The influence of Lactobacillus fermentum and Lactobacillus amylovorus
Obesity has become a major public health concern in developed countries and has suddenly become a growing health problem in developing countries as it presents itself as a risk factor for an array of chronic diseases including diabetes, cardiovascular disease, and cancer (DiBaise, 2008). Obesity occurs when energy input is greater than expenditure, coupled with chronic low-grade inflammation and gut hormone signaling responsible for metabolic roles in energy expenditure and body adiposity (Murphy, 2006; Sanz, 2008). Recent evidence has shown that the gut microbiota is directly involved in the regulation of how lipids are metabolized and changing the compositional abundance of the intestinal microflora alters whether excess energy is stored or expended (Turnbaugh, 2008; Ley, 2005; Raoult, 2008).
In a number of animal studies, it has been shown that the gut microbial populations and the capacity to harvest energy from the diet vary substantially between obese mice and their lean counterparts. This observation is of particular interest as it may propose a causative association for their dissimilarity in body composition. To demonstrate the proposed relationship between microflora, energy harvesting efficiency and body composition; gut microbial colonization of lean germ-free (gnotobiotic) mice with the distal gut microorganisms from conventionalized obese mice has been conducted to study the metabolic effects as an insight into causative sources of obesity (Turnbaugh et al, 2006). Within two weeks, the germ-free mice showed a dramatic increase in fat mass and heightened glycemia and insulinemia compared to their original germ-free microbiome, despite identical diets and similar consumption. The increase in blood glucose levels and adipose deposition seen in the colonized germ-free mice may deduce that the gut microbiome possess properties that determine how efficient the body is at harvesting energy from the diet, and whether it is stored or expended.
This link between microbial ecology in the gut and fat storage seen in animal studies appears to apply to human subjects as well. Human distal gut microbiota was analyzed at baseline and endpoint from 12 unrelated obese men and women fed either a randomly assigned fat-restricted or carbohydrate-restricted diet. Gut microbiota in obese subjects at baseline was found to be heavily populated with bacteria from the phylum Firmicutes and less with bacteria from Bacteriodetes, compared to lean subjects. At endpoint, bacteria from Bacteriodetes had increased, proportional to the amount of weight loss proposing that the presence of specific microbial populations in the gut may encompass properties responsible with weight management (Ley, 2006b).
Bacteria from the genus, Lactobacillus, have been used as probiotics for its capability to lower colonic pH through the production of lactic acid and thus, inhibiting the growth of pathogenic bacteria. The bacterial strains selected for the probiotic yogurt treatment formulations for this study are L. fermentum and L.amylovorus. These microbial strains were chosen for their promising health benefits as L. fermentum has been demonstrated to be capable of lowering serum lipids and the novel strain L. amylovorus may have an effect on cholesterol assimilation (Simons, 2005; Grill, 2000).
Given these findings, the current study looks to investigate whether the gut microflora may be altered through the consumption of probiotics, namely Lactobacillus fermentum and Lactobacillus amylovorus in order to populate the intestinal tract with the microbes which may transfer specific metabolic effects shown to have an impact on energy storage (DiBaise, 2008).
Materials and Methods
Obese, but otherwise healthy volunteers (n = 48) were recruited and initially screened for health status. Eligible subjects (n=30) underwent blood screening, and a complete physical examination including a complete medical history. Subjects who were diagnosed to have heart disease, kidney disease, diabetes mellitus, liver disease, lactose intolerance or had recently undergone major surgery were excluded from the study along with those who were taking medications known to affect lipid metabolism.
The study was a controlled diet, cross-over clinical investigation using a Latin square sequence design. The study consisted of three 43 day phases, each separated by a six week wash out interval. Subjects were randomized to one of three treatment arms: a) control yogurt; b) yogurt containing 1013 CFU of microencapsulated L. amylovorus bacteria; c) yogurt containing 1013 CFU microencapsulated L. fermentum bacteria. During each treatment period, subjects were provided with diets containing 35% of energy as fat, 50% carbohydrate and 15% protein (Table 1, Figure 1). All meals were prepared at the metabolic kitchen located at the Richardson Centre for Functional Foods and Nutraceuticals (RCFFN) using a three-day rotation menu. Individual basal energy requirements were determined using the Mifflin equation (Mifflin et al., 1990) and were multiplied by a physical activity factor of 1.7. The control and two treatment yogurts were consumed simultaneously with 4g of wheat bran with supper.
Body composition assessment
Body composition was analyzed using dual-energy X-ray absorptiometry (DEXA) at the beginning and end of each phase in order to determine overall body fat and lean body mass to assess body composition (General Electric Lunar Digital Prodigy Advance).
Fecal energy analysis
Bomb calorimetry, the most direct method used to determine fecal energy per unit weight (cal/g), was used to quantify energy losses (Parr 6300). Stool samples were collected in duplicate at the end of each study phase for a total of 6 samples per subject. Once samples were collected they were frozen at -80°C. The fecal samples were then freeze dried and ground to produce a homogenous sample. Samples were taken and weighed to a constant weight. Results of total fecal energy were expressed in calories per gram.
DNA extraction and RT-PCR for fecal microbial composition analysis
Fecal samples were thawed and resuspended in phosphate buffered saline (PBS). Then, 150 mg of the resuspended solution was washed in 1 ml of PBS and centrifuged. DNA was extracted from the pellets by using ZR Fecal DNA Kit (D6010, Zymo Research Corp., Orange, CA), which included a bead-beating step for the mechanical lysis of the microbial cells. DNA concentration and purity were determined spectrophotometrically by measuring the OD and A260/280 (Beckman DU/800, Beckman Coulter Inc., Fullerton, CA). The PCR primers used are listed in Table 2. Primers were assembled from the literature or newly designed and tested for specificity in silico. Primers were synthesized by University Core DNA Services (University of Calgary, Calgary, AB). Real-time PCR was carried out using an AB 7300 system (Applied Biosystems, Foster City, CA) and sequence detection software (Version 1.3; Applied Biosystems, Foster City, CA). Each reaction was run in triplicate in optical reaction plates (Applied Biosystems, Foster City, CA) sealed with optical adhesive film (Applied Biosystems, Foster City, CA). Amplification reactions were carried out with Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA) mixed with the selected primer set (Table 2) for each primer, and 2 µl (~12 ng) of genomic DNA. To evaluate the efficiency (E) of the amplification of each primer set, DNA templates were pooled (50 ng/reaction). Amplification efficiency was calculated from the slope of the standard curve generated from plotting the threshold cycle (CT) versus logarithmic values of different DNA concentrations using the following equation (Denman and McSweeney, 2005):
Relative quantification followed the mathematical model (Pfaffl, 2001):
Ri= [(Etarget)ΔCTtarget (Controli - SARAi)]/[(Eref)ΔCTref (Controli - SARAi)] (6)
All data were expressed as mean+/- SE. Statistical significance was set at P<0.05 for all analyses. Data that was not normally distributed underwent log transformation. Differences between treatments at baseline, midpoint and endpoint for body composition were compared by using the analysis of variance (ANOVA) model for determination of diet effects. When diet effects were found to be significant, Least Squares Means was used to identify differences between diet effects. Student's paired- t test was used to compare diet effects between baseline-midpoint and baseline-endpoint as well as differences of percent changes at endpoint relative to control between L. fermentum and L. amylovorus treatments.
The LSD multiple comparison test was conducted to detect significant differences among treatment groups in analyzing gut microbial composition parameters.
Data were analyzed with the use of SAS software (version 8.0; SAS Institute Inc, Cary, NC, USA).
Forty-eight subjects were initially recruited and thirty subjects (11 males and 19 females) completed the entire trial. Baseline characteristics of the study are displayed in Table 3.
Body weight and body compositions in response to treatments
No differences in body weight of subjects were observed at baseline or endpoint across the three treatments (Table 4). Twenty-eight subjects were available to undergo whole body scans over the course of the study and endpoint scans of all three phases were obtained from a smaller subgroup of subjects (n=15).
No treatment effects were observed at endpoint for total lean mass or total fat mass (Table 4). However, over the study period, body fat mass decreased by 3% (P=0.0158) from baseline in response to L. fermentum treatment, while L. amylovorus feeding reduced fat mass by 4% (P=0.0211) from baseline. In addition, body fat mass was decreased by 1% (P=0.0127) from baseline compared with control (Figure 3). These changes in total fat mass occurred despite no statistically significant shift in body weights across treatments.
Fecal energy expenditure in response to treatments
The fecal energy expenditure for L. fermentum, L. amylovorus, and control treatments were 5,056.8 + 46.5, 5,163.2 +65, and 5,068.9 + 58.9 cal/g respectively. There were no significant differences observed between the control, L. amylovorus and L. fermentum groups in endpoint fecal energy concentrations (n=28, p=0.67) (Figure 2).
Gut microbial abundance in response to treatments
The sub-study for the microbial composition of human fecal samples was conducted as a blind study, in which the investigators were not knowledgeable of the order of the treatments. All RT-PCR data are log-2 normalized and presented relative to control treatment in Table 5. All values were averaged across group and period and calculated relative to the control treatment, labeled by a zero in the table and presented in finalized, normalized format in Table 6 and presented graphically in Figure 3. There was a highly significant effect for Lactobacillus (P = 0.008), when the control was compared to L.fermentum and L.amylovorus. This was to be expected because both probiotic treatments, L.fermentum and L.amylovorus, contained active bacterial cultures from the genus Lactobacillus.
The feeding of Lactobacillus exerted a synergistic effect, directly or indirectly, on the Clostridium cluster IV group in the gut. Levels of Clostridium cluster IV were reduced in subjects administered treatment L.amylovorus (P= 0.038), when compared to control (Table 6). Fecal microbial abundances of Bacteroides, Bifidobacteria adolescentis, Cluster IV Ruminococcus, Roseburia spp., and E. rectale did not show significant differences with response to treatments over control (Figure 4).
It has been shown in previous reports that weight loss has a modulation effect on gut microflora composition, where gut microbial abundances of specific bacterial strains increase or decrease with initial weight loss. Obese subjects (BMI=32.8) who exhibit high weight loss (>4.0kg) show a subsequent increased gut bacterial abundance from the genera Bacteroides, Lactobacillus and Clostridium leptum (Santacruz et al, 2009). This observation presents the question of whether gut colonization of Bacteroides and other commensal microbes is solely a reactionary response to weight loss or if populating the gut microflora with specific beneficial bacterial strains through probiotic consumption would directly or indirectly contribute to weight loss.
In the present study, microbial inoculated yogurt was administered in effort to colonize the intestinal tract with predetermined bacterial strains known to have a beneficial health effect so as to alter the gut microbiome and ultimately the energy storage ability of the host. Mechanisms responsible for the metabolic effects on energy storage and lipids from the gut microbiota are speculated to be the inhibition of lipoprotein lipase promoting fat oxidation in muscle and limited adipocyte deposition from a heightened level of fasting-induced adipose factor (Fiaf) in the plasma and are distinctive in mice with ‘lean' microbiota (Backhëd, 2007; Cani, 2007). Supporting mechanisms are lower glycemia and insulinemia which both promote lipogenesis and decreased intestinal glucose absorption, all which stem from gut microbiota as an environmental factor affecting energy metabolism and host homeostasis (Turnbaugh, 2008).
At endpoint, there was no significant change in body weight across treatments whereas body adiposity in subjects was shown to be altered over all treatments, where subjects lost a total fat mass of 4%, 3% and 1% consuming treatments L. amylovorus, L. fermentum and control, respectively (Table 4, Figure 3). According to fecal energy expenditure results, the percentage fat loss observed in subjects was not shown to be due to excess energy from the diet lost through fecal energy as concentrations were not significantly different across treatments (Figure 2), thus suggesting that this observed fat loss originated from other metabolic mechanisms. This outcome indicates that a change in lipid metabolism occurred with respective treatments and that previously mentioned mechanisms may have been adopted due to an altered gut microbiome composition.
It has been shown in prior studies that weight loss alters the gut microbiome creating a higher Bacteroides to Firmicutes bacterial ratio, typical of a ‘lean' microflora (Sanz, 2008). Subjects with this ‘lean' microflora seem to possess attributes that protect them from the development of obesity as is outlined in the study by Bäckhed et al. Germ-free mice and mice with normal, conventional microflora were fed a typical Western diet, characterized by high fat and high sugar. The germ-free mice were found to remain lean and resist the development of obesity, whereas the colonized mice grew overweight despite identical diets and consumption. These findings suggest that characteristics of the microbiome are responsible for protecting or predisposing the host to obesity by way of altered lipid and carbohydrate metabolism depending on the gut microbial composition of an obese or lean subject (Bäckhed et al, 2006).
Furthermore, if these mechanisms are also characteristic in the lean human microbiome, characterized by a microflora favouring Bacteroidetes over Firmicutes, then the modulation of the gut microbiota in obese subjects in this fashion may alter their energy harvesting efficiency through the development of these lipid metabolism properties and positively affect their lipid profile and body composition.
This hypothesis may explain the decrease in body fat mass seen in all treatments, as the gut abundance of Bacteroides was not found to be significant over control proposing that all three treatments had relatively equivalent quantities of Bacteroides at endpoint but may have increased at a uniform rate throughout the study phases due to the consumption of Lactobacillus. Alternatively, a longer study period may have resulted in a greater proliferation of Bacteroides, as was shown in the study by Ley et al., where the abundance of Bacteroides multiplied with proportional weight loss in obese subjects (Ley, 2006b).
Moreover, L. amylovorus resulted in the largest fat reduction as well as the largest decrease in Clostridium cluster IV (C. leptum)speculating that the decline in the abundance of this bacteria may also correlate to the diminution of body adiposity. Typically, C. leptum is considered a commensal bacterial species and an increase in relative abundance would be favourable as it is involved in many physiological functions, particularly nutrient absorption, short chain fatty acid (SCFA) production, epithelial cell maturation and maintenance and weight loss (Santacruz, 2009; Zwielehener, 2009). However, a decrease in this microbe was observed in the L. amylovorus treatment reducing the colonic SCFA production and the subsequent energy provided by butyrate, acetate and propionate for the gut, muscle and liver, respectively. This decrease in endogenous energy provision would contribute to reducing total daily caloric supply and the energy harvesting ability of the gut microbiome (Nadal, 2009). This role in energy homeostasis may be a property of the altered microbiome along with increased beta oxidation and decreased adipose deposition as attributes of the ‘lean' microbiome, however this is yet to be elucidated. In addition, the decreased abundance of this microbe is warranted since it has been observed that with weight loss subsequent reduction in gut microbial abundances of Firmicutes occurs, of which phylum C.leptum belongs (Santacruz, 2009).
Subjects consuming L. fermentum also showed a 3% body fat mass loss over the three phases suggesting this treatment also had an effect on fat metabolism. Contrary to the L.amylovorus treatment, L. fermentum did not show a decrease in populations of Clostridium cluster IV bacteria, however, the levels of Lactobacillus spp. were significantly higher than control. This result indicates a significant increase in the lactic acid producing bacteria, creating an acidic microbial environment that inhibits the growth of pathogenic bacteria and promotes the proliferation of beneficial bacteria inevitably creating a microbiome ratio favouring Bacteroides over Firmicutes, which may contribute to the altered body adiposity.
The production of short chain fatty acids (SCFA) through the bacterial fermentation of undigested carbohydrates by the gut microflora, particularly the 4g of wheat bran included in the intervention diet may also have contributed to the loss of body fat mass as SCFA can supply up to 10% of the daily energy requirements (Sanz, 2008). This energy provision from SCFA is absorbed through the colon providing the main energy source for colonocytes and may cause excess energy from the diet to be lost through the feces (Hijova, 2007). However, no significant difference in fecal energy losses was shown across the three treatments over the three phases, therefore the production of SCFA does not seem to have contributed a direct part in fat loss due to treatments.
In summary, although treatment effects were not noted at endpoint, our results suggests that over the study period, body fat mass tended to be reduced by both L. fermentum and L. amylovorus treatments and a reduction in the abundance of Firmicutes was seen with L.amylovorus as the population of Clostridium cluster IV decreased. However, more studies are needed to elucidate the underlying mechanisms responsible for such an effect on fat mass yet it is evident that the composition of the gut microbiome plays an essential role in energy homeostasis and that modulation of the microbial environment may contribute to more effective protection from obesity (Tsai, 2009).
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