Biology subjects

Kana, O. Z.

Publications and source records attributed to Kana, O. Z..

3 recordsLinked to original sources

bioRxiv2024

A multiscale model of the mammalian liver circadian clock supports synchronization of autonomous oscillations by intercellular communication.

Expression of core circadian clock genes in hepatocytes across the liver lobule is temporally synchronized despite cell-autonomous oscillations in gene expression. This spatial synchronization has been attributed to an unknown intercellular coupling mechanism. Here we have developed multicellular computational models of the murine liver lobule with and without intercellular coupling to investigate the role of synchronization in circadian gene expression. Our models demonstrated that intercellular coupling was needed to generate sustained circadian oscillations with a near 24-hour period. Without coupling the simulated period was variable within the 21-28-hour range. Further model analysis revealed that a robust near-24-hour oscillation period can be generated with a wide range of circadian protein degradation rates. In contrast, only a small window of circadian gene transcription rates was able to generate realistic oscillatory periods. The coupled model accurately captured the temporal dynamics of circadian genes derived from single-nuclei transcriptomic data. Overall, this study provides novel insights into the mammalian hepatic circadian clock through modeling of spatial and temporal gene expression patterns and data-driven analysis.

systems biology↗

bioRxiv2022

Generative Modeling of Single Cell Gene Expression for Dose-Dependent Chemical Perturbations

Single cell sequencing provides a new opportunity to study the heterogeneity of chemical perturbation within tissues. However, exploring the combinatorial space of all cell type-chemical combinations is experimentally and financially unfeasible. This space is significantly expanded by the dose axis of chemical perturbation. Thus, computational tools are needed to predict responses not only across tissues, but also across doses while capturing the nuances of cell type specific gene expression. Variational autoencoders simplify the single cell expression space allowing cross cell type predictions using simple vector arithmetic. However, differing sensitivities and non-linearities make cell type specific gene expression predictions following treatment at higher doses challenging. Here we introduce single cell Variational Inference of Dose-Response (scVIDR) which achieves high dose and cell type specific predictions better than other state of the art algorithms. scVIDR predicts in vivo and in vitro dose-dependent gene expression across cell types in mouse liver, peripheral blood mononuclear cells, and cancer cell lines. We use regression to interpret the outputs of scVIDR. Additionally, we use scVIDR to order individual cells based on their sensitivities to a particular chemical by assigning a pseudo-dose value to each cell. Taken together, we show that scVIDR can effectively predict the dose and cell state dependent changes associated with chemical perturbations.

bioinformatics↗

bioRxiv2022

Predictive Models of Genome-wide Aryl Hydrocarbon Receptor DNA Binding Reveal Tissue Specific Binding Determinants

BackgroundThe Aryl Hydrocarbon Receptor (AhR) is an inducible transcription factor (TF) whose ligands include the environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). TCDD-mediated toxicity occurs through activation of AhR and its subsequent binding to the Dioxin Response Element (DRE), comprising the DNA motif 5-GCGTG-3. However, AhR binding in human tissues is highly dynamic and tissue specific. Approximately 50% of all experimentally verified AhR binding sites do not contain a DRE. Additionally, most accessible DREs are not bound by AhR. Identification of tissue specific AhR binding determinants is crucial for understanding downstream gene regulation and potential adverse outcomes of AhR activation. ResultsWe applied XGBoost, a supervised machine learning architecture, to predict the genome wide AhR binding status of DREs in open chromatin as a function of DNA sequence flanking the DRE, chromatin accessibility, histone modifications (HM), TF binding, and proximity of the DRE to gene promoters. We trained and validated our models using 5-fold cross validation to predict the binding status of DREs in AhR-activated MCF-7 breast cancer cells, primary human hepatocytes, and lymphoblastoid GM17212 cells, as well as AhR non-activated HepG2 hepatocellular carcinoma cells. Our results demonstrate highly accurate and robust models of AhR binding; and identify patterns of transcription factor binding and histone modifications predictive of AhR binding. These patterns are consistent within tissues but highly variable across tissues, which is suggestive of tissue-specific mechanisms of AhR binding. ConclusionsAhR binding is driven by a complex interplay of tissue-agnostic DNA sequence flanking its binding motif and tissue-specific local chromatin context.

genomics↗