Biology subjects

Artemov, A. V.

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

4 recordsLinked to original sources

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↗