Antonie van Leeuwenhoek (2015) 107:149–156 DOI 10.1007/s10482-014-0312-3

ORIGINAL PAPER

Pre-treatment with antibiotics and Escherichia coli to equalize the gut microbiota in conventional mice Caroline Linninge • Siv Ahrne´ • Go¨ran Molin

Received: 21 August 2014 / Accepted: 20 October 2014 / Published online: 31 October 2014 Ó Springer International Publishing Switzerland 2014

Abstract The composition of the gut microbiota can vary widely between individual mice of the same batch and thereby affect the resulting outcome in experimental studies. Therefore, an efficient method is needed to equalize the gut microbiota prior to the start of critical experiments. In order to minimize variations in gut microbiota between animals and provide the animals with a Gram-negative flora exposing lipopolysaccharides in the cell-walls, C57BL/6 mice were given a mixture of ampicillin, metronidazole and clindamycin in the drinking water for 3 days and then Escherichia coli for two additional days. Treatment with antibiotics alone or with antibiotics in combination with E. coli was well tolerated by all animals. Body weight and liver weight were not affected, although higher hepatic fat content was found in treated animals (p \ 0.05). The diversity of the gut microbiota was strongly reduced in animals treated with antibiotics and antibiotics in combination with E. coli (p \ 0.01), without affecting the total amount of bacteria. Cloned and sequenced 16S rRNA genes

C. Linninge (&)  S. Ahrne´  G. Molin Food Hygiene, Department of Food Technology, Engineering and Nutrition, Lund University, Box 124, 221 00 Lund, Sweden e-mail: [email protected] S. Ahrne´ e-mail: [email protected] G. Molin e-mail: [email protected]

showed high presence of Enterobacteriaceae and Porphymonadaceae in the treated animals. Analysis with Principal Component Analysis gave a clear separation of the composition in microbiota between different treatment groups. The described treatment efficiently equalized the gut microbiota and provided the animals with a strong abundance of Enterobacteriaceae without changing the total load of bacteria. This is a straightforward, lenient and efficient method of pre-treatment to equalize the gut microbiota of mice as a starting procedure of animal studies. Keywords Antibiotics  Escherichia coli  Equalization  Gut microbiota  Liver fat

Introduction The gut microbiota of laboratory mice has been shown to affect for example glucose tolerance (Bech-Nielsen et al. 2012), insulin tolerance, inflammation (Carvalho et al. 2012; Membrez et al. 2008) and fat accumulation (Ba¨ckhed et al. 2004; Cani et al. 2008). An obstacle in physiological and nutritional studies of rodents when the gut microbiota can be involved in the outcome is the huge variation between individual animals in the experimental set-up (Hufeldt et al. 2010a; Hufeldt et al. 2010b). Even if the animals are of the same in-bred strain, the same age, and delivered at the same time from the same producer (same batch), the microbiota

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can vary within wide limits between the different individuals (Axling et al. 2012). The composition of the microbiota may be decisive for the outcome of a metabolic experiment. For example, two substrains of C57BL/6 mice from two different producers responded differently to oat bran with respect to reduction of plasma cholesterol (Andersson et al. 2013). C57BL/6 mice were fed an atherogenic diet with oat bran or control fibres, and mice of the substrain C57BL/6NCrl responded to oat bran with lower plasma cholesterol, higher excretion of bile acids, and increased expression of the bile acid-producing hepatic enzymes CYP7A1 and CYP8B, while none of these effects were seen in C57BL/6JBomTac mice. However, there was an overall difference in the microbiota between C57BL/ 6NCrl and C57BL/6JBomTac (Andersson et al. 2013). In order to obtain more reproducible and reliable results and to possibly reduce the number of animals needed in animal studies, a simple method to equalize the gut microbiota of the animals, i.e. to decrease the variation between animals, prior to experimental start would be usable. Furthermore, the influence of lipopolysaccharides (LPS) originating from the gut microbiota has been found in dietary-driven metabolic mice models (Cani et al. 2007, 2008). It is not unusual that laboratory mice are strongly dominated by Grampositive taxa and thus lack the immunological stress of potent LPS as from Proteobacteria (Manichanh et al. 2010). Antibiotics are commonly used to cure bacterial infections. However, upon antibiotic use the gut microbial structure is substantially affected in both humans and rodents (Bech-Nielsen et al. 2012; Jakobsson et al. 2010; Manichanh et al. 2010). Compared to humans, the microbiota in laboratory rodents is often low in Enterobacteriaceae, and more diverse (Manichanh et al. 2010). In view of the strong pro-inflammatory effect of LPS from species of the Enterobacteriaceae family, and the proposed relevance for LPS as a triggering factor for type 2 diabetes and obesity (Cani et al. 2007, 2008), the low presence of Enterobacteriaceae in the gut microbiota can be a drawback when using rodent models in metabolic studies. There are relationships between different dysfunctions and a high prevalence of bacteria belonging to the Gram-negative phylum Proteobacteria in general and the family Enterobacteriaceae or even the species Escherichia coli in particular: For example, Betaproteobacteria (Larsen et al. 2010) and E. coli

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(Qin et al. 2010) were found to be enriched in patients with type 2 diabetes compared to healthy counterparts; centenarians had more Proteobacteria than adults between 25 and 78 years old (Biagi et al. 2010); E. coli in infants was associated with a higher risk of developing atopic dermatitis (Penders et al. 2007); and Enterobacteriaceae was significantly higher in obese/overweight children (Karlsson et al. 2012). Proteobacteria and perhaps especially Enterobacteriaceae with E. coli as a typical representative seem to be a crucial component of the microbiota in certain groups of urban humans, and involved either as marker or a long-term causal agent for ill-health (Karlsson et al. 2012; Larsen et al. 2010). Consequently, it can be important that animal models for simulating dysfunctions in humans harbour a substantial quantity of Enterobacteriaceae in the digestive tract. The aim of the present study was to clarify whether a three-day treatment with antibiotics alone or in combination with 2 days of administration with E. coli can equalize the gut microbiota of laboratory mice and establish a consistent presence of Enterobacteriaceae in all individuals. This treatment is primarily intended as a pre-treatment of animals used in studies related to dietary effects on immune system, metabolism and physiology where the gut-microbiota can be expected to have influence on the outcome, e.g. in evaluation of metabolic effects of dietary food supplements such as prebiotics and probiotics. The applied procedure was well tolerated by the animals but induced a rapid increase in liver fat.

Materials and methods Animals and experimental procedure Male mice of the C57BL/6NCrl strain, 8-weeks-old with body weight 18.2–21.8 g, were purchased from Charles River laboratories (Sulzfeld, Germany) and randomly divided into four groups (eight animals per group; 1 cage/group). The animals were kept in a temperaturecontrolled environment on a 12-h light/dark cycle. All experiments followed the national guidelines for the care and use of animals, and were approved by the Malmo¨/Lund regional ethical committee for laboratory animals (Ethical Permission, M209-11). Three groups were given a purified c-irradiated opensource diet based on AIN-76A (RD-diet), produced by

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Research Diets (New Brunswick, NJ, USA), while one group was given regular chow diet (LabFor, Lantma¨nnen, Sweden). Feed was administered ad libitum. For two groups on the RD-diet, an antibiotic cocktail was freshly prepared and given daily for 3 days in the drinking water, supplemented with 2 % (w/v) fructose. The antibiotics in the drinking water were 1 mg/ml ampicillin (Sigma-Aldrich, St Louis, MO), 1 mg/ml metronidazole (Sigma-Aldrich, St Louis, MO) and 0.3 mg/ml clindamycin (Pfizer AB, Sollentuna, Sweden). For 2 days following the antibiotic treatment, one of the antibiotic-treated groups was administered two non-pathogenic strains of E. coli, CCUG29300T (Culture Collection of Go¨teborg; T stands for type strain) and a C57BL/6-clone (isolate from the gut of a healthy C57BL/6 mouse). Escherichia coli was grown overnight in Luria– Bertani broth at 37 °C, and harvested bacteria were stored in glycerol buffer (Karlsson et al. 2011) at -80 °C until use. Water consumption was determined and changed daily and an average dose of 108 CFU of each E. coli strain was consumed by each mouse. The amount of consumed water was in general terms similar for all groups although animals on antibiotics tended to have slightly lower water intake. The third group on RD-diet served as control and the group on chow diet was control for the purified open-source diet. These groups were given regular drinking water supplemented with fructose so these mice consumed the same amount of sugar as the antibiotic-treated mice. The different animal groups are labeled as follows: mice on RD-diet without supplements of antibiotics or E. coli (Control-RD); mice on RD-diet that were given antibiotics (Antib-RD); mice on RD-diet given antibiotics and E. coli (Antib-Ec-RD) and mice on regular chow (Control-chow). Nine days after start of RD-diet or chow-diet, animals were sacrificed by CO2 inhalation. The abdomen and chest were opened and spleen, liver and cecum were carefully dissected and weighed. Liver and cecum tissue were rinsed in sterile phosphate buffered saline (PBS; Oxoid, England) and were immediately frozen until further analysis. Liver fat content Hepatic fat was determined gravimetrically by extraction in diethyl ether and petroleum ether (bp 40–60 °C;

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1:1) after hydrolysis with ethanol and 7.7 M HCl at 70–80 °C for 1 h (Schmid-Bondzynski-Ratslaff 1989). Microbial analyses DNA from cecum mucosa samples was isolated and purified in BioRobot EZ1 (EZ1 DNA Tissue Kit and tissue card; Qiagen, Hilden, Germany). Tissue samples were incubated with 380 or 570 ll Buffer G2 and 30 or 45 ll Proteinas K (Qiagen, Germany) depending on tissue weight (0.049–0.147 g) at 56 °C for 17 h. Twelve glass beads (2 mm in diameter) were added to each sample, which were further incubated for 2 hours and then shaken at 4 °C for 45 min to disintegrate the bacterial cell membranes. The supernatant was diluted with sterile PBS before extraction in the BioRobot EZ1. Terminal Restriction Fragment Length Polymorphism (T-RFLP) analysis was performed with MspI as previously described (Karlsson et al. 2011, 2012). Thresholds for both internal standard and terminal restriction fragments (T-RFs) were set to 20 fluorescence units. Bacterial abundance of Enterobacteriaceae, the Clostridium leptum group, Lactobacillus, Akkermansia muciniphilia-like bacteria, Desulfovibrio, Bifidobacterium, the Bacteroides fragilis group and total amount of bacteria were estimated in cecum mucosa using separate quantitative PCR assays according to Karlsson et al. (2012). Primers used for the qPCR assays have been used and published previously (Axling et al. 2012; Castillo et al. 2006; Fite et al. 2004; Matsuki et al. 2004; Rinttila¨ et al. 2004). In addition, one animal/group was screened for Fecalibacterium prausnitzzi (Sokol et al. 2008) but, since all had concentrations below detection limit, the assay was not run for the remaining animals. Detection limit was 102 gene copies/reaction for the C. leptum group, Lactobacillus, A. muciniphila-like bacteria, Desulfovibrio, Bifidobacterium and for the total bacteria, while the assays for Enterobacteriaceae and the B. fragilis group detected 103 gene copies/reaction. For standard curves, ten-fold dilution series of the target DNA were made in TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0). For the standard of Enterobacteriaceae the TE buffer was supplemented with 0.1 lg/ll herring sperm DNA (VWR International, West Chester, PA). Number of bacteria was expressed as log10 16S rRNA gene copies/g cecum tissue.

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Cloning and sequencing were performed, as described previously (Karlsson et al. 2011) to imply phylogenetic affiliations of the intestinal microbiota in animals treated with antibiotics (two animals/ group, 22 sequences/animal). Animals were selected based on T-RFLP profiles and animals with high diversity were chosen to find as many bacterial identities as possible, but noteworthy, all animals within the same group had almost the same microbial profile. Calculations Microbial diversity was estimated by calculation of richness (number of T-RFs) and Shannon and Simpson diversity indices as described by Karlsson et al. (2012) with the exception that T-RFs within 40–580 base pairs were included in the T-RFLP profile analysis and calculation. Statistical evaluation was performed in GraphPad Prism 6 using Kruskal–Wallis One Way ANOVA on ranks, and Dunn’s post hoc test if applicable. A p value of \0.05 was considered statistically significant. Multivariate statistical analysis was performed in SIMCA-P?12.0.1 (Umetrics AB, Umea˚, Sweden).

Results Treatment tolerance The antibiotics and supplemented E. coli were well tolerated by all animals as no adverse effects, such as diarrhea, behavioural changes or changes in body weight, were observed. No signs of disease or unexpected deaths occurred during the experimental period.

Effects on organ weights Short-term administration of antibiotics, alone (AntibRD) or in combination with E. coli (Antib-Ec-RD), resulted in significantly increased hepatic fat content compared to RD-diet alone (Control-RD), (p = 0.014 and p = 0.034, respectively; Table 1), and animals in the Antib-Ec-RD group had visually paler livers compared to other animals. However, liver weight did not differ according to treatment. Animals in groups Antib-RD and Antib-Ec-RD had significantly heavier cecum compared to animals in Control-RD (p =\0.001; Table 1). At dissection, the cecum content in animals of groups Antib-RD and Antib-Ec-RD was looser. Antib-RD animals had increased spleen weight compared to those in the Control-RD (p = 0.022). The treatments did not affect the body weight (Table 1). Equalization of the microbiota The individual microbiotas differed between mice of the two treatment groups Antib-RD and Antib-Ec-RD but the differences were diminished and nearly disappeared in the Antib-Ec-RD group (Fig. 1). Thus, the complexity of the microbiota in each individual mouse drastically decreased as only a few T-RFs dominated after antibiotic treatment. Multivariate statistical analysis with Principal Component Analysis (PCA) on T-RFLP profiles was used to describe the differences in gut microbiota on group-level, and a distinct difference in gut microbiota according to treatment was found (Fig. 1). The microbiota of the individual mice was after antibiotic treatment forming tight clusters, clearly separated from those without antibiotic treatment. This demonstrates a strong equalization effect of the treatments with antibiotic and antibiotic in combination with E. coli.

Table 1 Body weight, organ weights and liver fat content of mice on treatments intended to equalize the gut microbiota Treatment group

Body weight (g)

Liver (g)

Liver fat (% of liver weight)

Spleen (g)

Cecum (g)

Control-RD

22.0 (21.4–23.7)

1.09 (1.04–1.23)

2.75 (2.29–3.29)

0.067 (0.061–0.070)

0.237 (0.202–0.270)

Antib-RD

22.4 (22.4–23.3)

1.06 (1.05–1.15)

4.38* (3.31–5.21)

0.063* (0.061–0.065)

0.990*** (0.838–1.05)

Antib-Ec-RD

23.1 (22.3–24.0)

1.12 (1.04–1.19)

3.42* (3.32–4.29)

0.064 (0.060–0.069)

0.860*** (0.848–0.881)

Control-chow

22.6 (22.2–22.8)

1.24 (1.19–1.28)

2.67 (2.56–2.86)

0.063 (0.062–0.065)

0.413 (0.373–0.452)

Median values (interquartile ranges) are presented * Denotes significantly different compared to the Control-RD group, p \ 0.05 and *** denotes p \ 0.001

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12 10

Control-RD

8 6

PC 2; 18.1%

4 2 0

Antib-RD

-2

Control-chow

Antib-Ec-RD

-4 -6 -8 -10 -12 -15 -14 -13 -12 -11 -10 -9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

PC 1; 22.3%

8

9

10

11

12

13

14

15

16

17

SIMCA-P+ 12.0.1 - 2014-05-21 12:12:11 (UTC+1)

Fig. 1 Multivariate analysis with principal component analysis (PCA) of T-RFLP profiles from mice on different treatment. Animals treated with antibiotics, alone or in combination with E. coli, cluster tightly together, showing the equalization potential on the gut microbiota. Animals in the Control-RD group cluster

together but separated from those on antibiotics, while the microbiota of animals in the Control-chow group did not prove similarity to the same degree. PC1 22.3 %, PC2 18.1 %. Filled triangle Control-RD, open square Antib-RD, filled square Antib-Ec-RD, plus Control-chow

Diversity of the microbiota

account. In the present study both group Antib-RD and Antib-Ec-RD had lower diversity compared to Control-RD (p \ 0.01, Table 2). The majority of clones sequenced from animals treated with the antibiotics implied phylogenetic affiliation to Enterobacteriaceae (Escherichia) and Porphyromonadaceae (Parabacteroides), and, to a lesser extent, to Xanthomonadaceae, Lachnospiraceae and Enterococcaceae. Antibiotic treatment in combination with E. coli showed an even higher level of equalization towards domination of Escherichia than that provided by the antibiotic treatment alone, according to sequencing of clones obtained in this study (Fig. 2).

The diversity of the dominating taxa of the gut microbiota, accessed by calculation of richness and diversity indices from T-RFLP profiles, was strongly reduced in animals receiving RD-diet in combination with antibiotics, and antibiotics and E. coli, compared to those on RD-diet only. Richness was significantly decreased in animals of the Anti-RD and Anti-Ec-RD groups compared to Control-RD (p = 0.001 and p = 0.003, respectively). Evenness is also considered in the evaluation since both Shannon and Simpson diversity indices take both richness and evenness into

Table 2 Microbial diversity in cecum tissue of animals on treatments intended to equalize the gut microbiota Treatment group

No. of T-RFs

Shannon index

Simpson index

Control-RD

46 (41–50)

2.91 (2.70–2.95)

0.90 (0.88–0.91)

Antib-RD

3.5 (2.5–5)***

0.13 (0.07–0.44)**

0.05 (0.03–0.23)***

Antib-Ec-RD

4 (3–6)**

0.39 (0.10–0.85)**

0.19 (0.04–0.49)**

Control-chow

44 (39–54)

2.87 (2.37–3.29)

0.90 (0.75–0.94)

Median values (interquartile ranges) are presented * p \ 0.05, ** p \ 0.01 and *** p \ 0.001 compared to the Control-RD group

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Enterobacteriaceae Porphyromonadaceae Enterococcaceae Xanthomonadaceae Lachnospiraceae Ruminococcaceae 0

10

20

30

40

50

60

Total clones for each library (%)

Fig. 2 Identification of sequences in clone libraries to the level of family. The figure shows the bacterial families to which clones in each treatment group are related. Results are given as

percentage of total number of clones for each treatment group (22 sequences/animal). open square Antib-RD, filled square Antib-Ec-RD

Quantification of bacterial groups in the gut flora

with antibiotics (p = 0.584). The Control-chow group was included for comparison with the purified RD-diet (Control-RD). Animals on the chow diet did not have significantly different abundance of any of the tested bacterial groups compared to animals on the RD-diet (Table 3) even if the overall microbial composition differed as shown in Fig. 1.

Enterobacteriaceae was the only tested bacterial group that by the applied PCR-protocols could be quantified above the detection limit in animals treated with antibiotics. Enterobacteriaceae was significantly higher in animals treated with antibiotics alone or antibiotics in combination with E.coli compared to the control (Control-RD) (p \ 0.01; Table 3). In contrast, the number of 16S rRNA gene copies from the C. leptum group, Lactobacillus, Akkermansia muciniphilia-like bacteria and Desulfovibrio, were all significantly higher in animals without antibiotic treatment (p = 0.001–0.05, Table 3). The B. fragilis group and Bifidobacterium were below detection limit in all animals, the only exception being one animal in the Control-chow group that had Bifidobacterium just above detection limit (log10 5.98 gene copies/g cecum tissue). Notably, the total amount of bacteria was not reduced or significantly different in animals treated

Discussion Antibiotic treatment followed by administration of E. coli, resulted in an equalization of the gut microbiota. As indicated in Table 3, the equalization was quite powerful and imposed a selection pressure in favour of Enterobacteriaceae. In comparison, a combined treatment with ampicillin, metronidazole and neomycin in the drinking water (each at 1 g per liter) for 8 weeks to Swiss mice strongly reduced the total load of bacteria but similarly directed the microbiota towards a strong

Table 3 Abundance of selected bacterial groups in cecum tissue of mice on treatments intended to equalize the microbiota Treatment group

Enterobacteriacae log10 copies/g cecum tissue

Clostridium leptum group log10 copies/g cecum tissue

Lactobacillus log10 copies/g cecum tissue

Akkermansia muciniphilia-like log10 copies/g cecum tissue

Control-RD

ND

8.08 (7.98–8.14)

6.88 (6.52–7.21)

8.51 (8.35–8.80)

8.08 (7.98–8.14)

8.56 (8.34–8.72)

Antib-RD

8.18 (7.81–8.89)

ND***

ND*

ND***

ND***

8.70 (7.94–8.77)

Antib-Ec-RD

9.00 (8.77–9.22)**

ND**

ND***

ND**

ND**

8.30 (8.07–8.63)

Control-chow

ND

7.63 (7.05–8.08)

6.80 (6.51–7.27)

6.55 (6.34–6.74)

7.63 (7.05–8.08)

8.54 (8.16–9.04)

Median values (interquartile ranges) are presented ND not detected * Denotes p \ 0.05, ** p \ 0.01 and *** p \ 0.001 compared to the Control-RD group

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Desulfovibrio log10 copies/g cecum tissue

Total bacteria log10 copies/g cecum tissue

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abundance of Proteobacteria (Carvalho et al. 2012). However, it was not reported which type of Proteobacteria that predominated. When C57BL/6 mice were given ampicillin in the drinking water (1 g per litre) for 28 days, the diversity and richness of the microbiota decreased, but the inter-individual differences remained (Bech-Nielsen et al. 2012). A threeday antibiotic treatment of rats with a mixture of vancomycin and imipenum in the drinking water strongly reduced the total bacterial load directly after treatment, and a substantial reduction remained 3 months after the antibiotic treatment (Manichanh et al. 2010). Also the richness of the faecal microbiota was reduced, following a similar pattern. This treatment diminished Bacteroidetes and Firmicutes and increased Proteobacteria directly after treatment, but these effects disappeared after 1 month. However, individual differences in microbiota remained after the treatment (Manichanh et al. 2010). The outcome of the present treatment of mice was unexpected in the sense that it so efficiently diminished individual differences between animals and that it without affecting the total bacterial load significantly affected the abundance of the different bacterial groups. In the present study, some bacterial groups commonly found in the intestinal microbiota were quantified. The minor differences between the regular chow diet and the purified RD-diet did not significantly influence the abundance of the tested bacterial groups although the multivariate analysis showed that the microbiota of animals on the chow diet varied to a higher degree than in animals on the RD-diet. Antibiotics in the present study were chosen to impose a selective pressure on the indigenous microbiota in order to decrease the interindividual differences in the bacterial composition, and to some extent mimic an adverse human microbiota with element of dysbiosis, which frequently means overgrowth of E. coli in the intestinal tract (Bouhnik et al. 1999). Metronidazole is generally regarded as selectively active against anaerobic bacteria and ampicillin is a broad spectrum beta-lactam antibiotic inhibiting synthesis of the cell wall. Clindamycin has bacteriostatic effect mainly on Grampositive anaerobes, and this antibiotic is known to favour marked expansion of Enterobacteriaceae and to reduce the diversity of the gut bacterial flora (Buffie et al. 2012). Once the microbiota was suppressed, two strains of E. coli were used to boost the intestinal microbiota with Enterobacteriaceae, and to certify the

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presence of E. coli. Escherichia coli CCUG29300T is non-pathogenic and was chosen as it is the type strain of the species, and since it has previously been shown to increase the Enterobacteriaceae load in rodents (Fa˚k et al. 2008; Karlsson et al. 2011). The second E. coli strain originated from a healthy male C57BL/6 mouse but has never previously been used in any experimental study. Both strains were well tolerated by the animals. Multivariate analysis of the gut microbiota clearly showed that the experimental procedure using antibiotics in the present study was capable of equalizing the mice gut microbiota. Cloning and sequencing indicated that the supplementation with E. coli further equalized the microbiota. The strong selection power on the gut microbiota of the individual mice on antibiotics alone or in combination with E. coli is demonstrated in Fig. 1. The analysis reveal that the microbiota of the antibiotic group and, even more, the antibiotic and E. coli group clustered much more tightly together than that of the control groups. Considering the rapid accumulation of liver-fat in antibiotic treated mice seen in the present study, previous studies have implied the potential of antibiotics to change the gut microbial environment and affect liver fat content (Bergheim et al. 2008; Membrez et al. 2008). Furthermore, increased cecal load of E. coli has been linked to elevated hepatic lipid accumulation (Membrez et al. 2008), and administration of antibiotics at sub-therapeutic levels for 7 weeks has been shown to affect expression of genes related to regulation of hepatic lipid metabolism in mice (Cho et al. 2012). Also, Cani et al. (2007) showed that LPS originating from the microbiota in the gastro-intestinal tract can be the triggering factor for inflammation and obesity in mice. A high-fat diet increases the LPS-concentration in the blood and induce systemic inflammation, and the inflammation initiates processes leading to fat accumulation (Cani et al. 2008). Thus, the changes observed in the present study were presumably a result of the changed microbiota, but future studies have to be done to evaluate if this biased microbiota also in the long run can induce effects on the metabolism. In conclusion, a short term antibiotic treatment, alone or in combination with E. coli, efficiently equalized the gut microbiota between individual animals. Administration of E. coli certified a strong abundance of Enterobacteriaceae but without changing the total load of bacteria. The model described

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here, with a short, lenient and standardized procedure, can be useful as a pre-treatment of rodents to be used in different kind of physiologically, metabolically and nutritionally focused rodent models, and especially if the background effect of bacteria generating LPS are to be taken into account. Acknowledgments Dr. P. Ha˚kansson’s Foundation (Eslo¨v, Sweden), the Royal Physiographic Society in Lund, and the Functional Food Science Centre at Lund University, Sweden are greatly acknowledged for financial support. Conflict of interest The authors declare that they have no conflicts of interests.

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Pre-treatment with antibiotics and Escherichia coli to equalize the gut microbiota in conventional mice.

The composition of the gut microbiota can vary widely between individual mice of the same batch and thereby affect the resulting outcome in experiment...
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