Biology subjects

Bekiranov, S.

Publications and source records attributed to Bekiranov, S..

10 recordsLinked to original sources

bioRxiv2026

Dietary depletion of glutamine is atheroprotective

Heart attacks and strokes are late-stage complications of rupture of unstable atherosclerotic plaques. Stable plaques contain stabilizing matrix-producing fibrotic cells, largely smooth muscle cell (SMC)-derived. The molecular drivers of SMC phenotypic transitions to beneficial fibrotic or destabilizing inflammatory and calcifying phenotypes are unclear. Since atherosclerosis develops over decades, there is extensive interest in identifying dietary alterations that enhance plaque stability. We demonstrate that SMC acquire a fibrotic phenotype dependent on glutamine-derived metabolites supporting both catabolism and collagen synthesis. Moreover, dietary glutamine restriction decreases mortality of mice susceptible to atherosclerotic plaque rupture. Lesions from glutamine-restricted mice are smaller and have increased SMC investment. This study identifies dietary glutamine as a driver of cardiovascular mortality, suggesting a new strategy for reducing late-stage complications of atherosclerosis.

physiology↗

bioRxiv2025

CCAAT-enhancer binding protein delta functions as a tumor suppressor gene in acute myeloid leukemia

There is a continued need for identification of novel disease drivers of acute myeloid leukemia as many patients experience relapse and have poor clinical outcomes. Analyses from our study and publicly available datasets predicted CEBPD as a novel tumor suppressor gene in acute myeloid leukemia. Consistent with the analyses, CEBPD knockdown experiments showed activation of MAPK signaling with concomitant increase in cell growth rate, while upregulation experiments suggested induction of myeloid differentiation marker CD14 expression in AML cell lines OCI-AML2 and OCI-AML5. Consistent with a previous report, our genomics analyses and azacytidine treatment experiments suggested a role for DNA methylation in downregulation of CEBPD expression during AML pathogenesis. Altogether, our results provide experimental evidence for a tumor suppressor function of CEBPD in AML.

cancer biology↗

bioRxiv2023

DARDN: Identifying transcription factor binding motifs from long DNA sequences using multi-CNNs and DeepLIFT

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facilitate oncogenic transcription programs by cooperating with transcription factors bound at flanking distal regions. Identification of DNA sequence features from a broad genomic region that distinguish cancer-specific CTCF binding sites from regular CTCF binding sites can help find oncogenic transcription factors in a cancer type. However, the long DNA sequences without localization information makes it difficult to perform conventional motif analysis. Here we present DNAResDualNet (DARDN), a computational method that utilizes convolutional neural networks (CNNs) for predicting cancer-specific CTCF binding sites from long DNA sequences and employs DeepLIFT, a method for interpretability of deep learning models that explains the models output in terms of the contributions of its input features [1], for identifying DNA sequence features associated with cancer-specific CTCF binding. Evaluation on DNA sequences associated with CTCF binding sites in T-cell acute lymphoblastic leukemia (T-ALL) and other cancer types demonstrates DARDNs ability in classifying DNA sequences surrounding cancer-specific CTCF binding from control constitutive CTCF binding and identifying sequence motifs for transcription factors potentially active in each specific cancer type. We identified potential oncogenic transcription factors in T-ALL, acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Our work demonstrates the power of advanced machine learning and feature discovery approach in finding biologically meaningful information from complex high-throughput sequencing data.

bioinformatics↗

bioRxiv2023

DeepGSEA: Explainable Deep Gene Set Enrichment Analysis for Single-cell Transcriptomic Data

Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, due to the extreme heterogeneity of single-cell gene expression, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in specific applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, primarily due to interpretability challenges. In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the expressiveness of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained. The source code of DeepGSEA is available at https://github.com/Teddy-XiongGZ/DeepGSEA.

bioinformatics↗

bioRxiv2023

Mouse Model of Heart Attack and Stroke Shows Improved Survival with MPO Inhibition

Thromboembolic events, including myocardial infarction (MI) or stroke, caused by the rupture or erosion of unstable atherosclerotic plaques are the leading cause of death worldwide1. Unfortunately, the lack of a mouse model that develops advanced coronary atherosclerosis and that exhibits a high incidence of spontaneous plaque rupture with MI or stroke has greatly stymied development of more effective therapeutic approaches for reducing these events beyond what has been achieved with aggressive lipid lowering. Herein, we describe a novel mouse model that develops widespread advanced atherosclerosis including in coronary, brachiocephalic, and carotid arteries. These mice show high mortality following Western Diet feeding with clear evidence of plaque rupture, MI, and stroke. To validate the utility of this model, mice were treated with the drug candidate AZM198, which inhibits myeloperoxidase, an enzyme primarily produced by activated neutrophils and predictive of rupture of human atherosclerotic lesions2-7. AZM198 treatment resulted in marked improvements in survival with a greater than 60% decrease in the incidence of plaque rupture, MI, and stroke. In summary, our work describes a novel mouse model that closely replicates late-stage clinical events of advanced human atherosclerotic disease and evidence that this model can be used to identify and test potential new therapeutic agents to prevent major adverse cardiac events.

physiology↗

bioRxiv2023

Genome-scale chromatin interaction dynamic measurements for key components of the RNA Pol II general transcription machinery

BackgroundA great deal of work has revealed in structural detail the components of the machinery responsible for mRNA gene transcription initiation. These include the general transcription factors (GTFs), which assemble at promoters along with RNA Polymerase II (Pol II) to form a preinitiation complex (PIC) aided by the activities of cofactors and site-specific transcription factors (TFs). However, less well understood are the in vivo PIC assembly pathways and their kinetics, an understanding of which is vital for determining on a mechanistic level how rates of in vivo RNA synthesis are established and how cofactors and TFs impact them. ResultsWe used competition ChIP to obtain genome-scale estimates of the residence times for five GTFs: TBP, TFIIA, TFIIB, TFIIE and TFIIF in budding yeast. While many GTF-chromatin interactions were short-lived (< 1 min), there were numerous interactions with residence times in the several minutes range. Sets of genes with a shared function also shared similar patterns of GTF kinetic behavior. TFIIE, a GTF that enters the PIC late in the assembly process, had residence times correlated with RNA synthesis rates. ConclusionsThe datasets and results reported here provide kinetic information for most of the Pol II-driven genes in this organism and therefore offer a rich resource for exploring the mechanistic relationships between PIC assembly, gene regulation, and transcription. The relationships between gene function and GTF dynamics suggest that shared sets of TFs tune PIC assembly kinetics to ensure appropriate levels of expression.

biochemistry↗

bioRxiv2022

The potentiative cytotoxic effect of IGF1R and EGFR inhibition on the Head and Neck Cancer Proteome

Head and neck cancers are the sixth most common cancer worldwide. Combinatorial targeted therapy has the potential to reduce drug resistance and increase cytotoxicity to head and neck squamous cell carcinoma (HNSCC). Using drug combinations is especially important when targeting the epidermal growth factor receptor (EGFR) since we previously demonstrated that activation of the insulin-like growth factor 1 receptor (IGF1R) is a mechanism for resistance against EGFR inhibition and that a combination of an IGF1R inhibitor, BMS754807, and an EGFR inhibitor, BMS599626, robustly inhibited the growth of HNSCC cell lines in vitro. To examine the mechanism of cytotoxicity, we performed protein pathway activation mapping via reverse phase protein array (RPPA) analysis of 145 proteins and phosphoproteins in five HNSCC cell lines to map key proteins and phosphoproteins important in tumorigenesis. By performing principal component analysis, calculating log fold changes, and constructing protein networks, we were able to provide evidence to support the hypothesis that the combination of IGF1R and EGFR inhibitors has a potentiative effect on inhibiting receptor tyrosine kinase signaling. The effects of the individual drugs are amplified, demonstrating that the combination more robustly inhibits the pathways of both receptors.

cancer biology↗

bioRxiv2019

A Sir2-regulated locus control region in the recombination enhancer of Saccharomyces cerevisiae specifies chromosome III structure

The NAD+-dependent histone deacetylase Sir2 was originally identified in Saccharomyces cerevisiae as a silencing factor for HML and HMR, the heterochromatic cassettes utilized as donor templates during mating-type switching. MATa cells preferentially switch to MAT using HML as the donor, which is driven by an adjacent cis-acting element called the recombination enhancer (RE). In this study we demonstrate that Sir2 and the condensin complex are recruited to the RE exclusively in MATa cells, specifically to the promoter of a small gene within the right half of the RE known as RDT1. We go on to demonstrate that the RDT1 promoter functions as a locus control region (LCR) that regulates both transcription and long-range chromatin interactions. Sir2 represses the transcription of RDT1 until it is redistributed to a dsDNA break at the MAT locus induced by the HO endonuclease during mating-type switching. Condensin is also recruited to the RDT1 promoter and is displaced upon HO induction, but does not significantly repress RDT1 transcription. Instead condensin appears to promote mating-type switching efficiency and donor preference by maintaining proper chromosome III architecture, which is defined by the interaction of HML with the right arm of chromosome III, including MATa and HMR. Remarkably, eliminating Sir2 and condensin recruitment to the RDT1 promoter disrupts this structure and reveals an aberrant interaction between MATa and HMR, consistent with the partially defective donor preference for this mutant. Global condensin subunit depletion also impairs mating type switching efficiency and donor preference, suggesting that modulation of chromosome architecture plays a significant role in controlling mating type switching, thus providing a novel model for dissecting condensin function in vivo. Author summarySir2 is a highly conserved NAD+-dependent protein deacetylase and defining member of the sirtuin protein family. It was identified about 40 years ago in the budding yeast, Saccharomyces cerevisiae, as a gene required for silencing of the cryptic mating-type loci, HML and HMR. These heterochromatic cassettes are utilized as templates for mating-type switching, whereby a programmed DNA double-strand break at the MATa or MAT locus is repaired by gene conversion to the opposite mating type. The preference for switching to the opposite mating type is called donor preference, and in MATa cells, is driven by a cis-acting DNA element called the recombination enhancer (RE). It was believed that the only role for Sir2 in mating-type switching was silencing HML and HMR. However, in this study we show that Sir2 also regulates expression of a small gene (RDT1) in the RE that is activated during mating-type switching. The promoter of this gene is also bound by the condensin complex, and deleting this region of the RE drastically changes chromosome III structure and alters donor preference. The RE therefore appears to function as a complex locus control region (LCR) that links transcriptional control to chromatin architecture, and thus provides a new model for investigating the underlying mechanistic principles of programmed chromosome architectural dynamics.

molecular biology↗

bioRxiv2018

CNV Neurons Are Rare in Aged Human Neocortex

Megabase-scale somatic copy number variants (CNVs) alter allelic diversity in a subset of human neocortical neurons. Reported frequencies of CNV neurons range from [~]5% of neurons in some individuals to greater than 30% in other individuals. Genome-wide and familial studies implicitly assume a constant brain genome when assessing the genetic risk architecture of neurological disease, thus it is critical to determine whether divergent reports of CNV neuron frequency reflect normal individual variation or technical differences between approaches. We generated a new dataset of over 800 human neurons from 5 neurotypical individuals and developed a computational approach that measures single cell library quality based on Bayesian Information Criterion and identifies integer-like variant segments from population-level statistics. A brain CNV atlas was assembled using our new dataset and published data from 10 additional neurotypical individuals. This atlas reveals that the frequency of neocortical CNV neurons varies widely among individuals, but that this variability is not readily accounted for by tissue quality or CNV detection approach. Rather, the age of the individual is anti-correlated with CNV neuron frequency. Fewer CNV neurons are observed in aged individuals than young individuals.

neuroscience↗

bioRxiv2017

Second-generation method for analysis of chromatin binding using formaldehyde crosslinking kinetics

Formaldehyde crosslinking underpins many of the most commonly used experimental approaches in the chromatin field, especially in capturing site-specific protein-DNA interactions. Extending such assays to assess the stability and binding kinetics of protein-DNA interactions is more challenging, requiring absolute measurements with a relatively high degree of physical precision. We previously described an experimental framework called CLK, which uses time-dependent formaldehyde crosslinking data to extract chromatin binding kinetic parameters. Many aspects of formaldehyde behavior in cells are unknown or undocumented, however, and could potentially impact analyses of CLK data. Here we report biochemical results that better define the properties of formaldehyde crosslinking in budding yeast cells. These results have the potential to inform interpretations of standard chromatin assays including chromatin immunoprecipitation, and the chemical complexity we uncovered resulted in the development of an improved method for measuring binding kinetics using the CLK approach. Optimum conditions included an increased formaldehyde concentration and more robust glycine quench conditions. Notably, we find that formaldehyde crosslinking rates can vary dramatically for different protein-DNA interactions in vivo. Some interactions were crosslinked much faster than the time scale for macromolecular interaction, making them suitable for kinetic analysis. For other interactions, we find the crosslinking reaction occurred on the same time scale or slower than binding dynamics; for these it was in some cases possible to compute the in vivo equilibrium-binding constant but not on- and off-rates for binding. Selected TATA-binding protein-promoter interactions displayed dynamic behavior on the minute to several minutes time scale.

biochemistry↗