Biology subjects

Sokolov, A.

Publications and source records attributed to Sokolov, A..

26 records · Page 2Linked to original sources

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↗

bioRxiv2020

Machine Learning Identifies Novel Candidates for DrugRepurposing in Alzheimer's Disease

Clinical trials of novel therapeutics for Alzheimers Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. Repurposing can yield a useful therapeutic and also accelerate proof of concept studies that ultimately lead to a new molecular entity. We present a novel machine learning framework, DRIAD (Drug Repurposing In AD), that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD was validated on gene lists known to be associated with AD from other studies and subsequently applied to evaluate lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs were inspected for common trends among their nominal molecular targets and their "off-targets", revealing a high prevalence of kinases from the Janus (JAK), Unc-51-like (ULK) and NIMA-related (NEK) families. These kinase families are known to modulate pathways related to innate immune signaling, autophagy, and microtubule formation and function, suggesting possible disease-modifying mechanisms of action. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be evaluated in a clinical trial.

neuroscience↗

bioRxiv2019

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109 or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.

systems biology↗

bioRxiv2019

Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer

In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.\n\nMETADATA SUMMARY\n\nO_TBL View this table:\norg.highwire.dtl.DTLVardef@6edf3eorg.highwire.dtl.DTLVardef@1026e07org.highwire.dtl.DTLVardef@85a7baorg.highwire.dtl.DTLVardef@c6d7e5org.highwire.dtl.DTLVardef@87fccf_HPS_FORMAT_FIGEXP M_TBL C_TBL

cancer biology↗

bioRxiv2019

Effect of Alpha-Lactalbumin and Lactoferrin Oleic Acid Complexes on Chromatin Structural Organization

This work focuses on the study of multimeric alpha-lactalbumin oleic acid and lactoferrin oleic acid complexes. The purpose of the research is to study possible mechanisms involved in their pro-apoptotic activities, as seen in some tumor cell cultures. Complexes featuring oleic acid (OA) with human alpha-lactalbumin (hAl) or with bovine alpha-lactalbumin (bAl), and human lactoferrin (hLf) were investigated using small-angle neutron scattering (SANS). It was shown that while alpha-lactalbumin protein complexes were formed on the surface of polydisperse OA micelles, the lactoferrin complexes comprised a monodisperse system of nanoscale particles. Both hAl and hLf complexes appeared to interact with the chromatin of isolated nuclei affecting chromatin structural organization. The possible roles of these processes in the specific anti-tumor activity of these complexes are discussed.

biophysics↗

bioRxiv2019

No evidence for disruption of global patterns of nest predation in shorebirds

Kubelka et al. (Science, 9 November 2018, p. 680-683) claim that climate change has disrupted patterns of nest predation in shorebirds. They report that predation rates have increased since the 1950s, especially in the Arctic. We describe methodological problems with their analyses and argue that there is no solid statistical support for their claims.

ecology↗

bioRxiv2019

A draft genome sequence of the miniature parasitoid wasp, Megaphragma amalphitanum

Body size reduction, also known as miniaturization, is an important evolutionary process that affects a number of physiological and phenotypic traits and helps animals to conquer new ecological niches. However, this process is poorly understood at the molecular level. Here, we report genomic and transcriptomic features of arguably the smallest known insect - the parasitoid wasp, Megaphragma amalphitanum (Hymenoptera: Trichogrammatidae). In contrast to expectations, we find that the genome and transcriptome sizes of this parasitoid wasp are comparable to other members of the Chalcidoidea superfamily. Moreover, the gene content of M. amalphitanum compared to other chalcid wasps is remarkably conserved. Among the very rare cases of apparent gene loss is centrosomin, which encodes an important centrosome component; the absence of this protein might be related to the large number of anucleate neurons in M. amalphitanum. Intriguingly, we also observed significant changes in M. amalphitanum transposable element dynamics over time, whereby an initial burst was followed by suppression of activity, possibly due to a recent reinforcement of the genome defense machinery. Thus, while the M. amalphitanum genomic data reveal certain features that may be linked to the unusual biological properties of this organism, miniaturization is not associated with a large decrease in genome complexity.

genomics↗

bioRxiv2017

Distinct epigenetic shift in a subset of Glioma CpG island methylator phenotype (G-CIMP) during tumor recurrence

Histomorphology and current grading schemes are unable to predict glioma relapse and malignant tumor progression. We reported that the IDH-mutant associated Glioma-CpG Island Methylator Phenotype (G-CIMP) can be further divided into two clinically distinct subtypes independent of histopathological grading (G-CIMP-high and -low) with evidence of correlation with tumor progression. Here we performed a comprehensive epigenomic analysis of 74 longitudinally collected glioma samples (grade II-IV) to understand malignant recurrence from G-CIMP-high to G-CIMP-low. G-CIMP-low recurrence appeared in 12% of all gliomas and resemble IDH-wildtype primary glioblastoma. G-CIMP-low recurrence can be characterized by distinct epigenetic changes at candidate functional tissue enhancers with AP-1/SOX binding elements, stem cell-like epigenomic phenotype, and genomic instability. Finally, we defined a set of candidate biomarker signatures that predict recurrence of G-CIMP-low with clinically relevance on patient outcomes. Our study provides opportunity for refined clinical trial designs and therapeutic targets that limit progression to more aggressive G-CIMP-low phenotype.\n\nHIGHLIGHTSO_LIIndolent G-CIMP-high progresses to aggressive G-CIMP-low phenotype\nC_LIO_LIIncidence of G-CIMP-low recurrent tumors are 3 times greater than G-CIMP-low primary\nC_LIO_LIG-CIMP-low recurrent tumors share epigenomic features with IDH-wildtype primary GBM\nC_LIO_LIPredictive biomarkers of G-CIMP-low progression at primary diagnosis\nC_LI

cancer biology↗