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Estimating the Rate of Cell Type Degeneration from Epigenetic Sequencing of Cell-Free DNA

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Research in Computational Molecular Biology (RECOMB 2020)

Abstract

Cells die at different rates as a function of disease state, age, environmental exposure, and behavior [8, 10]. Knowing the rate at which cells die is a fundamental scientific question, with direct translational applicability. A quantifiable indication of cell death could facilitate disease diagnosis and prognosis, prioritize patients for admission into clinical trials, and improve evaluations of treatment efficacy and disease progression [1, 4, 14, 16]. Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising non-invasive biomarker for cell death.

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References

  1. Bowser, R., Turner, M.R., Shefner, J.: Biomarkers in amyotrophic lateral sclerosis: opportunities and limitations. Nat. Rev. Neurol. 7, 631–8 (2011)

    Article  Google Scholar 

  2. Houseman, E.A., Molitor, J., Marsit, C.J.: Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30, 1431–1439 (2014)

    Article  Google Scholar 

  3. Houseman, E.A., et al.: DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012). https://doi.org/10.1186/1471-2105-13-86

    Article  Google Scholar 

  4. Joka, D., et al.: Prospective biopsy-controlled evaluation of cell death biomarkers for prediction of liver fibrosis and nonalcoholic steatohepatitis. Hepatology 55, 455–64 (2012)

    Article  Google Scholar 

  5. Lehmann-Werman, R., et al.: Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc. Natl. Acad. Sci. 113, E1826–E1834 (2016)

    Article  Google Scholar 

  6. Liu, X., et al.: Comprehensive DNA methylation analysis of tissue of origin of plasma cell-free DNA by methylated CpG tandem amplification and sequencing (MCTA-Seq). Clin. Epigenetics 11, 93 (2019)

    Article  Google Scholar 

  7. Lokk, K., et al.: DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns. Genome Biol. 15, r54 (2014). https://doi.org/10.1186/gb-2014-15-4-r54

    Article  Google Scholar 

  8. Meier, P., Finch, A., Evan, G.: Apoptosis in development. Nature 407, 796–801 (2000)

    Article  Google Scholar 

  9. Moss, J., et al.: Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat. Commun. 9, 1–12 (2018)

    Article  Google Scholar 

  10. Nagata, S.: Apoptosis by death factor. Cell 88, 355–65 (1997)

    Article  Google Scholar 

  11. Rahmani, E., Schweiger, R., Shenhav, L., Eskin, E., Halperin, E.: A Bayesian framework for estimating cell type composition from DNA methylation without the need for methylation reference. In: Sahinalp, S.C. (ed.) RECOMB 2017. LNCS, vol. 10229, pp. 207–223. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56970-3_13

    Chapter  Google Scholar 

  12. Rahmani, E., et al.: Sparse PCA corrects for cell-type heterogeneity in epigenome-wide association studies. Nat. Methods 13, 443–445 (2016)

    Article  Google Scholar 

  13. Snyder, M.W., Kircher, M., Hill, A.J., Daza, R.M., Shendure, J.: Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell 164, 57–68 (2016)

    Article  Google Scholar 

  14. Turner, M.R., et al.: Mechanisms, models and biomarkers in amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. Frontotemporal Degener. 14, 19–32 (2013)

    Article  Google Scholar 

  15. Verber, N.S., et al.: Biomarkers in motor neuron disease: a state of the art review. Front. Neurol. 10, 291 (2019)

    Article  Google Scholar 

  16. Vila, M., Przedborski, S.: Targeting programmed cell death in neurodegenerative diseases. Nat. Rev. Neurosci. 4, 365–375 (2003)

    Article  Google Scholar 

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Correspondence to Noah Zaitlen .

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Caggiano, C. et al. (2020). Estimating the Rate of Cell Type Degeneration from Epigenetic Sequencing of Cell-Free DNA. In: Schwartz, R. (eds) Research in Computational Molecular Biology. RECOMB 2020. Lecture Notes in Computer Science(), vol 12074. Springer, Cham. https://doi.org/10.1007/978-3-030-45257-5_21

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  • DOI: https://doi.org/10.1007/978-3-030-45257-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45256-8

  • Online ISBN: 978-3-030-45257-5

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