Biology subjects

Artemov, A. V.

Publications and source records attributed to Artemov, A. V..

7 recordsLinked to original sources

bioRxiv2024

Unbiased profiling of multipotency landscapes reveals spatial modulators of clonal fate biases

The proportion of cell types varies systematically across the body, but it remains unclear how individual progenitor cells integrate positional information to establish patterns of cellular composition. In this study we profile the clonal landscape of the embryo, using single-cell lineage tracing of mouse embryos from neurulation until mid-gestation. To analyze the complex clonal patterns derived from highly multipotent progenitors, we developed clone2vec, which uses unsupervised learning to categorize individual clones into lineages based on shared transcriptional context. This revealed a body-wide gradient of clonal fate biases, in which anatomical position and clonal composition are mutually predictive. Comparison of clonal lineages revealed spatial transcription factor programs associated with dynamic cell biasing towards skeletal versus non-skeletal fates. Mosaic combinatorial perturbations targeting the Hedgehog pathway generated clones in which positional identity was mismatched with clonal composition, suggesting a potential signaling influence on somite patterning. We explore the effects of position and heterochrony on fate biases in cranial, trunk, and caudal neural crest clones. Altogether, our work demonstrates an effective practical approach for dissecting mechanisms of lineage specification.

developmental biology↗

bioRxiv2021

Cell-intrinsic functions of the transcription factor Bhlhe40 in activated B cells and T follicular helper cells restrain the germinal center reaction and prevent lymphomagenesis

The generation of high-affinity antibodies against pathogens and vaccines requires the germinal center (GC) reaction - a process that relies on a complex interplay between specialized effector subsets of B and CD4 T lymphocytes - GC B cells and T follicular helper (TFH) cells. Intriguingly, several key positive regulators of the GC reaction are common for both cell types. Here, we report that the transcription factor Bhlhe40 is a crucial cell-intrinsic negative regulator affecting both the B and T cell sides of the GC reaction. In activated CD4 T cells, Bhlhe40 was required to restrain proliferation thus limiting the number of TFH cells. In B cells, Bhlhe40 executed its function in the first days after immunization by selectively restricting the generation of the earliest GC B cells but not of early memory B cells or plasmablasts. Conditional Bhlhe40 inactivation confirmed cell-autonomous functions of Bhlhe40 in both GC B and TFH cells, while the GC phenotype was further enhanced upon loss of Bhlhe40 in both cell types. This negative regulation of the GC reaction by Bhlhe40 was of crucial importance, as Bhlhe40-deficient mice with progressing age succumbed to a B cell lymphoma characterized by accumulation of monoclonal GC B-like cells and polyclonal TFH cells in various tissues.

immunology↗

bioRxiv2020

An IDH-independent mechanism of DNA hypermethylation upon VHL inactivation in cancer

Hypermethylation of tumor suppressors and other aberrations of DNA methylation in tumors play a significant role in cancer progression. DNA methylation can be affected by various environmental conditions including hypoxia. The response to hypoxia is mainly achieved through activation of the transcription program associated with HIF1a transcription factor. VHL inactivation by genetic or epigenetic events, which also induces aberrant activation of HIF1a, is the most common driver event for renal cancer. With whole-genome bisulfite sequencing and LC-MS, we demonstrated that VHL inactivation induced global genome hypermethylation in human kidney cancer cells under normoxic conditions. This effect was reverted by exogenous expression of wild-type VHL. We show that global genome hypermethylation in VHL mutants can be explained by transcriptional changes in MDH and L2HGDH genes that cause the accumulation of 2-hydroxyglutarate--a metabolite that inhibits DNA demethylation by Tet enzymes. Unlike the known cases of DNA hypermethylation in cancer, 2-hydroxyglutarate was accumulated in IDH wild type cells. Key pointsO_LIInactivation of VHL gene leads to genome hypermethylation in kidney cancer cells. The DNA methylation phenotype can be rescued by endogenous expression of wild-type VHL. C_LIO_LIDNA hypermethylation can be attributed to the accumulation of a Tet inhibitor 2-hydroxyglutarate C_LIO_LIThe accumulation of 2-hydroxyglutarate in IDH wild-type cells is explained by gene expression changes in key metabolic enzymes (malate dehydrogenase MDH and 2-hydroxyglutrarate dehydrogenase L2HGDH). C_LI

cancer biology↗

bioRxiv2017

POLD replicates both strands of small kilobase-long replication bubbles initiated at a majority of human replication origins

Error-prone mutants of polymerase epsilon (POLE*) or polymerase delta (POLD1*) induce a mutator phenotype in human cancers. Here we show that the rate of mutations introduced by POLD1* is elevated by 50%, while the rate of POLE*-induced mutations is decreased twofold, within one kilobase from replication origins. These results support a model in which POLD1 replicates both the leading and the lagging strands within a kilobase from an origin. The magnitude of the mutational bias suggests that the probability of an individual origin to initiate replication exceeds 50%, which is much higher than previous estimates. Using additional data from nascent DNA sequencing and Okazaki fragments sequencing (OK-seq) experiments, we showed that a majority of origins are firing at each replication round, but the initiated replication fork does not propagate further than 1Kb in both directions. Analyses based on mutational data and on OK-seq data concordantly suggest that only approximately a quarter of fired origins result in a processive replication fork. Taken together, our results provide a new model of replication initiation.

molecular biology↗

bioRxiv2016

CpG traffic lights are markers of regulatory regions in humans

DNA methylation is involved in regulation of gene expression. Although modern methods profile DNA methylation at single CpG sites, methylation levels are usually averaged over genomic regions in the downstream analyses. In this study we demonstrate that single CpG methylation can serve as a more accurate predictor of gene expression compared to average promoter / gene body methylation. CpG positions with significant correlation between methylation and expression of a gene nearby (named CpG traffic lights) are evolutionary conserved and enriched for exact TSS positions and active enhancers. Among all promoter types, CpG traffic lights are especially enriched in poised promoters. Genes that harbor CpG traffic lights are associated with development and signal transduction. Methylation levels of individual CpG traffic lights vary between cell types dramatically with the increased frequency of intermediate methylation levels, indicating cell population heterogeneity in CpG methylation levels. Being in line with the concept of the inherited stochastic epigenetic variation, methylation of such CpG positions might contribute to transcriptional regulation. Alternatively, one can hypothesize that traffic lights are markers of absent gene expression resulting from inactivation of their regulatory elements. The CpG traffic lights provide a promising insight into mechanisms of enhancer activity and gene regulation linking methylation of single CpG to expression.

bioinformatics↗

bioRxiv2016

Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

Despite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.

bioinformatics↗

bioRxiv2016

VHL inactivation without hypoxia is sufficient to achieve genome hypermethylation

VHL inactivation is a key oncogenic event for renal carcinomas. In normoxia, VHL suppresses HIF1a-mediated response to hypoxia. It has previously been shown that hypoxic conditions inhibit TET-dependent hydroxymethylation of cytosines and cause DNA hypermethylation at gene promoters. In this work, we performed VHL inactivation by CRISPR/Cas9 and studied its effects on gene expression and DNA methylation. We showed that even without hypoxia, VHL inactivation leads to hypermethylation of the genome which mainly occurred in AP-1 and TRIM28 binding sites. We also observed promoter hypermethylation of several transcription regulators associated with decreased gene expression.

cancer biology↗