Artem Artemov

The clustering of CpG islands may constitute an important determinant of the 3D organization of interphase chromosomes

We used the 4C-Seq technique to characterize the genome-wide patterns of spatial contacts of several CpG islands located on chromosome 14 in cultured chicken lymphoid and erythroid cells. We observed a clear tendency for the spatial clustering of CpG islands present on the same and different chromosomes, regardless of the presence or absence of promoters within these CpG islands. Accordingly, we observed preferential spatial contacts between Sp1 binding motifs and other GC-rich genomic elements, including the DNA sequence motifs capable of forming G-quadruplexes. However, an anchor placed in a gene/CpG island-poor area formed spatial contacts with other gene/CpG island-poor areas on chromosome 14 and other chromosomes. These results corroborate the two-compartment model of the spatial organization of interphase chromosomes and suggest that the clustering of CpG islands constitutes an important determinant of the 3D organization of the eukaryotic genome in the cell nucleus. Using the ChIP-Seq technique, we mapped the genome-wide CTCF deposition sites in the chicken lymphoid and erythroid cells that were used for the 4C analysis. We observed a good correlation between the density of CTCF deposition sites and the level of 4C signals for the anchors located in CpG islands but not for an anchor located in a gene desert. It is thus possible that CTCF contributes to the clustering of CpG islands observed in our experiments.

In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state

Populations in developed nations throughout the world are rapidly aging, and the search for geroprotectors, or anti-aging interventions, has never been more important. Yet while hundreds of geroprotectors have extended lifespan in animal models, none have yet been approved for widespread use in humans. GeroScope is a computational tool that can aid prediction of novel geroprotectors from existing human gene expression data. GeroScope maps expression differences between samples from young and old subjects to aging-related signaling pathways, then profiles pathway activation strength (PAS) for each condition. Known substances are then screened and ranked for those most likely to target differential pathways and mimic the young signalome. Here we used GeroScope and shortlisted ten substances, all of which have lifespan-extending effects in animal models, and tested 6 of them for geroprotective effects in senescent human fibroblast cultures. PD-98059, a highly selective MEK1 inhibitor, showed both life-prolonging and rejuvenating effects. Natural compounds like N-acetyl-L-cysteine, Myricetin and Epigallocatechin gallate also improved several senescence-associated properties and were further investigated with pathway analysis. This work not only highlights several potential geroprotectors for further study, but also serves as a proof-of-concept for GeroScope, Oncofinder and other PAS-based methods in streamlining drug prediction, repurposing and personalized medicine.

Towards natural mimetics of metformin and rapamycin

Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals—safer, naturally-occurring compounds—that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors.

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.

Spatial organization of housekeeping genes in interphase nuclei

The spatial organization of the eukaryotic genome is closely related to its function. In particular, interactions of gene promoters with distant enhancer elements in active chromatin hubs and gene recruitment to common transcription factories play an important role in regulating gene transcription. Tissue-specific genes are mostly used as models to study the spatial interactions of genomic regulatory elements, while little is known as to what extent the spatial organization of chromosomes is guided by housekeeping genes, which are transcribed in the majority of cells and are considerably more abundant than transcribed tissue-specific genes. To address the issue, chromosome conformation capture on chip (4C) was employed in a genomewide probing of spatial contacts for the chicken housekeeping genes CARHSP1 and TRAP1, which are on chromosome 14. Their promoters showed a higher frequency of interactions with transcriptionally active chromosome regions and regions enriched in Sp1 general transcription factor-binding sites and CpG islands, which both mark the promoters of housekeeping genes. No such preferences were observed for a gene-poor chromosome 14 region. Further evidence for the association of housekeeping gene promoters was obtained in independent cytological visualization of nonmethylated CpG islands in individual human cell nuclei. CpG islands were observed to cluster in the nuclear space. The results testify that the interaction of housekeeping gene promoters is an important factor that determines the spatial organization of interphase chromosomes.

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.