Mammalian Genome

, 20:476

Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice

  • Enpeng Zhao
  • Mark P. Keller
  • Mary E. Rabaglia
  • Angie T. Oler
  • Donnie S. Stapleton
  • Kathryn L. Schueler
  • Elias Chaibub Neto
  • Jee Young Moon
  • Ping Wang
  • I-Ming Wang
  • Pek Yee Lum
  • Irena Ivanovska
  • Michele Cleary
  • Danielle Greenawalt
  • John Tsang
  • Youn Jeong Choi
  • Robert Kleinhanz
  • Jin Shang
  • Yun-Ping Zhou
  • Andrew D. Howard
  • Bei B. Zhang
  • Christina Kendziorski
  • Nancy A. Thornberry
  • Brian S. Yandell
  • Eric E. Schadt
  • Alan D. Attie
Article

DOI: 10.1007/s00335-009-9217-2

Cite this article as:
Zhao, E., Keller, M.P., Rabaglia, M.E. et al. Mamm Genome (2009) 20: 476. doi:10.1007/s00335-009-9217-2

Abstract

Type 2 diabetes results from severe insulin resistance coupled with a failure of β cells to compensate by secreting sufficient insulin. Multiple genetic loci are involved in the development of diabetes, although the effect of each gene on diabetes susceptibility is thought to be small. MicroRNAs (miRNAs) are noncoding 19–22-nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand the relationship between miRNA regulation and obesity-induced diabetes, we quantitatively profiled approximately 220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs profiled were expressed in all three tissues, with many miRNAs in each tissue showing significant changes in response to genetic obesity. Furthermore, several miRNAs in each tissue were differentially responsive to obesity in B6 versus BTBR mice, suggesting that they may be involved in the pathogenesis of diabetes. In liver there were approximately 40 miRNAs that were downregulated in response to obesity in B6 but not BTBR mice, indicating that genetic differences between the mouse strains play a critical role in miRNA regulation. In order to elucidate the genetic architecture of hepatic miRNA expression, we measured the expression of miRNAs in genetically obese F2 mice. Approximately 10% of the miRNAs measured showed significant linkage (miR-eQTLs), identifying loci that control miRNA abundance. Understanding the influence that obesity and genetics exert on the regulation of miRNA expression will reveal the role miRNAs play in the context of obesity-induced type 2 diabetes.

Supplementary material

335_2009_9217_MOESM1_ESM.eps (1.6 mb)
Supplementary Figure 1Relationship of fold change and statistical significance for miRNA expression changes in adipose tissue and liver of B6 and BTBR mice. Volcano plots (Cui and Churchill 2003) illustrate the relationship between p-value and the ratio of the miRNA copy number between obese versus lean mice in adipose tissue (left) and liver (right) for B6 (top) and BTBR (bottom) mice. Symbol size is proportional to log10 of the strain-specific average copy number for each miRNA. MiRNAs that showed obesity-dependent changes with FDRs ≤ 0.02 (Storey and Tibshirani 2003) are highlighted in red with their symbol number (EPS 1655 kb)
335_2009_9217_MOESM2_ESM.xls (367 kb)
Supplementary Table 1MiRNA profiling results in islet, adipose tissue and liver collected from B6-lean, B6-ob/ob, BTBR-lean and BTBR-ob/ob mice. Pancreatic islet, adipose tissue, and liver were harvested and miRNA measurements performed by LNA-based RT-PCR profiling. The miRNA abundance is shown as copy number/cell. Pools of RNA samples were used for the islet profiling, whereas 5 individuals were profiled for the adipose tissue and liver. For the adipose and liver profiling, Student’s t-test, reported as p-values and FDRs (Storey and Tibshirani 2003), were used to determine which miRNAs underwent statistically significant changes as a function of obesity in either B6 or BTBR mice. MiRNAs are ordered by relative abundance, showing that miR-211 was the most abundant miRNA in all 3 tissues (XLS 367 kb)
335_2009_9217_MOESM3_ESM.pdf (9 kb)
Supplementary Table 2Taqman-based real time PCR measurement on 10 individual islet samples per group confirms majority of LNA-based profiling results for selected miRNAs. MiRNA from the pooled islet samples which showed significant changes from LNA-based PCR profiling results were subsequently measured by Taqman-based PCR on 10 individual samples of B6-lean, B6-ob/ob, BTBR-lean and BTBR-ob/ob mice. MiRNA expression values are shown as ΔCT normalized to SnoRNA234 (a house-keeping, small nucleolar RNA molecule) ± standard error. The fold change (FC) for the effect of obesity (obesity/lean) is shown for B6 and BTBR mice; negative FC values show obesity-dependent reduction. A student’s t-test was used to determine statistical significance (Bonferroni-corrected p-values) between the 10 lean and obese mice for each strain. Correlation shows the Pearson’s correlation between the LNA-based measurement of the islet pooled samples and the average Taqman-based ΔCt measurement of the 10 individual islet samples selected. Expression profiles across the 4 experimental groups between Taqman and LNA PCR methods were highly correlated, except for miR-214. NS not significant (PDF 9 kb)
335_2009_9217_MOESM4_ESM.pdf (24 kb)
Supplementary Table 3Genomic location and position of hepatic miR-eQTL for 21 miRNAs. Twenty-one miRNAs which showed significant linkage in F2 liver are listed according to their LOD score. The genomic location and position of the QTL loci are provided. Cis-mapping miRNAs are indicated by *; all other miRNAs map in trans. # indicates the two miRNAs (let-7c and miR-16) that derive from more than one genomic location. As these both map in trans, we cannot distinguish which genomic position contributed to the QTL(PDF 23 kb)

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Enpeng Zhao
    • 1
  • Mark P. Keller
    • 1
  • Mary E. Rabaglia
    • 1
  • Angie T. Oler
    • 1
  • Donnie S. Stapleton
    • 1
  • Kathryn L. Schueler
    • 1
  • Elias Chaibub Neto
    • 2
  • Jee Young Moon
    • 2
  • Ping Wang
    • 3
  • I-Ming Wang
    • 4
  • Pek Yee Lum
    • 4
  • Irena Ivanovska
    • 4
  • Michele Cleary
    • 4
  • Danielle Greenawalt
    • 4
  • John Tsang
    • 4
  • Youn Jeong Choi
    • 3
  • Robert Kleinhanz
    • 4
  • Jin Shang
    • 5
  • Yun-Ping Zhou
    • 5
  • Andrew D. Howard
    • 5
  • Bei B. Zhang
    • 5
  • Christina Kendziorski
    • 3
  • Nancy A. Thornberry
    • 5
  • Brian S. Yandell
    • 2
  • Eric E. Schadt
    • 6
  • Alan D. Attie
    • 1
  1. 1.Biochemistry DepartmentUniversity of WisconsinMadisonUSA
  2. 2.Statistics DepartmentUniversity of WisconsinMadisonUSA
  3. 3.Biostatistics and Medical InformaticsUniversity of WisconsinMadisonUSA
  4. 4.Rosetta InpharmaticsSeattleUSA
  5. 5.Merck Research LaboratoriesRahwayUSA
  6. 6.Sage BionetworksSeattleUSA