Microbiota and Epigenetic Regulation of Inflammatory Mediators

  • Marlene RemelyEmail author
  • Heidrun Karlic
  • Irene Rebhan
  • Martina Greunz
  • Alexander G. Haslberger
Part of the Methods in Pharmacology and Toxicology book series (MIPT)


Bacteria and bacterial derived metabolites are known to influence the host epigenetic regulation patterns such as DNA methylation and histone modifications, thus altering the expression of critical genes in pathologic processes, for example in metabolic syndrome. Fermentation end products, especially butyrate and LPS (lipopolysaccharides), the latter being cell-wall components of gram-negative bacteria, have been suggested as bioactive metabolites influencing epigenetic modifications by directly influencing enzymes catalyzing epigenetic modifications, by altering the availability of substrates, or by interactions with receptors. Thus, identification and quantification of gut microbiota via molecular based methods are of importance to address different epigenetic patterns and gene expression. We discuss methods for microbiota, epigenetic methylation, and expression analysis of our own research which will have a role in future studies.

Key words

Microbiota and microbial epigenetic active products Quantification of DNA and RNA Gene expression analysis Methylation analysis Omics 



Avian myeloblastosis virus


Ammonium persulfate solution


Bisulfite genomic sequencing


Complementary DNA


Denaturing gradient gel electrophoresis


DNA-methyltransferase 1


Ethylenediaminetetraacetic acid


Free fatty acid receptor 3


Fluorescence resonance energy transfer


Histone deacetylases






Methylated DNA immunoprecipitation


Moloney murine leukemia virus


Methylation sensitive restriction enzyme


Nuclease factor kB


Posttranslational modification


Quantitative real-time polymerase chain reaction


Reverse transcription


Short chain fatty acids


Structural equation modelling

TE buffer

Tris-EDTA buffer




Toll-like receptor


  1. 1.
    Aziz Q, Doré J, Emmanuel A, Guarner F, Quigley EM (2013) Gut microbiota and gastrointestinal health: current concepts and future directions. Neurogastroenterol Motil 25(1):4–15CrossRefPubMedGoogle Scholar
  2. 2.
    Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK, Al-Soud WA, Sorensen SJ, Hansen LH, Jakobsen M (2010) Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 5(2), e9085CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Vrieze A, Holleman F, Zoetendal EG, de Vos WM, Hoekstra JB, Nieuwdorp M (2010) The environment within: how gut microbiota may influence metabolism and body composition. Diabetologia 53(4):606–613CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Creely SJ, McTernan PG, Kusminski CM, Fisher FM, Da Silva NF, Khanolkar M, Evans M, Harte AL, Kumar S (2007) Lipopolysaccharide activates an innate immune system response in human adipose tissue in obesity and type 2 diabetes. Am J Physiol Endocrinol Metab 292(3):E740–E747CrossRefPubMedGoogle Scholar
  5. 5.
    Remely M, Aumueller E, Jahn D, Hippe B, Brath H, Haslberger AG (2014) Microbiota and epigenetic regulation of inflammatory mediators in type 2 diabetes and obesity. Benef Microbes 5(1):33–43CrossRefPubMedGoogle Scholar
  6. 6.
    Remely M, Aumueller E, Merold C, Dworzak S, Hippe B, Zanner J, Pointner A, Brath H, Haslberger AG (2013) Effects of short chain fatty acid producing bacteria on epigenetic regulation of FFAR3 in type 2 diabetes and obesity. Gene 537(1):85–92CrossRefPubMedGoogle Scholar
  7. 7.
    Canani RB, Costanzo MD, Leone L, Bedogni G, Brambilla P, Cianfarani S, Nobili V, Pietrobelli A, Agostoni C (2011) Epigenetic mechanisms elicited by nutrition in early life. Nutr Res Rev 24(2):198–205CrossRefPubMedGoogle Scholar
  8. 8.
    Dahaliwal A (2013) DNA extraction and purification. Mater Methods 3(191)Google Scholar
  9. 9.
    Kotorashvili A, Ramnauth A, Liu C, Lin J, Ye K, Kim R, Hazan R, Rohan T, Fineberg S, Loudig O (2012) Effective DNA/RNA co-extraction for analysis of microRNAs, mRNAs, and genomic DNA from formalin-fixed paraffin-embedded specimens. PLoS One 7(4), e34683CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Wilfinger WW, Mackey K, Chomczynski P (1997) Effect of pH and ionic strength on the spectrophotometric assessment of nucleic acid purity. Biotechniques 22(3):474–476, 478-81PubMedGoogle Scholar
  11. 11.
    Muyzer G, de Waal EC, Uitterlinden AG (1993) Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol 59(3):695–700PubMedPubMedCentralGoogle Scholar
  12. 12.
    Neefs JM, Van de Peer Y, De Rijk P, Goris A, De Wachter R (1991) Compilation of small ribosomal subunit RNA sequences. Nucleic Acids Res 19(Suppl):1987–2015CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Muyzer G, Smalla K (1998) Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology. Antonie Van Leeuwenhoek 73(1):127–141CrossRefPubMedGoogle Scholar
  14. 14.
    Beutler E, Gelbart T, Kuhl W (1990) Interference of heparin with the polymerase chain reaction. Biotechniques 9(2):166PubMedGoogle Scholar
  15. 15.
    Willems M, Moshage H, Nevens F, Fevery J, Yap SH (1993) Plasma collected from heparinized blood is not suitable for HCV-RNA detection by conventional RT-PCR assay. J Virol Methods 42(1):127–130CrossRefPubMedGoogle Scholar
  16. 16.
    (1993) Recommendations of the International Council for Standardization in Haematology for Ethylenediaminetetraacetic Acid Anticoagulation of Blood for Blood Cell Counting and Sizing. International Council for Standardization in Haematology: Expert Panel on Cytometry. Am J Clin Pathol 100(4):371–372Google Scholar
  17. 17.
    Raabe BM, Artwohl JE, Purcell JE, Lovaglio J, Fortman JD (2011) Effects of weekly blood collection in C57BL/6 mice. J Am Assoc Lab Anim Sci 50(5):680–685PubMedPubMedCentralGoogle Scholar
  18. 18.
    Bustin SA (2000) Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol 25(2):169–193CrossRefPubMedGoogle Scholar
  19. 19.
    Griffiths-Jones S (2006) miRBase: the microRNA sequence database. Methods Mol Biol 342:129–138PubMedGoogle Scholar
  20. 20.
    Bui TV, Mendell JT (2010) Myc: maestro of MicroRNAs. Genes Cancer 1(6):568–575CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Langevin SM, Stone RA, Bunker CH, Grandis JR, Sobol RW, Taioli E (2010) MicroRNA-137 promoter methylation in oral rinses from patients with squamous cell carcinoma of the head and neck is associated with gender and body mass index. Carcinogenesis 31(5):864–870CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Langevin SM, Stone RA, Bunker CH, Lyons-Weiler MA, LaFramboise WA, Kelly L, Seethala RR, Grandis JR, Sobol RW, Taioli E (2011) MicroRNA-137 promoter methylation is associated with poorer overall survival in patients with squamous cell carcinoma of the head and neck. Cancer 117(7):1454–1462CrossRefPubMedGoogle Scholar
  23. 23.
    Egger G, Wielscher M, Pulverer W, Kriegner A, Weinhausel A (2012) DNA methylation testing and marker validation using PCR: diagnostic applications. Expert Rev Mol Diagn 12(1):75–92CrossRefPubMedGoogle Scholar
  24. 24.
    Noehammer C, Pulverer W, Hassler MR, Hofner M, Wielscher M, Vierlinger K, Liloglou T, McCarthy D, Jensen TJ, Nygren A, Gohlke H, Trooskens G, Braspenning M, Van Criekinge W, Egger G, Weinhaeusel A (2014) Strategies for validation and testing of DNA methylation biomarkers. Epigenomics 6(6):603–622CrossRefPubMedGoogle Scholar
  25. 25.
    Pulverer W, Hofner M, Preusser M, Dirnberger E, Hainfellner JA, Weinhaeusel A (2014) A simple quantitative diagnostic alternative for MGMT DNA-methylation testing on RCL2 fixed paraffin embedded tumors using restriction coupled qPCR. Clin Neuropathol 33(1):50–60CrossRefPubMedGoogle Scholar
  26. 26.
    Morris T, Lowe R (2012) Report on the Infinium 450 k methylation array analysis workshop: April 20, 2012 UCL, London, UK. Epigenetics 7(8):961–962CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Fortin JP, Fertig E, Hansen K (2014) shinyMethyl: interactive quality control of Illumina 450k DNA methylation arrays in R. F1000Res 3:175PubMedPubMedCentralGoogle Scholar
  28. 28.
    Fortin JP, Labbe A, Lemire M, Zanke BW, Hudson TJ, Fertig EJ, Greenwood CM, Hansen KD (2014) Functional normalization of 450 k methylation array data improves replication in large cancer studies. Genome Biol 15(11):503CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Martens JH, Stunnenberg HG (2013) BLUEPRINT: mapping human blood cell epigenomes. Haematologica 98(10):1487–1489CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Martens JH, Stunnenberg HG, Logie C (2011) The decade of the epigenomes? Genes Cancer 2(6):680–687CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Ptitsyn AA, Weil MM, Thamm DH (2008) Systems biology approach to identification of biomarkers for metastatic progression in cancer. BMC Bioinformatics 9(Suppl 9):S8CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Marlene Remely
    • 1
    Email author
  • Heidrun Karlic
    • 2
  • Irene Rebhan
    • 1
  • Martina Greunz
    • 1
  • Alexander G. Haslberger
    • 1
  1. 1.Department of Nutritional SciencesUniversity ViennaViennaAustria
  2. 2.Ludwig Boltzmann Institute for Leukemia Research and HematologyViennaAustria

Personalised recommendations