期刊论文详细信息
BMC Medical Genomics
Exploring the molecular causes of hepatitis B virus vaccination response: an approach with epigenomic and transcriptomic data
Christine Nardini2  Weili Yan1  Yi Cheng1  Youtao Lu2 
[1]Department of Clinical Epidemiology, Children’s Hospital of Fudan University, Shanghai, PR China
[2]Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG, Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, PR China
关键词: Omics;    Methylation;    Vaccine;    Hepatitis B virus;   
Others  :  797112
DOI  :  10.1186/1755-8794-7-12
 received in 2013-11-13, accepted in 2014-03-05,  发布年份 2014
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【 摘 要 】

Background

Variable responses to the Hepatitis B Virus (HBV) vaccine have recently been reported as strongly dependent on genetic causes. Yet, the details on such mechanisms of action are still unknown. In parallel, altered DNA methylation states have been uncovered as important contributors to a variety of health conditions. However, methodologies for the analysis of such high-throughput data (epigenomic), especially from the computational point of view, still lack of a gold standard, mostly due to the intrinsic statistical distribution of methylomic data i.e. binomial rather than (pseudo-) normal, which characterizes the better known transcriptomic data.

We present in this article our contribution to the challenge of epigenomic data analysis with application to the variable response to the Hepatitis B virus (HBV) vaccine and its most lethal degeneration: hepatocellular carcinoma (HCC).

Methods

Twenty-five infants were recruited and classified as good and non-/low- responders according to serological test results. Whole genome DNA methylation states were profiled by Illumina HumanMethylation 450 K beadchips. Data were processed through quality and dispersion filtering and with differential methylation analysis based on a combination of average methylation differences and non-parametric statistical tests. Results were finally associated to already published transcriptomics and post-transcriptomics to gain further insight.

Results

We highlight 2 relevant variations in poor-responders to HBV vaccination: the hypomethylation of RNF39 (Ring Finger Protein 39) and the complex biochemical alteration on SULF2 via hypermethylation, down-regulation and post-transcriptional control.

Conclusions

Our approach appears to cope with the new challenges implied by methylomic data distribution to warrant a robust ranking of candidates. In particular, being RNF39 within the Major Histocompatibility Complex (MHC) class I region, its altered methylation state fits with an altered immune reaction compatible with poor responsiveness to vaccination. Additionally, despite SULF2 having been indicated as a potential target for HCC therapy, we can recommend that non-responders to HBV vaccine who develop HCC are quickly directed to other therapies, as SULF2 appears to be already under multiple molecular controls in such patients. Future research in this direction is warranted.

【 授权许可】

   
2014 Lu et al.; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Suzuki MM, Bird A: DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet 2008, 9:465-476.
  • [2]Heyn H, Esteller M: DNA methylation profiling in the clinic: applications and challenges. Nat Rev Genet 2012, 13:679-692.
  • [3]Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, Delano D, Zhang L, Schroth GP, Gunderson KL, Fan J-B, Shen R: High density DNA methylation array with single CpG site resolution. Genomics 2011, 98:288-295.
  • [4]Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD, Pradhan S, Nelson SF, Pellegrini M, Jacobsen SE: Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 2008, 452:215-219.
  • [5]Meissner A, Mikkelsen TS, Gu H, Wernig M, Hanna J, Sivachenko A, Zhang X, Bernstein BE, Nusbaum C, Jaffe DB, Gnirke A, Jaenisch R, Lander ES: Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 2008, 454:766-770.
  • [6]Bock C: Analysing and interpreting DNA methylation data. Nat Rev Genet 2012, 13:705-719.
  • [7]Quackenbush J: Microarray data normalization and transformation. Nat Genet 2002, 32:496-501.
  • [8]Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci 2001, 98:5116-5121.
  • [9]Smyth G: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3:1544-6115.
  • [10]Yan K, Cai W, Cao F, Sun H, Chen S, Xu R, Wei X, Shi X, Yan W: Genetic effects have a dominant role on poor responses to infant vaccination to hepatitis B virus. J Hum Genet 2013, 58:293-297.
  • [11]WHO hepatitis B [http://www.who.int/csr/disease/hepatitis/whocdscsrlyo20022/en/index1.html webcite]
  • [12]Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Holko M, Ayanbule O, Yefanov A, Soboleva A: NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Res 2011, 39(suppl 1):D1005-D1010.
  • [13]Ihaka R, Gentleman R: R: a language for data analysis and graphics. J Comput Graph Stat 1996, 5:299-314.
  • [14]Touleimat N, Tost J: Complete pipeline for Infinium®Human Methylation 450 K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics 2012, 4:325-341.
  • [15]Wilcoxon F: Individual comparisons by ranking methods. Biometrics 1945, 1:80-83.
  • [16]Fisher RA: On the interpretation of χ2 from contingency tables, and the calculation of P. J R Stat Soc 1922, 85:87.
  • [17]Laurent L, Wong E, Li G, Huynh T, Tsirigos A, Ong CT, Low HM, Kin Sung KW, Rigoutsos I, Loring J, Wei C-L: Dynamic changes in the human methylome during differentiation. Genome Res 2010, 20:320-331.
  • [18]Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002, 30:207-210.
  • [19]Guo Y, Guo H, Zhang L, Xie H, Zhao X, Wang F, Li Z, Wang Y, Ma S, Tao J, Wang W, Zhou Y, Yang W, Cheng J: Genomic analysis of anti-hepatitis B virus (HBV) activity by small interfering RNA and lamivudine in stable HBV-producing cells. J Virol 2005, 79:14392-14403.
  • [20]Liu Y, Zhao J, Wang C, Li M, Han P, Wang L, Cheng Y-Q, Zoulim F, Ma X, Xu D-P: Altered expression profiles of microRNAs in a stable hepatitis B virus-expressing cell line. Chin Med J Engl Ed 2009, 122:10-14.
  • [21]Hsu S-D, Lin F-M, Wu W-Y, Liang C, Huang W-C, Chan W-L, Tsai W-T, Chen G-Z, Lee C-J, Chiu C-M, Chien C-H, Wu M-C, Huang C-Y, Tsou A-P, Huang H-D: miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 2010, 39(Database):D163-D169.
  • [22]Vergoulis T, Vlachos IS, Alexiou P, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas T, Hatzigeorgiou AG: TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 2011, 40:D222-D229.
  • [23]Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are MicroRNA targets. Cell 2005, 120:15-20.
  • [24]Betel D, Wilson M, Gabow A, Marks DS, Sander C: The microRNA.org resource: targets and expression. Nucleic Acids Res 2007, 36(Database):D149-D153.
  • [25]Rice JA: Mathematical Statistics and Data Analysis. Stamford, Connecticut: Cengage Learning; 2007.
  • [26]Nakano K, Whitaker JW, Boyle DL, Wang W, Firestein GS: DNA methylome signature in rheumatoid arthritis. Ann Rheum Dis 2013, 72:110-117.
  • [27]Bioconductor - methylumi [http://www.bioconductor.org/packages/2.10/bioc/html/methylumi.html webcite]
  • [28]Du P, Zhang X, Huang C-C, Jafari N, Kibbe W, Hou L, Lin S: Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinforma 2010, 11:587. BioMed Central Full Text
  • [29]Marabita F, Almgren M, Lindholm ME, Ruhrmann S, Fagerström-Billai F, Jagodic M, Sundberg CJ, Ekström TJ, Teschendorff AE, Tegnér J, Gomez-Cabrero D: An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform. Epigenetics 2013, 8:333-346.
  • [30]Varani K, Laghi-Pasini F, Camurri A, Capecchi PL, Maccherini M, Diciolla F, Ceccatelli L, Lazzerini PE, Ulouglu C, Cattabeni F, Borea PA, Abbracchio MP: Changes of peripheral A2A adenosine receptors in chronic heart failure and cardiac transplantation. FASEB J 2003, 17:280-282.
  • [31]Varani K, Caramori G, Vincenzi F, Adcock I, Casolari P, Leung E, MacLennan S, Gessi S, Morello S, Barnes PJ, Ito K, Chung KF, Cavallesco G, Azzena G, Papi A, Borea PA: Alteration of adenosine receptors in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2006, 173:398-406.
  • [32]Varani K, Vincenzi F, Tosi A, Gessi S, Casetta I, Granieri G, Fazio P, Leung E, MacLennan S, Granieri E, Borea PA: A2A adenosine receptor overexpression and functionality, as well as TNF-α levels, correlate with motor symptoms in Parkinson’s disease. FASEB J 2010, 24:587-598.
  • [33]Lai J-P, Sandhu DS, Yu C, Han T, Moser CD, Jackson KK, Guerrero RB, Aderca I, Isomoto H, Garrity-Park MM, Zou H, Shire AM, Nagorney DM, Sanderson SO, Adjei AA, Lee J-S, Thorgeirsson SS, Roberts LR: Sulfatase 2 up-regulates glypican 3, promotes fibroblast growth factor signaling, and decreases survival in hepatocellular carcinoma. Hepatol Baltim Md 2008, 47:1211-1222.
  • [34]Lai J-P, Thompson JR, Sandhu DS, Roberts LR: Heparin-degrading sulfatases in hepatocellular carcinoma: potential roles in pathogenesis and identification of therapeutic targets. Future Oncol Lond Engl 2008, 4:803-814.
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