- Research Paper
- Open Access
The nutrigenomic investigation of C57BL/6N mice fed a short-term high-fat diet highlights early changes in clock genes expression
© Springer-Verlag Berlin Heidelberg 2013
- Received: 17 December 2012
- Accepted: 27 March 2013
- Published: 16 April 2013
Mice fed long-term high-fat diets (HFD) are an established model for human metabolic disorders, such as obesity and diabetes. However, also the effects of short-term HFD feeding should be investigated to understand which are the first events that trigger the onset of a pre-disease condition, the so-called metabolic syndrome, that increases the risk of developing clinical diseases. In this study, C57BL/6N mice were fed a control diet (CTR) or a HFD for 1 (T1) or 2 weeks (T2). Metabolic and histological effects were examined. Cecum transcriptomes of HFD and CTR mice were compared at T2 by microarray analysis. Differentially expressed genes were validated by real-time PCR in the cecum and in the liver. After 2 weeks of diet administration, HFD mice showed an altered expression pattern in only seven genes, four of which are involved in the circadian clock regulatory pathway. Real-time PCR confirmed microarray results of the cecum and revealed the same trend of clock gene expression changes in the liver. These findings suggest that clock genes may play an important role in early controlling gut output systems in response to HFD in mice and that their expression change may also represent an early signaling of the development of an intestinal pro-inflammatory status.
- Clock genes
- Microarray analysis
- Short-term high-fat diet
High-fat (HF) feeding treatments have been described in the mouse by many authors, above all as a model to study obesity, hyperglycemic, hypercholesterolemic and hyperinsulinemic symptoms (Jump 2011), which are major risk factors for chronic diseases in humans, including type-2 diabetes, cardiovascular disease, hypertension, stroke and certain forms of cancer (Anderson et al. 2005). Most studies were based on long-term HF feeding treatments; however, also short-term effects should be evaluated to understand which are the first “alarm bells” for the development of a pre-disease condition, the so-called metabolic syndrome (de Wilde et al. 2008; Sasaki et al. 2010; Isken et al. 2010). This syndrome has attracted much attention, because it seems to be caused by an excess of nutrients (Kimokoti and Brown 2011). Most of these studies focused on the physiology and underlying molecular mechanisms in liver (Kreeft et al. 2005; Akagiri et al. 2008), skeletal muscle (Anderson et al. 2008) and adipose tissue (Akagiri et al. 2008; Kintscher et al. 2008), as these organs are targets of insulin-modulated metabolism. However, increasing evidence links the intake of a HF diet (HFD) to gut local inflammatory events, being gut the first organ to be exposed to dietary components. In particular, the mammalian small intestine quickly responds to HFD by undergoing pronounced morphological and functional adaptations to changes in the diet. de Wit and co-workers examined by microarray analysis the role of small intestine in the development of dietary fat–induced obesity and insulin resistance in C57BL/6J mice. They found that the biological processes influenced by feeding a high-fat diet were related to lipid metabolism, cell-cycle regulation and inflammation/immune response. For this last process, they could not draw a definitive conclusion on inflammatory status of the small intestine as a whole, because almost no gene showed a consistent up- or down-regulation in all the segments of the small intestine (de Wit et al. 2008). In the study conducted by Ding and co-workers, C57BL/6 mice were exposed to high-fat or low-fat diets and assessed for the expression of multiple pro-inflammatory cytokines in the small intestine and in the colon. Only TNFα was found to be up-regulated by the HF diet and this occurred specifically in the ileum and within a 2–6-week time frame from the beginning of HF diet administration, before diet-induced weight gain and increased fat mass could be observed (Ding et al. 2010). In this study, no detectable increase was identified in TNFα or other cytokines in plasma. Most of these studies focused on the small bowel. However, also the large bowel, so far not intensively explored, can be influenced by diet variation and is a potential target of inflammation (Chapman-Kiddell et al. 2010). In fact, a recent work of Patrone et al. (Patrone et al. 2012) reported that a short-term HF diet rich in soy oil modified the cecum microbiota of C57BL/6 mice. In light of this finding, in the present study we investigate whether a short-term HF diet also induces early changes in gene expression in the cecum of C57BL/6N mice.
Animals and diets
Composition of the experimental diets
g/kg of diet
Potatoes protein flour
Mineral and vitamin mixa
Amino acid mixb
Fatty acids compositionc
Gross energy, kJ/g
Protein, % energy
Carbohydrates, % energy
Lipid, % energy
Mice were anaesthetized with a mixture of Zoletil 100® (Virbac) and Rompun (Bayer) diluted in PBS at a ratio of 1:2:37. For each time point, animals were killed in the morning to avoid diurnal variability. In addition, to minimize possible temporal effects on analytical measurements, mice to be killed were chosen in turn from the two experimental groups. Following deep anesthesia, blood was collected by intracardiac puncture using a heparinized syringe. Plasma was obtained by centrifugation and stored at –20 °C until use. The large intestine was harvested via midline laparotomy and rinsed with PBS to remove luminal contents. Next, three consecutive segments were collected. The first 0.5-cm segments were processed for histological analysis. The last 1-cm segment was snap-frozen in liquid nitrogen and stored at −80 °C for subsequent RNA isolation.
Blood metabolites were analyzed at 37 °C by a clinical auto-analyzer (ILAB 600, Instrumentation Laboratory, Lexington, MA). Glucose, total protein, albumin, total cholesterol, triglycerides, urea and aspartate aminotransferase (AST/GOT) were determined using kits purchased from Instrumentation Laboratory (IL Test). Globulin was calculated as the difference between total protein and albumin. Non-esterified fatty acids (NEFA) were determined by commercial kits (Wako Chemicals GmbH, Neuss, Germany). Ceruloplasmin was analyzed using the method described by Sunderman and Nomoto (1970). Myeloperoxidase analysis was performed using the method described by Bradley et al. (1982). Serum amyloid A protein was assayed using an ELISA commercial kit (SAA, by ELISA, Tridelta, Ireland) and a microplate reader (The Synergy™ 2 BioTek Instruments, Inc., Winooski, Vermont, USA).
Sections of cecum, collected from all mice at each time point, were fixed in 10 % phosphate-buffered PFA at 4 °C for 24 h. PFA-fixed intestinal tissues were embedded in paraffin, sectioned to a thickness of 5 μm, stained with hematoxylin and eosin and observed by a light microscope. Inflammation was evaluated on stained sections in blind by two independent researchers on the basis of the following factors: leukocytes infiltration, tissue alteration (edema and mucosal injury) and presence of mucus in the lumen. A rating score ranging between 0 (no change from normal tissue) and 5 (lesions involved most areas and all layers of the intestinal section including mucosa and muscle) was applied for each factor. The sum of scores for inflammatory cell infiltration, tissue alteration and presence of mucus in the lumen was considered as the total inflammatory score (TIS) for each specimen. The crypt depth was also measured in 10 cryptae per mouse. Only complete cryptae with an intact layer of epithelial cells were chosen for the measurement. All evaluations were conducted with a micrometric ocular at a 100× magnification.
Total RNA was extracted from cecum samples collected at T2 using the Chomczynski–Sacchi method (Chomczynski and Sacchi 1987) and quantified using Qubit™ fluorometer (Invitrogen, Milan, Italy). The quality of RNA samples was ascertained using Experion (Bio-Rad Laboratories, Segrate, Milan, Italy).
Gene expression was measured on cecum samples of individual CTR and HF mice killed at T2, in a total of 6 biological replicates per group. In addition, two samples per group were hybridized twice on different arrays and treated as technical replicates. Custom Array™ 90 K arrays were produced by the Functional Genomics Center at the University of Verona using a Custom Array Synthesizer (CombiMatrix, Irvine, CA, USA). A total of 29,435 non-redundant probes were synthesized in triplicate, representing all the available mouse (Mus musculus) genes in GenBank database. Total RNA (2 μg) was amplified and labeled with Cy5 using RNA Ampulse amplification and labeling kit for CombiMatrix arrays (Kreatech Diagnostics, The Netherlands), according to the manufacturer’s recommendations. In short, the double-strand cDNA was obtained by reverse-transcribing the total RNA using T7 Oligo(dT) primer. Thereafter, cDNA was transcribed in vitro, to generate amplified antisense RNA (aRNA). aRNA was labeled with cyanine 5 fluorophore using a chemical ligation. After purification, labeled aRNA was quantified by Varian Cary spectrophotometer (Agilent Technologies—Life Sciences and Chemical) and 4 μg hybridized to the array, according to the standard protocol supplied with Custom Array™ 90 K (CombiMatrix, Irvine, CA, USA). Fluorescent signals were collected on a GenePix 4,000B scanner (Molecular Devices, Sunnyvale, CA, USA).
Data analysis of microarray experiment
After image processing, signal intensity for each spot was quantified using GenePix Pro software v7. Cy5 signal quality was checked with an implemented R pipeline that takes advantage of limma package functions from Bioconductor (Bolstad et al. 2003). The reproducibility of the experiments was assessed calculating the average Spearman’s rank correlation coefficient among all technical and biological replicates (r = 0.96) (range 0.99–0.91). After background correction, data were normalized (Gentleman et al. 2004) using the normal–exponential convolution (McGee and Chen 2006). Statistically significant differential expression between HF and control groups was tested using the limma package (Smyth 2004) on log2-transformed data. To increase the power of statistical analysis, we assigned weights to each array using a heteroscedastic linear model with shared array variance terms. In experiments with few replicate arrays, this approach outperforms the standard method of filtering data (Ritchie et al. 2006). The limma package was used to apply a Bayes model to moderate the standard errors of the estimated log-fold changes. This moderated t-statistic is computed for each probe and for each contrast. This has the same interpretation as an ordinary t-statistic, except that standard errors have been moderated across genes, that is, shrunk toward a common value, improving the statistical power of analyses, particularly in the case of small-size samples. Benjamini and Hochberg 5 % false discovery rate (q value ≤0.05) was used as threshold, to identify significant differentially expressed genes. Hierarchical clustering of all samples was computed based on genes differentially expressed with a q value ≤0.05.
RT and real-time PCR
Real-time PCR primers
Statistical analysis of blood parameters was carried out by ANOVA using the GLM procedure of SAS® (SAS Institute Inc., Cary, NC, USA, release 9.1), including the effects of time (T0, T1 and T2), sex (male and female), diet (CTR and HF) and their interactions. Significance threshold was set at P ≤ 0.05.
Growth and metabolic parameters
Plasma glucose, total cholesterol, triglycerides, urea, SAA levels in mice fed CTR or HF diet, after 7 (T1) and 14 days of treatment (T2)
7 days (T1)
14 days (T2)
Total cholesterol, mmol/l
Effect of high-fat diet on histological parameters
Effect of high-fat diet on the histology of the cecum of mice
T0 (n = 5)
T1 CTR (n = 5)
T1 HF (n = 6)
T2 CTR (n = 6)
T2 HF (n = 6)
Crypt depth, μm
150.00 ± 30.00
156.00 ± 26.08
155.00 ± 30.00
161.67 ± 20.41
156.67 ± 22.51
4.20 ± 2.69
6.60 ± 4.39
4.47 ± 2.77
5.16 ± 3.09
6.3 ± 3.54
Identification of genes in the cecum affected by high-fat diet
Top 10 of the differentially expressed genes in the cecum of mice at T2, in response to high-fat diet
Mus musculus aryl hydrocarbon receptor nuclear translocator–like (Arntl) or brain muscle Arnt-like-1 (Bmal1), mRNA
Mus musculus thyrotroph embryonic factor (Tef), transcript, mRNA
Mus musculus follistatin (Fst), mRNA
Mus musculus nuclear receptor subfamily 1, group D, member 2 (Nr1d2), mRNA
Mus musculus histocompatibility 2, Q region locus 10 (H2-Q10), mRNA
Mus musculus tetraspanin 4 (Tspan4), mRNA
Mus musculus hepatic leukemia factor (Hlf), mRNA
Mus musculus kinesin family member 26B (Kif26b), mRNA
Mus musculus solute carrier family 30, member 10 (Slc30a10), mRNA
Mus musculus nuclear factor, interleukin 3, regulated (Nfil3), mRNA
The other four significant genes are directly or indirectly participating in the regulation of the circadian clock system. Two (Bmal1, Nr1d2) are involved in the transcriptional/translational feedback loops controlling the circadian clock system and two (Tef, Hlf) are genes coding for PAR bZip (proline and acidic amino-acid-rich basic leucine zipper) transcription factors, whose circadian expression is driven by the core clock. In the list of the top ten genes sorted by adjusted p value, we also found Nfil3 (q value ≤0.2) (Table 5) also involved in the modulation of the circadian clock system.
Confirmation of clock genes differential expression by real-time PCR in cecum and liver
Relative expression ratio of the eight genes validated by real-time PCR in mice at T2 in response to the HF diet
Fold changea (HF/CTR)
Fold changea (HF/CTR)
Aryl hydrocarbon receptor nuclear translocator–like (Arntl) or brain muscle Arnt-like-1 (Bmal1)
Thyrotroph embryonic factor (Tef)
Hepatic leukemia factor (Hlf)
Nuclear receptor subfamily 1, group D, member 2 (Nr1d2) or Rev-erbß
Nuclear factor, interleukin 3, regulated (Nfil3) or E4 promoter-binding protein 4 (E4BP4)
Circadian locomotor output cycles kaput (Clock)
Cryptochrome 1 (photolyase-like) (Cry1)
Peroxisome proliferator activated receptor alpha (PPARα)
The present study investigated the early response of C57BL/6N mice to an increased consumption of dietary fat. The unusual high-fat diet was enriched in the main precursor of the arachidonic acid, reported to drive the metabolism to the synthesis of pro-inflammatory mediators (Calder 2002, 2003).
No increase in body weight gain was evident in mice fed the HF diet compared with CTR, in spite of the higher energy content. Also, histological parameters of intestinal tissue samples collected from HF mice showed neither morphological alterations nor leukocytes infiltration. It is quite likely that the short period of dietary treatment was not sufficient to produce visible effects on growth and to induce a detectable tissue alteration in the gut.
Total plasma cholesterol concentration displayed a significant increase in the HF group both at T1 and T2, as already described in previous studies (Paigen 1995; Gallou-Kabani et al. 2007). Moreover, Paigen (1995) observed that the increased cholesterol level was mostly due to the non-HDL fraction of cholesterol, thus reproducing the situation observed in studies on human metabolic syndrome.
Triglycerides levels in HF group were 34 % (T1) and 41 % (T2), lower than those of mice in the CTR groups. These results are in line with many studies in the murine species (Paigen 1995; Tsunoda et al. 1998) and in humans (Lichtenstein 2006). The decrease in triglyceride levels following fish oil administration has been in part ascribed by some authors to a decrease in the hepatic production of triglyceride-rich particles (VLDL, the lipoprotein responsible for transporting triglycerides for subsequent delipidation by peripheral tissue) or to an increase in the conversion rate of VLDL to LDL (Balk et al. 2006).
Serum amyloid A protein
Serum amyloid A is the major acute-phase protein in vertebrates (Uhlar and Whitehead 1999). Its level can increase 100- to 1,000-fold in response to acute inflammatory stimuli. Our results indicate a significant increase in SAA level at T2 in animals fed HF diet. However, the increase recorded was inconsistent with the presence of acute inflammation, since HF group displayed approximately twice the level of SAA compared with the CTR group. This observation was consistent with data from Lewis et al. (2004) that noticed that SAA level in atherosclerosis susceptible LDLR−/− mice fed high-fat diet was higher than in control mice but much lower than the one observed during an acute inflammatory response. Interestingly, in clinical studies in humans, Ridker et al.(2000) and Jousilahti et al. (2001) reported that small increases in SAA, if compared with the SAA level in an acute inflammation, have proven to be predictive values for cardiovascular disease events, hence indicating SAA as an early marker for inflammation increasing before the appearance of any evidence for distress.
Metabolic data, cholesterol and SAA in particular, suggest at T2 the beginning of a possible onset of inflammation.
Gene expression in gut was assessed at T2 by microarray analysis. The study revealed seven genes differentially expressed in HF mice compared with CTR mice (q value ≤0.05).
The Fst, H2-Q10 and Tspan4 are genes implicated in inflammatory processes. During an acute systemic inflammation activin A is released concurrently with follistatin as part of the circulatory cytokine cascade (Jones et al. 2004). Follistatin is down-regulated in HFD and consequently less able to counteract the pro-inflammatory activity of activins. H2-Q10 is down-regulated as well. The gene is part of the MHC complex; however, its specific function is poorly known. Conversely, Tspan4 is up-regulated by HFD. Among the functions of the tetraspanin trans-membrane protein, there is the modulation of the avidity of adhesion receptors involved in inflammation, and the regulation of cell migration and invasion (Yàñez-Mó et al. 2009). The HFD seems therefore on the one side to pave the way to an inflammatory response and on the other to weaken the potential reaction to the onset of an inflammatory status.
The other four differentially expressed genes were involved in molecular mechanisms regulating circadian rhythms. The Bmal1 gene, down-regulated in HF, is a transcription factor playing a central role in the regulation of circadian rhythms and seems to contribute to the regulation of adipogenesis and adipocyte functions (Shimba et al. 2005). The Nr1d2 gene, up-regulated in HF, is highly expressed during daylight and represses Bmal1 transcription (Guillaumond et al. 2005). It is also implicated in the regulation of many genes involved in lipid homeostasis in skeletal muscle cells (Ramakrishnan et al. 2005). The Tef and the Hlf gene, both up-regulated in HF, serve as mediators in transmitting circadian signals and in controlling various metabolic pathways and physiological functions. Finally, it is worth mentioning the Nfil3 gene coding for the E4BP4 protein that, although not significantly (q value = 0.18), appears down-regulated in HF. Nfil3 plays a regulatory role in the circadian transcriptional circuitry (Ueda et al. 2005).
Microarray data were validated by real-time PCR in the cecum and assessed in the liver, assumed to be the most responsive organs to diet interventions. Results confirmed all cecum microarray data and revealed the same trend of gene expression modulation in the liver, with the exception of PPARα expression, up-regulated in the liver of HF-fed mice, and of Clock expression, down-regulated in the liver in response to HF treatment.
In the last years, metabolic diseases have been shown to be associated with altered temporal organization of many physiological functions. Therefore, attention has been directed toward the investigation of links between circadian rhythms and functions, in particular of the gastrointestinal tract (GI). A number of investigations have been conducted in transgenic mice with disruptions or deficiencies of some clock genes fed high-fat diets or obesity-induced diets and have demonstrated that disruption of circadian rhythms leads to metabolic disorders (Hemmeryckx et al. 2011; Dallmann and Weaver 2010; Marcheva et al. 2010). By contrast, only a few studies have investigated the relationship existing between different diets and circadian clock regulation in wild-type mice. The effect of administering high-fat diet on the expression of clock genes has so far been studied in different tissues (Kohsaka et al. 2007; Barnea et al. 2009; Hsieh et al. 2010; Yanagihara et al. 2006), but to date, alterations of clock gene expression in the gut following administration of high-fat diet in wild-type mice have never been reported.
In the present work, we show for the first time that the administration of a high-fat diet leads to a change in the expression of clock genes in the cecum of C57BL/6N mice. Out of 30,000 genes investigated, only 7 genes appear to respond very early (after 2 weeks of treatment) to the administration of HF diets, in the presence of only slight alterations of metabolic parameters and no evidence of clear inflammation, and four of these genes were involved in the regulation of the circadian clock system. The variations we observed in cecum and liver gene expression are in accordance with known regulatory circuits (Kohsaka et al. 2007; Mitsui et al. 2001). Nr1d2, Tef and Hlf expression alterations in peripheral tissues following administration of high-fat diet had never been described by other authors.
Even if most of the complex molecular interactions underlying the circadian systems remain poorly understood, it is now evident that the circadian clock plays an important role in regulating metabolism and energy homeostasis in peripheral tissues by mediating the expression and the activity of certain metabolic enzymes and transport systems. This study suggests that also in the cecum of mice, as well as in the liver, diet directly influences clock genes expression and it is conceivable that their up- or down-regulation mediates alterations in metabolic processes (i.e., energy utilization and/or storage) by means of subtle transcriptional–translational feedback loops. The internal clock system is thought to allow organs to quickly adapt to environmental changes, and this might explain the changes in clock genes expression in the cecum and liver following high-fat-diet administration. Although the molecular mechanisms underlying the results observed remain unknown, we can suppose that cecum might be one of the first organs to be subject to changes in clock genes expression following a diet intervention.
Further studies are needed to better understand whether the low fold changes and the lack of inflammation might be ascribed to the short-term high-fat-diet treatment, as stated in previous works, or to our unusual high-fat diet model including soy oil, rich in unsaturated fatty acids, as fat source. For this reason, it could also be interesting to understand whether there is a correlation between the changes in the gut microbiota (Patrone et al. 2012) and in the gut gene expression following the HFD administration, al least in this model. Finally, it remains to be elucidated whether there is an association between the small increase in SAA and the change in some clock genes expression levels in the cecum and liver of HF-fed mice at T2 as an early signaling of the development of an intestinal pro-inflammatory status. In this view, clock genes could be very useful as “early markers” of pre-disease conditions for metabolic disorders.
We thank Alberto Ferrarini (Functional Genomics Center, University of Verona, Italy) for his technical assistance in the microarray experiment and Prof. Diego Fornasari (Department of Pharmacology, School of Medicine, Università degli Studi di Milano and Institute of Neuroscience, Milan, Italy) for the useful discussion. This work was conducted in the framework of the project “Nutrigenomics” supported by the “Fondazione Romeo ed Enrica Invernizzi,” Milan, Italy.
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