Biology subjects

Malovichko, M.

Publications and source records attributed to Malovichko, M..

4 recordsLinked to original sources

bioRxiv2024

Benzene metabolites increase vascular permeability by activating heat shock proteins and Rho GTPases

Benzene is a ubiquitous environmental and occupational pollutant abundant in household products, petrochemicals, and cigarette smoke. It is also a well-known carcinogen and hematopoietic toxin. Population-based studies indicate an increased risk of heart failure in subjects exposed to inhaled benzene, which coincides with the infiltration of immune cells into the myocardium. However, the mechanisms of benzene-induced cardiovascular disease remain unknown. Our data suggests that benzene metabolites trans,trans-muconaldehyde (MA), and hydroquinone (HQ) propagate endothelial activation and apoptosis analyzed by endothelial-specific microparticles in C57BL/6J mice plasma. Subcutaneous injections of MA and HQ increased vascular permeability by 1.54 fold and 1.27 fold correspondingly. In addition, the exposure of primary cardiac microvascular endothelial cells to MA increased vascular permeability detected by transendothelial monolayer resistance and by fluorescently labeled dextrans diffusion. The bulk RNA sequencing of endothelial cells exposed to MA for 2, 6, and 24 hours showed MA-dependent upregulation of heat shock-related pathways at 2 and 6 hours, dysregulation of GTPases at 6 hours, and altered cytoskeleton organization at 24 hours of exposure. We found that the HSP70 protein induced by MA in endothelial cells is colocalized with F-actin foci. HSP70 inhibitor 17AAG and HSP90 inhibitor JG98 attenuated MA-induced endothelial permeability, while HSP activator TRC enhanced endothelial leakage. Moreover, MA induced Rac1 GTPase activity, while Rho GTPase inhibitor Y-27632 attenuated MA-induced endothelial permeability. We showed that benzene metabolites compromised the endothelial barrier by altering HSP- and GTPase-related signaling pathways.

pharmacology and toxicology↗

bioRxiv2023

Targeting endothelial FOXO1 protects diabetic β-cells and improves wound healing

The forkhead box O1 (FOXO1) transcription factor plays critical roles in regulating not only metabolic activity but also angiogenesis in the vascular endothelium1-4. Our previous studies show that epsin endocytic adaptors can regulate both angiogenesis and lymphangiogenesis5-7. Endothelial cells (ECs) lining the inside of blood vessels are continuously exposed to circulating insulin and insulin-like growth factors (IGFs). Emerging evidences suggest that ECs can affect {beta}-cell function8-11. Excessive IGF2, especially elevated local IGF2 levels in islets, may represent a risk factor for developing diabetes12-15; however, the underlying molecular mechanisms by which aberrant angiogenesis and endothelium-derived factors regulate pancreatic {beta}-cell function in diabetes remain unclear. Here, we report that the pancreas of diabetic patients as well as the pancreas, skin, and plasma of streptozotocin/high fat diet (STZ/HFD)-induced diabetic mice and db/db mice contains excess IGF2, which can lead to {beta}-cell dysfunction and apoptosis. Single-cell transcriptomics combined with mass spectrometry analysis reveal that endothelial-specific knockout of FOXO1 increases circulating soluble and cell-membrane or intracellular expression levels of IGF type 2 receptor (IGF2R) and CCCTC-binding factor (CTCF), while decreasing IGF2 levels in diabetes. Both IGFR215-17 and CTCF18-21 can reduce IGF2 levels and may ameliorate {beta}-cell decline associated with excess IGF2 in diabetes. Furthermore, depletion of FOXO1, epsins, or knockdown of ULK1 inhibits autophagy formation in ECs, preventing degradation of vascular endothelial growth factor receptor 2 (VEGFR2) to promote angiogenesis and improve wound healing in diabetes. Our findings reveal that endothelial FOXO1 regulates epsin-dependent angiogenesis and affects {beta}-cell function and fate through CTCF and IGF2-IGF2R, providing a potential strategy for ameliorating diabetes and accelerating cutaneous wound healing.

cell biology↗

bioRxiv2022

Yttrium-iron garnet magnetometer in MEG: Advance towards multi-channel arrays

Recently, a new kind of sensor applicable in magnetoencephalography (MEG) has been presented: a solid-state yttrium-iron garnet magnetometer (YIGM). The feasibility of YIGM was proved in alpha-rhythm registration experiment. In the current paper, we propose the analysis of lead-field matrices for different possible YIGM multichannel on-scalp sensor layouts with respect to the information theory. We use real noise level of the new sensor to compute signal power, signal-to-noise ration (SNR) and total information capacity, and compare them with corresponding metrics that can be obtained with well-established MEG systems: based on superconducting quantum interference devices (SQUIDs) and optically-pumped magnetometers (OPMs). This simulation study is aimed to shed some light on the direction for further development of YIGM sensor, namely creation of multi-channel YIG-MEG.

neuroscience↗

bioRxiv2020

Deep Learning for Cortical Potential Imaging

Electroencephalography (EEG) is a well-established non-invasive technique to measure the brain activity, albeit with a limited spatial resolution. Variations in electric conductivity between different tissues distort the electric fields generated by cortical sources, resulting in smeared potential measurements on the scalp. One needs to solve an ill-posed inverse problem to recover the original neural activity. In this article, we present a generic method of recovering the cortical potentials from the EEG measurement by introducing a new inverse-problem solver based on deep Convolutional Neural Networks (CNN) in paired (U-Net) and unpaired (DualGAN) configurations. The solvers were trained on synthetic EEG-ECoG pairs that were generated using a head conductivity model computed using the Finite Element Method (FEM). These solvers are the first of their kind, that provide robust translation of EEG data to the cortex surface using deep learning. Providing a fast and accurate interpretation of the tracked EEG signal, our approach promises a boost to the spatial resolution of the future EEG devices.

neuroscience↗