Literature DB >> 22525352

Make way for the 'next generation': application and prospects for genome-wide, epigenome-specific technologies in endocrine research.

Richard D Emes1, William E Farrell.   

Abstract

Epigenetic changes, which target DNA and associated histones, can be described as a pivotal mechanism of interaction between genes and the environment. The field of epigenomics aims to detect and interpret epigenetic modifications at the whole genome level. These approaches have the potential to increase resolution of epigenetic changes to the single base level in multiple disease states or across a population of individuals. Identification and comparison of the epigenomic landscape has challenged our understanding of the regulation of phenotype. Additionally, inclusion of these marks as biomarkers in the early detection or progression monitoring of disease is providing novel avenues for future biomedical research. Cells of the endocrine organs, which include pituitary, thyroid, thymus, pancreas ovary and testes, have been shown to be susceptible to epigenetic alteration, leading to both local and systemic changes often resulting in life-threatening metabolic disease. As with other cell types and populations, endocrine cells are susceptible to tumour development, which in turn may have resulted from aberration of epigenetic control. Techniques including high-throughput sequencing and array-based analysis to investigate these changes have rapidly emerged and are continually evolving. Here, we present a review of these methods and their promise to influence our studies on the epigenome for endocrine research and perhaps to uncover novel therapeutic options in disease states.

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Year:  2012        PMID: 22525352     DOI: 10.1530/JME-12-0045

Source DB:  PubMed          Journal:  J Mol Endocrinol        ISSN: 0952-5041            Impact factor:   5.098


  11 in total

Review 1.  Transcriptional regulation by the Set7 lysine methyltransferase.

Authors:  Samuel T Keating; Assam El-Osta
Journal:  Epigenetics       Date:  2013-03-11       Impact factor: 4.528

2.  Current and emerging technology approaches in genomics.

Authors:  Yvette P Conley; Leslie G Biesecker; Stephen Gonsalves; Carrie J Merkle; Maggie Kirk; Bradley E Aouizerat
Journal:  J Nurs Scholarsh       Date:  2013-01-07       Impact factor: 3.176

3.  The results of molecular genetic testing for RET proto-oncogene mutations in patients with medullary thyroid carcinoma in a referral center after the two decade period.

Authors:  B Rovcanin; S Damjanovic; V Zivaljevic; A Diklic; M Jovanovic; I Paunovic
Journal:  Hippokratia       Date:  2016 Jul-Sep       Impact factor: 0.471

4.  Age-associated epigenetic change in chimpanzees and humans.

Authors:  Elaine E Guevara; Richard R Lawler; Nicky Staes; Cassandra M White; Chet C Sherwood; John J Ely; William D Hopkins; Brenda J Bradley
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-09-21       Impact factor: 6.237

Review 5.  Detection of cancer-specific epigenomic changes in biofluids: powerful tools in biomarker discovery and application.

Authors:  André Nogueira da Costa; Zdenko Herceg
Journal:  Mol Oncol       Date:  2012-08-16       Impact factor: 6.603

6.  Identification of DNA methylation biomarkers from Infinium arrays.

Authors:  Frank Wessely; Richard D Emes
Journal:  Front Genet       Date:  2012-08-25       Impact factor: 4.599

7.  A six months exercise intervention influences the genome-wide DNA methylation pattern in human adipose tissue.

Authors:  Tina Rönn; Petr Volkov; Cajsa Davegårdh; Tasnim Dayeh; Elin Hall; Anders H Olsson; Emma Nilsson; Asa Tornberg; Marloes Dekker Nitert; Karl-Fredrik Eriksson; Helena A Jones; Leif Groop; Charlotte Ling
Journal:  PLoS Genet       Date:  2013-06-27       Impact factor: 5.917

8.  Empirical testing of hypotheses about the evolution of genomic imprinting in mammals.

Authors:  David G Ashbrook; Reinmar Hager
Journal:  Front Neuroanat       Date:  2013-04-30       Impact factor: 3.856

Review 9.  Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks.

Authors:  Liam G Fearnley; Michael Inouye
Journal:  Int J Epidemiol       Date:  2016-04-26       Impact factor: 7.196

10.  Machine learning approaches to the social determinants of health in the health and retirement study.

Authors:  Benjamin Seligman; Shripad Tuljapurkar; David Rehkopf
Journal:  SSM Popul Health       Date:  2017-11-21
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