Biology subjects

Ganesh, P.

Publications and source records attributed to Ganesh, P..

6 recordsLinked to original sources

bioRxiv2025

Engineering Sustained-Release Broadly Neutralizing Antibody Formulations

Sustained serum levels of broadly neutralizing antibodies (bnAbs) are crucial for effective passive immunization against infectious diseases as protection persists only while these bnAbs remain at adequate concentrations within the body. Current obstacles, such as poor pharmacokinetics (PK) and burdensome administration, must be overcome to make bnAbs a viable option for pre- and post-exposure prophylaxis. In this work, we explore how a polymer-nanoparticle (PNP) hydrogel depot technology can be engineered to prolong protein delivery. In-vivo studies in mice and rats demonstrate prolonged protein release, and modeling efforts predict the impact of both the elimination half-life of the active pharmaceutical ingredient and hydrogel depot volume on overall pharmacokinetics. Moreover, flow cytometry characterization reveals that immune cell infiltration into the hydrogel depot can result in faster-than-expected release of antibody cargo on account of active transport via cellular uptake. We then demonstrate that co-formulation of antibodies with an anti-inflammatory agent reduces cellular infiltration and resulting active transport, further extending delivery and pharmacokinetics. Finally, multicompartmental modeling predicts the human PK profiles of clinically relevant HIV bnAbs delivered via subcutaneous hydrogel injection. These findings aid in the development of next generation hydrogel materials that stabilize and slowly release bnAbs for long-term pre-exposure immunoprophylaxis.

bioengineering↗

bioRxiv2025

All-Atom Modeling and Simulation of Biopolymer Interface: Dual Role of Antifouling Polymer Brushes

Antifouling polymers are highly valuable in a variety of applications, including antiviral coatings, targeted drug delivery, and marine coatings, where preventing unwanted protein adsorption is critical. Although extensive experimental studies have characterized polymer-protein interactions, computational studies remain limited due to the inherent difficulty of modeling and integrating these distinct components within a unified system. This study presents molecular modeling and simulation of polyelectrolyte and polyzwitterionic brushes--poly(dimethylaminoethyl methacrylate) (PDMAEMA), poly(2-(N-oxide-N,N-dimethylamino)ethyl methacrylate) (PNOMA), and poly(2-(N-3-sulfopropyl-N,N-dimethylammonium)ethyl methacrylate) (PSBMA)--grafted onto -quartz substrates in the presence of lysozyme protein. The brush models were developed to closely replicate experimentally synthesized brush samples and to provide detailed insights into structural and dynamical changes at the molecular level during protein adsorption. Using steered molecular dynamics simulations, we show that the PSBMA brush, due to its high local density, exhibits the greatest resistance to protein insertion. C root-mean-square deviation and interaction patterns analyses further reveal that PSBMA also induces the most significant destabilization of lysozyme, while PDMAEMA brush enhances protein stability through ion-mediated interactions. The PNOMA brush, while requiring the lowest force for protein adsorption, induces greater protein destabilization than the PDMAEMA brush primarily due to electrostatic repulsion caused by a short carbon spacer length. To the best of our knowledge, this study presents the first comprehensive and realistic model system--comprising nanomaterials, polymers, and proteins, specifically antifouling polyzwitterions and polyelectrolytes grafted onto -quartz substrates via linkers. These findings highlight the dual role of antifouling polymer brushes: resisting protein adsorption and modulating protein structural dynamics, offering valuable insights for the rational design of next-generation antifouling materials. TOC GRAPHICS O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/651893v1_ufig1.gif" ALT="Figure 1"> View larger version (72K): org.highwire.dtl.DTLVardef@9620b8org.highwire.dtl.DTLVardef@3e395dorg.highwire.dtl.DTLVardef@1282169org.highwire.dtl.DTLVardef@1a997f7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2024

Tunable Cytosolic Chloride Indicators for Real-Time Chloride Imaging in Live Cells

Chloride plays a crucial role in various cellular functions, and its level is regulated by a variety of chloride transporters and channels. However, to date, we still lack the capability to image instantaneous ion flux through chloride channels at single-cell level. Here, we developed a series of cell-permeable, pH-independent, chloride-sensitive fluorophores for real-time cytosolic chloride imaging, which we call CytoCl dyes. We demonstrated the ability of CytoCl dyes to monitor cytosolic chloride and used it to uncover the rapid changes and transient events of halide flux, which cannot be captured by steady-state imaging. Finally, we successfully imaged the proton-activated chloride channel-mediated ion flux at single-cell level, which is, to our knowledge, the first real-time imaging of ion flux through a chloride channel in unmodified cells. By enabling the imaging of single-cell level ion influx through chloride channels and transporters, CytoCl dyes can expand our understanding of ion flux dynamics, which is critical for characterization and modulator screening of these membrane proteins. A conjugable version of CytoCl dyes was also developed for its customization across different applications.

cell biology↗

bioRxiv2024

Adaptive Integration of Perceptual and Reward Information in an Uncertain World

Perceptual uncertainty and salience both impact decision-making, but how these factors precisely impact trial-and-error reinforcement learning is not well understood. Here, we test the hypotheses that (H1) perceptual uncertainty modulates reward-based learning and that (H2) economic decision-making is driven by the value and the salience of sensory information. For this, we combined computational modeling with a perceptual uncertainty-augmented reward-learning task in a human behavioral experiment (N = 98). In line with our hypotheses, we found that subjects regulated learning behavior in response to the uncertainty with which they could distinguish choice options based on sensory information (belief state), in addition to the errors they made in predicting outcomes. Moreover, subjects considered a combination of expected values and sensory salience for economic decision-making. Taken together, this shows that perceptual and economic decision-making are closely intertwined and share a common basis for behavior in the real world.

neuroscience↗

bioRxiv2022

Core material and surface chemistry of Layer-by-Layer (LbL) nanoparticles independently direct uptake, transport, and trafficking in preclinical blood-brain barrier (BBB) models

Development of new treatments for neurological disorders, especially brain tumors and neurodegenerative diseases, is hampered by poor accumulation of new therapeutic candidates in the brain. Drug carrying nanoparticles are a promising strategy to deliver therapeutics, but there is a major need to understand interactions between nanomaterials and the cells of the blood-brain barrier (BBB), and to what degree these interactions can be predicted by preclinical models. Here, we use a library of eighteen layer-by-layer electrostatically assembled nanoparticles (LbL-NPs) to independently assess the impact of nanoparticle core stiffness and surface chemistry on in vitro uptake and transport in three common assays, as well as intracellular trafficking in hCMEC/D3 endothelial cells. We demonstrate that nanoparticle core stiffness impacts the magnitude of material transported, while surface chemistry influences how the nanoparticles are trafficked within the cell. Finally, we demonstrate that these factors similarly dictate in vivo BBB transport using intravital imaging through cranial windows in mice, and we discover that a hyaluronic acid surface chemistry provides an unpredicted boost to transport. Taken together, these findings highlight the importance of considering factors such as assay geometry, nanomaterial labelling strategies, and fluid flow in designing preclinical assays to improve nanoparticle screening throughput for drug delivery to the brain.

bioengineering↗

bioRxiv2018

Nucl2Vec: Local alignment of DNA sequences using Distributed Vector Representation

The Next Generation Sequencing Technique (NGS) has provided affordable and fast method for generating genetic data. Generation of whole Genome Sequence and extract relevant information from this data is still a computationally expensive process. In this paper we demonstrate a novel approach for local alignment of DNA reads with respect to reference genome. For this process we have used Skip-gram model for creating encoding(Nucl2Vec) and k-nearest neighbor for the alignment. With our new approach we have reduced computation cost for local alignment, while achieving accuracy comparable to existing defacto standard BWA-MEM tool.\n\nIndex TermsGenome, Alignment, Local Alignment, k-nearest Neighbor, Distributed vector representation, Skip-gram

genomics↗