Research Article
Open Access
A Lactobacillus Cocktail Changes Gut Flora and Reduces Cholesterolemia and Weight Gain in Hyperlipidemia Mice
Shousong Yue1#, Bharathiraja Chinnapandi1#, Haitao Ge2, Xiaofeng Zou1, Xiaojing Chen2, Chuandong Wang2, Wei Hu2 and Jean-François Picimbon1#*
1Biotechnology Research Centre, Shandong Academy of Agricultural Sciences, Jinan, P.R. China
2State Key Laboratory of Microbial Technology, Shandong University, Jinan, P.R. China
#These authors contributed equally
*Corresponding author: Jean-François Picimbon, Functional Genomics and Proteomics of Chemical Ecology, Microbiology, Plant Transgenesis and Insect Control, Biotechnology Research Center, Shandong Academy of Agricultural Sciences, Jinan, P.R.China, Tel: +86-(531)-83175350; Fax:+86-(531)-83178156; E-mail:
@
Received: May 07, 2014; Accepted: May 31, 2014; Published: June 2, 2014
Citation: Yue S, Chinnapandi B, Ge H, Zou X, Chen X, et al. (2014) A lactobacillus Cocktail Changes Gut Flora and Reduces Cholesterolemia and Weight Gain in Hyperlipidemia Mice. SOJ Microbiol Infect Dis 2(2): 1-14.
http://dx.doi.org/10.15226/sojmid.2014.00119
We have developed a hyperlipidemia mouse model system to
test the effects of natural Lactobacillus bio-products on intestinal
microflora, organ physiology and lipid metabolism.
Using denaturing gradient gel electrophoresis, PCR, quantitative
real-time PCR and Southern blots, we show that Lactobacillus
has different effects on the intestinal microflora compared to
pharmaceutical drugs such as Simvastatin. The pool treated with
Lactobacillus (L. plantarum, L. acidophilus and L. casei) is found to
be closer than the control conditions. We report the existence of
twelve main gut bacterial strains related to lipid metabolism in mice
(Bacillus amyloliquefaciens, B. licheniformis, B. oleronius, Enterobacter
sp. dc6, Enterococcus faecium, Lactobacillus johnsonii, Lactococcus sp.
M3T8B4 and four uncultured bacteria); most of which are found to
be regulated by the cocktail of lactobacilli. In addition, results show
the reduced levels of cholesterolemia and weight in hyperlipidemia
mice as well as beneficial effects on LDL/HDL ratio, neutral lipid
accumulation, cholesterol removal and antioxidant activities
following treatments with Lactobacillus.
On the basis of these results we propose the use of
L. plantarum,
L. acidophilus and L. casei in a new bio-product cocktail not only
as a food complement to regulate the gut flora and prevent lipid
accumulation but also as an alternate therapy to pharmaceutical
drugs for the treatment of hyperlipidemia, obesity and all other
genetic disorders that cause severe deficiency in lipid metabolic
pathways
Keywords: Bioproduct; L. plantarum; L. acidophilus; L. casei;
Simvastatin; Ubac2; HDL; LDL; lipid metabolism.
AI: Atherosclerotic index; Blic: Bacillus licheniformis;
Bm9h: Bacillus M9H; Bole: Bacillus oleronius; Bamy: Bacillus
amyloliquifaciens; CH: Cholesterol; DGGE: denatured gradient
gel electrophoresis; DPPH: Di-Phenyl-Picryl-Hydrazyl; Edc6:
Enterococcus dc6; Efae: Enterococcus faecium; HDL: High density
lipoprotein; L8b4: Lactococcus M3T8B4; LDL: Low density lipoprotein; Ljoh: Lactobacillus johnsonii; NBT/BCIP: Nitro-
Blue-Tetrazolium,5-Bromo-4-Chloro-3’-IndolyPhosphate; RT-PCR:
real time PCR; SAFR: Superoxide anion free radicals; TG:
Triglycerides; Ubac1: Uncultured bacteria 1; Ubac2: Uncultured
bacteria 2; Ubac3: Uncultured bacteria 3; Ubac4: Uncultured
bacteria 4
Hyperlipidemia is associated to obesity, which is a major
worldwide health concern even though considering it as a
disease can be a matter of debate [1]. Being overweight due to the
accumulation of excess body fat has become epidemic and concern
individuals of all ages, including not only adults but also children
from many countries all over the world [2-4]. Understanding of
lipid metabolism, hyperlipidemia, weight gain, diabetes, cancers
and all forms of obesity remains complex [5]. Lipid accumulation
impacts a lot of endocrine and metabolic systems from heart
to ovaries, leading potentially to cardiovascular accidents and/
or cancer [6]. One of the most urgent challenges for human
health protection is therefore to develop strategies to tackle
hyperlipidemia before it generates disability or fatal issue.
In view of this challenge, it is important to note that in
animal models obesity can be strongly associated with changes
in the composition of the gut bacterial microflora [7]. In mice,
high-fat-diet intake is shown to induce severe changes in the
gut bacterial microflora only in a few days time. Normal mice
inoculated with the microflora of obese mice become readily
fatter than those inoculated with the microflora of leaner mice
[8]. Reversely, normal mice that received gut bacteria from mice
that lost weight following gastric bypass surgery also lost weight
[9-10]. Important host physiological systems such as the immune
response and lipid metabolism are tightly connected with the gut
bacterial microflora [11]. Beneficial bacteria in the gut are known
to stimulate the lymphoid tissue to produce antibodies directed
against specific pathogens or to prevent the development of
harmful microbes directly through the “barrier effect” at the
intestinal mucosa level [12]. Unbalanced gut flora can lead to
disease through generation of pro-atherosclerotic phospholipid
metabolites [13]. Lactobacilli or any other gut microbial flora are
well reported to keep the lipid metabolism in vascular biology,
as filed for the lipid composition, LDL/HDL ratio, atherosclerotic
index and neutral lipid accumulation. These results suggest that
appropriate medicine chemicals or bacterial probiotics should
be used for treatment of obesity and overweight to restore first
a positive microflora in order to prevent the development of
immune and/or cardiovascular diseases [14].
Although some gut bacterial phylotypes remain to be
identified, the composition of the gut microbiota is known in
both human and animal models as Actinobacteria, Bacteroidetes,
Firmicutes, Proteobacteria and in less extent Fusobacteria and
Verrucomicrobia [15]. The current view in animal models is that
hyperlipidemia is linked to unbalanced proportions between
such bacterial phylotypes; high cholesterolemia would be mainly
associated with high levels of Bacteroidetes and low levels of
Firmicutes [16,17]. Dietary supplements containing probiotics of
the Firmicutes group such as Lactobacillus spp., Bifidobacterium
spp., and Enterococcus spp., are frequently used to maintain
the health and weight of livestock accordingly [18]. Increasing
Lactobacillus strain-levels in the gut flora of newborn chicks and
ducks is associated with weight gain [19]. However, in humans,
the relationship between body lipid levels, gut microflora and
Lactobacillus is not so clear [20-23]. In contrast, abundance of
Fusobacterium and Tenacibaculum has been reported in older
subjects of the Amish population where it could be responsible
for colon cancer [24].
Humankind has been consuming probiotics throughout most of its history without inducing apparent hyperlipidemia or obesity in healthy adults [25,26]. However, a number of studies have reported weight gain in children given Lactobacillus spp. as a treatment for diarrhea, suggesting that perhaps probiotics could be related to hyperlipidemia and/or have various age-dependent effects [27,28].
Here, we present strong support that using a specific
Lactobacillus bio-product cocktail (L. plantarum, L. acidophilus
and L. casei) has performed as equal to or better than Simvastatin
on both blood lipid concentration and fat body distribution
during treatments of hyperlipidemia. Lactobacillus is shown not
only to prevent lipid accumulation during fat diet intake but also
to help maintain a normal gut microflora. Such a finding might
be very important to help develop a “natural” therapy against
the hyperlipidemic conditions, gain weight, obesity and related
metabolic diseases.
We initiated a four-points study to characterize key bacterial
phylotypes associated with hyperlipidemia, weight gain and
chemical versus bacterial treatment: 1) develop a mouse
model of hyperlipidemia, 2) identify bacterial strains specific
to the hyperlipidemic status (overweight and high blood lipid
concentration), 3) test the effects of Lactobacillus and 4) compare
with the effects of the pharmaceutical agent Simvastatin. Our
combined culture and molecular analysis allowed identification
of twelve dominant strains in the mouse fecal and gut samples. Then, using DGGE and qPCR, we show that except B. licheniformis,
strain-levels for all identified bacteria such as BM9H, B. oleronius,
B. amyloliquefaciens, Enterobacter sp. dc6, E. faecium, L. johnsonii,
M3T8B4, Ubac1, UBac2, Ubac3 and Ubac4 are significantly
affected by hyperlipidemia. Most importantly, bacterial strains
such as Ubac4 are significantly up-regulated in ill hyperlipidemic
mice but significantly down-regulated in model mice treated with
Lactobacillus. In addition, measuring various physiological and
metabolic parameters in the different groups of mice, we showed
that the pools of mice treated with Lactobacillus not only had a
gut flora closer to control conditions but clearly lost weight and
had seriously improved lipidemia and cholesterolemia. Finally,
we showed that each specific Lactobacillus single-strain of the
bacterial blend had very potent antioxidant activities.
Bio-product composition
In a preliminary study, we tested the effects of single-strain
lactobacillus probiotics on improvement of hyperlipimedia
(cholesterol-lowering). We found that single-strain lactobacillus
bio-products had no significant effects on cholesterol-lowering
and blood lipid concentration (Table 1).
The probiotics used in this study on hyperlipidemia were a
cocktail of three specific strains of Lactobacillus (Lactobacillus
plantarum SD02, L. acidophilus SD65 and L. casei SD07)
traditionally maintained in our laboratory. Growth of pure
cultures of the three bacterial strains was taken in MRS liquid
medium placed in an anaerobic workstation held permanently
at 37°
C (industrial platform). For bioproduct sample preparation,
bacterial cells of each strain were centrifuged at 2000 × g for 20
min at 4°
C. The bacterial cell pellets were resuspended in the ratio
of 109
CFU/ml in sterile saline water and stored at 4°
C until use.
Probiotic solutions were freshly prepared by mixing the three
bacterial suspensions in an equal volume just before treatment.
Mice received every day 0.3 ml of bioproducts administered
intra-gastrically using a stainless-steel needle.
Experimental mice model
The use of live mice in this study was approved by the
Shandong University Animal Research Ethics Committee and was
licensed by Shandong Province (governmental license SCXK Lu
20090001)
The experimental design is represented on (Figure 1A).
Seventy-seven weeks-old healthy male white mice (purchased
from Laboratory Animal Center, Shandong University, Jinan,
China) with a body weight of 20-22 grams were used for
experimental studies. Mice were housed separated in three
different groups maintained at 20 ± 2°
C and 50 ± 5% humidity
in a sterile or free pathogen environment (ethical clearance).
In step 1 (development of hyperlipidemic model ill-mice), mice
from Group 1 were fed during twenty days with standard diet
(wt/wt) composed of barley meal 20%, dehydrated cabbage
10%, soybean meal 20%, dry yeast 1%, bone powder 5%, corn
meal 16%, fish meal 10% and salt 2% (control-conditions), while
mice from Group 2 were fed with the same diet complemented
Table 1: Effects of
Lactobacillus single-strains on cholesterol-lowering and lipid metabolism.
|
Mice |
Cholesterol (mmol/L) |
Triglycrides (mmol/L) |
LDL (mmol/L) |
HDL (mmol/L) |
Step1 |
Fatty diet |
4.22±0.37a |
1.09±0.18a |
2.24±0.14a |
0.94±0.05a |
L. plantarum |
3.74±0.47a |
1.00±0.19a |
1.92±0.23a |
0.84±0.11a |
L. casei |
3.84±0.37a |
0.77±0.17a |
2.46±0.14a |
0.86±0.11a |
L. acidophilus |
3.99±0.37a |
1.12±0.29a |
2.3±0.14a |
1.08±0.10a |
Values represent mean ± SEM. Values followed by the same letter do not differ significantly.
Figure 1: Hyperlipidemia mice models. Step 1 (feeding phase): 1) Standard diet (control), 2) Ingestion high fat (HF), 3) Ingestion HF plus Lactobacillus; Step 2 (treatment phase): 4) Fat mice (ill-conditions) treated with placebo, 5) Fat mice treated with Lactobacillus cocktail, 6) Fat mice treated with Simvastatin.
with high fat. High lipid diet was composed of standard diet
(80%, wt/wt) plus lard 5%, egg 5% and whole milk powder 10%.
Mice from Group 3 were fed with high fat diet complemented
with lactobacillus bio-product. Bio-product was administered by
a daily injection of 0.3 ml (10 mL/kg of bodyweight). Groups 1
and 2 received a daily injection of 0.3 ml of saline water
In step 2 (treatment of hyperlipidemic model mice), doubleweighted
mice (40-44 grams) were divided into three additional
groups and reared as described before for fourteen days. During
this period, fat mice were fed with standard diet throughout the
whole experiment and had free access to water and food. Fat
mice from group 4 (ill-conditions) were injected daily with 0.3
ml of placebo (saline water), while fat mice from groups 5 and 6
were injected daily with 0.3 ml of probiotics (10 ml/kg of body
weight) and simvastatin solution (3.0 mg /kg of body weight),
respectively, following SFDA instructions.
Body weight and feed intakes of fat mice were recorded every
24 h starting from the beginning of step 1. At the same time,
mouse fecal droppings were collected in sterile Eppendorf tubes
and immediately stored at -70°C until further experiments.
Identification of bacterial DNA profiling
For Denaturing Gradient Gel Electrophoresis (DGGE)
fingerprinting analysis, PCR and Real-time PCR, microbial
genomic DNA was extracted from fecal pellets in the six groups
of mice using the phenol chloroform/isoamyl alcohol/ethanol
extraction method. Fecal pellets were first freeze-dried and
homogenized in 500 μl of extraction buffer (Tris–EDTA/
NaCl pH 8.0, 20 mg/ml proteinase K, 20% wt/vol SDS) before
incubation for 1 h at 37°C. Genomic DNA from the gut of control
and lactobacillus-treated mice of step 2 were prepared following
the same procedure. Extracted genomic DNA from feces and gut
was dissolved in sterile milli-Q water for a final concentration of
about 1 ug/ul and stored at -20°C. until further use in molecular
biology experiments (PCR, real-time PCR and Southern blots).
Prelude to DGGE, an amount of ten to twenty nanograms
of total genomic DNA was used as a template for the PCR
amplification of V3 region of 16S rDNA (ribosomal DNA) using
conserved universal bacterial primers: 16F 5’-CGC CCG GGG
CGC GCC CCG GGCGGG GCG GGG GCA CGG GGG G AGA GTT TGA
TCM TGG CTC AG-3’ and 16R 5’-TAC GGY TAC CTT GTT ACG
ACT T-3’ (Invitrogen, Shanghai, China). Such universal primers were mandatory to obtain the fingerprints of the main bacterial
community present in the different fecal samples [29]. PCR
amplification of 16S rDNA products (TransGen Biotech, Beijing,
China) was carried out in a Takara Master Thermal Cycler Dice
(Takara, Dalian, China) programmed for an initial denaturation
of 95°C for 3 min followed by 30 cycles of (94°C for 30 s), 50°C for
30 s, 72°C for 1 min and a final extension of 72°C for 7 min. The
16S rDNA PCR products were then used to compare the bacterial
profiles between the six samples of mouse feces.
Mixtures of PCR-derived 16S rDNA products were separated
on a 6% (wt/vol) polyacrylamide gel with a denaturant gradient
ranging from 40 to 65% optimized for V3 region. Electrophoresis
was run for 7 h in TAE 1x running buffer at constant voltage
(100 V) with a temperature of 60°C in a Junyi JY-TD331-DGGE
system (Dong Fang Electrophoresis Equipment Co. LTD, Beijing,
China). After electrophoresis, gels were stained for 15 min with
ethidium bromide and visualized under UV light. The final result
of DGGE images was analyzed using Quantity One® (1-D analysis
software, Version 4.4.0, Bio-Rad, Hercules, California, USA).
Isolation and identification of fecal bacteria
To identify most dominant bacteria in feces from mice in
the six different groups, fecal samples from Group 1-6 mice
were processed for bacterial cultures on LB medium using most
conventional methods. About twenty colonies in each series of
samples were selected for genomic DNA, extraction, PCR, cloning
and sequencing. Total genomic DNA was extracted from pure
cultured strains as described before and used as template (10
ng) in PCR reactions employing 16F and 16S universal primers
(Invitrogen, Shanghai, China; see before). Strain-specific 16S
rDNA PCR products were purified using Qiaquick Gel Extraction
Kit (Qiagen, Valencia, California, USA) and cloned into pMD-19-T
simple vector (Takara Biotechnology, Dalian, China). 16S rDNA
clones specific for single strains were sequenced on ABI-PRISM
3730 automated sequencing system using 16S or 16R primer,
Big Dye Ready Reaction DyeDeoxy Terminator Cycle Sequencing
kit and Applied Biosystems AmpliTaq DNA Polymerase (Perkin-
Elmer, Weiterstadt, Germany). Sequences obtained were
assembled using AutoAssembler (PE-Applied Biosystems, Foster
City, California, USA) and subjected to BLAST analysis using
the server at NCBI, identifying new strains or strains with 99%
identity to some known bacterial species (Acc. Nb. KC347584,
KC347585, KC347586, KC347587, KC347588, KC347589,
KC347590, KC347591, KC441061, KC441062, KC441063,
KC441064).
Universal 16S and 16R primers were then used to amplify
genomic DNA of strain-specific cultures and map Lactobacillus
johnsonii, Lactococcus sp. M3T8B4, Enterococcus faecium,
Enterobacter sp. dc6, Bacillus licheniformis, Bacillus oleronius,
Bacillus amyloliquefaciens, Bacillus BM9H, Ubac1, Ubac2, Ubac3
and Ubac4 on the DGGE profile (Table 2). Metagenomic DNA
was isolated from both mouse feces and bacterial cultures as
described before. The same procedure was used for DGGE (see
under Identification of bacterial DNA profiling). The two PCRDGGE
profiles were compared on the same 6% polyacrylamide gel, identifying Efae, Ubac3 and Ubac4 as main bacteria in the
mouse fecal samples of step 2.
Probe synthesis and Southern blotting analysis
PCR-amplified 16S-rDNA products were used as templates
for preparation of probes for Southern blots. Fecal microbial
16S rDNA (QIAamp DNA stool kit, Qiagen, USA) were amplified
using 16S primers in PCR (95°C 3 min, 30 cycles: 94°C 30 s, 50°C
30 s, 72°C 1 min, 72°C 7 min). The 16S rDNA PCR products were
then purified and labeled with DIG-dUTP (alkali labile) using 1 μg
of denatured DNA (DIG-High prime labeling kit, Roche Applied
Science, Mannheim, Germany). Prelude to Southern blot, gut
microbial 16S-rDNA products (1 μg) were amplified in PCR in
similar conditions using Ubac2 strain-specific primers (Table 2).
Strain-specific rDNA PCR products were then separated using
agarose gel electrophoresis and blotted onto Hybond nylon
membrane (GE Healthcare, Pittsburgh, Pennsylvania, USA) by
capillary transfer in 20x SSC buffer for 12 h at 37°C. Membranes
were then dried for 2 h at 80°C and pre-hybridized in 20 ml of
1x hybridization buffer (DIG Easy Hyb Roche Applied System,
Mannheim, Germany) for 2 h at 42°C in a vacuum hybridization
oven (model HB-1D, Techne®, Princeton, New Jersey, USA).
Hybridization was performed by incubating the membrane for
12h at 42°C 10 ml of 1x hybridization buffer containing a Ubac-
2-specific DIG-labeled DNA probe at a concentration of 25 ng/
ml. After hybridization, membranes were washed at 25°C with
SSC/SDS then blocked to prevent any non-specific binding of the
antibody (1:10 000 dilution). Finally, hybridization signals were
revealed using nitroblue tetrazolium salt/bromo-chloro-indolylphosphate
(NBT/BCIP, DIG-High prime labeling kit, Roche
Applied Science, Mannheim, Germany).
qPCR
Precise quantification (Ct) of the twelve main bacterial
strains identified in mouse fecal and gut samples was performed
by Real-time PCR using the MyiQTM Single-Color Real-Time
PCR Detection System on optical grade 96-well plates (Bio-Rad,
Hercules, USA). Real-time PCR reactions (20 μl) were carried
out in triplicates on three different samples (n=9). Bacterial
strain levels were measured by using the Fast Start Essential
DNA Green Master Kit (Roche), with 0.5 μmol of each forward
and reverse primer (Invitrogen, Shanghai, China) and 10 ng of
genomic DNA templates. Primers were those from (Table 2). The
Real-time PCR program was optimized for fecal and gut DNA:
95°C for 600 s and 45 cycles of 95°C for 20 s, 55°C for 20 s and
75°C for 15 s. After amplification, melting curve analysis was
performed by slow heating from 65°C to 95 °C (1°C per cycle for
10 sec) with fluorescence acquisition using 0.1°C intervals. The
threshold cycle (Ct) values and baseline settings were recorded
using the software implemented in MYiQ to allow calculation of
the prevalence levels for each test bacterial strain on the basis
of the equation 2^(-deltadeltaCt) [30]. Blic-levels were used as
internal reference. Bacterial strains of feces from mice under
control conditions and gut of ill mice were used as reference
series, respectively (control levels = 1). Statistical analysis of
qPCR data was performed using SPSS Statistics 22.
Target bacteria |
Sequence |
Expected product size |
Annealing
temperature ™ |
Bacillus amyloliquefaciens
Bamy-F
Bamy-R |
5’-tgttagggaagaacaagtgc-3’
5’-cctttacgcccaataattcc-3’ |
141 bps |
58ºC |
Bacillus licheniformis
BlichF
BlichR |
5’-gcttttagctaccacttgca-3’
5’-tttcgtccattgcggaagat-3’ |
177 bps |
58 ºC |
BacillusM9H
Bacil M9HF
Bacil M9HR |
5’-tttggtctgtaactgacgct-3’
5’-gaaaccctctaacacttagc-3’ |
107 bps |
58 ºC |
Bacillus oleronius
Bole-F
Bole-R |
5’-tgcagctaacgcattaagca-3’
5’-taaggttcttcgcgttgctt-3’ |
127 bps |
58 ºC |
Enterococcus dc6
Enterodc6F
Enterodc6R |
5’-ggtttaattcgatgcaacgc-3’
5’-caacatttcacaacacgaga-3’ |
135 bps |
58 ºC |
Enterococcus faecium
EnterofaeF
EnterofaeR |
5’-taacacttggaaacaggtgc-3’
5’-acctcaccaactagctaatg-3’ |
121 bps |
58 ºC |
Lactobacillus johnsonii
LjohnF
LjohnR |
5’-ataacacctggaaacagatgc-3’
5’-cgttaccttaccaactagct-3’ |
125 bps |
58 ºC |
LactococcusM3T8B4
LacM3T8B4-F
LacM3T8B4 |
5’tatctaaccagaaagggacg-3’
5’-ttgagccactgccttttaca-3’ |
136 bps |
58 ºC |
Uncultured bacterium 1
Uncbac1F
Uncbac1R |
5’-gcaaggttgaaactcaaagg-3’
5’-gtcaaaggatgtcaagacct-3’ |
108 bps |
58 ºC |
Uncultured bacterium2
Uncbac2-F
Uncbac2-R |
5’-ggtttaattcgaagcaacgc-3’
5’-acttaacccaacatctcagc-3’ |
143 bps |
58 ºC |
Uncultured bactrium3
Uncbac3-F
Uncbac3-R |
5’-taacacctggaaacagatgc-3’
5’-cgttaccttaccaactagct-3’ |
124 bps |
58 ºC |
Uncultured bactrium4
Uncbac4-F
Uncbac4-R |
5’-acggtatctaaccagaaagc-3’
5’-ggttaagccgaactttcaca-3’ |
141 bps |
58 ºC |
Table 2: Primers for quantification of twelve main bacterial strain levels in mouse gut and feces.
Table 3:Similarity matrix of the fecal bacterial profiling from mice subjected to illness and treatment &
conditions.
Mice |
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
(1) |
100 |
|
|
|
|
|
(2) |
72.8 |
100 |
|
|
|
|
(3) |
68.9 |
71.3 |
100 |
|
|
|
(4) |
67.9 |
65.9 |
68.9 |
100 |
|
|
(5) |
62.3 |
61.9 |
54.7 |
53.5 |
100 |
|
(6) |
29.8 |
35.7 |
33.9 |
35.7 |
22.1 |
100 |
(1): Standard diet, (2): High-Fat diet, (3): High-Fat diet and Lactobacillus complement (Step 1), (4): Ill mice, (5): Ill mice treated with Lactobacillus, (6):
Ill mice treated with Simvastatin (Step 2).
Gut collection and tissue measurements
At the end of step 1 (groups 1-3) and step 2 (groups 4-6),
mice were fasted for 12 h and euthanized. In each group of mice,
visceral organs such as kidney, gut, liver, spleen, pancreas and
the subcutaneous intra-abdominal adipose tissues (perirenal
and epididymal fat pads) were then collected aseptically under
a laminar airflow hood and weighted using a Sartorius BP211D (Sartorius, Gottingen, Germany). The gut was kept for DNA
extraction and identification of strain-specific levels following
treatments in step 2: 1) Control, 2) Lactobacillus treated mice.
Measurement of blood cholesterol and lipids
Blood samples were collected from the eye socket and kept
at 0°C for 30 minutes before centrifugation at 2000 × g for 15
minutes at 4°C to separate blood components. The cell pellets were discarded, while the serum samples were stored at -70°C
until measurement of lipidemia. Serum total cholesterol (TC),
triglycerides (TG), high-density lipoprotein (HDL) and lowdensity
lipoprotein (LDL) were determined using commercial
kits (Changchun Huili Bio-chemical Co., Ltd). Atherosclerotic
index (AI) was calculated by using the equation TC-HDL/HDL.
Data were analyzed using SPSS Statistical software. Data were
tested by analysis of variance (ANOVA). Means were compared
across groups by Ducan tests, significance being declared when
p ≤ 0.05 or p ≤ 0.01.
Determination of scavenging abilities of Lactobacillus
strains
The three strains of Lactobacillus (L. plantarum SD02, L.
acidophilus SD65 and L. casei SD07) were prepared as described
under “Bio-product composition”. Determination of cholesterol
removal rate was according to Brashears et al. [31]. Assessment
of the scavenging capacity of the DPPH radical by various
Lactobacillus strains was carried out according to the procedure
described by Wang et al. [32]. Assay of scavenging capacity
against the superoxide anion free radicals was according to Sah
et al. [33].
Results and Discussion
A mouse model of hyperlipidemia was developed to identify
specific bacteria associated with lipid metabolism and test the
effects of a new cocktail of Lactobacilli bio-products versus
Simvastatin on variation in composition of the intestinal gut flora
as well as on prevention and treatment of cholesterolemia and
weight gain (Figure 1).
Using universal primers in DGGE experiments amplified
various DNA bands, each representing a specific bacterial strain
(Figure 2). Interestingly, comparing DGGE profiles of the fecal
microbial community in the different groups of mice from Step
1 and 2 showed clear differences between mice fed with fat diet
and those fed with fat diet and Lactobacillus and between all
three groups in Step 2 (Figure 2A). Most notably, the bacterial
community diversity revealed by the DGGE fingerprinting
showed that specific bacterial strains were induced after high-fat
diet intake and/or treatment with Lactobacillus or Simvastatin
(Figure 2B). Sequencing bacterial cultures from mouse fecal
samples identified four “uncultured” bacteria (Ubac1, Ubac2,
Ubac3 and Ubac4), Lactobacillus johnsonii, Lactococcus sp.
M3T8B4, Enterococcus faecium, Enterobacter sp. dc6, Bacillus
amyloliquefaciens, B. licheniformis, B. oleronius and B. M9H.
Trillions of bacteria probably exist in the mouse flora playing a
key role in the regulation of the digestive tract and thereby many
other physiological systems such as development, brain function
and modulation of the immune system [34-37]. However, these
particular twelve strains are found to be an essential part of
the mouse fecal and gut microbial catalogue (Table 2). Using
universal primers to amplify DNA in fecal samples and such
twelve bacterial cultures showed similar DNA band profiles
between fecal samples of ill mice and bacterial strains such as
E. faecium, Ubac3 and Ubac4. Comparing DNA bands from DGGE
mapping also showed strong similarities between fecal samples of mice treated with Simvastatin and two main bacterial strains,
BM9H and Ubac1 (Figure 2C).
Whether these bacterial strains interfere with the mouse gut
flora and its metabolic functions in a beneficial manner needs to be
studied with caution. Many Bacillus bacteria such as B. anthracis,
B. cereus and B. subtilis are known to produce toxins that affect
human health through damage on both digestive and immune
systems [38-41]. Enteroccoci are well known highly resistant
human pathogens that can spread over the whole body and cause
various diseases from meningitis and bacteremia to endocarditis,
diverticulitis and urinary infections [42-44]. Ubac1 (KC347585)
is significantly related to E. faecium strain S4 (KC478508). If this
new Enteroccocus strain induced by Simvastatin has harmful
secondary effects similar to those from Bacillus and Enteroccocus
bacteria, a new remedy alternative to Simvastatin needs urgently
to be used to cure hyperlipidemia. In our study, using drug
therapy (Simvastatin) is found to affect the gut microflora much
more than a Lactobacillus therapy (Figures 2 & 3, Table 3). Many
potentially toxic bacterial strains are up-regulated by Simvastatin
(Figure 2). In contrast, levels of potentially beneficial bacteria
such as Ubac2 are reduced by Simvastatin treatment (Figure 3).
The similarity matrix shows index value pairs of 22.1-35.7 for
the gut flora of mice treated with Simvastatin compared to other
treatments, while index value pairs of 53.5-62.3 are found for the
gut flora of mice treated with Lactobacillus (Table 3).
Simvastatin is known for a lot of side effects such as diarrhea,
swelling, weight gain, increased thirst, nausea, abdominal pain,
a loss of appetite, constipation and water retention. Persistent
liver and gastro-intestinal disorders up to diabetic problems
are observed in patients on Simvastatin or any other statin drug
(MRC/BHF Heart Protection Study Collaborative Group, 2009)
[45]. Our results show that one major cause for the secondary
effects of the chronic usage of Simvastatin is most probably to be an
altered gut flora. Many antibiotic chemicals used in medicine and
animal rearing are well known to be efficient to treat pathogenic
bacterial strain-levels but also to have long-term effects on the
composition of the normal gut flora thus predisposing the body
to develop new illness [46,47]. Strains of bacterial pathogens
may become dominant due to loss of bacterial diversity in
the gut following specific antibiotic chemical treatments [48].
Meanwhile, use of antibiotics and chemical treatments are clearly
prohibited for pregnant women. The probability for the child to
develop diseases such as asthma is rather high if the mother takes
antibiotics [49]. More natural treatments or specific co-treatment
with bioproducts are necessary to cure bacterial infections while
maintaining normal strain-levels in the gut flora.
Levels of abundance have been determined precisely using
qRT-PCR for the twelve bacterial strains in all six groups of mice
(Table 4 & Figure 4). Internal reference was Blic-levels for which
no statistically significant differences were found between mean
Ct values of different treatments (Table 4). Results show that
Lactobacillus, Lactococcus, Enterococcus and other uncultured
bacterial species from the genus Enterococcus are significantly
down-regulated by fat diet intake (Figure 4). We show the
occurrence of specific bacterial strains that are significantly

Figure 2: Fatty diet, Lactobacillus- and Simvastatin-associated changes in the mouse fecal microbial flora. A: DGGE profiles (left) and One Quantity analysis (right) of the fecal microbial flora in mice after fatty diet intake (step 1) and post-treatment phase (step 2). A right: 1: Standard diet, 2: Fatty diet, 3: Fatty diet and Lactobacillus complement, 4: ill mice, 5: ill mice treated with Lactobacillus, 6: ill mice treated with Simvastatin. Line 4 corresponds to over-weighted mice developing pathological conditions. The DGGE profiles were constructed using universal primers tuned to the conserved V3 region of bacterial genomes. The DGGE profile of feces from ill mice (line 4) was characterized by a high density of bacteria, in contrast to ill mice treated with Lactobacillus (line 5) or Simvastatin (line 6). The bacterial DGGE profiles completely differed between ill mice treated with Lactobacillus and those treated with Simvastatin. M: Molecular weight markers (Lambda DNA/Hind III Plus Markers: 3130, 9416, 6557, 4361, 2322, 2027, 564, 125). B: Analysis results of lane comparison from the DGGE profiles using Quantity One 4.4.0 software. Significant differences are found between the bacterial profiles of mice from groups 1 to 6. “+” shows specific bacterial-strain levels increased after uptake of fatty diet in absence of Lactobacillus during step 1 (2) and in ill mice of step 2 (4). The arrow shows a peak of bacteria in ill mice treated with Lactobacillus (5). The dash shows bacterial strain-levels decreased in ill mice after Simvastatin treatment in step 2 (6). The asterisks show specific bacterial-strain levels increased in ill mice after Simvastatin treatment (6). C: DGGE mapping of bacterial strains identified in mouse fecal samples. DGGE profiles of Ljoh, L8b4, Ubac1, Ubac2, Ubac3 and Ubac4 (left) and Edc6, Efae, Blic, Bm9h, Bole, Bamy, M3T8 (right) compared to fecal DNAs from mice of step 2 amplified with 16R-16S universal primers. Markers (Lambda DNA/Hind III Plus) are shown on the left of the gel. Line 4 corresponds to over-weighted mice developing pathological conditions. DGGE mapping shows prevalence of the twelve bacterial strains in step 2. Bacillus M9H and uncultured bacteria-1 (Ubac1) strain-levels are found to increase significantly over Simvastatin treatment (*). Arrowheads indicate diagnostic bands for Efae, Ubac3 and Ubac4 enriched in mouse fecal samples of step2. Blic: Bacillus licheniformis, Bamy: Bacillus amyloliquifaciens, Bole: Bacillus oleronius, Bm9h: Bacillus M9H, Edc6: Enterococcus dc6, Efae: Enterococcus faecium, Ljoh: Lactobacillus johnsonii, M3T8: Lactococcus M3T8B4, Ubac1: Uncultured bacteria 1, Ubac2: Uncultured bacteria 2, Ubac3: Uncultured bacteria 3, Ubac4: Uncultured bacteria 4.

Figure 3: Regulation of bacterial strain-levels by Lactobacillus and Simvastatin. Southern blots of 16S rDNA products from gut genomic DNA of mice from the groups 1 to 6. 1: mice subjected to standard diet, 2: mice subjected to fatty diet, 3: mice subjected to fatty diet complemented with Lactobacillus, 4: ill mice treated with placebo, 5: ill mice treated with Lactobacillus, 6: ill mice treated with Simvastatin. Using Ubac2 strain-specific DNA probe show significantly decreased bacterial strain-levels following Simvastatin treatment (see line 6).
Table 4: Similarity matrix of the fecal bacteria profiling from mice subjected to illness and treatment conditions.
(1): Standard diet, (2): Fatty diet, (3): Fatty diet and Lactobacillus complement (Step 1), (4): Ill mice, (5): Ill mice treated with Lactobacillus, (6): Ill mice treated with Simvastatin (Step 2).
decreased or increased after high fat diet in our mouse model of
hyperlipidemia. E. faecium and Ubac4 bacterial strain-levels are
found to be abnormally high (tenfold increased) in fecal samples
of ill-mice that have been subjected to heavy fat diet intake (Step
2; Figure 4A). In ill mice, levels of L. johnsonii and Ubac3 were
increased by a factor of about 3. In contrast, bacterial levels for
B. amyloliquefaciens, B. M9H, Enterococcus dc6, L sp. M3T8B4
and Ubac1, were seriously altered in step 2. Ubac2 strain-levels
were reduced by a factor of 1000 over fat diet intake (Figure 4A).
However, some of these bacteria (E. faecium, B. M9H, B. oleronius,
Ubac2 and Ubac4) are found to be specifically regulated by
Lactobacillus cocktail (Figures 4B). An increase of about 50% was
noticed for E. faecium following Lactobacillus treatment. This may
be very beneficial for the mice. E. faecium and some other strains
of Enteroccocus bacteria are known to have positive effects on
intestinal microbial flora and immune function in particular in
mice [50,51]. Lactobacillus also significantly increased levels of
Ubac2 in the mouse gut and strongly reduced (by about 30%)
the levels of Ubac4 (Figure 4B). Thus, our results show that
Lactobacillus could have two-sided beneficial effects. It could
help stimulate beneficial bacterial-strain levels such as Ubac2
and in the same time significantly reduce the levels of more
harmful bacteria such as Ubac4. On the basis of these results, we
propose that our new probiotic cocktail could have numerous
beneficial effects on animal and human gut microflora and could
be a particularly good alternative to treatments with Simvastatin
and any other even more toxic statin drugs.
In addition, our results show that specific bacterial strainlevels
such as those of Efae and Ubac4 that are found to be
strongly associated to hyperlipidemic conditions might be useful
probes for diagnosis of obesity risks and diabetes using fecal
samples of patients with metabolic problems. This needs to be
investigated very precisely in order to develop new formulations
against most severe conditions of hyperlipidemia. The function of the four uncultured bacteria Ubac1
(KC347585; 98% identical to E. faecium strain S4, KC478508),
Ubac2 (KC441062; 98% identical to L. taiwanensis, NR-044507),
Ubac3 (KC441063; also 98% identical to L. taiwanensis, NR-
044507) and Ubac4 (KC441064; 98% identical to L. murinus,
AB326349) is unknown and needs to be investigated to study in
details the beneficial effects of Lactobacillus. Lactobacillus species
such as L. taiwanensis are known to increase in the gut of mice fed
with high-fat diet in agreement with our study [52]. L. johnsonii
strain-levels are shown to increase over fat diet intake (Figure 4).
A certain number of Lactobacilli have also been described in the
gut of rats reared with sucrose [53]. Lactobacilli are known to be
crucial to regulate sugar as well as polyphosphate physiological
levels [54]. They are also known to decrease inflammation
and muscle atrophy in acute leukemia mouse models [55]. L.
murinus strains have even been shown to enhance intestinal
cell proliferation and therefore maintain gastrointestinal cell
turnover [56]. Both Lactobacillus and Enteroccocus are known
to play a key role in sugar and protein digestion for vitamin
and short fatty acid synthesis as well as in immunomodulation,
pathogen inhibition and epithelial cell attachment as part of
the “acidophilus complex” [57]. A strain such as L. johnsonii is
traditionally used to attenuate Helicobacter pylori-associated
gastritis [58]. Thus, our current knowledge about the function
of gut and fecal bacteria strongly suggests that high-fat diet
could alter many various physiological functions by altering the
composition of the gut microflora. Our results typically show that
beneficial bacterial strain-levels could be dangerously down regulated
while other much more toxic bacterial pathogens could
be developed in the gut in response to ingestion and accumulation
of high amounts of fat. Treatments such as our Lactobacillus
cocktail may be extremely useful to maintain strain-levels in the
gut microbial flora and thereby good health conditions.
However, it has to be taken into consideration that the

Figure 4: Quantitative real-time PCR analysis of bacterial strains from mouse gut and feces. A: Relative abundance of the twelve bacterial strains identified in fecal samples from mice fed with standard diet (1), mice fed with standard diet complemented with high fat (2), mice fed with high fat diet complemented with Lactobacillus cocktail (3) and ill-model mice (4). Strain-levels in mice fed with standard diet are used as control (= 1). B: Relative abundance of the twelve bacterial strains identified in the gut samples from ill-model mice treated with placebo (G1) or Lactobacillus (G2). Strain-levels in the gut of ill mice from step 2 are used as control (=1). In A and B, bacterial-strain levels in each sample are compared to levels of the bacterial strain Blic used as reference (Table S3). The value 2^(-delta delta Ct) is calculated using the mean Ct value (n=9). Blic: Bacillus licheniformis, Bamy: Bacillus amyloliquifaciens, Bole: Bacillus oleronius, Bm9h: Bacillus M9H, Edc6: Enterococcus dc6, Efae: Enterococcus faecium, Ljoh: Lactobacillus johnsonii, M3T8: Lactococcus M3T8B4, Ubac1: Uncultured bacteria 1, Ubac2: Uncultured bacteria 2, Ubac3: Uncultured bacteria 3, Ubac4: Uncultured bacteria 4.
stimulatory effects of Lactobacillus on bacterial strain-levels
including BM9H, Ubac2 or Efae were not observed during step
1 (development of illness over excessive fat diet intake). All the
twelve strain levels were found to be severely down-regulated
in step 1 in the absence or presence of Lactobacillus (Figures
3&4). This indicates perhaps that one of the short-term effects of
excessive fat intake is a completely down-regulation of the entire
gut flora. A similar down-regulation of bacterial strain-levels is
observed in the case of various disorders such as inflammatory
bowel disease (IBD), Crohn’s disease (CD), ulcerative colitis and colon cancer [59-62]. In contrast, long-term effects of fat
diet intake could lead to increased levels of harmful bacteria in
absence of Lactobacillus. We describe a case of hyperlipidemic
mouse model where many strains such as BM9H, Efae and Ubac2
were completely abolished in step 1 and where two strains
(Ubac4 and Enteroccocus) were increased by a factor of 10 in feces
of hyperlipidemic mice after a long-term fat diet (step 2; Figure 4).
If this is good or bad for the model mice needs to be explored
in further details using ,BM9H, Enteroccocus, Ubac2 or Ubac4
single strain experiments. Different doses of Lactobacillus and/

Figure 5: Blood lipid concentration (in Mmol/liter) of cholesterol (CH), triglycrides (TG), high density lipoprotein (HDL) and low density lipoprotein (LDL) in mice subjected to hyperlipidemia, bacterial bioproduct and pharmaceutical treatment conditions. 1: Standard diet, 2: Fatty diet, 3: Fatty diet and Lactobacillus complement (Step 1), 4: Hyperlipidemic mice, 5: Hyperlipidemic mice treated with Lactobacillus, 6: Hyperlipidemic mice treated with Simvastatin (Step 2). Bars represent mean ± SEM. Bars followed by the same letter do not differ significantly (P≤0.05).
Figure 6: Lactobacillus single-strain effects on cholesterol (CH) removal rate (A) and scavenging rate against 2,2-diphenyl-1-picrylhydrazyl (DPPH)
(B) and superoxide anion free radicals (SAFR) (C). Number atop the bar is the mean percent value for each treatment.
|
Fecal |
Mean Difference |
Std Error |
Sig. |
95% Confidence interval |
Lower Bound |
Upper Bound |
Blic |
1-2 |
-6.27000* |
1.13160 |
0.001 |
-8.8795 |
-3.6605 |
1-3 |
-2.72667* |
1.13160 |
0.043 |
-5.3361 |
-0.1172 |
1-4 |
-1.21333 |
1.13160 |
0.315 |
-3.8228 |
1.3961 |
2-3 |
3.54333* |
1.13160 |
0.014 |
.9339 |
6.1528 |
2-4 |
5.05667* |
1.13160 |
0.002 |
2.4472 |
7.6661 |
3-4 |
1.51333 |
1.13160 |
0.218 |
-1.0961 |
4.1228 |
Bamy |
1-2 |
-3.69667* |
0.27197 |
0.000 |
-4.3238 |
-3.0695 |
1-3 |
-0.32000 |
0.27197 |
0.273 |
-.9472 |
0.3072 |
1-4 |
-0.93000* |
0.27197 |
0.009 |
-1.5572 |
-.3028 |
2-3 |
3.37667* |
0.27197 |
0.000 |
2.7495 |
4.0038 |
2-4 |
2.76667* |
0.27197 |
0.000 |
2.1395 |
3.3938 |
3-4 |
-0.61000 |
0.27197 |
0.055 |
-1.2372 |
0.0172 |
Bole |
1-2 |
-0.04333 |
0.38394 |
0.913 |
-0.9287 |
.8420 |
1-3 |
1.94333* |
0.38394 |
0.001 |
1.0580 |
2.8287 |
1-4 |
1.53667* |
0.38394 |
0.004 |
0.6513 |
2.4220 |
2-3 |
1.98667* |
0.38394 |
0.001 |
1.1013 |
2.8720 |
2-4 |
1.58000* |
0.38394 |
0.003 |
0.6946 |
2.4654 |
3-4 |
-0.40667 |
0.38394 |
0.320 |
-1.2920 |
0.4787 |
BM9H |
1-2 |
-1.54667* |
0.23682 |
0.000 |
-2.0928 |
-1.0006 |
1-3 |
1.38333* |
0.23682 |
0.000 |
.8372 |
1.9294 |
1-4 |
-4.46333* |
0.23682 |
0.000 |
-5.0094 |
-3.9172 |
2-3 |
2.93000* |
0.23682 |
0.000 |
2.3839 |
3.4761 |
2-4 |
-2.91667* |
0.23682 |
0.000 |
-3.4628 |
-2.3706 |
3-4 |
-5.84667* |
0.23682 |
0.000 |
-6.3928 |
-5.3006 |
Edc6 |
1-2 |
3.46000* |
0.12891 |
0.000 |
3.1627 |
3.7573 |
1-3 |
1.98667* |
0.12891 |
0.000 |
1.6894 |
2.2839 |
1-4 |
2.50000* |
0.12891 |
0.000 |
2.2027 |
2.7973 |
2-3 |
-1.47333* |
0.12891 |
0.000 |
-1.7706 |
-1.1761 |
2-4 |
-0.96000* |
0.12891 |
0.000 |
-1.2573 |
-.6627 |
3-4 |
0.51333* |
0.12891 |
0.004 |
0.2161 |
.8106 |
Efae |
1-2 |
-0.92333* |
0.13427 |
0.000 |
-1.2330 |
-.6137 |
1-3 |
1.19000* |
0.13427 |
0.000 |
.8804 |
1.4996 |
1-4 |
-4.64333* |
0.13427 |
0.000 |
-4.9530 |
-4.3337 |
2-3 |
2.11333* |
0.13427 |
0.000 |
1.8037 |
2.4230 |
2-4 |
-3.72000* |
0.13427 |
0.000 |
-4.0296 |
-3.4104 |
3-4 |
-5.83333* |
0.13427 |
0.000 |
-6.1430 |
-5.5237 |
Ljoh |
1-2 |
-2.46667* |
0.26379 |
0.000 |
-3.0750 |
-1.8584 |
1-3 |
0.13333 |
0.26379 |
0.627 |
-.4750 |
0.7416 |
1-4 |
-2.86333* |
0.26379 |
0.000 |
-3.4716 |
-2.2550 |
2-3 |
2.60000* |
0.26379 |
0.000 |
1.9917 |
3.2083 |
2-4 |
-0.39667 |
0.26379 |
0.171 |
-1.0050 |
0.2116 |
3-4 |
-2.99667* |
0.26379 |
0.000 |
-3.6050 |
-2.3884 |
M3T84B |
1-2 |
0.33667* |
0.11928 |
0.022 |
0.0616 |
0.6117 |
1-3 |
-0.41000* |
0.11928 |
0.009 |
-0.6851 |
-.1349 |
1-4 |
0.35333* |
0.11928 |
0.018 |
0.0783 |
0.6284 |
2-3 |
-0.74667* |
0.11928 |
0.000 |
-1.0217 |
-0.4716 |
2-4 |
0.01667 |
0.11928 |
0.892 |
-.2584 |
0.2917 |
3-4 |
0.76333* |
0.11928 |
0.000 |
.4883 |
1.0384 |
Ubac1 |
1-2 |
-0.90667* |
0.16974 |
0.001 |
-1.2981 |
-0.5152 |
1-3 |
-0.17333 |
0.16974 |
0.337 |
-0.5648 |
0.2181 |
1-4 |
0.92000* |
0.16974 |
0.001 |
0.5286 |
1.3114 |
2-3 |
0.73333* |
0.16974 |
0.003 |
0.3419 |
1.1248 |
2-4 |
1.82667* |
0.16974 |
0.000 |
1.4352 |
2.2181 |
3-4 |
1.09333* |
0.16974 |
0.000 |
0.7019 |
1.4848 |
Ubac2 |
1-2 |
2.24000* |
0.52052 |
0.003 |
1.0397 |
3.4403 |
1-3 |
4.21667* |
0.52052 |
0.000 |
3.0163 |
5.4170 |
1-4 |
4.91667* |
0.52052 |
0.000 |
3.7163 |
6.1170 |
2-3 |
1.97667* |
0.52052 |
0.005 |
0.7763 |
3.1770 |
2-4 |
2.67667* |
0.52052 |
0.001 |
1.4763 |
3.8770 |
3-4 |
.70000 |
0.52052 |
0.216 |
-.5003 |
1.9003 |
Ubac3 |
1-2 |
-1.35000* |
0.40266 |
0.010 |
-2.2785 |
-0.4215 |
1-3 |
-.56000 |
0.40266 |
0.202 |
-1.4885 |
0.3685 |
1-4 |
-3.05667* |
0.40266 |
0.000 |
-3.9852 |
-2.1281 |
2-3 |
0.79000 |
0.40266 |
0.085 |
-0.1385 |
1.7185 |
2-4 |
-1.70667* |
0.40266 |
.003 |
-2.6352 |
-.7781 |
3-4 |
-2.49667* |
0.40266 |
.000 |
-3.4252 |
-1.5681 |
Ubac4 |
1-2 |
-2.78333* |
0.17491 |
.000 |
-3.1867 |
-2.3800 |
1-3 |
1.38667* |
0.17491 |
0.000 |
0.9833 |
1.7900 |
1-4 |
-4.61667* |
0.17491 |
0.000 |
-5.0200 |
-4.2133 |
2-3 |
4.17000* |
0.17491 |
0.000 |
3.7667 |
4.5733 |
2-4 |
-1.83333* |
0.17491 |
0.000 |
-2.2367 |
-1.4300 |
3-4 |
-6.00333* |
0.17491 |
0.000 |
-6.4067 |
-5.6000 |
Blic |
1-2 |
1.15667 |
1.29497 |
0.406 |
-2.0120 |
4.3253 |
Bamy> |
1-2 |
2.8667* |
0.19435 |
0.000 |
2.3911 |
3.3422 |
Bole |
1-2 |
0.74333* |
0.27954 |
0.038 |
0.0593 |
1.4273 |
BM9H |
1-2 |
1.67000* |
0.20607 |
0.000 |
1.1658 |
2.1742 |
Edc6 |
1-2 |
1.23333* |
0.15266 |
0.000 |
0.8598 |
1.6069 |
Efae |
1-2 |
0.78000* |
0.18211 |
0.005 |
0.3344 |
1.2256 |
Ljoh |
1-2 |
0.78000* |
0.26021 |
0.024 |
0.1433 |
1.4167 |
M3T84B |
1-2 |
0.40333* |
0.09039 |
0.004 |
0.1822 |
0.6245 |
Ubac1 |
1-2 |
1.35667* |
0.18899 |
0.000 |
0.8942 |
1.8191 |
Ubac2 |
1-2 |
-0.20333 |
0.19431 |
0.336 |
-0.6788 |
0.2721 |
Ubac3 |
1-2 |
0.45667 |
0.44272 |
0.342 |
-0.6266 |
1.5400 |
Ubac4 |
1-2 |
1.61667* |
0.11271 |
0.000 |
1.3409 |
1.8925 |
Mice |
Body weight (g) |
Liver
weight (g) |
Kidney weight (g) |
Spleen
weight (g) |
Pancreas
weight (g) |
Epididymal fat pad (g) |
Perirenal
fat pad (g) |
(1) |
31.7±0.81A |
1.57±0.11a |
0.515±0.036a |
0.122±0.014a |
0.055±0.0056a |
0.785±0.055A |
0.219±.019A |
(2) |
38.4±2.03B |
1.89±0.13a |
0.509±0.022a |
0.134±0.011a |
0.0796±0.0078a |
1.978±0.123B |
0.398±.045B |
(3) |
30.6±0.85A |
1.59±0.056a |
0.441±0.035a |
0.145±0.075a |
0.0716±0.0101a |
0.951±0.048A |
0.131±.023A |
(4) |
39.2±0.90a |
1.88±0.056a |
0.553±0.044a |
0.108±0.0097a |
0.073±0.0081a |
1.27±0.13A |
0.263±0.049a |
(5) |
39.8±1.79a |
1.93±0.052a |
0.546±0.035a |
0.143±0.0037b |
0.083±0.015a |
1.75±0.25A |
0.316±0.031a |
(6) |
44.17±1.84a |
2.06±0.15a |
0.633±0.027a |
0.126±0.011a |
0.104±0.0018a |
2.24±0.15B |
0.495±0.064b |
Table 5: Bodyweight and weight of internal organs of mice subjected to hyperlipidemia, bacterial bioproduct and pharmaceutical treatment conditions.
Values represent mean±SEM. Values followed by different small and capital letters indicate significant level at P≤0.05 and P≤0.01, respectively. (1): Standard diet, (2): Fatty diet, (3): Fatty diet and Lactobacillus complement (Step 1), (4): Hyperlipidemia-mice, (5): Hyperlipidemia-mice treated with Lactobacillus, (6): Hyperlipidemia-mice treated with Simvastatin (Step 2).
or more appropriate ratios of bacteria in the bio-product cocktail
may help boost specific bacterial strain-levels of the gut flora in
the short term. A slight increase is seen for L. johnsonii, M3T8B4,
Ubac1 and Ubac3 during co-ingestion of fat diet and Lactobacillus
(Figure 4). Long-term and short-term effects of Lactobacillus
on the composition of the gut flora may be very different. They
may well depend on individuals [63-67]. However, our results in
mice show that the effects of Lactobacillus mainly depend on the
concentration of ingested fat. Analyzing Efae and Ubac2 strainlevels,
Lactobacillus is found to have a stimulatory effect during
standard food diet intake but a rather inhibitory effect during
intake of high fat diet (Figures 4A-B). In contrast, Ubac4 levels
are reduced by Lactobacillus in both Step 1 and Step 2 (Figures
4A-B). All together, this suggests that effects of Lactobacillus may
strongly depend on the diet but that very specific noxious gut
bacterial strains such as Ubac4 could be targeted independently
to diet conditions.
In Human, there is a clear correlation between gut flora,
phenotype diversity, food diet and blood pressure [68]. However,
very little is known about microflora, regulation of singlebacterial
strains, metagenomics, genes and control of high
blood lipid levels. In mice, the gut flora is known to regulate
fat metabolism [69]. Interestingly, in our study, overweight, fat
pad accumulations, hypercholesterolemia and high blood lipid
concentration are diagnosed for mice overfed with fat diet in step
1 where gut bacterial diversity is seriously affected (Figures 2-5
& Tables 3-5). This illustrates a strong association between gut
flora and lipid metabolism. Importantly, our results show that
overfeeding mice with high-fat diet lead to significant weight
gain and increased epididymal fat pad mass, but that the addition
of Lactobacillus in the diet clearly maintains normal body weight
and weight of specific tissues such as the liver, the adipose
capsule of the kidney and the epididymal fat (Table 5, Step 1). In
the pool of fat mice, we show that treatments with Lactobacillus
and Simvastatin over fourteen-days period did not re-establish
normal conditions in regard to body weight and/or specific organ
weight. Epididymal fat pad even increased after probiotic or
medical treatment (Table 5, Step 2). However, it appeared very
clearly that Lactobacillus bio-product treatments in high-fat diet
fed mice had more significant effects on body weight and weight
of kidney, pancreas and perirenal/epididymal fat pads than Simvastatin-based therapy (Table 5, Step 2).
In regard to blood biochemical parameters, our results
show that overfeeding mice with high-fat diet lead to a severe
accumulation of neutral lipids in the blood circuitry (Figure 5,
Step 1). No differences were found in cholesterol and triglyceride
level numbers as well as in LDL/HDL ratios between fat mice
treated with Lactobacillus and those treated with Simvastatin. In
the two treatments, decreased cholesterol and triglyceride blood
concentrations were detected in comparison with untreated
ill-mice (Figure 5, Step 2), suggesting that bioproducts and
drugs could both have beneficial effects on lipid metabolism in
individuals with a fatty-acid metabolism disorder. However,
bioproducts are shown to improve cholesterolemia also during
fat intake (Figure 5). Other studies in rodent models as well as in
Human indicate that probiotic bacteria could be used to improve
the lipid profile as an alternative or a supplement for antibiotic
therapy [70-72]. However, our results show clear support for the
further idea that Lactobacillus can prevent hyperlipidemia when
added as a complement of fat food. Significantly lower cholesterol,
triglyceride, HDL/LDL ratios were found in the blood from mice
of step 1 treated with Lactobacillus (Figure 5). In addition, while
atherosclerotic index as high as 3.47-4.43 was observed in mice
fed with high-fat diet (indicative of severe disease conditions), AI
values were reduced to 2.00-2.41 in Lactobacillus-treated mice
similarly to control healthy and Simvastatin-treated mice (Figure
5). In a further attempt to justify how the Lactobacillus works for
the in vivo pharmacology, we measured additional biochemical
parameters for the lipid metabolism and oxidation in mice
(cholesterol removal rate and scavenging rates of diphenylpicrylhydrazyl
and superoxide anion free radicals). We find that L.
plantarum SD02, L. paracasei SD07 and L. acidophilus SD65 used
as single-strain significantly contribute to cholesterol removal.
A CH removal rate of 30-50% is observed following treatment
with each strain of Lactobacilli (Figure 6A). In addition, each
Lactobacillus strain of the bioproduct is shown to have a very high
scavenging ability against DPPH (1,1-diphenyl-2-picrylhydrazyl).
In DPPH-scavenging assay total antioxidant capacity was found to
be superior to 90% for all the three single-bacterial strains tested
at the dose implemented in the bioproduct (109 CFU/ml). These
results indicated a strong antioxidant activity in the bioproduct
(Figure 6B). Finally, L. plantarum is found to have a rather low scavenging ability against superoxide anion free radicals, but
the two other Lactobacillus single strains (L. paracasei and L.
acidophilus) are both found to have potent superoxide anion
scavenger activities. The SAFR scavenging rate value of L.
plantarum is only of about 25%, but the SAFR scavenging rate
values observed with L. paracasei and L. acidophilus are of
about 88 and 96%, respectively (Figure 6C). This is particularly
important since O·̄2 anion is known as one of the major causes
of apoptosis and cell death in various tissues following all many
different mechanisms [73,74].
Our results therefore demonstrate that a food complemented
with our new Lactobacillus cocktail can not only have a strong
beneficial effect for the gut flora but also for many biochemical
parameters of general body condition especially in affections
related to cholesterolemia and thereby altered hepatic
metabolism [75-78].
A specific cocktail of Lactobacillus bacteria is found to act on
the gut flora and to have both curative and preventive effects on
the accumulation of lipids in the blood and fat in various organ
tissues of the digestive tract. We show that a precise composition
of L. plantarum SD02, L. acidophilus SD65 and L. casei SD07 helps
maintain gut flora, reduce blood lipid concentration, control
cholesterol levels, stimulate antioxidant activities and avoid gain
of weight in a hyperlipidemia mouse model system. If the cocktail
works in an obese mouse model system, a domestic animal
such as dog and cat and/or an animal model of the industrial
production such as chicken, cow, duck, goat, goose, hen, horse,
mouton, ox, piglet and rabbit needs to be investigated in details.
Our Lactobacillus cocktail may be beneficial in dolphins. It may
also be beneficial in fishes, mollusks and turtle s. Importantly, it
may be very crucial to help digestion and thereby reproduction
of endangered animals such as Cheetah, Gorilla, Panda, Rhino,
Tiger and other legendary animals such as the Deer of Hainan
Island. It may also help improve digestion in wild animals such
as wolves and white bears and thereby their adaptation to new
unnatural environment. In addition, the cocktail of L. plantarum
SD02, L. acidophilus SD65 and L. casei SD07 may before all
have very beneficial effects in patients suffering both lipid
metabolism pathology and Simvastatin therapy. We think that
our Lactobacillus preparation applied for human health can be
extremely benefic not only to eliminate the secondary effects
due to chemical drugs such as Simvastatin but also to put a brake
on the development of pathological conditions including cancer,
diabetes and obesity.
J.F.P. is High Level Oversea Scientist and Taishan Scholar
(NO.tshw20091015). We acknowledge the support of grants
ZR2011CM046 from Natural Sciences Foundation of Shandong
Province and 2012BAK17B05 from National Key Technologies R
& D Program.
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