Biology subjects

Marri, D. K.

Publications and source records attributed to Marri, D. K..

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

Prediction of mammalian tissue-specific CLOCK-BMAL1 binding to E-box motifs

The mammalian circadian clock is based on a core intracellular gene regulatory network, coordinated by communication between the central nervous system and peripheral tissues like the liver. Transcriptional and translational feedback loops underlie the molecular mechanism of circadian oscillation and generate its 24 h periodicity. The Brain and muscle Arnt-like protein-1 (Bmal1) forms a heterodimer with Circadian Locomotor Output Cycles Kaput (Clock) that binds to E-box gene regulatory elements, activating transcription of clock genes. In this work we aimed to develop a predictive model of genome-wide CLOCK-BMAL1 binding to E-box motifs. We found over-representation of the canonical E-box motif CACGTG in BMAL1-bound regions in accessible chromatin of the mouse liver, heart and kidney. We developed three different tissue-specific machine learning models based on DNA sequence, DNA sequence plus DNA shape, and DNA sequence and shape plus histone modifications. Combining DNA sequence with DNA shape and histone modification features yielded improved transcription factor binding site prediction. Further, we identified the genomic and epigenomic features that best correlate to the binding of BMAL1 to DNA. The DNA shape features Electrostatic Potential, Minor Groove Width and Propeller Twist together with the histone modifications H3K27ac, H3K4me1, H3K36me3, and H3K4me3 were the features most highly predictive of DNA binding by BMAL1 across all three tissues.

bioinformatics↗