26 research outputs found
Age-related trajectories of DNA methylation network markers: a parenclitic network approach to a family-based cohort of patients with Down Syndrome
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 familybased 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
ΠΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΠΏΠ΅ΡΠ΅Π³ΡΡΠ·ΠΊΠΈ ΠΏΡΠ°Π²ΡΡ ΠΊΠ°ΠΌΠ΅Ρ ΡΠ΅ΡΠ΄ΡΠ° Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΎΡΡΡΠΎΠΉ ΠΌΠ°ΡΡΠΈΠ²Π½ΠΎΠΉ ΡΡΠΎΠΌΠ±ΠΎΡΠΌΠ±ΠΎΠ»ΠΈΠ΅ΠΉ Π»Π΅Π³ΠΎΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΠ’-Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ
The study group included 147 patients at the stage of preparation for emergency surgical treatment of acute massive PE in the period from March 2012 to December 2019 inclusive. As CT indicators of overload of the right chambers of the heart, the usual CT indicators that do not require the use of expert β class computed tomographs were taken β they were the superior vena cava, inferior vena cava, unpaired vein; reflux of the contrast drug into the inferior vena cava; reflux of the contrast drug into the hepatic veins. In the course of the study, a comparative analysis of the average pressure in the pulmonary artery with the above CT indicators was performed. The most stable statistical relationship with the indicators of mean pressure in the pulmonary artery was demonstrated by CT parameters β the diameter of the unpaired vein and the reflux of the contrast agent into the hepatic veins. Based on the results of the work, a method for calculating the actual values of the average pressure in the pulmonary artery based on the CT parameter of the diameter of the unpaired vein is proposed.Π Π³ΡΡΠΏΠΏΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π²ΠΎΡΠ»ΠΎ 147 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π½Π° ΡΡΠ°ΠΏΠ΅ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ ΠΊ ΡΠΊΡΡΡΠ΅Π½Π½ΠΎΠΌΡ Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΌΡ Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΎΡΡΡΠΎΠΉ ΠΌΠ°ΡΡΠΈΠ²Π½ΠΎΠΉ ΡΡΠΎΠΌΠ±ΠΎΡΠΌΠ±ΠΎΠ»ΠΈΠΈ Π»Π΅Π³ΠΎΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ (Π’ΠΠΠ) Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ Ρ ΠΌΠ°ΡΡΠ° 2012 Π³. ΠΏΠΎ Π΄Π΅ΠΊΠ°Π±ΡΡ 2019 Π³. Π²ΠΊΠ»ΡΡΠΈΡΠ΅Π»ΡΠ½ΠΎ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΠ’-ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΠΏΠ΅ΡΠ΅Π³ΡΡΠ·ΠΊΠΈ ΠΏΡΠ°Π²ΡΡ
ΠΊΠ°ΠΌΠ΅Ρ ΡΠ΅ΡΠ΄ΡΠ° Π²Π·ΡΡΡ ΠΎΠ±ΡΡΠ½ΡΠ΅ ΠΠ’-ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ, Π½Π΅ ΡΡΠ΅Π±ΡΡΡΠΈΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΎΠ² ΡΠΊΡΠΏΠ΅ΡΡΠ½ΠΎΠ³ΠΎ ΠΊΠ»Π°ΡΡΠ°, ΠΈΠΌΠΈ ΡΡΠ°Π»ΠΈ Π²Π΅ΡΡ
Π½ΡΡ ΠΏΠΎΠ»Π°Ρ Π²Π΅Π½Π°, Π½ΠΈΠΆΠ½ΡΡ ΠΏΠΎΠ»Π°Ρ Π²Π΅Π½Π°, Π½Π΅ΠΏΠ°ΡΠ½Π°Ρ Π²Π΅Π½Π°; ΡΠ΅ΡΠ»ΡΠΊΡ ΠΊΠΎΠ½ΡΡΠ°ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ° Π² Π½ΠΈΠΆΠ½ΡΡ ΠΏΠΎΠ»ΡΡ Π²Π΅Π½Ρ; ΡΠ΅ΡΠ»ΡΠΊΡ ΠΊΠΎΠ½ΡΡΠ°ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ° Π² ΠΏΠ΅ΡΠ΅Π½ΠΎΡΠ½ΡΠ΅ Π²Π΅Π½Ρ. Π Ρ
ΠΎΠ΄Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΡΠ΅Π΄Π½Π΅Π³ΠΎ Π΄Π°Π²Π»Π΅Π½ΠΈΡ Π² Π»Π΅Π³ΠΎΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ Ρ Π²ΡΡΠ΅ΡΠΊΠ°Π·Π°Π½Π½ΡΠΌΠΈ ΠΠ’-ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌΠΈ. ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠΎΠΉΡΠΈΠ²ΡΡ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΡΡ Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Ρ Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌΠΈ ΡΡΠ΅Π΄Π½Π΅Π³ΠΎ Π΄Π°Π²Π»Π΅Π½ΠΈΡ Π² Π»Π΅Π³ΠΎΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π»ΠΈ ΠΠ’-ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ β Π΄ΠΈΠ°ΠΌΠ΅ΡΡ Π½Π΅ΠΏΠ°ΡΠ½ΠΎΠΉ Π²Π΅Π½Ρ ΠΈ ΡΠ΅ΡΠ»ΡΠΊΡ ΠΊΠΎΠ½ΡΡΠ°ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ° Π² ΠΏΠ΅ΡΠ΅Π½ΠΎΡΠ½ΡΠ΅ Π²Π΅Π½Ρ. ΠΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ ΡΠ°Π±ΠΎΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΡΠ°ΡΡΠ΅ΡΠ° ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΡΡΠ΅Π΄Π½Π΅Π³ΠΎ Π΄Π°Π²Π»Π΅Π½ΠΈΡ Π² Π»Π΅Π³ΠΎΡΠ½ΠΎΠΉ Π°ΡΡΠ΅ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΠΠ’-ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ° βΠ΄ΠΈΠ°ΠΌΠ΅ΡΡ Π½Π΅ΠΏΠ°ΡΠ½ΠΎΠΉ Π²Π΅Π½Ρβ
Biocompatibility of Bare Nanoparticles Based on Silicon and Gold for Nervous Cells
This work aimed to investigate the biocompatibility of bare (ligand-free) lasersynthesized nanoparticles (NPs) based on silicon (Si) and gold (Au) with primary hippocampal cultures. 1%, 5% and 7% of culture medium were replaced by 0.1 mg/mL NP solution on day 14 of culture development in vitro. Our studies revealed that the NPs caused a dose-dependent cytotoxic effect, which was manifested by an increase the number of dead cells and a decrease of the spontaneous functional calcium activity of neural networks. Au NPs revealed less pronounced cytotoxic effect than Si ones and it can be explained by larger size and better solubility of Si NPs.
Keywords: bare nanoparticles, primary hippocampal cultures, neurotoxicit
Cytokine profile in hospitalized patients with COVID-19 of different severity
Analysis of cytokine profile markers in conjunction with the clinical manifestations of coronavirus disease 2019 (COVID-19) can provide valuable information about the pathogenetic manifestations of the disease, and therefore, in the future, determine drugs that affect the cytokine storm and have an anti-inflammatory effect.Aim. To identify correlations between the parameters of the developed cytokine profile and the clinical course in hospitalized patients with COVID-19 of different severity.Material and methods. The study included 70 hospitalized patients with a confirmed diagnosis of COVID-19, with a mean age of 58 [50;69] years, including 40 men (57%) and 30 women (43%). The average lung involvement according to computed tomography (CT) at admission was CT-2 [1;3]. Peripheral venous blood was taken at admission, which averaged 7 [6; 8] days from the symptom onset. Standard biochemical parameters were studied, as well as 47 cytokines and chemokines using the Multiplex system (Merck KGaA, Darmstadt, Germany).Results. Correlations was found between the lung involvement degree and the level of IL-8 (r=0,31, p<0,05), IL-15 (r=0,35, p<0,05), IL-18 (r=0,31, p<0,05), MCP-1 (r=0,36, p<0,05), MIG (r=0,50, p<0,05), TNF-Ξ± (r=0,41, p<0,05). An inverse correlation was also found in the level of blood oxygen saturation with the same indicators as follows: IL-8 (r=-0,27, p<0,05), IL-15 (r=-0,34, p<0,05), IL-18 (r=-0,31, p<0,05), MCP-1 (r=-0,40, p<0,05), MIG (r=-0,56, p<0,05), TNF-Ξ± (r=-0,45, p<0,05). IL-6 levels were significantly elevated in patients with severe COVID-19 (CT3, CT4), while no increase in IL-6 was observed in patients with moderate disease (CT1, CT2). It is noteworthy that in patients with diabetes, the highest values of IL-12, IL-9 were recorded.Conclusion. Hyperinflammatory syndrome in severe COVID-19 is manifested by high levels of IL-6, MIG, MDC, MCP-1, M-CSF, TNF-Ξ±, Ξ², IL-8, IL-18, IL-15. With the CT-1 and CT-2, an increase in only the level of IL-18, IL-8 is noted. The identified patterns prove and make it possible to explain a number of systemic inflammatory changes that occur with COVID-19
Intracellular Neuroprotective Mechanisms in Neuron-Glial Networks Mediated by Glial Cell Line-Derived Neurotrophic Factor.
peer reviewedGlial cell line-derived neurotrophic factor (GDNF) has a pronounced neuroprotective effect in various nervous system pathologies, including ischaemic brain damage and neurodegenerative diseases. In this work, we studied the effect of GDNF on the ultrastructure and functional activity of neuron-glial networks during acute hypoxic exposure, a key damaging factor in numerous brain pathologies. We analysed the molecular mechanisms most likely involved in the positive effects of GDNF. Hypoxia modelling was performed on day 14 of culturing primary hippocampal cells obtained from mouse embryos (E18). GDNF (1βng/ml) was added to the culture medium 20βmin before oxygen deprivation. Acute hypoxia-induced irreversible changes in the ultrastructure of neurons and astrocytes led to the loss of functional Π‘a(2+) activity and neural network disruption. Destructive changes in the mitochondrial apparatus and its functional activity characterized by an increase in the basal oxygen consumption rate and respiratory chain complex II activity during decreased stimulated respiration intensity were observed 24 hours after hypoxic injury. At a concentration of 1βng/ml, GDNF maintained the functional metabolic network activity in primary hippocampal cultures and preserved the structure of the synaptic apparatus and number of mature chemical synapses, confirming its neuroprotective effect. GDNF maintained the normal structure of mitochondria in neuronal outgrowth but not in the soma. Analysis of the possible GDNF mechanism revealed that RET kinase, a component of the receptor complex, and the PI3K/Akt pathway are crucial for the neuroprotective effect of GDNF. The current study also revealed the role of GDNF in the regulation of HIF-1Ξ± transcription factor expression under hypoxic conditions
ΠΠ΅Π»ΠΊΠΎΠ²ΡΠΉ ΡΠΎΡΡΠ°Π² ΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½ ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ² ΠΏΡΠΈ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ ΠΏΠ΅ΡΠ΅Π½ΠΈ ΠΈ ΠΏΠΎΡΠΊΠΈ
Organ transplantation is an effective treatment for many end-stage diseases. However, reperfusion injury constitutes a major complication of transplantation, which is associated with microcirculatory disorders and aggregation of blood corpuscles. Red blood cells (RBC) play an essential role in maintaining hemodynamic and rheological properties of the blood. Moreover, the study of mechanisms of changes in RBC functional indices is an urgent task. The main indicator of RBC functioning is the stability of RBC membrane structure. The issue of RBC membrane modification in organ transplantation has not been studied so far. Objective: to study the protein composition of RBC membranes, their aggregation and electrokinetic parameters in liver and kidney recipients, as well as in related kidney and liver fragment donors before and after operation. Research materials. Blood of 12 kidney recipients and 5 related kidney donors, 8 liver recipients and 4 related liver fragment donors β 1β2 hours before surgery, 1 week, 1, 2, 7, 10, 12 months after surgery. The control group consisted of 8 healthy volunteers. Research methods. Protein separation was done by Laemmli electrophoresis. RBC electrophoretic mobility, which characterizes the electrokinetic properties of cells, was measured by microelectrophoresis. Aggregation was calculated microscopically by counting unaggregated RBCs. Obtained values were compared by Mann-Whitney U test. Results. Examination of the RBC membrane of kidney recipients revealed a significant decrease in the amount of Band 3 protein and glycophorin before and after transplantation. Band 3 protein levels reduced at 1 month, glycophorin reduced at 7 months after surgery, with a maximum decrease in these protein fractions by more than 50% by 7 days compared with control values. There was also a decrease in spectrin content for 2 months after surgery with a maximum decrease of 30% by 1 month. In liver recipients, analysis of RBC membrane proteins revealed a decrease in the amount of glycophorin before surgery and further decrease at 2 months of post-transplant period. The maximum decrease in this index was 72% by 7 days after surgery. In addition, there was a fall in spectrin and Band 3 protein levels at 1 month by more than 60% relative to the control values. In donors, there were changes in the protein fraction of RBC membranes in the long-term post-operative period: spectrin and Band 3 protein levels reduced by 2 times at month 2 in kidney donors, while glycophorin levels reduced by 2.3 times at month 1 after operation in liver donors. Similarly, both groups of donors had increased actin levels at month 1 after surgery. The revealed changes in protein levels in the protein phase of RBC membranes were combined with functional indices of RBCs. In kidney recipients, decreased RBC electrophoretic mobility and increased aggregation were detected at 2 months. In liver recipients, the changes in these indicators were at 1 month. A decrease in RBC electrophoretic mobility was detected in donors of both groups. Conclusion. Changes in RBC membrane electronegativity are associated with changes in glycophorin and Band 3 protein levels, whereas in RBC aggregation process in liver/kidney recipients, the structural and functional disorders in the interrelationships of such membrane proteins as spectrin, Band 3 protein, and glycophorin, are significant factors. Alteration of actin determines inhibition of RBC aggregation growth in donors.Π’ΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΡ ΠΎΡΠ³Π°Π½ΠΎΠ² ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΠ΅ΡΠΌΠΈΠ½Π°Π»ΡΠ½ΡΠΌΠΈ ΡΡΠ°Π΄ΠΈΡΠΌΠΈ ΡΡΠ΄Π° ΡΡΠΆΠ΅Π»ΡΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ. ΠΠ΄Π½Π°ΠΊΠΎ ΡΠ΅ΡΡΠ΅Π·Π½ΡΠΌ ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠ΅ΠΌ ΠΏΡΠΈ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ ΡΠ²Π»ΡΡΡΡΡ ΡΠ΅ΠΏΠ΅ΡΡΡΠ·ΠΈΠΎΠ½Π½ΡΠ΅ ΠΏΠΎΠ²ΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΡ, ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ²ΡΠ·Π°Π½Ρ Ρ ΠΌΠΈΠΊΡΠΎΡΠΈΡΠΊΡΠ»ΡΡΠΎΡΠ½ΡΠΌΠΈ Π½Π°ΡΡΡΠ΅Π½ΠΈΡΠΌΠΈ ΠΈ Π°Π³ΡΠ΅Π³Π°ΡΠΈΠ΅ΠΉ ΡΠΎΡΠΌΠ΅Π½Π½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² ΠΊΡΠΎΠ²ΠΈ. ΠΡΠΈΡΡΠΎΡΠΈΡΡ ΠΈΠ³ΡΠ°ΡΡ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ ΡΠΎΠ»Ρ Π² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠ°Π½ΠΈΠΈ Π³Π΅ΠΌΠΎΠ΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ΅ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΠΎΠΉΡΡΠ² ΠΊΡΠΎΠ²ΠΈ, ΠΈ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΈΡ
ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ΠΉ. ΠΡΠ½ΠΎΠ²Π½ΡΠΌ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΌ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΡΠΈΡΡΠΎΡΠΈΡΠ° ΡΠ»ΡΠΆΠΈΡ ΡΡΠ°Π±ΠΈΠ»ΡΠ½ΠΎΡΡΡ ΡΡΡΡΠΊΡΡΡΡ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½Ρ. ΠΠΎΠΏΡΠΎΡ ΠΎ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΡΠΈΡΡΠΎΡΠΈΡΠ°ΡΠ½ΠΎΠΉ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½Ρ ΠΏΡΠΈ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ ΠΎΡΠ³Π°Π½ΠΎΠ² Π½Π° ΡΠ΅Π³ΠΎΠ΄Π½ΡΡΠ½ΠΈΠΉ Π΄Π΅Π½Ρ Π½Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½.Π¦Π΅Π»Ρ: ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ Π±Π΅Π»ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π° ΠΌΠ΅ΠΌΠ±ΡΠ°Π½ ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ², ΠΈΡ
Π°Π³ΡΠ΅Π³Π°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠΈΠ½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Ρ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠ΅ΡΠ΅Π½ΠΈ ΠΈ ΠΏΠΎΡΠΊΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΡΡ
Π΄ΠΎΠ½ΠΎΡΠΎΠ² ΠΏΠΎΡΠΊΠΈ ΠΈ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠ° ΠΏΠ΅ΡΠ΅Π½ΠΈ Π΄ΠΎ ΠΈ Π² Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ΅ ΠΏΠΎΡΠ»Π΅ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΠ΅ΡΠΈΠΎΠ΄Π°.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠΎΠ²Ρ 12 ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠΎΡΠΊΠΈ, 5 ΡΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΡΡ
Π΄ΠΎΠ½ΠΎΡΠΎΠ² ΠΏΠΎΡΠΊΠΈ, 8 ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠ΅ΡΠ΅Π½ΠΈ ΠΈ 4 ΡΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΡΡ
Π΄ΠΎΠ½ΠΎΡΠΎΠ² ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠ° ΠΏΠ΅ΡΠ΅Π½ΠΈ Π²ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ΅ β Π·Π° 1β2 ΡΠ°ΡΠ° Π΄ΠΎ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ, ΡΠ΅ΡΠ΅Π· 1 Π½Π΅Π΄Π΅Π»Ρ, 1, 2, 7, 10, 12 ΠΌΠ΅ΡΡΡΠ΅Π² ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ. ΠΡΡΠΏΠΏΡ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ 8 Π·Π΄ΠΎΡΠΎΠ²ΡΡ
Π΄ΠΎΠ±ΡΠΎΠ²ΠΎΠ»ΡΡΠ΅Π².ΠΠ΅ΡΠΎΠ΄Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. Π Π°Π·Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π±Π΅Π»ΠΊΠΎΠ² ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΡΠ»Π΅ΠΊΡΡΠΎΡΠΎΡΠ΅Π·Π° ΠΏΠΎ ΠΡΠΌΠΌΠ»ΠΈ. ΠΠ»Π΅ΠΊΡΡΠΎΡΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΡΡ ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½ΠΎΡΡΡ ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ², Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠΈΠ½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ²ΠΎΠΉΡΡΠ²Π° ΠΊΠ»Π΅ΡΠΎΠΊ, ΠΈΠ·ΠΌΠ΅ΡΡΠ»ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΌΠΈΠΊΡΠΎΡΠ»Π΅ΠΊΡΡΠΎΡΠΎΡΠ΅Π·Π°. ΠΠ³ΡΠ΅Π³Π°ΡΠΈΡ ΡΠ°ΡΡΡΠΈΡΡΠ²Π°Π»ΠΈ ΠΌΠΈΠΊΡΠΎΡΠΊΠΎΠΏΠΈΡΠ΅ΡΠΊΠΈ, ΠΏΡΡΠ΅ΠΌ ΠΏΠΎΠ΄ΡΡΠ΅ΡΠ° Π½Π΅Π°Π³ΡΠ΅Π³ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ². Π‘ΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π²Π΅Π»ΠΈΡΠΈΠ½ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ ΠΏΠΎ U-ΠΊΡΠΈΡΠ΅ΡΠΈΡ ΠΠ°Π½Π½Π°βΠ£ΠΈΡΠ½ΠΈ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½Ρ ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ² ΠΊΡΠΎΠ²ΠΈ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠΎΡΠΊΠΈ Π²ΡΡΠ²ΠΈΠ»ΠΎ Π·Π½Π°ΡΠΈΠΌΠΎΠ΅ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° Π±Π΅Π»ΠΊΠ° ΠΏΠΎΠ»ΠΎΡΡ 3 ΠΈ Π³Π»ΠΈΠΊΠΎΡΠΎΡΠΈΠ½Π° Π΄ΠΎ ΠΈ ΠΏΠΎΡΠ»Π΅ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ. Π£ΡΠΎΠ²Π΅Π½Ρ Π±Π΅Π»ΠΊΠ° ΠΏΠΎΠ»ΠΎΡΡ 3 Π±ΡΠ» ΡΠ½ΠΈΠΆΠ΅Π½ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 1 ΠΌΠ΅ΡΡΡΠ°, Π³Π»ΠΈΠΊΠΎΡΠΎΡΠΈΠ½Π° β Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 7 ΠΌΠ΅ΡΡΡΠ΅Π² ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ Ρ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΌ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ΠΌ Π΄Π°Π½Π½ΡΡ
ΡΡΠ°ΠΊΡΠΈΠΉ Π±Π΅Π»ΠΊΠΎΠ² Π±ΠΎΠ»Π΅Π΅ ΡΠ΅ΠΌ Π½Π° 50% ΠΊ 7-ΠΌ ΡΡΡΠΊΠ°ΠΌ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ. Π’Π°ΠΊΠΆΠ΅ ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π»ΠΎΡΡ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ ΡΠΏΠ΅ΠΊΡΡΠΈΠ½Π° Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 2 ΠΌΠ΅ΡΡΡΠ΅Π² ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ Ρ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΌ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ΠΌ Π½Π° 30% ΠΊ 1 ΠΌΠ΅ΡΡΡΡ. Π£ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠ΅ΡΠ΅Π½ΠΈ Π°Π½Π°Π»ΠΈΠ· Π±Π΅Π»ΠΊΠΎΠ² ΠΌΠ΅ΠΌΠ±ΡΠ°Π½Ρ ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ² Π²ΡΡΠ²ΠΈΠ» ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° Π³Π»ΠΈΠΊΠΎΡΠΎΡΠΈΠ½Π° Π΄ΠΎ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΠΈ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅Π΅ Π΅Π³ΠΎ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 2 ΠΌΠ΅ΡΡΡΠ΅Π² ΠΏΠΎΡΡΡΡΠ°ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΠ΅ΡΠΈΠΎΠ΄Π°. ΠΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ΅ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ β Π½Π° 72% β Π±ΡΠ»ΠΎ ΠΎΡΠΌΠ΅ΡΠ΅Π½ΠΎ ΠΊ 7-ΠΌ ΡΡΡΠΊΠ°ΠΌ ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, Π½Π°Π±Π»ΡΠ΄Π°Π»ΠΎΡΡ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΡΠΏΠ΅ΠΊΡΡΠΈΠ½Π° ΠΈ Π±Π΅Π»ΠΊΠ° ΠΏΠΎΠ»ΠΎΡΡ 3 Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 1 ΠΌΠ΅ΡΡΡΠ° Π±ΠΎΠ»Π΅Π΅ ΡΠ΅ΠΌ Π½Π° 60% ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ. Π£ Π΄ΠΎΠ½ΠΎΡΠΎΠ² ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π² Π±Π΅Π»ΠΊΠΎΠ²ΠΎΠΉ ΡΡΠ°ΠΊΡΠΈΠΈ ΡΡΠΈΡΡΠΎΡΠΈΡΠ°ΡΠ½ΡΡ
ΠΌΠ΅ΠΌΠ±ΡΠ°Π½ ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π»ΠΈΡΡ Π² ΠΎΡΠ΄Π°Π»Π΅Π½Π½ΡΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ: Ρ Π΄ΠΎΠ½ΠΎΡΠΎΠ² ΠΏΠΎΡΠΊΠΈ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ Π² 2 ΡΠ°Π·Π° ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΡΠΏΠ΅ΠΊΡΡΠΈΠ½Π° ΠΈ Π±Π΅Π»ΠΊΠ° ΠΏΠΎΠ»ΠΎΡΡ 3 ΠΎΡΠΌΠ΅ΡΠ°Π»ΠΎΡΡ Π½Π° 2-ΠΉ ΠΌΠ΅ΡΡΡ, Ρ Π΄ΠΎΠ½ΠΎΡΠΎΠ² ΠΏΠ΅ΡΠ΅Π½ΠΈ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ Π³Π»ΠΈΠΊΠΎΡΠΎΡΠΈΠ½Π° Π² 2,3 ΡΠ°Π·Π° β ΠΊ 1-ΠΌΡ ΠΌΠ΅ΡΡΡΡ ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ. Π’Π°ΠΊΠΆΠ΅ Π² ΠΎΠ±Π΅ΠΈΡ
Π³ΡΡΠΏΠΏΠ°Ρ
Π΄ΠΎΠ½ΠΎΡΠΎΠ² ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π»ΡΡ ΡΠΎΡΡ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π°ΠΊΡΠΈΠ½Π° ΠΊ 1-ΠΌΡ ΠΌΠ΅ΡΡΡΡ ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ. ΠΡΡΠ²Π»Π΅Π½Π½ΡΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° Π±Π΅Π»ΠΊΠΎΠ² Π² Π±Π΅Π»ΠΊΠΎΠ²ΠΎΠΉ ΡΠ°Π·Π΅ ΡΡΠΈΡΡΠΎΡΠΈΡΠ°ΡΠ½ΡΡ
ΠΌΠ΅ΠΌΠ±ΡΠ°Π½ ΡΠΎΡΠ΅ΡΠ°Π»ΠΈΡΡ Ρ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΌΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌΠΈ ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ². Π£ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠΎΡΠΊΠΈ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΠΠ€ΠΠ ΠΈ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ Π°Π³ΡΠ΅Π³Π°ΡΠΈΠΈ Π½Π°Π±Π»ΡΠ΄Π°Π»ΠΎΡΡ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 2 ΠΌΠ΅ΡΡΡΠ΅Π², Ρ ΡΠ΅ΡΠΈΠΏΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠ΅ΡΠ΅Π½ΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π·Π°ΡΠΈΠΊΡΠΈΡΠΎΠ²Π°Π½Ρ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 1 ΠΌΠ΅ΡΡΡΠ°. Π£ Π΄ΠΎΠ½ΠΎΡΠΎΠ² ΠΎΠ±Π΅ΠΈΡ
Π³ΡΡΠΏΠΏ Π±ΡΠ»ΠΎ Π²ΡΡΠ²Π»Π΅Π½ΠΎ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ ΠΠ€ΠΠ.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. Π‘ΠΎΠ²ΠΎΠΊΡΠΏΠ½ΠΎΡΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΏΠΎΠΊΠ°Π·Π°Π»Π°, ΡΡΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΡΠ»Π΅ΠΊΡΡΠΎΠΎΡΡΠΈΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½ ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ² ΡΠΎΠΏΡΡΠΆΠ΅Π½ΠΎ Ρ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ Π³Π»ΠΈΠΊΠΎΡΠΎΡΠΈΠ½Π° ΠΈ Π±Π΅Π»ΠΊΠ° ΠΏΠΎΠ»ΠΎΡΡ 3, ΡΠΎΠ³Π΄Π° ΠΊΠ°ΠΊ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ Π°Π³ΡΠ΅Π³Π°ΡΠΈΠΈ ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ² Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠΎΡΠ»Π΅ ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΈ ΠΏΠ΅ΡΠ΅Π½ΠΈ/ΠΏΠΎΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΡΠΌΠΈ ΡΠ°ΠΊΡΠΎΡΠ°ΠΌΠΈ ΡΠ²Π»ΡΡΡΡΡ ΡΡΡΡΠΊΡΡΡΠ½ΠΎ-ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠ΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΡ Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Π΅ΠΉ ΡΠ°ΠΊΠΈΡ
ΠΌΠ΅ΠΌΠ±ΡΠ°Π½Π½ΡΡ
Π±Π΅Π»ΠΊΠΎΠ², ΠΊΠ°ΠΊ ΡΠΏΠ΅ΠΊΡΡΠΈΠ½, Π±Π΅Π»ΠΎΠΊ ΠΏΠΎΠ»ΠΎΡΡ 3, Π³Π»ΠΈΠΊΠΎΡΠΎΡΠΈΠ½. ΠΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π°ΠΊΡΠΈΠ½Π° ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅Ρ ΡΠ΄Π΅ΡΠΆΠΈΠ²Π°Π½ΠΈΠ΅ ΡΠΎΡΡΠ° Π°Π³ΡΠ΅Π³Π°ΡΠΈΠΈ ΡΡΠΈΡΡΠΎΡΠΈΡΠΎΠ² Ρ Π΄ΠΎΠ½ΠΎΡΠΎΠ²
Age-related trajectories of DNA methylation network markers: A parenclitic network approach to a family-based cohort of patients with Down Syndrome
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