Epigenetically reprogrammed methylation landscape drives the DNA self-assembly and serves as a universal cancer biomarker

Abu Ali Ibn Sina, Laura G. Carrascosa (), Ziyu Liang, Yadveer S. Grewal, Andri Wardiana, Muhammad J. A. Shiddiky, Robert A. Gardiner, Hemamali Samaratunga, Maher K. Gandhi, Rodney J. Scott, Darren Korbie and Matt Trau ()
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Abu Ali Ibn Sina: The University of Queensland
Laura G. Carrascosa: The University of Queensland
Ziyu Liang: The University of Queensland
Yadveer S. Grewal: The University of Queensland
Andri Wardiana: The University of Queensland
Muhammad J. A. Shiddiky: The University of Queensland
Robert A. Gardiner: The University of Queensland
Hemamali Samaratunga: The University of Queensland
Maher K. Gandhi: The University of Queensland
Rodney J. Scott: The University of Newcastle
Darren Korbie: The University of Queensland
Matt Trau: The University of Queensland

Nature Communications, 2018, vol. 9, issue 1, 1-13

Abstract: Abstract Epigenetic reprogramming in cancer genomes creates a distinct methylation landscape encompassing clustered methylation at regulatory regions separated by large intergenic tracks of hypomethylated regions. This methylation landscape that we referred to as Methylscape is displayed by most cancer types, thus may serve as a universal cancer biomarker. To-date most research has focused on the biological consequences of DNA Methylscape changes whereas its impact on DNA physicochemical properties remains unexplored. Herein, we examine the effect of levels and genomic distribution of methylcytosines on the physicochemical properties of DNA to detect the Methylscape biomarker. We find that DNA polymeric behaviour is strongly affected by differential patterning of methylcytosine, leading to fundamental differences in DNA solvation and DNA-gold affinity between cancerous and normal genomes. We exploit these Methylscape differences to develop simple, highly sensitive and selective electrochemical or colorimetric one-step assays for the detection of cancer. These assays are quick, i.e., analysis time ≤10 minutes, and require minimal sample preparation and small DNA input.

Date: 2018
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DOI: 10.1038/s41467-018-07214-w

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