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Age-related trajectories of DNA methylation network markers: A parenclitic network approach to a family-based cohort of patients with Down Syndrome

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  • Krivonosov, Mikhail
  • Nazarenko, Tatiana
  • Bacalini, Maria Giulia
  • Vedunova, Maria
  • Franceschi, Claudio
  • Zaikin, Alexey
  • Ivanchenko, Mikhail

Abstract

Despite the fact that the cause of Down Syndrome (DS) is well established, the underlying molecular mechanisms that contribute to the syndrome and the phenotype of accelerated aging remain largely unknown. DNA methylation profiles are largely altered in DS, but it remains unclear how different methylation regions and probes are structured into a network of interactions. We develop and generalize the Parenclitic Networks approach that enables finding correlations between distant CpG probes (which are not pronounced as stand-alone biomarkers) and quantifies hidden network changes in DNA methylation. DS and a family-based cohort (including healthy siblings and mothers of persons with DS) are used as a case study. Following this approach, we constructed parenclitic networks and obtained different signatures that indicate (i) differences between individuals with DS and healthy individuals; (ii) differences between young and old healthy individuals; (iii) differences between DS individuals and their age-matched siblings, and (iv) difference between DS and the adult population (their mothers). The Gene Ontology analysis showed that the CpG network approach is more powerful than the single CpG approach in identifying biological processes related to DS phenotype. This includes the processes occurring in the central nervous system, skeletal muscles, disorders in carbohydrate metabolism, cardiopathology, and oncogenes. Our open-source software implementation is accessible to all researchers. The software includes a complete workflow, which can be used to construct Parenclitic Networks with any machine learning algorithm as a kernel to build edges. We anticipate a broad applicability of the approach to other diseases.

Suggested Citation

  • Krivonosov, Mikhail & Nazarenko, Tatiana & Bacalini, Maria Giulia & Vedunova, Maria & Franceschi, Claudio & Zaikin, Alexey & Ivanchenko, Mikhail, 2022. "Age-related trajectories of DNA methylation network markers: A parenclitic network approach to a family-based cohort of patients with Down Syndrome," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    • Handle: RePEc:eee:chsofr:v:165:y:2022:i:p2:s0960077922010426
    DOI: 10.1016/j.chaos.2022.112863
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    References listed on IDEAS

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    1. Leland H. Hartwell & John J. Hopfield & Stanislas Leibler & Andrew W. Murray, 1999. "From molecular to modular cell biology," Nature, Nature, vol. 402(6761), pages 47-52, December.
    2. Peter Henneman & Arjan Bouman & Adri Mul & Lia Knegt & Anne-Marie van der Kevie-Kersemaekers & Nitash Zwaveling-Soonawala & Hanne E J Meijers-Heijboer & A S Paul van Trotsenburg & Marcel M Mannens, 2018. "Widespread domain-like perturbations of DNA methylation in whole blood of Down syndrome neonates," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-19, March.
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