Age-related trajectories of DNA methylation network markers: A parenclitic network approach to a family-based cohort of patients with Down Syndrome. (December 2022)

• Record Type: Journal Article
• Authors: Krivonosov, Mikhail; Nazarenko, Tatiana; Bacalini, Maria Giulia; Vedunova, Maria; Franceschi, Claudio; Zaikin, Alexey; Ivanchenko, Mikhail
• Is Part Of: Chaos, solitons and fractals. Volume 165:Part 2(2022)
• Publication Date: 2022-12
• Subjects: Down Syndrome -- Parenclitic network -- DNA methylation -- Complex networks -- Aging; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2022.112863
• Languages: English
• ISSNs: 0960-0779

Abstract

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. Highlights: A new generalization of parenclitic approach based on machine learning kernels. The CpG network identifies coupled genes and biological processes related to DS phenotype. Open-source software implementation for generalized parenclitic approach is publicly available. DS specific epigenetic patterns become increasingly common in older mothers.