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`DOI: 10.3748/wjg.v20.i47.17727
`
`World J Gastroenterol 2014 December 21; 20(47): 17727-17736
` ISSN 1007-9327 (print) ISSN 2219-2840 (online)
`© 2014 Baishideng Publishing Group Inc. All rights reserved.
`
`WJG 20th Anniversary Special Issues (17): Intestinal microbiota
`Impact of the gut microbiota on rodent models of human
`disease
`
`TOPIC HIGHLIGHT
`
`Axel Kornerup Hansen, Camilla Hartmann Friis Hansen, Lukasz Krych, Dennis Sandris Nielsen
`
`Axel Kornerup Hansen, Camilla Hartmann Friis Hansen,
`Section of Experimental Animal Models, Department of Veteri-
`nary Disease Biology, University of Copenhagen, 1871 Freder-
`iksberg, Denmark
`Lukasz Krych, Dennis Sandris Nielsen, Department of Food
`Science, Faculty of Sciences, University of Copenhagen, 1958
`Frederiksberg, Denmark
`Author contributions: All authors contributed to this paper
`equally.
`Correspondence to: Axel Kornerup Hansen, Professor, DVSc,
`DVM, DipECLAM, Section of Experimental Animal Models, De-
`partment of Veterinary Disease Biology, University of Copenha-
`gen, 57 Thorvaldsensvej, 1871 Frederiksberg,
`Denmark. akh@sund.ku.dk
`Telephone: +45-353-32726 Fax: +45-353-32755
`Received: February 27, 2014 Revised: September 30, 2014
`Accepted: November 18, 2014
`Published online: December 21, 2014
`
`Abstract
`Traditionally bacteria have been considered as either
`pathogens, commensals or symbionts. The mammal
`gut harbors 1014 organisms dispersed on approximately
`1000 different species. Today, diagnostics, in contrast
`to previous cultivation techniques, allow the identifica-
`tion of close to 100% of bacterial species. This has
`revealed that a range of animal models within differ-
`ent research areas, such as diabetes, obesity, cancer,
`allergy, behavior and colitis, are affected by their gut
`microbiota. Correlation studies may for some diseases
`show correlation between gut microbiota composition
`and disease parameters higher than 70%. Some dis-
`ease phenotypes may be transferred when recolonizing
`germ free mice. The mechanistic aspects are not clear,
`but some examples on how gut bacteria stimulate re-
`ceptors, metabolism, and immune responses are dis-
`cussed. A more deeper understanding of the impact of
`microbiota has its origin in the overall composition of
`the microbiota and in some newly recognized species,
`
`such as Akkermansia muciniphila, Segmented filamen-
`tous bacteria and Faecalibacterium prausnitzii, which
`seem to have an impact on more or less severe disease
`in specific models. Thus, the impact of the microbiota
`on animal models is of a magnitude that cannot be
`ignored in future research. Therefore, either models
`with specific microbiota must be developed, or the mi-
`crobiota must be characterized in individual studies and
`incorporated into data evaluation.
`
`© 2014 Baishideng Publishing Group Inc. All rights reserved.
`
`Key words: Animal models; Gut microbiota; Diabetes;
`Obesity; Cancer; Allergy; Behavior; Colitis
`
`Core tip: Full characterization of the gut microbiota of
`animal models has revealed that animal models within
`different research areas, such as diabetes, obesity,
`cancer, allergy, behavior and colitis, are highly affected
`by their gut microbiota. The mechanistic aspects are
`not clear; however, the impact of the microbiota on
`animal models is of a magnitude that cannot be ig-
`nored in future research. Therefore, either models with
`specific microbiota must be developed, or the micro-
`biota must be characterized in individual studies and
`incorporated into data evaluation.
`
`Hansen AK, Friis Hansen CH, Krych L, Nielsen DS. Impact of
`the gut microbiota on rodent models of human disease. World J
`Gastroenterol 2014; 20(47): 17727-17736 Available from: URL:
`http://www.wjgnet.com/1007-9327/full/v20/i47/17727.htm DOI:
`http://dx.doi.org/10.3748/wjg.v20.i47.17727
`
`INTRODUCTION
`Host-microbiota relationship
`The gut is an ideal incubation chamber for bacteria
`adapted to the mammal body temperature and the anaer-
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`Hansen AK et al. Gut microbiota and rodent models
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`Actinobacteria
`Mollicutes
`Spirochaetes
`Proteobacteria
`Verrucomicrobia
`Bacteroidetes
`Firmicutes
`
`100%
`90%
`80%
`70%
`60%
`50%
`40%
`30%
`20%
`10%
`0%
`
`Ileum Caecum Faeces
`
`Figure 1 The approximate composition of the gut microbiota in the ileum,
`cecum and feces of mice[3,7,8,11,16].
`
`obic environment. Thousands of years of co-existence
`has led to such adaptation, and the mammal gut harbors
`1014 organisms dispersed over approximately 1000 dif-
`ferent species, dependent on how the cut-offs are set for
`similarity. Within the traditional approach to laboratory
`animal bacteriology, bacteria have been considered as
`either pathogens, commensals or symbionts; however,
`there seems to be a need for a broader understanding
`of this. When first inside the gut, the bacteria will be fed
`and will be allowed to propagate, while the host organ-
`ism will benefit from otherwise unavailable products of
`microbial digestion. Generally, pathogenicity is not in
`the interest of the microorganism, because it induces a
`strong and eradicating immune response from the host,
`and even in the case of microbial victory in this battle,
`the end result may be the death of the host and the need
`for the microbe to relocate to a new habitat. The host
`immune system, on the other hand, needs to protect the
`host from invasion without being so aggressive that it
`loses the microbe and thereby all its benefits.
`
`Complexity of microbial impact on the host
`A more advanced understanding of the impact of the
`microbiota takes into consideration both the overall
`composition and the balance between the members of
`the microbiota, as well as some newly recognized species,
`which, by themselves, seem to have an effect on the spe-
`cific models. Some of these have a symbiotic effect, while
`others push disease development in a more detrimental
`direction. However, same species may act in favor of the
`development of one disease, while being more protective
`against another disease, and the mechanistic potential of
`the species may differ between different parts of the gut.
`For most of these bacteria, it is their abundance, rather
`than their qualitative presence or absence, which are re-
`
`sponsible for their effect on the host[1-4]. The microbiota
`is normally not very diverse in the upper part of the gut,
`e.g. in the ileum, where there is a huge accumulation of
`lymphatic tissue available for stimulation[3,5-10]. It gradually
`becomes more diverse as the gut contents pass through
`the large intestine and become feces (Figure 1)[3,5-11]. In
`both man and mouse, a microbiota with a low diversity is
`indicative of an increased risk of developing inflammato-
`ry disease[12,13]. Furthermore, in animals, a microbiota that
`is roughly similar in the upper part of the gut, may differ
`substantially in the lower part of the gut and vice versa[3,14].
`Finally, there might be essential differences between the
`effects of the various species at different ages of the
`animals, which may explain why some species favor the
`development of one disease, while protecting against an-
`other.
`
`Modern microbiological identification techniques
`Over recent decades, new methods based upon molecu-
`lar biology diagnostics have been developed. Such meth-
`ods, which include quantitative real-time polymerase
`chain reaction (qPCR) assays[15], pyrosequencing[16] and
`metagenomic sequencing, have permitted identification
`of close to 100% of the gut’s operational taxonomic
`units (OTU), which include both cultivable and non-
`cultivable bacterial species, and in principle, viral, eukary-
`otes and Archea[17], although they are seldom specifically
`tested for at present. In contrast, previous cultivation
`techniques only allowed cultivation and identification of
`10%-20% of the bacterial species present in the gut[18].
`These molecular biology-based tools have enabled de-
`tailed correlation studies. Such studies have revealed that
`a range of animal models within a range of different
`research areas are affected by their gut microbiota[19].
`
`GENERAL MECHANISMS UNDERLYING
`THE GUT MICROBIOTA EFFECT
`As described below, the impact of the microbiota on an-
`imal models is well documented, while the mechanisms
`underlying this are less clear. Some hypotheses, though,
`make more sense than others. As techniques for the
`full characterization of the microbiota have been devel-
`oped over the last decade, we are only now beginning to
`achieve an understanding of how the microbiota actually
`exerts its effect on the host; however, some examples
`can be given.
`
`Window of opportunity
`In early life, there is a window for the induction of oral
`tolerance in the gut[20]. This seems essential to avoid in-
`flammatory disease later in life[21]. Molecular structures
`in bacteria known as microbial-associated molecular pat-
`terns (MAMP) stimulate pattern-recognition receptors
`(PRR) in the host, thereby inducing innate responses[22].
`Among the most important PRRs are the toll-like recep-
`tors (TLR), which are present in different types on a
`range of different cell types[22-29] (Figure 2). An impor-
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`Hansen AK et al. Gut microbiota and rodent models
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`Mum
`
`AMP-activated
`protein kinase
`
`H
`
`H
`
`H
`
`C
`
`H
`
`H
`
`C
`
`H
`
`H
`
`C
`
`H
`
`H
`
`C
`
`H
`
`C
`
`H
`H
`
`C
`
`H
`
`C
`
`H
`
`O
`
`O
`
`C
`
`OH
`
`OH
`
`Short chain fatty acid
`
`GPR
`
`Lipids
`
`Lipids
`
`Lipids
`
`CH2OH
`O
`
`OH
`OH
`
`OH
`OH
`
`CH2OH
`O
`
`OH
`OH
`
`OH
`OH
`
`CH2OH
`O
`
`OH
`OH
`
`OH
`OH
`
`Acetyl-CoA carboxylase
`fatty acid synthase
`
`Lipopolysaccharide
`
`TLR4
`
`Flagellin
`
`TLR5
`
`Peptidoglycan
`Lipoproteins
`
`Lipoteichoic acid
`Polysaccharide A
`
` Zymosan
`
`TLR10
`
`TLR6
`
`TLR2
`
`TLR1
`
`Figure 2 Examples of some theories on potential pathways for the impact of gut microbiota on animal models of human disease. Bacterial colonization may
`double the density of capillaries in the small intestinal epithelium, thereby promoting intestinal monosaccharide absorption[28]. Undigested food components may be
`fermented into SCFAs and subsequently act as signals for GPRs of importance for the development of obesity[26,29]. Bacteria may express several key enzymes rele-
`vant for hepatic lipogenesis[27,50], and hepatic and muscular fatty acid oxidation[31]. Molecular structures in the cell walls of bacteria may act as MAMPs, which stimulate
`TLRs on the host cells to induce innate immune responses. The complex of TLR1, TLR2, TLR6 and TLR10 is expressed on a range of cell types such as enterocytes,
`macrophages, dendritic cells, natural killer cells, mast cells, T cells, B cells, neutrophilic cells and Schwann cells, and may be stimulated by various MAMPs, e.g. pep-
`tidoglycan, from Gram positive bacteria cell types[21,23-25,31-33]. TLR4, expressed by e.g. macrophages, dendritic cells, mast cells, natural killer cells and enterocytes, is
`stimulated by lipopolysaccharides from Gram negative bacteria[30], while flagellin from various bacteria may stimulate TLR5 expressed by e.g. mucosal dendritic cells
`and macrophages[33]. Mucin-degrading Akkermansia muciniphila may reduce the mucus layer to increase TLR-stimulation[79]. SCFAs: Short chain fatty acids; GPR:
`G-protein receptor; MAMP: Microbial-associated molecular pattern; TLR: Toll-like receptor.
`
`tant example of a MAMP is lipopolysaccharides (LPS),
`which are important parts of the cell wall of Gram
`negative bacteria[30], such as Proteobacteria[31], from
`which it stimulates TLR4. Another important example is
`peptidoglycan, found in the cell walls of Gram positive
`bacteria, which stimulates TLR2[32] and flagellin deriving
`from flagellated bacteria, leading to stimulation of TLR5[33].
`Therefore, as different types of MAMPs stimulate differ-
`ent TLR’s dispersed on a variety of different cell types[23],
`and as MAMPs are also dispersed and shared between
`members of the microbiota[22], there is a vast range of
`innate host responses to bacteria.
`
`Adult life stimulation
`The age of the animal also makes a difference. For
`example, stimulation of TLR1, TLR2 and TLR4 in early
`life leads to higher production of interleukin (IL)-6 than
`stimulation later in life[34]. Germ free animals have more
`T helper cells type 2 (TH2) and less TH1 cells[35], as the
`
`stimulation of the gut lamina propria dendritic cells, e.g.
`by polysaccharide A (PSA) from Bacteroides fragilis, induces
`IL-12 secretion, which favors TH1 at the cost of TH2[36].
`Host-bacterial interactions, probably mediated through
`glucagon-like peptide 2 (GLP-2), seem to control the gut
`barrier function[37]. Metabolic endotoxaemia is responsible
`for the phenomenon whereby excess intake of dietary
`fat increases plasma LPS levels[38,39], which in mice, is
`a sufficient molecular mechanism to trigger metabolic
`diseases, such as obesity and diabetes[40].
`
`EXAMPLES OF SOME ANIMAL
`MODELS UNDER IMPACT OF THE GUT
`MICROBIOTA
`Impact of germ free status
`The clearest documentation of a general microbial impact
`on rodent models is observed when comparing a conven-
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`Hansen AK et al. Gut microbiota and rodent models
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`Table 1 Examples of rodent models in which germ free status
`has a documented impact
`
`Model
`Models with increased disease incidence or severity
` β-lactoglobulin induced mouse[51]
` NOD mouse[42]
` MyD88 KO NOD mouse[42]
` Restrained mouse[43]
`Models with decreased disease incidence or severity
` Ovalbumin-specific TCR TG mouse[44]
` Swiss-Webster mouse[45]
` Collagen induced rat[52]
` HLA-B27 TG rat[53]
` IL-2 KO mouse[54,55]
` IL-10 KO mouse[56]
` TCRα KO mouse[57]
` Dextran sulfate sodium induced mouse[46]
` SAMP1/Yit mouse[47]
` Adoptive T-cell transfer in the mouse[48]
` Carrageenan, LPS, or formalin induced mouse[49]
`
` C57BL/6 mouse[65]
` C57BL/6 mouse[65]
`
`Disease
`
`Allergy
`Type 1 diabetes
`Type 1 diabetes
`Stress
`
`Allergy
`Anxiety
`Arthritis
`IBD
`IBD
`IBD
`IBD
`IBD
`IBD
`IBD
`Inflammatory
`pain
`Obesity
`Type 2 diabetes
`
`NOD: Non-obese diabetic; MyD88: Myeloid differentiation primary
`response gene 88; KO: Knockout; TCR: T cell receptor; TG: Transgenic;
`HLA-B27: Human leucocyte antigen subtype B27; IL-2: Interleukin 2;
`SAMP1/Yit: Senescence accelerated mice prone line 1 Yakult; LPS: Lipo-
`polysaccharide. IBD: Inflammatory bowel disease.
`
`tional model with a microbiota with a germ free version.
`In several studies, this has revealed essential differences
`in disease expression (Table 1)[22,41-57]. Although germ free
`mice eat more, they are leaner, and they have less body
`fat compared with conventional mice because they are
`less efficient in extracting energy from their diet[50]. Germ
`free mice have increased expression of obesity-related
`peptides, such as glucagon-like peptide 1 (GLP-1) in the
`brain[58], which is relevant, because central GLP-1 reduces
`food intake in rats[59]. Germ free mice also behave differ-
`ently from microbiota-harboring mice and this behavior
`may be normalized by colonization[43]. However, for this
`phenotype there also seems to be an important time win-
`dow in early life[60]. Germ free mice with a mutation caus-
`ing a defect in the skin barrier suffer from a more severe
`B-lymphoproliferative disorder, because they express sig-
`nificantly higher levels of the proinflammatory cytokine
`thymic stromal lymphopoietin[61]. Inflammatory bowel dis-
`ease (IBD) occurs either because of a TH1/TH17 response
`(Crohn’s disease) or a TH2 response (ulcerative colitis) to
`gut commensals[62]. Therefore, IBD under germ free con-
`ditions does not develop at all in, e.g. Human Leucocyte
`Antigen subtypes B27 (HLA-B27) transgenic rats[53] and
`IL-10 knockout mice[56]. For the IL-10 knockout mice[63]
`it does not occur even under barrier protected conditions
`(Table 1). IL-2 knockout mice may, under germ free con-
`ditions, show mild focal intestinal inflammation[64] (Table
`1).
`
`Impact of fluctuations in the gut microbiota composition
`Within animal models of the metabolic syndrome, there
`
`seems to be an association between the gut microbiota
`and at least some of the metabolic parameters. For ex-
`ample, in leptin-deficient obese mice, there is a strong
`correlation between glycated hemoglobin levels and the
`composition of the gut microbiota[1]. Further, these mice
`have significantly more Firmicutes and fewer Bacteriode-
`tes members compared with their wild-type and heterozy-
`gous litter mates[10]. Their obese phenotype may be trans-
`ferred with the microbiota by recolonizing germ free lean
`wild-type mice[65]. In C57 Black substrain 6 (C57BL/6)
`mice on both high and low calorie diet, continuous oral
`ampicillin improves glucose tolerance[66,67]. However, this
`effect is mainly caused by an early life impact on glucose
`tolerance, and the effect ceases immediately after ter-
`mination of treatment; thereafter, the glucose tolerance
`may even decrease[68,69]. Several studies describe cross-
`talk between the brain and the gut through both the vagal
`system and the hypothalamus-pituitary-adrenal (HPA)
`axis[70]. Stressing animal models changes their micro-
`biota[71], and the composition of the gut microbiota has
`an impact on responses in rodent stress tests[72,73]. Innate
`immune system cytokines, such as IL-1, IL-6 and tumor
`necrosis factor α (TNFα), which may originate from a
`gut microbiota provocation, induce “sickness behav-
`ior”, changing the priorities of the organism to enhance
`recovery and survival[74]. However, metabolites formed
`by microbial decomposition in the gut may also have a
`direct impact on the brain[75]. In mouse models of atopic
`dermatitis, more than 70% of the variation observed in
`the local tissue cytokine response may be shared with the
`variation in gut microbiota[76]. Changes in the structure
`of the microbial community seem to reduce the number,
`as well as the size, of tumors in azoxymethane/dextran
`sodium sulfate (AOM/DSS) colon cancer-induced mice,
`and tumor induction may be achieved by colonizing germ
`free mice with microbiota from induced mice[77].
`
`EXAMPLES OF THE IMPACTS OF
`SPECIFIC BACTERIAL SPECIES
`Verrucomicrobioa
`Akkermansia muciniphila (A. muciniphila) is a Gram negative
`bacterium, which in mice is the only species belonging to
`the phylum Verrucomicrobia[78]. It interacts via its mucin
`degrading capabilities with enteroendocrine cells to mod-
`ulate gut barrier function, and it is capable of producing
`certain short chain fatty acids (SCFA’s) with a direct ac-
`tion on the receptor G-protein receptor 43 (GPR43)[79].
`Abundance of A. muciniphila is reduced in mice with
`obesity and type 2 diabetes[80], and it gradually disappears
`as aging leptin deficient obese mice develop insulin resis-
`tance[1]. In non-obese diabetic (NOD) mice it becomes
`more abundant when mice are fed a gluten-free diet,
`which decreases the incidence of type 1 diabetes[81]. Early
`life treatment with vancomycin in NOD mice allows A.
`muciniphila to become a dominant gut microbiota mem-
`ber, which reduces the incidence of type 1 diabetes[3],
`but enhances susceptibility to allergic asthma[82], which
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`is in accordance with other studies showing allergy and
`diabetes to counteract one another in NOD mice[83,84].
`Induction of IBD in mice with dextran sodium sulfate
`(DSS) reduces the number of extracellular vesicles de-
`rived from A. muciniphila, and feeding DSS induced mice
`such vesicles reduces the severity of IBD[85], which fits
`well with observations in humans[4]. However, it not only
`reduces the severity of diseases: its presence is correlated
`with higher severity when infecting mice with Salmonella
`typhimurium[86], and AOM/DSS colon cancer-induced
`mice have an increased abundance of A. muciniphila[77],
`which may be explained by its ability to downregulate the
`natural killer cell receptor, NKG2D, which is part of the
`anti-carcinogenic defense[87].
`
`Firmicutes
`Segmented filamentous bacteria (SFB’s) are clostridia-re-
`lated Gram-positive bacteria[88]. The term has been applied
`for decades to describe intestinal bacteria of a uniform
`morphology[89]. However, today the term refers to one
`single species, also known as Candidatus Savagella[90]. SFBs
`induce secretion of the pro-inflammatory cytokine IL-17
`from TH17 cells[91], which in the adult mouse is correlated
`with a low number of regulatory T cells[92]. The presence
`of SFB’s differs between mice from different vendors[92],
`and SFB positive NOD mice have a significantly lower
`incidence of type 1 diabetes compared with SFB nega-
`tive ones[93]. In the adoptive transfer severe combined
`immune deficiency (SCID) mouse model of IBD, SFBs
`are essential for the induction of severe inflammation[48].
`Furthermore, SFBs and the induced TH17 are important
`in the defense against intestinal pathogens. For example,
`mice infected with Citrobacter rodentium, a potent murine
`colon pathogen, exhibit severe symptoms if they lack
`SFBs[91].
`IBD in IL-10 knockout mice is enhanced by Enterococ-
`cus fecalis[94,95], which is probably linked to its production of
`gelatinase[96].
`Faecalibacterium prausnitzii (F. prausnitzii) is a clostridia-
`related bacterium[97] linked to a protective effect against
`human Crohn’s disease[98]. Oral feeding of F. prausnitzii
`reduced the severity of 2,4,6-trinitrobenzenesulfonic
`acid (TNBS)-induced colitis in mice, and some studies
`indicated that this may also be the case in both multidrug
`resistance gene deficient (mdr1a knockout)[99] and in the
`DSS-induced mouse models of colitis[100].
`High abundances of Lactobacillus spp. and bifidobac-
`teria are correlated strongly with low levels of inflamma-
`tion in mice[101] and leptin in rats[102], which also fits well
`with these bacteria acting protectively against IBD in
`IL-10 knockout mice[103], allergic sensitization in mice[104],
`and myocardial infarction in rats[102]. Lachnospiraceae seems
`quantitatively correlated to improved glucose tolerance
`in leptin-deficient obese mice[1].
`In stressed mice, there is correlation between their Fir-
`micutes levels and their responses in the stress tests[73].
`Ingestion of Lactobacillus rhamnosus in mice regulates their
`emotional behavior and central γ-aminobutyric acid (GABA)
`
`Hansen AK et al. Gut microbiota and rodent models
`
`receptor expression via the vagus nerve[72].
`
`Bacteroidetes
`A high abundance of the Gram negative family Prevotel-
`laceae, perhaps restricted to one unclassified genus, in
`the gut of leptin-deficient obese mice correlated with
`impaired glucose tolerance[1].By contrast, in AOM/DSS
`induced colon cancer mice, a high abundance of Pre-
`votellaceae correlated with a low tumor burden[77]. P. copri,
`which has been correlated with the development of ar-
`thritis in humans, seems to increase the severity of DSS
`induced colitis in mice[5]. Caspase-3 knockout mice exhibit
`a lower inflammatory response to DSS induction of
`colitis compared with wild-type mice; however, this pro-
`tective effect of the mutation is decreased by cohousing
`knockout mice with wild-type mice, which significantly
`increases the abundance of Prevotella spp. in the knockout
`mice[105].
`Bacteroides vulgatus seems to enhance IBD in HLA-B27
`transgenic rats[106] and IL-10 knockout mice[95], and in
`the Bio Breeding (BB) rat, a spontaneous type 1 diabetes
`model. The fecal microbiota differ and contain an in-
`creased number of Bacteroides spp. before onset of diabe-
`tes[107]. As in all other mammals, Bacteroides spp. form an
`important part of the Bacteroidetes fraction of the rodent
`gut[16]. These Gram negative bacteria are important for the
`processing of complex molecules to simpler ones in the
`gut[108]: complex glycans are their key source of energy[109].
`B. fragilis toxins cause symptoms of diarrhea and IBD
`in germ-free mice[110], and they induce colonic tumors
`strongly in multiple intestinal neoplasia (MIN) mice[111].
`On the other hand, B. fragilis PSA, which is important for
`the inflammatory gut response to pathogens[36], also pro-
`tects against Helicobacter hepaticus-induced colitis in mice;
`probably via the prevention of IL-17 secretion[112]. Feeding
`the maternal immune activation (MIA) mouse model with
`B. fragilis reduces symptoms of autism, which is probably
`linked to the normalization of the levels of a specific gut
`metabolite[113].
`The abundance of Alistipes spp., a bacterium of the
`Rikenellaceae family, seems to increase when mice are
`stressed by grid floor housing[73].
`
`Proteobacteria
`Escherichia coli (E. coli) enhances IBD in HLA-B27 over-
`expressing rats[106], although E. coli Nissle stabilizes the
`enteric barrier in mice[114]. When reducing type 1 diabetes
`by pre-weaning treatment of NOD mice with vancomy-
`cin, a vast increase in the abundance of Proteobacteria
`in the pups was observed[3].
`
`Actinobacteria
`Bifidobacterium spp. in rodents have a positive impact on
`the regulatory and innate immunity[101,115]. Perinatal sup-
`plementation of B. longum reduced TH1 and TH2 responses
`in allergen sensitized mice[104]. On the other hand, their
`numbers are also increased in gluten-fed NOD mice with
`a high incidence of type 1 diabetes compared with NOD
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`mice on a gluten-free diet[81].
`
`DISCUSSION
`The information gained over the last decade on how
`the entire microbiota, as well as some of its individual
`members, affect animal models of very different types,
`has prompted the scientific community to incorporate
`this in future production and quality assurance of animal
`models. It is not possible to regard these matters from a
`“Specific pathogen-free” concept, as some of the spe-
`cies act in favor of the development of one disease,
`while against the development of another disease, e.g.
`SFB’s both protect against type 1 diabetes and induces a
`TH17 response in favor of the development of Crohn’s
`disease. Furthermore, the balance between the different
`fractions of the microbiota is also likely to make a dif-
`ference. Ultimately, it is often a quantitative rather than
`a qualitative presence that makes the difference. There-
`fore, it is likely that we will see more tailor-made rodent
`models, i.e. commercial breeders and research groups
`have sought to produce animals with a specific microbi-
`ota for the conditions under test. One obvious idea may
`be to breed such animals by selective breeding; however,
`this does not seem to increase the microbiota similarity,
`although the microbiota of offspring show a clear clus-
`tering with the mother’s microbiota[116,117]. It is probably
`rational to inoculate germ free mice with a tailor-made
`microbiota around weaning, as they are conventionalized
`in SPF conditions[118]. The window for induction of oral
`tolerance in animal models may also be turned around,
`such that a low bacterial stimulation in the open phase
`of this window may be essential to develop target diseas-
`es in the model. When stimulated later on, the nature of
`this stimulation is also essential, because commonly used
`disease models in rodents are driven by specific subsets
`of T cells[19]. Another alternative will be to characterize
`the microbiota composition for animals in sensitive stud-
`ies and incorporate this in the data evaluation by chemo-
`metric or multifactorial statistical means. The impact of
`the gut microbiota on animal models is of a magnitude
`that cannot be neglected in the future.
`
`2
`
`REFERENCES
`1
`Ellekilde M, Krych L, Hansen CH, Hufeldt MR, Dahl K,
`Hansen LH, Sørensen SJ, Vogensen FK, Nielsen DS, Hansen
`AK. Characterization of the gut microbiota in leptin deficient
`obese mice - Correlation to inflammatory and diabetic pa-
`rameters. Res Vet Sci 2014; 96: 241-250 [PMID: 24556473 DOI:
`10.1016/j.rvsc.2014.01.007]
`Brown CT, Davis-Richardson AG, Giongo A, Gano KA,
`Crabb DB, Mukherjee N, Casella G, Drew JC, Ilonen J, Knip
`M, Hyöty H, Veijola R, Simell T, Simell O, Neu J, Wasserfall
`CH, Schatz D, Atkinson MA, Triplett EW. Gut microbiome
`metagenomics analysis suggests a functional model for the
`development of autoimmunity for type 1 diabetes. PLoS
`One 2011; 6: e25792 [PMID: 22043294 DOI: 10.1371/journal.
`pone.0025792]
`3 Hansen CH, Krych L, Nielsen DS, Vogensen FK, Hansen LH,
`Sørensen SJ, Buschard K, Hansen AK. Early life treatment with
`
`5
`
`4
`
`vancomycin propagates Akkermansia muciniphila and reduc-
`es diabetes incidence in the NOD mouse. Diabetologia 2012; 55:
`2285-2294 [PMID: 22572803 DOI: 10.1007/s00125-012-2564-7]
`Png CW, Lindén SK, Gilshenan KS, Zoetendal EG, McSwee-
`ney CS, Sly LI, McGuckin MA, Florin TH. Mucolytic bacteria
`with increased prevalence in IBD mucosa augment in vitro
`utilization of mucin by other bacteria. Am J Gastroenterol 2010;
`105: 2420-2428 [PMID: 20648002 DOI: 10.1038/ajg.2010.281]
`Scher JU, Sczesnak A, Longman RS, Segata N, Ubeda C,
`Bielski C, Rostron T, Cerundolo V, Pamer EG, Abramson
`SB, Huttenhower C, Littman DR. Expansion of intestinal
`Prevotella copri correlates with enhanced susceptibility to
`arthritis. Elife 2013; 2: e01202 [PMID: 24192039 DOI: 10.7554/
`eLife.01202]
`6 Ubeda C, Taur Y, Jenq RR, Equinda MJ, Son T, Samstein M,
`Viale A, Socci ND, van den Brink MR, Kamboj M, Pamer EG.
`Vancomycin-resistant Enterococcus domination of intestinal
`microbiota is enabled by antibiotic treatment in mice and
`precedes bloodstream invasion in humans. J Clin Invest 2010;
`120: 4332-4341 [PMID: 21099116 DOI: 10.1172/jci43918]
`7 Hildebrandt MA, Hoffmann C, Sherrill-Mix SA, Keilbaugh
`SA, Hamady M, Chen YY, Knight R, Ahima RS, Bushman
`F, Wu GD. High-fat diet determines the composition of the
`murine gut microbiome independently of obesity. Gastro-
`enterology 2009; 137: 1716-1724.e1-e2 [PMID: 19706296 DOI:
`10.1053/j.gastro.2009.08.042]
`8 Antonopoulos DA, Huse SM, Morrison HG, Schmidt TM,
`Sogin ML, Young VB. Reproducible community dynamics of
`the gastrointestinal microbiota following antibiotic perturba-
`tion. Infect Immun 2009; 77: 2367-2375 [PMID: 19307217 DOI:
`10.1128/IAI.01520-08]
`9 Wilson KH, Brown RS, Andersen GL, Tsang J, Sartor B.
`Comparison of fecal biota from specific pathogen free and
`feral mice. Anaerobe 2006; 12: 249-253 [PMID: 17070078 DOI:
`10.1016/j.anaerobe.2006.09.002]
`10 Ley RE, Bäckhed F, Turnbaugh P, Lozupone CA, Knight RD,
`Gordon JI. Obesity alters gut microbial ecology. Proc Natl
`Acad Sci USA 2005; 102: 11070-11075 [PMID: 16033867 DOI:
`10.1073/pnas.0504978102]
`11 Walk ST, Blum AM, Ewing SA, Weinstock JV, Young VB.
`Alteration of the murine gut microbiota during infection with
`the parasitic helminth Heligmosomoides polygyrus. Inflamm
`Bowel Dis 2010; 16: 1841-1849 [PMID: 20848461 DOI: 10.1002/
`ibd.21299]
`12 Hildebrand F, Nguyen TL, Brinkman B, Yunta RG, Cauwe B,
`Vandenabeele P, Liston A, Raes J. Inflammation-associated
`enterotypes, host genotype, cage and inter-individual effects
`drive gut microbiota variation in common laboratory mice.
`Genome Biol 2013; 14: R4 [PMID: 23347395 DOI: 10.1186/gb-
`2013-14-1-r4]
`13 Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W,
`Guan Y, Shen D, Peng Y, Zhang D, Jie Z, Wu W, Qin Y, Xue
`W, Li J, Han L, Lu D, Wu P, Dai Y, Sun X, Li Z, Tang A,
`Zhong S, Li X, Chen W, Xu R, Wang M, Feng Q, Gong M, Yu J,
`Zhang Y, Zhang M, Hansen T, Sanchez G, Raes J, Falony
`G, Okuda S, Almeida M, LeChatelier E, Renault P, Pons N,
`Batto JM, Zhang Z, Chen H, Yang R, Zheng W, Li S, Yang
`H, Wang J, Ehrlich SD, Nielsen R, Pedersen O, Kristiansen
`K, Wang J. A metagenome-wide association study of gut
`microbiota in type 2 diabetes. Nature 2012; 490: 55-60 [PMID:
`23023125 DOI: 10.1038/nature11450]
`14 Pang W, Vogensen FK, Nielsen DS, Hansen AK. Faecal
`and caecal microbiota profiles of mice do not cluster in the
`same way. Lab Anim 2012; 46: 231-236 [PMID: 22723645 DOI:
`10.1258/la.2012.011128]
`15 Bergström A, Licht TR, Wilcks A, Andersen JB, Schmidt
`LR, Grønlund HA, Vigsnaes LK, Michaelsen KF, Bahl MI.
`Introducing GUt low-density array (GULDA): a validated ap-
`proach for qPCR-based intestinal microbial community anal-
`ysis. FEMS Microbiol Lett 2012; 337: 38-47 [PMID: 22967145
`
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`December 21, 2014|Volume 20|Issue 47|
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`DOI: 10.1111/1574-6968.12004]
`16 Krych L, Hansen CH, Hansen AK, van den Berg FW, Nielsen
`DS. Quantitatively different, yet qualitatively alike: a meta-
`analysis of the mouse core gu