Biology subjects

Kohar, V.

Publications and source records attributed to Kohar, V..

5 recordsLinked to original sources

bioRxiv2022

Video based phenotyping platform for the laboratory mouse

Automated detection of complex animal behavior remains a challenge in neuroscience. Developments in computer vision have greatly advanced automated behavior detection and allow high-throughput preclinical and mechanistic studies. An integrated hardware and software solution is necessary to facilitate the adoption of these advances in the field of behavioral neurogenetics, particularly for non-computational laboratories. We have published a series of papers using an open field arena to annotate complex behaviors such as grooming, posture, and gait as well as higher-level constructs such as biological age and pain. Here, we present our, integrated rodent phenotyping platform, JAX Animal Behavior System (JABS), to the community for data acquisition, machine learning-based behavior annotation and classification, classifier sharing, and genetic analysis. The JABS Data Acquisition Module (JABS-DA) enables uniform data collection with its combination of 3D hardware designs and software for real-time monitoring and video data collection. JABS-Active Learning Module (JABS-AL) allows behavior annotation, classifier training, and validation. We introduce a novel graph-based framework (ethograph) that enables efficient boutwise comparison of JABS-AL classifiers. JABS-Analysis and Integration Module (JABS-AI), a web application, facilitates users to deploy and share any classifier that has been trained on JABS, reducing the effort required for behavior annotation. It supports the inference and sharing of the trained JABS classifiers and downstream genetic analyses (heritability and genetic correlation) on three curated datasets spanning 168 mouse strains that we are publicly releasing alongside this study. This enables the use of genetics as a guide to proper behavior classifier selection. This open-source tool is an ecosystem that allows the neuroscience and genetics community for shared advanced behavior analysis and reduces the barrier to entry into this new field.

animal behavior and cognition↗

bioRxiv2020

Genes adapt to outsmart gene targeting strategies in mutant mouse strains by skipping exons to reinitiate transcription and translation

Gene disruption in mouse embryonic stem cells or zygotes is a conventional genetics approach to identify gene function in vivo. However, because different gene-disruption strategies use different mechanisms to disrupt genes, the strategies can result in diverse phenotypes in the resulting mouse model. To determine whether different gene-disruption strategies affect the phenotype of resulting mutant mice, we characterized Rhbdf1 mouse mutant strains generated by three commonly used strategies--definitive-null, targeted knockout (KO)-first, and CRISPR/Cas9. We find that Rhbdf1 responds differently to distinct KO strategies, for example, by skipping exons and reinitiating translation to potentially yield gain-of-function alleles rather than the expected null or severe hypomorphic alleles. Our analysis also revealed that at least 4% of mice generated using the KO-first strategy show conflicting phenotypes, suggesting that exon skipping is a widespread phenomenon occurring across the genome. Additionally, our study emphasizes that at least 35% of mouse and 45% of human protein-coding genes could be predisposed to targeted KO-first- and CRISPR/Cas9-mediated unexpected translation. Our findings have significant implications for the application of genome editing in both basic research and clinical practice.

genetics↗

bioRxiv2019

Modeling a gene regulatory network of EMT hybrid states for mouse embryonic skin cells

Epithelial-mesenchymal transition (EMT) plays a crucial role in embryonic development and tumorigenesis. Although EMT has been extensively studied with both computational and experimental methods, the gene regulatory mechanisms governing the transition are not yet well understood. Recent investigations have begun to better characterize the complex phenotypic plasticity underlying EMT using a computational systems biology approach. Here, we analyzed recently published single-cell RNA sequencing data from E9.5 to E11.5 mouse embryonic skin cells and identified the gene expression patterns of both epithelial and mesenchymal phenotypes, as well as a clear hybrid state. By integrating the scRNA-seq data and gene regulatory interactions from the literature, we constructed a gene regulatory network model governing the decision-making of EMT in the context of the developing mouse embryo. We simulated the network using a recently developed mathematical modeling method, named RACIPE, and observed three distinct phenotypic states whose gene expression patterns can be associated with the epithelial, hybrid, and mesenchymal states in the scRNA-seq data. Additionally, the model is in agreement with published results on the composition of EMT phenotypes and regulatory networks. We identified Wnt signaling as a major pathway in inducing the EMT and its role in driving cellular state transitions during embryonic development. Our findings demonstrate a new method of identifying and incorporating tissue-specific regulatory interactions into gene regulatory network modeling.\n\nAuthor SummaryEpithelial-mesenchymal transition (EMT) is a cellular process wherein cells become disconnected from their surroundings and acquire the ability to migrate through the body. EMT has been observed in biological contexts including development, wound healing, and cancer, yet the regulatory mechanisms underlying it are not well understood. Of particular interest is a purported hybrid state, in which cells can retain some adhesion to their surroundings but also show mesenchymal traits. Here, we examine the prevalence and composition of the hybrid state in the context of the embryonic mouse, integrating gene regulatory interactions from published experimental results as well as from the specific single cell RNA sequencing dataset of interest. Using mathematical modeling, we simulated a regulatory network based on these sources and aligned the simulated phenotypes with those in the data. We identified a hybrid EMT phenotype and revealed the inducing effect of Wnt signaling on EMT in this context. Our regulatory network construction process can be applied beyond EMT to illuminate the behavior of any biological phenomenon occurring in a specific context, allowing better identification of therapeutic targets and further research directions.

systems biology↗

bioRxiv2019

Random Parametric Perturbations of Gene Regulatory Circuit Uncover State Transitions in Cell Cycle

Many biological processes involve precise cellular state transitions controlled by complex gene regulation. Here, we use budding yeast cell cycle as a model system and explore how a gene regulatory circuit encodes essential information of state transitions. We present a generalized random circuit perturbation (RACIPE) method, specifically for circuits containing heterogeneous regulation types, and its usage to analyze both stable steady states and oscillatory states from an ensemble of circuit models with random kinetic parameters. The stable steady states form robust clusters with a circular structure that are associated with cell cycle phases. We show that this circular structure in the clusters is consistent with single cell RNA-seq data. The oscillatory states specify irreversible state transitions along cell cycle progression. Furthermore, we identify possible mechanisms to understand irreversible state transitions from steady states of random models. We expect this approach to be robust and generally applicable to unbiasedly predict dynamical transitions of a gene regulatory circuit.

systems biology↗

bioRxiv2018

Role of noise and parametric variation in the dynamics of gene regulatory circuits

Stochasticity in gene expression impacts the dynamics and functions of gene regulatory circuits. Intrinsic noises, including those that are caused by low copy number of molecules and transcriptional bursting, are usually studied by stochastic analysis methods, such as Gillespie algorithm and Langevin simulation. However, the role of extrinsic factors, such as cell-to-cell variability and heterogeneity in microenvironment, is still elusive. To evaluate the effects of both intrinsic and extrinsic noises, we develop a new method, named sRACIPE, by integrating stochastic analysis with random circuit perturbation (RACIPE) method. Unlike traditional methods, RACIPE generates and analyzes an ensemble of mathematical models with random kinetic parameters. Previously, we have shown that the gene expression from random models form robust and functionally related clusters. Under the framework of this randomization-based approach, here we develop two stochastic simulation schemes, aiming to reduce the computational cost without sacrificing the convergence of statistics. One scheme uses constant noise to capture the basins of attraction, and the other one uses simulated annealing to detect the stability of states. By testing the methods on several gene regulatory circuits, we found that high noise, but not large parameter variation, merges clusters together. Our approach quantifies the robustness of a gene circuit in the presence of noise and sheds light on a new mechanism of noise induced hybrid states. We have implemented sRACIPE into a freely available R package.

systems biology↗