Nonviral Platform for Expression of Recombinant Protein in Insect Cells.

Nonviral transfection has been used to express various recombinant proteins, therapeutics, and virus-like particles (VLP) in mammalian and insect cells. Virus-free methods for protein expression require fewer steps for obtaining protein expression by eliminating virus amplification and measuring the infectivity of the virus. The nonviral method uses a nonlytic plasmid to transfect the gene of interest into the insect cells instead of using baculovirus, a lytic system. In this chapter, we describe one of the transfection methods, which uses polyethyleneimine (PEI) as a DNA delivery material into the insect cells to express the recombinant protein in both adherent and suspension cells.

A computational model to uncover the biophysical underpinnings of neural firing heterogeneity in dissociated hippocampal cultures.

Calcium (Ca2+ ) imaging reveals a variety of correlated firing in cultures of dissociated hippocampal neurons, pinpointing the non-synaptic paracrine release of glutamate as a possible mediator for such firing patterns, although the biophysical underpinnings remain unknown. An intriguing possibility is that extracellular glutamate could bind metabotropic receptors linked with inositol trisphosphate (IP3 ) mediated release of Ca2+ from the endoplasmic reticulum of individual neurons, thereby modulating neural activity in combination with sarco/endoplasmic reticulum Ca2+ transport ATPase (SERCA) and voltage-gated Ca2+ channels (VGCC). However, the possibility that such release may occur in different neuronal compartments and can be inherently stochastic poses challenges in the characterization of such interplay between various Ca2+ channels. Here we deploy biophysical modeling in association with Monte Carlo parameter sampling to characterize such interplay and successfully predict experimentally observed Ca2+ patterns. The results show that the neurotransmitter level at the plasma membrane is the extrinsic source of heterogeneity in somatic Ca2+ transients. Our analysis, in particular, identifies the origin of such heterogeneity to an intrinsic differentiation of hippocampal neurons in terms of multiple cellular properties pertaining to intracellular Ca2+ signaling, such as VGCC, IP3 receptor, and SERCA expression. In the future, the biophysical model and parameter estimation approach used in this study can be upgraded to predict the response of a system of interconnected neurons.

Identification of optimal flow rate for culture media, cell density, and oxygen toward maximization of virus production in a fed-batch baculovirus-insect cell system.

In recent times, it has been realized that novel vaccines are required to combat emerging disease outbreaks, and faster optimization is required to respond to global vaccine demands. Although, fed-batch operations offer better productivity, experiment-based optimization of a new fed-batch process remains expensive and time-consuming. In this context, we propose a novel computational framework that can be used for process optimization and control of a fed-batch baculovirus-insect cell system. Since the baculovirus expression vector system (BEVS) is known to be widely used platforms for recombinant protein/vaccine production, we chose this system to demonstrate the identification of optimal profile. Toward this, first, we constructed a mathematical model that captures the time course of cell and virus growth in a baculovirus-insect cell system. Second, the proposed model was used for numerical analysis to determine the optimal operating profiles of control variables such as culture media, cell density, and oxygen based on a multiobjective optimal control formulation. Third, a detailed comparison between batch and fed-batch culture was perfromed along with a comparison between various alternatives of fed-batch operation. Finally, we demonstrate that a model-based quantification of controlled feed addition in fed-batch culture is capable of providing better productivity as compared to a batch culture. The proposed framework can be utilized for the estimation of optimal operating regions of different control variables to achieve maximum infected cell density and virus yield while minimizing the substrate/media, uninfected cell, and oxygen consumption.

Computational framework to understand the clinical stages of COVID-19 and visualization of time course for various treatment strategies.

Coronavirus disease 2019 is known to be regulated by multiple factors such as delayed immune response, impaired T cell activation, and elevated levels of proinflammatory cytokines. Clinical management of the disease remains challenging due to interplay of various factors as drug candidates may elicit different responses depending on the staging of the disease. In this context, we propose a computational framework which provides insights into the interaction between viral infection and immune response in lung epithelial cells, with an aim of predicting optimal treatment strategies based on infection severity. First, we formulate the model for visualizing the nonlinear dynamics during the disease progression considering the role of T cells, macrophages and proinflammatory cytokines. Here, we show that the model is capable of emulating the dynamic and static data trends of viral load, T cell, macrophage levels, interleukin (IL)-6 and TNF-α levels. Second, we demonstrate the ability of the framework to capture the dynamics corresponding to mild, moderate, severe, and critical condition. Our result shows that, at late phase (>15 days), severity of disease is directly proportional to pro-inflammatory cytokine IL6 and tumor necrosis factor (TNF)-α levels and inversely proportional to the number of T cells. Finally, the simulation framework was used to assess the effect of drug administration time as well as efficacy of single or multiple drugs on patients. The major contribution of the proposed framework is to utilize the infection progression model for clinical management and administration of drugs inhibiting virus replication and cytokine levels as well as immunosuppressant drugs at various stages of the disease.

Analytics Pipeline for Visualization of Single Cell RNA Sequencing Data from Brochoaveolar Fluid in COVID-19 Patients: Assessment of Neuro Fuzzy-C-Means and HDBSCAN.

Since the mutation in SARS-COV2 poses new challenges in designing vaccines, it is imperative to develop advanced tools for visualizing the genetic information. Specially, it remains challenging to address the patient-to-patient variability and identify the signature for severe/critical conditions. In this endeavor we analyze the large-scale RNA-sequencing data collected from broncho-alveolar fluid. In this work, we have used PCA and tSNE for the dimension-reduction. The novelty of the current work is to depict a detailed comparison of k-means, HDBSAN and neuro-fuzzy method in visualization of high-dimension data on gene expression. Clinical Relevance- The subpopulation profiling can be used to study the patient-to patient variability when infected by SARS-COV-2 and its variants. The distribution of cell types can be relevant in designing new drugs that are targeted to control the distribution of epithelial cells T cells and macrophages.

Mathematical modeling of viral infection dynamics and immune response in SARS-CoV-2: A computational framework for testing drug efficacy.

SARS-CoV-2 has emerged to cause the outbreak of COVID-19, which has expanded into a worldwide human pandemic. Although detailed experimental data on animal experiments would provide insight into drug efficacy, the scientists involved in these experiments would be exposed to severe risks. In this context, we propose a computational framework for studying infection dynamics that can be used to capture the growth rate of viral replication and lung epithelial cell in presence of SARS-CoV-2. Specifically, we formulate the model consisting of a system of non-linear ODEs that can be used for visualizing the infection dynamics in a cell population considering the role of T cells and Macrophages. The major contribution of the proposed simulation method is to utilize the infection progression model in testing the efficacy of the drugs having various mechanisms and analyzing the effect of time of drug administration on virus clearance.Clinical Relevance-The proposed computational framework incorporates viral infection dynamics and role of immune response in Covid-19 that can be used to test the impact of drug efficacy and time of drug administration on infection mitigation.

Dissociation of the G protein ßγ from the Gq-PLCß complex partially attenuates PIP2 hydrolysis.

Phospholipase C ß (PLCß), which is activated by the Gq family of heterotrimeric G proteins, hydrolyzes the inner membrane lipid phosphatidylinositol 4,5-bisphosphate (PIP2), generating diacylglycerol and inositol 1,4,5-triphosphate (IP3). Because Gq and PLCß regulate many crucial cellular processes and have been identified as major disease drivers, activation and termination of PLCß signaling by the Gαq subunit have been extensively studied. Gq-coupled receptor activation induces intense and transient PIP2 hydrolysis, which subsequently recovers to a low-intensity steady-state equilibrium. However, the molecular underpinnings of this equilibrium remain unclear. Here, we explored the influence of signaling crosstalk between Gq and Gi/o pathways on PIP2 metabolism in living cells using single-cell and optogenetic approaches to spatially and temporally constrain signaling. Our data suggest that the Gßγ complex is a component of the highly efficient lipase GαqGTP-PLCß-Gßγ. We found that over time, Gßγ dissociates from this lipase complex, leaving the less-efficient GαqGTP-PLCß lipase complex and allowing the significant partial recovery of PIP2 levels. Our findings also indicate that the subtype of the Gγ subunit in Gßγ fine-tunes the lipase activity of Gq-PLCß, in which cells expressing Gγ with higher plasma membrane interaction show lower PIP2 recovery. Given that Gγ shows cell- and tissue-specific subtype expression, our findings suggest the existence of tissue-specific distinct Gq-PLCß signaling paradigms. Furthermore, these results also outline a molecular process that likely safeguards cells from excessive Gq signaling.

Smart Data Analytics approach to model Complex Biochemical Oscillations in Hippocampal Neurons.

Calcium spiking can be used for drug screening studies in pharmaceutical industries. However, performing experiments for multiple drugs and doses are highly expensive. The oscillatory behavior of calcium spiking data demonstrates extreme nonlinearity and phase singularity. This makes it more challenging to construct physics-based models for the experimental observations. In this scenario, data based modelling, such as Artificial Neural Networks (ANN), and thereafter the model based prediction of calcium profiles may offer a cost-effective and time saving solution. Therefore, a novel ANN building algorithm is presented in the current work, where data based simultaneous estimation of ANN architecture and nonlinear activation function stands out as the main highlight. The resultant ANN was then used to learn the oscillatory behavior in calcium ion concentration data, obtained from hippocampal neurons of rats by fluorescent labelling and confocal imaging. The paper shows that the novel technique can be used in general for emulating biochemical oscillations (with or without drug injection) and can be implemented to predict the cell-drug responses for intermediated doses. The proposed algorithm can also be used for obtaining high resolution data from low resolution experimental measurements.

Confocal imaging of cytosolic Ca2+ and fuzzy clustering reveal the circuit topology details underlying synchronization in hippocampal neurons.

Neuronal synchronization contributes to various cognitive functions and disruption in synchronicity may lead to various diseased conditions. However, measurement of synchronicity at a higher spatial resolution remains challenging. Specifically, investigation on understanding the role of network topology in tuning the network activity and synchronicity remains sparse. In this context, we propose imaging of intracellular Ca2+ in primary cultures of hippocampal neurons using Fluo-4 as the fluorescent indicator using the confocal microscope. In order to identify the synchronous response from a set of heterogeneous Ca2+ spiking, we present fuzzy clustering of the oscillatory responses. Further, the synchronicity was measured through evaluation of the correlation between Ca2+ spiking trends. Confocal imaging and analysis show that neuronal connectivity and topology play an essential role in tuning the synchronicity of the neuronal network.

Confocal Imaging and k-Means Clustering of GABAB and mGluR Mediated Modulation of Ca2+ Spiking in Hippocampal Neurons.

Imaging cytosolic calcium in neurons is emerging as a new tool in neurological disease diagnosis, drug screening, and toxicity testing. Ca2+ oscillation signatures show a significant variation depending on GPCR targeting agonists. Quantification of Ca2+ spike trains in ligand induced Ca2+ oscillations remains challenging due to their inherent heterogeneity in primary culture. Moreover, there is no framework available for identification of optimal number of clusters and distance metric to cluster Ca2+ spike trains. Using quantitative confocal imaging and clustering analysis, we show the characterization of Ca2+ spiking in GPCR targeting drug-treated primary culture of hippocampal neurons. A systematic framework for selection of the clustering method instead of an intuition-based method was used to optimize the cluster number and distance metric. The results discern neurons with diverse Ca2+ response patterns, including higher amplitude fast spiking and lower spiking responses, and their relative percentage in a neuron population in absence and presence of GPCR-targeted drugs. The proposed framework was employed to show that the  clustering pattern of Ca2+ spiking can be controlled using GABAB and mGluR targeting drugs. This approach can be used for unbiased measurement of neural activity and identification of spiking population with varying amplitude and frequencies, providing a platform for high-content drug screening.

Comparison of Calcium Dynamics and Specific Features for G Protein-Coupled Receptor-Targeting Drugs Using Live Cell Imaging and Automated Analysis.

G protein-coupled receptors (GPCRs) are targets for designing a large fraction of the drugs in the pharmaceutical industry. For GPCR-targeting drug screening using cell-based assays, measurement of cytosolic calcium has been widely used to obtain dose-response profiles. However, it remains challenging to obtain drug-specific features due to cell-to-cell heterogeneity in drug-cell responses obtained from live cell imaging. Here, we present a framework combining live cell imaging of a cell population and a feature extraction method for classification of responses of drugs targeting GPCRs CXCR4 and α2AR. We measured the calcium dynamics using confocal microscopy and compared the responses for SDF-1α and norepinephrine. The results clearly show that the clustering patterns of responses for the two GPCRs are significantly different. Additionally, we show that different drugs targeting the same GPCR induce different calcium response signatures. We also implemented principal component analysis and k means for feature extraction and used nondominated (ND) sorting for ranking a group of drugs at various doses. The presented approach can be used to model a cell population as a mixture of subpopulations. It also offers specific advantages, such as higher spatial resolution, classification of responses, and ranking of drugs, potentially providing a platform for high-content drug screening.