Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012;7(4):e34872.
doi: 10.1371/journal.pone.0034872. Epub 2012 Apr 4.

Differential expression of microRNAs in adipose tissue after long-term high-fat diet-induced obesity in mice

Dionysios V Chartoumpekis  1 , Apostolos Zaravinos, Panos G Ziros, Ralitsa P Iskrenova, Agathoklis I Psyrogiannis, Venetsana E Kyriazopoulou, Ioannis G Habeos
Affiliations

Differential expression of microRNAs in adipose tissue after long-term high-fat diet-induced obesity in mice

Dionysios V Chartoumpekis et al. PLoS One. 2012.

Abstract

Obesity is a major health concern worldwide which is associated with increased risk of chronic diseases such as metabolic syndrome, cardiovascular disease and cancer. The elucidation of the molecular mechanisms involved in adipogenesis and obesogenesis is of essential importance as it could lead to the identification of novel biomarkers and therapeutic targets for the development of anti-obesity drugs. MicroRNAs (miRNAs) have been shown to play regulatory roles in several biological processes. They have become a growing research field and consist of promising pharmaceutical targets in various fields such as cancer, metabolism, etc. The present study investigated the possible implication of miRNAs in adipose tissue during the development of obesity using as a model the C57BLJ6 mice fed a high-fat diet.C57BLJ6 wild type male mice were fed either a standard (SD) or a high-fat diet (HFD) for 5 months. Total RNA was prepared from white adipose tissue and was used for microRNA profiling and qPCR.Twenty-two of the most differentially expressed miRNAs, as identified by the microRNA profiling were validated using qPCR. The results of the present study confirmed previous results. The up-regulation of mmu-miR-222 and the down-regulation of mmu-miR-200b, mmu-miR-200c, mmu-miR-204, mmu-miR-30a*, mmu-miR-193, mmu-miR-378 and mmu-miR-30e* after HFD feeding has also been previously reported. On the other hand, we show for the first time the up-regulation of mmu-miR-342-3p, mmu-miR-142-3p, mmu-miR-142-5p, mmu-miR-21, mmu-miR-146a, mmu-miR-146b, mmu-miR-379 and the down-regulation of mmu-miR-122, mmu-miR-133b, mmu-miR-1, mmu-miR-30a*, mmu-miR-192 and mmu-miR-203 during the development of obesity. However, future studies are warranted in order to understand the exact role that miRNAs play in adipogenesis and obesity.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Differentially expressed miRNAs between control mice (standard diet) and sample mice (high-fat diet) mice.
The SD #1 and #2 indicate technical replicates. The diagram shows the top ranking 26 differentially expressed miRNAs based on the comparison samples versus control. Each bar represents the fold change between the sample mice versus the control mouse. Since slide no. 3 is a dye swap (control vs. samples) the values have been reversed to fit slides no. 1 and 2. A fold change >1 indicates up-regulation after HFD feeding, whereas a fold change <1 indicates down-regulation after HFD feeding. SD; standard diet, HFD; high-fat diet.
Figure 2
Figure 2. Unsupervised hierarchical cluster analysis diagram based on 529 probe sets with highest variation in SD vs. HFD mice.
Color saturation is directly proportional to measured expression ratio magnitude. Rows represent individual probe set. Columns represent experimental sample. Red bars indicate high expression. Green bars indicate low expression. Slides 1 and 2: control (SD) mice, slide3: sample (HFD) mice. SD; standard diet. HFD; high-fat diet.
Figure 3
Figure 3. Three-dimensional PCA clustering.
The observed samples were represented as a linear combination of the principal components with associated gene scores. The three-dimensional PCA clustering revealed 13 miRNA clusters.
Figure 4
Figure 4. K-means clustering identified 10 clusters.
This non-hierarchial method initially takes the number of components of the population equal to the final required number of clusters. In this step itself the final required number of clusters is chosen such that the points are mutually farthest apart. Next, it examines each component in the population and assigns it to one of the clusters depending on the minimum distance. The centroid's position is recalculated everytime a component is added to the cluster and this continues until all the components are grouped into the final required number of clusters. A. K-means clustering of profiles and B. Centroids.
Figure 5
Figure 5. Terrain (map) analysis, depicting the gene clusters, as well as the links among the clustered genes.
A. Each miRNA cluster is depicted with a different color. B. The correlations among the various miRNAs are depicted with lines.
Figure 6
Figure 6. Figure of merit (FOM) vs. no. of clusters graph for the k-means cluster algorithm.
A figure of merit is an estimate of the predictive power of a clustering algorithm. The lower the adjusted FOM value is, the higher the predictive power of the algorithm. The value of the adjusted FOM for the k-means run decreases steeply until the number of clusters reaches 7, after which it levels out. This suggests that, for this data set, k-means performs optimally for 7 clusters and that any additional clusters produced will not add to the predictive value of the algorithm.
Figure 7
Figure 7. miRNA relative expression in mice fed a standard or a high-fat diet for 5 months.
The miRNA levels were measured by quantitative RT-PCR in white adipose tissue from mice fed a standard or a high-fat diet for 5 months (n = 8 for each diet type). The RT-PCR was performed in triplicate wells for each individual sample. Bars show means±standard deviation. * p<0.0001, † p<0.001. SD; standard diet, HFD; high-fat diet.
Figure 8
Figure 8. Hierarchical clustering (HCL) and Principal Component Analysis (PCA).
HCL (A) and PCA analysis (B) for 18 mmu-miRs validated by qPCR. The clustering results were similar to those acquired by the miRNA profiling.
Figure 9
Figure 9. Correlation between microarrays and qPCR.
A. The fold changes were calculated using the ΔΔCt comparative quantification method. Mmu-miR-141 was not detected in “control mice” (SD), hence no fold change was calculated. We identified an agreement between the microarrays and the qPCR results. B. Scatterplot and Correlation between qPCR and microarrays (Pearson's correlation = 0.888).
Figure 10
Figure 10. GO terms annotation of the validated target genes of the up-regulated mmu-miRNAs after HFD feeding.
The most significant functions of the enriched miRNAs are highlighted in red color.
Figure 11
Figure 11. GO terms annotation of the validated target genes of the down-regulated mmu-miRNAs after HFD feeding.
The most significant functions of the enriched miRNAs are highlighted in red color.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. James PT, Leach R, Kalamara E, Shayeghi M. The worldwide obesity epidemic. Obes Res. 2001;9(Suppl 4):228S–233S. - PubMed
    1. Calle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer. 2004;4:579–591. - PubMed
    1. Bergman RN, Kim SP, Hsu IR, Catalano KJ, Chiu JD, et al. Abdominal obesity: role in the pathophysiology of metabolic disease and cardiovascular risk. Am J Med. 2007;120:S3–8; discussion S29–32. - PubMed
    1. Kershaw EE, Flier JS. Adipose tissue as an endocrine organ. J Clin Endocrinol Metab. 2004;89:2548–2556. - PubMed
    1. Despres JP. Abdominal obesity as important component of insulin-resistance syndrome. Nutrition. 1993;9:452–459. - PubMed
-