Skip to main content

Advertisement

SpringerLink
Log in

A micro-RNA expression signature for human NAFLD progression

  • Original Article—Liver, Pancreas, and Biliary Tract
  • Published:
Journal of Gastroenterology Aims and scope Submit manuscript

Abstract

Background

The spectrum of nonalcoholic fatty liver disease (NAFLD) describes disease conditions deteriorating from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) to cirrhosis (CIR) to hepatocellular carcinoma (HCC). From a molecular and biochemical perspective, our understanding of the etiology of this disease is limited by the broad spectrum of disease presentations, the lack of a thorough understanding of the factors contributing to disease susceptibility, and ethical concerns related to repeat sampling of the liver. To better understand the factors associated with disease progression, we investigated by next-generation RNA sequencing the altered expression of microRNAs (miRNAs) in liver biopsies of class III obese subjects (body mass index ≥40 kg/m2) biopsied at the time of elective bariatric surgery.

Methods

Clinical characteristics and unbiased RNA expression profiles for 233 miRs, 313 transfer RNAs (tRNAs), and 392 miscellaneous small RNAs (snoRNAs, snRNAs, rRNAs) were compared among 36 liver biopsy specimens stratified by disease severity.

Results

The abundances of 3 miRNAs that were found to be differentially regulated (miR-301a-3p and miR-34a-5p increased and miR-375 decreased) with disease progression were validated by RT-PCR. No tRNAs or miscellaneous RNAs were found to be associated with disease severity. Similar patterns of increased miR-301a and decreased miR-375 expression were observed in 134 hepatocellular carcinoma (HCC) samples deposited in The Cancer Genome Atlas (TCGA).

Conclusions

Our analytical results suggest that NAFLD severity is associated with a specific pattern of altered hepatic microRNA expression that may drive the hallmark of this disorder: altered lipid and carbohydrate metabolism. The three identified miRNAs can potentially be used as biomarkers to access the severity of NAFLD. The persistence of this miRNA expression pattern in an external validation cohort of HCC samples suggests that specific microRNA expression patterns may permit and/or sustain NAFLD development to HCC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  1. Charlton M. Nonalcoholic fatty liver disease: a review of current understanding and future impact. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2004;2(12):1048–58.

    Google Scholar 

  2. Chalasani N, Younossi Z, Lavine JE, et al. The diagnosis and management of non-alcoholic fatty liver disease: practice guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology. 2012;55(6):2005–23.

  3. Ekstedt M, Franzen LE, Mathiesen UL, et al. Long-term follow-up of patients with NAFLD and elevated liver enzymes. Hepatology. 2006;44(4):865–73.

    Article  CAS  PubMed  Google Scholar 

  4. Cheung O, Puri P, Eicken C, et al. Nonalcoholic steatohepatitis is associated with altered hepatic MicroRNA expression. Hepatology. 2008;48(6):1810–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Vickers KC, Roteta LA, Hucheson-Dilks H, Han L, Guo Y. Mining diverse small RNA species in the deep transcriptome. Trends Biochem Sci. 2015;40(1):4–7.

    Article  CAS  PubMed  Google Scholar 

  6. Han L, Vickers KC, Samuels DC, Guo Y. Alternative applications for distinct RNA sequencing strategies. Brief Bioinform. 2014;16(4):629–39.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Frye M, Watt FM. The RNA methyltransferase Misu (NSun2) mediates Myc-induced proliferation and is upregulated in tumors. Curr Biol. 2006;16(10):971–81.

    Article  CAS  PubMed  Google Scholar 

  8. Berg M, Agesen TH, Thiis-Evensen E, et al. Distinct high resolution genome profiles of early onset and late onset colorectal cancer integrated with gene expression data identify candidate susceptibility loci. Mol Cancer. 2010;9:100.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Bartlett JM, Thomas J, Ross DT, et al. Mammostrat as a tool to stratify breast cancer patients at risk of recurrence during endocrine therapy. Breast Cancer Res. 2010;12(4):R47.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Wei FY, Suzuki T, Watanabe S, et al. Deficit of tRNA(Lys) modification by Cdkal1 causes the development of type 2 diabetes in mice. J Clin Invest. 2011;121(9):3598–608.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kirchhoff K, Machicao F, Haupt A, et al. Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated with impaired proinsulin conversion. Diabetologia. 2008;51(4):597–601.

    Article  CAS  PubMed  Google Scholar 

  12. Omori S, Tanaka Y, Takahashi A, et al. Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes. 2008;57(3):791–5.

    Article  CAS  PubMed  Google Scholar 

  13. Yasukawa T, Suzuki T, Ueda T, Ohta S, Watanabe K. Modification defect at anticodon wobble nucleotide of mitochondrial tRNAs(Leu)(UUR) with pathogenic mutations of mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes. J Biol Chem. 2000;275(6):4251–7.

    Article  CAS  PubMed  Google Scholar 

  14. Yasukawa T, Kirino Y, Ishii N, et al. Wobble modification deficiency in mutant tRNAs in patients with mitochondrial diseases. FEBS Lett. 2005;579(13):2948–52.

    Article  CAS  PubMed  Google Scholar 

  15. Kirino Y, Goto Y, Campos Y, Arenas J, Suzuki T. Specific correlation between the wobble modification deficiency in mutant tRNAs and the clinical features of a human mitochondrial disease. Proc Natl Acad Sci USA. 2005;102(20):7127–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kleiner DE, Brunt EM, Van Natta M, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 2005;41(6):1313–21.

    Article  PubMed  Google Scholar 

  17. Fujii M, Shibazaki Y, Wakamatsu K, et al. A murine model for non-alcoholic steatohepatitis showing evidence of association between diabetes and hepatocellular carcinoma. Med Mol Morphol. 2013;46(3):141–52.

    Article  CAS  PubMed  Google Scholar 

  18. Guo Y, Bosompem A, Mohan S, et al. Transfer RNA detection by small RNA deep sequencing and disease association with myelodysplastic syndromes. BMC Genom. 2015;16:727.

    Article  Google Scholar 

  19. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. 2011;17:10–2.

  20. Guo Y, Zhao S, Sheng Q, et al. Multi-perspective quality control of Illumina exome sequencing data using QC3. Genomics. 2014;103(5–6):322–8.

    Google Scholar 

  21. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Berezikov E, Robine N, Samsonova A, et al. Deep annotation of Drosophila melanogaster microRNAs yields insights into their processing, modification, and emergence. Genome Res. 2011;21(2):203–15.

  23. Rajagopalan R, Vaucheret H, Trejo J, Bartel DP. A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Gene Dev. 2006;20(24):3407–25.

  24. Westholm JO, Ladewig E, Okamura K, Robine N, Lai EC. Common and distinct patterns of terminal modifications to mirtrons and canonical microRNAs. RNA. 2012;18(2):177–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Larter CZ, Yeh MM. Animal models of NASH: getting both pathology and metabolic context right. J Gastroenterol Hepatol. 2008;23(11):1635–48.

    Article  PubMed  Google Scholar 

  26. Chan PP, Lowe TM. GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 2009;37(Database issue):D93–7.

    Article  CAS  PubMed  Google Scholar 

  27. Flicek P, Amode MR, Barrell D, et al. Ensembl 2014. Nucleic Acids Res. 2014;42(Database issue):D749–55.

    Article  CAS  PubMed  Google Scholar 

  28. Guo Y, Zhao S, Ye F, Sheng Q, Shyr Y. MultiRankSeq: multiperspective approach for RNAseq differential expression analysis and quality control. Biomed Res Int. 2014;2014:248090.

    PubMed  PubMed Central  Google Scholar 

  29. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc B. 2005;67:301–20.

    Article  Google Scholar 

  30. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Tibshirani R. Regression shrinkage and selection via the Lasso. J Roy Stat Soc B Met. 1996;58(1):267–88.

    Google Scholar 

  32. Domencich TA, McFadden D, Charles River Associates. Urban travel demand : a behavioral analysis : a Charles River Associates research study. New York: American Elsevier; 1975. vol xv, p. 215.

  33. Hirsch RM, Slack JR, Smith RA. Techniques of trend analysis for monthly water-quality data. Water Resour Res. 1982;18(1):107–21.

    Article  Google Scholar 

  34. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402–8.

    Article  CAS  PubMed  Google Scholar 

  35. Wattacheril J, Seeley EH, Angel P, et al. Differential intrahepatic phospholipid zonation in simple steatosis and nonalcoholic steatohepatitis. PLoS One. 2013;8(2):e57165.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Guo Y, Bosompem A, Zhong X, Clark T, Shyr Y, Kim AS. A comparison of microRNA sequencing reproducibility and noise reduction using mirVana and TrIzol isolation methods. Int J Comput Biol Drug Des. 2014;7(2–3):102–12.

    Article  CAS  PubMed  Google Scholar 

  37. Odiase EO. The risk level of liver cancer cases among smokers in developing countries. Eur J Cancer. 2012;48:S13.

    Article  Google Scholar 

  38. Varnholt H. The role of microRNAs in primary liver cancer. Ann Hepatol. 2008;7(2):104–13.

    CAS  PubMed  Google Scholar 

  39. Jiang J, Gusev Y, Aderca I, et al. Association of microRNA expression in hepatocellular carcinomas with hepatitis infection, cirrhosis, and patient survival. Clin Cancer Res. 2008;14(2):419–27.

  40. Ladeiro Y, Couchy G, Balabaud C, et al. MicroRNA profiling in hepatocellular tumors is associated with clinical features and oncogene/tumor suppressor gene mutations. Hepatology. 2008;47(6):1955–63.

    Article  CAS  PubMed  Google Scholar 

  41. Wong QWL, Lung RWM, Law PTY, et al. MicroRNA-223 is commonly repressed in hepatocellular carcinoma and potentiates expression of Stathmin1. Gastroenterology. 2008;135(1):257–69.

    Article  CAS  PubMed  Google Scholar 

  42. Li WQ, Chen C, Xu MD, et al. The rno-miR-34 family is upregulated and targets ACSL1 in dimethylnitrosamine-induced hepatic fibrosis in rats. FEBS J. 2011;278(9):1522–32.

    Article  CAS  PubMed  Google Scholar 

  43. Concepcion CP, Han YC, Mu P, et al. Intact p53-dependent responses in miR-34-deficient mice. PLoS Genet. 2012;8(7):e1002797.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chang Y, Yan W, He XX, et al. miR-375 inhibits autophagy and reduces viability of hepatocellular carcinoma cells under hypoxic conditions. Gastroenterology. 2012;143(1):177-U357.

    Article  Google Scholar 

  45. Liu AM, Poon RTP, Luk JM. MicroRNA-375 targets Hippo-signaling effector YAP in liver cancer and inhibits tumor properties. Biochem Bioph Res Co. 2010;394(3):623–7.

    Article  CAS  Google Scholar 

  46. Ascha MS, Hanouneh IA, Lopez R, Tamimi TA, Feldstein AF, Zein NN. The incidence and risk factors of hepatocellular carcinoma in patients with nonalcoholic steatohepatitis. Hepatology. 2010;51(6):1972–8.

    Article  PubMed  Google Scholar 

  47. Takuma Y, Nouso K. Nonalcoholic steatohepatitis-associated hepatocellular carcinoma: our case series and literature review. World J Gastroenterol. 2010;16(12):1436–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Yasui K, Hashimoto E, Komorizono Y, et al. Characteristics of patients with nonalcoholic steatohepatitis who develop hepatocellular carcinoma. Clin Gastroenterol Hepatol. 2011;9(5):428–33 (e50).

    Article  PubMed  Google Scholar 

  49. Pirola CJ, Fernandez Gianotti T, Castano GO, et al. Circulating microRNA signature in non-alcoholic fatty liver disease: from serum non-coding RNAs to liver histology and disease pathogenesis. Gut. 2015;64(5):800–12.

    Article  CAS  PubMed  Google Scholar 

  50. Cermelli S, Ruggieri A, Marrero JA, Ioannou GN, Beretta L. Circulating microRNAs in patients with chronic hepatitis C and non-alcoholic fatty liver disease. PLoS One. 2011;6(8):e23937.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yamada H, Suzuki K, Ichino N, et al. Associations between circulating microRNAs (miR-21, miR-34a, miR-122 and miR-451) and non-alcoholic fatty liver. Clin Chim Acta. 2013;424:99–103.

    Article  CAS  PubMed  Google Scholar 

  52. Tan Y, Ge G, Pan T, Wen D, Gan J. A pilot study of serum microRNAs panel as potential biomarkers for diagnosis of nonalcoholic fatty liver disease. PLoS One. 2014;9(8):e105192.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Celikbilek M, Baskol M, Taheri S, et al. Circulating microRNAs in patients with non-alcoholic fatty liver disease. World J Hepatol. 2014;6(8):613–20.

    PubMed  PubMed Central  Google Scholar 

  54. Estep M, Armistead D, Hossain N, et al. Differential expression of miRNAs in the visceral adipose tissue of patients with non-alcoholic fatty liver disease. Aliment Pharmacol Ther. 2010;32(3):487–97.

    Article  CAS  PubMed  Google Scholar 

  55. Sharma H, Estep M, Birerdinc A, et al. Expression of genes for microRNA-processing enzymes is altered in advanced non-alcoholic fatty liver disease. J Gastroenterol Hepatol. 2013;28(8):1410–5.

    Article  CAS  PubMed  Google Scholar 

  56. Jiang ZC, Tang XM, Zhao YR, Zheng L. A functional variant at miR-34a binding site in toll-like receptor 4 gene alters susceptibility to hepatocellular carcinoma in a Chinese Han population. Tumour Biol. 2014;35(12):12345–52.

    Article  CAS  PubMed  Google Scholar 

  57. Li W, Xie L, He X, et al. Diagnostic and prognostic implications of microRNAs in human hepatocellular carcinoma. Int J Cancer. 2008;123(7):1616–22.

    Article  CAS  PubMed  Google Scholar 

  58. Castro RE, Ferreira DM, Afonso MB, et al. miR-34a/SIRT1/p53 is suppressed by ursodeoxycholic acid in the rat liver and activated by disease severity in human non-alcoholic fatty liver disease. J Hepatol. 2013;58(1):119–25.

    Article  CAS  PubMed  Google Scholar 

  59. Min HK, Kapoor A, Fuchs M, et al. Increased hepatic synthesis and dysregulation of cholesterol metabolism is associated with the severity of nonalcoholic fatty liver disease. Cell Metab. 2012;15(5):665–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Streba LA, Vere CC, Rogoveanu I, Streba CT. Nonalcoholic fatty liver disease, metabolic risk factors, and hepatocellular carcinoma: an open question. World J Gastroenterol. 2015;21(14):4103–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Zhou P, Jiang W, Wu L, Chang R, Wu K, Wang Z. miR-301a is a candidate oncogene that targets the homeobox gene Gax in human hepatocellular carcinoma. Dig Dis Sci. 2012;57(5):1171–80.

    Article  CAS  PubMed  Google Scholar 

  62. Chang Y, Yan W, He X, et al. miR-375 inhibits autophagy and reduces viability of hepatocellular carcinoma cells under hypoxic conditions. Gastroenterology. 2012;143(1):177–87.

    Article  CAS  PubMed  Google Scholar 

  63. Li N, Fu H, Tie Y, et al. miR-34a inhibits migration and invasion by down-regulation of c-Met expression in human hepatocellular carcinoma cells. Cancer Lett. 2009;275(1):44–53.

    Article  CAS  PubMed  Google Scholar 

  64. Yang P, Li QJ, Feng Y, et al. TGF-beta-miR-34a-CCL22 signaling-induced Treg cell recruitment promotes venous metastases of HBV-positive hepatocellular carcinoma. Cancer Cell. 2012;22(3):291–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. He XX, Chang Y, Meng FY, et al. MicroRNA-375 targets AEG-1 in hepatocellular carcinoma and suppresses liver cancer cell growth in vitro and in vivo. Oncogene. 2012;31(28):3357–69.

    Article  CAS  PubMed  Google Scholar 

  66. Liu AM, Poon RT, Luk JM. MicroRNA-375 targets Hippo-signaling effector YAP in liver cancer and inhibits tumor properties. Biochem Biophys Res Commun. 2010;394(3):623–7.

    Article  CAS  PubMed  Google Scholar 

  67. Robertson CL, Srivastava J, Siddiq A, et al. Astrocyte elevated gene-1 (AEG-1) regulates lipid homeostasis. J Biol Chem. 2015;290(29):18227–36.

  68. Septer S, Edwards G, Gunewardena S, et al. Yes-associated protein is involved in proliferation and differentiation during postnatal liver development. Am J Physiol Gastrointest Liver Physiol. 2012;302(5):G493–503.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The National Institute of Diabetes And Digestive and Kidney Diseases of the National Institutes of Health supported the research reported in this publication, specifically through NIH grants DK020593 (Vanderbilt Diabetes Research and Training Center), 5UL1 RR024975-03 (CTSA), P30 CA68485 (Vanderbilt Ingram Cancer Center), DK058404 (Vanderbilt Digestive Disease Research Center), and R01 DK091748. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA

    Yan Guo, Quanghu Sheng & Shilin Zhao

  2. Department of Surgery, Vanderbilt University, MRBIV 8465A Langford Hall, 2213 Garland Ave, Nashville, TN, 37232, USA

    Yanhua Xiong & Charles Robb Flynn

  3. Columbia University College of Physicians and Surgeons, New York, NY, USA

    Julia Wattacheril

Authors
  1. Yan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanhua Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Quanghu Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shilin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Julia Wattacheril
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Charles Robb Flynn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Charles Robb Flynn.

Ethics declarations

Conflict of interest

None.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 276 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Y., Xiong, Y., Sheng, Q. et al. A micro-RNA expression signature for human NAFLD progression. J Gastroenterol 51, 1022–1030 (2016). https://doi.org/10.1007/s00535-016-1178-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00535-016-1178-0

Keywords

Search

Navigation