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

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    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

    ΠŸΡ€ΠΎΠ³Π½ΠΎΠ·ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ стСпСни ΠΏΠ΅Ρ€Π΅Π³Ρ€ΡƒΠ·ΠΊΠΈ ΠΏΡ€Π°Π²Ρ‹Ρ… ΠΊΠ°ΠΌΠ΅Ρ€ сСрдца Ρƒ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² с острой массивной тромбоэмболиСй Π»Π΅Π³ΠΎΡ‡Π½ΠΎΠΉ Π°Ρ€Ρ‚Π΅Ρ€ΠΈΠΈ Π½Π° основании Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² КВ-диагностики

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    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 – class computed tomographs were taken – 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 – 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’ Π³Ρ€ΡƒΠΏΠΏΡƒ исслСдования вошло 147 ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² Π½Π° этапС ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠΈ ΠΊ экстрСнному хирургичСскому Π»Π΅Ρ‡Π΅Π½ΠΈΡŽ острой массивной тромбоэмболии Π»Π΅Π³ΠΎΡ‡Π½ΠΎΠΉ Π°Ρ€Ρ‚Π΅Ρ€ΠΈΠΈ (ВЭЛА) Π² ΠΏΠ΅Ρ€ΠΈΠΎΠ΄ с ΠΌΠ°Ρ€Ρ‚Π° 2012 Π³. ΠΏΠΎ Π΄Π΅ΠΊΠ°Π±Ρ€ΡŒ 2019 Π³. Π²ΠΊΠ»ΡŽΡ‡ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ. Π’ качСствС КВ-ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ ΠΏΠ΅Ρ€Π΅Π³Ρ€ΡƒΠ·ΠΊΠΈ ΠΏΡ€Π°Π²Ρ‹Ρ… ΠΊΠ°ΠΌΠ΅Ρ€ сСрдца взяты ΠΎΠ±Ρ‹Ρ‡Π½Ρ‹Π΅ КВ-ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ, Π½Π΅ Ρ‚Ρ€Π΅Π±ΡƒΡŽΡ‰ΠΈΠ΅ использования ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… Ρ‚ΠΎΠΌΠΎΠ³Ρ€Π°Ρ„ΠΎΠ² экспСртного класса, ΠΈΠΌΠΈ стали вСрхняя полая Π²Π΅Π½Π°, ниТняя полая Π²Π΅Π½Π°, нСпарная Π²Π΅Π½Π°; Ρ€Π΅Ρ„Π»ΡŽΠΊΡ контрастного ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚Π° Π² ниТнюю ΠΏΠΎΠ»ΡƒΡŽ Π²Π΅Π½Ρƒ; Ρ€Π΅Ρ„Π»ΡŽΠΊΡ контрастного ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚Π° Π² ΠΏΠ΅Ρ‡Π΅Π½ΠΎΡ‡Π½Ρ‹Π΅ Π²Π΅Π½Ρ‹. Π’ Ρ…ΠΎΠ΄Π΅ исслСдования ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ ΡΡ€Π°Π²Π½ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹ΠΉ Π°Π½Π°Π»ΠΈΠ· срСднСго давлСния Π² Π»Π΅Π³ΠΎΡ‡Π½ΠΎΠΉ Π°Ρ€Ρ‚Π΅Ρ€ΠΈΠΈ с Π²Ρ‹ΡˆΠ΅ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹ΠΌΠΈ КВ-показатСлями. НаиболСС ΡƒΡΡ‚ΠΎΠΉΡ‡ΠΈΠ²ΡƒΡŽ ΡΡ‚Π°Ρ‚ΠΈΡΡ‚ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ взаимосвязь с показатСлями срСднСго давлСния Π² Π»Π΅Π³ΠΎΡ‡Π½ΠΎΠΉ Π°Ρ€Ρ‚Π΅Ρ€ΠΈΠΈ продСмонстрировали КВ-ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹ – Π΄ΠΈΠ°ΠΌΠ΅Ρ‚Ρ€ Π½Π΅ΠΏΠ°Ρ€Π½ΠΎΠΉ Π²Π΅Π½Ρ‹ ΠΈ Ρ€Π΅Ρ„Π»ΡŽΠΊΡ контрастного ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚Π° Π² ΠΏΠ΅Ρ‡Π΅Π½ΠΎΡ‡Π½Ρ‹Π΅ Π²Π΅Π½Ρ‹. По Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π°ΠΌ Ρ€Π°Π±ΠΎΡ‚Ρ‹ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠ° расчСта фактичСских Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ срСднСго давлСния Π² Π»Π΅Π³ΠΎΡ‡Π½ΠΎΠΉ Π°Ρ€Ρ‚Π΅Ρ€ΠΈΠΈ Π½Π° основании КВ-ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π° β€œΠ΄ΠΈΠ°ΠΌΠ΅Ρ‚Ρ€ Π½Π΅ΠΏΠ°Ρ€Π½ΠΎΠΉ вСны”

    Biocompatibility of Bare Nanoparticles Based on Silicon and Gold for Nervous Cells

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    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

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    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.

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    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 (1 ng/ml) was added to the culture medium 20 min 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 1 ng/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

    Π‘Π΅Π»ΠΊΠΎΠ²Ρ‹ΠΉ состав ΠΈ Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Π΅ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½ эритроцитов ΠΏΡ€ΠΈ трансплантации ΠΏΠ΅Ρ‡Π΅Π½ΠΈ ΠΈ ΠΏΠΎΡ‡ΠΊΠΈ

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    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 – 1–2 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.Врансплантация ΠΎΡ€Π³Π°Π½ΠΎΠ² являСтся эффСктивным ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ лСчСния ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² с Ρ‚Π΅Ρ€ΠΌΠΈΠ½Π°Π»ΡŒΠ½Ρ‹ΠΌΠΈ стадиями ряда тяТСлых Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ. Однако ΡΠ΅Ρ€ΡŒΠ΅Π·Π½Ρ‹ΠΌ ослоТнСниСм ΠΏΡ€ΠΈ трансплантации ΡΠ²Π»ΡΡŽΡ‚ΡΡ Ρ€Π΅ΠΏΠ΅Ρ€Ρ„ΡƒΠ·ΠΈΠΎΠ½Π½Ρ‹Π΅ поврСТдСния, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ связаны с микроциркуляторными Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΡΠΌΠΈ ΠΈ Π°Π³Ρ€Π΅Π³Π°Ρ†ΠΈΠ΅ΠΉ Ρ„ΠΎΡ€ΠΌΠ΅Π½Π½Ρ‹Ρ… элСмСнтов ΠΊΡ€ΠΎΠ²ΠΈ. Π­Ρ€ΠΈΡ‚Ρ€ΠΎΡ†ΠΈΡ‚Ρ‹ ΠΈΠ³Ρ€Π°ΡŽΡ‚ ΡΡƒΡ‰Π΅ΡΡ‚Π²Π΅Π½Π½ΡƒΡŽ Ρ€ΠΎΠ»ΡŒ Π² ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠ°Π½ΠΈΠΈ гСмодинамичСских ΠΈ рСологичСских свойств ΠΊΡ€ΠΎΠ²ΠΈ, ΠΈ ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠΎΠ² измСнСния ΠΈΡ… Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ являСтся Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΠΉ Π·Π°Π΄Π°Ρ‡Π΅ΠΉ. ΠžΡΠ½ΠΎΠ²Π½Ρ‹ΠΌ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΌ функционирования эритроцита слуТит ΡΡ‚Π°Π±ΠΈΠ»ΡŒΠ½ΠΎΡΡ‚ΡŒ структуры ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½Ρ‹. Вопрос ΠΎ ΠΌΠΎΠ΄ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ эритроцитарной ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½Ρ‹ ΠΏΡ€ΠΈ трансплантации ΠΎΡ€Π³Π°Π½ΠΎΠ² Π½Π° сСгодняшний дСнь Π½Π΅ исслСдован.ЦСль: ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ Π±Π΅Π»ΠΊΠΎΠ²ΠΎΠ³ΠΎ состава ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½ эритроцитов, ΠΈΡ… Π°Π³Ρ€Π΅Π³Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… ΠΈ элСктрокинСтичСских ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ Ρƒ Ρ€Π΅Ρ†ΠΈΠΏΠΈΠ΅Π½Ρ‚ΠΎΠ² ΠΏΠ΅Ρ‡Π΅Π½ΠΈ ΠΈ ΠΏΠΎΡ‡ΠΊΠΈ, Π° Ρ‚Π°ΠΊΠΆΠ΅ родствСнных Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ² ΠΏΠΎΡ‡ΠΊΠΈ ΠΈ Ρ„Ρ€Π°Π³ΠΌΠ΅Π½Ρ‚Π° ΠΏΠ΅Ρ‡Π΅Π½ΠΈ Π΄ΠΎ ΠΈ Π² Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ΅ послСопСрационного ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π°.ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Ρ‹ исслСдования. ΠšΡ€ΠΎΠ²ΡŒ 12 Ρ€Π΅Ρ†ΠΈΠΏΠΈΠ΅Π½Ρ‚ΠΎΠ² ΠΏΠΎΡ‡ΠΊΠΈ, 5 родствСнных Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ² ΠΏΠΎΡ‡ΠΊΠΈ, 8 Ρ€Π΅Ρ†ΠΈΠΏΠΈΠ΅Π½Ρ‚ΠΎΠ² ΠΏΠ΅Ρ‡Π΅Π½ΠΈ ΠΈ 4 родствСнных Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ² Ρ„Ρ€Π°Π³ΠΌΠ΅Π½Ρ‚Π° ΠΏΠ΅Ρ‡Π΅Π½ΠΈ Π²ΠΎ Π²Ρ€Π΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ΅ – Π·Π° 1–2 часа Π΄ΠΎ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ, Ρ‡Π΅Ρ€Π΅Π· 1 нСдСлю, 1, 2, 7, 10, 12 мСсяцСв послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ. Π“Ρ€ΡƒΠΏΠΏΡƒ контроля составили 8 Π·Π΄ΠΎΡ€ΠΎΠ²Ρ‹Ρ… Π΄ΠΎΠ±Ρ€ΠΎΠ²ΠΎΠ»ΡŒΡ†Π΅Π².ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹ исслСдования. Π Π°Π·Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π±Π΅Π»ΠΊΠΎΠ² ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ элСктрофорСза ΠΏΠΎ Лэммли. Π­Π»Π΅ΠΊΡ‚Ρ€ΠΎΡ„ΠΎΡ€Π΅Ρ‚ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½ΠΎΡΡ‚ΡŒ эритроцитов, Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡŽΡ‰ΡƒΡŽ элСктрокинСтичСскиС свойства ΠΊΠ»Π΅Ρ‚ΠΎΠΊ, измСряли ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ микроэлСктрофорСза. ΠΠ³Ρ€Π΅Π³Π°Ρ†ΠΈΡŽ рассчитывали микроскопичСски, ΠΏΡƒΡ‚Π΅ΠΌ подсчСта Π½Π΅Π°Π³Ρ€Π΅Π³ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… эритроцитов. Π‘Ρ€Π°Π²Π½Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… Π²Π΅Π»ΠΈΡ‡ΠΈΠ½ ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ ΠΏΠΎ U-ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΡŽ ΠœΠ°Π½Π½Π°β€“Π£ΠΈΡ‚Π½ΠΈ.Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. ИсслСдованиС ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½Ρ‹ эритроцитов ΠΊΡ€ΠΎΠ²ΠΈ Ρ€Π΅Ρ†ΠΈΠΏΠΈΠ΅Π½Ρ‚ΠΎΠ² ΠΏΠΎΡ‡ΠΊΠΈ выявило Π·Π½Π°Ρ‡ΠΈΠΌΠΎΠ΅ сниТСниС количСства Π±Π΅Π»ΠΊΠ° полосы 3 ΠΈ Π³Π»ΠΈΠΊΠΎΡ„ΠΎΡ€ΠΈΠ½Π° Π΄ΠΎ ΠΈ послС провСдСния трансплантации. Π£Ρ€ΠΎΠ²Π΅Π½ΡŒ Π±Π΅Π»ΠΊΠ° полосы 3 Π±Ρ‹Π» сниТСн Π² Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ 1 мСсяца, Π³Π»ΠΈΠΊΠΎΡ„ΠΎΡ€ΠΈΠ½Π° – Π² Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ 7 мСсяцСв послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ с ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΌ ΡƒΠΌΠ΅Π½ΡŒΡˆΠ΅Π½ΠΈΠ΅ΠΌ Π΄Π°Π½Π½Ρ‹Ρ… Ρ„Ρ€Π°ΠΊΡ†ΠΈΠΉ Π±Π΅Π»ΠΊΠΎΠ² Π±ΠΎΠ»Π΅Π΅ Ρ‡Π΅ΠΌ Π½Π° 50% ΠΊ 7-ΠΌ суткам ΠΎΡ‚Π½ΠΎΡΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ контроля. Π’Π°ΠΊΠΆΠ΅ Ρ€Π΅Π³ΠΈΡΡ‚Ρ€ΠΈΡ€ΠΎΠ²Π°Π»ΠΎΡΡŒ сниТСниС содСрТания спСктрина Π² Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ 2 мСсяцСв послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ с ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΌ сниТСниСм Π½Π° 30% ΠΊ 1 мСсяцу. Π£ Ρ€Π΅Ρ†ΠΈΠΏΠΈΠ΅Π½Ρ‚ΠΎΠ² ΠΏΠ΅Ρ‡Π΅Π½ΠΈ Π°Π½Π°Π»ΠΈΠ· Π±Π΅Π»ΠΊΠΎΠ² ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½Ρ‹ эритроцитов выявил сниТСниС количСства Π³Π»ΠΈΠΊΠΎΡ„ΠΎΡ€ΠΈΠ½Π° Π΄ΠΎ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ дальнСйшСС Π΅Π³ΠΎ ΡƒΠΌΠ΅Π½ΡŒΡˆΠ΅Π½ΠΈΠ΅ Π² Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ 2 мСсяцСв посттрасплантационного ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π°. МаксимальноС сниТСниС показатСля – Π½Π° 72% – Π±Ρ‹Π»ΠΎ ΠΎΡ‚ΠΌΠ΅Ρ‡Π΅Π½ΠΎ ΠΊ 7-ΠΌ суткам послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ. ΠšΡ€ΠΎΠΌΠ΅ Ρ‚ΠΎΠ³ΠΎ, наблюдалось сниТСниС количСства спСктрина ΠΈ Π±Π΅Π»ΠΊΠ° полосы 3 Π² Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ 1 мСсяца Π±ΠΎΠ»Π΅Π΅ Ρ‡Π΅ΠΌ Π½Π° 60% ΠΎΡ‚Π½ΠΎΡΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ контроля. Π£ Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ² измСнСния Π² Π±Π΅Π»ΠΊΠΎΠ²ΠΎΠΉ Ρ„Ρ€Π°ΠΊΡ†ΠΈΠΈ эритроцитарных ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½ Ρ€Π΅Π³ΠΈΡΡ‚Ρ€ΠΈΡ€ΠΎΠ²Π°Π»ΠΈΡΡŒ Π² ΠΎΡ‚Π΄Π°Π»Π΅Π½Π½Ρ‹ΠΉ ΠΏΠ΅Ρ€ΠΈΠΎΠ΄ послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ: Ρƒ Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ² ΠΏΠΎΡ‡ΠΊΠΈ сниТСниС Π² 2 Ρ€Π°Π·Π° количСства спСктрина ΠΈ Π±Π΅Π»ΠΊΠ° полосы 3 ΠΎΡ‚ΠΌΠ΅Ρ‡Π°Π»ΠΎΡΡŒ Π½Π° 2-ΠΉ мСсяц, Ρƒ Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ² ΠΏΠ΅Ρ‡Π΅Π½ΠΈ сниТСниС Π³Π»ΠΈΠΊΠΎΡ„ΠΎΡ€ΠΈΠ½Π° Π² 2,3 Ρ€Π°Π·Π° – ΠΊ 1-ΠΌΡƒ мСсяцу послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ. Π’Π°ΠΊΠΆΠ΅ Π² ΠΎΠ±Π΅ΠΈΡ… Π³Ρ€ΡƒΠΏΠΏΠ°Ρ… Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ² рСгистрировался рост ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ Π°ΠΊΡ‚ΠΈΠ½Π° ΠΊ 1-ΠΌΡƒ мСсяцу послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ. ВыявлСнныС измСнСния количСства Π±Π΅Π»ΠΊΠΎΠ² Π² Π±Π΅Π»ΠΊΠΎΠ²ΠΎΠΉ Ρ„Π°Π·Π΅ эритроцитарных ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½ ΡΠΎΡ‡Π΅Ρ‚Π°Π»ΠΈΡΡŒ с Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹ΠΌΠΈ показатСлями эритроцитов. Π£ Ρ€Π΅Ρ†ΠΈΠΏΠΈΠ΅Π½Ρ‚ΠΎΠ² ΠΏΠΎΡ‡ΠΊΠΈ сниТСниС ЭЀПЭ ΠΈ ΡƒΠ²Π΅Π»ΠΈΡ‡Π΅Π½ΠΈΠ΅ Π°Π³Ρ€Π΅Π³Π°Ρ†ΠΈΠΈ наблюдалось Π² Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ 2 мСсяцСв, Ρƒ Ρ€Π΅Ρ†ΠΈΠΏΠΈΠ΅Π½Ρ‚ΠΎΠ² ΠΏΠ΅Ρ‡Π΅Π½ΠΈ измСнСния Π΄Π°Π½Π½Ρ‹Ρ… ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ зафиксированы Π² Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ 1 мСсяца. Π£ Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ² ΠΎΠ±Π΅ΠΈΡ… Π³Ρ€ΡƒΠΏΠΏ Π±Ρ‹Π»ΠΎ выявлСно ΡƒΠΌΠ΅Π½ΡŒΡˆΠ΅Π½ΠΈΠ΅ ЭЀПЭ.Π—Π°ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅. Π‘ΠΎΠ²ΠΎΠΊΡƒΠΏΠ½ΠΎΡΡ‚ΡŒ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² ΠΏΠΎΠΊΠ°Π·Π°Π»Π°, Ρ‡Ρ‚ΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΡΠ»Π΅ΠΊΡ‚Ρ€ΠΎΠΎΡ‚Ρ€ΠΈΡ†Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½ эритроцитов сопряТСно с ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ содСрТания Π³Π»ΠΈΠΊΠΎΡ„ΠΎΡ€ΠΈΠ½Π° ΠΈ Π±Π΅Π»ΠΊΠ° полосы 3, Ρ‚ΠΎΠ³Π΄Π° ΠΊΠ°ΠΊ Π² процСссС Π°Π³Ρ€Π΅Π³Π°Ρ†ΠΈΠΈ эритроцитов Ρƒ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² послС трансплантации ΠΏΠ΅Ρ‡Π΅Π½ΠΈ/ΠΏΠΎΡ‡ΠΊΠΈ Π·Π½Π°Ρ‡ΠΈΠΌΡ‹ΠΌΠΈ Ρ„Π°ΠΊΡ‚ΠΎΡ€Π°ΠΌΠΈ ΡΠ²Π»ΡΡŽΡ‚ΡΡ структурно-Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Π΅ Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΡ взаимосвязСй Ρ‚Π°ΠΊΠΈΡ… ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½Π½Ρ‹Ρ… Π±Π΅Π»ΠΊΠΎΠ², ΠΊΠ°ΠΊ спСктрин, Π±Π΅Π»ΠΎΠΊ полосы 3, Π³Π»ΠΈΠΊΠΎΡ„ΠΎΡ€ΠΈΠ½. ИзмСнСниС Π°ΠΊΡ‚ΠΈΠ½Π° опрСдСляСт сдСрТиваниС роста Π°Π³Ρ€Π΅Π³Π°Ρ†ΠΈΠΈ эритроцитов Ρƒ Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ²

    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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    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
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