 4.3
4.0 5.6 9.1 1.2 1.4 6.2 1.4
108 108 108 109 109 109 108
2.8 2.7 6.8 0.4 0.4 0.5 1.5
1.6 2.6 4.6 5.7 1.3 7.1 1.2
108 108 108 108 109 109 105
0.4 0.4 0.6 0.5 0.5 0.9 1.0
108 108 108 108 108 108
The behaviour of the three probiotic strains in water was different in stationary phase: L. acidophilus johnsonii (CFU/mL) decreased from 8.8$108 to 1.5$108 and from 1.1$108 to 8.5$107 after 1 h and 2 h duodenal digestion (Table 1B), respectively. L. casei shirota (CFU/mL) decreased from 1.0$109 to 1.2$108 and from 7.4$108 to 1.2$108 after 1 h and 2 h duodenal digestion (Table 1B), respectively. L. casei immunitas (CFU/mL) decreased from 5.4$108 to 5.0$107 and from 1.5$108 to 5.0$105 after 1 h and 2 h duodenal digestion (Table 1B), respectively. L. acidophilus johnsonii (CFU/mL) decreased from 9.3$108 to 8.9$104 and from 7.4$108 to 4.4$104 after 1 h and 2 h (Table 1C), respectively, in logarithmic phase during simulated dynamic gastric digestion plus duodenal digestion when using milk as food vehicle. L. casei shirota (CFU/mL) decreased from 6.7$108 to 5.7$109 and from 5.4$108 to 3.7$109 after 1 h and 2 h duodenal digestion (Table 1C), respectively, and L. casei immunitas (CFU/mL) decreased from 6.9$108 to 1.5$108 and from 4.0$108 to 1.4$108 after 1 h and 2 h duodenal digestion (Table 1C), respectively when using milk as food vehicle. L. acidophilus johnsonii (CFU/mL) ranged from 1.3$109 to 1.3$109 and from 4.0$108 to 3.8$104 after 1 h and 2 h (Table 1D), respectively when milk was used as food matrix in stationary phase during simulated dynamic gastric digestion plus duodenal digestion. L. casei shirota (CFU/mL) ranged from 1.8$109 to 1.8$109 and from 2.3$109 to 5.6$105 after 1 h and 2 h duodenal digestion (Table 1D), respectively, and L. casei immunitas (CFU/mL) ranged from 5.4$108 to 5.4$108 after 1 h and from 1.6$108 to 1.2$105 after 2 h duodenal digestion (Table 1D). Each strain was examined separately for differences in the area under the curve (AUC) as a summary statistic for the relationship between counts and time. There was no significant difference between the logarithmic and stationary phases for either the water or milk matrix when in gastric digestion conditions, gastric plus duodenal conditions for 1 h or gastric plus duodenal conditions for
2 h. The effect of strain on AUC was also examined. There was no significant difference of strain in logarithmic or stationary phase for either the water or milk matrix when in gastric digestion conditions, gastric plus duodenal conditions for 1 h or gastric plus duodenal conditions for 2 h. 3.3. Recovery (%) during gastric and gastric plus duodenal digestion The recovery (%) of the three strains during simulated gastric and gastric plus duodenal digestion in water and milk is shown in Fig. 2. The recovery for L. acidophilus johnsonii strain was 39.2% and 82.2% in water under gastric digestions in logarithmic and stationary phase, respectively. Higher recovery was observed with L. casei shirota in water, with 43.8% and 93.3% in logarithmic and stationary phase, respectively. Lower recoveries were observed with this strain after gastric plus duodenal in logarithmic phase (17.9% and 12.6% after 1 h and 2 h incubation, respectively), whereas an increase (97.4%) was found after 1 h gastric plus duodenal digestion in stationary phase. The recovery for L. casei immunitas strain was 34.2% under gastric digestion in logarithmic phase, with a slight increase (44.7%) in stationary phase. Similarly to L. casei shirota, higher recovery (160%) was observed after 1h gastric plus duodenal digestion in stationary phase. The statistical analysis of recovery (%) of Lactobacillus strains in logarithmic phase showed a significant higher recovery (p ¼ 0.012) for L. acidophilus johnsonii than L. casei immunitas and for L. casei shirota (p ¼ 0.033) than L. casei immunitas in duodenal digestion after 2 h incubation (Fig. 2A). No other significant differences were observed in the water matrix among the three strains in gastric and in duodenal conditions. In contrast, there were statistically significant differences in the milk matrix (logarithmic phase) among the three strains under
A. Lo Curto et al. / Food Microbiology 28 (2011) 1359e1366
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Fig. 2. Recovery (%) of Lactobacillus strains during simulated gastric and gastric plus duodenal digestion in water (A) and milk (B). (black bars gastric digestion; white bars gastric plus duodenal digestion after 1 h; grey bars gastric plus duodenal digestion after 2 h in logarithmic phase). (dotted black bars gastric digestion; dotted white bars gastric plus duodenal digestion after 1 h; dotted grey bars gastric plus duodenal digestion after 2 h in stationary phase).
gastric and duodenal conditions (p < 0.05), except for L. casei shirota vs L. casei immunitas (p ¼ 0.39) during duodenal digestion after 1 h incubation (Fig. 2B). Higher recoveries were observed using milk as food matrix for all the tested strains, L. casei shirota showing the highest recovery (197%) after gastric digestion in logarithmic phase and L. acidophilus johnsonii in stationary phase (176%). Significant difference in milk among the three strains were observed in duodenal digestion after 1 h incubation for L. casei shirota vs L. casei immunitas (p < 0.001) and L. casei shirota vs L. acidophilus johnsonii (p < 0.001) and in duodenal digestion after 2 h incubation with L. casei shirota vs L. acidophilus johnsonii (p ¼ 0.036) (Fig. 2B). Finally, considering Lactobacillus strains in stationary phase, the statistical analysis of recovery (%) in both matrices showed that there was significant difference (p < 0.05) in water among the three strains under gastric conditions except for L. casei shirota vs L. acidophilus johnsonii (p ¼ 0.07), and during duodenal digestion after 2 h for L. acidophilus johnsonii vs L. casei immunitas (p ¼ 0.30) and L. casei shirota vs L. casei immunitas (p ¼ 0.12) (Fig. 2A). 3.4. Lactic acid production The lactic acid analysis of samples in logarithmic phase using milk as vehicle showed that the initial concentration of lactic acid (3.4 mM) with L. acidophilus johnsonii cultures detected in the first gastric sample increased up to 36.7 mM and 40.6 mM after 1 and 2 h duodenal digestion, respectively. The total lactic acid production was 27.3 mM in the gastric samples, 98.7 mM and 111.2 mM after 1 and 2 h duodenal digestion, respectively (Table 2). The starting concentration of lactic acid produced by L. casei shirota was 3.8 mM in the first gastric sample and increased up to
13.0 mM after 1 h duodenal digestion and up to 23.6 mM after 2 h duodenal digestion. Thus, the total lactic acid produced was 44.1 mM in the gastric samples, 81.7 mM and 135.7 mM after 1 h and 2 h duodenal digestion, respectively (Table 2).
Table 2 Concentration (mM) of lactic acid produced during gastric digestion plus duodenal digestion after 1 h and 2 h incubation in log phase. Time (min)
L. acidophilus johnsonii
Gastric digestion 9 3.4 1.2 18 2.7 1.1 27 2.9 0.7 36 3.2 0.1 45 3.4 0 54 8.1 2.2 63 3.7 2.5 Gastric plus duodenal digestion (1 h) 69 3.2 0.2 78 4.5 1.5 87 6.1 2.6 96 9.0 4.9 105 11.7 3.8 114 27.5 2.0 123 36.7 0.1 Gastric plus duodenal digestion (2 h) 129 4.1 1.7 138 5.4 1.9 147 6.5 1.7 156 8.7 2.0 165 12.5 4.1 174 33.6 0.5 183 40.6 1
L. casei shirota
L. casei immunitas
3.8 4.5 5.9 6.5 7.5 9.5 6.3
1.9 2.3 3.8 3.4 4.2 4.2 2.3
3.1 3.2 3.8 4.0 4.5 10.3 3.5
6.2 8.5 9.8 12.0 15.8 16.3 13.0
3.2 0.7 1.3 2.9 2.4 0.4 0.1
4.8 8.0 8.5 11.5 14.1 46.8 26.9
2.3 0.6 0.6 2.1 1.3 0.5 1.3
7.5 11.7 11.7 13.4 22.7 45.1 23.6
1.5 3.1 3.7 3.4 0.5 3.9 4.9
7.2 11.0 13.9 17.0 24.6 57.6 38.5
2.0 0.8 0.8 0.5 0.9 3.7 1.1
1.7 1.2 2.8 6.0 5.9 5.8 2.2
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The lactic acid concentration in L. casei immunitas cultures was 3.1 mM in the first gastric sample, which increased up to 26.9 mM after 1 h duodenal digestion and up to 38.5 mM after 2 h, with a total production of 32.4 mM during gastric digestion, 120.7 mM and 169.8 mM after 1 h and 2 h duodenal digestion, respectively (Table 2). There was no significant effect of count or strain on lactic acid production at the time of gastric digestion (Fig. 3A) or gastric plus duodenal conditions for 1 h (Fig. 3B) or 2 h (Fig. 3C). The lactic acid analysis was also performed in stationary phase using milk as food vehicle. L. acidophilus johnsonii cultures showed a lactic acid concentration of 6.9 mM in the first gastric sample which increased up to 35.7 mM after 1 h duodenal digestion and up to 24.0 mM after 2 h. The total lactic acid production in the gastric samples was 66.7 mM, 154.3 mM and 150.6 mM after 1 h and 2 h duodenal digestion, respectively (Table 3). The initial concentration of lactic acid produced by L. casei shirota was 7.4 mM in the first gastric sample which increased up to 65.9 mM and 98.2 mM after 1 and 2 h duodenal digestion, respectively (Table 3). The total lactic acid concentration produced was 76.8 mM in gastric samples, 194.0 mM after 1 h and 236.5 mM after 2 h duodenal digestion.
A
The lactic acid concentration produced by L. casei immunitas was 4.3 mM in the first gastric sample and increased up to 5.2 mM after 1 h duodenal incubation and up to 5.6 mM after 2 h. The total lactic acid production was 49.6 mM in gastric samples, 93.7 mM and 101.2 mM after 1 and 2 h duodenal digestion, respectively (Table 3). There was no significant effect of count or strain on lactic acid production at the time of gastric digestion (Fig. 4A). There was a significant effect of count (p ¼ 0.009) and strain (p ¼ 0.008) on lactic acid production at the time of gastric plus duodenal conditions for 1 h (Fig. 4B). There was a significant effect of strain (p ¼ 0.009) but not of strain on lactic acid production at the time of gastric plus duodenal conditions for 2 h (Fig. 4C). 4. Discussion The results obtained in this research have shown a great survival of all three probiotic strains after in vitro gastric and gastric plus duodenal digestion. We have recently observed good survival rates of six Lactobacillus rhamnosus strains during simulated in vivo digestion using MRS as delivery vehicle (Pitino et al.,
B
C
Fig. 3. Relationship between lactic acid concentration and bacterial count in the log phase following gastric digestion (A); following gastric and 1 h duodenal digestion (B); following gastric and 2 h duodenal digestion (C). All figures have data points represented by “o” for immunitas strain, “Δ” for johnsonii strain and “þ” for shirota strain. The regression immunitas; “e lines represent the fit to the linear model, lactic acid concentration w bacterial count þ strain. Each level in the factor “strain” is represented by a different line ( e” johnsonii; “- - -” shirota). The slope of the lines is the regression coefficient for the “bacterial count” variable.
A. Lo Curto et al. / Food Microbiology 28 (2011) 1359e1366 Table 3 Concentration (mM) of lactic acid produced during gastric digestion plus duodenal digestion at 1 h and 2 h incubation in stat phase. Time (min)
L. acidophilus johnsonii
Gastric digestion 9 6.9 3.5 18 7.8 4.3 27 7.2 3.2 36 7.8 4.0 45 8.3 3.3 54 13.2 2.8 63 15.4 1.9 Gastric plus duodenal digestion (1 h) 69 11.2 3.3 78 12.3 4.3 87 11.9 3.1 96 12.9 4.2 105 17.8 4.0 114 52.5 1.1 123 35.7 0.5 Gastric plus duodenal digestion (2 h) 129 15,7 0.1 138 10.4 1.8 147 9.3 1.8 156 12.4 2.2 165 14.8 3.3 174 64.0 4.9 183 24.0 4.0
L. casei shirota
L. casei immunitas
7.4 8.9 9.4 9.9 11.1 22.6 7.5
2.1 2.9 3.5 4.2 4.7 4.0 0.1
4.3 4.9 5.4 5.0 6.4 16.0 7.5
0.3 0.2 0.0 0.2 0 0.7 2.0
10.8 11.0 12.0 12.4 13.7 68.0 65.9
1.9 2.5 2.8 3.4 2.7 3.6 1.3
6.2 8.7 9.4 9.9 11.1 43.1 5.2
0.5 4.1 4.6 5.3 4.0 0.6 0.3
9.1 10.4 11.0 13.0 16.0 78.6 98.2
2.9 4.4 4.1 5.3 3.0 3.5 1.3
6.2 8.0 8.3 9.3 9.8 53.8 5.6
1.0 2.6 3.5 3.7 3.4 0.0 0.6
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2010). Since the lactic acid production is an important index of adaptation of bacteria which secrete lactic acid as end-product of lactose fermentation, the survival of probiotic strains was confirmed by data obtained measuring the production of lactic acid. Our experimental data showed that lactic acid levels secreted by all three strains confirmed a good fit and growth of these bacteria during digestion. These results were further supported by an accurate statistical analysis, which confirmed their validity. The results suggested some differences among the three strains used, with generally higher survival in milk compared with water. This observation could be related to the lower buffering capacity of the water compared to milk (Holzapfel et al., 2001). The milk buffering effect can protect strains against the harmful action of gastric and duodenal environment (Sirò et al., 2008). Moreover, the probiotic strains harvested during stationary growth phase, which better represent the conditions in which the commercial products are sold, survived better compared to the strains harvested in logarithmic phase growth (Pochart et al., 1992; Marteau et al., 1997). L. acidophilus subsp. johnsonii showed good survival in water in both logarithmic phase and stationary phase. The 100% of recovery rate in water in stationary phase showed that adaptation to gastric and duodenal conditions makes possible to reach the intestine live and vital (Makelainen et al., 2009). The high survival shown in the
Fig. 4. Relationship between lactic acid concentration and bacterial count in the stationary phase following gastric digestion (A); following gastric and 1 h duodenal digestion (B); following gastric and 2 h duodenal digestion (C). All figures have data points represented by “o” for immunitas strain, “Δ” for johnsonii strain and “þ” for shirota strain. The regression lines represent the fit to the linear model, lactic acid concentration w bacterial count þ strain. Each level in the factor “strain” is represented by a different line immunitas; “e e” johnsonii; “- - -” shirota). The slope of the lines is the regression coefficient for the “bacterial count” variable. (
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stationary phase is probably due to the ability of the strain to better adapt to changing pH in the gastric compartment. The same strain in milk showed a good survival both in logarithmic and stationary growth phase. L. casei subsp. shirota strain showed a similar behaviour to L. acidophilus subsp. johnsonii in water, with good survival in both logarithmic and stationary phase. The difference between the two phases could be related to the more susceptibility to environmental stresses by bacteria in log phase as previously reported (Heller, 2001). L. casei subsp. shirota showed excellent survival in milk matrix in both phases; in logarithmic phase the recovery rate was higher than L. acidophilus subsp. johnsonii. L. casei subsp. immunitas showed a different trend in water compared to other studied strains. In water matrix, the survival rate was very low both in log phase and stationary phase. Instead, the % of recovery in milk matrix was similar to L. casei subsp. shirota. The results showed that the best probiotic strain was L. acidophilus subsp. johnsonii for its highest survival in both tested foods. Moreover the other two probiotic tested strains showed good enough survival in upper gastrointestinal tract. This research confirms some previous reports on the survival of L. casei strains found in some commercial products (Spandhaak et al., 1998; Oozeer et al., 2006; Tormo Carnicer et al., 2006; Tuohy et al., 2007). Acknowledgements This research has been funded by the Biotechnology and Biological Research Sciences Council (BBSRC, UK). Appendix. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.fm.2011.06.007. References Chapman, C.M., Gibson, G.R., Rowland, I., 2011. Health benefits of probiotics: are mixtures more effective than single strains. Eur. J. Nutr. 50, 1e17. Charalampopoulos, D., Pandiella, S.S., Webb, C., 2003. Evaluation of the effect of malt, wheat and barley extracts on the viability of potentially probiotic lactic acid bacteria under acidic conditions. Int. J. Food Microbiol. 82, 133e141. Cimperman, L., Bayless, G., Best, K., Diligente, A., Mordarski, B., Oster, M., Smith, M., Vatakis, F., Wiese, D., Steiber, A., Katz, J., 2011. A Randomised, double-blind, placebo-controlled pilot study of Lactobacillus reuteri ATCC 55730 for the prevention of antibiotic-associated diarrhea in hospitalized adults. J. Clin. Gastroenterol. doi:10.1097/MCG.0b013e3182166a42. Corcoran, B.M., Stanton, C., Fitzgerald, G.F., Ross, R.P., 2005. Survival of probiotic lactobacilli in acidic environments is enhanced in the presence of metabolizable sugars. Appl. Environ. Microbiol. 71, 3060e3067. Hartke, A., Bouche, S., Gansel, X., Boutibonnes, P., Auffray, Y., 1994. Starvationinduced stress resistance in Lactococcus lactis subsp. lactis IL1403. Appl. Environ. Microbiol. 60, 3474e3478. Heller, K.J., 2001. Probiotic bacteria in fermented foods: product characteristics and starter organisms. Am. J. Clin. Nutr. 73, 374Se379S.
Holzapfel, W.H., Haberer, P., Geisen, R., Bjorkroth, J., Schillinger, U., 2001. Taxonomy and important features of probiotic microorganisms in food and nutrition. Am. J. Clin. Nutr. 73, 365Se373S. Makelainen, H., Forssten, S., Olli, K., Granlund, L., Rautonen, N., Ouwehand, A.C., 2009. Probiotic lactobacilli in semi-soft cheese survive in the simulated human gastrointestinal tract. Int. Dairy J. 19, 675e683. Mandalari, G., Faulks, R.M., Rich, G.T., Lo Turco, V., Picout, D.R., Lo Curto, R.B., Bisignano, G., Dugo, P., Dugo, G., Waldron, K.W., Ellis, P.R., Wickham, M.S.J., 2008. Release of protein, lipid and vitamin E from almond seeds during digestion. J. Agric. Food Chem. 56, 3409e3416. Marciani, L., Wickham, M.S.J., Bush, D., Faulks, R.M., Wright, J., Fillery-Travis, A., Spiller, R., Gowland, P., 2006. Magnetic resonance imaging of the behaviour of oil-in-water emulsions in the gastric lumen of man. Brit. J. Nutr. 95, 331e339. Marciani, L., Bush, D., Wright, P., Wickham, M.S.J., Pick, B., Wright, J., Faulks, R.M., Fillery-Travis, A.J., Spiller, R.C., Gowland, P.A., 2005. Monitoring of gallbladder and gastric coordination by EPI. J. Mag. Res. Imag 21, 82e85. Marciani, L., Ramanathan, C., Tyler, D.J., Young, P., Manoj, P., Wickham, M.S.J., FilleryTravis, A., Spiller, R.C., Gowland, P.A., 2001. Fat emulsification measured using NMR transverse relaxation. J. Mag. Res. Imag 153, 1e6. Marciani, L., Wickham, M.S.J., Wright, J., Bush, D., Faulks, R.M., Fillery-Travis, A., Gowland, P.A., Spiller, R.C., 2003. Magnetic resonance imaging (MRI) insights into how fat emulsion stability alters gastric emptying. Gastroent 124, A581. Marteau, P., Minekus, M., Havenaar, R., Huis in’t Veld, J.H., 1997. Survival of lactic acid bacteria in a dynamic model of the stomach and small intestine: validation and the effects of bile. J. Dairy Sci. 80, 1031e1037. Mater, D.D.G., Bretigny, L., Firmesse, O., Flores, M.J., Mogenet, A., Bresson, J.L., Corthier, G., 2005. Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus survive gastrointestinal transit of healthy volunteers consuming yogurt. FEMS Microbiol. Lett. 250, 185e187. Oozeer, R., Leplingard, A., Mater, D.D., Mogenet, A., Michelin, R., Seksek, I., Marteau, P., Dore, J., Bresson, J.L., Corthier, G., 2006. Survival of Lactobacillus casei in the human digestive tract after consumption of fermented milk. Appl. Environ. Microbiol. 72, 5615e5617. Ortega, N., Reguant, J., Romero, M.P., Macià, A., Motilva, M.J., 2009. Effect of fat content on the digestibility and bioaccessibility of cocoa polyphenol by an in vitro digestion model. J. Agric. Food Chem. 57, 5743e5749. Pitino, I., Randazzo, C.L., Mandalari, G., Lo Curto, A., Faulks, R.M., Le Marc, Y., Bisignano, C., Caggia, C., Wickham, M.S.J., 2010. Survival of Lactobacillus rhamnosus strains in the upper gastrointestinal tract. Food Microbiol. 27, 1121e1127. Pochart, P., Marteau, P., Bouhnik, Y., Goderel, I., Bourlioux, P., Rambaud, J.C., 1992. Survival of bifidobacteria ingested via fermented milk during their passage through the human small intestine: an in vivo study using intestinal perfusion. Am. J. Clin. Nutr. 55, 78e80. Rallu, F., Gruss, A., Maguin, E., 1996. Lactococcus lactis and stress. Antonie Leeuwenhoek 70, 243e251. Rook, G.A., Brunet, L.R., 2005. Microbes, immunoregulation, and the gut. Gut 54, 317e320. Sirò, I., Kàpolna, E., Kapolna, B., Lugasi, A., 2008. Functional food. Product development, marketing and consumer acceptance. Appetite 51, 456e467. Spandhaak, S., Havenaar, R., Schaafsma, G., 1998. The effect of consumption of milk fermented by Lactobacillus casei strain Shirota on the intestinal microflora and immune parameters in humans. Eur. J. Clin. Nutr. 52, 899e907. Tao, S., Lu, Y., Zhang, D., Yang, Y., Yang, Y., Lu, X., Sai, D., 2009. Assessment of oral bioaccessibility of organochlorine pesticides in soil using an in vitro gastrointestinal model. Environ. Sci. Technol. 43, 4524e4529. Tormo Carnicer, R., Infante Pina, D., Rosello Mayans, E., Bartolome Comas, R., 2006. Intake of fermented milk containing Lactobacillus casei DN-114 001 and its effect on gut flora. An Pediatr 65, 448e453. Tuohy, K.M., Pinart-Gilberga, M., Jones, M., Hoyles, L., McCartney, A.L., Gibson, G.R., 2007. Survivability of a probiotic Lactobacillus casei in the gastrointestinal tract of healthy human volunteers and its impact on the faecal microflora. J. Appl. Microbiol. 102, 1026e1032. Vardakou, M., Mercuri, A., Barker, S.A., Craig, D.Q., Faulks, R.M., Wickham, M.S.J., 2011. Achieving antral grinding forces in biorelevant in vitro models: comparing the USP dissolution apparatus II and the dynamic gastric model with human in vivo data. AAPS PharmSciTech. doi:10.1208/s12249-011-9616-z.