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Biology subjects

Bazarevich, M.

Publications and source records attributed to Bazarevich, M..

3 recordsLinked to original sources

Accelerated Aging Signatures in 3D Genome Organization and Transcriptome in Schizophrenia

Schizophrenia is a severe neuropsychiatric disorder that affects the behavioral, emotional and cognitive state of patients. Despite its substantial heritability, the molecular etiology of the disease remains poorly understood. Many schizophrenia-associated genetic variants reside in non-coding regions, and exert their effects through distal regulatory elements of the genome. In this context, the three-dimensional organization of the genome is expected to play a decisive role in establishing contacts between these regulatory elements and their target genes, thereby mediating schizophrenia-associated dysregulation of gene expression. Here, we present a novel Hi-C dataset providing an unprecedented view of three-dimensional genome organization in post-mortem schizophrenia brain samples. Our findings indicate that most changes occur at long-range genomic distances while local architecture of topologically-associated domains remains largely intact. However, neurons display localized and functionally relevant loop differences, particularly in regulatory regions associated with neurodevelopmental processes. Global characteristics of higher-order chromatin organization show accelerated aging alteration pattern in schizophrenia, and downstream analysis of transcriptomic data in schizophrenia brain samples further confirms that schizophrenia is associated with accelerated aging.

bioinformatics↗

Extensive long-range polycomb interactions and weak compartmentalization are hallmarks of human neuronal 3D genome

Chromatin architecture regulates gene expression and shapes cellular identity, particularly in neuronal cells. Specifically, polycomb group (PcG) proteins enable establishment and maintenance of neuronal cell type by reorganizing chromatin into repressive domains that limit the expression of fate-determining genes and sustain distinct gene expression patterns in neurons. Here, we map the 3D genome architecture in neuronal and non-neuronal cells isolated from the Wernickes area of four human brains and comprehensively analyze neuron-specific aspects of chromatin organization. We find that genome segregation into active and inactive compartments is greatly reduced in neurons compared to other brain cells. Furthermore, neuronal Hi-C maps reveal strong long-range interactions, forming a specific network of PcG-mediated contacts in neurons that is nearly absent in other brain cells. These interacting loci contain developmental transcription factors with repressed expression in neurons and other mature brain cells. But only in neurons, they are rich in bivalent promoters occupied by H3K4me3 histone modification together with H3K27me3, which points to a possible functional role of PcG contacts in neurons. Importantly, other layers of chromatin organization also exhibit a distinct structure in neurons, characterized by an increase in short-range interactions and a decrease in long-range ones.

genomics↗

Hi-C Resolution Enhancement with Genome Sequence Data

The increasing interest in chromatin conformation inside the nucleus and the availability of genome-wide experimental data make it possible to develop computational methods that can increase the quality of the data and thus overcome the limitations of high experimental costs. Here we develop a deep-learning approach for increasing Hi-C data resolution by appending additional information about genome sequence. In this approach, we utilize two different deep-learning algorithms: the image-to-image model, which enhances Hi-C resolution by itself, and the sequence-to-image model, which uses additional information about the underlying genome sequence for further resolution improvement. Both models are combined with the simple head model that provides a more accurate enhancement of initial low-resolution Hi-C data. The code is freely available in a GitHub repository: https://github.com/koritsky/DL2021_HI-C.

systems biology↗